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Purpose: The purpose of this study was to investigate associations between digital urine color and paper urine color with other urine indices to assess hydration status. Methods: Twelve male subjects (mean ± standard deviation; age, 26 ± 8 years; body mass, 57.8 ± 5.3 kg; height, 177.5 ± 8.9 cm; VO2max, 57.8 ± 5.8 ml·kg−1·min−1) performed four exercise trials in the heat. Before and following exercise trials, subjects provide urine samples. Urine samples were measured using a digital urine color chart on a portable device screen. Urine samples were also assessed with urine specific gravity (USG), urine osmolality (UOsmo), and a validated paper urine color chart. Results: There were extremely large associations found between digital urine color and paper urine color (r = 0.926, p < 0.001). Correlation coefficients showing associations with USG and UOsmo were similar between digital urine color (USG, r = 0.695, p < 0.001; UOsmo, r = 0.555, p < 0.001) and paper urine color (USG, r = 0.713, p < 0.001; UOsmo, r = 0.570, p < 0.001). Bland–Altman analysis indicated that no proportional bias was observed between digital and paper urine colors (bias, − 0.148; SD of bias, 0.492; 95% LOA, − 1.11, 0.817; p = 0.094). Conclusions: Strong associations were found between digital and paper urine colors with no proportional bias. Furthermore, the degree of associations with USG and UOsmo was similar between digital and paper urine color. These results indicate that digital urine color is a useful tool to assess hydration status and this method could be used as an alternative method to using paper urine color. [ABSTRACT FROM AUTHOR]

A novel high speed microcompression platen tester was developed in order to measure the out-of-plane compressive modulus of thin materials. The instrument is capable of subjecting a sample of thickness 20 μm or greater to a transverse compressive pulse over a time interval ranging from approximately 2 ms to several seconds, and can therefore be used to collect data under conditions representative of those in a high speed calender nip. In this study, free layers of coating formulations normally used to coat paper were prepared and tested using the microcompression platen tester described above. Tests were conducted at high speeds, with a pulse duration of 2 ms during the compressive stroke, and at 23 °C to simulate room temperature calendering conditions. The compressive modulus of the coating did not correlate strongly with the modulus of its constituent latex. Latex content, however, strongly affected coating compressive modulus. A sharp increase in the compressive modulus was observed at the coating critical pigment volume concentration (CPVC)—essentially the latex concentration at which the coating layer porosity is reduced to zero. Pigment size distribution and pigment morphology also affected the compressive modulus of coating in a manner consistent with packing theory. [ABSTRACT FROM AUTHOR]

This paper discusses the main scientific results of the XXV International Conference on Data Analytics and Management in Data-Intensive DomainsDomains, held on October 24–27, 2022 in Moscow, Russia. The motivation and goals of the conference, main areas of focus, related conferences, and associated events are considered. The program of the current year's conference is described, including the topics of invited and sectional reports. Conclusions are drawn based on the results of the analysis of the scientific contribution of the conference. A list of papers selected by the conference program committee for the special issue of the journal is attached. The papers relate to the areas of image analysis and processing, the application of machine learning methods in medicine, astronomy, materials science, and extracting information from texts. [ABSTRACT FROM AUTHOR]

This paper examines commonly applied methods of data analysis. Predicated on these methods, the main issue pertains to the plausibility of the studies' end products, that is, their conclusions. I argue that the methods chosen often lead to unwarranted conclusions: the data analyses chosen tend to produce looked-for null rejections even though the null may be much more plausible on prior grounds. Two aspects of data analyses applied cause obvious problems. First, researchers tend to dismiss "preliminary" findings when the findings contradict the expected outcome of the research question (the "screen-picking" issue). Second, researchers rarely acknowledge that small p-values should be expected when the number of observations runs into the tens of thousands (the "large N" issue). This obviously enhances the chance for a null rejection even if the null hypothesis holds for all practical purposes. The discussion elaborates on these two aspects to explain why researchers generally avoid trying to mitigate false positives via supplementary data analyses. In particular, for no apparent good reasons, most research studiously avoids the use of hold-out samples. An additional topic in this paper concerns the dysfunctional consequences of the standard ("A-journal") publication process, which tends to buttress the use of research methods prone to false or unwarranted null-rejections. [ABSTRACT FROM AUTHOR]

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time-consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, which utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements, are learned and inferred from data. During the inference stage, the maximum a posterior (MAP) estimation framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper offers an analysis of data from the second phase of a project entitled Understanding and Addressing Everyday Sexisms in Australian Universities, which involved interviewing key stakeholders with an understanding of and/or experiences of 'Everyday Sexisms' within the academy. The paper demonstrates how women understand themselves as inherently unwelcome within higher education in Australia, and illustrates how this manifests through experiences, complaints procedures and seemingly banal everyday gendered and racilaised interactions. The authors show how complaints procedures often operate to further harass women who have experienced sexist harassment at work. The paper concludes by considering how the shared experiences of minoritised people within universities can pave the way for new ways of understanding diversity and working together to co-create a more equitable Higher Education. [ABSTRACT FROM AUTHOR]

THz spectroscopy in the time domain was explored in combination with multivariate data analysis, for quantitative determination of chemical and mechanical properties of historic paper, such as lignin content, tensile strength, and ash content. Using partial least squares (PLS) regression, it was shown that quantitative prediction of the material properties is possible, which indicates the potential of THz spectroscopy for chemical characterisation of complex organic materials of natural origin. In addition, the results demonstrate that THz spectra and PLS loading weights for lignin content differ significantly, which leads to the conclusion that THz spectra of composite macromolecular materials do not represent sums of spectra of the individual components. This supports the premise that THz spectra reflect intermolecular interactions. The study was carried out using 250 historical paper samples from the sixteenth century to present. Although the measurements were performed in vacuum to improve the quality of spectra, THz spectroscopy is in principle non-destructive. This research therefore reinforces the role of THz spectroscopy in characterisation of valuable historic materials, where invasive analysis is often not possible. [ABSTRACT FROM AUTHOR]

