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Three-dimensional (3D) cell cultures, including organ-on-a-chip (OOC) devices, offer the possibility to mimic human physiology conditions better than 2D models. The organ-on-a-chip devices have a wide range of applications, including mechanical studies, functional validation, and toxicology investigations. Despite many advances in this field, the major challenge with the use of organ-on-a-chips relies on the lack of online analysis methods preventing the real-time observation of cultured cells. Mass spectrometry is a promising analytical technique for real-time analysis of cell excretes from organ-on-a-chip models. This is due to its high sensitivity, selectivity, and ability to tentatively identify a large variety of unknown compounds, ranging from metabolites, lipids, and peptides to proteins. However, the hyphenation of organ-on-a-chip with MS is largely hampered by the nature of the media used, and the presence of nonvolatile buffers. This in turn stalls the straightforward and online connection of organ-on-a-chip outlet to MS. To overcome this challenge, multiple advances have been made to pre-treat samples right after organ-on-a-chip and just before MS. In this review, we summarised these technological advances and exhaustively evaluated their benefits and shortcomings for successful hyphenation of organ-on-a-chip with MS.

Oxidation of the monoterpene Δ3-carene (C10H16) is a potentially important and understudied source of atmospheric secondary organic aerosol (SOA). We present chamber-based measurements of speciated gas and particle phases during photochemical oxidation of Δ3-carene. We find evidence of highly oxidized organic molecules (HOMs) in the gas phase and relatively low-volatility SOA dominated by C7-C10 species. We then use computational methods to develop the first stages of a Δ3-carene photochemical oxidation mechanism and explain some of our measured compositions. We find that alkoxy bond scission of the cyclohexyl ring likely leads to efficient HOM formation, in line with previous studies. We also find a surprising role for the abstraction of primary hydrogens from methyl groups, which has been calculated to be rapid in the α-pinene system, and suggest more research is required to determine if this is more general to other systems and a feature of autoxidation. This work develops a more comprehensive view of Δ3-carene photochemical oxidation products via measurements and lays out a suggested mechanism of oxidation via computationally derived rate coefficients.

Objective We systematically evaluated the global research trends in robotic application on the spine through bibliometric analysis and mapping knowledge domains. Methods A systematic literature search was performed of the PubMed and Web of Science, including the Science Citation Index Expanded, databases. The number, countries, journals, and authors of the publications, total citations, average publication year, and institution sources were analyzed using Microsoft Excel, the Online Analysis Platform of Bibliometrics, and VOSviewer. The hotspots were analyzed and visualized using VOSviewer. Results We identified a total of 2135 publications. The United States ranked first in the number of publications (n = 824; 38.63%) and frequency of citations (n = 29,075). Northwestern University had the highest number of publications (n = 67) and Harvard University the highest number of citations (n = 4198). The Journal of NeuroEngineering and Rehabilitation published the largest number of reports (n = 73), and the most frequently cited journal was Nature (n = 3844 citations). The research hotspots were divided into 3 categories analyzed by VOSviewer: rehabilitation, basic science, and surgery. According to the average publication year, the most recent hotspot was radiation exposure, and the earliest hotspot was radiosurgery. Conclusions The number of studies of robotic application on the spine has continued to increase. The United States was the greatest contributor to robotic applications on the spine. Robot-assisted rehabilitation for neurological and orthopedic lesions is still a major research hotspot. The range of robotic applications on the spine has expanded from assisted rehabilitation to assisted rehabilitation and surgery.

The analytical capabilities of various schemes of the relative movement of the flows of an analyzed aqueous phase and an extractant gas in chromatomembrane gas extraction were compared. It was demonstrated that a direct-flow scheme is preferable when the efficiency of the system increases with the flow rate of the extractant gas (online analysis), whereas a two-dimensional scheme is preferable in the analysis of preliminarily selected samples.

BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease, the progression of which current drug therapy cannot reverse. This study analyzed current research hotspots and future research trends in IPF through bibliometric methods, with the aim of providing a reference for new therapeutic strategies. METHODS Publications on IPF obtained from the Web of Science Core Collection database, The Literature Metrology Online Analysis Platform, and CiteSpace were used to analyze publication characteristics. VOSviewer was used to conduct keywords co-occurrence analysis and analyze research hotspots. RESULTS A total of 7,016 publications related to IPF were identified from 2011 to 2020. The most contributions were from the USA and the five research institutions with the largest number of publications were all from that country. The American Journal of Respiratory and Critical Care Medicine was the most cited journal and had an incontrovertible academic impact with five of the top 10 high-cited references published in this journal. G Raghu was the academic authority in this domain in terms of both the number of publications and the most citations. By analyzing keywords, we identified three IPF research hotspot clusters, which are "clinical research", "pathogenesis research" and "diagnosis research" respectively. CONCLUSIONS We evaluated all publications concerning IPF research in the past decade through bibliometric analysis. The current research hotspot in this field is drug therapy for the condition using nintedanib and pirfenidone. Future research will focus on conducting multi-center randomized controlled trials to explore and evaluate new therapeutic drugs for IPF. It is hoped that this study can provide information and data support for further research and the development of new therapeutic drugs.

The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.

