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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.

Purpose Aging populations and increasing quality of life requirements have attracted growing efforts to study chronic postsurgical pain (CPSP). However, a diverse range of factors are involved in CPSP development, which complicates efforts to predict and treat this disease. To advance research in this field, our study aimed to use bibliometric analysis to quantitatively and qualitatively evaluate CPSP research and predict research hot spots over the last 10 years. Methods Relevant publications between 2011 and 2020 were extracted from the Web of Science Core Collection database. CiteSpace software (v5.7.R2) and the Online Analysis Platform of Literature Metrology were used to analyze research attributes including countries and authors, keywords and co-occurrence, and burst detection to predict trends and hot spots. Results A total of 2493 publications were collected with the number of annual publications showing nearly threefold increase over the past decade. Articles were the primary publication type with the United States as the leading country and the center of national collaboration. Johns Hopkins University provided the leading influence within the CPSP field. Postoperative pain, multimodal analgesia, quality of life, opioid, microglia, cesarean delivery, inguinal hernia, chronification, genetic polymorphism, and lidocaine were the top 10 clusters in co-occurrence cluster analysis. Moreover, burst detection was shown that epidural analgesia, nerve injury, total hip arthroplasty were the new hot spots within the CPSP field. Conclusion Bibliometric mapping not only defined the overall structure of CPSP-related research but its collective information provides crucial assistance to direct ongoing research efforts. The prominent keywords including "risk factor" and "multimodal analgesia" indicate that CPSP prevention and new treatment methods remain hot spots. Nonetheless, the recognition that CPSP is complex and changeable, proposes comprehensive biopsychosocial approaches are needed, and these will be essential to improve CPSP interventions and outcomes.

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.

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.

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.

In the study of animal behaviour, annotation and analysis is largely done manually either directly in the field or from recordings. An emerging field, computational ethology, is challenging this approach by using machine learning to automate the process. However, the use of such methods in general is complicated by a lack of modularity, leading to high cost and long development times. At the same time, the benefits of implementing a fully automated pipeline are often minuscule. We propose online analysis as a way to gain more from automating the process, such as making it easier to ensure that equipment is properly configured and calibrated, enabling the recording equipment to follow the animals, and even enabling closed-loop experiments. In this work, we discuss the requirements and challenges for such a system and propose an implementation based on modern IT infrastructure. Finally, we demonstrate the system in case studies of bats and mongoose. As more and more methods and algorithms are developed we expect online systems to enable new experimental setups to study behaviour, leading to new insights in the field.

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

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...

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.

Organ-on-a-chip technology is used to study biological processes that involve multiple cell types and temporal changes like, homeostasis, metabolism and responses to chemical triggers. Main benefits of organ-on-a-chip systems include: improved mimicking of the in vivo situation, easy manipulation of the microenvironment and low reagent consumption. Exploiting the unique dynamic aspects of organ-on-a-chip technology, such as liquid flow, automated online measurement of parameters by sensors or online coupling to analytical equipment becomes feasible. Apart from the challenge to detect drug uptake and chemical changes in real-time with high resolution at the microscale, the biggest challenge, is detection of the analyte of interest in cell culture medium, as this contains high amounts of salts, sugars and proteins required by the living cells. In this review online and in situ analytical techniques integrated with organ-on-a-chip devices are discussed with special emphasis on maintaining the biological relevance, achieving analytical compatibility, system integration and final applicability.

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.

The huge consumption of modern plastic with improper disposals has posed a great threat to the environment and human beings. To put these post-consumer plastics into the recycling chain, laser induced breakdown spectroscopy (LIBS) has been established as a potential tool for rapid grading of plastics online. Meanwhile, LIBS is a powerful tool for plastic investigations in the laboratory with the capacity of simultaneous qualitative and quantitative analysis. The present review describes the development and prospect of plastic investigations using LIBS, highlighting the intrinsic advantages of LIBS for plastic recycling. Moreover, the selection of hardware components and analysis methods are discussed, which may help to start a research on plastic using LIBS. National Environmental Agency (NEA) National Research Foundation (NRF) The authors are grateful for the financial support from SCARCE, which is supported by the National Research Foundationgs2:, Prime Minister's Office, Singapore, the Ministry of National Development, Singapore, and National Environment Agency, Ministry of Sustainability and the Environment, Singapore under the Closing the Waste Loop R&D Initiative as part of the Urban Solutions & Sustainability - Integration Fund (Award No. USS-IF-2018-4).