Background: Assessing and communicating software engineering research can be challenging. Design science is recognized as an appropriate research paradigm for applied research, but is rarely explicitly used as a way to present planned or achieved research contributions in software engineering. Applying the design science lens to software engineering research may improve the assessment and communication of research contributions. Aim: The aim of this study is 1) to understand whether the design science lens helps summarize and assess software engineering research contributions, and 2) to characterize different types of design science contributions in the software engineering literature. Method: In previous research, we developed a visual abstract template, summarizing the core constructs of the design science paradigm. In this study, we use this template in a review of a set of 38 award winning software engineering publications to extract, analyze and characterize their design science contributions. Results: We identified five clusters of papers, classifying them according to their different types of design science contributions. Conclusions: The design science lens helps emphasize the theoretical contribution of research output—in terms of technological rules—and reflect on the practical relevance, novelty and rigor of the rules proposed by the research. [ABSTRACT FROM AUTHOR]

This paper is the first of a series whose aim is to respond the paper 'Scientific integrity in the digital age: data fabrication, published in Research on Chemical Intermediates, DOI ' by Khaled, and four related papers by the same author. The validity of the experimental findings with typical straight lines passing through the origin is discussed. This part presents clear-cut evidence from the literature that the variation of weight loss with time, constructed during corrosion and corrosion control research studies, yields typical straight lines passing through the origin, depending on the solubility of the corrosion products. This part also raises, and at the same time answers, the question: 'are experimental results with typical straight lines passing through the origin necessarily referring to data fabrication?' [ABSTRACT FROM AUTHOR]

Purpose: To compare (1) fluid, food and nutrient intake obtained with a paper versus an online version of a 7-day food record and (2) user's acceptability of both versions of the food record. Methods: A cross-over study was carried out in 2010 in France. A total of 246 participants aged 18-60 years reported their food and fluid intake using both versions of the 7-day food record, separated by a 7- to 14-day washout period. To help participants in estimating consumed portions, both versions of the food record were supported by a photographic booklet of standard portions and containers. At the end of the study protocol, participants completed a questionnaire designed to assess the acceptability of the two questionnaires. Results: The reported water intake of fluids was significantly higher with the online version compared with the paper version (respectively 1348 ± 36 and 1219 ± 34 mL/day, p < 0.0001). No difference was found between methods in terms of energy intake and the consumption of most food categories, macro- and micronutrients. Furthermore, 77 % of the participants preferred the online method to the paper version. Conclusions: Fluid intake, but not food intake, reported with the online 7-day food record was higher in comparison with the paper version. In addition, the online version was preferred by users. In population surveys, the online record is therefore a relevant alternative, and even a preferred alternative in the case of fluid intake, to the paper record. [ABSTRACT FROM AUTHOR]

As one of the most popular topics currently, big data has played an important role in both academic research and practical applications. However, in the manufacturing industry, it is difficult to make full use of the research results for production optimization and/or management due to the low quality of real workshop data. Typical quality problems of real workshop data include the information match degree, missing recessive data, and false error identification. The conventional data analysis methods cannot handle most such issues because these methods fail to consider professional insights into and domain knowledge about the data. The main motivation of this paper is to explore methods for analyzing and evaluating big data with domain knowledge. For this purpose, real production data from a semiconductor manufacturing workshop are adopted as the data object. First, a series of data analysis techniques with domain knowledge are developed for diagnosing the imperfections. Then, corresponding data processing techniques with domain knowledge are proposed for solving those data quality problems according to specific flaws in the data. Furthermore, this paper proposes quantitative calculation methods of data value density to determine the extent to which data quality can be improved by the proposed data processing techniques. Case studies are conducted to demonstrate that data analysis and processing techniques with domain knowledge can effectively handle data quality problems of real workshop data in terms of the information match degree, missing recessive data, and false error identification. The work in this paper has the potential to be further extended and applied to other big data applications beyond the manufacturing industry. [ABSTRACT FROM AUTHOR]

The Psychonomic Society (PS) adopted New Statistical Guidelines for Journals of the Psychonomic Society in November 2012. To evaluate changes in statistical reporting within and outside PS journals, we examined all empirical papers published in PS journals and in the Experimental Psychology Society journal, The Quarterly Journal of Experimental Psychology (QJEP), in 2013 and 2015, to describe these populations before and after effects of the Guidelines. Comparisons of the 2013 and 2015 PS papers reveal differences associated with the Guidelines, and QJEP provides a baseline of papers to reflect changes in reporting that are not directly influenced by the Guidelines. A priori power analyses increased from 5% to 11% in PS papers, but not in QJEP papers (2%). The reporting of effect sizes in PS papers increased from 61% to 70%, similar to the increase for QJEP from 58% to 71%. Only 18% of papers reported confidence intervals (CIs) for means; only two PS papers in 2015 reported CIs for effect sizes. Although variability statistics are important to understanding data, and to further analysis, they were only reported as numbers in just over half of the PS journal papers. Almost all PS and QJEP papers relied exclusively on null hypothesis significance testing to guide interpretation of the data. Changes associated with the Guidelines are in the desired direction with respect to reporting effect sizes and power analyses but are not yet reflected in researchers' practices in describing their data, addressing data assumptions, and thinking beyond the p value when interpreting their data. [ABSTRACT FROM AUTHOR]