Legalization of Cannabis in countries, like Canada, and global demand for non-hallucinating chemical components, such as Cannabidiols (CBD), have stimulated the increased interest from academics, industry, and regulatory agencies. Subsequent research publications in scientific journals in this field are expected to grow rapidly. However, there have been few research reviews that have quantified patterns in research publications concerning cannabis, nor a literature-based perspective on the historical development, current status, and future direction of cannabis research. Here, a bibliometric analysis is performed to address this gap in the scientific literature. A total of 1167 relevant articles (Supplementary file 1) were screened and analyzed using three software tools: HistCite, CiteSpace, and Bibliometric Online Analysis Platform. The performances of relevant countries, institutions, authors, and journals were presented, and the evolutionary trends of different categories were revealed. The historical development of cannabis and CBD research can be clearly divided into three stages, which focus on the chemistry, pharmacology, and molecular biology aspects of Cannabis sativa in general and then a focus on CBD related publications. A timeline was drawn to highlight the major trends in the literature, including scientific discoveries. In the end, several suggestions for future research directions in this field are provided.

Online analysis and monitoring of solution phase chemistry by way of nuclear magnetic resonance spectroscopy on a recirculating sample from an external reaction vessel (FlowNMR) has proven to be a valuable tool for understanding the dynamic behaviour of complex solution-phase systems in real time. A variety of flow cells and setups have been used at both low and high magnetic field strengths for various applications, and the choice of materials, dimensions and components can have a profound impact on the quality and relevance of the data obtained. Here we review some fundamental engineering aspects of FlowNMR setups to help avoid common pitfalls and work towards establishing good practice quality guidelines (GxP) for FlowNMR investigations in academia and industry.

As a kind of convenient, fast, and online analysis technology, point-of-care testing (POCT) technology has been widely used in disease diagnosis, health management, environmental monitoring and emergency response analysis. To date, it is a hot spot to develop novel strategy for the detection of various targets based on the simple, rapid and sensitive signal readout and different signal production, transfer and amplification strategies in the field of POCT. The introduction of some nanozymes and functionalized nanomaterials with special catalytic properties further accelerate the application of simple signal readout such as color, temperature, pressure, weight and distance, which greatly promotes the development of simple readout-based POCT methods, and expands the analytical applications of such methods. In this paper, we summarized the principles, classifications, and the applications of these emerging POCT methods for the analysis of life-related targets and reviewed their current challenges and future application prospects.

To improve the extraction performance of the silica aerogel, a melamine-terephthalaldehyde polymer was used to hybridize silica aerogel, and the hybridized aerogel was coated on the surface of stainless steel wire to prepare a fiber-filled extraction tube through placing four wires into a polyetheretherketone tube. The tube was combined with high-performance liquid chromatography, then the online extraction and detection were established. Several polycyclic aromatic hydrocarbons (PAHs) were selected as the target analytes. Under the optimum extraction and desorption conditions, the limit of detection was as low as 3.0 ng L−1, and the linear range was 0.01–20.0 μg L−1. The enrichment factors of PAHs were in the range of 1724–2393. Three environmental water samples of mineral water, tap water and river water were analyzed by this method, and the recoveries that spiked at 1.0–10.0 μg L−1 were between 80.5–126%. It showed many advantages compared with other methods, such as better sensitivity, faster detection and online analysis.

A test that allows one to determine the compliance of the experimental data with the classical (orthodox) field dependence is described. The test is presented in two versions: using the Elinson–Schrednik approximation of the current–voltage characteristics in the classical Fowler–Nordheim coordinates, as well as the modern Forbes–Deane approximation with modified Murphy–Good coordinates. The use of the test is demonstrated by the study of a multitip emitter based on carbon nanotubes. A technique of online (real-time) calculation of the effective parameters of the emitter and testing is presented.

The method of alpha-olefin oligomerization employing a heterogeneous catalyst for production of base oils used in motor and transmission oils manufacturing is considered, which has better performance and is eco-friendlier than methods employing homogeneous highly corrosive catalysts like BF3, or AlCl3. New methods and technology for online analysis of the reaction products and process flows have been developed, allowing automated process control.

In the Big Data warehouse context, a column-oriented NoSQL database system is considered as the storage model which is highly adapted to data warehouses and online analysis. Indeed, the use of NoSQL models allows data scalability easily and the columnar store is suitable for storing and managing massive data, especially for decisional queries. However, the column-oriented NoSQL DBMS do not offer online analysis operators (OLAP). To build OLAP cubes corresponding to the analysis contexts, the most common way is to integrate other software such as HIVE or Kylin which has a CUBE operator to build data cubes. By using that, the cube is built according to the row-oriented approach and does not allow to fully obtain the benefits of a column-oriented approach. In this article, the focus is to define a cube operator called MC-CUBE (MapReduce Columnar CUBE), which allows building columnar NoSQL cubes according to the columnar approach by taking into account the non-relational and distributed aspects when data warehouses are stored.

Prompt gamma neutron activation analysis (PGNAA) is a non-destructive online measurement nuclear analysis method. With its unique advantages, it has been widely used in online analysis of industrial materials such as coal, cement, and minerals in recent years. However, there are many kinds of literature on PGNAA in the field of industrial materials detection, and there are still a few concluding articles. To this end, based on the principle of PGNAA online analysis, the status quo and development of the real-time online detection of industrial material components in the field are reviewed and discussed by consulting a large number of domestic and foreign PGNAA related literature and data, to facilitate the reference of relevant scientific researchers.

Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears.

