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Healthcare‐associated infections (HAIs) are a global public health threat. Italy is one of the countries with the highest prevalence of HAI. Hand hygiene (HH) is a pillar of infection prevention and control. Monitoring HH is necessary to improve HH compliance, and direct observation is considered the gold standard. Transcription and analysis of data collected during direct observation of HH compliance with the WHO paper form are time‐consuming. We collected, during a 9‐day observation period, HH opportunities and compliance both with a smartphone application (SpeedyAudit) and with the WHO paper form. Then, we investigated the difference in the required time for data transcription and analysis between the WHO paper form and the use of the app. The difference in the required time for data transcription and analysis was significant with a mean time of 2 s using the app and about 14–54 min/day using paper form (p =.004) while no significant difference was found in measured compliance rates between the two data collecting methods. HH monitoring with an app is time‐saving, and the app we used was easy to use. [ABSTRACT FROM AUTHOR]

Accurate and up‐to‐date biodiversity forecasts enable robust planning for environmental management and conservation of landscapes under a wide range of uses. Future predictions of the species composition of ecological communities complement more frequently reported species richness estimates to better characterize the different dimensions of biodiversity. The models that make community composition forecasts are calibrated with data on species' geographic patterns for the present, which may not be good proxies for future patterns. The future establishment of novel communities represents data on species interactions unaccounted for by these models. However, detecting them in a systematic way presents challenges due to the lack of monitoring data for landscapes with high environmental turnover, where such communities are likely to establish. Here, we propose lightweight monitoring over both ecological and anthropogenic disturbance gradients using passive sensors (i.e., those that operate continuously without much human input) to detect novel communities with the aim of updating models that make community composition forecasts. Monitoring over these two gradients should maximize detection of novel communities and improve understanding of relationships between community composition and environmental change. Further, barriers regarding cost and effort are reduced by using relatively few sensors requiring minimal upkeep. Ongoing updates to community composition forecasts based on novel community data and better understanding of the associated uncertainty should improve future decision‐making for both resource management and conservation efforts. [ABSTRACT FROM AUTHOR]

Conventional nature conservation has attempted to protect biodiversity, ecological integrity (i.e., composition and biomass/abundance of native species), and ecosystem services in parallel, assuming generally positive relationships among them. However, many native species are potentially weakened by and maladapted to a new, warmer climate. Such native species may not be able to offer ecosystem services at previous levels. If the relationship between ecosystem services and native species is no longer positive due to climate change, pursuing ecological integrity could be ineffective for maximizing ecosystem services. Hence, there is room to investigate future relationships among biodiversity, ecological integrity, and ecosystem services. This investigation can be done by describing the changes in ecosystem services as a function of biodiversity and ecological integrity. Herein, biodiversity and ecological integrity can be expressed by phylogenetic diversity and species intactness indices based on abundance, respectively. According to my hypothetical figure, the metrics of biodiversity and ecological integrity are potentially not positively but rather negatively related to ecosystem services. The hypothetical figure can be examined and improved using three different approaches: computer simulations (e.g., the LANDIS‐II forest simulation model), collection of actual data (e.g., measuring ecosystem services of some sites with psychrophilic and thermophilic species), and questionnaire surveys about cultural services (e.g., using digitally manipulated photographs and generative adversarial networks). Thus, we can successfully enable practitioners to understand new relationships, which will be relevant to ecosystem management under changing climates. [ABSTRACT FROM AUTHOR]

Near Infrared Spectroscopy (NIRS), as one of analysis technologies, has shown promisingly industrial applications for significant properties for recent decades such as fast response, preciseness, non-intrusion etc. Here, the authors employed NIRS coupled with a series of physical and chemical tests to assess the ageing condition of oil-paper insulation, which is responsible for the main insulation type of high-voltage power transformer. Among these procedures, the data analytic algorithms are of utmost importance to determine the evaluation precision. After plenty of trial-and-error, Savitzky-Golay (S-G) convolution was finally utilised to de-noise samples and improve the spectral data quality. The competitive adaptive reweighted sampling (CARS) was used to select the optimal wavelength combination of NIRS, which is found able to fully extract the effectively spectral information and reduce dimensions of spectral data. Based on the above-mentioned techniques, the quantitative analysis model of NIRS was established by partial least squares (PLS), which could synthetically process the spectral data and the degree of polymerisation (DP) of paper samples. The results indicated that compared with the traditional detection methods, the NIRS analysis is a powerful and informative tool to characterise the condition of oil-paper insulation without intrusion or damage to transformers. [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor ( IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. [ABSTRACT FROM AUTHOR]

Background: Britain is rich in longitudinal population cohort studies that posit valuable data resources for social science. However, education researchers currently underutilize these resources., Aims: The current paper (1) outlines the power and benefits of secondary data analyses for educational science and (2) provides a practical guide for education researchers on the characteristics, data, and accessibility of British population cohort studies., Methods: We identified eight British population cohort studies from the past 40 years that collected scholastic performance data during primary and secondary schooling, including (1) Avon Longitudinal Study of Parents And Children (ALSPAC), (2) Twins Early Development Study (TEDS), (3) Effective Pre-School, Primary and Secondary Education Project (EPPSE), (4) Millennium Cohort Study (MCS), (5) Born in Bradford (BiB), (6) Next Steps (LYSPE1), (7) Understanding Society (US), and (8) Our Future (LYSPE2). Participants across these studies were born between 1989 and 2010, and followed up at least once and up to 68 times, over periods of 7 to 29 years. For each study, we summarize here the context and aims, review the assessed variables, and describe the process for accessing the data., Conclusions: We hope this article will encourage and support education researchers to widely utilize existing population cohort studies to further advance education science in Britain and elsewhere., (© 2020 British Psychological Society.)

