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Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses—such as the analysis of longitudinal data—reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of longitudinal data of 11 articles published in PLOS ONE. Inclusion criteria were the availability of data and author consent. We investigated the types of methods and software used and whether we were able to reproduce the data analysis using open source software. Most articles provided overview tables and simple visualisations. Generalised Estimating Equations (GEEs) were the most popular statistical models among the selected articles. Only one article used open source software and only one published part of the analysis code. Replication was difficult in most cases and required reverse engineering of results or contacting the authors. For three articles we were not able to reproduce the results, for another two only parts of them. For all but two articles we had to contact the authors to be able to reproduce the results. Our main learning is that reproducing papers is difficult if no code is supplied and leads to a high burden for those conducting the reproductions. Open data policies in journals are good, but to truly boost reproducibility we suggest adding open code policies. [ABSTRACT FROM AUTHOR]

The Journal of Physiology and British Journal of Pharmacology jointly published an editorial series in 2011 to improve standards in statistical reporting and data analysis. It is not known whether reporting practices changed in response to the editorial advice. We conducted a cross-sectional analysis of reporting practices in a random sample of research papers published in these journals before (n = 202) and after (n = 199) publication of the editorial advice. Descriptive data are presented. There was no evidence that reporting practices improved following publication of the editorial advice. Overall, 76-84% of papers with written measures that summarized data variability used standard errors of the mean, and 90-96% of papers did not report exact p-values for primary analyses and post-hoc tests. 76-84% of papers that plotted measures to summarize data variability used standard errors of the mean, and only 2-4% of papers plotted raw data used to calculate variability. Of papers that reported p-values between 0.05 and 0.1, 56-63% interpreted these as trends or statistically significant. Implied or gross spin was noted incidentally in papers before (n = 10) and after (n = 9) the editorial advice was published. Overall, poor statistical reporting, inadequate data presentation and spin were present before and after the editorial advice was published. While the scientific community continues to implement strategies for improving reporting practices, our results indicate stronger incentives or enforcements are needed. [ABSTRACT FROM AUTHOR]

Brazil counts among the countries the hardest hit by the Covid-19 pandemic. A great deal has been said about the negative role played by President Bolsonaro's denialism, but relatively few studies have attempted to measure precisely what impact it actually had on the pandemic. Our paper conducts econometric estimates based on observational data at municipal level to quantitatively assess the 'Bolsonaro effect' over time from March 2020 to December 2022. To our knowledge, this paper presents the most comprehensive investigation of Bolsonaro's influence in the spread of the pandemic from two angles: considering Covid-19 mortality and two key transmission mitigation channels (social distancing and vaccination); and exploring the full pandemic cycle (2020–2022) and its dynamics over time. Controlling for a rich set of relevant variables, our results find a strong and persistent 'Bolsonaro effect' on the death rate: municipalities that were more pro-Bolsonaro recorded significantly more fatalities. Furthermore, evidence suggests that the president's attitude and decisions negatively influenced the population's behaviour. Firstly, pro-Bolsonaro municipalities presented a lower level of compliance with social distancing measures. Secondly, vaccination was relatively less widespread in places more in favour of the former president. Finally, our analysis points to longer-lasting and damaging repercussions. Regression results are consistent with the hypothesis that the 'Bolsonaro effect' impacted not only on Covid-19 vaccination, but has affected vaccination campaigns in general thereby jeopardizing the historical success of the National Immunization Program in Brazil. [ABSTRACT FROM AUTHOR]

The skill of analyzing and interpreting research data is central to the scientific process, yet it is one of the hardest skills for students to master. While instructors can coach students through the analysis of data that they have either generated themselves or obtained from published articles, the burgeoning availability of preprint articles provides a new potential pedagogical tool. We developed a new method in which students use a cognitive apprenticeship model to uncover how experts analyzed a paper and compare the professional's cognitive approach to their own. Specifically, students first critique research data themselves and then identify changes between the preprint and final versions of the paper that were likely the results of peer review. From this activity, students reported diverse insights into the processes of data presentation, peer review, and scientific publishing. Analysis of preprint articles is therefore a valuable new tool to strengthen students' information literacy and understanding of the process of science. [ABSTRACT FROM AUTHOR]

The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis. [ABSTRACT FROM AUTHOR]

Formalistic tasks are widely utilized in modern companies due to their ability to increase productivity and contribute to the achievement of corporate goals at a lower cost. However, these tasks are often meet with resistance from individuals because they do not provide direct short-term rewards for their efforts. Drawing on social cognitive theory, this study examined the influence of individual quality and organizational attachment on the completion of formalistic tasks. To address this, the study conducted a questionnaire survey to collect data from 602 Chinese respondents and built a structural equation model for data analysis. Through empirical research, the study confirmed the positive role of individual quality, including knowledge and personality, in the completion of formalistic tasks. Furthermore, the study proved that avoidant attachment could significantly weaken the effect of some components of individual quality on formalistic task completion. This paper is the first to reveal the influence of individual and environmental factors on individuals' completion of formalistic tasks, progressing from bottom to top. The implications of these results are discussed. [ABSTRACT FROM AUTHOR]

The introduction of computer-based testing in high-stakes examining in higher education is developing rather slowly due to institutional barriers (the need of extra facilities, ensuring test security) and teacher and student acceptance. From the existing literature it is unclear whether computer-based exams will result in similar results as paper-based exams and whether student acceptance can change as a result of administering computer-based exams. In this study, we compared results from a computer-based and paper-based exam in a sample of psychology students and found no differences in total scores across the two modes. Furthermore, we investigated student acceptance and change in acceptance of computer-based examining. After taking the computer-based exam, fifty percent of the students preferred paper-and-pencil exams over computer-based exams and about a quarter preferred a computer-based exam. We conclude that computer-based exam total scores are similar as paper-based exam scores, but that for the acceptance of high-stakes computer-based exams it is important that students practice and get familiar with this new mode of test administration. [ABSTRACT FROM AUTHOR]

Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan-another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lusa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Based on three decades of citation data from across scientific fields of science, we study trends in impact factor biased self-citations of scholarly journals, using a purpose-built and easy to use citation based measure. Our measure is given by the ratio between i) the relative share of journal self-citations to papers published in the last two years, and ii) the relative share of journal self-citations to papers published in preceding years. A ratio higher than one suggests that a journal’s impact factor is disproportionally affected (inflated) by self-citations. Using recently reported survey data, we show that there is a relation between high values of our proposed measure and coercive journal self-citation malpractices. We use our measure to perform a large-scale analysis of impact factor biased journal self-citations. Our main empirical result is, that the share of journals for which our measure has a (very) high value has remained stable between the 1980s and the early 2000s, but has since risen strongly in all fields of science. This time span corresponds well with the growing obsession with the impact factor as a journal evaluation measure over the last decade. Taken together, this suggests a trend of increasingly pervasive journal self-citation malpractices, with all due unwanted consequences such as inflated perceived importance of journals and biased journal rankings. [ABSTRACT FROM AUTHOR]

Background: The widespread reluctance to share published research data is often hypothesized to be due to the authors' fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically. Methods and Findings: We related the reluctance to share research data for reanalysis to 1148 statistically significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical significance. Conclusions: Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies. [ABSTRACT FROM AUTHOR]

Citation distributions are crucial for the analysis and modeling of the activity of scientists. We investigated bibliometric data of papers published in journals of the American Physical Society, searching for the type of function which best describes the observed citation distributions. We used the goodness of fit with Kolmogorov-Smirnov statistics for three classes of functions: log-normal, simple power law and shifted power law. The shifted power law turns out to be the most reliable hypothesis for all citation networks we derived, which correspond to different time spans. We find that citation dynamics is characterized by bursts, usually occurring within a few years since publication of a paper, and the burst size spans several orders of magnitude. We also investigated the microscopic mechanisms for the evolution of citation networks, by proposing a linear preferential attachment with time dependent initial attractiveness. The model successfully reproduces the empirical citation distributions and accounts for the presence of citation bursts as well. [ABSTRACT FROM AUTHOR]

This study aims to investigate green finance's impact on the synergy between ESG and green innovation (ESGGI) performance and examine the potential influence played by stakeholders' green concerns on this impact. Hence, we calculated the synergy of ESGGI performance based on the entropy method and the coupled coordination degree model and conducted regression analyses on the data of 1143 Chinese companies from 2012 to 2020. The results reveal a remarkable inverted U-shaped relationship between green finance and the synergy of ESGGI performance. Additionally, the green concerns of the government and the media can efficiently moderate green finance's impact on the synergy of ESGGI performance. Further tests show that green finance's impact on the synergy of ESGGI performance of SOEs, HPEs, and HTEs is more significant. This paper demonstrates an association between green finance and the synergy of ESGGI performance, which yields new insights for companies to implement green transformation and sustainable development. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods. [ABSTRACT FROM AUTHOR]

Bootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the analysis of data where predictors are collinear. This paper develops a nonparametric bootstrap-quantile approach for the estimation of ridge parameter in the linear regression model. The proposed method is illustrated using some popular and widely used ridge estimators, but this idea can be extended to any ridge estimator. Monte Carlo simulations are carried out to compare the performance of the proposed estimators with their baseline counterparts. It is demonstrated empirically that MSE obtained from our suggested bootstrap-quantile approach are substantially smaller than their baseline estimators especially when collinearity is high. Application to real data sets reveals the suitability of the idea. [ABSTRACT FROM AUTHOR]

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time. [ABSTRACT FROM AUTHOR]

Objectives: The paper presents data on the two problems of misconduct and marginality in management, business and economics (MBE) journals and their practices to combat these problems. Design: Data was collected in three phases. First, all publicly retracted papers in MBE journals were identified through keywords searches in 7 major databases (n = 1329 journals). Second, a focused survey was distributed to editors involved in such retractions (n = 64; response rate = 28%). Finally, a survey was administered to all active journals in the seven databases to collect data on editors’ perceptions and practices related to the two problems (n = 937, response rate = 31.8%). Frequency analyses, cross tabulations, and qualitative analyses of open answers were used to examine the data. Results: 184 retracted papers in MBE journals were identified in 2005–2015 (no retraction was found before 2005). From 2005–2007 to 2012–2015, the number of retractions increased by a factor ten with an all-time high in 2015. The survey to journals with reported retractions illustrates how already a few cases of suspected misconduct put a strain on the editorial workload. The survey to all active journals revealed that 42% of the respondents had started to use software to screen all submitted papers, and that a majority recognized the problem of marginality, as indicated by salami-style submissions. According to some editors, reviewers easily spot such submissions whereas others argued that authors may submit thinly sliced papers in parallel to several journals, which means that this practice is only discovered post-publication. The survey question on ways to support creative contributions stimulated a rich response of ideas regarding editorial vision, engaged boards and developmental approaches. The study uses data from three specialized fields, but its findings may be highly relevant to many journals in the social sciences. [ABSTRACT FROM AUTHOR]