The present study was an attempt to determine whether rates of probability discounting would vary as a function of the type of data-collection method, mode of task administration, type of data analysis, and/or type of outcome being discounted. Some of the 650 participants completed the task using the fill-in-the-blank method (FITB) while others completed it using a version of the multiple-choice (MC) method. Some participants completed the task in paper-pencil format and others did so online. All participants discounted seven different outcomes, and the data were analyzed using a hyperbolic equation and a hyperbolic-like equation, and by calculating the area under the discounting curve. Steeper rates of probability discounting were observed for the FITB than the MC method and for participants who completed the task online than in paper-pencil format. The different methods of analyzing the data sometimes led to different conclusions. Lastly, different rates of probability discounting were observed across the different outcomes being discounting. Implications and recommendations for researchers who study probability discounting are discussed. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the laboratory induction in the intersexuality of Gambusia (G.) affinis mosquitofish using paper mill effluent. Study used analysis of variance and multiple linear regression statistical analyses during data analysis. Study reveals that an androgenic-like compound, androgen, or a water-borne androgen precursor serves as responsible agent for poeciliid fishes' intersex condition.

By using the traditional active E2E (end-to-end) detection method to locate the congested link in IP network, we can only judge whether the network is congestion or not, and the congested cause cannot be effectively known. However, the passive detection method needs to acquire the syslog of all routers in the managed network, which requires a large amount of data. In this paper, a kind of multimodal spatiotemporal correlation fuzzy inference method for network health monitoring method was proposed through combining with the method of active E2E detection and passive detection. First, the set of network-congested links is obtained based on the Bayesian theorem through E2E path performance detection; then, the router syslog associated with the congested links will be acquired for a period of time, and the abnormal events in the syslog are detected based on the principle of mutual information (MI), so as to find the causes of network congestion. Based on the multimodal spatiotemporal correlation fuzzy paradigms combining with the active end-to-end detection and passive detection technology, the amount of data analysis will be greatly reduced and the operation speed will be improved. Simulation experiments and actual experiments verified the accuracy of the method this paper proposed. [ABSTRACT FROM AUTHOR]

Little research exists that addresses the equivalence in collectivistic cultures of paper- versus Internet-based surveys. This study addressed this gap and examined the measurement equivalence of individual innovativeness scales between Internet surveys and paper-based surveys within a collectivistic culture (with China serving as our example). The study analyzed and compared survey data from both paper and web-based surveys using confirmatory factor analysis. The assessment of invariance included the levels of configural, metric, scalar, and covariance invariance. The means and variance of latent variables were also compared. The results show that measurements are invariant at the two levels (configural and metric), and the covariances between latent variables are also equivalent, but the mean and variance differences of latent variables are apparent. The results indicate that when conducting research in collectivistic cultures and collecting data from distinct survey modes, researchers should concern themselves with the potential of extreme response patterns and the inclination of social desirability responding, as well as considering the measurement invariance across survey modes. [ABSTRACT FROM AUTHOR]

In the recent paper [1], a new scaling theory of electron localization was proposed. We show that numerical data for the quasi-one-dimensional Anderson model do not support predictions of this theory. [ABSTRACT FROM AUTHOR]

In the present school-based study, a convenience sample of 477 students in grades 6-9 and second year in high school from a city and a smaller town recorded daily occurrence and intensity of headaches in a standard paper diary during a 3-week period. Total headache activity (headache sum), number of headache days, intensity level and duration for weekly headaches were estimated. Approximately 85% of the adolescents had experienced headache of any intensity level during the 3-week recording period. On the average, they reported 2.5 headache days per week and a mean intensity level for headache episodes of 1.7. Our estimates for headache of any intensity level (1-5) occurring at least once a week was surprisingly high (73.8%). For the highest intensity level across the whole 3-week period, almost identical proportions of mild and moderate headaches were reported by students (22.3-22.5%), while about twice as many (40.7%) had experienced severe headaches. Girls consistently reported more headaches than boys, in particular of the moderate and severe intensity types. Students in the city also reported more frequent and intense headaches than those in the town. Peak headache activity was observed at noon and in the afternoon and in the days from the middle of the week until weekend. The use of prospective recordings in diaries will further advance our knowledge on the prevalence and characteristics of recurrent headaches among children and adolescents in community samples. [ABSTRACT FROM AUTHOR]

This paper considers theoretical foundations and mathematical methods of data analysis under the conditions of the Rice statistical distribution. The problem involves joint estimation of the signal and noise parameters. It is shown that this estimation requires the solution of a complex system of essentially nonlinear equations with two unknown variables, which implies significant computational costs. This study is aimed at mathematical optimization of computer algebra methods for numerical solution of the problem of Rician data analysis. As a result of the optimization, the solution of the system of two nonlinear equations is reduced to the solution of one equation with one unknown variable, which significantly simplifies algorithms for the numerical solution of the problem, reduces the amount of necessary computational resources, and enables the use of advanced methods for parameter estimation in information systems with priority of real-time operation. Results of numerical experiments carried out using Wolfram Mathematica confirm the effectiveness of the developed methods for two-parameter analysis of Rician data. The data analysis methods considered in this paper are useful for solving many scientific and applied problems that involve analysis of data described by the Rice statistical model. [ABSTRACT FROM AUTHOR]