Objective: The aim of this study is to measure the knowledge level of pharmacists working in Turkey about COVID-19 Material and Method: The online questionnaire consisting of 16 questions in the form of true-false, developed by the authors after a comprehensive review of the literature, was delivered to pharmacists via social media and professional platforms (such as WhatsApp, Gmail, Instagram, LinkedIn) using the Google form. 364 pharmacists from all over Turkey who filled out the questionnaire were included in the study. Result and Discussion: In this study carried out in Turkey, it was determined that 99.5% of the pharmacists had good knowledge. When the factors affecting the level of knowledge of COVID-19 were examined, in our study, no relationship was found with the independent variables since the level of knowledge was homogeneously high. In the literature, it is reported that age, years of professional experience, having a master's and doctorate education are associated with better knowledge, while the length of experience is associated with the level of knowledge, while in some studies it is stated that no feature is related. However, the fact that it is a cross-sectional study creates a limitation in explaining the cause-effect relationship. Although the number of participants met the sample, it would have been desirable to have more. © 2022 University of Ankara. All rights reserved.

Background: Approximately 50% of cataracts are associated with genetic factors. Genetic etiology and molecular mechanisms based on gene research increase the understanding of cataracts and provide direction for diagnosis and intervention. In the present study, SCIE papers related to the modeling of cataract gene research from 2010–2019 were evaluated and qualitative and quantitative analyses with modeling performed.Methods: The SCIE database was searched on July 6, 2021 for cataract gene publications and relevant papers published since 2010 were considered for review. Subsequently, 1,904 SCIE papers associated with cataract genes from 2010–2019 were analyzed using a bibliometric method. The publication, country, institution, journal, references, knowledgebase, keywords, and research hotspots of the papers were analyzed using an online analysis platform of literature metrology, bibliographic item co-occurrence matrix builder (BICOMB), CiteSpace V, and VOS viewer analysis tool.Results: 78 countries published the related articles, and the United States ranks of America had the most publications. Two thousand seven hundred and eighty three institutions contributed to the related publications. Fudan University had the most publications. The reference clusters of SCI papers were clustered into six categories, namely, causing congenital cataract-microcornea syndrome, functional snp, cataractous lenses, a1 mutation, foxe3 mutation, cell adhesion gene pvrl3, nid1 gene. The key words representing the research frontiers were cerebrotendinous xanthomatosis (2017-2019), oxidative stress (2017–2019).Conclusion: This study provided a systematic, objective and comprehensive analysis of the literature related to gene research of cataract. Moreover, this study demonstrated the current hotspots and the future trends in the field of gene research of cataract. This review will help ophthalmologist to discern the dynamic evolution of cataract gene research, as well as highlight areas for future research.

Research in the writing of this final assignment takes the title Analysis of the Online Sale and Purchase Deed Registration System Application for Sukoharjo District Land Deed Maker Officers, where the PPAT itself consists of leaders and office and field staff who bring files to the ATR / BPN office in Sukoharjo, this research applies the method UML (Unified Model Language), in the process. This is to find out the constraints of PPAT in examining the registration of land sale and purchase deeds in the Sukoharjo regency. The data that was collected were processed qualitatively and analyzed descriptively and analytically. The results showed; 1) The function of registering via PPAT Online in assisting PPAT in the transfer of land rights in Sukoharjo Regency is to assist in the process of changing names that arise from the seller to the buyer. 2) The juridical consequences if both parties do not fulfill obligations such as taxation in making deed of transfer of land rights, namely that PPAT will not process the transfer of rights to land and / or buildings if it is not equipped with proof of payment of BPHTB and PPh taxes so that the consequences.

The selection and supply of electric locomotives based on the life cycle contract requires a more thorough review of indicators of their effectiveness implemented in operation. This study examines the main operation indicators currently used: coefficients of operational availability, technical availability, intrinsic availability (instantaneous), availability of the locomotive fleet, performed ton-kilometer operation, and electric power consumption. However, these indicators are not comprehensive and do not provide an overall economic assessment of operation. For a comprehensive assessment of the effectiveness of operation of locomotives, an online calculation of the life cycle cost is proposed. This involves directly using operating and service data for its performance, and the calculation of an indicator — life cycle specific cost (LCSC). The online LCSC is a uniform criterion associated with the energy efficiency of the performed transportation, volume of the ton-kilometer operation, operation expenses, reliability of electric locomotive, and its equipment availability ratio (EAR). The LCSC analysis enables assessment of deviations of the operation effectiveness indicators from those planned, and determining a strategy for improvement of the electric locomotive. It helps to define peculiarities in the operation of an electric locomotive and to forecast its condition and potential properties. Additionally, the LCSC facilitates analyzing the condition of a group of electric locomotives when changing the operation technology (for example, operation starting with increased rolling stock weight), including assessment of the extent of economic benefits of fulfilled measures.Currently, scientists of the JSC “VNIIZhT” and specialists of LLC “Ural Locomotives” are conducting tests on the electric locomotives 2EC6 on the Oktyabrskaya railroad using the “Online LCC” server. This allows online analysis of information collected on the operation and servicing of locomotives under the life cycle contract, pro viding effective and prompt control of the electric locomotive fleet.