In recent years, the relationship of collaboration among scientists and the citation impact of papers have been frequently investigated. Most of the studies show that the two variables are closely related: An increasing collaboration activity (measured in terms of number of authors, number of affiliations, and number of countries) is associated with an increased citation impact. However, it is not clear whether the increased citation impact is based on the higher quality of papers that profit from more than one scientist giving expert input or other (citation-specific) factors. Thus, the current study addresses this question by using two comprehensive data sets with publications (in the biomedical area) including quality assessments by experts (F1000Prime member scores) and citation data for the publications. The study is based on more than 15,000 papers. Robust regression models are used to investigate the relationship between number of authors, number of affiliations, and number of countries, respectively, and citation impact-controlling for the papers' quality (measured by F1000Prime expert ratings). The results point out that the effect of collaboration activities on impact is largely independent of the papers' quality. The citation advantage is apparently not quality related; citation-specific factors (e.g., self-citations) seem to be important here. [ABSTRACT FROM AUTHOR]

This study presents the outcomes of a semi‐systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory‐driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory‐driven MMLA research and how this acceleration can extend or even create new theoretical knowledge. Practitioner notesWhat is already known about this topicMultimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena.MMLA can help us monitor learning activity at the micro‐level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces.MMLA provide new opportunities to support students' learning.What this paper addsSome MMLA works use theory, but, overall, the role of theory is currently limited.The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory.Most of the theory‐driven MMLA papers use theory 'as is' and do not consider the analytical and synthetic role of theory or aim to contribute to it.Implications for practice and/or policyIf the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories.MMLA research is mature enough to contribute to learning theory, and more research should aim to do so.MMLA researchers and practitioners, including technology designers, developers, educators and policy‐makers, can use this review as an overview of the current state of theory‐driven MMLA. [ABSTRACT FROM AUTHOR]

The paper by G. Zammarchi I et al i . exploits Markov chain to analyse web usability of a University website using eye tracking methodology. This special issue of I Statistical Analysis and Data Mining i collects papers presented at the 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Cassino, Italy, 11-13 September 2019. [Extracted from the article]

This paper provides an overall picture of women's representation and gender parity in the field of Agricultural Economics by constructing and analyzing a database of agricultural economists in China. We find that female scholars "occupy half the sky" in number, but not all the way up to the higher‐ranking academic positions. Women lag behind men in terms of research performance and academic promotion; even more so, gender disparity becomes more prominent when moving up the ranking ladder. A closer examination of agricultural economists of different age cohorts indicates that the gender gap, both in numbers engaged in the profession and academic performance measured by Chinese paper publications and nation‐level projects chaired, is narrowing. However, gaps in the number of high‐quality paper publications and the time span before promotion, alongside the phenomenon of a "leaky pipeline" in academia, are growing significantly over time. The representation of "star scientists" shows similar, yet steeper trends. In the absence of detailed studies, the paper explores possible explanations of the converging gender gap in scale but increasing gender disparity that is termed a "leaky pipeline." It concludes that the Policy of Enrollment Expansion in Higher Education provides vulnerable young females with more opportunities to access higher education, which increases both the number and proportion of women in the profession. [ABSTRACT FROM AUTHOR]

Multivariate time series forecasting is widely used in various fields in real life. Many time series prediction models have been proposed. The current forecasting model lacks the mining of correlation between sequences based on different periods and correlation of periodical features between different periods when dealing with data. In this paper, we propose a multivariate data prediction model FAFTransformer based on the reorganization of multi‐periodic features, which first extracts the multi‐periodic information of the time series using the method of frequency domain analysis. The temporal dependencies within sequences are then captured using convolution based on different periods, and the correlations between sequences are learned by combining the multivariate attention mechanism to obtain the intra‐sequence and inter‐sequence correlations under the same period. Finally, period fusion is proposed to capture the correlation of period characteristics between different periods. The experimental results show that the model achieves the best results on multiple datasets compared to the latest seven predictive models for time‐series data. [ABSTRACT FROM AUTHOR]

A network is constructed by nodes and links, thus the node degree and the link strength appear as underlying quantities in network analysis. While the power-law distribution of node degrees is verified as a basic feature of numerous real networks, we investigate whether the link strengths follow the power-law distribution in weighted networks. After testing 12 different paper cocitation networks with 2 methods, fitting in double-log scales and the Kolmogorov-Smirnov test ( K- S test), we observe that, in most cases, the link strengths also follow the approximate power-law distribution. The results suggest that the power-law type distribution could emerge not only in nodes and informational entities, but also in links and informational connections. [ABSTRACT FROM AUTHOR]