El interés desarrollado en los últimos años por las conductas sexuales de hombres que tienen sexo con hombres (HSH) no ha tenido correlato en un análisis similar protagonizado por mujeres (MSM). Esta investigación aborda la temática con el objetivo de describir el comportamiento de las mujeres mexicanas que tienen sexo con otras mujeres, desde una perspectiva que prioriza la salud sexual. El punto de partida es el paradigma crítico y la metodología cualitativa, conjugando entrevistas abiertas y el uso del método sociométrico en el análisis de la esfera virtual. Se presentan los perfiles y la caracterización de conductas y subrayan la necesidad de actuar desde la promoción de la salud y la equidad. In recent years, the issue of women who have sex with women (WSW) has not received the same attention as that of men who have sex with men (MSM). Focusing on sexual health as a priority, our study addresses this topic with the objective of describing the behaviour of Mexican women who have sex with other women. We based the study on a critical paradigm and qualitative methodology, combining interviews with a sociometric method for online analysis. The conclusions present profiles and behavioral characterisation while highlighting the need to act to promote health and equity.

The standardization of the online government service platform is of great significance to simplify the procedures and improve the efficiency of administrative licensing agencies. In this paper, the definition of online government service platform, from the law, policy background, the practice level, carry out the work of government services online analysis summarizes the necessity, the role of online government service platform and the existing problems, and analysis the reasons from the dimension of standardization, standardization can be targeted to summarize proposed online government service platform construction of three aspects of the role.

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.

Marine remote sensing (MRS) data provide an important tool for advancing global change research. However, the existing product service practices are insufficient for meeting the needs of a full-experience online application. This paper introduces a framework named SatANA, which is unified by a data tiling method with a spatial-aware feature, for integrated and intelligent improvements in visualization, storage and computing. The SatANA framework is supported by a hybrid database storage ideal for the cloud storage of massive MRS data. The raw data are displayed and roamed on a virtual globe through the Internet as tiles, enhancing their spatial awareness, that can be intelligently used for visualization result tuning, data storage preloading and distributed computing optimized indexing. To verify its feasibility and effectiveness, we applied this framework to a platform called SatCO2, which is devoted to providing convenient access to and the efficient utilization of MRS data.

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.

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.

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 development of online analysis methods for industrial electric arc furnaces (EAFs) has been a major research topic in recent years. Process control becomes even more important in the future due to the increase in both the usage of recycled metal as charge material and the metal recycling rate. For the industry to keep up with the pace, reliable chemical analysis of the slag together with online information about the furnace operation status is essential. Herein, optical emission spectroscopy is used to obtain the information about the electric arc together with radiative properties and surface temperature of the molten bath in an industrial EAF. The arc is visible for high alloyed steel grades within 5–30 min before tapping and the arc spectra are dominated by the optical emissions from the slag components. The plasma properties of the electric arc are determined with emission lines from atomic chromium, iron, and calcium. The time evolution of the spectra for high alloyed and carbon steel grades are compared with each other to provide a better understanding of the differences in the spectra between these two steel grades.

The aim of this work is to obtain a useful anomaly definition for online analysis of time series. The idea is to develop an anomaly concept which is sustainable for long-lived and frequent streamings. As a solution, we provide an adaptation of the discord concept, which has been successfully used for anomaly detection on time series. An online approach implies the frequent processing of a data streaming for timely providing anomaly alerts. This requires a modification since discord search is not exactly decomposable in its original definition. With a statistical approach, allowing to rate the significance of the discords of each analysis, it has been possible to obtain a solution where the number of false positives is minimized. The new online anomalies are called significant online discords (sods). As a novel feature, sod search determines the quantity of anomalies in the time series under investigation. The search for sods has been implemented and its properties validated with synthetic and real data. As a result, we found that sods can be considered as a useful new tool for anomaly detection in fast streaming time series or Big Data contexts.

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.

Improvements in computational resources over the last decade are enabling a new era of computational prediction and design of novel materials. The resulting resources are databases such as the Materials Project (www.materialsproject.org), which is harnessing the power of supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials, to design novel materials, and to make the data available for free to the community, together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for more than 70,000 materials and millions of associated materials properties. The software infrastructure carries out thousands of calculations per week, enabling screening and predictions for both novel solids as well as molecular species with targeted properties. As the rapid growth of accessible computed materials properties continues, the next frontier is harnessing that information for automated learning and accelerated discovery. In this article, we highlight some of the emerging and exciting efforts, and successes, as well as current challenges using descriptor-based and machine-learning methods for data-accelerated materials design.

