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2,264 results on '"Identification methods"'

251. Detection of Staphylococcus aureus and enterotoxin genes from meat products by using conventional and modern identification methods

Author: ola fathy younes, Hemmat M. Ibrahim, Ahmed Afifi maarouf, Mohamed Hassan, and Reham Amin
Subjects: Identification methods, Negative sample, Enterotoxin, Biology, medicine.disease_cause, law.invention, Enterotoxin gene, Coagulase-Positive Staphylococcus, law, Staphylococcus aureus, Multiplex polymerase chain reaction, medicine, General Earth and Planetary Sciences, Food science, Polymerase chain reaction, General Environmental Science
Abstract: One hundred meat product samples (minced meat, kofta, burger and sausage, 25 of each) were collected and examined for bacteriological evaluation and detection of Staphylococcus aureus by conventional bacteriological methods and by polymerase chain reaction PCR. The mean value of S. aureus counts cfu/g were 0.63×102 ±0.19×102, 0.17×102 ±0.06×102, 0.96×102 ±0.24×102 and 1.1×102 ±0.15×102, respectively. Concerning S.aureus, bacteriological results revealed that the prevalence in minced meat, kofta, burger and sausage was 16%, 12%, 16%, 20%, respectively. In addition, 5 samples out of 100 ones were unaccepted as they were exceeded the permissible limit of E.O.S 2005. Moreover, four random S. aureus samples (2 positive and 2 negative) were reexamined by polymerase chain reaction (PCR) and just one sample was negative by the bacteriological method for S. aureus showed positive results with PCR (false negative sample). Multiplex Polymerase Chain Reaction (m-PCR) was applied for detection of genes responsible for enterotoxins production (sea, seb, sec, sed and see) from identified coagulase positive Staphylococcus aureus. They were detected in the examined minced meat and sausage samples.
Published: 2019
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252. A maximum correntropy criterion based recursive method for output-only modal identification of time-varying structures under non-Gaussian impulsive noise

Author: Zhenjiang Yue, Lei Yu, Li Liu, and Jie Kang
Subjects: Identification methods, Acoustics and Ultrasonics, Computer science, Mechanical Engineering, Modal analysis, Gaussian, Kernel Bandwidth, Condensed Matter Physics, symbols.namesake, Modal, Test case, Mechanics of Materials, Robustness (computer science), Outlier, symbols, Algorithm
Abstract: This work considers the problem of output-only identification for time-varying structures in a recursive manner under non-Gaussian impulsive noise. Presently, the most existing identification methods are based on classical least-squares (LS) criterion due to its mathematical tractability, computational simplicity and optimality under Gaussian assumption. However, the performance of the LS based methods deteriorates seriously in non-Gaussian situations, especially when responses are corrupted by impulsive noise (outliers) which are frequently encountered in real test cases. To deal with this problem, a maximum correntropy criterion based recursive modal identification method is proposed in this paper, and a class of heavy-tailed alpha-stable distribution is adopted to model non-Gaussian impulsive noise. The effect of the kernel bandwidth parameter in proposed method is discussed and a rational value is given for the model structure selection. Finally, the proposed method is comparatively assessed against its LS counterpart via a numerical example and a laboratory time-varying structure experiment. The comparisons have illustrated the advantages of the proposed method on the modal frequency estimates in terms of estimation accuracy and robustness under the non-Gaussian impulsive noise.
Published: 2019
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253. Picosecond Infrared Laser Desorption Mass Spectrometry Identifies Medulloblastoma Subgroups on Intrasurgical Timescales

Author: Taira Kiyota, Sunit Das, Isabelle Ferry, Howard J. Ginsberg, James T. Rutka, Megan Wu, Arash Zarrine-Afsar, David G. Munoz, Claudia M. Kuzan-Fischer, Betty Luu, Ahmed Aman, Michael D. Taylor, and Michael Woolman
Subjects: 0301 basic medicine, Oncology, Identification methods, Cancer Research, medicine.medical_specialty, Malignant brain tumor, Tumor resection, Mass spectrometry, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, Humans, Cerebellar Neoplasms, Grading (tumors), Medulloblastoma, Intraoperative Care, business.industry, Linear discriminant analysis, medicine.disease, Prediction probability, 3. Good health, 030104 developmental biology, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, 030220 oncology & carcinogenesis, business
Abstract: Medulloblastoma (MB) is a pediatric malignant brain tumor composed of four different subgroups (WNT, SHH, Group 3, Group 4), each of which are a unique biological entity with distinct clinico-pathological, molecular, and prognostic characteristics. Although risk stratification of patients with MB based on molecular features may offer personalized therapies, conventional subgroup identification methods take too long and are unable to deliver subgroup information intraoperatively. This limitation prevents subgroup-specific adjustment of the extent or the aggressiveness of the tumor resection by the neurosurgeon. In this study, we investigated the potential of rapid tumor characterization with Picosecond infrared laser desorption mass spectrometry (PIRL-MS) for MB subgroup classification based on small molecule signatures. One hundred and thirteen ex vivo MB tumors from a local tissue bank were subjected to 10- to 15-second PIRL-MS data collection and principal component analysis with linear discriminant analysis (PCA-LDA). The MB subgroup model was established from 72 independent tumors; the remaining 41 de-identified unknown tumors were subjected to multiple, 10-second PIRL-MS samplings and real-time PCA-LDA analysis using the above model. The resultant 124 PIRL-MS spectra from each sampling event, after the application of a 95% PCA-LDA prediction probability threshold, yielded a 98.9% correct classification rate. Post-ablation histopathologic analysis suggested that intratumoral heterogeneity or sample damage prior to PIRL-MS sampling at the site of laser ablation was able to explain failed classifications. Therefore, upon translation, 10-seconds of PIRL-MS sampling is sufficient to allow personalized, subgroup-specific treatment of MB during surgery. Significance: This study demonstrates that laser-extracted lipids allow immediate grading of medulloblastoma tumors into prognostically important subgroups in 10 seconds, providing medulloblastoma pathology in an actionable manner during surgery.
Published: 2019
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254. The applicability of vortex identification methods for complex vortex structures in axial turbine rotor passages

Author: Hong-kai Yang, Wei-hao Zhang, Xia Cao, and Yu-fan Wang
Subjects: Identification methods, Physics, Angle of attack, Mechanical Engineering, 020101 civil engineering, 02 engineering and technology, Mechanics, Aerodynamics, Condensed Matter Physics, Turbine rotor, 01 natural sciences, Turbine, 010305 fluids & plasmas, 0201 civil engineering, Axial turbine, Vortex, Physics::Fluid Dynamics, Mechanics of Materials, Condensed Matter::Superconductivity, Modeling and Simulation, 0103 physical sciences, Streamlines, streaklines, and pathlines
Abstract: The complex vortex structures in the flow around turbine rotor passages, with weak or strong, large or small vortices, interacting with each other, often generate most of aerodynamic loss in turbomachines. Therefore, it is important to identify the vortex structures accurately for the flow field analysis and the aerodynamic performance optimization for turbomachines. In this paper, by using 4 vortex identification methods (the Q criterion, the Ω method, the Liutex method and the Ω-Liutex method), the vortices are identified in turbine rotor passages. In terms of the threshold selection, the results show that the Ω method and the Q-Liutex method are more robust, by which strong and weak vortices can be visualized simultaneously over a wide range of thresholds. As for the display consistency of the vortex identification methods and the streamlines, it is shown that the Liutex method gives results coinciding best with the streamlines in identifying strong vortices, while the Ω-Liutex method gives results the most consistent with the streamlines in identifying weak vortices. As to the relationship among the loss, the vortices and the shear, except for the Q criterion, the other three methods can distinguish the vortical regions from the high shear regions. And the flow losses in turbine rotor passages are often related to high shear zones, while there is a small loss within the core of the vortex. In order to obtain the variation of vortices in the turbine rotor passages at different working points, the Liutex method is applied in 2 cases of a turbine with different angles of attack. The identification results show that the strengths of the tip leakage vortex and the upper passage vortex are weaker and the distance between them is closer at a negative angle of attack. This indicates that the Liutex method is an effective method, and can be used to analyze the vortex structures and their evolution in turbine rotor passages.
Published: 2019
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255. Identification methods on blank-die interfacial heat transfer coefficient in press hardening

Author: Lan Yue, Yuan Chen, Yongfeng Li, Ji He, and Shuhui Li
Subjects: Identification methods, Materials science, 020209 energy, Energy Engineering and Power Technology, 02 engineering and technology, Heat transfer coefficient, Hot stamping, Mechanics, Blank, Industrial and Manufacturing Engineering, Infrared thermometer, 020401 chemical engineering, Heat flux, Heat transfer, 0202 electrical engineering, electronic engineering, information engineering, Hardening (metallurgy), 0204 chemical engineering
Abstract: The interfacial heat transfer behaviors between blank and tools play a great effect on final mechanical property of hot stamping parts. Accurate description on the interfacial heat transfer coefficient (IHTC) can make great improvement on simulation accuracy of hot stamping process. However, it is still a big challenge due to the difficulty in direct measurement on instantaneous interface temperature, pressure and heat flux. So an accurate and efficient identification method of IHTC based on indirect measurement is indispensable. In this paper, an improved test machine with a high-accuracy infrared thermometer and well-controlled interface pressure is constructed. Two groups of IHTC identification methods, including Inverse Heat Conduction (IHC) and Finite-Element-Method based OPTimization (FEM-OPT) methods are developed. Several numerical procedures have been implemented for determining the IHTC between the blank and tools. The error of the proposed methods is further analyzed and calibrated by finite element simulation and experiments. The identified IHTC curves with double peaks reveal the interfacial heat transfer behavior during hot stamping clearly. The comparison among different identification methods shows that the constant equivalent IHTC is not a rational assumption, and IHC method gives the best accuracy on temperature prediction, FEM-OPT method with temperature dependent IHTC takes the next place.
Published: 2019
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256. Screening for substance use in pregnancy and the newborn

Author: Sydney S Kelpin, Kathryn Polak, and Mishka Terplan
Subjects: Identification methods, medicine.medical_specialty, Substance-Related Disorders, Patient interview, Context (language use), Prenatal care, Biological Testing, 03 medical and health sciences, Neonatal Screening, 0302 clinical medicine, Pregnancy, 030225 pediatrics, medicine, Humans, business.industry, Infant, Newborn, Prenatal Care, medicine.disease, Substance abuse, Family medicine, Pediatrics, Perinatology and Child Health, Female, Pregnant Women, Substance use, business
Abstract: Substance use during pregnancy is common, costly and associated with maternal and newborn health consequences. Assessment of substance use should be integrated into prenatal care. Substance use identification methods include patient interview, screening instruments, and biological testing. In this review, we critically evaluate screening and testing for substance use during pregnancy, highlighting the benefits and barriers of integrated assessment into prenatal care. We also discuss the limitations and negative consequences that should be considered when implementing screening and/or testing procedures. Lastly, we provide recommendations for the ethical implementation of screening and testing for substance use in the context of prenatal care.
Published: 2019
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257. Data‐driven method for the quantitative fault diagnosability analysis of dynamic systems

Author: Fangzhou Fu, Zhigang Wu, Wenbo Li, Linlin Li, and Dayi Wang
Subjects: Identification methods, 0209 industrial biotechnology, Control and Optimization, Computer science, Control engineering, 02 engineering and technology, System configuration, Fault (power engineering), Computer Science Applications, Data-driven, Human-Computer Interaction, 020901 industrial engineering & automation, Control and Systems Engineering, Control theory, Electrical and Electronic Engineering, Moore–Penrose pseudoinverse
Abstract: The main objective of this work is to propose a method for the data-driven evaluation of fault diagnosability in dynamic systems. This technique promises to be an efficient way to analyse the structural properties of unknown systems and can be used to optimise a system configuration, including sensor placement. The basic idea behind this method, which suitably integrates direct identification methods into the fault diagnosability evaluation, lies in the invariability of the characteristics of the dynamic behaviour corresponding to different fault cases. Data-driven realisations of distinguishability based on both fault-free data and fault data are proposed. Furthermore, a novel efficient method for computing distinguishability via the pseudoinverse is proposed. Finally, the effectiveness of the proposed method is illustrated via numerical and practical examples.
Published: 2019
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258. Evaluating next‐generation sequencing (NGS) methods for routine monitoring of wild bees: Metabarcoding, mitogenomics or NGS barcoding

Author: Morgan Gueuning, Juerg E. Frey, Simon Blaser, Christophe J. Praz, Matthias Albrecht, Dominik Ganser, and Eva Knop
Subjects: 0106 biological sciences, 0301 basic medicine, Identification methods, conservation biology, False positives and false negatives, molecular identification, Computational biology, Biology, 010603 evolutionary biology, 01 natural sciences, DNA barcoding, DNA, Mitochondrial, DNA sequencing, 03 medical and health sciences, Genetics, False positive paradox, Animals, DNA Barcoding, Taxonomic, survey, Resource Article, insects, Ecology, Evolution, Behavior and Systematics, Molecular identification, Specimen identification, RESOURCE ARTICLES, High-Throughput Nucleotide Sequencing, Bees, Molecular and Statistical Advances, 030104 developmental biology, Genetics, Population, Monitoring data, pollinators, Metagenomics, Biotechnology
Abstract: Implementing cost‐effective monitoring programs for wild bees remains challenging due to the high costs of sampling and specimen identification. To reduce costs, next‐generation sequencing (NGS)‐based methods have lately been suggested as alternatives to morphology‐based identifications. To provide a comprehensive presentation of the advantages and weaknesses of different NGS‐based identification methods, we assessed three of the most promising ones, namely metabarcoding, mitogenomics and NGS barcoding. Using a regular monitoring data set (723 specimens identified using morphology), we found that NGS barcoding performed best for both species presence/absence and abundance data, producing only few false positives (3.4%) and no false negatives. In contrast, the proportion of false positives and false negatives was higher using metabarcoding and mitogenomics. Although strong correlations were found between biomass and read numbers, abundance estimates significantly skewed the communities' composition in these two techniques. NGS barcoding recovered the same ecological patterns as morphology. Ecological conclusions based on metabarcoding and mitogenomics were similar to those based on morphology when using presence/absence data, but different when using abundance data. In terms of workload and cost, we show that metabarcoding and NGS barcoding can compete with morphology, but not mitogenomics which was consistently more expensive. Based on these results, we advocate that NGS barcoding is currently the seemliest NGS method for monitoring of wild bees. Furthermore, this method has the advantage of potentially linking DNA sequences with preserved voucher specimens, which enable morphological re‐examination and will thus produce verifiable records which can be fed into faunistic databases.
Published: 2019

259. Сучасний стан та перспективи діагностики захворювань легень, спричинених нетуберкульозними мікобактеріями

Author: A.I. Barbova and O.A. Zhurilo
Subjects: Identification methods, medicine.medical_specialty, Tuberculosis, biology, business.industry, Human immunodeficiency virus (HIV), микобактериоз, нетуберкулезные микобактерии, фенотипическая диагностика, генетическая диагностика, Disease, mycobacteriosis, nontuberculous mycobacteria, phenotypic diagnostics, genetic diagnostics, medicine.disease_cause, biology.organism_classification, medicine.disease, medicine, мікобактеріоз, нетуберкульозні мікобактерії, фенотипова діагностика, генотипова діагностика, Identification (biology), Nontuberculous mycobacteria, Intensive care medicine, business
Abstract: Nowadays, in the world, various patterns of phenotypic and genotypic diagnostics of M. tuberculosis and nontuberculous mycobacteria, which are the causative agents of mycobacteriosis, are being introduced into practice. This is necessary in order to minimize the time of identification of mycobacteria. Using these methods allows you to get complete information about the causative agent of tuberculosis and mycobacteriosis in the case of the disease with a small bacterial excretion.The aim of the work is to assess the situation regarding the spread of mycobacteriosis diseases and analysis of prospects for the phenogenotypic diagnosis of these diseases.To the literary review, information is driven about the causative agents of mycobacteriosis – nontuberculous mycobacteria. Various approaches to their classification are characterized. The modern methods of revealing and authentication of nontuberculous mycobacteria are expounded; the estimation of their efficiency is conducted. Attention is paid to modern methods of genotypic diagnosis of mycobacteriosis. The analysis of the main scientific sources shows that the current problem is a progressive increasing in the incidence of mycobacteriosis and there are difficulties in laboratory diagnostics of their pathogens.Conclusions. Currently, mycobacteriosis are a serious problem for both clinicians and laboratory specialists. A significant variety of pathogens, the complexity of the pre-analytical stage, the ambiguity of microscopic examination and cultivation methods, the introduction of principally new identification methods into practice require the development of regulatory documents that would regulate the algorithms of laboratory diagnosis and identification of etiologically significant NTMB. This issue is particularly acute due to a significant increase in the incidence of HIV infection and increasing in the number of patients with severe immunodeficiencies., Сейчас в мире внедряют в практику разнообразные схемы фенотипической и генотипической диагностики M. tuberculosis и нетуберкулезных микобактерий, которые являются возбудителями микобактериозов. Это нужно для того, чтобы максимально сократить сроки идентификации микобактерий. Использование этих методов позволяет получить полную информацию о возбудителе туберкулеза и микобактериозов в случае заболевания с небольшим бактериовыделением.Цель работы — оценка ситуации относительно распространения заболеваний микобактериозами и анализ перспектив фено- и генотипической диагностики этих заболеваний.В литературном обзоре приведены сведения о возбудителях микобактериозов — нетуберкулезных микобактериях. Охарактеризованы различные подходы к их классификации. Изложены современные методы выявления и идентификации нетуберкулезных микобактерий, проведена оценка их эффективности. Уделено внимание современным методам генотипической диагностики микобактериозов. Анализ основных научных источников свидетельствует, что актуальной проблемой в настоящее время является прогрессирующее увеличение заболеваемости микобактериозами. Наблюдаются трудности в лабораторной диагностике их возбудителей.Выводы. В настоящее время микобактериозы представляют серьезную проблему как для врачей-клиницистов, так и для специалистов лабораторной службы. Значительное разнообразие возбудителей, сложности проведения преаналитического этапа, неоднозначность методов микроскопического исследования и культивирования, внедрение в практику принципиально новых методов идентификации требуют разработки нормативных документов, которые бы регламентировали алгоритмы лабораторной диагностики и идентификации этиологически значимых нетуберкулезных микобактерий. Особенно остро этот вопрос стоит в связи со значительным ростом заболеваемости ВИЧ-инфекцией и увеличением количества пациентов с выраженными иммунодефицитами., Нині в світі впроваджують у практику різноманітні схеми фенотипової і генотипової діагностики M. tuberculosis та нетуберкульозних мікобактерій, які є збудниками мікобактеріозів. Це потрібно для того, щоб максимально скоротити терміни ідентифікації мікобактерій. Використання цих методів дає змогу дістати повнішу інформацію щодо збудника туберкульозу і мікобактеріозів у разі захворювання з невеликим бактеріовиділенням.Мета роботи — оцінка ситуації щодо поширення захворювань на мікобактеріоз та аналіз перспектив фено- і генотипової діагностики цих захворювань.У літературному огляді наведено відомості про збудників мікобактеріозів — нетуберкульозних мікобактерій. Схарактеризовано різні підходи до їхньої класифікації. Викладено сучасні методи виявлення і ідентифікації нетуберкульозних мікобактерій, проведено оцінку їхньої ефективності. Великої уваги надано методам генотипової діагностики мікобактеріозів. Аналіз основних наукових джерел свідчить, що актуальною проблемою сьогодення є прогресуюче збільшення захворюваності на мікобактеріози. Лабораторна діагностика їхніх збудників ускладнена.Висновки. На сьогодні мікобактеріози є серйозною проблемою як для лікарів-клініцистів, так і для фахівців лабораторної служби. Значна різноманітність збудників, складнощі з проведенням преаналітичного етапу, неоднозначність методів мікроскопічного дослідження і культивування, впровадження в практику принципово нових методів ідентифікації вимагають розробки нормативних документів, що регламентують алгоритми лабораторної діагностики і ідентифікації етіологічно значущих нетуберкульозних мікобактерій. Особливо гостро це питання стоїть у зв’язку зі значним зростанням захворюваності на ВІЛ-інфекцію і збільшенням кількості пацієнтів з виразними імунодефіцитами.
Published: 2019
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260. A review of fish identification methods applied on small fish