Microblog has become the "first scenario" under which the public learn about the epidemic situation and express their opinions. Public sentiment mining based on microblog data can provide a reference for the government's information disclosure, public sentiment guidance and formulation of epidemic prevention and control policy. In this paper, about 200,000 pieces of text data were collected from Jan. 1 to Feb. 26, 2020 from Sina Weibo, which is the most popular microblog website in China. And a public sentiment analysis framework suitable for Chinese-language scenarios was proposed. In this framework, a sentiment dictionary suitable for Chinese-language scenarios was constructed, and Baidu's Sentiment Analysis API was used to calculate the public sentiment indexes. Then, an analysis on the correlation between the public sentiment indexes and the COVID-19 case indicators was made. It was discovered that there is a high correlation between public sentiments and incidence trends, in which negative sentiment is of statistical significance for the prediction of epidemic development. To further explore the source of public negative sentiment, the topics of the public negative sentiment on Weibo was analyzed, and 20 topics in five categories were got. It is found that there is a strong linkage between the hot spots of public concern and the epidemic prevention and control policies. If the policies cover the hot spots of public concern in a timely and effective manner, the public negative sentiment will be effectively alleviated. The analytical framework proposed in this paper also applies to the public sentiment analysis and policy making for other major public events. [ABSTRACT FROM AUTHOR]

A number of publishers and funders, including PLOS, have recently adopted policies requiring researchers to share the data underlying their results and publications. Such policies help increase the reproducibility of the published literature, as well as make a larger body of data available for reuse and re-analysis. In this study, we evaluate the extent to which authors have complied with this policy by analyzing Data Availability Statements from 47,593 papers published in PLOS ONE between March 2014 (when the policy went into effect) and May 2016. Our analysis shows that compliance with the policy has increased, with a significant decline over time in papers that did not include a Data Availability Statement. However, only about 20% of statements indicate that data are deposited in a repository, which the PLOS policy states is the preferred method. More commonly, authors state that their data are in the paper itself or in the supplemental information, though it is unclear whether these data meet the level of sharing required in the PLOS policy. These findings suggest that additional review of Data Availability Statements or more stringent policies may be needed to increase data sharing. [ABSTRACT FROM AUTHOR]

Background: There is increasing interest to make primary data from published research publicly available. We aimed to assess the current status of making research data available in highly-cited journals across the scientific literature. Methods and Results: We reviewed the first 10 original research papers of 2009 published in the 50 original research journals with the highest impact factor. For each journal we documented the policies related to public availability and sharing of data. Of the 50 journals, 44 (88%) had a statement in their instructions to authors related to public availability and sharing of data. However, there was wide variation in journal requirements, ranging from requiring the sharing of all primary data related to the research to just including a statement in the published manuscript that data can be available on request. Of the 500 assessed papers, 149 (30%) were not subject to any data availability policy. Of the remaining 351 papers that were covered by some data availability policy, 208 papers (59%) did not fully adhere to the data availability instructions of the journals they were published in, most commonly (73%) by not publicly depositing microarray data. The other 143 papers that adhered to the data availability instructions did so by publicly depositing only the specific data type as required, making a statement of willingness to share, or actually sharing all the primary data. Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available. Conclusion: A substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement. [ABSTRACT FROM AUTHOR]

The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight. [ABSTRACT FROM AUTHOR]

Citizen science is a research practice that relies on public contributions of data. The strong recognition of its educational value combined with the need for novel methods to handle subsequent large and complex data sets raises the question: Is citizen science effective at science? A quantitative assessment of the contributions of citizen science for its core purpose – scientific research – is lacking. We examined the contribution of citizen science to a review paper by ornithologists in which they formulated ten central claims about the impact of climate change on avian migration. Citizen science was never explicitly mentioned in the review article. For each of the claims, these ornithologists scored their opinions about the amount of research effort invested in each claim and how strongly the claim was supported by evidence. This allowed us to also determine whether their trust in claims was, unwittingly or not, related to the degree to which the claims relied primarily on data generated by citizen scientists. We found that papers based on citizen science constituted between 24 and 77% of the references backing each claim, with no evidence of a mistrust of claims that relied heavily on citizen-science data. We reveal that many of these papers may not easily be recognized as drawing upon volunteer contributions, as the search terms “citizen science” and “volunteer” would have overlooked the majority of the studies that back the ten claims about birds and climate change. Our results suggest that the significance of citizen science to global research, an endeavor that is reliant on long-term information at large spatial scales, might be far greater than is readily perceived. To better understand and track the contributions of citizen science in the future, we urge researchers to use the keyword “citizen science” in papers that draw on efforts of non-professionals. [ABSTRACT FROM AUTHOR]

Observer bias and other “experimenter effects” occur when researchers’ expectations influence study outcome. These biases are strongest when researchers expect a particular result, are measuring subjective variables, and have an incentive to produce data that confirm predictions. To minimize bias, it is good practice to work “blind,” meaning that experimenters are unaware of the identity or treatment group of their subjects while conducting research. Here, using text mining and a literature review, we find evidence that blind protocols are uncommon in the life sciences and that nonblind studies tend to report higher effect sizes and more significant p-values. We discuss methods to minimize bias and urge researchers, editors, and peer reviewers to keep blind protocols in mind. [ABSTRACT FROM AUTHOR]