Bibliometric analysis was carried out for champion data comparisons among prestigious nuclear research institutes (PNRI) existed in Japan, the U. S., France, and Germany. The analysis was relied on database INIS (IAEA), ECD (DOE), WOS (Thomson), and SCOPUS (Elsevier). INIS is advanced, key ex-post evaluating tool for determining general research paper-based champion. Over the 30-year time span of research paper publication, the world champion among 11 PNRI is JAERI confirmed by INIS but ORNL confirmed by ECD, WOS, and SCOPUS, the latter two collected journal submitted research paper. Five years ago JAERI is the 3rd ranked institutes following ORNL and ANL. INIS database results revealed that CEA/Grenoble is the French domestic champion regarding research paper publication. Five years ago it was CEA/Saclay. Results from analytical tools used in bibliometric studies should be viewed with careful consideration to learn of any influencing factors because different characteristics exhibited by individual databases can sometimes generate conflicting bibliometric results. This was true among INIS, ECD, WOS, and SCOPUS when looking at trends especially between 5-year periods. [ABSTRACT FROM AUTHOR]

This paper serves as an introduction to the special issue of Prevention Science entitled, "Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science." This special issue includes a collection of original papers from multiple disciplines that apply individual-level data synthesis methodologies, including IDA, individual participant meta-analysis, and other related methods to harmonize and integrate multiple datasets from intervention trials of the same or similar interventions. This work builds on a series of papers appearing in a prior Prevention Science special issue, entitled "Who Benefits from Programs to Prevent Adolescent Depression?" (Howe, Pantin, & Perrino, 2018). Since the publication of this prior work, the use of individual-level data synthesis has increased considerably in and outside of prevention. As such, there is a need for an update on current and future directions in IDA, with careful consideration of innovations and applications of these methods to fill important research gaps in prevention science. The papers in this issue are organized into two broad categories of (1) evidence synthesis papers that apply best practices in data harmonization and individual-level data synthesis and (2) new and emerging design, psychometric, and methodological issues and solutions. This collection of original papers is followed by two invited commentaries which provide insight and important reflections on the field and future directions for prevention science. [ABSTRACT FROM AUTHOR]

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments. [ABSTRACT FROM AUTHOR]

This bibliographic study presents a historical perspective on Korea's construction engineering and management (CEM) research and reports a 27-year analysis of papers written by Korean CEM researchers between 1993 and 2019. Frequency analysis of the collected papers was performed according to three main perspectives: the knowledge area, research topics, and methods. Knowledge area analysis shows that research fields have become more diversified and subdivided. Construction technology, cost management, and planning and schedule management have consistently maintained the top three positions for the past three decades. In addition, quality management, resource management, and risk management were also ranked high in the 2000s. In the 2010s, safety management, environmental management, quality management, and construction industry and business were ranked high. These changes reveal how Korean society's and industry's demands have changed over the years. Furthermore, the findings from keyword frequency analysis revealed that the focus of CEM research was mostly on cost and scheduling in the 1990s and on scheduling, risk, cost, and construction automation in the 2000s. The advent of Building Information Modeling (BIM) in the 2010s was an epochal change. This study found that the research methods frequently used by CEM researchers were simulation, optimization, regression analysis, computer vision and image processing, 3D modeling, case-based reasoning, genetic algorithm, case study, economic analysis, and artificial neural network. [ABSTRACT FROM AUTHOR]

The paper discusses the main scientific results of the XXIV International Conference "Data Analytics and Management in Data Intensive Domains" DAMDID/RCDL-2022, held on October 4–7, 2022, in St. Petersburg, Russia. A historical review of the development of the conference was carried out. Motivation, goals of the conference, main areas of the subject, related conferences, and held associated events are considered. The conference program of the current year is characterized, including the topics of invited and sectional reports. Conclusions are drawn on the basis of the results of the analysis of the scientific contribution of the conference. Attached is a list of papers selected by the conference program committee for a special issue of the journal. The papers relate to areas of data integration, data analysis in astronomy and biomedicine, machine learning applications, image analysis and processing, and information extraction from texts. [ABSTRACT FROM AUTHOR]

The main purpose of the present paper is to investigate the problem of the nonparametric estimation of the expectile regression in which the response variable is scalar while the covariate is a random function. More precisely, an estimator is constructed by using the local linear k Nearest Neighbor procedures (kNN). The main contribution of this study is the establishment of the Uniform consistency in Number of Neighbors of the constructed estimators. These results are established under fairly general structural conditions on the classes of functions and the underlying models. The usefulness of our result for the smoothing parameter automatic selection is discussed. Some simulation studies are carried out to show the finite sample performances of the kNN estimator. The theoretical uniform consistency results, established in this paper, are (or will be) key tools for many further developments in functional data analysis. [ABSTRACT FROM AUTHOR]

Globally, there is a growing momentum toward achieving universal access to clean energy, particularly electricity, as it plays a crucial role in improving the overall quality of life. Addressing the challenge of universal electricity access requires the development of various mechanisms that enable individuals to access electricity at affordable rates while having the freedom to choose their service providers. In many countries, governments are actively involved in enhancing electricity access, but this effort can put strain on state finances, leading to increased fiscal deficits. This paper aims to investigate the relationship between power expenditure and state finances in Jammu and Kashmir, India, in light of the push for improved electricity access. Using data from the RBI Bulletins for the period 1990–91 to 2019–20, the paper employs panel data analysis to investigate the relationship between power expenditure and gross fiscal deficit. Results indicate a positive and significant correlation between expenditure on power and state gross fiscal deficit in Jammu and Kashmir, indicating lower fiscal sustainability with higher power expenditure. For the purpose of reducing the pressure on state finances caused by enormous domestic power subsidies, the study recommended targeted subsidies with a focus on small energy projects utilizing renewable energy sources and energy-saving technologies, as well as incentives and regulations to promote energy conservation. [ABSTRACT FROM AUTHOR]