Author: Tien-Chieh Hung, Gonzalo Castillo, and Marade Sandford
Subjects: Identification methods, Fishery, Ecology, Aquaculture, business.industry, %22">Fish , Management, Monitoring, Policy and Law, Aquatic Science, Biology, business
Published: 2019
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261. Hydrocarbon accumulation period and geochemical discrimination of gas-bearing properties of deep tight sandstone in Kuqa depression, China

Author: Chao Ding, Zhao Libin, Zhang Tonghui, Sun Xiongwei, Zhilong Huang, Yang Xuejun, Guo Xiaobo, and Han Wei
Subjects: Identification methods, chemistry.chemical_classification, Fuel Technology, Hydrocarbon, chemistry, General Chemical Engineering, Geochemistry, Period (geology), Energy Engineering and Power Technology, General Chemistry, Reservoir fluid, Geotechnical Engineering and Engineering Geology, Geology
Abstract: In order to clarify the hydrocarbon accumulation period of deep tight sandstone in Dabei-Keshen area of Kuqa depression, and to develop identification methods of reservoir fluid properties, a serie...
Published: 2019
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262. A damage identification method for a thin plate structure based on PVDF sensors and strain mode

Author: Hong Ming, Xin Xu, Cui Hongyu, and Peng Weiqiang
Subjects: Identification methods, Materials science, Strain (chemistry), Engineering structures, Mechanical Engineering, Acoustics, Mode (statistics), 02 engineering and technology, 021001 nanoscience & nanotechnology, Vibration, Identification (information), 020303 mechanical engineering & transports, 0203 mechanical engineering, Structure based, 0210 nano-technology, Eigensystem realization algorithm
Abstract: Damage identification methods for engineering structures based on vibration parameters have the advantages of easy detection and high precision; however, structural strain information is more sensitive to structural damage than displacement information. Traditional resistance strain sensors have low accuracy and poor stability when measuring structural strains. Therefore, this paper uses a highly sensitive polyvinylidene fluoride dynamic strain sensor to identify structural damage in a thin plate. The polyvinylidene fluoride sensor is used to obtain structural strain response information, and structural modal parameters are identified using operational modal identification methods based on the natural excitation technique and the eigensystem realization algorithm. This paper uses a damage index based on mode shape and flexibility. A new damage index based on the LU decomposition of the flexibility matrix is used to identify the damage of the thin plate structure. The effectiveness of the modal identification methods and the new damage index is validated via an elastic thin plate experiment. The results show that the modal identification method and the new damage index proposed in this paper can identify damage in a thin plate structure. Sensor comparison experiments also show that compared with a resistance strain sensor, the polyvinylidene fluoride sensor has higher damage sensitivity, better damage recognition and the ability to recognize farther from the sensor.
Published: 2019
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263. Comparisons and analyses of vortex identification between Omega method and Q criterion

Author: Yu-ning Zhang, Xiao-yu Wang, and Chaoqun Liu
Subjects: Identification methods, Physics, Mechanical Engineering, Structure (category theory), 020101 civil engineering, 02 engineering and technology, Condensed Matter Physics, 01 natural sciences, Omega, 010305 fluids & plasmas, 0201 civil engineering, Vortex, Identification (information), Flow (mathematics), Mechanics of Materials, Condensed Matter::Superconductivity, Modeling and Simulation, 0103 physical sciences, Statistical physics, Selection (genetic algorithm), Flow statistics
Abstract: The present paper presents comparisons of the vortex identification between the omega method and the Q criterion based on the data of a classical flow. From the comparisons of the vortex structure together with the flow statistics, some important conclusions are drawn on the validity of the two methods, as follows. The omega method can identify various kinds of vortices with different intensities (e.g., the strong vortex, the medium vortex and the weak vortex). For the Q criterion, due to the subjective threshold selection, only the strong vortex with weak deformations could be identified. Finally, some emerging topics related with the advanced vortex identification methods are briefly discussed.
Published: 2019
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264. Automatic detection of coronal loops using guided filtering and wavelet transform

Author: Song Feng and Li Zhang
Subjects: Physics, Identification methods, Channel (digital image), Computer Networks and Communications, Wavelet transform, Plasma, Function (mathematics), Coronal loop, Magnetic field, Computational physics, Control and Systems Engineering, Coronal plane, Physics::Space Physics, Astrophysics::Solar and Stellar Astrophysics
Abstract: The corona is the outer layer of the solar asmosphere. The bright loop-like structures in the corona, which are called coronal loops, are hot plasma to be bound to the solar coronal magnetic fields. Studying these magnetic features can help to better understand the dynamics of coronal magnetic fields and coronal oscillations, and further clearify the debate of coronal heating. Accurate identifying coronal loops are crucial for the relevant studies. However, due to the complexity of the coronal magnetic fields, it is very difficult to identify and extract the loop-like structures. In this case, we propose a novel algorithm based on guided filtering and wavelet transform modulus maxima to automatically detect and extract them. The steps are as follows: we use (1) a fuzzy function to enhance the contrast of solar corona images; (2) guided filtering to highlight the edges of coronal loops; (3) wavelet transform modulus maxima to identitify the edges of coronal loops; (4) image binarization to extract coronal loops; (5) morphological operate to remove those non-loop structures. The images observed by the Transition and Coronal Explorer (TRACE) and the Atmospheric Imaging Assembly on the Solar Dynamics (SDO/AIA) intruments in the 171 A channel are selected to illustrate the porcess and further evaluate the performance of the algorithm. The results demonstrate that the proposed algorithm has significant advantages over provious identification methods and the detected and extracted structures can be further applied to scientific researchs.
Published: 2019
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265. Colorimetric and test stripe-based assay of bacteria by using vancomycin-modified gold nanoparticles

Author: Fu-Gen Wu, Qi You, Xiaodong Zhang, and Yang Chen
Subjects: Identification methods, medicine.drug_class, Antibiotics, 02 engineering and technology, 010402 general chemistry, 01 natural sciences, Tap water, Materials Chemistry, medicine, Electrical and Electronic Engineering, Instrumentation, Detection limit, Orange juice, Chromatography, biology, Chemistry, Metals and Alloys, 021001 nanoscience & nanotechnology, Condensed Matter Physics, biology.organism_classification, 0104 chemical sciences, Surfaces, Coatings and Films, Electronic, Optical and Magnetic Materials, Colloidal gold, Vancomycin, 0210 nano-technology, Bacteria, medicine.drug
Abstract: Bacteria are the most principal pathogens threatening human health. Rapid, simple, and sensitive identification and assay for bacteria are greatly desired for clinical diagnosis and environmental monitoring. In this work, a commonly used antibiotic vancomycin (Van) was used to synthesize gold nanoparticles (Au NPs) by a simple one-pot reaction, and the resultant Van-coated Au NPs (denoted as Van-AuNPs) can selectively interact with Gram-positive bacteria and thus be used to differentiate Gram-positive bacteria from Gram-negative ones through the change of their red color. On this basis, Van-AuNPs-based visual colorimetric assay was established for Gram-positive bacteria. The detection limits of bare eyes for Gram-positive S. aureus, M. luteus, and B. subtilis are 1 × 109, 1 × 109, and 1 × 109 cells/mL, respectively, according to the color of the spots on the test strip without the use of any microscopic devices. We also used this method to detect S. aureus in orange juice and tap water with excellent recoveries. Compared with conventional bacterial identification methods, this assay is rapid and easy, and is promising to be used as a rapid method for the identification and quantification of bacteria in clinics, environment, and fieldwork.
Published: 2019
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266. Copula-based frequency analysis of drought with identified characteristics in space and time: a case study in Huai River basin, China

Author: Mingzhong Xiao, Yuelong Zhu, and Zhongbo Yu
Subjects: Identification methods, Atmospheric Science, geography, Frequency analysis, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Copula (linguistics), 0207 environmental engineering, Drainage basin, Conditional probability, 02 engineering and technology, 01 natural sciences, law.invention, law, Climatology, Environmental science, 020701 environmental engineering, Extreme value theory, 0105 earth and related environmental sciences
Abstract: Droughts are regional phenomena that dynamic varied along the time; however, the spatiotemporal dynamic processes of drought were usually ignored in the drought identification methods. To better understand the space-time structure of drought, a space-time continuum drought identification method was used to identify drought intensity, duration, and area in Huai River basin, and their multiple dependences were modeled by copulas. The Intensity-Area-Frequency (IAF) curves were used to model the regional variation of drought in this study. Without identified space-time structure of drought, the construction of IAF curves is usually focusing on the decreasing drought intensity with increasing areal extent. However, stronger drought intensity was found to be followed by larger areal extent when considering the space-time structure of drought, and a copula-based method was introduced to better constructive IAF curves. Conditional probabilities of several drought characteristics taking extreme values were further analyzed. Compared to probabilities estimated by univariate frequency analysis, results indicate that occurrence frequency of drought was underestimated when neglecting any natural characteristic of drought, and underestimation was more serious for the more extreme situations of drought. Furthermore, owing to stronger dependences of drought characteristics from short-term to long-term, more serious underestimations of occurrence frequency of drought were found for the drought from short-term to long-term when neglecting any natural characteristic of drought.
Published: 2019
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267. Prompt prediction of spillovers in urban street networks by using rolling time occupancy data

Author: Sheng Bowen, Sheng Jin, Xiang Song, Dongfang Ma, and Dianhai Wang
Subjects: Identification methods, Focus (computing), Occupancy, Spillover effect, Computer science, General Engineering, Econometrics, Transportation
Abstract: Prompt and accurate prediction of spillovers is essential in traffic control systems. Many studies on spillover focus on the identification methods, but little attention is paid to spillover predic...
Published: 2019
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268. Dental stem cells – Sources and identification methods

Author: Anjali Narwal, Anita Hooda, and Shruti Gupta
Subjects: Identification methods, lcsh:RT1-120, Cell type, reservoir, lcsh:Nursing, business.industry, Regeneration (biology), Mesenchymal stem cell, lcsh:R, lcsh:Medicine, General Medicine, Pluripotent, Regenerative medicine, Dental stem cells, stem cells, Medicine, Stem cell, Medical science, business, Neuroscience
Abstract: The banking of mesenchymal cells from the umbilical cord and harvesting them for future use is the current trend in medical science. Such sources are reservoirs of stem cells. Over the past decade, the field of dentistry has embossed its presence by taking major lead in the field of regenerative medicine and more precisely in the field of stem cells. These stem cells have the capacity for regeneration and repair by converting into any other cell type. However, these cells require signals for differentiation in a timely manner. Tooth and its associated structures have been discovered as the latest reservoirs of stem cells. In this review, a light has been thrown on such sources and their identification has been emphasized.
Published: 2019

269. La revolución empírica en economía

Author: Daniel Otero Robles and Boris Salazar Trujillo
Subjects: Cultural Studies, Identification methods, History, Literature and Literary Theory, Revolução empírica, Computer science, revolución empírica, experimentos naturales, synthetic control, Control (management), Psychological intervention, econometria, econometría, Semantic network, econometrics, credibility, credibilidade, empirical revolution, Natural (music), variables instrumentales, HB71-74, instrumental variables, controle sintético, Data science, control sintético, credibilidad, Economics as a science, experimentos naturais, variáveis instrumentais, natural experiments
Abstract: Resumen Este artículo intenta explicar el avance de la revolución empírica. Plantea que la búsqueda de experimentos naturales y cuasinaturales, allí donde estuvieran disponibles, para mejorar la credibilidad de los métodos de identificación, condujo a la creación y aplicación de nuevas herramientas econométricas y a su posterior propagación transversal a un número creciente de campos de la economía. La aplicación de los nuevos diseños y herramientas de investigación a la evaluación de intervenciones de política en todo el mundo, activó un potente sistema de retroalimentación que va de las intervenciones a su evaluación mediante nuevas herramientas econométricas, a la publicación de artículos académicos y a la generación de nuevas intervenciones. Usando redes de cocitación y redes semánticas de los artículos que introdujeron las nuevas herramientas, encontramos trazos de su impacto creciente sobre la práctica de los economistas, y de la emergencia de tres agrupaciones de investigadores como efecto de la irrupción del control sintético en 2003. Abstract This article proposes an explanation to the empirical revolution in economics. It argues that the search for natural and quasi-natural experiments, wherever they were available, looking for more credible identification methods, led to the creation and application of new econometric tools and to their further propagation to an increasing number of fields in Economics. The application of the new tools and research designs to the evaluation of policy interventions all over the world activated a powerful feedback systems, working from interventions to their evaluations, by means of new econometric tools, to the publication of academic articles and back to the generation of new interventions. By using networks of cocitation and semantic networks of the articles that introduced the new tools, we found traces of their impact over the practice of economists, and over the emergence of three groupings of researchers as an effect of the arrival of synthetic control in 2003. Resumo Este artigo tenta explicar o avanço da revolução empírica. Propõe que a busca de experimentos naturais e cuasi naturais, ali onde estivessem disponíveis, para melhorar a credibilidade dos métodos de identificação, conduziu à criação e aplicativo de novas ferramentas econométricas e a sua posterior propagação transversal a um número crescente de campos da economia. O aplicativo dos novos desenhos e ferramentas de investigação à avaliação de intervenções de política em todo mundo, ativou um potente sistema de retroalimentação que vai das intervenções a sua avaliação mediante novas ferramentas econométricas, e a sua posterior propagação transversal a um número crescente de campos da economía, à publicação de artigos acadêmicos e à geração de novas intervenções. Usando redes de cocitación e redes semánticas dos artigos que introduziram as novas ferramentas, encontramos traços de seu impacto crescente sobre a prática dos economistas, e da emergência de três agrupamentos de pesquisadores como efeito da irrupción do controle sintético em 2003.
Published: 2019

270. Early detection of bacteria using SPR imaging and event counting: experiments withListeria monocytogenesandListeria innocua

Author: Alexandra Morlay, Thierry Livache, Marine Boulade, Yoann Roupioz, Michael Canva, Paul G. Charette, Félix Piat, Loïc Leroy, Laboratoire Charles Fabry / Biophotonique, Laboratoire Charles Fabry (LCF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS), Laboratoire Nanotechnologies Nanosystèmes (LN2 ), Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Sherbrooke (UdeS)-École supérieure de Chimie Physique Electronique de Lyon (CPE)-Centre National de la Recherche Scientifique (CNRS), Chimie pour la Reconnaissance et l’Etude d’Assemblages Biologiques (CREAB), SYstèmes Moléculaires et nanoMatériaux pour l’Energie et la Santé (SYMMES), Institut de Chimie du CNRS (INC)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Prestodiag, Université Paris-Sud - Paris 11 (UP11)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS), Université de Sherbrooke (UdeS)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-École Supérieure de Chimie Physique Électronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and ANR-17-EURE-0003,CBH-EUR-GS,CBH-EUR-GS(2017)
Subjects: Identification methods, Spr imaging, Chromatography, biology, Chemistry, General Chemical Engineering, Early detection, 02 engineering and technology, General Chemistry, 010402 general chemistry, 021001 nanoscience & nanotechnology, medicine.disease_cause, biology.organism_classification, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, 01 natural sciences, Rapid detection, 0104 chemical sciences, Listeria monocytogenes, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Surface plasmon resonance imaging, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, medicine, Listeria, 0210 nano-technology, Bacteria
Abstract: International audience; Foodborne pathogens are of significant concern in the agrifood industry and the development of associated rapid detection and identification methods are of major importance. This paper describes the novel use of resolution-optimized prism-based surface plasmon resonance imaging (RO-SPRI) and data processing for the detection of the foodborne pathogens Listeria monocytogenes and Listeria innocua. With an imaging spatial resolution on the order of individual bacteria (2.7 AE 0.5 mm Â 7.9 AE 0.6 mm) over a field of view 1.5 mm 2 , the RO-SPRI system enabled accurate counting of individual bacteria on the sensor surface. Using this system, we demonstrate the detection of two species of Listeria at an initial concentration of 2 Â 10 2 CFU mL À1 in less than 7 hours. The surface density of bacteria at the point of positive detection was 15 AE 4 bacteria per mm 2. Our approach offers great potential for the development of fast specific detection systems based on affinity monitoring.
Published: 2019
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271. Toward data-driven production simulation modeling: dispatching rule identification by machine learning techniques

Author: Timothy Sprock, Satoshi Nagahara, and Moneer Helu
Subjects: Identification methods, 0209 industrial biotechnology, Computer science, business.industry, Simulation modeling, 02 engineering and technology, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Data-driven, Identification (information), 020901 industrial engineering & automation, Operation control, Robustness (computer science), General Earth and Planetary Sciences, Production (economics), Artificial intelligence, Human decision, business, computer, 0105 earth and related environmental sciences, General Environmental Science
Abstract: Production simulation is useful to predict and optimize future production. However, it requires effort and expertise to create accurate simulation models. For instance, operational control rules, such as job sequencing rules, are modeled based on interviews with shop-floor managers and some assumptions since those rules are tacit in general. In this paper, we consider a data-driven approach to model operational control rules. We develop job sequencing rule identification methods that model rules from production data using machine learning techniques. These methods are evaluated based on accuracy and robustness against uncertainty in human decision making using virtual and real production data.
Published: 2019
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272. Identification of hadronic tau lepton decays using a deep neural network