As the core part of the new generation of information technology, the Internet of Things has accumulated a large number of real-time data streams of various types and structures. The data stream is generated at an extremely fast speed, and its content and distribution characteristics are all in high-speed dynamic changes, which must be processed in real time. Therefore, the feature learning algorithm is required to support incremental updates and learn the characteristics of high-speed dynamic change data in real time. Most of the current machine learning models for processing big data belong to the static learning model. The batch learning method makes it impossible to analyze data streams in real time, and the learning ability of dynamic data streams is poor. Therefore, this paper proposes an incremental high-order deep learning model to extend the data from the vector space to the tensor space and update the parameters and structure of the network model in the high-order tensor space. In the process of parameter updating, the first-order approximation concept is introduced to avoid incrementing parameters by the iterative method and to improve the parameter update efficiency, so that the updated model can quickly learn the characteristics of dynamically changing big data and satisfy the real-time requirements of big data feature learning while maintaining the original knowledge of the neural network model as much as possible. To evaluate the performance of the proposed model, experiments were performed on real image data sets-MNIST, and the model was evaluated for stability, plasticity, and run time. The experimental results show that the model not only has the ability to incrementally learn the characteristics of new data online but also retains the ability to learn the original data features, improve the model update efficiency, and maximize the online analysis and real-time processing of dynamic data streams.

Data streams are pervasive but implementing online analysis of streaming data is often nontrivial as data streams can have different, domain-specific formats. Regardless of the stream, the analysis task is essentially the same: features are extracted from the stream, e.g., to employ machine learning and data mining methods. We present the Modular Integrated Distributed Analysis System (midas) for constructing distributed online stream processing systems for heterogeneous data. The midas framework makes it possible to process raw data streams, extract features, perform machine learning and make the results available through an HTTP API for easy integration with various applications. midas is agnostic with regard to the type of data stream and is suitable for multiple domains. Keywords: Data streams, Online analysis, Distributed systems, Machine learning

Early warning of coal and gas outburst based on gas emission characteristics is a more feasible and effective outburst prediction method. In order to make up for the limitations of the existing traditional contact coal and gas outburst prediction methods, the gas emission dynamic characteristics were used to carry out coal and gas outburst warnings, and the Xiayukou Coal Mine outburst warning system based on gas emission characteristics was constructed. The field application practice shows that the system can evaluate and warn the dangerous state and development trend of the working face stably and reliably. The system has tracked and inspected the tunnel length in the underground mine over 500m, and caught many abnormal gas phenomena in advance, and realized the real-time monitoring, online analysis, and intelligent early warning of coal and gas. It has extremely important practical significance for improving mine gas control and daily safe and efficient production.

The classification and identification of coal ash contributes to recycling and reuse of metallurgical waste. This work explores the combination of the laser-induced breakdown spectroscopy (LIBS) technique and independent component analysis-wavelet neural network (ICA-WNN) for the classification analysis of coal ash. A series of coal ash samples were compressed into pellets and prepared for LIBS measurements. At first, principal component analysis (PCA) was used to identify and remove abnormal spectra in order to optimize the training set for the WNN model. And then, ICA was employed to select and optimize input variables for the WNN model. The classification of coal ash was carried out by using the WNN model with optimized model parameters (the number of hidden neurons (NHN), the number of iterations (NI), the learning rate (LR) and the momentum) and input variables optimized by ICA. Under the optimized WNN model parameters, the coal ash samples for test sets were identified and classified by using WNN and artificial neural network (ANN) models, and the WNN model shows a better classification performance. It was confirmed that the LIBS technique coupled with the WNN method is a promising approach to achieve the online analysis and process control of the coal industry.

Objectives: Pharyngeal high-resolution impedance manometry (HRIM) studies can be analysed by automated impedance manometry (AIM)-analysis to objectively asses swallowing. The aim of this study was to evaluate the reliability of a recently developed online AIM analysis portal. Methods: A database of 24 pharyngeal HRIM and videofluoroscopy studies (n = 18 oropharyngeal dysphagia patients and n = 6 controls; mean age 66, range 58–71 years) was established. Five observers of varying expertise analysed the database twice using AIMplot-online. Two experienced observers also performed the analysis using original AIMplot. Intra- and inter-rater reliability was determined by the intra-class correlation coefficient (ICC). Results from AIMplot and AIMplot-online and their relation to penetration aspiration scale (PAS) scores were compared. Results: The expert analysis confirmed that both analysis platforms differentiated patients from controls and, amongst patients, significant swallowing dysfunction. Intra- and inter-rater reliability were substantial to excellent; however, expertise and the inclusion of highly aberrant cases influenced the reliability of some measures. Conclusion: In conclusion, swallow function variables can be reliably derived using online software-based analysis and are altered in relation to increasing levels of swallowing dysfunction. By making the AIM analysis widely available via the Internet, the diagnostic potential of pharyngeal HRIM recordings can be fully and independently assessed.