This paper takes the specific environment covered by vegetation as the research object, carries out modeling and analysis, takes the large-scale fading model of wireless channel as the basis of data processing, researches the transmission law of electromagnetic wave in a typical vegetation environment, which can be divided into four situations. The signal attenuation in each case is theoretically derived and numerically simulated. From the view point of supporting vegetation environment channel, the large-scale channel measurement system is built to meet the actual needs, such as bandwidth, frequency, vegetation coverage, etc. the final vegetation environment channel model under the large-scale fading model is obtained. The results show that the path gain of four scenarios respectively are 81.3 dB, 36.5 dB, 1.6 dB, 1.5 dB, the value of path gain index is within the range of 2~3.5, four scenarios shadow fading standard deviation values are 7.1, 4.8, 10.1, 9.2, reflects the change of received power at the point caused by random factors such as reflection, absorption and scattering. In addition, the proposed channel model improves the gain about 15% compared with the tradition SUI model within vegetation coverage scene. The design process of the proposed model is carried out in the order of "studied the existing foundation → analyzed the existing problems → proposed the optimization scheme → simulation and verification results → actual measurement system". The advantage of paper's method is that, when the signal frequency, transceiver distance, antenna height and vegetation environment characteristic parameters are given, the statistical analysis results of wireless channel data are obtained. The purpose of the proposed work establishes a signal propagation prediction model under the vegetation environment, realizes a theoretical basis for channel simulation, and provides the basis of anti-fading technologies. [ABSTRACT FROM AUTHOR]

Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When expression data of multiple (p) genes in multiple (K) conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, with computational complexity of O(p2K log K), the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a faster alternative of CFGL named rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p2K) and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in both simulation study and real data analysis. We used RCFGL to jointly estimate the gene co-expression networks in different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL. Author summary: Inferring gene co-expression networks can be useful for understanding pathway activity and gene regulation. While jointly estimating co-expression networks of multiple conditions, taking into account condition specificity, such as information about an edge being present only in a specific condition or an edge being present across all the conditions, substantially increases the power. In this paper, a computationally rapid condition adaptive method for jointly estimating gene co-expression networks of multiple conditions is proposed. The novelty of the method is demonstrated through a broad range of simulation studies and a real data analysis with multiple brain regions from a genetically diverse cohort of rats. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to assess the efficiency of the European Union's public sector from a quality of governance approach, employing a two-step methodology. In the first stage, EU states' efficiency scores are determined using Data Envelopment Analysis. Once quantifying public efficiency, the second step of the methodology examines the determinants of efficiency using a quantile regression estimation technique, with an emphasis on demographic features, corruption, economic freedom, and governmental digitalization. The novelty is provided by the assessment of the public sector's efficiency and efficacy from a broader approach in order to determine the efficiency scores of European Union countries, as well as the key factors that may impact the public sector's performance. The main findings, namely that governance quality can be considered an important resource in analysing public performance and that human resources, freedom, democracy, corruption, and digitalization have an impact on efficiency, are important considerations not only for policymakers but also for society, researchers, and the academic community. Reform measures should strive to improve both the technical and democratic components of public institutions in order to more effectively and transparently allocate public resources, while taking into consideration local and national peculiarities., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Negri, Dincă. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

When there is an extensive number of inputs and outputs compared to the number of DMUs, one of the drawbacks of Data Envelopment Analysis appears, which incorrectly classifies inefficient DMUs, as efficient ones. Accordingly, the DEA ranking power becomes further moderated. To improve the ranking power, this paper renders the details of an algorithm that presents a model combining the Principal Component Analysis and the Slacks-Based Measure (PCA-SBM) which reduces the number of the incorrectly determined efficient DMUs. Also to complete ranking of DMUs, the algorithm presents a Super-Efficiency model integrated with PCA (PCA-Super SBM) which can rank the efficient DMUs (extreme and non-extreme). Whereas the most important previous models for ranking efficient units cannot rank non-extreme ones. Additionally, in most previous studies, DEA models combined with PCA fail to handle negative data, while, the presented models can cover this data. Two case studies (pharmaceutical companies listed on the Iranian stock market and bank branches) are manipulated to demonstrate the applicability and performance of the algorithm. To show the superiority of the presented models, the SBM model without PCA and the Super SBM model without PCA have been implemented on the data of both cases. In comparing the two methods (PCA-SBM and SBM), the PCA-SBM model has higher ranking power (five efficient DMUs versus nineteen in the case of pharmaceutical companies and four efficient DMUs versus twenty-nine in the case of bank branches). Also in comparing the PCA-Super SBM and Super SBM, the PCA-Super SBM model works more powerfully in complete ranking. As the Super SBM model cannot rank non-extreme units unlike the PCA-Super SBM. Consequently, the presented algorithm works successfully in ranking the DMUs completely (inefficient, extreme, and non-extreme efficient) with low complexity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Yazdi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper explores the concept of communities as complex adaptive systems in the context of violence against women (VAW) prevention. Using thematic network analysis on data from 80 semi-structured interviews with community members in Samoa, we found that communities exhibit many properties of complex adaptive systems. Within nested systems, diverse and dynamic agents interact based on their knowledge and attitudes, which changes over time, leading to emergent and unpredictable outcomes. The functioning of communities and their response to VAW is a product of non-linear and emerging relationships and interactions between systems components at the community level. The approach we propose for conceptualising communities as complex adaptive systems provides a structured method for designing and evaluating community-based interventions that are grounded in the local context and existing resources. With in-depth knowledge of how a community works, interventions can be better equipped to address wicked problems such as VAW. [ABSTRACT FROM AUTHOR]

We present an R-package for predictive modelling, CARRoT (Cross-validation, Accuracy, Regression, Rule of Ten). CARRoT is a tool for initial exploratory analysis of the data, which performs exhaustive search for a regression model yielding the best predictive power with heuristic 'rules of thumb' and expert knowledge as regularization parameters. It uses multiple hold-outs in order to internally validate the model. The package allows to take into account multiple factors such as collinearity of the predictors, event per variable rules (EPVs) and R-squared statistics during the model selection. In addition, other constraints, such as forcing specific terms and restricting complexity of the predictive models can be used. The package allows taking pairwise and three-way interactions between variables into account as well. These candidate models are then ranked by predictive power, which is assessed via multiple hold-out procedures and can be parallelised in order to reduce the computational time. Models which exhibited the highest average predictive power over all hold-outs are returned. This is quantified as absolute and relative error in case of continuous outcomes, accuracy and AUROC values in case of categorical outcomes. In this paper we briefly present statistical framework of the package and discuss the complexity of the underlying algorithm. Moreover, using CARRoT and a number of datasets available in R we provide comparison of different model selection techniques: based on EPVs alone, on EPVs and R-squared statistics, on lasso regression, on including only statistically significant predictors and on stepwise forward selection technique. [ABSTRACT FROM AUTHOR]