Author: Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Dragicevic, M., Del Valle, A. Escalante, Fruhwirth, R., Jeitler, M., Krammer, N., Lechner, L., Liko, D., Mikulec, I, Paulitsch, P., Pitters, F. M., Schieck, J., Schofbeck, R., Schwarz, D., Templ, S., Waltenberger, W., Wulz, C-E, Chekhovsky, V, Litomin, A., Makarenko, V, Darwish, M. R., De Wolf, E. A., Janssen, T., Kello, T., Lelek, A., Sfar, H. Rejeb, Van Mechelen, P., Van Putte, S., Van Remortel, N., Blekman, F., Bols, E. S., Delcourt, M., El Faham, H., Lowette, S., Moortgat, S., Morton, A., Muller, D., Sahasransu, A. R., Tavernier, S., Van Doninck, W., Van Mulders, P., Beghin, D., Bilin, B., Clerbaux, B., De Lentdecker, G., Favart, L., Grebenyuk, A., Kalsi, A. K., Lee, K., Mahdavikhorrami, M., Makarenko, I, Moureaux, L., Petre, L., Popov, A., Postiau, N., Starling, E., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., Wezenbeek, L., Cornelis, T., Dobur, D., Knolle, J., Lambrecht, L., Mestdach, G., Niedziela, M., Roskas, C., Samalan, A., Skovpen, K., Tytgat, M., Vermassen, B., Vit, M., Benecke, A., Bethani, A., Bruno, G., Bury, F., Caputo, C., David, P., Delaere, C., Donertas, I. S., Giammanco, A., Jaffel, K., Jain, Sa, Lemaitre, V, Mondal, K., Prisciandaro, J., Taliercio, A., Teklishyn, M., Tran, T. T., Vischia, P., Wertz, S., Alves, G. A., Hensel, C., Moraes, A., Alda Junior, W. L., Gallo Pereira, M. Alves, Ferreira Filho, M. Barroso, Malbouisson, H. Brandao, Carvalho, W., Chinellato, J., Da Costa, E. M., Da Silveira, G. G., Damiao, D. De Jesus, De Souza, S. Fonseca, Figueiredo, D. Matos, Herrera, C. Mora, Amarilo, K. Mota, Mundim, L., Nogima, H., Teles, P. Rebello, Santoro, A., Silva Do Amaral, S. M., Sznajder, A., Thiel, M., Da Silva De Araujo, F. Torres, Pereira, A. Vilela, Bernardes, C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gregores, E. M., Lemos, D. S., Mercadante, P. G., Novaes, S. F., Padula, Sandra S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Dimitrov, A., Ivanov, T., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Cheng, T., Javaid, T., Mittal, M., Yuan, L., Ahmad, M., Bauer, G., Dozen, C., Hu, Z., Martins, J., Wang, Y., Yi, K., Chapon, E., Chen, G. M., Chen, H. S., Chen, M., Iemmi, F., Kapoor, A., Leggat, D., Liao, H., Liu, Z-A, Milosevic, V, Monti, F., Sharma, R., Tao, J., Thomas-Wilsker, J., Wang, J., Zhang, H., Zhao, J., Agapitos, A., An, Y., Ban, Y., Chen, C., Levin, A., Li, Q., Lyu, X., Mao, Y., Qian, S. J., Wang, D., Wang, Q., Xiao, J., Lu, M., You, Z., Gao, X., Okawa, H., Lin, Z., Xiao, M., Avila, C., Cabrera, A., Florez, C., Fraga, J., Mejia Guisao, J., Ramirez, F., Ruiz Alvarez, J. D., Salazar Gonzalez, C. A., Giljanovic, D., Godinovic, N., Lelas, D., Puljak, I, Antunovic, Z., Kovac, M., Sculac, T., Brigljevic, V, Ferencek, D., Majumder, D., Roguljic, M., Starodumov, A., Susa, T., Attikis, A., Christoforou, K., Erodotou, E., Ioannou, A., Kole, G., Kolosova, M., Konstantinou, S., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Saka, H., Finger, M., J, R., Kveton, A., Ayala, E., Jarrin, E. Carrera, Kamel, A. Ellithi, Salama, E., Lotfy, A., Mahmoud, M. A., Bhowmik, S., Dewanjee, R. K., Ehataht, K., Kadastik, M., Nandan, S., Nielsen, C., Pata, J., Raidal, M., Tani, L., Veelken, C., Eerola, P., Forthomme, L., Kirschenmann, H., Osterberg, K., Voutilainen, M., Bharthuar, S., Brucken, E., Garcia, F., Havukainen, J., Kim, M. S., Kinnunen, R., Lampen, T., Lassila-Perini, K., Lehti, S., Linden, T., Lotti, M., Martikainen, L., Myllymaki, M., Ott, J., Siikonen, H., Tuominen, E., Tuominiemi, J., Luukka, P., Petrow, H., Tuuva, T., Amendola, C., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J. L., Ferri, F., Ganjour, S., Givernaud, A., Gras, P., de Monchenault, G. Hamel, Jarry, P., Lenzi, B., Locci, E., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. O., Savoy-Navarro, A., Titov, M., G. B., Yu, Ahuja, S., Beaudette, F., Bonanomi, M., Perraguin, A. Buchot, Busson, P., Cappati, A., Charlot, C., Davignon, O., Diab, B., Falmagne, G., Ghosh, S., de Cassagnac, R. Granier, Hakimi, A., Kucher, I, Motta, J., Nguyen, M., Ochando, C., Paganini, P., Rembser, J., Salerno, R., Sarkar, U., Sauvan, J. B., Sirois, Y., Tarabini, A., Zabi, A., Zghiche, A., Agram, J-L, Andrea, J., Apparu, D., Bloch, D., Bourgatte, G., Brom, J-M, Chabert, E. C., Collard, C., Darej, D., Fontaine, J-C, Goerlach, U., Grimault, C., Le Bihan, A-C, Nibigira, E., Van Hove, P., Asilar, E., Beauceron, S., Bernet, C., Boudoul, G., Camen, C., Carle, A., Chanon, N., Contardo, D., Depasse, P., El Mamouni, H., Fay, J., Gascon, S., Gouzevitch, M., Ille, B., Laktineh, I. B., Lattaud, H., Lesauvage, A., Lethuillier, M., Mirabito, L., Perries, S., Shchablo, K., Sordini, V, Torterotot, L., Touquet, G., Vander Donckt, M., Viret, S., Adamov, G., Lomidze, I, Tsamalaidze, Z., Botta, V, Feld, L., Klein, K., Lipinski, M., Meuser, D., Pauls, A., Roewert, N., Schulz, J., Teroerde, M., Dodonova, A., Eliseev, D., Erdmann, M., Fackeldey, P., Fischer, B., Hebbeker, T., Hoepfner, K., Ivone, F., Mastrolorenzo, L., Merschmeyer, M., Meyer, A., Mocellin, G., Mondal, S., Mukherjee, S., Noll, D., Novak, A., Pook, T., Pozdnyakov, A., Rath, Y., Reithler, H., Roemer, J., Schmidt, A., Schuler, S. C., Sharma, A., Vigilante, L., Wiedenbeck, S., Zaleski, S., Dziwok, C., Fluegge, G., Ahmad, W. Haj, Hlushchenko, O., Kress, T., Nowack, A., Pistone, C., Pooth, O., Roy, D., Sert, H., Stahl, A., Ziemons, T., Zotz, A., Petersen, H. Aarup, Martin, M. Aldaya, Asmuss, P., Baxter, S., Bayatmakou, M., Behnke, O., Martinez, A. Bermudez, Bhattacharya, S., Bin Anuar, A. A., Borras, K., Brunner, D., Campbell, A., Cardini, A., Cheng, C., Colombina, F., Rodriguez, S. Consuegra, Silva, G. Correia, Danilov, V, De Silva, M., Didukh, L., Eckerlin, G., Eckstein, D., Banos, L. I. Estevez, Filatov, O., Gallo, E., Geiser, A., Giraldi, A., Grohsjean, A., Guthoff, M., Jafari, A., Jomhari, N. Z., Jung, H., Kasem, A., Kasemann, M., Kaveh, H., Kleinwort, C., Kruecker, D., Lange, W., Lenz, T., Lidrych, J., Lipka, K., Lohmann, W., Mankel, R., Melzer-Pellmann, I-A, Morentin, M. Mendizabal, Metwally, J., Meyer, A. B., Meyer, M., Mnich, J., Mussgiller, A., Otarid, Y., Adan, D. Perez, Pitzl, D., Raspereza, A., Lopes, B. Ribeiro, Rubenach, J., Saggio, A., Saibel, A., Savitskyi, M., Scham, M., Scheurer, V, Schuetze, P., Schwanenberger, C., Singh, A., Ricardo, R. E. Sosa, Stafford, D., Tonon, N., Van De Klundert, M., Walsh, R., Walter, D., Wen, Y., Wichmann, K., Wiens, L., Wissing, C., Wuchterl, S., Aggleton, R., Albrecht, S., Bein, S., Benato, L., Connor, P., De Leo, K., Eich, M., Feindt, F., Froehlich, A., Garbers, C., Garutti, E., Gunnellini, P., Hajheidari, M., Haller, J., Hinzmann, A., Kasieczka, G., Klanner, R., Kogler, R., Kramer, T., Kutzner, V, Lange, J., Lange, T., Lobanov, A., Malara, A., Nigamova, A., Rodriguez, K. J. Pena, Rieger, O., Schleper, P., Schroeder, M., Schwandt, J., Sonneveld, J., Stadie, H., Steinbrueck, G., Tews, A., Zoi, I, Bechtel, J., Brommer, S., Burkart, M., Butz, E., Caspart, R., Chwalek, T., De Boer, W., Dierlamm, A., Droll, A., El Morabit, K., Faltermann, N., Giffels, M., Gosewisch, J. O., Gottmann, A., Hartmann, F., Heidecker, C., Husemann, U., Keicher, P., Koppenhoefer, R., Maier, S., Metzler, M., Mitra, S., Mueller, Th, Neukum, M., Nuernberg, A., Quast, G., Rabbertz, K., Rauser, J., Savoiu, D., Schnepf, M., Seith, D., Shvetsov, I, Simonis, H. J., Ulrich, R., Van der Linden, J., Von Cube, R. F., Wassmer, M., Weber, M., Wieland, S., Wolf, R., Wozniewski, S., Wunsch, S., Anagnostou, G., Daskalakis, G., Geralis, T., Kyriakis, A., Loukas, D., Stakia, A., Diamantopoulou, M., Karasavvas, D., Karathanasis, G., Kontaxakis, P., Koraka, C. K., Manousakis-Katsikakis, A., Panagiotou, A., Papavergou, I, Saoulidou, N., Theofilatos, K., Tziaferi, E., Vellidis, K., Vourliotis, E., Bakas, G., Kousouris, K., Papakrivopoulos, I, Tsipolitis, G., Zacharopoulou, A., Adamidis, K., Bestintzanos, I, Evangelou, I, Foudas, C., Gianneios, P., Katsoulis, P., Kokkas, P., Manthos, N., Papadopoulos, I, Strologas, J., Csanad, M., Farkas, K., Gadallah, M. M. A., Lokos, S., Major, P., Mandal, K., Mehta, A., Pasztor, G., Radl, A. J., Suranyi, O., Veres, G., I, Bartok, M., Bencze, G., Hajdu, C., Horvath, D., Sikler, F., Veszpremi, V., Czellar, S., Karancsi, J., Molnar, J., Szillasi, Z., Teyssier, D., Raics, P., Trocsanyi, Z. L., Ujvari, B., Csorgo, T., Nemes, F., Novak, T., Choudhury, S., Komaragiri, J. R., Kumar, D., Panwar, L., Tiwari, P. C., Bahinipati, S., Kar, C., Mal, P., Mishra, T., Bindhu, V. K. Muraleedharan Nair, Nayak, A., Saha, P., Sur, N., Swain, S. K., Vats, D., Bansal, S., Beri, S. B., Bhatnagar, V, Chaudhary, G., Chauhan, S., Dhingra, N., Gupta, R., Kaur, A., Kaur, M., Kaur, S., Kumari, P., Meena, M., Sandeep, K., Singh, J. B., Virdi, A. K., Ahmed, A., Bhardwaj, A., Choudhary, B. C., Gola, M., Keshri, S., Kumar, A., Naimuddin, M., Priyanka, P., Ranjan, K., Shah, A., Bharti, M., Bhattacharya, R., Bhowmik, D., Dutta, S., Gomber, B., Maity, M., Palit, P., Rout, P. K., Saha, G., Sahu, B., Sarkar, S., Sharan, M., Singh, B., Thakur, S., Behera, P. K., Behera, S. C., Kalbhor, P., Muhammad, A., Pradhan, R., Pujahari, P. R., Sikdar, A. K., Dutta, D., Jha, V, Kumar, V, Mishra, D. K., Naskar, K., Netrakanti, P. K., Pant, L. M., Shukla, P., Aziz, T., Dugad, S., Kumar, M., Banerjee, S., Chudasama, R., Guchait, M., Karmakar, S., Kumar, S., Majumder, G., Mazumdar, K., Alpana, K., Dube, S., Kansal, B., Laha, A., Pandey, S., Rane, A., Rastogi, A., Sharma, S., Bakhshiansohi, H., Khazaie, E., Zeinali, M., Chenarani, S., Etesami, S. M., Khakzad, M., Najafabadi, M. Mohammadi, Grunewald, M., Abbrescia, M., Aly, R., Aruta, C., Colaleo, A., Creanza, D., De Filippis, N., De Palma, M., Di Florio, A., Di Pilato, A., Elmetenawee, W., Fiore, L., Gelmi, A., Gul, M., Iaselli, G., Ince, M., Lezki, S., Maggi, G., Maggi, M., Margjeka, I, Mastrapasqua, V, Merlin, J. 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M., Kim, M., Koenig, E., Konigsberg, J., Korytov, A., K. H., Lo, Matchev, K., Menendez, N., Mitselmakher, G., Madhu, A. Muthirakalayil, Rawal, N., Rosenzweig, D., Rosenzweig, S., Rotter, J., Shi, K., Sturdy, J., Yigitbasi, E., Zuo, X., Adams, T., Askew, A., Habibullah, R., Hagopian, V, Johnson, K. F., Khurana, R., Kolberg, T., Martinez, G., Prosper, H., Schiber, C., Viazlo, O., Yohay, R., Zhang, J., Baarmand, M. M., Butalla, S., Elkafrawy, T., Hohlmann, M., Verma, R. Kumar, Noonan, D., Rahmani, M., Yumiceva, F., Adams, M. R., Gonzalez, H. Becerril, Cavanaugh, R., Chen, X., Dittmer, S., Evdokimov, O., Gerber, C. E., Hangal, D. A., Hofman, D. J., Merrit, A. H., Mills, C., Oh, G., Roy, T., Rudrabhatla, S., Tonjes, M. B., Varelas, N., Viinikainen, J., Wang, X., Wu, Z., Ye, Z., Alhusseini, M., Dilsiz, K., Gandrajula, R. P., Koseyan, O. K., Merlo, J-P, Mestvirishvili, A., Nachtman, J., Ogul, H., Onel, Y., Penzo, A., Snyder, C., Tiras, E., Amram, O., Blumenfeld, B., Corcodilos, L., Davis, J., Eminizer, M., Gritsan, A., Kyriacou, S., Maksimovic, P., Roskes, J., Swartz, M., Vami, T. A., Abreu, A., Anguiano, J., Barrera, C. Baldenegro, Baringer, P., Bean, A., Bylinkin, A., Flowers, Z., Isidori, T., Khalil, S., King, J., Krintiras, G., Kropivnitskaya, A., Lazarovits, M., Lindsey, C., Marquez, J., Minafra, N., Murray, M., Nickel, M., Rogan, C., Royon, C., Salvatico, R., Sanders, S., Schmitz, E., Smith, C., Takaki, J. D. Tapia, Warner, Z., Williams, J., Wilson, G., Duric, S., Ivanov, A., Kaadze, K., Kim, D., Maravin, Y., Mitchell, T., Modak, A., Nam, K., Rebassoo, F., Wright, D., Adams, E., Baden, A., Baron, O., Belloni, A., Eno, S. C., Hadley, N. J., Jabeen, S., Kellogg, R. G., Koeth, T., Mignerey, A. C., Nabili, S., Palmer, C., Seidel, M., Skuja, A., Wang, L., Wong, K., Abercrombie, D., Andreassi, G., Bi, R., Brandt, S., Busza, W., Cali, I. A., Eysermans, J., Freer, C., Ceballos, G. Gomez, Goncharov, M., Harris, P., Hu, M., Klute, M., Kovalskyi, D., Krupa, J., Lee, Y-J, Mironov, C., Paus, C., Rankin, D., Roland, C., Roland, G., Shi, Z., Stephans, G. S. F., Wang, Z., Wyslouch, B., Chatterjee, R. M., Evans, A., Hansen, P., Hiltbrand, J., Jain, Sh, Krohn, M., Kubota, Y., Mans, J., Revering, M., Rusack, R., Saradhy, R., Schroeder, N., Strobbe, N., Wadud, M. A., Bloom, K., Bryson, M., Claes, D. R., Fangmeier, C., Finco, L., Golf, F., Joo, C., Kravchenko, I, Musich, M., Reed, I, Siado, J. E., Snow, G. R., Tabb, W., Yan, F., Zecchinelli, A. G., Agarwal, G., Bandyopadhyay, H., Hay, L., Iashvili, I., Kharchilava, A., Mclean, C., Nguyen, D., Pekkanen, J., Rappoccio, S., Williams, A., Alverson, G., Barberis, E., Haddad, Y., Hortiangtham, A., Li, J., Madigan, G., Marzocchi, B., Morse, D. M., Nguyen, V, Orimoto, T., Parker, A., Skinnari, L., Tishelman-Charny, A., Wamorkar, T., Wang, B., Wisecarver, A., Wood, D., Bueghly, J., Chen, Z., Gilbert, A., Gunter, T., Hahn, K. A., Liu, Y., Odell, N., Schmitt, M. H., Velasco, M., Band, R., Bucci, R., Das, A., Dev, N., Goldouzian, R., Hildreth, M., Anampa, K. Hurtado, Jessop, C., Lannon, K., Lawrence, J., Loukas, N., Lutton, D., Marinelli, N., Mcalister, I, Mccauley, T., Mcgrady, C., Mohrman, K., Musienko, Y., Ruchti, R., Siddireddy, P., Townsend, A., Wayne, M., Wightman, A., Zarucki, M., Zygala, L., Bylsma, B., Cardwell, B., Durkin, L. S., Francis, B., Hill, C., Ornelas, M. Nunez, Wei, K., Winer, B. L., Yates, B. R., Addesa, F. M., Bonham, B., Das, P., Dezoort, G., Elmer, P., Frankenthal, A., Greenberg, B., Haubrich, N., Higginbotham, S., Kalogeropoulos, A., Kopp, G., Kwan, S., Lange, D., Marlow, D., Mei, K., Ojalvo, I, Olsen, J., Stickland, D., Tully, C., Norberg, S., Bakshi, A. S., Barnes, V. E., Chawla, R., Das, S., Gutay, L., Jones, M., Jung, A. W., Karmarkar, S., Kondratyev, D., Liu, M., Negro, G., Neumeister, N., Paspalaki, G., Peng, C. C., Piperov, S., Purohit, A., Schulte, J. F., Stojanovic, M., Thieman, J., Wang, F., Xiao, R., Xie, W., Dolen, J., Parashar, N., Baty, A., Decaro, M., Dildick, S., Ecklund, K. M., Freed, S., Gardner, P., Geurts, F. J. M., Li, W., Padley, B. P., Redjimi, R., Shi, W., Leiton, A. G. Stahl, Zhang, L., Bodek, A., de Barbaro, P., Demina, R., Dulemba, J. L., Fallon, C., Ferbel, T., Galanti, M., Garcia-Bellido, A., Hindrichs, O., Khukhunaishvili, A., Ranken, E., Taus, R., Chiarito, B., Chou, J. P., Gandrakota, A., Gershtein, Y., Halkiadakis, E., Hart, A., Heindl, M., Karacheban, O., Laflotte, I, Lath, A., Montalvo, R., Nash, K., Osherson, M., Salur, S., Schnetzer, S., Somalwar, S., Stone, R., Thayil, S. A., Thomas, S., Wang, H., Acharya, H., Delannoy, A. G., Fiorendi, S., Spanier, S., Bouhali, O., Dalchenko, M., Delgado, A., Eusebi, R., Gilmore, J., Huang, T., Kamon, T., Luo, S., Malhotra, S., Mueller, R., Overton, D., Rathjens, D., Safonov, A., Akchurin, N., Damgov, J., Hegde, V, Kunori, S., Lamichhane, K., Mengke, T., Muthumuni, S., Peltola, T., Volobouev, I, Whitbeck, A., Appelt, E., Greene, S., Gurrola, A., Johns, W., Melo, A., Ni, H., Padeken, K., Romeo, F., Sheldon, P., Tuo, S., Velkovska, J., Arenton, M. W., Cox, B., Cummings, G., Hakala, J., Hirosky, R., Joyce, M., Ledovskoy, A., Li, A., Neu, C., Lara, C. E. Perez, Tannenwald, B., White, S., Wolfe, E., Poudyal, N., Black, K., Bose, T., Caillol, C., Dasu, S., De Bruyn, I, Everaerts, P., Fienga, F., Galloni, C., He, H., Herndon, M., Herve, A., Hussain, U., Lanaro, A., Loeliger, A., Loveless, R., Sreekala, J. Madhusudanan, Mallampalli, A., Mohammadi, A., Pinna, D., Savin, A., Shang, V, Smith, W. H., Teague, D., Trembath-Reichert, S., Vetens, W., Universidad de Cantabria, Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Leprince-Ringuet (LLR), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), CMS, A. Tumasyan, W. Adam, J.W. Andrejkovic, T. Bergauer, S. Chatterjee, M. Dragicevic, A. Escalante Del Valle, R. Fr??hwirth, M. Jeitler, N. Krammer, L. Lechner, D. Liko, I. Mikulec, P. Paulitsch, F.M. Pitter, J. Schieck, R. Sch??fbeck, D. Schwarz, S. Templ, W. Waltenberger, C.-E. Wulz, V. Chekhovsky, A. Litomin, V. Makarenko, M.R. Darwish, E.A. De Wolf, T. Janssen, T. Kello, A. Lelek, H. Rejeb Sfar, P. Van Mechelen, S. Van Putte, N. Van Remortel, F. Blekman, E.S. Bol, J. D'Hondt, M. Delcourt, H. El Faham, S. Lowette, S. Moortgat, A. Morton, D. M??ller, A.R. Sahasransu, S. Tavernier, W. Van Doninck, P. Van Mulder, D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, L. Favart, A. Grebenyuk, A.K. Kalsi, K. Lee, M. Mahdavikhorrami, I. Makarenko, L. Moureaux, L. P??tr??, A. Popov, N. Postiau, E. Starling, L. Thoma, M. Vanden Bemden, C. Vander Velde, P. Vanlaer, L. Wezenbeek, T. Corneli, D. Dobur, J. Knolle, L. Lambrecht, G. Mestdach, M. Niedziela, C. Roska, A. Samalan, K. Skovpen, M. Tytgat, B. Vermassen, M. Vit, A. Benecke, A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I.S. Donerta, A. Giammanco, K. Jaffel, Sa. Jain, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio, M. Teklishyn, T.T. Tran, P. Vischia, S. Wertz, G.A. Alve, C. Hensel, A. Morae, W.L. Ald?? J??nior, M. Alves Gallo Pereira, M. Barroso Ferreira Filho, H. Brandao Malbouisson, W. Carvalho, J. Chinellato, E.M. Da Costa, G.G. Da Silveira, D. De Jesus Damiao, S. Fonseca De Souza, D. Matos Figueiredo, C. Mora Herrera, K. Mota Amarilo, L. Mundim, H. Nogima, P. Rebello Tele, A. Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De Araujo, A. Vilela Pereira, C.A. Bernarde, L. Calligari, T.R. Fernandez Perez Tomei, E.M. Gregore, D.S. Lemo, P.G. Mercadante, S.F. Novae, Sandra S. Padula, A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, G. Sultanov, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov, T. Cheng, T. Javaid, M. Mittal, L. Yuan, M. Ahmad, G. Bauer, C. Dozen, Z. Hu, J. Martin, Y. Wang, K. Yi, E. Chapon, G.M. Chen, H.S. Chen, M. Chen, F. Iemmi, A. Kapoor, D. Leggat, H. Liao, Z.-A. Liu, V. Milosevic, F. Monti, R. Sharma, J. Tao, J. Thomas-Wilsker, J. Wang, H. Zhang, J. Zhao, A. Agapito, Y. An, Y. Ban, C. Chen, A. Levin, Q. Li, X. Lyu, Y. Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao, M. Lu, Z. You, X. Gao, H. Okawa, Z. Lin, M. Xiao, C. Avila, A. Cabrera, C. Florez, J. Fraga, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar Gonz??lez, D. Giljanovic, N. Godinovic, D. Lela, I. Puljak, Z. Antunovic, M. Kovac, T. Sculac, V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov, T. Susa, A. Attiki, K. Christoforou, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou, J. Mousa, C. Nicolaou, F. Ptocho, P.A. Razi, H. Rykaczewski, H. Saka, M. Finger, A. Kveton, E. Ayala, E. Carrera Jarrin, A. Ellithi Kamel, E. Salama, A. Lotfy, M.A. Mahmoud, S. Bhowmik, R.K. Dewanjee, K. Ehataht, M. Kadastik, S. Nandan, C. Nielsen, J. Pata, M. Raidal, L. Tani, C. Veelken, P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen, S. Bharthuar, E. Br??cken, F. Garcia, J. Havukainen, M.S. Kim, R. Kinnunen, T. Lamp??n, K. Lassila-Perini, S. Lehti, T. Lind??n, M. Lotti, L. Martikainen, M. Myllym??ki, J. Ott, H. Siikonen, E. Tuominen, J. Tuominiemi, P. Luukka, H. Petrow, T. Tuuva, C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S. Ganjour, A. Givernaud, P. Gra, G. Hamel de Monchenault, P. Jarry, B. Lenzi, E. Locci, J. Malcle, J. Rander, A. Rosowsky, M.??. Sahin, A. Savoy-Navarro, M. Titov, G.B. Yu, S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, A. Cappati, C. Charlot, O. Davignon, B. Diab, G. Falmagne, S. Ghosh, R. Granier de Cassagnac, A. Hakimi, I. Kucher, J. Motta, M. Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, U. Sarkar, J.B. Sauvan, Y. Siroi, A. Tarabini, A. Zabi, A. Zghiche, J.-L. Agram, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C. Collard, D. Darej, J.-C. Fontaine, U. Goerlach, C. Grimault, A.-C. Le Bihan, E. Nibigira, P. Van Hove, E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B. Ille, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L. Mirabito, S. Perrie, K. Shchablo, V. Sordini, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret, G. Adamov, I. Lomidze, Z. Tsamalaidze, V. Botta, L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Paul, N. R??wert, J. Schulz, M. Teroerde, A. Dodonova, D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, T. Hebbeker, K. Hoepfner, F. Ivone, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, L. Vigilante, S. Wiedenbeck, S. Zaleski, C. Dziwok, G. Fl??gge, W. Haj Ahmad, O. Hlushchenko, T. Kre, A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl, T. Ziemon, A. Zotz, H. Aarup Petersen, M. Aldaya Martin, P. Asmu, S. Baxter, M. Bayatmakou, O. Behnke, A. Berm??dez Mart??nez, S. Bhattacharya, A.A. Bin Anuar, K. Borra, D. Brunner, A. Campbell, A. Cardini, C. Cheng, F. Colombina, S. Consuegra Rodr??guez, G. Correia Silva, V. Danilov, M. De Silva, L. Didukh, G. Eckerlin, D. Eckstein, L.I. Estevez Bano, O. Filatov, E. Gallo, A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem, M. Kasemann, H. Kaveh, C. Kleinwort, D. Kr??cker, W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann, R. Mankel, I.-A. Melzer-Pellmann, M. Mendizabal Morentin, J. Metwally, A.B. Meyer, M. Meyer, J. Mnich, A. Mussgiller, Y. Otarid, D. P??rez Ad??n, D. Pitzl, A. Raspereza, B. Ribeiro Lope, J. R??benach, A. Saggio, A. Saibel, M. Savitskyi, M. Scham, V. Scheurer, P. Sch??tze, C. Schwanenberger, A. Singh, R.E. Sosa Ricardo, D. Stafford, N. Tonon, M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, L. Wien, C. Wissing, S. Wuchterl, R. Aggleton, S. Albrecht, S. Bein, L. Benato, P. Connor, K. De Leo, M. Eich, F. Feindt, A. Fr??hlich, C. Garber, E. Garutti, P. Gunnellini, M. Hajheidari, J. Haller, A. Hinzmann, G. Kasieczka, R. Klanner, R. Kogler, T. Kramer, V. Kutzner, J. Lange, T. Lange, A. Lobanov, A. Malara, A. Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, M. Schr??der, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbr??ck, A. Tew, I. Zoi, J. Bechtel, S. Brommer, M. Burkart, E. Butz, R. Caspart, T. Chwalek, W. De Boer, A. Dierlamm, A. Droll, K. El Morabit, N. Faltermann, M. Giffel, J.o. Gosewisch, A. Gottmann, F. Hartmann, C. Heidecker, U. Husemann, P. Keicher, R. Koppenh??fer, S. Maier, M. Metzler, S. Mitra, Th. M??ller, M. Neukum, A. N??rnberg, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simoni, R. Ulrich, J. Van Der Linden, R.F. 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A, Kaadze, K, Maravin, Y, Mitchell, T, Modak, A, Nam, K, Rebassoo, F, Wright, D, Adams, E, Baden, A, Baron, O, Belloni, A, Eno, S, Hadley, N, Jabeen, S, Kellogg, R, Koeth, T, Mignerey, A, Nabili, S, Palmer, C, Seidel, M, Skuja, A, Wang, L, Wong, K, Abercrombie, D, Andreassi, G, Bi, R, Brandt, S, Busza, W, Cali, I, D'Alfonso, M, Eysermans, J, Freer, C, Gomez Ceballos, G, Goncharov, M, Harris, P, Hu, M, Klute, M, Kovalskyi, D, Krupa, J, Mironov, C, Paus, C, Rankin, D, Roland, C, Roland, G, Shi, Z, Stephans, G, Wang, Z, Wyslouch, B, Chatterjee, R, Evans, A, Hansen, P, Hiltbrand, J, Krohn, M, Kubota, Y, Mans, J, Revering, M, Rusack, R, Saradhy, R, Schroeder, N, Strobbe, N, Wadud, M, Bloom, K, Bryson, M, Claes, D, Fangmeier, C, Finco, L, Golf, F, Joo, C, Kravchenko, I, Musich, M, Reed, I, Siado, J, Snow, G, Tabb, W, Yan, F, Zecchinelli, A, Agarwal, G, Bandyopadhyay, H, Hay, L, Iashvili, I, Kharchilava, A, Mclean, C, Nguyen, D, Pekkanen, J, Rappoccio, S, Williams, A, Alverson, G, Barberis, E, Haddad, Y, Hortiangtham, A, Li, J, Madigan, G, Marzocchi, B, Morse, D, Nguyen, V, Orimoto, T, Parker, A, Skinnari, L, Tishelman-Charny, A, Wamorkar, T, Wang, B, Wisecarver, A, Wood, D, Bueghly, J, Chen, Z, Gilbert, A, Gunter, T, Hahn, K, Liu, Y, Odell, N, Schmitt, M, Velasco, M, Band, R, Bucci, R, Das, A, Dev, N, Goldouzian, R, Hildreth, M, Hurtado Anampa, K, Jessop, C, Lannon, K, Lawrence, J, Loukas, N, Lutton, D, Marinelli, N, Mcalister, I, Mccauley, T, Mcgrady, C, Mohrman, K, Musienko, Y, Ruchti, R, Siddireddy, P, Townsend, A, Wayne, M, Wightman, A, Zarucki, M, Zygala, L, Bylsma, B, Cardwell, B, Durkin, L, Francis, B, Hill, C, Nunez Ornelas, M, Wei, K, Winer, B, Yates, B, Addesa, F, Bonham, B, Das, P, Dezoort, G, Elmer, P, Frankenthal, A, Greenberg, B, Haubrich, N, Higginbotham, S, Kalogeropoulos, A, Kopp, G, Kwan, S, Lange, D, Marlow, D, Mei, K, Ojalvo, I, Olsen, J, Stickland, D, Tully, C, Norberg, S, Bakshi, A, Barnes, V, Chawla, R, Das, S, Gutay, L, Jones, M, Jung, A, Karmarkar, S, Kondratyev, D, Liu, M, Negro, G, Neumeister, N, Paspalaki, G, Peng, C, Piperov, S, Purohit, A, Schulte, J, Stojanovic, M, Thieman, J, Wang, F, Xiao, R, Xie, W, Dolen, J, Parashar, N, Baty, A, Decaro, M, Dildick, S, Ecklund, K, Freed, S, Gardner, P, Geurts, F, Li, W, Padley, B, Redjimi, R, Shi, W, Stahl Leiton, A, Zhang, L, Bodek, A, de Barbaro, P, Demina, R, Dulemba, J, Fallon, C, Ferbel, T, Galanti, M, Garcia-Bellido, A, Hindrichs, O, Khukhunaishvili, A, Ranken, E, Taus, R, Chiarito, B, Chou, J, Gandrakota, A, Gershtein, Y, Halkiadakis, E, Hart, A, Heindl, M, Karacheban, O, Laflotte, I, Lath, A, Montalvo, R, Nash, K, Osherson, M, Salur, S, Schnetzer, S, Somalwar, S, Stone, R, Thayil, S, Thomas, S, Wang, H, Acharya, H, Delannoy, A, Fiorendi, S, Spanier, S, Bouhali, O, Dalchenko, M, Delgado, A, Eusebi, R, Gilmore, J, Huang, T, Kamon, T, Luo, S, Malhotra, S, Mueller, R, Overton, D, Rathjens, D, Safonov, A, Akchurin, N, Damgov, J, Hegde, V, Kunori, S, Lamichhane, K, Mengke, T, Muthumuni, S, Peltola, T, Volobouev, I, Whitbeck, A, Appelt, E, Greene, S, Gurrola, A, Johns, W, Melo, A, Ni, H, Padeken, K, Romeo, F, Sheldon, P, Tuo, S, Velkovska, J, Arenton, M, Cox, B, Cummings, G, Hakala, J, Hirosky, R, Joyce, M, Ledovskoy, A, Li, A, Neu, C, Perez Lara, C, Tannenwald, B, White, S, Wolfe, E, Poudyal, N, Black, K, Bose, T, Caillol, C, Dasu, S, De Bruyn, I, Everaerts, P, Fienga, F, Galloni, C, He, H, Herndon, M, Herve, A, Hussain, U, Lanaro, A, Loeliger, A, Loveless, R, Madhusudanan Sreekala, J, Mallampalli, A, Mohammadi, A, Pinna, D, Savin, A, Shang, V, Smith, W, Teague, D, Trembath-Reichert, S, Vetens, W, Damarseçkin, Serdal, European Commission, European Research Council, Ministerio de Ciencia, Innovación y Universidades (España), Principado de Asturias, Agencia Estatal de Investigación (España), Department of Physics, Helsinki Institute of Physics, Physics, Elementary Particle Physics, Faculty of Sciences and Bioengineering Sciences, Vriendenkring VUB, and CMS Collaboration
Subjects: electron, Technology, jet [quark], Physics - Instrumentation and Detectors, particle identification: efficiency, 13000 GeV-cms, Particle, 09 Engineering, PARTICLE PHYSICS, LARGE HADRON COLLIDER, CMS, тау-лептон, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), tau: particle identification, Particle identification methods, cluster finding, particle identification [tau], scattering [p p], [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], gluon: jet, глубокие нейронные сети, High energy physics, Experimental particle physics, LHC, p p: scattering, p p: colliding beams, B: decay, tau: hadronic decay, interaction: gauge, interaction: model, transverse momentum: missing-energy, new physics: search for, mass spectrum: transverse, black hole: quantum, vector boson: mass, W': leptonic decay, sensitivity, leptoquark: coupling, CERN LHC Coll, leptoquark: mass: lower limit, anomaly, channel cross section: upper limit, effective field theory, Higgs, Detectors and Experimental Techniques, quark: jet, Instruments & Instrumentation, physics.ins-det, Instrumentation, Mathematical Physics, 02 Physical Sciences, efficiency [particle identification], tau, hadronic decay, Physics, Settore FIS/01 - Fisica Sperimentale, Instrumentation and Detectors (physics.ins-det), calibration and fitting methods, Nuclear & Particles Physics, поиск кластеров, calibration and fitting method, colliding beams [p p], performance, Particle Physics - Experiment, data analysis method, neural network, p p, scattering, распознавание образов, FOS: Physical sciences, tau: decay modes, 114 Physical sciences, decay modes, quark, Particle identification method, particle identification: performance, statistical analysis, jet [gluon], детекторные системы, muon, Pattern recognition, TeV, ddc:530, ddc:610, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], адронные распады, tau, decay, Calibration and fitting methods, Science & Technology, hep-ex, Large detector systems for particle and astroparticle physics, High Energy Physics::Phenomenology, Cluster finding, идентификация частиц, gluon, jet, Physics and Astronomy, efficiency, performance [particle identification], decay modes [tau], Large detector systems for particle and astroparticle physic, High Energy Physics::Experiment, hadronic decay [tau], identification methods, experimental results
Abstract: CMS Collaboration: et al., A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τh) that originate from genuine tau leptons in the CMS detector against τh candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τh candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine τh to pass the discriminator against jets increases by 10–30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τh reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved τh reconstruction method are validated with LHC proton-proton collision data at √s = 13 TeV., Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIABelgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science — EOS” — be.h project n. 30820817; the Be¼ing Municipal Science&Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy—EXC 2121 “Quantum Universe”—390833306, and under project number 400140256—GRK2497; the Lendület (“Momentum”) Program and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Science and Higher Education and the National Science Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Higher Education, projects no. 0723-2020-0041 and no. FSWW-2020-0008, and the Russian Foundation for Basic Research, project No. 19-42-703014 (Russia); MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, and the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Stavros Niarchos Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; theWelch Foundation, contract C-1845; and theWeston Havens Foundation (U.S.A.).
Published: 2022
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273. Overview of methods for determining the depth distribution of elements in X-ray fluorescence analysis.