Researchers need maps to effectively navigate increasingly voluminous literatures. This is no less the case in the field of problem structuring methods (PSMs). This paper offers an atlas of the journal literature of the theoretical development of, what are currently acknowledged to be, the four main PSMs up to their consolidation in 1989. A thorough contextual appreciation of the structure and dynamics of this literature sets the stage for addressing some of its specific aspects, for which an atlas is especially effective as an orientation device. Substantiated suggestions for exploratory excursions, as well as potential pitfalls, are accentuated, the overall aim being to provide researchers with navigational support that may assist their research objectives. Based on evidence uncovered from the atlas, a number of issues current in the PSM field are discussed, including the use of the collective descriptor 'family', the extent to which PSMs find their origins, and belong, in the wider field of operational research and the identification of sources that have hitherto received little or no acknowledgment but which merit attention as precursors and promising contributors to PSM research. The paper is accompanied by an electronic supplement containing the basic data of the atlas from which additional maps may be designed and constructed. [ABSTRACT FROM AUTHOR]

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Background: Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. Objective: To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. Methods: The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Results: Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Conclusion: Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. Patient or Public Contribution: This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members. [ABSTRACT FROM AUTHOR]

According to current research in bibliometrics, percentiles (or percentile rank classes) are the most suitable method for normalizing the citation counts of individual publications in terms of the subject area, the document type, and the publication year. Up to now, bibliometric research has concerned itself primarily with the calculation of percentiles. This study suggests how percentiles (and percentile rank classes) can be analyzed meaningfully for an evaluation study. Publication sets from four universities are compared with each other to provide sample data. These suggestions take into account on the one hand the distribution of percentiles over the publications in the sets (universities here) and on the other hand concentrate on the range of publications with the highest citation impact-that is, the range that is usually of most interest in the evaluation of scientific performance. [ABSTRACT FROM AUTHOR]

Citation data for institutes are generally provided as numbers of citations or as relative citation rates (as, for example, in the Leiden Ranking). These numbers can then be compared between the institutes. This study aims to present a new approach for the evaluation of citation data at the institutional level, based on regression models. As example data, the study includes all articles and reviews from the Web of Science for the publication year 2003 ( n = 886,416 papers). The study is based on an in-house database of the Max Planck Society. The study investigates how much the expected number of citations for a publication changes if it contains the address of an institute. The calculation of the expected values allows, on the one hand, investigating how the citation impact of the papers of an institute appears in comparison with the total of all papers. On the other hand, the expected values for several institutes can be compared with one another or with a set of randomly selected publications. Besides the institutes, the regression models include factors which can be assumed to have a general influence on citation counts (e.g., the number of authors). [ABSTRACT FROM AUTHOR]

It is shown that under certain circumstances in particular for small data sets, the recently proposed citation impact indicators I3(6PR) and R(6, k) behave inconsistently when additional papers or citations are taken into consideration. Three simple examples are presented, in which the indicators fluctuate strongly and the ranking of scientists in the evaluated group is sometimes completely mixed up by minor changes in the database. The erratic behavior is traced to the specific way in which weights are attributed to the six percentile rank classes, specifically for the tied papers. For 100 percentile rank classes, the effects will be less serious. For the six classes, it is demonstrated that a different way of assigning weights avoids these problems, although the nonlinearity of the weights for the different percentile rank classes can still lead to (much less frequent) changes in the ranking. This behavior is not undesired because it can be used to correct for differences in citation behavior in different fields. Remaining deviations from the theoretical value R(6, k) = 1.91 can be avoided by a new scoring rule: the fractional scoring. Previously proposed consistency criteria are amended by another property of strict independence at which a performance indicator should aim. [ABSTRACT FROM AUTHOR]

Background Existing tools for rapid cognitive assessment in HIV-positive individuals with mild cognitive deficits lack sensitivity or do not meet psychometric requirements for tracking changes in cognitive ability over time. Methods Seventy-five nondemented HIV-positive patients were evaluated with the Montreal Cognitive Assessment (MoCA), a brief battery of standardized neuropsychological tests, and computerized tasks evaluating frontal-executive function and processing speed. Rasch analyses were applied to the MoCA data set and subsequently to the full set of data from all tests. Results The MoCA was found to adequately measure cognitive ability as a single, global construct in this HIV-positive cohort, although it showed poorer precision for measuring patients of higher ability. Combining the additional tests with the MoCA resulted in a battery with better psychometric properties that also better targeted the range of abilities in this cohort. Conclusion This application of modern test development techniques shows a path towards a quick, quantitative, global approach to cognitive assessment with promise both for initial detection and for longitudinal follow-up of cognitive impairment in patients with HIV infection. [ABSTRACT FROM AUTHOR]