This special issue contains substantially revised and extended versions of some of the best papers presented at the 22nd International Conference on Fundamental Approaches to Software Engineering in 2019. All papers share the common theme that they are either concerned with model-based analysis of systems or they develop methods in its service. [ABSTRACT FROM AUTHOR]

Context: Artificial Intelligence (AI) has rapidly developed over the past several decades. Several related AI approaches, such as Machine Learning (ML), have been applied to research on landscape patterns and ecological processes. Objectives: Our goal was to review the methods of AI, particularly ML, used in studies related to landscape ecology and the main topics addressed. We aimed to assess the trend in the number of ML papers and the methods used therein, and provide a synopsis and prospectus of current use and future applications of ML in landscape ecology. Methods: We conducted a systematic literature search and selected 125 papers for review. These were examined and scored according to multiple criteria regarding methods and topic. We applied quantitative statistical methods, including cluster analysis based on titles, abstracts, and keywords and a non-metric multidimensional scaling based on attributes assigned during the review. We used Random Forests machine learning to describe the differences between identified clusters in terms of the topics and methods they included. Results: The most frequent method found was Random Forests, but it is noteworthy to mention the increasing popularity of tools related to Deep Learning. The topics cover both ecologically oriented issues and the landscape-human interface. There has been a rapid increase in ML and AI methods in landscape ecology research, with Deep Learning and complex multi-step pipeline AI methods emerging in the last several years. Conclusions: The rapid increase in the number of ML papers in landscape ecology research, and the range of methods employed in them, suggest explosive growth in application of these methods in landscape ecology. The increase of Deep Learning approaches in the most recent years suggest a major change in analytical paradigms and methodologies that we feel may transform the field and enable analyses of more complex pattern process relationships across vaster data sets than has been possible previously. [ABSTRACT FROM AUTHOR]

The analysis of collaborative contribution networks and hot spots in earthquake research from a global perspective will reveal some rules and trends in this field that will provide references and macroguidance for scientific researchers in the field. With the help of data analysis software Excel and visualization software VOSviewer, this study mainly uses two methods of bibliometric and social network analysis to analyze 66,296 papers on the subject of "earthquake" collecting in Web of Science in 2009–2018 from multiple perspectives. In the past 10 years, scientific research output in the earthquake field has steadily increased year by year with an evident cross subject. The contribution of co-publishing is much higher than that of single publishing, and it has a higher number of citations and displays. Half of the 50 keywords with the highest frequency of co-occurrence are the continuation of hot spots in 1900–2010. The evolution of hot spots shows that in the average publishing year of 2013.5–2015.0, hot spots have changed from old to new, but few new hot spots have appeared in the past 2 years. [ABSTRACT FROM AUTHOR]

The study aimed to examine how emotional design and digitalization/mobilization of textbooks influence students' emotion, cognitive load, and cognitive performance. Research samples were 147 undergraduate students enrolling in the "Introduction to Internet" course at a university. They were randomly divided into three groups: 51 students in the control group using traditional paper textbooks; 49 students in the experimental group I using e-textbooks with emotional design; 47 students in the experimental group II consisted using e-textbooks without emotional design. The MANCOVA was adopted to analyze gathered data. The results revealed learners' positive emotion was significantly higher when using e-textbooks with emotional design than when using paper textbooks. Three types of cognitive load was significantly better when using e-textbooks with emotional design than when using e-textbooks without emotional design and paper textbooks. Cognitive efficiency was significantly better when using two types of e-textbooks (with and without emotional design) than when using paper textbooks. In a word, "emotional design and digitalization/mobilization" of textbooks (i.e. e-textbooks with emotional design) would significantly improve positive emotion; emotional design would significantly decrease intrinsic and extraneous cognitive load when using e-textbooks, and significantly increase germane cognitive load; digitalization/mobilization of textbooks (i.e. e-textbooks) significantly improved cognitive efficiency. What the study learned was that learners might firstly adopt e-textbooks with emotional design, and those without emotional design secondly, and paper textbooks rank last; viz., digitalized/mobilized textbooks (namely e-textbooks) were better than paper textbooks. Practical and theoretical implications and suggestions were raised for practitioners and researchers to take as references. [ABSTRACT FROM AUTHOR]

During the epidemic, online teaching became the mainstream. Online teaching evaluation aims to systematically test teachers' teaching process according to certain teaching objectives and standards, and evaluate its value, advantages and disadvantages, so as to improve the quality of teaching. It is not only an important part of the teaching process, but also the basis of all effective and successful teaching. In this paper, we propose an online teaching evaluation method based on Epistemic Neural Network (ENN), which is an evolutionary intelligence method. In terms of uncertainty modeling, ENN's design innovation provides the improvement effect of geometric progression in terms of statistical quality and calculation cost. Therefore, it is very suitable for teaching evaluation, which is an evaluation process guided by a variety of uncertain factors. Specifically, this paper considers the content and grade standards of online teaching evaluation from five aspects. (1) Teachers' syllabus, teaching progress, teaching plan, courseware and other teaching documents and teaching materials; (2) Abide by teaching discipline, the implementation of teaching plan and the completion of teaching tasks; (3) Teaching attitude, teaching investment, teaching and educating people, and the comprehensive quality of teachers; (4) Whether the concepts taught in the course are accurate, the expression is clear, whether the key points are prominent and whether the difficulties are clearly explained; (5) The depth, breadth and frontier of teaching content, and the amount of classroom information. According to the above five evaluation indexes which involves the big data analysis, we train ENN to get an evaluation score that can evaluate the teacher's online teaching process. In addition, we also test the average evaluation time to verify the effectiveness. [ABSTRACT FROM AUTHOR]