Author: Musílek, Ladislav, Prokeš, Radek, and Trojek, Tomáš
Subjects: *X-ray spectroscopy, *X-ray fluorescence, *HISTORICAL literacy, *ART objects, *SURFACES (Technology), *CULTURAL property
Abstract: X-ray fluorescence analysis is a frequently used analytical method in a number of areas for many decades, including the research of objects of cultural heritage. Especially in this area, the irreplaceable advantage is the fact that the measurement can be performed non-destructively and non-invasively on the examined object as a whole. It affects only a relatively thin layer at the surface of the object under investigation, nevertheless, this layer can have a complex structure (various coatings, gilding or other metallization, paint layers, etc.). Information about it can be a valuable contribution both to historical knowledge and to restoration work. During the development of the method, therefore, several procedures were elaborated to estimate the homogeneity or possible inhomogeneities of the investigated layer. In principle, the simplest one is to measure at different beam angles. However, this also changes the depth in the material into which the radiation penetrates. Without changing the measurement geometry, it is possible to use simultaneous detection of two different energy lines of the characteristic radiation of the investigated element (e.g., K α and K β) and to evaluate the depth distribution on the basis of their ratio. Finally, the most sophisticated, but also the most informative, is the confocal arrangement of the spectrometer, where the focus, i.e. the intersection of the beams of incident and emitted radiation very narrowly collimated by the capillary optics, shifts to the depth of the measured material. This review paper summarizes the principles and possibilities of these methods, their advantages and limitations, and thus gives information for their use for specific needs. The use is illustrated by examples of specific measurements of art objects, realized in various laboratories, but especially in the laboratory of the authors of this paper. • XRF analysis can measure only the thin layers of the material surfaces under investigation. • For some cultural heritage objects, it is useful to know the inhomogeneities of surface layers. • Inhomogeneities can be identified by measuring at different angles of the radiation beam. • Two different lines of characteristic radiation of the detected element can also be used. • The most complex information can be obtained by measuring in confocal geometry. [ABSTRACT FROM AUTHOR]
Published: 2022
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274. Music emotion recognition based on combination of multiple features and neural network

Author: Chenguang Zhang, Zhuang Chen, and Jinming Yu
Subjects: Identification methods, Artificial neural network, InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), Computer science, business.industry, Speech recognition, Deep learning, Emotion classification, Representation (arts), Artificial intelligence, Emotion recognition, business, Set (psychology), GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Music emotion recognition
Abstract: Most music messages express emotional signals. The emotional classification of music can organize and retrieve music more conveniently. Music emotion recognition refers to the music emotion detected from people's annotations. Considering the low music emotion recognition rate of traditional music emotion recognition models, a music emotion recognition model based on deep learning is proposed in this paper. It uses deep learning to analyze the dynamic emotion recognition of music and adopts the VA model to generate a set of VA values for a song. Specifically, the related background and emotion classification model of music emotion recognition is first elaborated. Then, the representation of emotion is explained. Next, the overall framework of dynamic music emotion recognition is designed. Finally, the convolutional long- and short-time neural network and BiLSTM are combined to perform music dynamic VA, and this method is compared with other related identification methods.
Published: 2021
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275. Comparative Assessment of Matrix-assisted Laser Desorption Ionization-time of Flight Mass Spectrometry (MALDI-TOF-MS) and Conventional Methods in the Identification of Clinically Relevant Yeasts