High-tech synthesis equipment is relatively common in the pharmaceutical industry, where researchers use high-throughput robotic platforms to optimize reaction conditions or screen catalysts. But these facilities are still rare in academia, and many PhD students in synthetic chemistry have little or no experience with the technology. Academic programs are now aiming to close that skills gap and redefine what it means to be a synthetic chemist. One of these programs is based at the Centre for Rapid Online Analysis of Reactions (ROAR), an automated synthesis suite that opened this year at Imperial College London. ROAR’s launch is part of a broader drive to transform synthetic chemistry into a truly data-driven discipline and may herald a time when automated synthesis suites are as ubiquitous at universities as nuclear magnetic resonance facilities. A stint in process development at BASF, a role analyzing Australia’s natural gas supply chain, and a summer in private

Time-domain nuclear magnetic resonance (NMR) is used for the characterization of wheat infested with Sitophilus zeamais. NMR parameters (T21, T22, P21, P22, and A21/22) were achieved by biexponential analysis combined with a discrete method. Sound wheat, S. zeamais, and S. zeamais/wheat binary mixture are all explored by this method to find the changes in the process of the number increase and growth stage. Based on different sets of NMR parameters, the classification and quantification of the stage and number are made by linear discriminant analysis and partial least squares regression with very high accuracy. The weight and moisture content information lack will make the misclassification rate increase but not by more than 7%. This NMR-based hidden-insect detection method, with fast and nondestructive advantages, was confirmed by X-ray and had a high potential to be equipped in the online analysis system.

Currently, there is growing interest from the producers and the vegetable processing industry to use from farm to fork, non-destructive analysis techniques as a routine step to ensure the quality and safety of horticultural products. This interest, coinciding with the development of new instruments adapted for use both in the field and on the production line, has led to Near Infrared Spectroscopy (NIRS) becoming an increasingly practicable option for meeting the demand. The aim of this work was to develop and optimize NIRS analysis methodology using two spectrophotometers: the first is the MicroNIR™ 1700, a manual, portable instrument based on Linear Variable Filter (LVF) technology, ideally suited to analysing horticultural products in the field, and the other is the Matrix-F, based on Fourier Transform (FT) NIR technology and suitable for online analysis in the processing industries. A total of 230 summer squashes were used to predict the quality (dry matter and soluble solid content) and safety (nitrate content) parameters. For the MicroNIR™ 1700, the comparison between the equations developed confirmed that taking of point spectra (static mode) was the most suitable way of analysis to measure both the quality and safety parameters. In the case of the Matrix-F instrument, it was confirmed that a single spectrum taken online for the intact product as it moves on the conveyor belt (dynamic mode) is enough to establish the product’s quality and safety during industrial processing, thus allowing it to be incorporated easily and conveniently into the production line.

This study explored the potential of using sentiment analysis of tweets to predict referendum choices (Brexit). The feasibility of using StreamKM++ in the massive online analysis framework was exam...

This article addresses a recursive parameter estimation algorithm for a hidden Markov model (HMM). The work focuses on an HMM with multiple states that are assumed to follow from a multivariate Gaussian distribution. The novelty of this study lies in a state transition probability calculation technique that simplifies the application of the backward stage of the forward–backward algorithm. For sequential observation analysis, the complexity of the created recursive algorithm for learning the HMM parameters is merely linear. Meanwhile, the classical Baum–Welch algorithm has second order complexity; therefore, it cannot be applied in online analysis situations. The properties of the proposed recursive expectation–maximization (EM) algorithm were explored by a computer simulation solving test examples and demonstrate that this algorithm can be efficiently applied to solve online tasks related to HMM parameter estimation.

Die Quantifizierung gesundheitsschädlicher Substanzen an Arbeitsplätzen wurde in den letzten Jahrzehnten immer wichtiger und ist auch weiterhin von großem Interesse. Die Rahmenbedingungen für Gefahrstoffmessungen am Arbeitsplatz haben sich gleichzeitig aufgrund des technischen Fortschritts und fortwährender Grenzwertabsenkungen geändert. Somit ist es essenziell, die analytischen Methoden diesen Änderungen anzupassen. Das Institut für Arbeitsschutz der Deutschen Gesetzlichen Unfallversicherung (IFA) bietet zur Qualitätssicherung für Messstellen verschiedene Ringversuche für gesundheitsschädliche Stoffe am Arbeitsplatz an einer dynamischen Prüfgasstrecke an. Im Zuge kontinuierlicher Verbesserung wurden Online-Messmethoden in den Prozess der Prüfgasherstellung implementiert. Der Einsatz eines Online-Gaschromatographen ermöglicht die selektive Quantifizierung der Substanzen im Prüfgasgemisch und sichert somit die Qualität des Prüfgases, z. B. während der Ringversuche für organische Lösemittel und flüchtige organische Verbindungen.

Ionic liquids (ILs) are becoming important solvents in commerce, but monitoring their purity and performance in industrial applications presents new challenges. Fiber welding technology utilizes ILs to mold and shape natural fibers (cotton, hemp, flax, silk, and wool) into morphologies that are typically attained only using synthetic, petroleum-based non-biodegradable plastics. The result is an atom-efficient process that up-converts fibrous substrates to value-added products and materials. A key aspect of bringing this and other IL-enabled technologies to market relies on efficient monitoring and recycling of IL-based solvents. Implementing online IL quality monitoring enhances the unit economics of these processes. Here, we characterize and report conductivity measurements, refractometry, and ATR-FTIR spectroscopy techniques for online IL monitoring during an industrial fiber welding process. The online analysis enables more efficient recycling of the IL solvent, increasing the process efficiency and product quality.