‎The process of high speed milling (HSM) is regarded as one of the most sophisticated and complicated manufacturing operations‎. ‎In the past four decades‎, ‎many investigations have been conducted on this process aiming to better understand its nature and‎ ‎improve the surface quality of the products as well as extending tool life‎. ‎To achieve these goals‎, ‎it is necessary to form a‎ ‎general descriptive reference model of the milling process using experimental data‎, ‎thermo-mechanical analysis‎, ‎statistical or‎ ‎artificial-intelligent (AI) models‎. ‎Besides‎, ‎increasing demands for more efficient milling processes‎, ‎qualified surface finishing‎, and modeling techniques have propelled the development of more effective modeling methods and approaches‎. ‎In the first part this dissertation‎, ‎an‎ ‎extensive literature survey of the state-of-the-art modeling techniques of milling processes is carried out‎, ‎more specifically‎ ‎of recent advances and applications of AI-based modeling techniques‎. ‎The comparative study of the available methods as well as the‎ ‎suitability of each method for corresponding types of experiments is also presented‎. ‎In addition‎, ‎the weaknesses of each method as‎ ‎well as open research challenges are presented‎. ‎Therefore‎, ‎a comprehensive comparison of recent developments in the field will‎ ‎be a guideline for choosing the most suitable modeling technique for this process regarding its goals‎, ‎conditions‎, ‎and specifications‎. ‎After comprehensive study of the available methods for modeling HSM processes‎, ‎to build up a proper condition monitoring system‎, ‎sensor signals are to be utilized to form a reference model which non-intrusively reflects the performance of the system‎. ‎Therefore‎, ‎a desired reference model has to apply more efficient feature extraction and artificial intelligence (AI) techniques to be more repeatable and generalizable‎. ‎Since milling signals are complex‎, ‎a time-frequency analysis method‎, ‎namely wavelet‎, ‎is applied for feature extraction‎. ‎Considering the high dimension of the wavelet features‎, ‎clustering methods are used for dimension reduction and also as an interpretation layer between the signal feature extraction subsystem and artificial intelligence blocks‎. ‎This research illustrates the performance of artificial intelligence based techniques for modeling of high speed end milling experimental data‎. Studied and developed methods are applied on wavelet features of force and vibration signals to illustrate the repeatability and accuracy of their results‎. ‎It is shown that the proposed structure as well as the developed artificial intelligent method can present the status of the process and can be applied for fault diagnosis and TCM purposes‎. ‎It is also discussed that how application of available data mining methods with a proper structure may improve the performance of existing reference models towards more efficient utilization of available experimental data and easily generalizable reference models‎. DOCTOR OF PHILOSOPHY (EEE)

Data mining techniques refer to extraction techniques that are performed on large sets/volume of data or the big data. Big data mining is used to extract and retrieve desired information or pattern from a large quantity of data. Today, we need a system which maintains the performance of “Big data” and for analyzing this big data we use various logics. In online marketing, price of any product is highly transparent. It is used to increase sales and margin by many online applications. As a part of this research, in this paper, we have discussed what is big data, what logics are used in big data mining in the form of algorithms, and how it is implemented in big data. How the customer is benefited by analysis of dynamic product price of a product? Various data such as customer attributes, customer visit attributes, and purchase history of a customer have been collected in major form for our study.

This study develops two novel charging strategies for lithium-ion batteries, designed to prevent the onset of lithium plating when the cells are charged at low ambient temperatures. Commercially available 3.1 Ah 18650-type cells with NCA and graphite electrodes have been selected for this study. Experimental results highlight that for these cells, lithium plating can be detected when the cells are charged with a traditional constant-current constant-voltage (CC-CV) profile at an ambient temperature of 5 °C and a charge rate of 1C. The occurrence of lithium plating is known to lead to a considerable capacity reduction. To avoid the onset of lithium plating with minimum impact on the charge time, two optimal charging strategies are proposed. The first is based on detecting the onset of lithium plating through the online analysis of the voltage relaxation profile (VRP). The second is to manage the cell charging process to achieve a pre-defined rate of battery degradation per charge cycle. Experimental results highlight that the capacity fade of cells using the proposed charging strategies can be significantly reduced compared to when charged using the conventional CC-CV approach by 45% and 70% respectively while minimizing their impact on the charging speed.