At present, there is little research on the application of wireless sensor networks in the agricultural field. In order to improve the operating performance and practical effects of the intelligent agricultural system, based on data mining technology, this paper uses ZigBee wireless sensor network as the networking technology to cover all aspects of crops under the guidance of the concept of sustainable agricultural development, and realizes the data collection and remote-control process of the agricultural production process, and conducts data analysis and processing through data mining. Moreover, in order to improve the performance of the model, this paper proposes to use the single-point crossover multiple-generation genetic algorithm to optimize the weights and thresholds of the BP neural network to establish the Multi-Generation Genetic Algorithm Back Propagation MGABP model. In addition, in application, the analytic hierarchy process is introduced as the guidance mechanism of neural networks. Finally, this paper designs experiments to analyze the performance of the system constructed in this paper, and uses mathematical statistics to perform statistics on experimental results. The experimental analysis and statistical diagrams of various parameters shows the outcome of this study. The research results show that the intelligent agricultural system model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

Purpose: Life cycle assessment (LCA) is a data-intensive methodology; therefore, experts usually focus collection efforts on a few activities, while generic data on remaining activities are taken from databases. Even though increased availability of databases has facilitated LCA takeoff, assuring data quality is fundamental to ensure meaningful results and reliable interpretation.Methods: Ecoinvent has become a global reference for inventory data. Its current version released three impact partition modeling options—the recycled content, "allocation at the point of substitution" (APOS), and consequential models—whose adequate choice is crucial for yielding meaningful assessments. Tutorials and manuals describe the distribution algorithm that backs each system model, to ground decision-making regarding the best fit to a study's goals. We performed a systematic literature review to investigate—within the papers published on the International Journal of LCA (IJLCA)—how transparently authors addressed the system model choices.Results and discussion: About 70% of LCA practitioners continued to use earlier versions of ecoinvent after version 3 was launched in 2013. The number of papers using versions 3.x only showed an increased growth trend 2 years later. Eighty-three papers actually adopted the newest version of the database. From those, only 29 papers clearly mentioned the adopted system model. Our SLR also suggests a trend regarding authorship profile of LCA-related studies: the number of studies conducted by practitioners aware of the intricacies of sound modeling of background and foreground data might have been surpassed by those conducted by non-LCA specialists who use LCA as a supporting tool for investigations in applied fields, and merely scratch the surface.Conclusions: Our results point to a need for a caveat: ecoinvent users must take time to understand the general concept behind each system model and practice one of the most important actions when performing an LCA—state methodological choices clearly. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

Massive teaching resources will cause serious teaching efficiency problems for online teaching, and traditional online teaching models are even inferior to traditional classroom teaching in terms of teaching effects. Based on this, this paper analyzes massive educational resources and builds a scalable computer interactive education system based on large-scale multimedia data analysis. Moreover, this paper sets the role of the system according to the actual teaching situation, and constructs the functional module of the system structure. In addition, this paper uses computer simulation technology to analyze interactive technology and make technical improvements to make interactive technology the core technology of the computer interactive education system, and get an extensible interactive education system based on the characteristics of network teaching. Then helps to monitor and access the performance of an interactive educational system. Furthermore, this paper designs an experiment to evaluate the performance of the computer interactive education system, which is mainly carried out from two aspects: interactive evaluation and teaching evaluation. From the experimental research results, we can see that this system can effectively improve the quality of teaching. [ABSTRACT FROM AUTHOR]

Global cities play a significant role in world economy as they serve as key hubs of economic activity and trade. These cities are centers of innovation, finance, culture, and commerce, attracting businesses and entrepreneurs from all over the world. They are characterized by their openness, diversity, and their ability to attract and retain talent. This paper includes a bibliometric analysis of the structure of global cities through examining the literature on global cities, including the document type, country/territory distribution, institution distribution, geographical distribution of authors, specially most active authors and their interests or research areas, relationships between principal authors and more relevant journals, and the research hot spots. The input data consists of journal articles archived by the Web of Science from 1991 to 2023, and the analysis is performed using SciMAT and VOS Viewer. The result of this paper would provide valuable insights into the state of research on this topic, including who is conducting research, where it is being conducted, what types of publications are being produced, and which themes are having the most impact on the field. Such an analysis would be useful for researchers, policymakers, and other stakeholders interested in understanding the role of global cities in the world economy. [ABSTRACT FROM AUTHOR]

This paper examines how an individual's labor supply responds immediately to her spouse's health shock in an aging household and how she adjusts her labor supply over time after her spouse's health shock. Different from previous work, this paper considers the subsequent health evolution following the spouse's health shock by proposing an adaptation model where the long-term labor supply adjustment of an individual is allowed to depend on her spouse's health evolution after the initial shock. Analysis of the 1996-2012 data from the Health and Retirement Study (HRS) suggests that in the short run, both husbands and wives change their labor supply very little when their spouses become ill, but in the long run, a husband's labor supply adjustment does vary with his wife's current health status after her initial health shock. In contrast, the wife's annual work hours are not affected by her husband's health shock in the long run, regardless of husband's subsequent health status. Households with an ill wife are probably at greater risk than those with an unhealthy husband in the long run, which may be attributed to the role that women have traditionally played in the household. [ABSTRACT FROM AUTHOR]

The article discusses the papers presented at the European Conference on Machine Learning (ECML) and the European Conference on Principles of Data Mining and Knowledge Discovery (PKDD). It notes that the papers are related to machine learning and data mining communities including "Sparse kernel SVMs via cutting-plane training," which features the state of the art efficiency in inducting and evaluating support vector machine (SVM) models. Other topics of the papers are also presented.