BACKGROUND Lung adenocarcinoma (LUAD) is a type of non-small cell carcinoma. Its pathogenesis is being explored and there is no cure for the disease. MATERIAL AND METHODS The Gene Expression Omnibus (GEO) was searched to obtain data on expression of messenger RNA. GEO2R, an interactive web tool, was used to calculate the differentially expressed genes (DEGs) in LUAD. All the DEGs from different datasets were imported into VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) to identify the intersection of the DEGs. An online analysis tool, the Database for Annotation, Visualization, and Integrated Discovery (DAVID), was used to help understand the biological meaning of DEG enrichment in LUAD. Cytoscape 3.7.2 was used to perform centrality analysis and visualize hub genes and related networks. Furthermore, the prognostic value of the hub genes was evaluated with the Kaplan-Meier plotter survival analysis tool. RESULTS The GEO database was used to obtain RNA sequencing information for LUAD and normal tissue from the GSE118370, GSE136043, and GSE140797 datasets. A total of 376 DEGs were identified from GSE118370, 248 were identified from GSE136403, and 718 DEGs were identified from GSE140797. The 10 genes with the highest degrees of expression - the hub genes - were CAV1, TEK, SLIT2, RHOJ, DGSX, HLF, MEIS1, PTPRD, FOXF1, and ADRB2. In addition, Kaplan-Meier survival evaluation showed that CAV1, TEK, SLIT2, HLF, MEIS1, PTPRD, FOXF1, and ADRB2 were associated with favorable outcomes for LUAD. CONCLUSIONS CAV1, TEK, SLIT2, HLF, MEIS1, PTPRD, FOXF1, and ADRB2 are hub genes in the DEG interaction network for LUAD and are involved in the development of and prognosis for the disease. The mechanisms underlying these genes should be the subject of further studies.

Detecting a panic behavior within a human crowd is of a high importance as it allows to prevent disasters. Online analysis of video streams, real-time processing and accurate detection are required to ensure effective surveillance of the crowded places. However, these requirements are not simultaneously fulfilled by the existing techniques. Rapid advances in artificial intelligence are investing the power for automatic public surveillance and timely detection of a possible abnormal behavior. Thus, the aim of the present work is to propose an online, real-time and effective technique for panic behavior detection. It relies on a handcrafted feature that accounts for the characteristics of the crowd to understand people behaviors and a long short-term memory neural network to predict future feature values. Experiments are performed on well-known datasets of panic situations to evaluate the performance and accuracy of the proposed algorithm. Results show the system yields excellent performances in terms of accuracy and processing time with respect to the state of the art techniques.

Ganoderma lucidum, as the symbol of traditional Chinese medicine, is one of the most economically important medicinal fungi and has received attention worldwide. The number of scientific research publications about G. lucidum increased rapidly. However, there have been few research reviews concerning the research trend of G. lucidum. Here we used Biblioshiny to analyze 3,286 documents from 1936 to 2019. These documents were screened and analyzed using four software tools: R-package bibliometrix, HisCite, Citespace, and Bibliometric Online Analysis Platform. We presented the performance of relevant sources, authors, institutions, and countries. We analyzed the most highly cited documents of G. lucidum to define the research hotspots and research trends in this field.

Background Femoroacetabular impingement (FAI) has attracted increasing attention over the past few decades. We aim to evaluate FAI research and predict research hot spots quantitatively and qualitatively. Methods The publications in FAI research between 2000 and 2019 were assimilated from the Web of Science Core Collection of Clarivate Analytics. The retrieved data were evaluated by the bibliometric method. Software CiteSpace 5.7.R1, VOSviewer 1.6.15, and the Online Analysis Platform of Literature Metrology ( http://bibliometric.com/ ) were used to analyze and identify the hot spots and trends in this field. Results A total of 2471 originals articles that fulfilled the study requirements were obtained. The number of manuscripts on FAI has experienced rapid growth, especially after 2009. The United States of America was the leading country for publication and to the collaboration network. FAI, osteoarthritis, hip arthroscopy, labral reconstruction, pathomorphology, outcome, rehabilitation, and joint cartilage are some of the high-frequency keywords in co-occurrence cluster analysis and cocited reference cluster analysis. Burst detection analysis of top keywords revealed that outcomes, instability, labral reconstruction, adolescent, and risk factor were newly emerged research hot spots. Conclusion The understanding of FAI has been improved significantly during the past two decades. Present studies focused on identifying the optimal method to treat labral pathology, outcome assessment of either surgeries or conservative managements, and predicting midterm and long-term outcomes. Together these studies exert critical implications for decision-making and management for FAI.

Detecting anomalies, in many cases, is only the first step. Often, it is merely the first step in a process that seeks to recover from an anomalous situation so that the system is back to a normal state. Manual analysis is a possible approach; however, this can be time consuming and error prone. Ideally, it would be desirable to have an automated means for resolving anomalies, which is invoked whenever one or more anomalies are detected. In this paper, we present a tool, TADS (Transformation of Anomalies in Data Streams), that creates automated recommendations based upon the observed streaming data. This is accomplished by utilizing the recently introduced concept of dynamic action rules with the Massive Online Analysis (MOA) data streaming platform. In experimental results, we demonstrate that TADS is able to correctly generate recommendations 100% of the time for over half the experiments and over 90% for all but two experimental conditions. In addition, only seconds are required to generate the personalized recommendation for each anomaly. Hence, the results indicate that TADS is a viable approach for correcting anomalies within a streaming environment.