Author: Jitendra Kumar Meena, Suneeta Meena, U Sasirekha, Aroop Mohanty, and Neelam Kaistha
Subjects: Identification methods, Chromatography, food.ingredient, business.industry, maldi-tof-ms, General Engineering, Infectious Disease, Matrix assisted laser desorption ionization time of flight, Healthcare Technology, yeast, cma, Mass spectrometry, Matrix-assisted laser desorption/ionization, food, Internal Medicine, candida, identification, Medicine, Agar, business
Abstract: Background: Candida species are generally identified by conventional methods such as germ tube or morphological appearance on cornmeal agar (CMA), biochemical methods using API kits, and molecular biological techniques. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) has revolutionized the identification of fungi reducing the turnaround time of days to minutes. Purpose: To compare the performance of MALDI-TOF MS and conventional methods in the identification of clinically relevant yeasts. Materials and methods: In this study, Candida identifications on CMA are compared with the results obtained on MALDI-TOF MS (Bruker Daltonics, Bremen, Germany). Discrepant results were confirmed by sequencing internal transcribed spacer (ITS) regions of rDNA. Results: A total of 114 clinical Candida species isolated from blood cultures were isolated and identified with conventional methods as well as with the MALDI-TOF-MS system. The agreement between the two test results were analyzed using Inter-rater reliability analysis (Cohen's Kappa) in SPSS Software Version 24 (IBM Corp., Armonk, NY). Overall, there was substantial agreement (Cohen's kappa=0.763) between the two methods. A value between 0.61 and 0.80 is classified under substantial. The most frequently isolated bloodstream Candida species included Candida albicans, C. tropicalis, C. parapsilosis, C.lusitaniae, C. glabrata which were accurately identified by MALDI-TOF-MS. When compared with conventional identification methods, MALDI-TOF-MS results are more reliable and rapid for Candida identification.
Published: 2021
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276. Performance of MALDI–TOF Mass Spectrometry in the Philippines

Author: Tatsuya Ukawa, Melissa Catli, Koya Ariyoshi, Alyannah C. Celis, Maria Cecilia P. Belo, Jaira Mauhay, Morichika Osa, Shuichi Suzuki, Chris Smith, Bhim Gopal Dhoubhadel, Zita dela Merced, Nobu Saito, Christopher M. Parry, Dorcas Valencia Umipig, Shingo Tamaki, Annavi Marie G. Villanueva, and Elizabeth Freda O. Telan
Subjects: 0301 basic medicine, Identification methods, Veterinary medicine, Diagnostic methods, biochemical methods, Definitive Therapy, Concordance, Philippines, 030106 microbiology, Biology, Article, 03 medical and health sciences, LMIC, Species level, Ionization time of flight, General Immunology and Microbiology, Public Health, Environmental and Occupational Health, bacterial infection, MALDI–TOF MS, MALDI-TOF Mass Spectrometry, VITEK2, 030104 developmental biology, Infectious Diseases, Initial cost, Medicine
Abstract: Identification of the causative pathogen in infectious diseases is important for surveillance and to guide treatment. In low- and middle-income countries (LMIC), conventional culture and identification methods, including biochemical methods, are reference-standard. Biochemical methods can lack sensitivity and specificity and have slow turnaround times, causing delays in definitive therapy. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI–TOF MS) is a rapid and accurate diagnostic method. Most studies comparing MALDI–TOF MS and biochemical methods are from high-income countries, with few reports from LMIC with tropical climates. The aim of this study was to assess the performance of MALDI–TOF MS compared to conventional methods in the Philippines. Clinical bacterial or fungal isolates were identified by both MALDI–TOF MS and automated (VITEK2) or manual biochemical methods in the San Lazaro Hospital, Metro Manila, the Philippines. The concordance between MALDI–TOF MS and automated (VITEK2) or manual biochemical methods was analyzed at the species and genus levels. In total, 3530 bacterial or fungal isolates were analyzed. The concordance rate between MALDI–TOF MS and biochemical methods was 96.2% at the species level and 99.9% at the genus level. Twenty-three isolates could not be identified by MALDI–TOF MS. In this setting, MALDI–TOF MS was accurate compared with biochemical methods, at both the genus and the species level. Additionally, MALDI–TOF MS improved the turnaround time for results. These advantages could lead to improved infection management and infection control in low- and middle-income countries, even though the initial cost is high., Tropical Medicine and Infectious Disease, 6(3), art. no. 112; 2021
Published: 2021

277. Tracking the Ancestry of Known and ‘Ghost’ Homeologous Subgenomes in Model Grass Brachypodium Polyploids

Author: John P. Vogel, Robert Hasterok, Sean P. Gordon, Joanna Lusinska, Bruno Contreras-Moreira, Antonio Díaz-Pérez, Luis A. Inda, Rubén Sancho, Des Marais Dl, and Pilar Catalán
Subjects: Identification methods, biology, Extant taxon, Phylogenetics, Evolutionary biology, Genus, Chromosome, Brachypodium, Ploidy, biology.organism_classification, Genome
Abstract: Unraveling the evolution of plant polyploids is a challenge when their diploid progenitor species are extinct or unknown or when their progenitor genome sequences are unavailable. The subgenome identification methods cannot adequately retrieve the homeologous genomes that are present in the allopolyploids if they do not take into account the potential existence of unknown progenitors. We addressed this challenge in the widely distributed dysploid grass genus Brachypodium, which is a model genus for temperate cereals and biofuel grasses. We used a transcriptome-based phylogeny and newly designed subgenome detection algorithms coupled with a comparative chromosome barcoding analysis. Our phylogenomic subgenome detection pipeline was validated in Triticum allopolyploids, which have known progenitor genomes, and was used to infer the identities of three extant and four ‘ghost’ subgenomes in six Brachypodium polyploids (B. mexicanum, B. boissieri, B. retusum, B. phoenicoides, B. rupestre and B. hybridum), of which five contain undescribed homeologous subgenomes. The existence of the seven Brachypodium progenitor genomes in the polyploids was confirmed by their karyotypic barcode profiles. Our results demonstrate that our subgenome detection method is able to uncover the ancestral genomic components of both allo- and autopolyploids.
Published: 2021
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278. Clinical utility of 16S rRNA PCR in pleural infection

Author: Holly Ciesielczuk, Jonathan Lambourne, and Temi Lampejo
Subjects: Adult, Male, 0301 basic medicine, Microbiology (medical), Fastidious organism, Identification methods, Adolescent, 030106 microbiology, medicine.disease_cause, Polymerase Chain Reaction, Microbiology, Pneumococcal Infections, Young Adult, 03 medical and health sciences, 0302 clinical medicine, RNA, Ribosomal, 16S, Streptococcus pneumoniae, medicine, Humans, 030212 general & internal medicine, Child, Pathogen, Aged, Retrospective Studies, Aged, 80 and over, business.industry, Pleural infection, Infant, General Medicine, Middle Aged, Pleural Diseases, medicine.disease, 16S ribosomal RNA, United Kingdom, Empyema, Child, Preschool, Etiology, Pleura, Female, business
Abstract: Pleural infections cause major morbidity and mortality, particularly amongst paediatric and elderly populations. The aetiology is broad, but pleural culture fails to yield a causative pathogen in approximately 40 % of cases. Alternative pathogen identification methods are therefore required. The aim of the study was to investigate the yield from and impact on patient care when performing 16S rRNA PCR on culture-negative pleural fluid specimens and to determine whether any individual laboratory parameters were associated with a positive 16S rRNA PCR result. We conducted a study on 90 patients with suspected pleural infection, who had a culture-negative pleural fluid specimen, which underwent 16S rRNA PCR analysis between August 2017 and June 2019. This study was undertaken at a large NHS Trust in London, UK. Thirty-one per cent of culture-negative pleural fluid specimens tested by 16S rRNA PCR yielded a positive PCR result. Our data demonstrated that 16S rRNA PCR detected a significantly higher proportion of Streptococcus pneumoniae (PP=0.0025) compared with culture-based methods. Of the 25 16S rRNA PCR results that were positive for a causative pathogen, 76 % had a direct impact on clinical management. No single laboratory variable was found to be associated with a positive 16S rRNA PCR result. The findings from our real-world evaluation highlight the importance of 16S rRNA PCR in confirming pleural infection when the aetiology is unknown, and its direct, positive impact on clinical management.
Published: 2021
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279. Gradient-based and multi-innovation gradient-based iterative identification methods for dual-diode photovoltaic cell models

Author: Xiangxiang Meng and Yan Ji
Subjects: Identification methods, Materials science, Gradient based algorithm, Photovoltaic system, Electronic engineering, Diode, Dual (category theory)
Published: 2021
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280. Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC

Author: J. Rauser, John Perry Cumalat, Swagata Mukherjee, K. J. Pena Rodriguez, K. Hurtado Anampa, Maryam Zeinali, Patrick Jarry, Kevin Nash, Thea Klaeboe Aarrestad, Gourab Saha, Valentina Dutta, Toni Sculac, Todor Ivanov, T. Bose, Inkyu Park, Virgil E Barnes, Andrzej Novak, Graham Wilson, Zviad Tsamalaidze, Alessio Ghezzi, Anup Kumar Sikdar, Gerry Bauer, Tai Sakuma, Simone Scarfi, Vukasin Milosevic, Nikkie Deelen, Radia Redjimi, E. M. Da Costa, Prakash Thapa, Giovanni Abbiendi, Olmo Cerri, M. Ramirez-Garcia, Oliver Gutsche, Joachim Baechler, Abideh Jafari, Johannes Haller, Vadim Oreshkin, Byung Hun Oh, Francesca Cavallari, Anterpreet Kaur, Alexey Volkov, Yutaro Iiyama, Dmitri Konstantinov, Francisco Yumiceva, Ritva Kinnunen, Petar Maksimovic, Angelo Giacomo Zecchinelli, Angela Giraldi, Andreas Pfeiffer, Alessandra Cappati, Gilvan Alves, O. Gonzalez Lopez, J. Wang, M. J. Kim, Paolo Dini, S. Bheesette, Olivér Surányi, Fabio Ravera, Maren Tabea Meinhard, Rogelio Reyes-Almanza, Mehmet Kaya, Intae Yu, Anna Elliott-Peisert, Vuko Brigljevic, Lucia Silvestris, David Taylor, B. Jayatilaka, Josry Metwally, Aran Garcia-Bellido, Debarati Roy, Furkan Dolek, Franco Ligabue, Patrick Janot, Jan Tomsa, Thorsten Chwalek, Geng-Yuan Jeng, Irene Bachiller, Tobar Navarro Tobar, Kenneth Long, Andrea Carlo Marini, Alexander Tapper, Vyacheslav Krutelyov, Deborah Pinna, Kinga Anna Wozniak, S. Choi, Stephen Sanders, Caterina Aruta, Flavia Cetorelli, M. A. Mahmoud, Jingzhou Zhao, Branislav Ristic, Balashangar Kailasapathy, Nikola Godinovic, Hualin Mei, Altan Cakir, Paolo Meridiani, Aron Soha, Pedro Silva, Hyunyong Kim, Elizabeth Starling, Nan Lu, René Caspart, Regina Demina, A. Khan, V. K. Muraleedharan Nair Bindhu, Reyer Band, Micael Verissimo de Araujo, Ivan Mikulec, Francesco Micheli, Gul Gokbulut, Alexandre Hakimi, Eric Appelt, Andris Skuja, Nicolaus Kratochwil, Ravi Janjam, Siddharth Narayanan, Kirill Skovpen, L. V. Kardapoltsev, Sourabh Dube, Maria Savina, Leonid Sukhikh, Oz Amram, A. A. Abdelalim, Cecilia Elena Gerber, D. Domínguez Damiani, Diego Beghin, Oleg Teryaev, I. Babounikau, A. Ranieri, Izzeddin Suat Donertas, Livia Soffi, R. B. Garg, Christopher Seez, S. Qazi, Ada Solano, B. De La Cruz, Jose F Benitez, Samuel Higginbotham, Salvatore Costa, J. Puerta Pelayo, Indara Suarez, Anton Karneyeu, Leonard Apanasevich, M. Vander Donckt, Amitabh Lath, Angela Mehta, A. Kasem, Devdatta Majumder, Elisabetta Manca, Alex Dorsett, Christoph Grab, Henning Kirschenmann, Maciej Górski, Andrea Malara, A. Escalante Del Valle, Thomas Bergauer, Savvas Kyriacou, Denis Rathjens, Carlos Willmott, R. Del Burgo, Jorge Fraga, Ankush Reddy Kanuganti, Ivica Puljak, Hans Reithler, Ben Bylsma, Roberto Tenchini, Jochen Schieck, Antonio Cassese, Xuejun Wang, Ivan Ovtin, Giorgio Apollinari, Korbinian Schweiger, Anna Kropivnitskaya, P. de Barbaro, Tahereh Sadat Niknejad, Costas Foudas, Anna Zacharopoulou, David Hamilton, C. A. Salazar González, Colin Bernet, Ugur Kiminsu, G. P. Van Onsem, Martti Raidal, Matthias Schröder, A. Rossi, Alberto Santoro, D. Soldi, H. S. Kim, C. A. Mondragon Herrera, Grigory Safronov, Charalambos Nicolaou, Yousen Zhang, Niki Saoulidou, Sebastian Wozniewski, M. A. Shah, F. Pandolfi, Emanuele Usai, J. Seixas, Merve Ince, Adel Terkulov, Caglar Zorbilmez, Meng Xiao, Jacobo Konigsberg, Bryan Cardwell, Alexandre Zabi, P. C. Tiwari, Emrah Tiras, Burin Asavapibhop, Xunwu Zuo, Sercan Sen, James Bueghly, Matthew Nguyen, D. P. Stickland, V. Rodríguez Bouza, E. Gurpinar Guler, Pedro G Mercadante, Y. Musienko, Dominique Gigi, Teruki Kamon, Raffaello D'Alessandro, Hanwen Wang, Raghunath Pradhan, Andrei Sobol, H. Bakhshiansohi, Gabriella Pasztor, Sebastian Templ, Toyoko Orimoto, Robert Hirosky, Kyungwook Nam, James Buchanan, Etiennette Auffray, Maxim Pieters, M. C. Fouz, Aurélien Carle, Maurizio Biasini, Andrius Juodagalvis, Inna Makarenko, Victor Shang, S. Sánchez Navas, Nicolas Stylianou, Hans-Christian Kaestli, G. L. Pinna Angioni, Tom Cornelis, Shivali Malhotra, O. Davignon, Tanmay Mudholkar, Ennio Monteil, Aldo Penzo, T. R. Fernandez Perez Tomei, F. De Guio, Jose Chinellato, Richard Cavanaugh, D. R. Muller, A. Yagil, Sinan Sagir, Ulrich Heintz, M. Kovac, Bjorn Burkle, Minseok Oh, Bolek Wyslouch, Nicolas Tonon, Artur Gottmann, Muhammad Imran Malik Awan, Alexander Dierlamm, Tomasz Gadek, Aimilios Ioannou, Sridhara Dasu, Emilio Meschi, W. T. Lin, Dylan Hsu, Pierluigi Paolucci, N. Vanegas Arbelaez, Federico Siviero, Andrés G Delannoy, Christopher Hill, Kerstin Borras, Jennifer Ngadiuba, Giacomo Fedi, Liliana Teodorescu, Marta Felcini, Maria Spiropulu, Claudia Pistone, Wolfgang Adam, Christian Dorfer, Matteo Cremonesi, Erhan Gülmez, Kaori Maeshima, Aurelijus Rinkevicius, Martin Erdmann, P. Busson, Aaron Bundock, Katerina Lipka, Martin Kirakosyan, Negin Shafiei, Kenichi Hatakeyama, Demetra Tsiakkouri, Shawn Zaleski, Nitish Dhingra, Stephen Butalla, Raman Khurana, H.Q. Zhang, J. Mejia Guisao, Y-J Lee, Alexander Spiridonov, Jaana Kristiina Heikkilä, Aliya Nigamova, Rishi Patel, Zuhal Seyma Demiroglu, I. A. Melzer-Pellmann, Matthew Herndon, J. Suarez Gonzalez, Alexandros Attikis, Y. D. Oh, Fabrizio Palla, Andrew Wisecarver, Felipe Ramirez, Michal Szleper, Chia-Ming Kuo, P. Cao, Marco Rovere, Bradley Cox, Marco Toliman Lucchini, You-ying Li, Berkan Kaynak, Samuel Bein, Nikitas Loukas, Marc Osherson, J. H. Kim, Daniel Spitzbart, Georgios Bakas, Y. Wang, P. Major, Patricia McBride, Krzysztof Doroba, Marc Dünser, Vipin Bhatnagar, T. Liu, Anna Woodard, Nadia Pastrone, Candan Dozen, Neil Schroeder, Andreas Hinzmann, Marcin Konecki, Dylan Rankin, Andrew Brinkerhoff, Francesca Nessi-Tedaldi, C. Adloff, Yongho Jeong, Mauricio Thiel, Thomas Ferguson, Ilya Gorbunov, Metin Yalvac, Stephan Lammel, V. Trofimov, John Strologas, J. Fernandez Menendez, Peter Timothy Cox, Torben Lange, Michael Wayne Arenton, Andreas Werner Jung, Chandiprasad Kar, Vilius Cepaitis, Roberto Rossin, Elisa Fontanesi, Vasilije Perovic, Thomas James, Artur Lobanov, Nathaniel Pastika, Sevgi Tekten, Kyle Tos, Pieter David, Janek Bechtel, Ioannis Evangelou, C. K. Mackay, Marcos Cerrada, Jose Flix, A. Vorobyev, E. E. Boos, Alexander Ershov, Andreas Psallidas, C. Vander Velde, R. Bhattacharya, Mate Csanad, H. S. Chen, Michael Mulhearn, Jovan Milosevic, Kadri Ozdemir, Heriberto Castilla-Valdez, F. Vazquez Valencia, Alexander Nikitenko, Jasvinder A. Singh, M. Rahmani, A. A. 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Sturdy J., Zuo X., Adams T., Askew A., Diaz D., Habibullah R., Hagopian S., Hagopian V., Johnson K.F., Khurana R., Kolberg T., Martinez G., Prosper H., Schiber C., Yohay R., Zhang J., Baarmand M.M., Butalla S., Elkafrawy T., Hohlmann M., Kumar Verma R., Noonan D., Rahmani M., Saunders M., Yumiceva F., Adams M.R., Apanasevich L., Becerril Gonzalez H., Cavanaugh R., Chen X., Dittmer S., Evdokimov O., Gerber C.E., Hangal D.A., Hofman D.J., Mills C., Oh G., Roy T., Tonjes M.B., Varelas N., Viinikainen J., Wang X., Wu Z., Ye Z., Alhusseini M., Dilsiz K., Durgut S., Gandrajula R.P., Haytmyradov M., Khristenko V., Koseyan O.K., Merlo J.-P., Mestvirishvili A., Moeller A., Nachtman J., Ogul H., Onel Y., Ozok F., Penzo A., Snyder C., Tiras E., Wetzel J., Amram O., Blumenfeld B., Corcodilos L., Eminizer M., Gritsan A.V., Kyriacou S., Maksimovic P., Mantilla C., Roskes J., Swartz M., Vami T.A., Baldenegro Barrera C., Baringer P., Bean A., Bylinkin A., Isidori T., Khalil S., King J., Krintiras G., Kropivnitskaya A., Lindsey C., Minafra N., Murray M., Rogan C., Royon C., Sanders S., Schmitz E., Tapia Takaki J.D., Williams J., Wilson G., Duric S., Ivanov A., Kaadze K., Kim D., Maravin Y., Mitchell T., Modak A., Mohammadi A., Rebassoo F., Wright D., Adams E., Baden A., Baron O., Belloni A., Eno S.C., Feng Y., Hadley N.J., Jabeen S., Jeng G.Y., Kellogg R.G., Koeth T., Mignerey A.C., Nabili S., Seidel M., Skuja A., Tonwar S.C., Wang L., Wong K., Abercrombie D., Allen B., Bi R., Brandt S., Busza W., Cali I.A., D'Alfonso M., Gomez Ceballos G., Goncharov M., Harris P., Hsu D., Hu M., Klute M., Kovalskyi D., Krupa J., Lee Y.-J., Luckey P.D., Maier B., Marini A.C., Mironov C., Narayanan S., Niu X., Paus C., Rankin D., Roland C., Roland G., Shi Z., Stephans G.S.F., Sumorok K., Tatar K., Velicanu D., Wang T.W., Wang Z., Wyslouch B., Chatterjee R.M., Evans A., Hansen P., Hiltbrand J., Krohn M., Kubota Y., Lesko Z., Mans J., Revering M., Rusack R., Saradhy R., Schroeder N., Strobbe N., Wadud M.A., Acosta J.G., Oliveros S., Bloom K., Claes D.R., Fangmeier C., Finco L., Golf F., Gonzalez Fernandez J.R., Joo C., Kravchenko I., Siado J.E., Snow G.R., Tabb W., Yan F., Agarwal G., Bandyopadhyay H., Hay L., Iashvili I., Kharchilava A., McLean C., Nguyen D., Pekkanen J., Rappoccio S., Alverson G., Barberis E., Freer C., Haddad Y., Hortiangtham A., Li J., Madigan G., Marzocchi B., Morse D.M., Nguyen V., Orimoto T., Parker A., Skinnari L., Tishelman-Charny A., Wamorkar T., Wang B., Wisecarver A., Wood D., Bueghly J., Chen Z., Gilbert A., Gunter T., Hahn K.A., Odell N., Schmitt M.H., Sung K., Velasco M., Bucci R., Dev N., Goldouzian R., Hildreth M., Hurtado Anampa K., Jessop C., Lannon K., Loukas N., Marinelli N., McAlister I., Meng F., Mohrman K., Musienko Y., Ruchti R., Siddireddy P., Wayne M., Wightman A., Wolf M., Zygala L., Alimena J., Bylsma B., Cardwell B., Durkin L.S., Francis B., Hill C., Lefeld A., Winer B.L., Yates B.R., Bonham B., Das P., Dezoort G., Elmer P., Greenberg B., Haubrich N., Higginbotham S., Kalogeropoulos A., Kopp G., Kwan S., Lange D., Lucchini M.T., Luo J., Marlow D., Mei K., Ojalvo I., Olsen J., Palmer C., Piroue P., Stickland D., Tully C., Norberg S., Barnes V.E., Chawla R., Das S., Gutay L., Jones M., Jung A.W., Liu M., Negro G., Neumeister N., Peng C.C., Piperov S., Purohit A., Schulte J.F., Stojanovic M., Trevisani N., Wang F., Wildridge A., Xiao R., Xie W., Dolen J., Parashar N., Baty A., Dildick S., Ecklund K.M., Freed S., Geurts F.J.M., Li W., Padley B.P., Redjimi R., Roberts J., Rorie J., Shi W., Stahl Leiton A.G., Bodek A., De Barbaro P., Demina R., Dulemba J.L., Fallon C., Ferbel T., Galanti M., Garcia-Bellido A., Hindrichs O., Khukhunaishvili A., Ranken E., Taus R., Chiarito B., Chou J.P., Gandrakota A., Gershtein Y., Halkiadakis E., Hart A., Heindl M., Hughes E., Kaplan S., Karacheban O., Laflotte I., Lath A., Montalvo R., Nash K., Osherson M., Salur S., Schnetzer S., Somalwar S., Stone R., Thayil S.A., Thomas S., Acharya H., Delannoy A.G., Spanier S., Bouhali O., Dalchenko M., Delgado A., Eusebi R., Gilmore J., Huang T., Kamon T., Luo S., Malhotra S., Mueller R., Overton D., Pernie L., Rathjens D., Safonov A., Akchurin N., Damgov J., Hegde V., Kunori S., Lamichhane K., Mengke T., Muthumuni S., Peltola T., Undleeb S., Volobouev I., Whitbeck A., Appelt E., Greene S., Gurrola A., Janjam R., Johns W., Maguire C., Melo A., Ni H., Padeken K., Romeo F., Sheldon P., Tuo S., Velkovska J., Arenton M.W., Cox B., Cummings G., Hakala J., Hirosky R., Joyce M., Ledovskoy A., Li A., Neu C., Tannenwald B., Wolfe E., Karchin P.E., Poudyal N., Thapa P., Black K., Bose T., Buchanan J., Caillol C., Dasu S., De Bruyn I., Everaerts P., Galloni C., He H., Herndon M., Herve A., Hussain U., Lanaro A., Loeliger A., Loveless R., Madhusudanan Sreekala J., Mallampalli A., Pinna D., Savin A., Shang V., Smith W.H., Teague D., Trembath-Reichert S., UAM. Departamento de Física Teórica, Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Leprince-Ringuet (LLR), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Institut Pluridisciplinaire Hubert Curien (IPHC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Institut de Physique Nucléaire de Lyon (IPNL), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), CMS, Universidad de Cantabria, Özdemir, Kadri, Yerevan Physics Institute, Institut für Hochenergiephysik, Institute for Nuclear Problems, Universiteit Antwerpen, Vrije Universiteit Brussel, Université Libre de Bruxelles, Ghent University, Université Catholique de Louvain, Centro Brasileiro de Pesquisas Fisicas, Universidade do Estado do Rio de Janeiro (UERJ), Universidade Estadual Paulista (UNESP), Universidade Federal do ABC (UFABC), Bulgarian Academy of Sciences, University of Sofia, Beihang University, Tsinghua University, Institute of High Energy Physics, Peking University, Sun Yat-Sen University, Fudan University, Zhejiang University, Universidad de Los Andes, Universidad de Antioquia, Mechanical Engineering and Naval Architecture, Faculty of Science, Institute Rudjer Boskovic, University of Cyprus, Charles University, Escuela Politecnica Nacional, Universidad San Francisco de Quito, Egyptian Network of High Energy Physics, Fayoum University, National Institute of Chemical Physics and Biophysics, University of Helsinki, Lappeenranta University of Technology, Université Paris-Saclay, Institut Polytechnique de Paris, IPHC UMR 7178, Institut de Physique Nucléaire de Lyon, Georgian Technical University, I. Physikalisches Institut, III. Physikalisches Institut A, III. Physikalisches Institut B, Deutsches Elektronen-Synchrotron, University of Hamburg, Karlsruher Institut fuer Technologie, NCSR Demokritos, National and Kapodistrian University of Athens, National Technical University of Athens, University of Ioánnina, Eötvös Loránd University, Wigner Research Centre for Physics, Institute of Nuclear Research ATOMKI, University of Debrecen, Karoly Robert Campus, Indian Institute of Science (IISc), HBNI, Panjab University, University of Delhi, Indian Institute of Technology Madras, Bhabha Atomic Research Centre, Tata Institute of Fundamental Research-A, Tata Institute of Fundamental Research-B, Indian Institute of Science Education and Research (IISER), Isfahan University of Technology, Institute for Research in Fundamental Sciences (IPM), University College Dublin, INFN Sezione di Bari, Università di Bari, Politecnico di Bari, INFN Sezione di Bologna, Università di Bologna, INFN Sezione di Catania, Università di Catania, INFN Sezione di Firenze, Università di Firenze, INFN Laboratori Nazionali di Frascati, INFN Sezione di Genova, Università di Genova, INFN Sezione di Milano-Bicocca, Università di Milano-Bicocca, INFN Sezione di Napoli, Università di Napoli Federico II, Università della Basilicata, Università G. 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Lebedev Physical Institute, Lomonosov Moscow State University, Novosibirsk State University (NSU), National Research Tomsk Polytechnic University, Tomsk State University, VINCA Institute of Nuclear Sciences, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Universidad Autónoma de Madrid, Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Colombo, University of Ruhuna, European Organization for Nuclear Research, Paul Scherrer Institut, Institute for Particle Physics and Astrophysics (IPA), Universität Zürich, National Central University, National Taiwan University (NTU), Science and Art Faculty, Physics Department, Bogazici University, Istanbul Technical University, Istanbul University, National Academy of Science of Ukraine, Kharkov Institute of Physics and Technology, University of Bristol, Rutherford Appleton Laboratory, Imperial College, Brunel University, Baylor University, Catholic University of America, University of Alabama, Boston University, Brown University, Davis, University of California, Riverside, San Diego, Santa Barbara, California Institute of Technology, Carnegie Mellon University, University of Colorado Boulder, Cornell University, Fermi National Accelerator Laboratory, University of Florida, Florida State University, Florida Institute of Technology, University of Illinois at Chicago (UIC), University of Iowa, Johns Hopkins University, University of Kansas, Kansas State University, Lawrence Livermore National Laboratory, University of Maryland, Massachusetts Institute of Technology, University of Minnesota, University of Mississippi, University of Nebraska-Lincoln, State University of New York at Buffalo, Northeastern University, Northwestern University, University of Notre Dame, Ohio State University, Princeton University, University of Puerto Rico, Purdue University, Purdue University Northwest, Rice University, University of Rochester, the State University of New Jersey, University of Tennessee, Texas A and M University, Texas Tech University, Vanderbilt University, University of Virginia, Wayne State University, University of Wisconsin-Madison, Vienna University of Technology, Technology and Maritime Transport, Universidade Estadual de Campinas (UNICAMP), Federal University of Rio Grande Do sul, Universidade Federal de Mato Grosso do Sul (UFMS), Universidade Federal de Pelotas, Nanjing Normal University, University of Chinese Academy of Sciences, Cairo University, Helwan University, Zewail City of Science and Technology, Suez University, British University in Egypt, Université de Haute Alsace, Erzincan Binali Yildirim University, Brandenburg University of Technology, Assiut University, Eötvös Lorand University, IIT Bhubaneswar, Institute of Physics, G.H.G. Khalsa College, Shoolini University, University of Hyderabad, University of Visva-Bharati, Indian Institute of Technology (IIT), Sharif University of Technology, University of Science and Technology of Mazandaran, Energy and Sustainable Economic Development, Centro Siciliano di Fisica Nucleare e di Struttura della Materia, Università di Napoli 'Federico II', Consejo Nacional de Ciencia y Tecnología, Uzbekistan Academy of Sciences, St. Petersburg State Polytechnical University, Budker Institute of Nuclear Physics, University of Belgrade, Sri Lanka, Universität Zurich, Ecole Polytechnique Federale Lausanne, Stefan Meyer Institute for Subatomic Physics, IN2P3-CNRS, Şirnak University, Research Center of Experimental Health Science, Beykent University, Application and Research Center for Advanced Studies, Mersin University, Piri Reis University, Adiyaman University, Ozyegin University, Izmir Institute of Technology, Necmettin Erbakan University, Bozok Universitetesi Rektörlügü, Marmara University, Milli Savunma University, Kafkas University, Istanbul Bilgi University, Hacettepe University, University of Southampton, IPPP Durham University, Bethel University, Karamanoǧlu Mehmetbey University, Ain Shams University, Bingol University, Sinop University, Istanbul, Erciyes University, Texas A and M University at Qatar, Sağır, Sinan, Damarseçkin, Serdal, Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), European Commission, Centre National de la Recherche Scientifique (France), European Research Council, Principado de Asturias, Ministerio de Economía y Competitividad (España), Sirunyan, A. M., Tumasyan, A., Adam, W., Bergauer, T., Dragicevic, M., Del Valle, A. Escalante, Fruehwirth, R., Jeitler, M., Krammer, N., Lechner, L., Liko, D., Pitters, F. M., Rad, N., Schieck, J., Schoefbeck, R., Spanring, M., Templ, S., Waltenberger, W., Wulz, C-E, Zarucki, M., Litomin, A., Gonzalez, J. Suarez, Darwish, M. R., De Wolf, E. A., Janssen, X., Kello, T., Lelek, A., Pieters, M., Sfar, H. Rejeb, Van Haevermaet, H., Van Mechelen, P., Van Putte, S., Van Remortel, N., Blekman, F., Bols, E. S., Chhibra, S. S., De Clercq, J., Lontkovskyi, D., Lowette, S., Moortgat, S., Morton, A., Muller, D., Python, Q., Tavernier, S., Van Doninck, W., Van Mulders, P., Beghin, D., Bilin, B., Clerbaux, B., De Lentdecker, G., Dorney, B., Favart, L., Grebenyuk, A., Kalsi, A. 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A., Calligaris, L., Tomei, T. R. Fernandez Perez, Gregores, E. M., Lemos, D. S., Mercadante, P. G., Novaes, S. F., Padula, Sandra S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Dimitrov, A., Ivanov, T., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Cheng, T., Fang, W., Guo, Q., Mittal, M., Wang, H., Yuan, L., Ahmad, M., Bauer, G., Hu, Z., Wang, Y., Yi, K., Chapon, E., Chen, G. M., Chen, H. S., Chen, M., Javaid, T., Kapoor, A., Leggat, D., Liao, H., Liu, Z-A, Sharma, R., Spiezia, A., Tao, J., Thomas-wilsker, J., Wang, J., Zhang, H., Zhang, S., Zhao, J., Agapitos, A., Ban, Y., Chen, C., Huang, Q., Levin, A., Li, Q., Lu, M., Lyu, X., Mao, Y., Qian, S. J., Wang, D., Wang, Q., Xiao, J., You, Z., Gao, X., Xiao, M., Avila, C., Cabrera, A., Florez, C., Fraga, J., Sarkar, A., Segura Delgado, M. A., Jaramillo, J., Mejia Guisao, J., Ramirez, F., Ruiz Alvarez, J. D., Salazar Gonzalez, C. A., Vanegas Arbelaez, N., Giljanovic, D., Godinovic, N., Lelas, D., Antunovic, Z., Kovac, M., Sculac, T., Ferencek, D., Majumder, D., Roguljic, M., Starodumov, A., Susa, T., Ather, M. W., Attikis, A., Erodotou, E., Ioannou, A., Kole, G., Kolosova, M., Konstantinou, S., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Saka, H., Tsiakkouri, D., Finger, M., Finger Jr, M. Jr, Kveton, A., Tomsa, J., Ayala, E., Jarrin, E. Carrera, Abdalla, H., Abdelalim, A. A., Assran, Y., Lotfy, A., Mahmoud, M. A., Bhowmik, S., De Oliveira, A. Carvalho Antune, Dewanjee, R. K., Ehataht, K., Kadastik, M., Pata, J., Raidal, M., Veelken, C., Eerola, P., Forthomme, L., Kirschenmann, H., Osterberg, K., Voutilainen, M., Brucken, E., Garcia, F., Havukainen, J., Karimaki, V, Kim, M. S., Kinnunen, R., Lampen, T., Lassila-Perini, K., Lehti, S., Linden, T., Siikonen, H., Tuominen, E., Tuominiemi, J., Luukka, P., Tuuva, T., Amendola, C., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J. L., Ferri, F., Ganjour, S., Givernaud, A., Gras, P., de Monchenault, G. Hamel, Jarry, P., Lenzi, B., Locci, E., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. O., Savoy-Navarro, A., Titov, M., Yu, G. B., Ahuja, S., Beaudette, F., Bonanomi, M., Perraguin, A. Buchot, Busson, P., Charlot, C., Davignon, O., Diab, B., Falmagne, G., de Cassagnac, R. Granier, Hakimi, A., Lobanov, A., Perez, C. Martin, Nguyen, M., Ochando, C., Paganini, P., Rembser, J., Salerno, R., Sauvan, J. B., Sirois, Y., Zabi, A., Zghiche, A., Agram, J-L, Andrea, J., Bloch, D., Bourgatte, G., Brom, J-M, Chabert, E. 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J., Clement, E., Cussans, D., Flacher, H., Goldstein, J., Heath, G. P., Heath, H. F., Kreczko, L., Krikler, B., Paramesvaran, S., Sakuma, T., El Nasr-Storey, S. Seif, Smith, V. J., Stylianou, N., Taylor, J., Titterton, A., Bell, K. W., Brew, C., Brown, R. M., Cockerill, D. J. A., Ellis, K., V, Harder, K., Harper, S., Linacre, J., Manolopoulos, K., Newbold, D. M., Olaiya, E., Petyt, D., Reis, T., Schuh, T., Shepherd-Themistocleous, C. H., Thea, A., Tomalin, I. R., Williams, T., Bainbridge, R., Bloch, P., Bonomally, S., Borg, J., Breeze, S., Buchmuller, O., Bundock, A., Chahal, G. S., Colling, D., Dauncey, P., Davies, G., Della Negra, M., Fedi, G., Hall, G., Iles, G., Langford, J., Lyons, L., Magnan, A-M, Malik, S., Martelli, A., Nash, J., Pesaresi, M., Raymond, D. M., Richards, A., Rose, A., Scott, E., Seez, C., Shtipliyski, A., Stoye, M., Tapper, A., Uchida, K., Virdee, T., Wardle, N., Webb, S. N., Winterbottom, D., Zecchinelli, A. G., Cole, J. E., Hobson, P. R., Khan, A., Kyberd, P., Mackay, C. K., Reid, I. D., Teodorescu, L., Zahid, S., Abdullin, S., Brinkerhoff, A., Call, K., Caraway, B., Dittmann, J., Hatakeyama, K., Kanuganti, A. R., Madrid, C., Mcmaster, B., Pastika, N., Sawant, S., Smith, C., Wilson, J., Bartek, R., Dominguez, A., Uniyal, R., Hernandez, A. M. Varga, Buccilli, A., Charaf, O., Cooper, S., Di Croce, D., Gleyzer, S., Henderson, C., Perez, C. U., Rumerio, P., West, C., Akpinar, A., Albert, A., Arcaro, D., Cosby, C., Demiragli, Z., Gastler, D., Rohlf, J., Salyer, K., Sperka, D., Spitzbart, D., Yuan, S., Zou, D., Benelli, G., Burkle, B., Coubez, X., Cutts, D., Duh, Y. T., Hadley, M., Heintz, U., Hogan, J. M., Kwok, K. H. M., Laird, E., Landsberg, G., Lau, K. T., Narain, M., Sagir, S., Syarif, R., Usai, E., Wong, W. Y., Yu, D., Zhang, W., Band, R., Brainerd, C., Breedon, R., Sanchez, M. Calderon De la Barca, Chertok, M., Conway, J., Conway, R., Cox, P. T., Erbacher, R., Flores, C., Funk, G., Jensen, F., Ko, W., Kukral, O., Lander, R., Mulhearn, M., Pellett, D., Pilot, J., Shi, M., Taylor, D., Tos, K., Tripathi, M., Yao, Y., Zhang, F., Bachtis, M., Cousins, R., Dasgupta, A., Hamilton, D., Hauser, J., Ignatenko, M., Iqbal, M. A., Lam, T., Mccoll, N., Nash, W. A., Regnard, S., Saltzberg, D., Schnaible, C., Stone, B., Valuev, V., Burt, K., Chen, Y., Clare, R., Gary, J. W., Hanson, G., Karapostoli, G., Long, O. R., Manganelli, N., Negrete, M. Olmedo, Si, W., Wimpenny, S., Zhang, Y., Branson, J. G., Cittolin, S., Cooperstein, S., Deelen, N., Duarte, J., Gerosa, R., Gilbert, D., Letts, J., Masciovecchio, M., May, S., Padhi, S., Pieri, M., Tadel, M., Vartak, A., Wurthwein, F., Yagil, A., Amin, N., Campagnari, C., Citron, M., Dorsett, A., Incandela, J., Kilpatrick, M., Marsh, B., Mei, H., Ovcharova, A., Qu, H., Quinnan, M., Richman, J., Sarica, U., Stuart, D., Wang, S., Bornheim, A., Cerri, O., Lawhorn, J. M., Lu, N., Mao, J., Newman, H. B., Ngadiuba, J., Nguyen, T. Q., Spiropulu, M., Vlimant, J. R., Wang, C., Xie, S., Zhang, Z., Zhu, R. Y., Alison, J., Andrews, M. B., Ferguson, T., Mudholkar, T., Paulini, M., Cumalat, J. P., Ford, W. T., Macdonald, E., Patel, R., Perloff, A., Stenson, K., Ulmer, K. A., Wagner, S. R., Alexander, J., Cheng, Y., Chu, J., Cranshaw, D. J., Datta, A., Frankenthal, A., Mcdermott, K., Monroy, J., Patterson, J. R., Quach, D., Ryd, A., Sun, W., Tan, S. M., Tao, Z., Thom, J., Wittich, P., Zientek, M., Albrow, M., Alyari, M., Apollinari, G., Apresyan, A., Apyan, A., Bauerdick, L. A. T., Beretvas, A., Berry, D., Berryhill, J., Bhat, P. C., Burkett, K., Butler, J. N., Canepa, A., Cerati, G. B., Cheung, H. W. K., Chlebana, F., Cremonesi, M., Elvira, V. D., Freeman, J., Gecse, Z., Gray, L., Green, D., Grunendahl, S., Gutsche, O., Harris, R. M., Hasegawa, S., Heller, R., Herwig, T. C., Hirschauer, J., Jayatilaka, B., Jindariani, S., Johnson, M., Joshi, U., Klabbers, P., Klijnsma, T., Klima, B., Kortelainen, M. J., Lammel, S., Lincoln, D., Lipton, R., Liu, T., Lykken, J., Maeshima, K., Mason, D., Mcbride, P., Merkel, P., Mrenna, S., Nahn, S., Pedro, K., Pena, C., Prokofyev, O., Ravera, F., Hall, A. Reinsvold, Ristori, L., Schneider, B., Sexton-Kennedy, E., Smith, N., Soha, A., Spalding, W. J., Spiegel, L., Stoynev, S., Strait, J., Taylor, L., Tkaczyk, S., Tran, N., Uplegger, L., Vaandering, E. W., Weber, H. A., Woodard, A., Acosta, D., Avery, P., Bourilkov, D., Cadamuro, L., Errico, F., Field, R. D., Guerrero, D., Joshi, B. M., Kim, M., Konigsberg, J., Korytov, A., Lo, K. H., Matchev, K., Menendez, N., Mitselmakher, G., Rosenzweig, D., Shi, K., Sturdy, J., Zuo, X., Adams, T., Askew, A., Diaz, D., Habibullah, R., Hagopian, S., Johnson, K. F., Khurana, R., Kolberg, T., Martinez, G., Prosper, H., Schiber, C., Yohay, R., Zhang, J., Baarmand, M. M., Butalla, S., Elkafrawy, T., Hohlmann, M., Verma, R. Kumar, Noonan, D., Rahmani, M., Saunders, M., Yumiceva, F., Adams, M. R., Apanasevich, L., Gonzalez, H. Becerril, Cavanaugh, R., Chen, X., Dittmer, S., Evdokimov, O., Gerber, C. E., Hangal, D. A., Hofman, D. J., Mills, C., Oh, G., Roy, T., Tonjes, M. B., Varelas, N., Viinikainen, J., Wang, X., Wu, Z., Ye, Z., Alhusseini, M., Dilsiz, K., Durgut, S., Gandrajula, R. P., Haytmyradov, M., Koseyan, O. K., Merlo, J-P, Mestvirishvili, A., Moeller, A., Nachtman, J., Ogul, H., Onel, Y., Ozok, F., Penzo, A., Snyder, C., Tiras, E., Wetzel, J., Amram, O., Blumenfeld, B., Corcodilos, L., Eminizer, M., Gritsan, A., Kyriacou, S., Maksimovic, P., Mantilla, C., Roskes, J., Swartz, M., Vami, T. A., Barrera, C. Baldenegro, Baringer, P., Bean, A., Bylinkin, A., Isidori, T., Khalil, S., King, J., Krintiras, G., Kropivnitskaya, A., Lindsey, C., Minafra, N., Murray, M., Rogan, C., Royon, C., Sanders, S., Schmitz, E., Takaki, J. D. Tapia, Williams, J., Wilson, G., Duric, S., Ivanov, A., Kaadze, K., Kim, D., Maravin, Y., Mitchell, T., Modak, A., Mohammadi, A., Rebassoo, F., Wright, D., Adams, E., Baden, A., Baron, O., Belloni, A., Eno, S. C., Feng, Y., Hadley, N. J., Jabeen, S., Jeng, G. Y., Kellogg, R. G., Koeth, T., Mignerey, A. C., Nabili, S., Seidel, M., Skuja, A., Tonwar, S. C., Wang, L., Wong, K., Abercrombie, D., Allen, B., Bi, R., Brandt, S., Busza, W., Cali, I. A., Ceballos, G. Gomez, Goncharov, M., Harris, P., Hsu, D., Hu, M., Klute, M., Kovalskyi, D., Krupa, J., Lee, Y-J, Luckey, P. D., Maier, B., Marini, A. C., Mironov, C., Narayanan, S., Niu, X., Paus, C., Rankin, D., Roland, C., Roland, G., Shi, Z., Stephans, G. S. F., Sumorok, K., Tatar, K., Velicanu, D., Wang, T. W., Wang, Z., Wyslouch, B., Chatterjee, R. M., Evans, A., Hansen, P., Hiltbrand, J., Jain, Sh, Krohn, M., Kubota, Y., Lesko, Z., Mans, J., Revering, M., Rusack, R., Saradhy, R., Schroeder, N., Strobbe, N., Wadud, M. A., Acosta, J. G., Oliveros, S., Bloom, K., Claes, D. R., Fangmeier, C., Finco, L., Golf, F., Fernandez, J. R. Gonzalez, Joo, C., Siado, J. E., Snow, G. R., Tabb, W., Yan, F., Agarwal, G., Bandyopadhyay, H., Hay, L., Iashvili, I., Kharchilava, A., Mclean, C., Nguyen, D., Pekkanen, J., Rappoccio, S., Alverson, G., Barberis, E., Freer, C., Haddad, Y., Hortiangtham, A., Li, J., Madigan, G., Marzocchi, B., Morse, D. M., Orimoto, T., Parker, A., Skinnari, L., Tishelman-Charny, A., Wamorkar, T., Wang, B., Wisecarver, A., Wood, D., Bueghly, J., Chen, Z., Gilbert, A., Gunter, T., Hahn, K. A., Odell, N., Schmitt, M. H., Sung, K., Velasco, M., Bucci, R., Dev, N., Goldouzian, R., Hildreth, M., Anampa, K. Hurtado, Jessop, C., Lannon, K., Loukas, N., Marinelli, N., Meng, F., Mohrman, K., Musienko, Y., Ruchti, R., Siddireddy, P., Wayne, M., Wightman, A., Wolf, M., Zygala, L., Alimena, J., Bylsma, B., Cardwell, B., Durkin, L. S., Francis, B., Hill, C., Lefeld, A., Winer, B. L., Yates, B. R., Bonham, B., Das, P., Dezoort, G., Elmer, P., Greenberg, B., Haubrich, N., Higginbotham, S., Kalogeropoulos, A., Kopp, G., Kwan, S., Lange, D., Lucchini, M. T., Luo, J., Marlow, D., Mei, K., Olsen, J., Palmer, C., Piroue, P., Stickland, D., Tully, C., Norberg, S., Barnes, V. E., Chawla, R., Das, S., Gutay, L., Jones, M., Jung, A. W., Liu, M., Negro, G., Neumeister, N., Peng, C. C., Piperov, S., Purohit, A., Schulte, J. F., Stojanovic, M., Trevisani, N., Wang, F., Wildridge, A., Xiao, R., Xie, W., Dolen, J., Parashar, N., Baty, A., Dildick, S., Ecklund, K. M., Freed, S., Geurts, F. J. M., Li, W., Padley, B. P., Redjimi, R., Roberts, J., Rorie, J., Shi, W., Leiton, A. G. Stahl, Bodek, A., de Barbaro, P., Demina, R., Dulemba, J. L., Fallon, C., Ferbel, T., Galanti, M., Garcia-Bellido, A., Hindrichs, O., Khukhunaishvili, A., Ranken, E., Taus, R., Chiarito, B., Chou, J. P., Gandrakota, A., Gershtein, Y., Halkiadakis, E., Hart, A., Heindl, M., Hughes, E., Kaplan, S., Karacheban, O., Lath, A., Montalvo, R., Nash, K., Osherson, M., Salur, S., Schnetzer, S., Somalwar, S., Stone, R., Thayil, S. A., Thomas, S., Acharya, H., Delannoy, A. G., Spanier, S., Bouhali, O., Dalchenko, M., Delgado, A., Eusebi, R., Gilmore, J., Huang, T., Kamon, T., Luo, S., Malhotra, S., Mueller, R., Overton, D., Pernie, L., Rathjens, D., Safonov, A., Akchurin, N., Damgov, J., Kunori, S., Lamichhane, K., Mengke, T., Muthumuni, S., Peltola, T., Undleeb, S., Whitbeck, A., Appelt, E., Greene, S., Gurrola, A., Janjam, R., Johns, W., Maguire, C., Melo, A., Ni, H., Padeken, K., Romeo, F., Sheldon, P., Tuo, S., Velkovska, J., Arenton, M. W., Cox, B., Cummings, G., Hakala, J., Hirosky, R., Joyce, M., Ledovskoy, A., Li, A., Neu, C., Tannenwald, B., Wolfe, E., Karchin, P. E., Poudyal, N., Thapa, P., Black, K., Bose, T., Buchanan, J., Caillol, C., Dasu, S., Everaerts, P., Galloni, C., He, H., Herndon, M., Herve, A., Hussain, U., Lanaro, A., Loeliger, A., Loveless, R., Sreekala, J. Madhusudanan, Mallampalli, A., Pinna, D., Savin, A., Smith, W. H., Teague, D., Trembath-Reichert, S., CMS Collaboration, Candelise, V., DA ROLD, A., DELLA RICCA, G., and ET AL (the CMS, Collaboration)
Subjects: Technology, Photon, Physics - Instrumentation and Detectors, heavy ion: scattering, 13000 GeV-cms, Physics::Instrumentation and Detectors, electron: rapidity, particle identification [electron], Particle, 01 natural sciences, 09 Engineering, photon: particle identification, 030218 nuclear medicine & medical imaging, High Energy Physics - Experiment, rapidity dependence, QUARK-GLUON PLASMA, electron: pair production, High Energy Physics - Experiment (hep-ex), Particle identification methods, 0302 clinical medicine, energy resolution: measured, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], scattering [p p], Detectors and Experimental Techniques, PERSPECTIVE, Nuclear Experiment, Instrumentation, Instruments & Instrumentation, physics.ins-det, Mathematical Physics, Boson, Physics, Range (particle radiation), Luminosity (scattering theory), Large Hadron Collider, 02 Physical Sciences, CMS, Settore FIS/01 - Fisica Sperimentale, Bremsstrahlung, Instrumentation and Detectors (physics.ins-det), measured [energy resolution], Nuclear & Particles Physics, CERN LHC Coll, Pseudorapidity, High energy physics, Experimental particle physics, LHC, Radiation-hard detectors, Si microstrip and pad detectors, Radiation damage to detector materials (solid state), Particle tracking detectors (Solid-state detectors), radiation: damage, CMS: upgrade, tracking detector, semiconductor detector: design, semiconductor detector: microstrip, performance, Hadron-Hadron scattering (experiments), Top physics, p p: scattering, p p: colliding beams, top: single production, Large Detector Systems For Particle and Astroparticle Physics, Quark-Gluon Plasma, PARTICLE PHYSICS, rapidity [electron], ddc:620, colliding beams [p p], Particle Physics - Experiment, CERN Lab, Large detector systems for particle and astroparticle physics, energy loss, FOS: Physical sciences, bremsstrahlung, 114 Physical sciences, Nuclear physics, 03 medical and health sciences, Particle identification method, particle identification: performance, scattering [heavy ion], 0103 physical sciences, 5020 GeV-cms/nucleon, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], ddc:610, pair production [electron], Engineering & allied operations, lead, Science & Technology, electron: particle identification, 010308 nuclear & particles physics, LARGE HADRON COLLIDER, hep-ex, particle identification [photon], Física, resolution, leptonic decay [Z0], Physics and Astronomy, performance [particle identification], Z0: leptonic decay, Large detector systems for particle and astroparticle physic, Physics::Accelerator Physics, High Energy Physics::Experiment, identification methods, Energy (signal processing), experimental results
Abstract: CERN for the benefit of the CMS collaboration: et al., The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016–2018, corresponding to an integrated luminosity of 136 fb^-1. Results obtained from lead-lead collision data collected at √(sNN)=5.02 TeV are also presented. Innovative techniques are used to reconstruct the electron and photon signals in the detector and to optimize the energy resolution. Events with electrons and photons in the final state are used to measure the energy resolution and energy scale uncertainty in the recorded events. The measured energy resolution for electrons produced in Z boson decays in proton-proton collision data ranges from 2 to 5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material. The energy scale in the same range of energies is measured with an uncertainty smaller than 0.1 (0.3)% in the barrel (endcap) region in proton-proton collisions and better than 1 (3)% in the barrel (endcap) region in heavy ion collisions. The timing resolution for electrons from Z boson decays with the full 2016–2018 proton-proton collision data set is measured to be 200 ps., Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, and 765710 (European Union); the Programa de Excelencia María de Maeztu, and the Programa Severo Ochoa del Principado de Asturias
Published: 2021
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281. Online Learning Algorithm-Based High-Efficient Control of IPM motors