Purpose: To look at the bibliometric publication trends on 'Urolithiasis' and aspects of treatment and training associated with it over a period of 16 years from 2000 to 2015. To this end, we conducted this study to look at the publication trends associated with urolithiasis, including the use of simulation, laser technology, and all types of interventions for it. Materials and methods: We performed a systematic review of the literature using PubMed over the last 16 years, from January 2000 to December 2015 for all published papers on 'Urolithiasis'. While there were no language restrictions, English language articles and all non-English language papers with published English abstracts were also included. Case reports, animal and laboratory studies, and those studies that did not have a published abstract were excluded from our analysis. We also analyzed the data in two time periods, period-1 (2000-2007) and period-2 (2008-2015). Results: During the last 16 years, a total of 5343 papers were published on 'Urolithiasis', including 4787 in English language and 556 in non-English language. This included papers on URS ( n = 1200), PCNL ( n = 1715), SWL ( n = 887), open stone surgery ( n = 87), laparoscopic stone surgery ( n = 209), pyelolithotomy ( n = 35), simulation in Endourology ( n = 82), and use of laser for stone surgery ( n = 406). When comparing the two time periods, during period 2, the change was +171% ( p = 0.007), +279% ( p < 0.001), and −17% ( p = 0.2) for URS, PCNL, and SWL, respectively. While there was a rise in laparoscopic surgery (+116%), it decreased for open stone surgery (−11%) and pyelolithotomy (−47%). A total of 82 papers have been published on simulation for stone surgery including 48 papers for URS (67% rise in period-2, p = 0.007), and 34 papers for PCNL (480% rise in period-2, p < 0.001). A rising trend for the use of laser was also seen in period 2 (increase of 126%, p < 0.02, from 124 papers to 281 papers). Conclusions: Published papers on intervention for Urolithiasis have risen over the last 16 years. While there has been a steep rise of URS and minimally invasive PCNL techniques, SWL and open surgery have shown a slight decline over this period. A similar increase has also been seen for the use of simulation and lasers in Endourology. [ABSTRACT FROM AUTHOR]

In a world with an increasing population, an alternative for aiding healthcare systems is the use of sensors and wearable devices for monitoring patient physiological data. The analysis of collected data can help guide health services or the self-care of patients. This paper explores literature related to the collection and analysis of physiological data in smart environments by means of a systematic mapping study, organized in three steps: (1) identification of research questions; (2) elaboration of the search process; (3) definition of the criteria for filtering results. Papers were added using the snowball sampling method. This work encompassed 5870 papers published in the past 11 years, up to April 2019. The final selection resulted in 32 papers. Among these, 25 works collected cardiac data, 23 used Wi-Fi, Bluetooth, GSM, or ZigBee technologies, and 14 used techniques for the analysis of physiological data. Mapping verified the more prevalent trends and technologies in the collected and analyzed physiological data from smart environments. The filtering process allowed for a focus on communication technologies and vital sign data types. Three general questions (GQ), two specific questions (SQ), and two statistical questions (STQ) were answered. Similar reviews have already been conducted focusing on sensors, rather than collecting techniques and physiological data analysis. This denotes an opportunity for further studies in the vital sign analysis area. [ABSTRACT FROM AUTHOR]

Although data sharing is one of the primary measures proposed to improve the integrity and quality of published research, studies show it remains the exception not the rule. The current study examines the availability of data in papers reporting the results of analyses of empirical data from original research in high-impact addiction journals. Thirteen high-impact journals with data sharing policies were selected from those included in the substance abuse category of the 2018 Clarivate Analytics' Journal Citation Report. The first 10 full or short original research reports that included empirical data in the most recent complete issue of each journal were electronically searched and reviewed for reference to where their data can be obtained and for a formal data sharing statement. Only eight of the 130 papers contained a data sharing statement in their text or supplementary online materials, and just one contained a direct link to the data analyzed. Data sharing was rare in the 13 high-impact addiction journals reviewed. The nature of the data reported in addiction journals might partly explain this. Currently, data sharing is not a procedure likely to improve the quality and integrity of published addiction research. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things, big data, cloud computing and other industries, the data center network, as the core infrastructure of the computing industry, is becoming increasingly important. Wireless communication technology is developing rapidly. This paper uses visible light wireless communication technology to build a wireless data center network, solving the problems of complex wiring and expansion difficulties in the existing data center network. Aiming at the problems of insufficient spectrum, low bandwidth utilization and large energy consumption in the existing wireless data center, this paper adopts the visible light wireless communication technology based on LED, and proposes a new wireless data center network structure HCDCN. Firstly, work have been done to analyze the feasibility of introducing data centers in this technology, and transform server racks in combination with the characteristics of the technology. Secondly, the construction process of the structure is introduced and a new coding rule is proposed. In order to increase the routing efficiency and fault tolerance, the rack top link routing path is added, and an efficient routing algorithm is designed. Finally, the aggregate throughput of HCDCN under different scales is obtained through experiments, and the network performance of HCDCN structure is compared with that of OWCells and Mesh structure. Experiments have proved that HCDCN's network performance is better than the other two structures under the same conditions. The new wireless data center network proposed in this paper has largely alleviated the bandwidth shortage of hotspot servers. [ABSTRACT FROM AUTHOR]