Analytical performance models have been shown very efficient in analyzing, predicting, and improving the performance of distributed computing systems. However, there is a lack of rigorous analytical models for analyzing the transient behaviour of serverless computing platforms, which is expected to be the dominant computing paradigm in cloud computing. Also, due to its unique characteristics and policies, performance models developed for other systems cannot be directly applied to modelling these systems. In this work, we propose an analytical performance model that is capable of predicting several key performance metrics for serverless workloads using only their average response time for warm and cold requests. The introduced model uses realistic assumptions, which makes it suitable for online analysis of real-world platforms. We validate the proposed model through extensive experimentation on AWS Lambda. Although we focus primarily on AWS Lambda due to its wide adoption in our experimentation, the proposed model can be leveraged for other public serverless computing platforms with similar auto-scaling policies, e.g., Google Cloud Functions, IBM Cloud Functions, and Azure Functions.

BACKGROUND Cardiopulmonary resuscitation (CPR) is a topic of great scientific and clinical interest that has received much attention in the past decade. Our study aimed to predict the trends in CPR research activities and evaluate hot topics via bibliometric means, quantitatively and qualitatively. MATERIAL AND METHODS All data were collected from a search of the Web of Science Core Collection on May 12, 2020. Retrieved information was investigated with bibliometric analysis by CiteSpace and VOSviewer software and the Online Analysis Platform of Literature Metrology to analyze and predict the trends and hotspots in this field. RESULTS Our search returned a total of 9563 articles and reviews on CPR published from 2010 through 2019. The number of original research studies on CPR has been increasing annually. The journal Resuscitation published the greatest number of manuscripts involved CPR, and the leading country and institution with regard to contributions on CPR were the United States and the University of Pennsylvania. Keyword co-occurrence/co-citation-cluster analysis showed that the most popular terms associated with CPR occurred in the manner of cluster labels, such as therapeutic hypothermia and treatment recommendation, among others. In addition, palliative care, sepsis, extracorporeal membrane oxygenation, and brain injury were identified as new foci through burst detection analysis. CONCLUSIONS Our study showed that the scientific research focus on CPR is switching from traditional therapeutic treatments to a public health practice, with in-depth understanding and development of CPR-related techniques expanding over the past decade. These results demonstrate trends in the CPR research and detected the possible neo-foci for ensuing research.

We present Dads, the first distributed, online, scalable, and cost-effective dynamic slicer for continuously-running distributed programs with respect to user-specified budget constraints. Dads is distributed by design to exploit distributed and parallel computing resources. With an online analysis, it avoids tracing hence the associated time and space costs. Most importantly, Dads achieves and maintains practical scalability and cost-effectiveness tradeoffs according to a given budget on analysis time by continually and automatically adjusting the configuration of its analysis algorithm on the fly via reinforcement learning. Against eight real-world Java distributed systems, we empirically demonstrated the scalability and cost-effectiveness merits of Dads. The open-source tool package of Dads with a demo video is publicly available.

The present study was designed to identify writer's gender trough online handwriting and drawing analysis. Two groups - one of 126 males (mean age 24.65, SD=2.45) and the other of 114 females (mean age 24.51, SD=2.50) participants were recruited in the experiment. They were asked to perform seven writing and drawing tasks utilizing a digitizing tablet and a special writing device. Seventeen writing features grouped into five categories have been considered. The experiment's results show that the set of considered features enable to discriminate between male and female writers investigating their performance while copying a house drawing (task 2), writing words in capital letters (task 3) and writing a complete sentence in cursive letters (task 7), in particular focusing on Ductus (number of strokes) and Time categories of writing features.

A method for the fast isolation, propagation, and characterization of very low count bacteriophages active against pathogenic bacterial strains is described in this study. Bacteriophages with a count of 10(2) phage particles were dynamically adhered from the maximum 10 mL blood plasma sample onto the nanostructured part of the fused silica capillary. One-step propagation of phage particles of genus Kayvirus inside the etched capillary on 10(4) Staphylococcus aureus host cells increased their number to 6 X 10(4) phage particles. Phage particles were concentrated online and separated by capillary electrophoretic methods. No phage replication occurred when the phage-resistant S. aureus or Escherichia coli cells were used. Two-step phage propagation in the capillary allowed an increase in the total virion count to up to 6 x 10(5) phage particles and subsequent off-line matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis of the phage zone collected after capillary electrophoresis. Relative standard deviations of the phage peak area were at most 2.3%. We expect that the method of isolating bacteriophages from blood plasma and their simultaneous identification will facilitate clinical studies of phage preparations and contribute to pharmacokinetics studies during phage therapy. This approach is also suitable for capturing and enriching new phages from the environment when a susceptible indicator strain is available.