Drawing on the theory of social action in organizational and institutional sociology, this paper examines the behavioral consequences of plural logics of action. It addresses the question based on the empirical case of plural medical systems that are composed of both biomedicine and alternative medicine. Applying mixed methods of a cross-national panel data analysis and a content analysis of medical journal articles, it finds that plural systems affect health outcomes negatively when tensions between biomedicine and alternative medicine are unaddressed. In contrast, plural systems produce tangible health benefits when biomedicine and alternative medicine are coordinated through government policies or by health care organizations/professionals. This paper proposes plurality coordination as an important mechanism that modifies the behavioral consequences of plural logics. This proposition contributes to providing theoretical answers to the sociological puzzle that plural logics of action produce inconsistent behavioral consequences. [ABSTRACT FROM AUTHOR]

The funding statement for this paper is incorrect. The correct funding statement is: This study was supported by the Thammasat Postdoctoral Fellowship (grant number TUPD7/2564; https://research.tu.ac.th/tu-research-funds) and the Thailand Science Research and Innovation Fundamental Fund for the fiscal year 2023. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.By Teerawat Simmachan; Noppachai Wongsai; Sangdao Wongsai and Rattana LerdsuwansriReported by Author; Author; Author; Author [Extracted from the article]

Background: In order to make informed decisions about how best to support children and young people with disabilities, effective strategies that facilitate active and meaningful participation in school are required. Clinical factors, diagnosis or impairments somewhat helpful in determining what should be provided in interventions. However, clinical factors alone will not offer a clear view of how to support participation. It is helpful then to look at wider psychosocial and environmental factors. The aim of this review was to synthesise evidence of psychosocial and environmental factors associated with school participation of 4–12 year old children with disabilities to inform the development of participation-fostering interventions. Methods: A systematic search and synthesis using realist methods was conducted of published research. Papers had to include consideration of psychosocial and/or environment factors for school participation of children with disabilities. The review was completed in accordance with the Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) and Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Papers were identified via Boolean search of the electronic databases MEDLINE, CINAHL, PhycINFO and ERIC (January 2006-October 2018). Appraisal focussed on contributions in terms of whether the articles are appropriate for the review (relevance) and research quality (rigour). Data were analyzed using content and thematic analysis methods using a realist framework. A narrative synthesis of results was reported. Results and implications: We identified 1828 papers in the initial search. Seventy two papers were included in the final synthesis. Synthesis of findings led to three overarching mechanisms representing psychosocial factors for children (1) identity (2) competence and (3) experience of mind and body. Environmental aspects (context) compromised five interrelated areas: (1) structures and organization, (2) peers, (3) adults, (4) space and (5) objects. Our synthesis provides insights on how professionals may organize efforts to improve children’s participation. Consideration of these findings will help to proactively deal with suboptimal participation outcomes. Development of theoretically determined assessments and interventions for management of school participation are now required. [ABSTRACT FROM AUTHOR]

In this project I investigate the use and possible misuse of p values in papers published in five (high-ranked) journals in experimental psychology. I use a data set of over 135’000 p values from more than five thousand papers. I inspect (1) the way in which the p values are reported and (2) their distribution. The main findings are following: first, it appears that some authors choose the mode of reporting their results in an arbitrary way. Moreover, they often end up doing it in such a way that makes their findings seem more statistically significant than they really are (which is well known to improve the chances for publication). Specifically, they frequently report p values “just above” significance thresholds directly, whereas other values are reported by means of inequalities (e.g. “p<.1”), they round the p values down more eagerly than up and appear to choose between the significance thresholds and between one- and two-sided tests only after seeing the data. Further, about 9.2% of reported p values are inconsistent with their underlying statistics (e.g. F or t) and it appears that there are “too many” “just significant” values. One interpretation of this is that researchers tend to choose the model or include/discard observations to bring the p value to the right side of the threshold. [ABSTRACT FROM AUTHOR]

Removal models were proposed over 80 years ago as a tool to estimate unknown population size. More recently, they are used as an effective tool for management actions for the control of non desirable species, or for the evaluation of translocation management actions. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important features of the data and we discuss the shift in the required assumptions for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique. [ABSTRACT FROM AUTHOR]

Charities play an increasingly important role in helping people experiencing poverty. However, institutionalized charity shifts the burden of poverty reduction away from the state and exposes recipients to stress and stigma. In this paper, we examine whether the need for institutionalized charity can be offset through enhanced state support. As in other countries, the Australian government responded to the COVID-19 pandemic by substantially increasing the level of income support to citizens through several temporary payments. We draw on this natural experiment and time-series data from the two largest charity organizations in Queensland, Australia to examine how these payments altered the demand for institutionalized charity. We model these data using difference-in-difference regression models to approximate causal effects. By exploiting the timing and varying amounts of the payments, our analyses yield evidence that more generous income support reduces reliance on charity. Halving the demand for charity requires raising pre-pandemic income-support by AUD$42/day, with supplements of approximately AUD$18/day yielding the greatest return on investment. [ABSTRACT FROM AUTHOR]