The inverter is considered the core of the PV power plant. The inverter's failure leads to generation loss and decreases plant availability. So, it is required to investigate a clear Root Cause Analysis (RCA) to deduce the failure causes and implement the required corrective action in addition to the preventive action to avoid more inverter failure, hereby maintaining the plant available to a certain value. This paper discusses real-time mode operation data analysis of the PV grid-connected inverter due to real central inverter incidents in Benban solar park located in Egypt.The central inverter plays an important role in the Mega-Scale PV power plant. The main function of this inverter is to convert the DC power produced by the PV modules to AC power to be injected into the utility grid by considering specific characteristics based on the grid code. The availability of any PV power plant directly depends on the healthy inverter's operation. The more increases for the installed inverters, the less availability loss in the case of inverter partial or catastrophic failures. So, it is required to focus on the failure causes of the central inverter by implementing a technical analysis using the available operational data. The monitored data of the central inverter in the PV power plant is classified into two types. The first type is the continuous time data stored in the memory. It represents the waveforms of inverter outputs like voltage, current, frequency, .... etc. Unfortunately, in case of a catastrophic failure, the central inverter is completely charred, and the continuous time data is lost due to storage memory damage. The second type is the operation data that is recorded by the SCADA system (per one-minute interval). Hereby, the operation data is the sole data in the case of the completely charred inverter. The representation of the operational data in curves indicates symptoms that can be used for the RCA processes. The investigation outcomes include three results. The first result is detecting the signature of the IGBT thermal stress on the voltage balance of the DC link capacitor. The second result is verifying a scenario for the cause of the IGBT failure by implementing a technical mathematical model based on the detected symptoms that denote the fault signature which is considered the thread-tip for detecting the failure cause. The third result is the simulating scenario for the interpretation of a DC link capacitors explosion due to the short circuit fault that occurred due to IGBT failure. The investigation in this paper is performed based on operation data analysis of the PV grid-connected inverter (central type) due to a real incident. The analysis methodology is based on mathematical calculation for the IGBT junction temperature using the measured heatsink temperature. The study concludes that after the IGBT failure occurred, it was a short circuit for a while and closed the terminals of the DC link capacitors. So, the DC link capacitors exploded and produced heavy sparks that led to enough fire to burn the inverter container completely. [ABSTRACT FROM AUTHOR]

The current special issue of Prevention Science indicates that momentum in using individual participant data (IPD) and integrative data analysis (IDA) to combine and synthesize findings in prevention science has accelerated over the past decade. In this commentary, we focus on two general themes involving methods for harmonizing measures and findings of effect heterogeneity. We describe methods for harmonization as retrospective psychometrics, requiring that we attend to the assumptions necessary for accurate measurement, but adjust our methods given the constraints of working with existing datasets that often involve different measures in different studies. We point to novel approaches for increasing confidence that semantic matching and empirical modeling used in these studies will yield accurate and valid measurements that can be combined in IDA. We also review findings about effect heterogeneity, emphasizing the importance of using etiologic and action theories to identify and evaluate sources of such effects. We note that all of the papers in this issue deserve careful attention, as they illustrate how prevention scientists are approaching the complexities of IDA and exploring novel methods for overcoming its challenges. [ABSTRACT FROM AUTHOR]

With the development of world economy, sports development has also become a symbol of national strength. Nowadays, sports competition has become an important activity for all countries to show their strength, and also an important bridge to connect all countries and build friendship. In the process of challenging the limit, human beings gradually master the action essentials of various sports, and the action difference of athletes directly affects the grades of the competition. Therefore, in the process of sports training, athletes of various countries will focus on the standard of action of each athlete. In traditional sports training, veteran athletes with rich experience are usually used as coaches to guide athletes empirically. However, the difference of coaches will also lead to the difference of sports effects. At the same time, coaches' grasp of athletes' action standards is more based on subjective observation, which cannot be accurately measured, so there may be large differences and errors in their grasp of action standards. In this paper, we propose an intelligent action recognition and correction system to assist coaches to measure and evaluate athletes' actions more accurately, and give correction suggestions according to standard actions. Our system uses RGB-D sensors to analyze athletes' skeleton key points in real time. The different between the athlete's action and the standard action is calculated through the connection between the joint key points. In this paper, we also use the timing tracking algorithm to comprehensively evaluate the consistency of the action with the standard. We verified the feasibility of the recognition correction system through the actual movement and measurement of athletes. The experiment shows that our system can accurately measure the movements of athletes, and has more accurate measurement results and correction suggestions than the traditional coach naked eye measurement. [ABSTRACT FROM AUTHOR]

In this paper we use the DBLP data to investigate the co-author relationship in a real bibliographic network and predict the interactions between co-authors. We analysis the research trend of authors and conferences based on keywords extracted from paper titles. We can understand research fields and change of research trend to find appropriate co-authors and conferences to submit our work. We also find potential co-authors for an existing author in DBLP data by using a variety of similarity measures and a random walk model. It can be useful for building a recommendation system. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various papers within the issue on topics including analysis of queueing models, development of approximations for systems with abandonment, and link between empirical data and abandonment models.

The latent block model is used to simultaneously rank the rows and columns of a matrix to reveal a block structure. The algorithms used for estimation are often time consuming. However, recent work shows that the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models and the groups posterior distribution to converge as the size of the data increases to a Dirac mass located at the actual groups configuration. Based on these observations, the algorithm Largest Gaps is proposed in this paper to perform clustering using only the marginals of the matrix, when the number of blocks is very small with respect to the size of the whole matrix in the case of binary data. In addition, a model selection method is incorporated with a proof of its consistency. Thus, this paper shows that studying simplistic configurations (few blocks compared to the size of the matrix or very contrasting blocks) with complex algorithms is useless since the marginals already give very good parameter and classification estimates. [ABSTRACT FROM AUTHOR]