Considering the growing importance of the field of chemobrionics since the term was coined in 2015 and the increase in the number of published papers, it has become necessary to catalogue all the papers published to date. Here, we present the chemobrionics database, which lists all the chemical gardens synthesised according to their anion, cation and experimental protocol. The aim of this database is to encourage the study and dissemination of chemical gardens in order to find new experimental avenues in the field of chemobrionics. As this is such a fruitful field, the database is continuously updated. [ABSTRACT FROM AUTHOR]

This paper investigates whether and how export growth to China varies following shocks in bilateral political relations between China and six Southeast Asian countries—Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam—between 2000 and 2019. High‐frequency news data is used to quantify bilateral political relations between each of the six countries and China. Then, a net cooperation index, plus separate indices of political cooperation and conflict, are placed in an augmented gravity model of trade situated in a vector autoregression framework alongside real exchange rate and industrial production variables to examine the short‐ (months) and long‐run (years) effects of shocks to bilateral political relations on each countryʼs exports growth to China. The results reveal that political relations with China played a role in Thailandʼs and Vietnamʼs exports growth to China but not in Indonesiaʼs, Malaysiaʼs, the Philippinesʼ and Singaporeʼs, and contribute new findings to the literature on politics and trade. Code and data for the analysis is available at: https://github.com/tradepolsrepository/tradepols.git. [ABSTRACT FROM AUTHOR]

StreamSAXS is a Python‐based small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user‐friendly tool for analysis of both batch data files and real‐time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug‐in framework, which enables developers to extend the built‐in workflow tasks. Another feature is the support for both already acquired and real‐time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large‐scale acquisition systems for end‐to‐end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios. [ABSTRACT FROM AUTHOR]

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks. [ABSTRACT FROM AUTHOR]

This paper investigates past systems engineering research structure by developing a network of research topics from an extensive corpus of publications. The bibliometric data analysis from the published research articles provides valuable information on past progress and future scientific discipline opportunities. Topic modelling, a form of unsupervised machine‐learning‐based natural language processing, is applied to extract the main topics from the titles and abstracts of a wide range of papers published about systems engineering. The co‐occurrence of research topics in these papers provides the data for generating network diagrams. A visual and quantitative network analysis of these topics revealed several cliques and clusters of research topics in systems engineering. Systems engineering researchers should consider these relationships between these research topics to plan a systems engineering research project. [ABSTRACT FROM AUTHOR]

A response by Alex De Visscher to a letter to the editor about his article "What Does the G-Index Really Measure?" in a 2011 issue is presented.

This communication comments on the three papers by the FDA CDER research team on the regulatory perspective of the missing data problem. The focus is on two topics: causal estimand and sensitivity analysis. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Product–service systems (PSS) have the potential to align businesses' financial incentives with environmental objectives. Conceptual and qualitative research on non‐ownership consumption suggests that such offerings benefit from dense local networks, which motivate system adoption and use. However, geographic network effects and their magnitude have not been examined with field data capturing revealed behavior. This paper leverages a large field dataset from a system for reusable take‐out food containers to evaluate the effect of increased geographic network density of participating restaurants on (a) the acquisition of new users and (b) the frequency of system use. Based on fixed effects Poisson panel models, this paper finds statistically significant and practically meaningful positive effects of increased geographic network density on acquiring new users. Notably, marginal effects of increased geographic network density on user acquisition diminish as networks get denser. In terms of frequency of use, no significant effects of geographic network density are identified. These results contribute to the literature on non‐ownership consumption by presenting nuanced field evidence of indirect, cross‐side network effects in PSS. Furthermore, findings encourage businesses and policymakers to promote PSS with dense local networks. [ABSTRACT FROM AUTHOR]

International collaborative papers are increasingly common in journals of many disciplines. These types of papers are often cited more frequently. To identify the coauthorship trends within Library and Information Science (LIS), this study analyzed 7,489 papers published in six leading publications (ARIST, IP&M, JAMIA, JASIST, MISQ, and Scientometrics) over the last three decades. Logistic regression tested the relationships between citations received and seven factors: authorship type, author's subregion, country income level, publication year, number of authors, document type, and journal title. The main authorship type since 1995 was national collaboration. It was also the dominant type for all publications studied except ARIST, and for all regions except Africa. For citation counts, the logistic regression analysis found all seven factors were significant. Papers that included international collaboration, Northern European authors, and authors in high-income nations had higher odds of being cited more. Papers from East Asia, Southeast Asia, and Southern Europe had lower odds than North American papers. As discussed in the bibliometric literature, Merton's Matthew Effect sheds light on the differential citation counts based on the authors' subregion. This researcher proposes geographies of invisible colleagues and a geographic scope effect to further investigate the relationships between author geographic affiliation and citation impact. [ABSTRACT FROM AUTHOR]

The article discusses the common statistical errors seen in published scientific studies or review papers. It highlights the two of the myriad problems identified in science reporting such as the misuse of statistics and the overinterpretation of data. The most often used statistical tests seen in the medical literature are also explored including the t-test and Chi-square test.