Electroactive biofilms are routinely characterized in-operando by dynamic electrochemical measurement techniques such as cyclic voltammetry or electrochemical impedance spectroscopy. Since electrical signals can be recorded and processed very quickly, these techniques allow to investigate slow and fast electron transfer processes. In contrast, the dynamics of species production rates are usually not addressed because standard measurement techniques for the quantification of reaction products such as gas chromatography are slow. Instead it is often assumed that species production rates are either directly proportional to the current - under so called turnover conditions - or equal zero - under so called non-turnover conditions. To challenge this assumption, we measured species production rates of a biofilm electrode with a high time resolution by differential electrochemical mass spectrometry (DEMS). An acetate oxidizing biofilm electrode was placed just micrometers away from the mass spectrometer inlet in which enabled us to observe CO2 production directly at the electrode during cyclic voltammetry (CV) and potential steps. The measurement results showed that the CO2 production deviates significantly from the expected value calculated from the current by Faraday’s law under certain operating conditions. We analyze this effect in detail and show that it can be explained with biofilm storage capacities for charge and substrate. These capacities are quantified by deconvoluting the faradaic and non-faradaic currents. [1] Also, the onset of the complete oxidation of acetate to CO2 during CVs was determined to be just 22 mV above the standard potential for acetate oxidation. Determining this value by directly measuring CO2 instead of current is advantageous because capacitive effects can be excluded. [1] In conclusion, we demonstrate that electrical current and CO2 production can be partly decoupled in biofilm electrodes and that DEMS is a valuable technique for analyzing processes in such electrodes. [1] Kubannek, F., Schröder, U., Krewer, U. (2018). Revealing metabolic storage processes in electrode respiring bacteria by differential electrochemical mass spectrometry. Bioelectrochemistry, 121, 160–168, doi: 10.1016/j.bioelechem.2018.01.014

Smart cities should be built on harmonious relationships with the environment, with rational measures to use environmental resources in many ways. Resources essential to human life such as water and energy must have special attention. With the advance of technologies use, human being becomes one of the main promoters of information and solutions for smart cities. Stream machine learning is an approach where data becomes available sequentially, called data stream, which adapts the prediction model with fixed window-sized data stream. The objective of this work is to analyze the effectiveness of buildings energy consumption using stream learning. To achieve this goal, MOA 11 Massive Online Analysis tool was used to evaluate the performance of the selected stream learning algorithms on an an open database. Batch algorithms were also used as testbed, to analyze the performance of stream algorithms related to them. Our work shows that stream learning can show good results in this scenario.

The evolution in the use of social media has raised a new communication paradigm, that is changing people's behavior, their way to create relationships and doing business; this evolution is visible in the use of social media, that every day increase their follower´s number, aspect that create a business opportunity for companies, among them the banks, who see in them an economic and effective opportunity to approach and know the market, as well as to generate engagement with their target audience. This research is focused on analyzing the business management models of the Ecuador's financial institutions on Facebook. This was done considering the activity of the private banks with more followers and the work they do on this social network. The results presented here, were obtained with the help of the online analysis tool, Fanpage Karma.

The increasing demand of the horticultural sector in terms of quality and safety assurance stresses the need of the producers and the agri-food industry of implementing non-destructive analysis techniques. Near infrared spectroscopy (NIRS) has proven to be an increasingly practical option for satisfying this demand. Recently a new generation of NIRS instruments has been developed, being necessary their previous evaluation before their incorporation for quality and safety assurance along the food supply chain. For this purpose, 230 summer squashes, grown outdoors in the province of Cordoba (Spain), were analyzed to determine quality (dry matter content (DMC) and soluble solid content (SSC)) and safety (nitrate content) parameters using two spectrophotometers, MicroNIRTM Pro 1700 and Matrix-F, ideally suited for the in situ and online analysis, respectively. A linear calibration strategy - modified partial least squares regression, MPLS - were used for the development of predictive models. The results obtained showed NIRS technology, by means of new generation sensors, is a potential tool for the non-destructive measurement of DMC (RPDcv = 1.76 and RPDcv = 1.98), SSC (RPDcv = 1.62 and RPDcv = 1.63) and nitrate content (RPDcv = 1.77 and RPDcv = 1.36), for the MicroNIRTM Pro 1700 and Matrix-F, respectively. This would enable to improve the quality and safety control of this vegetable throughout the whole supply chain, i.e. in field and in the processing plant.

Introduction Over the past few decades, the incidence of Rhabdomyolysis (RM) has significantly increased. The prognosis is substantially worse if renal failure develops. Many problems remain to be addressed regarding the pathogenesis, prevention, and treatment of RM. The purpose of this study was to identify and characterize the top 100 most cited publications regarding rhabdomyolysis (RM) by performing a bibliometric analysis. Methods Publications focusing on RM were identified from the Science Citation Index-Expanded (SCI-E) of the Web of Science Core Collection (WoSCC). Bibliographic information was collected, including year of publication, authorship, publishing journals, institution, country of origin and keywords. CiteSpace V5.6.R2 and the Online Analysis Platform of Literature Metrology were used for descriptive analysis. Results The 100 most cited articles were published between 1995 and 2016, with citation numbers ranging from 116 to 904. The United States (60) has been the largest contributor to RM research. Hartford Hospital and University of Texas were found to be the most productive institutions, with five articles each. Thompson, PD, who authored six articles, was the most productive author. The American Journal of Cardiology published the most articles (5), followed by the New England Journal of Medicine (4). The top three co-cited journals were the New England Journal of Medicine (74), Lancet (59) and JAMA (54). Conclusions This study provides valuable information on the study of RM. These findings may be used to guide clinical decision-making and identify new research fields.