In real time, everything requires monitoring and controlling, especially in case of the protecting food from getting spoiled. In this paper, an Internet of Thing (IoT) based framework for food monitoring is proposed to protect food from getting spoiled due to changes in the environmental conditions during storage. In the existing scenario the prediction has been done based on the recorded sensed data and detailed analysis have been done to identify the factors affecting the food to get spoiled. Automated controlling mechanism is proposed in this work for controlling the environmental parameters with adaptive Naive Byes prediction and IoT. In the proposed work, environmental parameters like temperature, humidity, moisture, light, etc., which affect on the quality of the nutritional values of food are considered which spoil the food, if they are not in the advisable range of their values. In this work online analysis would be done to predict the nutritional condition of the food to avoid the spoilage of the food. This will help to save food from getting spoiled and reduces the incidental losses in the business. All the sensed data will be stored on a cloud and the analysis would be performed for prediction of the environmental condition at the storage place to avoid food spoilage by changing to the suitable environmental condition, at the place. In the proposed work adaptive Naive Bayes method is used for prediction of environmental condition at the place where food is stored and the harmful changes are monitored and action will be taken to provide advisable condition at the stored location.

Angiostrongylus vasorum has spread farther north in Europe. In this study, two autochthonous findings from dogs from Finland are described: In February 2014, the infection was diagnosed in a 10-month-old labrador retriever, and in February 2017, in a three-year-old French bulldog. These diagnoses were based on direct detection of the larvae from faeces of the dogs. The dogs had no history of travel to or import from abroad; the first lived in Southern Finland and the other in Western Finland, about 150 km apart. The dogs had no clinical signs attributable to angiostrongylosis. An online questionnaire was used to survey the extent to which veterinarians in Finland have self-reportedly observed canine A vasorum infections. A total of 38 veterinarians authorised to work in Finland answered the questionnaire in December 2017, and 9 (24%) of them reported having seen one or more dogs with A vasorum infection in Finland. The results suggest that at least five individual dogs with A vasorum infection would have been seen in Finland, three of which had an apparently autochthonous infection. While the geographical distribution of A vasorum in Finland remains largely unknown, findings have started to appear from domestic dogs. It remains possible that some veterinarians could have misdiagnosed, for example, Crenosoma vulpis larvae as those of A vasorum, and the findings without confirmation using antigen test could be due to coprophagy and passage of ingested larvae; however, this does not change the main conclusion that can be made: A vasorum is already multifocally present in Finland. Increasing awareness about A vasorum is important in areas where it is emerging and spreading. © 2019 British Veterinary Association.

Background Hepatocellular carcinoma (HCC) is one of the most common malignant cancers with a poor prognosis. For decades, more and more biomarkers were found to effect on HCC prognosis, but these studies were scattered and there were no unified identifiers. Therefore, we built the database of prognostic biomarkers and models for hepatocellular carcinoma (dbPHCC). Methods dbPHCC focuses on biomarkers which were related to HCC prognosis by traditional experiments rather than high-throughput technology. All of the prognostic biomarkers came from literatures issued during 2002 to 2014 in PubMed and were manually selected. dbPHCC collects comprehensive information of candidate biomarkers and HCC prognosis. Results dbPHCC mainly contains 567 biomarkers: 323 proteins, 154 genes, and 90 microRNAs. For each biomarker, the reference information, experimental conditions, and prognostic information are shown. Based on two available patient cohort data sets, an exemplified prognostic model was constructed using 15 phosphotransferases in dbPHCC. The web interface does not only provide a full range of browsing and searching, but also provides online analysis tools. dbPHCC is available at http://lifecenter.sgst.cn/dbphcc/ Conclusions dbPHCC provides a comprehensive and convenient search and analysis platform for HCC prognosis research. General significance dbPHCC is the first database to focus on experimentally verified individual biomarkers, which are related to HCC prognosis. Prognostic markers in dbPHCC have the potential to be therapeutic drug targets and may help in designing new treatments to improve survival of HCC patients. This article is part of a Special Issue entitled “System Genetics” Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Security-related concerns in elastic cloud applications call for a risk-based approach due to the inherent trade-offs among security and other nonfunctional requirements, such as performance. To this end, the authors advocate a solution that can be efficiently realized through modeling the application behavior as a Markov decision process, on top of which probabilistic model checking is applied. The article explains the main steps in this approach and illustrates its use in online analysis and decision making regarding elasticity decisions. The runtime analysis is capable of providing evidence for key security-related aspects of the running applications, such as the probability of data leakage in the next hour.