Background: Soil-transmitted helminths affect almost 2 billion people globally. Hookworm species contribute to most of the related morbidity. Hookworms mainly cause anaemia, due to blood loss at the site of the attachment of the adult worms to the human intestinal mucosa. The World Health Organization (WHO) aims to eliminate hookworm morbidity by 2030 through achieving a prevalence of moderate and heavy intensity (M&HI) infections below 2%. In this paper, we aim to assess the suitability of this threshold to reflect hookworm-attributable morbidity. Methodology/Principal findings: We developed a hierarchical statistical model to simulate individual haemoglobin concentrations in association with hookworm burdens, accounting for low haemoglobin values attributable to other causes. The model was fitted to individual-level data within a Bayesian framework. Then, we generated different endemicity settings corresponding to infection prevalence ranging from 10% to 90% (0% to 55% M&HI prevalence), using 1, 2 or 4 Kato-Katz slides. For each scenario, we estimated the prevalence of anaemia due to hookworm. Our results showed that on average, haemoglobin falls below the WHO threshold for anaemia when intensities are above 2000 eggs per gram of faeces. For the different simulated scenarios, the estimated prevalence of anaemia attributable to hookworm ranges from 0% to 30% (95%-PI: 24% - 36%) being mainly associated to the prevalence of M&HI infections. Simulations show that a 2% prevalence of M&HI infections in adults corresponds to a prevalence of hookworm-attributable anaemia lower than 1%. Conclusions/Significance: Our results support the use of the current WHO thresholds of 2% prevalence of M&HI as a proxy for hookworm morbidity. A single Kato-Katz slide may be sufficient to assess the achievement of the morbidity target. Further studies are needed to elucidate haemoglobin dynamics pre- and post- control, ideally using longitudinal data in adults and children. Author summary: Soil-transmitted helminths affect almost 2 billion people globally. About 65% of the related morbidity is attributable to hookworm infection, especially anaemia, which is caused by adult worms attaching to the intestinal mucosa of the human host to feed on blood. The World Health Organization (WHO) defines the target for the elimination of morbidity as reaching moderate-to-heavy intensity infections prevalence < 2%. In this paper, we aim to assess the suitability of the current WHO threshold to reflect hookworm-attributable anaemia. To this end, we developed a statistical model to simulate individual haemoglobin concentrations in association with hookworm burdens in adults, accounting for low haemoglobin values attributable to other causes, such as malnutrition and malaria. We used model outcomes to estimate the prevalence of hookworm-attributable anaemia in different endemicity scenarios. Our predictions suggested that anaemia is mainly associated to moderate-to-heavy intensity infection and that the 2% threshold corresponds to a prevalence of hookworm-attributable anaemia <1%. These results support the use of the current WHO threshold as a proxy for hookworm morbidity. Our predictions represent a first step towards better quantifying the prevalence of anaemia due to hookworm infection. [ABSTRACT FROM AUTHOR]

Rising migration numbers and the resulting increase in economic and sociocultural heterogeneity in societies all over the world are theorised to put pressure on the sustainable use of common-pool resources [CPRs]. Increased heterogeneity is argued to decrease trust and diversify interests between resource users, leading to overuse and decline of natural and man-made CPRs. The aim of this paper is to understand cooperative behaviour under economic and sociocultural heterogeneity in CPRs, through the analyses of experimental data including 344 subjects from the United Kingdom and the Netherlands, and 144 subjects from India. Multilevel regression, ordinal logistic regression, linear conditional-contribution profiles [LCPs] and agent-based models [ABMs] are used to analyse and replicate experimental outcomes on the micro- and macro-level. Results show that the combination of economic and sociocultural heterogeneity affects cooperation negatively when the decision-situation is perceived as unfair, but that neither economic nor sociocultural heterogeneity on themselves affect cooperation negatively. Economic heterogeneity is even found to affect cooperation positively relative to homogeneity. Player type classification based on LCP scores shows that experimental outcomes can be interpreted with player types, and ABM simulations validate the experimental results by replicating the main outcomes. [ABSTRACT FROM AUTHOR]

Given the budgetary restrictions on scientific research and the increasing need to better inform conservation actions, it is important to identify the patterns and causes of biases in research effort. We combine bibliometric information from a literature review of almost 16,500 peer-reviewed publications on a well-known group of 286 species, the Order Carnivora, with global datasets on species' life history and ecological traits to explore patterns in research effort. Our study explores how species' characteristics influenced the degree to which they were studied (measured as the number of publications). We identified a wide variation in intensity of research effort at both Family and Species levels, with some of the least studied being those which may need protection in future. Our findings hint at the complex role of human perspectives in setting research agendas. We found that better-studied species tended to be large-bodied and have a large geographic range whilst omnivory had a negative relationship with research effort. IUCN threat status did not exhibit a strong relationship with research effort which suggests that the conservation needs of individual species are not major drivers of research interest. This work is the first to use a combination of bibliometric analysis and biological data to quantify and interpret gaps in research knowledge across an entire Order. Our results could be combined with other resources, such as Biodiversity Action Plans, to prioritise and co-ordinate future research effort, whilst our methods can be applied across many scientific disciplines to describe knowledge gaps. [ABSTRACT FROM AUTHOR]

This paper explores the determinants of carbon taxation by focusing on recent efforts to tax carbon in Washington State. The paper identifies citizen demand, energy interests, and tax structure as possible factors in the emergence of carbon taxation. Washington has relatively high demand for climate action, and is unique with respect to its high reliance on indirect taxation and its low level of local fossil fuel interests. I argue this context, especially the high reliance on indirect taxation, makes taxation more appealing than other carbon pricing alternatives. I then test whether local tax structure shapes preferences for carbon taxation using county level opinion data. I find evidence that support for carbon taxation is higher in states with higher gasoline taxes, but no effect from sales and sin taxes. I conclude summarizing various accounts of each initiative's failure, and highlight a few lessons from the efforts to tax carbon in Washington state. [ABSTRACT FROM AUTHOR]