Breath analysis is a promising method for metabolomics studies and clinical diagnosis, as it enables the observation of metabolites in a convenient and noninvasive way. In this work, an atmospheric pressure photoionization (APPI) source was modified for online analysis of exhaled breath by coupling with quadrupole time-of-flight mass spectrometry (QTOFMS). Three parameters, namely, the capillary voltage, the sampling flow and the curtain gas flow of the APPI source, were optimized. Five healthy volunteers, three males and two females, were enrolled to test the performance of modified APPI-QTOFMS by analyzing their exhaled breath. A total of 21 compounds were tentatively identified, and four metabolites, namely, dimethyl selenoxide, δ-valerolactam, hydroxymandelic acid and palmitic amide were detected in the exhaled breath for the first time. The result shows that modified APPI-QTOFMS can be used for the online study of exhaled breath. Graphical abstract.

Present work demonstrates the PCB waste characterization using solid state route. Microwave and resistive heating have been used for heating the PCB waste sample and inter-comparison studies have been carried out in order to understand the nature of volatiles and residue obtained during the two different heating processes. GC -MS was used to analyze the volatiles, collected during microwave heating and shows the presence of CO2 as major volatile. Simultaneous TG -DTA -EGA (FTIR) was used in air and inert atmospheres for online analysis of volatiles where CO2 was observed as major volatile in inert atmosphere along with moisture while CO evolution was also confirmed as major evolved gas along with CO2 and moisture in air atmosphere.

In data mining, various techniques are applied on large sets of data. These large sets of data are known as big data. For extraction and retrieval of desired information or pattern from big amount of data and quantity of knowledge, we tend to use huge data processing. To maintain working of “big data,” various logics are used. Today, because of many online applications, product price is highly dynamic and current price can be compared by the customers by using different applications. In our work, we have used modified K-means clustering method and divided online data. Dataset is divided into 10 clusters for analysis. In the last section, the conclusion based on I/O time and computational time is given.

The analysis of biological samples (bioanalysis) always needs intensive sample cleanup (sample preparation) before it gets injected into the analytical instruments. In bioanalysis, sample preparation has an essential role because of the complexity of the biological matrix. Among popular sample preparation techniques, microextraction techniques received high attention because of needs of low volume of used solvent and sample. The sorbent may has high influence on the method selectivity and recovery. Because of their high surface area, ultra-small size, high physical and chemical stability properties, nanomaterials attained huge attention in sample preparation for analytical and bioanalytical trends. In this chapter, the different types and applications of nanomaterials in microextraction analysis and bioanalysis will be summarized and discussed. In addition, recently published papers on the relevance of nanomaterials as sorbents in microextraction for bioanalysis will be presented and the application of nanomaterials for online analysis procedures will be included.

Introduction: In recent years, there has been an increase in the number of medical residency programs in all regions of Brazil, only in the last 4 years, from 2014 to 2018, there was an increase from 18,953 general spots offered for the first year of medical residency to 26,094, 37% more, this was also observed in General Surgery Residencies around Brazil. As a result, the number of general surgeons has increased substantially, from 12,430 in 2008 to 34,065 in 2018, an absolute percentage increase of 174%. These new surgeons are bringing with them their theoretical and practical knowledge already updated, regarding new techniques and surgical modalities. In this context, in the last 2 decades, video-assisted surgery has become the choice for numerous procedures in Brazil and in the World, considering its benefits for patients, such as less postoperative hospital stay and less metabolic response to surgical trauma, for example. The current study correlates the data between the increase in the number of spots in medical residency programs, related to the graduation of new surgeons in Brazil, to the increase of videolaparoscopic surgeries in the public health system. Methodology: Review and online analysis of the national electronic public health registry database - DATASUS. The most performed Surgeries in Brazil were chosen for our data analysis. Results: The results were divided into five geographically distributed areas in Brazil. At the end of the current study, the number of video-assisted surgeries increased by 233%, accompanied by a 63% increase in the number of medical residency openings and a 174% absolute increase in the number of new surgeons in Brazil at the same period. Conclusion: The current study showed that the increase in the number of video-assisted surgeries in the country is related to the exponential increase in the number of medical residency spots and, consequently, to the number of new graduated general surgeons. This increase occurs more and more in a context of videolaparoscopic techniques, revealing multiple benefits already recognized for patients. We could conclude, therefore, that the increase in the number of medical residencies in general surgery - which are increasingly teaching videolaparoscopic techniques - has contributed as a complementary factor to the increase in the number of video-assisted surgeries observed in all regions of Brazil.

A number of analytical techniques are available for the compositional analysis of pharmaceutical drugs, but these techniques are expensive, destructive, and laboratory based. The drug industry needs a less expensive, quick, nondestructive technique that can be used for the in situ, online analysis of drug samples. For more than two decades, laser-induced plasma spectroscopy, also known as laser-induced breakdown spectroscopy (LIBS), has been adopted as a viable technique in various fields of compositional analysis of materials. LIBS has a wide range of applications in the drug industry such as the identification of active pharmaceutical ingredients, excipients, and binders. In this chapter, we try to illustrate the capability of LIBS, in conjunction with statistical methods, for the compositional analysis of a variety of drug samples. Chemometric analysis of a LIBS spectral dataset has been found to be very useful in the discrimination of drug samples with similar elemental compositions.