Author: M. Kigure, T. Yang, Song Xu, M. Shibuya, R. Honda, Seiji Hashimoto, and Takahiro Kawaguchi
Subjects: Identification methods, Artificial neural network, Computer science, Rotor (electric), law, Magnet, Online learning, Control (management), Torque, Object (computer science), Automotive engineering, law.invention
Abstract: An Interior Permanent Magnet Synchronous Motor(IPMSM) has a structure in which a permanent magnet is arranged inside the rotor, and reluctance torque can be used combined with magnet torque. It is easy to achieve high efficiency and has been widely used in home appliances, industrial equipment, automobiles, etc. This paper presents a learning method that optimizes the efficiency of IPMSMs by using a multilayer neural network structure without using identification methods or estimation methods even when the parameters of the controlled object varied. The effectiveness of the proposed method is verified by simulation in comparison with the conventional method.
Published: 2021
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282. Substructural Identification Methods for Parameter Estimation of Railway Track Dynamic Systems

Author: Sheng-Nan Wang, Ling Ye, and Hua-Peng Chen
Subjects: Identification methods, Parametric analysis, Computer science, Estimation theory, InformationSystems_INFORMATIONSYSTEMSAPPLICATIONS, Mechanical Engineering, Aerospace Engineering, Railway transportation, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Automotive engineering, Identification (information), Safe operation, General Materials Science, Civil and Structural Engineering
Abstract: Real-time identification and monitoring of the railway track dynamic parameters are necessary to ensure the safe operation of the railway transportation systems. Traditional global structur...
Published: 2021
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283. Highly efficient maximum-likelihood identification methods for bilinear systems with colored noises

Author: Ximei Liu, Meihang Li, and Yamin Fan
Subjects: Identification methods, Bilinear systems, Colored, business.industry, Computer science, Maximum likelihood, Pattern recognition, Artificial intelligence, business
Abstract: As a special class of nonlinear systems, bilinear systems can naturally describe many industrial production process. This paper mainly discussed the highly efficient iterative identification methods for bilinear systems with autoregressive moving average noise. Firstly, the input-output representation of the bilinear systems is derived through eliminating the unknown state variables in the model. Then based on the maximum-likelihood principle and the negative gradient search principle, a maximum-likelihood gradient-based iterative (ML-GI) algorithm is proposed to identify the parameters of the bilinear systems with colored noises. For further improving the computational efficiency, the original identification model is divided into three sub-identification models with smaller dimensions and fewer parameters, and a hierarchical maximum-likelihood gradient-based iterative (H-ML-GI) algorithm is derived by using the hierarchical identification principle. A gradient-based iterative (GI) algorithm is given for comparison. Finally, the algorithms are verified by a simulation example. The simulation results show that the proposed algorithms are effective for identifying bilinear systems with colored noises and the H-ML-GI algorithm has a higher computational efficiency and a faster convergence rate than the ML-GI algorithm and the GI algorithm.
Published: 2021
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284. Output-only structural modal identification methods based on neural network

Author: D.W. Liu, H. Li, and Yuequan Bao
Subjects: Identification methods, Modal, Artificial neural network, business.industry, Computer science, Pattern recognition, Artificial intelligence, business
Published: 2021
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285. Comparing cortico-motor hotspot identification methods in the lower extremities post-stroke: MEP amplitude vs. latency

Author: Jasmine Cash, Mark G. Bowden, Charalambos C. Charalambous, E.C. Wonsetler, John H. Kindred, and J.B. Ergle
Subjects: 0301 basic medicine, Identification methods, Adult, Male, medicine.medical_specialty, Neuronavigation, medicine.medical_treatment, Article, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Medicine, Humans, Latency (engineering), Evoked potential, Muscle, Skeletal, Stroke, Aged, Retrospective Studies, Brain Mapping, business.industry, General Neuroscience, Motor Cortex, Middle Aged, medicine.disease, Evoked Potentials, Motor, Gait, Transcranial Magnetic Stimulation, Transcranial magnetic stimulation, 030104 developmental biology, Amplitude, Cross-Sectional Studies, Lower Extremity, Female, business, 030217 neurology & neurosurgery
Abstract: Transcranial magnetic stimulation (TMS) is a technique used to probe and measure cortico-motor responses of the nervous system. However, lower extremity (LE) specific methodology has been slow to develop. In this retrospective analysis, we investigated what motor evoked potential metric, amplitude (MEP(amp)) or latency (MEP(lat)), best distinguished the motor-cortical target, i.e. hotspot, of the tibialis anterior and soleus post-stroke. Twenty-three participants with stroke were included in this investigation. Neuronavigation was used to map hotspots, derived via MEP(amp) and MEP(lat), over a 3 cm × 5 cm grid. Distances between points with the greatest response within a session and between days were compared. Both criterion, amplitude and latency, provided poor identification of locations between trials within a session, and between multiple visits. Identified hotspots were similar only 15% and 8% of the time between two assessments within the same session, for amplitude and latency respectively. However, MEP(amp) was more consistent in identifying hotspots, evidenced by locations being less spatially distant from each other (Amplitude: 1.4 cm (SD 0.10) Latency: 1.7 (SD 1.04), P=0.008) within a session and between days (Amplitude: 1.3 cm (SD 0.95), Latency 1.9 cm (SD 1.14), P=0.004). While more work is needed to develop LE specific methodology for TMS, especially as it applies to investigating gait impairments, MEP(amp) appears to be a more consistent criterion for hotspot identification when compared to MEP(lat). It is recommended that future works continue to use MEP(amp) when identifying tibialis anterior and soleus hotspots using neuronavigation.
Published: 2021

286. Identification of Fungi Isolated from Oral Cavity of Patients with HIV Using MALDI–TOF MS

Author: Szymon Andrusiów, Zuzanna Pawlak, Adriana Janczura, and Magdalena Pajączkowska
Subjects: Identification methods, 0303 health sciences, 030306 microbiology, business.industry, lcsh:R, Human immunodeficiency virus (HIV), lcsh:Medicine, MALDI–TOF MS, HIV, oral cavity fungal flora, General Medicine, Oral cavity, medicine.disease_cause, Buccal mucosa, Article, Corpus albicans, Microbiology, Candida, 03 medical and health sciences, Matrix-assisted laser desorption/ionization, Medicine, Identification (biology), business, 030304 developmental biology
Abstract: Background: A growing incidence of invasive fungal infections, especially among immunocompromised patients, has given increased significance to microbiological diagnostics of yeast-like fungi. More accurate and faster fungi identification methods that can compete with classical methods are being searched for. In this paper, classical microbiological methods are compared to MALDI–TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry). Methods: The diagnostic material was collected from buccal mucosa from 98 adults, including 69 with HIV. Only positive cultures were included in the study. Results: Matching results were obtained in 45 samples, and there were nonmatching results in 35 samples, with the majority of these in the study group, constituting 50% of identifications within this group. A particularly common mistake resulting from the use of classical methods is the false identification of C. dubliniensis as C. albicans. Additionally, C. tropicalis proves to be difficult to identify. Conclusions: Our results and literature data suggest that MALDI–TOF MS should be considered an effective alternative to classical methods in terms of fungi identification, especially among HIV-positive patients, due to the different morphology of fungal colonies.
Published: 2021
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287. Research progress on distribution, sources, identification, toxicity, and biodegradation of microplastics in the ocean, freshwater, and soil environment