Objectives The purpose of this study was to characterize the publication fate of a recent 2-year sample of manuscripts declined by Academic Emergency Medicine ( AEM), the journal of the Society for Academic Emergency Medicine. Methods This was a retrospective analysis of manuscripts submitted to AEM in 2010 and 2011 that were declined by the AEM editorial review process. An online search was conducted for each declined paper, to determine whether or not it was published in another clinical/scientific journal after being declined by AEM. The investigators used Scopus and Google Scholar, using the submitting author's name, the verbatim title, and key words and phrases from the title, to search for subsequent publication of each paper. Results Of 1,542 manuscript submissions to the journal in 2010 and 2011, 1,052 papers were declined. Of these, 693 (65.9%) were subsequently published elsewhere, in a total of 229 journals: 362 papers in 22 different EM journals, 81 in 14 EM subspecialty journals, 237 in 185 non- EM journals, and 13 in eight nursing journals. Papers were published a median of 16.7 months (interquartile range [ IQR] = 11.8 to 22.0 months) after being declined at AEM. Of the 229 journals, 19 do not have h-indices. The median h-index of the remaining 210 journals is 36 ( IQR = 17 to 64; maximum = 229; AEM's h-index is 78). Thirty of these 210 journals, publishing 43 papers, have higher h-indices than AEM; the other 650 papers were published in journals either with lower h-indices than AEM's ( n = 180 journals) or in journals without h-indices ( n = 19 journals). U.S. and non-U.S. authors had similar rates of subsequent publication (65.3% vs 66.6%, p = 0.69) for papers initially declined by AEM. Papers in the educational advances category were less likely to be subsequently published than those in the original contributions (p < 0.0001) and brief reports (p = 0.0137) categories. Conclusions Nearly two-thirds of manuscripts declined by SAEM's journal are eventually published elsewhere, in a large number and wide variety of both EM and non- EM journals, in a median of 16.7 months. Authors of manuscripts declined by AEM should consider submission elsewhere, as subsequent publication of these manuscripts in another journal is probable. [ABSTRACT FROM AUTHOR]

The aim of this paper is to extend our knowledge about the power-law relationship between citation-based performance and coauthorship patterns in papers in the natural sciences. We analyzed 829,924 articles that received 16,490,346 citations. The number of articles published through coauthorship accounts for 89%. The citation-based performance and coauthorship patterns exhibit a power-law correlation with a scaling exponent of 1.20 ± 0.07. Citations to a subfield's research articles tended to increase 2.1.20 or 2.30 times each time it doubled the number of coauthored papers. The scaling exponent for the power-law relationship for single-authored papers was 0.85 ± 0.11. The citations to a subfield's single-authored research articles increased 2.0.85 or 1.89 times each time the research area doubled the number of single-authored papers. The Matthew Effect is stronger for coauthored papers than for single-authored. In fact, with a scaling exponent <1.0 the impact of single-authored papers exhibits a cumulative disadvantage or inverse Matthew Effect. [ABSTRACT FROM AUTHOR]

Whilst learning analytics is still nascent in most African higher education institutions, many African higher education institutions use learning platforms and analytic services from providers outside of the African continent. A critical consideration of the protection of data privacy on the African continent and its implications for learning analytics in African higher education is therefore needed. In this paper, we map the current state of legal and regulatory environments and frameworks on privacy to establish their implications for learning analytics. This scoping review of privacy regulations in 32 African countries, complemented by 15 scholarly papers, revealed that there are numerous national and regional legislation and regulatory frameworks, providing clear pointers pertaining to (student) data privacy to governments, higher education institutions and researchers. As such, the findings of this research have implications for African higher education to ensure not only legal compliance but also to oversee and safeguard student data privacy as part of their fiduciary duty. This research provides crucial insights regarding the importance of context for thinking about the expansion and institutional adoption of learning analytics. Practitioner notesWhat is already known about this topic Personal data have become commodified and are regarded as a valuable commercial asset.The commercial value of data relies on the collection and analysis of increasing volumes, granularity, variety and velocity of personal data (both identifiable and aggregated).Africa and African higher education are regarded as new data frontiers to be exploited.What this paper adds This paper, for the first time, makes an attempt to map privacy legislation and academic research on (student) data privacy in the African continent.Maps key implications for African higher educations to consider in collecting, analysing, using and sharing student data.It provides pointers for a research agenda pertaining to student data privacy on the African continent.Implications for practice and/or policy African higher education institutions should consider student data privacy when entering into service level agreements with educational technology and platform providers.African governments should develop common data sharing frameworks to facilitate cross‐border data transfer.Current African data privacy legislation provides important implications for the adoption and institutionalisation of learning analytics.African higher education also has to consider the ethical aspects of learning analytics. [ABSTRACT FROM AUTHOR]