Classical statistical analysis of data can be complemented or replaced with data analysis based on machine learning. However, in certain disciplines, such as education research, studies are frequently limited to small datasets, which raises several questions regarding biases and coincidentally positive results. In this study, we present a refined approach for evaluating the performance of a binary classification based on machine learning for small datasets. The approach includes a non-parametric permutation test as a method to quantify the probability of the results generalising to new data. Furthermore, we found that a repeated nested cross-validation is almost free of biases and yields reliable results that are only slightly dependent on chance. Considering the advantages of several evaluation metrics, we suggest a combination of more than one metric to train and evaluate machine learning classifiers. In the specific case that both classes are equally important, the Matthews correlation coefficient exhibits the lowest bias and chance for coincidentally good results. The results indicate that it is essential to avoid several biases when analysing small datasets using machine learning. [ABSTRACT FROM AUTHOR]

Background: Understanding causes and contributors to maternal mortality is critical from a quality improvement perspective to inform decision making and monitor progress toward ending preventable maternal mortality. The indicator "maternal death review coverage" is defined as the percentage of maternal deaths occurring in a facility that are audited. Both the numerator and denominator of this indicator are subject to misclassification errors, underreporting, and bias. This study assessed the validity of the indicator by examining both its numerator—the number and quality of death reviews—and denominator—the number of facility-based maternal deaths and comparing estimates of the indicator obtained from facility- versus district-level data. Methods and findings: We collected data on the number of maternal deaths and content of death reviews from all health facilities serving as birthing sites in 12 districts in three countries: Argentina, Ghana, and India. Additional data were extracted from health management information systems on the number and dates of maternal deaths and maternal death reviews reported from health facilities to the district-level. We tabulated the percentage of facility deaths with evidence of a review, the percentage of reviews that met the World Health Organization defined standard for maternal and perinatal death surveillance and response. Results were stratified by sociodemographic characteristics of women and facility location and type. We compared these estimates to that obtained using district-level data. and looked at evidence of the review at the district/provincial level. Study teams reviewed facility records at 34 facilities in Argentina, 51 facilities in Ghana, and 282 facilities in India. In total, we found 17 deaths in Argentina, 14 deaths in Ghana, and 58 deaths in India evidenced at facilities. Overall, >80% of deaths had evidence of a review at facilities. In India, a much lower percentage of deaths occurring at secondary-level facilities (61.1%) had evidence of a review compared to deaths in tertiary-level facilities (92.1%). In all three countries, only about half of deaths in each country had complete reviews: 58.8% (n = 10) in Argentina, 57.2% (n = 8) in Ghana, and 41.1% (n = 24) in India. Dramatic reductions in indicator value were seen in several subnational geographic areas, including Gonda and Meerut in India and Sunyani in Ghana. For example, in Gonda only three of the 18 reviews conducted at facilities met the definitional standard (16.7%), which caused the value of the indicator to decrease from 81.8% to 13.6%. Stratification by women's sociodemographic factors suggested systematic differences in completeness of reviews by women's age, place of residence, and timing of death. Conclusions: Our study assessed the validity of an important indicator for ending preventable deaths: the coverage of reviews of maternal deaths occurring in facilities in three study settings. We found discrepancies in deaths recorded at facilities and those reported to districts from facilities. Further, few maternal death reviews met global quality standards for completeness. The value of the calculated indicator masked inaccuracies in counts of both deaths and reviews and gave no indication of completeness, thus undermining the ultimate utility of the measure in achieving an accurate measure of coverage. [ABSTRACT FROM AUTHOR]

Background: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. Methodology/Principal Findings: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of ‘steel structure’ for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations. Conclusions/Significance: Authors’ network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations. [ABSTRACT FROM AUTHOR]

Introduction: Efficient utilisation of surgical resources is essential when providing surgical care in low-resources settings. Countries are developing plans to scale up surgery, though insufficiently based on empirical evidence. This paper investigates the determinants of hospital efficiency in district hospitals in three African countries., Methods: Three-month data, comprising surgical capacity indicators and volumes of major surgical procedures collected from 61 district-level hospitals in Malawi, Tanzania, and Zambia, were analysed. Data envelopment analysis was used to calculate average hospital efficiency scores (max. = 1) for each country. Quantile regression analysis was selected to estimate the relationship between surgical volume and production factors. Two-stage bootstrap regression analysis was used to estimate the determinants of hospital efficiency., Results: Average hospital efficiency scores were 0.77 in Tanzania, 0.70 in Malawi and 0.41 in Zambia. Hospitals with high efficiency scores had significantly more surgical staff compared with low efficiency hospitals (DEA score<1). Hospitals that scored high on the most commonly utilised surgical capacity index were not the ones with high surgical volumes or high efficiency. The number of surgical team members, which was lowest in Zambia, was strongly, positively correlated with surgical productivity and efficiency., Conclusion: Hospital efficiency, combining capacity measures and surgical outputs, is a better indicator of surgical performance than capacity measures, which could be misleading if used alone for surgical planning. Investment in the surgical workforce, in particular, is critical to improving district hospital surgical productivity and efficiency., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper, differential gene expression, and spectral shape analysis. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-distributed stochastic neighbor embedding (t-SNE). Although Mapper shows promise in analyzing high-dimensional data, tools to statistically analyze Mapper graphical structures are limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis. [ABSTRACT FROM AUTHOR]

This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp–Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features. [ABSTRACT FROM AUTHOR]