Author: Yanshen Li, Yongning Wu, Huihui Liu, Juan Li, Anqi Chen, Yanli You, Qinghui Sun, Chen Wang, Guibin Jiang, and Shupeng Yang
Subjects: Identification methods, Pollutant, Pollution, congenital, hereditary, and neonatal diseases and abnormalities, Microplastics, Creatures, media_common.quotation_subject, nutritional and metabolic diseases, Biodegradation, Environmental chemistry, Environmental science, skin and connective tissue diseases, General Environmental Science, media_common
Abstract: Microplastics (MPs) are distributed in the oceans, freshwater, and soil environment and have become major pollutants. MPs are generally referred to as plastic particles less than 5 mm in diameter. They consist of primary microplastics synthesized in microscopic size manufactured production and secondary microplastics generated by physical and environmental degradation. Plastic particles are long-lived pollutants that are highly resistant to environmental degradation. In this review, the distribution and possible sources of MPs in aquatic and terrestrial environments are described. Moreover, the adverse effects of MPs on natural creatures due to ingestion have been discussed. We also have summarized identification methods based on MPs particle size and chemical bond. To control the pollution of MPs, the biodegradation of MPs under the action of different microbes has also been reviewed in this work. This review will contribute to a better understanding of MPs pollution in the environment, as well as their identification, toxicity, and biodegradation in the ocean, freshwater, and soil, and the assessment and control of microplastics exposure.
Published: 2021
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288. A comprehensive metagenomics framework to characterize organisms relevant for planetary protection

Author: David Danko, James N. Benardini, Maria A. Sierra, Lisa Guan, Daniel Butler, Christopher E. Mason, Chandrima Bhattacharya, Arman Seuylemezian, Kasthuri Venkateswaran, Dmitry Meleshko, Nitin Kumar Singh, Krista Ryon, Katerina Kuchin, and Jason M. Wood
Subjects: Microbiology (medical), Identification methods, Planetary protection, Cleaning methods, Biology, Microbiology, lcsh:Microbial ecology, Space exploration, 03 medical and health sciences, Microbial ecology, Planetary Protection, Cleanroom, Extremophile, Humans, Spacecraft, Microbial profiling, 030304 developmental biology, 0303 health sciences, 030306 microbiology, business.industry, Research, Spacecraft Assembly Facility, Space Flight, Environment, Controlled, 13. Climate action, Metagenomics, lcsh:QR100-130, Metagenome, Biochemical engineering, business
Abstract: BackgroundClean rooms of the Space Assembly Facility (SAF) at the Jet Propulsion Laboratory (JPL) at NASA are the final step of spacecraft cleaning and assembly before launching into space. Clean rooms have stringent methods of air-filtration and cleaning to minimize microbial contamination for exoplanetary research and minimize the risk of human pathogens, but they are not sterile. Clean rooms make a selective environment for microorganisms that tolerate such cleaning methods. Previous studies have attempted to characterize the microbial cargo through sequencing and culture-dependent protocols. However, there is not a standardized metagenomic workflow nor analysis pipeline for spaceflight hardware cleanroom samples to identify microbial contamination. Additionally, current identification methods fail to characterize and profile the risk of low-abundance microorganisms.ResultsA comprehensive metagenomic framework to characterize microorganisms relevant for planetary protection in multiple cleanroom classifications (from ISO-5 to ISO-8.5) and sample types (surface, filters, and debris collected via vacuum devices) was developed. Fifty-one metagenomic samples from SAF clean rooms were sequenced and analyzed to identify microbes that could potentially survive spaceflight based on their microbial features and whether the microbes expressed any metabolic activity or growth. Additionally, an auxiliary testing was performed to determine the repeatability of our techniques and validate our analyses. We find evidence that JPL clean rooms carry microbes with attributes that may be problematic in space missions for their documented ability to withstand extreme conditions, such as psychrophilia and ability to form biofilms, spore-forming capacity, radiation resistance, and desiccation resistance. Samples from ISO-5 standard had lower microbial diversity than those conforming to ISO-6 or higher filters but still carried a measurable microbial load.ConclusionsAlthough the extensive cleaning processes limit the number of microbes capable of withstanding clean room condition, it is important to quantify thresholds and detect organisms that can inform ongoing Planetary Protection goals, provide a biological baseline for assembly facilities, and guide future mission planning.
Published: 2021

289. Bloodstream infection by Rhodococcus corynebacterioides in a pediatric patient diagnosed with high-risk retinoblastoma.

Author: Méndez-Cruz AR, Félix-Bermúdez GE, Aguilar-Escobar DV, Vega-Vega L, Morales-Estrada AI, and Contreras-Rodríguez A
Subjects: Animals, Humans, Child, Child, Preschool, RNA, Ribosomal, 16S genetics, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Anti-Bacterial Agents therapeutic use, Retinoblastoma drug therapy, Sepsis diagnosis, Sepsis drug therapy, Rhodococcus, Retinal Neoplasms complications, Retinal Neoplasms drug therapy, Bacteremia diagnosis, Bacteremia drug therapy, Bacteremia microbiology
Abstract: Rhodococcus is a pathogen that is known to cause infections in animals and humans, mainly in cases of immunocompromised patients. A case of a pediatric cancer patient suffering from a bloodstream infection caused by Rhodococcus corynebacterioides was described in this work. Gram positive rods were isolated from blood cultures. The target bacterium was identified using a combination of biochemical tests, the MALDI-TOF mass spectrometry technique, and the analysis of the 16S rRNA sequence. Moreover, an antimicrobial susceptibility test was performed using the E-test. The isolated bacterium was identified as R. corynebacterioides. The 3-year-old patient was successfully treated with vancomycin and meropenem. This is the first published report of R. corynebacterioides in a pediatric patient diagnosed with retinoblastoma that developed a bloodstream infection. R. corynebacterioides should be considered among the opportunistic infectious agents affecting pediatric cancer patients., (Copyright © 2022 Asociación Argentina de Microbiología. Publicado por Elsevier España, S.L.U. All rights reserved.)
Published: 2023
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290. 中-高含水期低渗透储层优势渗流通道研究 --以温米油田温西一区块三间房组储层为例

Author: 惠钢, 王友净, 李佳鸿, 康波, 葛启兵, and 戴立波
Abstract: Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Published: 2015

291. Mycobacterium abscessus Complex Infections in Humans.

Author: Lee, Meng-Rui, Sheng, Wang-Huei, Hung, Chien-Ching, Yu, Chong-Jen, Lee, Li-Na, and Hsueh, Po-Ren
Abstract: Mycobacterium abscessus complex comprises a group of rapidly growing, multidrug-resistant, nontuberculous mycobacteria that are responsible for a wide spectrum of skin and soft tissue diseases, central nervous system infections, bacteremia, and ocular and other infections. M. abscessus complex is differentiated into 3 subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. massiliense, and M. abscessus subsp. bolletii. The 2 major subspecies, M. abscessus subsp. abscessus and M. abscessus subsp. massiliense, have different erm(41) gene patterns. This gene provides intrinsic resistance to macrolides, so the different patterns lead to different treatment outcomes. M. abscessus complex outbreaks associated with cosmetic procedures and nosocomial transmissions are not uncommon. Clarithromycin, amikacin, and cefoxitin are the current antimicrobial drugs of choice for treatment. However, new treatment regimens are urgently needed, as are rapid and inexpensive identification methods and measures to contain nosocomial transmission and outbreaks. [ABSTRACT FROM AUTHOR]
Published: 2015
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292. High resolution melting analysis (HRM) as a new tool for the identification of species belonging to the Lactobacillus casei group and comparison with species-specific PCRs and multiplex PCR.

Author: Iacumin, Lucilla, Ginaldi, Federica, Manzano, Marisa, Anastasi, Veronica, Reale, Anna, Zotta, Teresa, Rossi, Franca, Coppola, Raffaele, and Comi, Giuseppe
Subjects: *LACTOBACILLUS casei, *BACTERIAL typing, *BACTERIA classification, *POLYMERASE chain reaction, *LACTIC acid bacteria, *COMPARATIVE studies
Abstract: The correct identification and characterisation of bacteria is essential for several reasons: the classification of lactic acid bacteria (LAB) has changed significantly over the years, and it is important to distinguish and define them correctly, according to the current nomenclature, avoiding problems in the interpretation of literature, as well as mislabelling when probiotic are used in food products. In this study, species-specific PCR and HRM (high-resolution melting) analysis were developed to identify strains belonging to the Lactobacillus casei group and to classify them into L. casei , Lactobacillus paracasei and Lactobacillus rhamnosus . HRM analysis confirmed to be a potent, simple, fast and economic tool for microbial identification. In particular, 201 strains, collected from International collections and attributed to the L. casei group, were examined using these techniques and the results were compared with consolidated molecular methods, already published. Seven of the tested strains don't belong to the L. casei group. Among the remaining 194 strains, 6 showed inconsistent results, leaving identification undetermined. All the applied techniques were congruent for the identification of the vast majority of the tested strains (188). Notably, for 46 of the strains, the identification differed from the previous attribution. [ABSTRACT FROM AUTHOR]
Published: 2015
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293. El Delito Con Rostro: Los comienzos de la identificación de delincuentes en Uruguay.

Author: Fessler, Daniel
Abstract: This article aims to analyze tensions at the beginning of the twentieth century between the two main methods of identifying criminal offenders ("bertillonnage" and dactyloscopy). It therefore highlights the need to produce efficient control mechanisms, converting the police into an increasingly specialized agency better placed to identify and pursue what was perceived as a growth in criminality. In the eyes of the dominant groups, working class and dangerous people are considered one and the same, with the latter nucleus including certain sectors of immigrants and groups seen as a new form of "crime": the social. [ABSTRACT FROM AUTHOR]
Published: 2015
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294. An algorithm for estimating poles of systems.

Author: Mi, Wen
Abstract: An algorithm of estimating poles of dynamic systems is proposed. It is proved that poles of rational functions are solutions of an algebra equation which can be obtained by taking backward shift operator. An illustrative example is presented to show the efficiency of estimating poles of systems with distinguished and multiple poles cases. [ABSTRACT FROM PUBLISHER]
Published: 2013
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295. Newton iterative identification method for nonlinear systems

Author: Miao Yin
Subjects: Identification methods, 0209 industrial biotechnology, Iterative method, Computer science, Model transformation, MathematicsofComputing_NUMERICALANALYSIS, 020206 networking & telecommunications, 02 engineering and technology, Data modeling, Nonlinear system, Identification (information), symbols.namesake, 020901 industrial engineering & automation, Simple (abstract algebra), 0202 electrical engineering, electronic engineering, information engineering, symbols, Applied mathematics, computer, Newton's method, computer.programming_language
Abstract: This paper discusses the identification methods for nonlinear systems. Using the Newton method and the iterative technique the Newton-based iterative algorithm is derived to determine the parameters of the nonlinear systems. In addition, in order to improve the computational efficiency, the Newton iterative algorithm based on the model transformation is derived. The basic principle is to transform a complex nonlinear system into a linear or simple nonlinear system. Finally, a numerical example is given to analyze the proposed methods.
Published: 2021
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296. Covid Detection from X-RAY and CT Scans using Transfer Learning – A Study

Author: Mohd Talib, Naman, Shailender Kumar, and Praduman Verma
Subjects: Identification methods, 0209 industrial biotechnology, 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), Computer science, 02 engineering and technology, medicine.disease_cause, medicine.disease, World health, Pneumonia, 020901 industrial engineering & automation, Pandemic, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Medical emergency, Transfer of learning, Coronavirus
Abstract: CoronaVirus cases have reached 55.6 million (as of 19th Nov 2020) out of which about 9 million cases are in India alone. Out of these 55.6 million people, nearly 1.34 million people have unfortunately lost their life which is about 3% of the total cases. The disease has become quite a risky infection that has spread quickly across the world and has been announced as a Pandemic by the World Health Organization (WHO). As the greater part of the Covid patients show respiratory manifestations (basically Pneumonia), Chest X-Ray Images can turn into an elective answer to help analyze COVID-19 positive patients from suspected partners. Anyway because of the unexpected episode of the COVID-19 pandemic, radiologists are currently confronting extraordinary weight in analyzing gigantic amounts of X-Ray Images. Other than being tedious and careful, the assessment cycle can become blunder inclined thinking about the huge measure of picture information and the outstanding task at hand. This article focuses on an examination of various procedures identified with the discovery of Coronavirus or pneumonia disease from the X-Ray Images or CT-Scan pictures. It additionally gives an investigation and examination of move learning calculations that were utilized to execute these Coronavirus identification methods.
Published: 2021
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297. A Deep Neural Inferencing Approach of Assistive Philippine Traffic Light Recognition: An Augmented Transfer Learning Approach

Author: Aimee G. Acoba, Nenita D. Guerrero, Christopher Franco Cunanan, Cherry D. Casuat, and Ma. Ian P. Delos Trinos
Subjects: Identification methods, Computer science, business.industry, Computational intelligence, 02 engineering and technology, Machine learning, computer.software_genre, Identification (information), Traffic signal, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Recognition system, 020201 artificial intelligence & image processing, Artificial intelligence, Transfer of learning, business, computer
Abstract: Study on the identification of traffic signals plays an important role not only for intelligent cars but also for traditional cars and their drivers. Various identification methods have been suggested over the years for traffic light identification, unfortunately not in the Philippine environments. In this study, the proponents developed a traffic light recognition system that could be used in crossroads/crosswalks in the Philippines for traffic light recognition. The researchers trained and tested the dataset using Yolo v3 and resulted in a mean average precision (mAP) of 0.88. The system has been tested by traffic lights in the Philippine environment, which has a good result in terms of traffic light detection and recognition.
Published: 2021
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298. PCR-based rapid diagnostic tools for the authentication of medicinal mistletoe species

Author: Goya Choi, Sungyu Yang, Wook Kim, Byeong Cheol Moon, and Pureum Noh
Subjects: Pharmacology, Identification methods, Genetic Markers, Plants, Medicinal, Traditional medicine, ved/biology, Macrosolen tricolor, ved/biology.organism_classification_rank.species, Taxillus chinensis, Pharmaceutical Science, Amplicon, Biology, Diagnostic tools, Polymerase Chain Reaction, Mistletoe, Complementary and alternative medicine, Drug Discovery, Viscum coloratum, Molecular Medicine, Species identification, Maturase K, Plant Preparations
Abstract: Background Taxilli Herba (TH) and Visci Herba (VH), defined as the leaves and branches of the mistletoe species Taxillus chinensis and Viscum coloratum, respectively, are popular herbal medicines in East Asia. However, commercial TH and VH products are frequently adulterated with related inauthentic mistletoe species, posing efficacy and safety concerns. Accurate species identification of herbal medicinal products is a prerequisite for quality control, but traditional morphological identification methods are hampered by difficulties in discriminating among closely related species and in identifying the source materials in processed products. Purpose This study aimed to develop sequence-characterized amplified region (SCAR) markers and a multiplex-SCAR assay for rapid and accurate identification of authentic TH and VH. Methods The matK region was sequenced in a total of 20 samples from five mistletoe species, namely T. chinensis and V. coloratum, and three species often found in adulterated herbal medicines, T. sutchuenensis, V. articulatum, and Macrosolen tricolor. Species-specific nucleotide polymorphisms were identified and short regions (21–22 bp) containing at least two species-specific nucleotides close to the 3ʹ end were incorporated into SCAR primers that produced uniquely sized PCR amplicons for each species. The five SCAR primer sets were also combined into a multiplex-SCAR assay. Results The SCAR primers successfully generated amplicons of the expected size for each target species even with low-DNA templates or with templates containing DNA from multiple samples. No amplification was observed in non-target species. The SCAR markers and the multiplex-SCAR assay successfully identified commercial TH and VH products that were counterfeit or adulterated in both dried and processed products. Conclusion This is the first report to illustrate discrimination of genuine medicinal mistletoe species with DNA-based marker assays, enabling rapid and accurate species identification. The SCAR assays developed in this study will facilitate the standardization of commercial mistletoe products.
Published: 2021

299. Prognosis of sexual dimorphism with unfused hyoid bone: Artificial intelligence informed decision making with discriminant analysis

Author: Hitesh Chawla, Ashish Tyagi, Piyush Bhardwaj, and Parul Tiwari
Subjects: Identification methods, Adult, Male, Adolescent, Body height, Decision Making, Overfitting, Biology, Pathology and Forensic Medicine, Young Adult, Artificial Intelligence, Statistics, Classifier (linguistics), Humans, Aged, Aged, 80 and over, Sex Characteristics, Hyoid bone, Hyoid Bone, Discriminant Analysis, Middle Aged, Linear discriminant analysis, Prognosis, Sex Determination by Skeleton, Sexual dimorphism, Forensic Anthropology, Female, Metric (unit)
Abstract: Depending on the metric and non-metric skeletal features of various bones, forensic experts proposed diverse sex identification methods. The main focus of the present study is to calculate sexual dimorphism in human unfused or disarticulated hyoid bone and compared it with studies conducted by different researchers. For this study, 293 unfused hyoid bones were accumulated and investigated from 173 male and 120 female cadavers of the northwest Indian population from the age of 15 to 80 years. Initially, discriminant analysis was performed on the dataset to predict sex and to get an idea for the crucial variables for sexual dimorphism. Later, significant variables predicted by the discriminant analysis were used for machine learning approaches to improve accuracy for sex determination. The standard scaler method is used for pre-processing of the data before machine learning analysis and to prevent overfitting and underfitting, 70 % of the whole dataset was utilized in the training of the model and the remaining data were used for testing the model. According to the discriminant analysis, body length (BL) and body height (BH) were found to be highly significant for the sex determination and predicted sex with 75.1 % accuracy. However, implementation of machine learning approaches such as the XG Boost classifier increased the accuracy to 83 % with sensitivity, and specificity scores of 0.81 and 0.84, respectively. Moreover, the ROC-AUC score achieved by the XG Boost classifier is 0.89; indicating machine learning investigation can improve the sex determination accuracy up to the appropriate standard.
Published: 2021

300. Micro and Nanoplastics Identification: Classic Methods and Innovative Detection Techniques

Author: Luciana Dini, Marco Rossi, Stefania Mariano, Marco Fidaleo, Stefano Tacconi, Mariano, Stefania, Tacconi, Stefano, Fidaleo, Marco, Rossi, Marco, and Dini, Luciana
Subjects: Identification methods, Scientific instrument, Microplastics, Computer science, Atomic force microscopy, detection, Holographic imaging, 02 engineering and technology, 010501 environmental sciences, 021001 nanoscience & nanotechnology, 01 natural sciences, nanoplastics, methods, microplastics, nanoplastics, characterization, microscopy, spectroscopy, analytical methods, environmental matrices, Identification (biology), Biochemical engineering, Small particles, Microplastics, nanoplastics, methods, detection, 0210 nano-technology, 0105 earth and related environmental sciences
Abstract: Micro and nanoplastics are fragments with dimensions less than a millimeter invading all terrestrial and marine environments. They have become a major global environmental issue in recent decades and, indeed, recent scientific studies have highlighted the presence of these fragments all over the world even in environments that were thought to be unspoiled. Analysis of micro/nanoplastics in isolated samples from abiotic and biotic environmental matrices has become increasingly common. Hence, the need to find valid techniques to identify these micro and nano-sized particles. In this review, we discuss the current and potential identification methods used in microplastic analyses along with their advantages and limitations. We discuss the most suitable techniques currently available, from physical to chemical ones, as well as the challenges to enhance the existing methods and develop new ones. Microscopical techniques (i.e. dissect, polarized, fluorescence, scanning electron, atomic force microscopy) are one of the most used identification methods for micro/nanoplastics, but they have the limitation to produce incomplete results in analyses of small particles. At present, the combination with chemical analysis (i.e. spectroscopy) allows this limit to be overcome. Moreover, recent studies introduced other approaches thus improving this research area. For example, holographic imaging in microscope configuration has been used to image microplastics directly in unfiltered water, demonstrating the ability to recognize microplastics, to discriminate them from diatoms and to differentiate between different sizes, shapes, and plastic types. The development of new analytical instruments coupled with one another or with conventional and innovative microscopies could solve the current problems in micro/nanoplastics identification.
Published: 2021
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