The adoption of Information and Communication Technologies in early childhood education is crucial for adapting traditional classrooms to the digital era. Over time, young children are increasingly using touch screen technologies such as tablets at home and in early childhood settings. However, the literature shows that there is a significant gap in knowledge of using this technology in early childhood education. Most researchers have focused on the pedagogical theory behind using touch screen devices, but there have not been many empirical studies about how these technologies affect students' learning processes. This paper presents three learning experiences where early childhood students perform educational activities using tablet computers, interactive whiteboards, and paper cards. The results show that students who used the technology were more motivated and achieved better results that those who used paper cards. Lay Description: What is already known about this topic: The use of ICT in childhood education offers new possibilities for teachers to provide new and more visual digital learning content to their students.Touch technologies seem suitable for young students. Because their motor skills are not fully developed yet, interacting with computers through a mouse and a keyboard is more difficult than doing it through natural gestures.There are many studies in higher education levels that demonstrate that proper use of ICT in education can lead to an increase of the students' motivation and learning. What this paper adds: Three empirical studies in three different childhood education classrooms to shed some light about how ICT can impact positively students learning.A comparison of interactive whiteboards and tablet computers benefits regarding more traditional methodologies in childhood education.Insights about how hardware and software can be combined to provide students a suitable learning environment in childhood education classrooms. Implications for practice and/or policy: Teachers should consider how they will create the workgroups. It is highly advisable to create heterogeneous groups taking into account the students' skills. This way, members of the same group can help each other, creating a richer learning scenario.Several teachers who participated in our studies said that they were not able to use ICT in their classrooms because they did not have enough digital competences. Our studies and the results obtained led them consider integrating technology in their classrooms. The good results obtained by the students who worked with technology changed the teachers' perspective about the use of technology in the classroom.Students showed a great interest in the use of tablet computers and interactive whiteboards, which translated into higher motivation compared with the students who solved the activities on paper. [ABSTRACT FROM AUTHOR]

The counting of papers and citations is fundamental to the assessment of research productivity and impact. In an age of increasing scientific collaboration across national borders, the counting of papers produced by collaboration between multiple countries, and citations of such papers, raises concerns in country-level research evaluation. In this study, we compared the number counts and country ranks resulting from five different counting methods. We also observed inflation depending on the method used. Using the 1989 to 2008 physics papers indexed in ISI's Web of Science as our sample, we analyzed the counting results in terms of paper count (research productivity) as well as citation count and citation-paper ratio (CP ratio) based evaluation (research impact). The results show that at the country-level assessment, the selection of counting method had only minor influence on the number counts and country rankings in each assessment. However, the influences of counting methods varied between paper count, citation count, and CP ratio based evaluation. The findings also suggest that the popular counting method (whole counting) that gives each collaborating country one full credit may not be the best counting method. Straight counting that accredits only the first or the corresponding author or fractional counting that accredits each collaborator with partial and weighted credit might be the better choices. [ABSTRACT FROM AUTHOR]

Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience. [ABSTRACT FROM AUTHOR]

It is a matter of debate whether the discipline independence in discipline formation narrows its interdisciplinarity. It is also less well understood how disruptive works emerge in investigative practice rather than a theory‐driven approach. Aggregation‐induced emission (AIE) is an atypical photophysical phenomenon, in which the whole (aggregate) is brighter than the sum of its parts (single molecule). Through measuring and computing the cognitive extent and evolution of research on AIE, including topics, epistemic‐social collaborative networks, interdisciplinarity, emergent concepts, core concept networks and knowledge flow, this study shows that a cross‐research scales concept and its practice can establish new bridges in the sciences and promote disruptive work. Focusing on mesoscale entities, scientists from many different branches of science are involved in theoretical research on mechanisms, as well as developing different AIE systems for applications. The data analysis in this study provides details showing how non‐reductionist concepts based on new scientific discoveries cross traditional disciplinary boundaries and aggregate interdisciplinary research. The emergence and evolution of the AIE field implies that scientists may be motivated to embrace nonreductionist ideas at different research scales, leading to a more permeable field boundary. [ABSTRACT FROM AUTHOR]

In this article, we report on the status of graphs in 21 scientific agricultural journals indexed in Thomson Reuters' Web of Knowledge. We analyze the authors' use of graphs in this context in relation to the quality of these journals as measured by their 2-year impact factors. We note a substantial variability in the use of graphs in this context: For one journal, 100% of the papers include graphs, whereas for others only about 50% of them include graphs. We also show that higher impact agricultural journals publish more papers with graphs and that there are more graphs in these papers than in those in journals with lower impact factors ( r = +0.40). [ABSTRACT FROM AUTHOR]