In financial time series there are time periods in which market indices values or assets prices increase or decrease monotonically. We call those events "price runs", "elementary uninterrupted trends" or just "uninterrupted trends". In this paper we study the distribution of the duration of uninterrupted trends for the daily indices DJIA, NASDAQ, IPC and Nikkei 225 during the period of time from 10/30/1978 to 08/07/2020 and we compare the simple geometric statistical model with [Formula: see text] consistent with the EMH to the empirical data. By a fitting procedure, it is found that the geometric distribution with parameter [Formula: see text] provides a good model for uninterrupted trends of short and medium duration for the more mature markets; however, longest duration events still need to be statistically characterized. Estimated values of the parameter p were also obtained and confirmed by calculating the mean value of p fluctuations from empirical data. Additionally, the observed trend duration distributions for the different studied markets are compared over time by means of the Anderson-Darling (AD) test, to the expected geometric distribution with parameter [Formula: see text] and to a geometric distribution with a free parameter p, making possible to assess and compare different market geometric behavior for different dates as well as to measure the fraction of time runs duration from studied markets are consistent with the geometric distribution with [Formula: see text] and in parametric free way., Competing Interests: The authors have declared that no competing interests exist.

This paper aims to understand the characteristics of domestic big data jobs requirements through k-means text clustering, help enterprises, and employees to identify big data talents, and promote the further development of big data-related research. Firstly, the crawler software is used to crawl the recruitment information about "big data" on the zhaopin.com recruitment website. Then, Jieba word segmentation and K-means text clustering are used to cluster big data recruitment positions, and the number of clustering was determined by the average sum of squares within the group. Finally, big data jobs are divided into 10 categories, and the urban distribution, salary level, education requirements, and experience requirements of big data jobs are discussed and analyzed from the perspectives of the overall data set and clustering results, to clarify the characteristics of big data job demands. The analysis results show that the job demands of big data are mainly distributed in first-tier cities and new first-tier cities. Enterprises are more inclined to job seekers with a college degree or bachelor's degree and more than one year's relevant experience. There are wage differences among different types of jobs. The higher the position, the higher the requirement for education and experience will be. [ABSTRACT FROM AUTHOR]

Differences in institutions have garnered much attention in recent years as one of the primary reasons for long-term economic performance, and a vast corpus of theoretical and empirical research demonstrates that financial development can have a substantial impact on economic and financial performance. In fast-growing emerging markets, the demand for financial services has considerably increased, which amplified the need for an efficient regulatory framework to deal with the sensitive nature of financial activities. Surprisingly, the existing literature on the impact of institutions and finance is focused on developed economies, while emerging markets are rarely focused on. Since emerging markets are becoming increasingly important as a result of the swift evolution of institutional and financial transformation, it is crucial to examine how the growth of financial markets reacts to institutional quality. This study investigates the role of institutions in promoting finance in emerging economies using the balanced panel dataset of 21 emerging market. Given the endogenous relationship between institutions and finance, the paper applies a two-stage least squares estimator to test the settler mortality hypothesis in this context. Aligned with theoretical intuition, the empirical result shows that settler mortality is a relevant instrument to institutions. Furthermore, the study performs robustness, using framework that deals with heterogeneity, and cross-section dependence. This empirical effort validates La Porta's Law and Finance Hypothesis in the context of emerging markets. The results of the study are found robust across the variety of alternative measures of institutions and finance, and standard error correction specifications. Therefore, the study offers useful insight into policy implications. [ABSTRACT FROM AUTHOR]

Twitter location inference methods are developed with the purpose of increasing the percentage of geotagged tweets by inferring locations on a non-geotagged dataset. For validation of proposed approaches, these location inference methods are developed on a fully geotagged dataset on which the attached Global Navigation Satellite System coordinates are used as ground truth data. Whilst a substantial number of location inference methods have been developed to date, questions arise pertaining the generalizability of the developed location inference models on a non-geotagged dataset. This paper proposes a high precision location inference method for inferring tweets' point of origin based on location mentions within the tweet text. We investigate the influence of data selection by comparing the model performance on two datasets. For the first dataset, we use a proportionate sample of tweet sources of a geotagged dataset. For the second dataset, we use a modelled distribution of tweet sources following a non-geotagged dataset. Our results showed that the distribution of tweet sources influences the performance of location inference models. Using the first dataset we outweighed state-of-the-art location extraction models by inferring 61.9%, 86.1% and 92.1% of the extracted locations within 1 km, 10 km and 50 km radius values, respectively. However, using the second dataset our precision values dropped to 45.3%, 73.1% and 81.0% for the same radius values. [ABSTRACT FROM AUTHOR]

In estimation theory, the use of auxiliary information significantly improves precision while estimating population parameters. In this paper, an efficient class of ratio cum median estimators of the population median is suggested using simple random sampling without replacement. The expressions for bias and mean square error of the proposed class are derived theoretically. The condition for the asymptotic optimum estimator is obtained with its bias and mean square error expressions. Under certain realistic conditions, the asymptotic optimum estimator is more proficient, based on analytical and numerical comparisons with some existing estimators that are members of the suggested class of estimators. The superiority of the proposed ratio cum median estimators is shown through real data applications. Such a new proposed estimator will be useful in the future for data analysis and making decisions. [ABSTRACT FROM AUTHOR]

The proliferation of Social Media and Open Web data has provided researchers with a unique opportunity to better understand human behavior at different levels. In this paper, we show how data from Open Street Map and Twitter could be analyzed and used to portray detailed Human Emotions at a city wide level in two cities, San Francisco and London. Neural Network classifiers for fine-grained emotions were developed, tested and used to detect emotions from tweets in the two cites. The detected emotions were then matched to key locations extracted from Open Street Map. Through an analysis of the resulting data set, we highlight the effect different days, locations and POI neighborhoods have on the expression of human emotions in the cities. [ABSTRACT FROM AUTHOR]