While the Cretaceous Normal Polarity Superchron has documented instances of brief reversed polarity intervals, the absence of accurate age determinations for such abrupt shifts poses a challenge in leveraging them as reliable reference tiepoints. This study presents a cyclostratigraphic analysis of gamma‐ray data from the DSDP Site 402A. The identification of Milankovitch cycles allowed us to construct a 405‐kyr astronomically‐tuned age model for two reversed events that occurred in the Aptian. Our results estimate an age of 117.03 ± 0.14 Ma for the Chron M"‐1r" or ISEA and 116.17 ± 0.14 Ma for reversal "2," with timespans of ∼20 and ∼10 kyr, respectively. Sedimentation patterns are influenced by orbital eccentricity cycles with an average sedimentation rate of 5 cm/kyr. The short time intervals related to these reversals exposes the difficulty in their detection in cores through paleomagnetic analysis as well as in deep‐tow surveys. Plain Language Summary: The Cretaceous Normal Polarity Superchron (CNPS) was characterized as a "quiet zone," which includes no geomagnetic reversals during ∼38 Myr. A number of studies propose significant fluctuations in geomagnetic intensity, with distinct features observed in paleosecular variations, including periods of reversal events, and the actual occurrence, timespan and ages for these rapid magnetozones have been under debate in literature. This paper provides the age and constrains the timespan of two of these debated reversals (i.e., M"‐1r" or ISEA, and "2") during the CNPS. The determination of the age of the reversals during the CNPS is a fundamental support for global correlations. Key Points: Orbital tuning for M‐1r (ISEA) has an age of 117.03 ± 0.14 Ma and a timespan of 20 kyrThe reversal "2" has an age of 116.16 ± 0.14 Ma and a minimum timespan of 10 kyrDSDP Site 402A has an average 5 cm/kyr sedimentation rate and eccentricity Milankovitch cycles rules sedimentation patterns [ABSTRACT FROM AUTHOR]

Hydrothermal liquefaction is a new, sustainable pathway to generate biogenic liquids from organic resources. The technology is compatible with a wide variety of resources such as lignocellulosic resources, organic waste, algae, and sewage sludge. The chemistry is complex and predictions of yields are notoriously difficult. Understanding and modeling of hydrothermal liquefaction is currently mostly based on a simplified biochemical analysis and product yield data. This paper presents a large dataset of 2439 experiments in batch reactors that were extracted from 171 publications in the scientific literature. The data include biochemical composition data such as fiber content and composition, proteins, lipids, carbohydrates, and ash. The experimental conditions are recorded for each experiment as well as the reported yields. The objective of this paper is to make a large database available to the scientific community. This database is analyzed with machine‐learning tools. The results show that there is no consensus on the analysis techniques, experimental procedures, and reported data. There are many inconsistencies across the literature that should be improved by the scientific community. Machine‐learning tools with a large dataset allow the generation of reliable yield production tools with a large application field. Given the accuracy of the data, the overall precision of prediction in an extrapolation to new results can be expected to be around 10%. [ABSTRACT FROM AUTHOR]

With the widespread popularity of electric vehicles in the domestic market, large‐scale electric vehicle user data has been collected and stored. Highly accurate user‐level charging load prediction has a wide range of application scenarios and great business value. However, most existing EV load prediction methods are modelled from the charging station perspective, ignoring the user's travel habits and charging demand. Therefore, this paper proposes a temporal spatial neural network based on graph attention and Autoformer to predict electric vehicle charging load. Firstly, the urban map of Wuhan is rasterized. Then, driving and charging data from the user level are aggregated into the raster module according to the time sequence, and a spatio‐temporal graph data structure of user travel trajectory is constructed. Finally, the temporal spatial neural network is used to construct the EV charging load prediction model from the user's perspective. The experimental results show that, compared with other baseline prediction methods, the proposed method effectively improves the accuracy of the EV charging load prediction model by fully exploiting the distribution of EV user clusters in time and geographic space. [ABSTRACT FROM AUTHOR]

Background: Instructional videos constitute a pivotal component in flipped learning. Despite their significance, there is a dearth of research specifically dedicated to instructional videos within the context of flipped classrooms. This paucity has led to an empirical void in verifying the efficacy of instructional videos in flipped learning environments. Objectives: The present study endeavours to contribute to the extant literature on flipped pedagogical practices by providing empirical evidence regarding the effectiveness of instructional videos in flipped learning environments. Methods: This study employs a convergent mixed‐methods design. Forty‐five instructional videos in three subtypes were administered in two classes over a 15‐week semester. Data, both quantitative (log data from the learning management system) and qualitative (from focus group discussions at two time points), were concurrently collected from a flipped class (n = 25) and a blended class (n = 28) with the aim of gauging student engagement and learning outcomes in the flipped class and understanding video‐viewing features typical of the flipped class. Results: Both datasets corroborated that participants in the flipped class outperformed their counterparts in the blended class in terms of video‐watching, peer interaction, knowledge retention, and transfer skills. Conclusions: The instructional videos within the flipped classroom setting serve to enhance student engagement in the learning process and improve learning outcomes. The efficacy of these instructional videos in flipped learning can be further augmented through strategic approaches in both their design and implementation. Lay Description: What is currently known about this topic: The flipping pedagogy is conducive to learning.Videos play a crucial role in flipped learning.Videos prepared students for classroom activities.However, engaging students with pre‐class videos presents a challenge. What this paper adds: This paper furnishes direct empirical evidence supporting the effectiveness of instructional videos in flipped learning.It uncovers factors affecting student engagement with videos in flipped learning.It offers insights into the design and implementation of videos in flipped learning. Implications for practice: Considering facilitating conditions and affective factors when creating videos.Acknowledging learner characteristics when designing videos.Utilizing instructional strategies when implementing videos. [ABSTRACT FROM AUTHOR]

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace‐based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self‐reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self‐efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self‐reports collected before the start of the MOOC offers better understanding of trace‐based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self‐reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow. Lay Description: What is already known about this topic: Researchers have made extensive use of data from online learning platforms—often referred to as learning trace data—to understand how learners engage in online learning activities.Surveys are often used to gather information about learners' motivation, perceptions, and other internal factors, to complement the insight gleaned from learning traces and thus create a more complete picture of the learning behaviour.When analysing survey data, the focus is usually on individual factors without considering how different factors are connected and affect one another.Psychological networks analysis is a novel analytic approach to studying complex phenomena both in psychology and education. What this paper adds: The paper uses psychological networks as a method for examining mutual relations among learners' internal factors (e.g., motivation, perceptions) measured through self‐reports before and after a course.By combining conventional statistical analysis of self‐reports with psychological networks analysis, the paper develops a comprehensive picture of learners' internal factors measured through self‐reports, one that accounts for both individual and interconnected nature of those factors.This comprehensive approach to information extraction from surveys allows for better understanding of trace‐based learner profiles, that is, profiles mined from data about learners' interaction with online learning activities. Implications for practice and/or policy: The presented method paves a way for a comprehensive analysis of the data collected from learners through surveys.It helps us gain a better understanding of learners' engagement with online learning activities.It can advance the evaluation of digital educational programs that aim to encourage changes in health‐related behaviour. [ABSTRACT FROM AUTHOR]

Background: Painting is the foundational expression across all art forms and is one of the key creative practices for fostering students' aesthetic ability and creativity within fine arts courses. Collaborative painting in the form of socially shared regulation of learning (SSRL) can be recognized as an effective strategy for enhancing creativity in both individual and group work. However, the absence of contextual experiences and collaborative spaces poses challenges for students in cultivating their creativity in painting. Objectives: The present study explores students' creativity, socially shared regulation (SSR) and positive painting behaviours using an SSRL‐based collaborative painting approach in the metaverse (Meta‐CP). Methods: Via a quasi‐experimental design, a total of 40 Chinese students in fifth grade were recruited and were randomly divided into an experimental group and a control group, with 20 students utilizing the Meta‐CP approach, while the remaining 20 students followed the conventional SSRL‐based paper‐and‐brush collaborative painting (C‐CP) approach. The Meta‐CP approach not only offers an authentic painting context and a collaborative space but also facilitates the collaborative process through the SSRL framework. The data collection included students' creative tendency, painting works and painting process. Results and Conclusions: The Meta‐CP approach effectively enhances students' creativity concerning adventure, curiosity and imagination, as well as results in the creation of more distinctive and logical artistic works. Furthermore, the approach significantly improved the quality of students' SSR. Additionally, students utilizing the Meta‐CP approach displayed more positive painting behaviours compared with those employing the C‐CP approach. Lay Description: What is already known about this topic:Collaborative painting is an effective strategy for enhancing creativity.Socially shared regulation of learning (SSRL) fosters collaborative learning.Students in collaborative painting may lack a contextual and synchronized painting space.The metaverse benefits in terms of contextual experiences and collaborative spaces. What this paper adds:Using an SSRL‐based collaborative painting approach in the metaverse (Meta‐CP) is effective in improving students' creativity.The Meta‐CP approach has the potential to improve students' quality of socially shared regulation (SSR), and positive painting behaviours. Implications for practice and/or policy:The metaverse provides an immersive synchronized painting space for art education to stimulate students' creative potential.The metaverse with proper learning strategies (e.g., SSRL) provides an effective collaborative learning environment for creation. [ABSTRACT FROM AUTHOR]

Suitable and effective matching area selection is crucial for gravity matching‐aided navigation. In this paper, an all‐field extended extremum algorithm based on an adaptive threshold (AT‐AEE) is proposed for matching area selection in the Arctic Sea. The gradient data is obtained by using the convolution of gravity reference graph data and all‐field extended extremum parameters. Then, the adaptive threshold method was employed to determine the optimal gradient threshold based on gravity anomaly data across various test areas. Data points with gradients exceeding the specified threshold are identified as local candidate points for matching areas. The test areas containing a certain proportion of local candidate points are designated as the suitable matching areas. Nine test areas in the Arctic Sea with different gravity change characteristics were chosen for simulation experiments to verify the performance of the proposed algorithm. Simulation experiments showed that superior navigation positioning results could be obtained in the matching areas selected by the AT‐AEE algorithm. Compared to traditional algorithm, the matching areas derived from the AT‐AEE algorithm performed with a better consistency in the gravity matching navigation results. In suitable matching areas with the proportion of local candidate points reaching 70%, the average positioning errors could be reduced to less than 1.5 n miles. [ABSTRACT FROM AUTHOR]

The switching power supply is widely used in the secondary equipment of the power grid. The failure of the switching power supply will cause the protection device to malfunction, leading to a risk of large‐scale power failure. In this paper, a real‐time monitoring method based on only output voltage information is proposed. The experiment shows that this parameter‐reduced approach, relying only on voltage information, outperforms the existing method in detection accuracy. [ABSTRACT FROM AUTHOR]