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The leaders of business are a continued focus of interest in management research and in the broader society. Their attributes speak to social mobility, inequality, and who holds positions of power and influence in society. This article examines the attributes of the ten highest-ranked executives of the largest corporate enterprises in the United States—the Fortune 100—and compares how they have changed over the past 40 years, a period when many assumptions about businesses and the people who run them have changed. While there has been significant change in some areas, such as the increase in the proportion of women and foreign-born executives and the rise in outside hiring, there is no evidence of an increase in younger leaders who advance faster than their predecessors and spend an ever-shorter time with their employer. In fact, top executives now are as old as their peers were in the 1950s, and their tenure with their employer is rising. [ABSTRACT FROM AUTHOR]

This study documented three domains of journal research quality using metrics from six major publishers and two major database services for a large sample of kinesiology-related journals: Rigor/soundness, usage, and prestige. All journal metrics were highly skewed and variable. Median acceptance rates and initial review time were consistent with rigorous peer-review. Four variables were strongly interrelated (0.849 < r < 0.963) and indicate typical usage of 1 to 3 citations for articles in these journals. The 5-year Eigenfactor Score had a relatively weaker association (0.510 < r < 0.758) with the usage metrics, supporting it as an estimate of the prestige afforded these kinesiology journals in Scopus instead of usage. Care must be taken to interpret multiple journal metrics; taking into account the skew, large variability, and confounding factors such as journal size and subject. Differences across quality domains, metrics, and bias from numerous contextual factors refute the insidious misuse of any single journal metric as quality indictor for ranking journals. [ABSTRACT FROM AUTHOR]

Identification of road crash Hotspots related to adverse weather is critical in implementing effective measures for reducing road crashes. A reliable hotspot identification method and firm criteria for evaluation are essential for accurately predicting crash risk Hotspots related to adverse weather. These steps will significantly enhance the accuracy of hotspot predictions and improve road safety efforts. This study investigates significant road safety issues related to adverse weather conditions in Jordan by testing hotspot identification methods (HSIDs) for crashes occurring under these conditions. The study applies and compares several hotspot ranking techniques including the Crash rate method (CR), the Crash frequency method (CF), the potential for improvement using predicted crashes (PI), and the Empirical Bayes method (EB), using a detailed dataset of weather-related crashes. The HSID methods are systematically tested using several statistical tests and assessed to determine the reliable approach/es for detecting hazardous sites during adverse weather conditions. The analysis encompasses urban and rural road segments, involving their operational and road characteristics and various crash characteristics. By integrating modern statistical tools, the study provides a detailed assessment of crash-identified Hotspots under adverse weather conditions and highlights major findings. The findings provide significant insights for decision-makers, emphasizing the importance of targeted initiatives and better road safety measures in identifying hotspots. Based on the findings, the study proposes some techniques for improving road safety related to poor weather Given the study's limitations, further and more comprehensive research is recommended to address these limitations and reach more conclusive results. This offers a strong foundation for decision-makers and traffic safety authorities to put into practice strategies to lower crash rates and enhance safety during inclement weather. [ABSTRACT FROM AUTHOR]

Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems. [ABSTRACT FROM AUTHOR]

We provide computationally efficient methods for unranking derangements and ménage permutations. That is, we provide a polynomial-time algorithm to extract the k th such permutation under the lexicographic ordering. More generally, we show that there exists a polynomial-time algorithm for unranking words in lexicographic order whenever there exists a polynomial-time algorithm for counting the number of such words with a given prefix. We use rook theory to give a polynomial-time algorithm for counting the number of derangements and ménage permutations with a given prefix and in turn use this to give an unranking algorithm. This has applications to combinatorics, probability, modeling, and data compression. [ABSTRACT FROM AUTHOR]

This study documented the associations and variability of four prestige metrics for kinesiology-related journals. Four prestige metrics for the year 2022 were collected from three database services for 334 kinesiology-related journals. The prestige measures were highly skewed and had large variability, with medical journals having the highest values. There were strong nonlinear and heteroscedastic associations between all four prestige indicators, however rankings of top 10% journals based on these metrics had considerable disagreement between metrics. The substantial variability of journal prestige metrics was larger than the variation previously reported for scholar subjective perceptions of journal prestige. The prestige metrics provided inconsistent and unrealistically precise estimates of kinesiology journal prestige. Great care should be exercised in interpreting prestige metrics and scholar perceptions of journal prestige in diverse disciplines like kinesiology. [ABSTRACT FROM AUTHOR]

Security and privacy have become major issues. Although no one seriously claims that privacy and security are identical, most sources treat them as if they are. Differentiating between privacy and security is essential, for example, in those situations where there is a trade-off between the two concepts, or when a regulator imposes rules concerning privacy or security or both. Moreover, differentiation can support the selection of proper means of defence. Therefore, there is a gap between the need to differentiate and the availability of an appropriate methodology to do so. This article introduces a robust methodology for differentiating between privacy and security, based on four basic components: the target of the attack, the nature of the cost, the presence of a trade-off, and the existence of consent. Since many situations involve both privacy and security, the methodology ranks their level independently. The research addresses several goals: alignment with the prevalent definitions and concepts in the literature; achieving objectivity; and a non-dichotomous classification. The application of this methodology was demonstrated in an empirical study with $n = 155$ n = 155 valid participants. The empirical study indicated significant differences between intuitive classification and methodology-based classification, thereby emphasising the need for this methodology. [ABSTRACT FROM AUTHOR]

Understanding how precipitation fluctuates geographically and temporally over a specific place due to climate change is critical. Generally, simulations of general circulation models (GCM) under different scenarios are downscaled to the local scale to study the impact of climate change on precipitation. However, selecting suitable GCMs for the given study area is one of the most hectic tasks, as the performance of GCMs may vary with respect to space and timescale. Therefore, the current study ranks twenty-seven CMIP 6 (Coupled Modelled Intercomparison Project Phase 6) GCMs in simulating precipitation over India for nine times series, including daily, monthly, yearly, and six extreme series extracted with annual maximum and peak over threshold methods. The gridded daily rainfall data provided by the India Meteorological Department (IMD) are used as the observed data. The GCMs' outputs are corrected for the systematic bias using the linear scaling method. The performance of a GCM is assessed with three statistical performance metrics, namely NSE, RMSE, and R2. The GCMs' ranks are determined using a multi-criterion decision-making technique named the modified technique of order preference by similarity to an ideal solution (mTOPSIS) for every grid point and nine timescales (i.e., daily, monthly, yearly, and six extreme series). From the results, for the entire India, the top ten recommended CMIP 6 GCMs are FGOALS-g3, HadGEM3-GC31-MM, EC-Earth3, BCC-CSM2-MR, CNRM-CM6-1-HR, CanESM5, AWI-ESM-1-1-LR, MPI-ESM-1-2-HR, IITM-ESM, and INM-CM5-0. The identified best-performing models provide insightful information for better regional climate projections and underscore the necessity of considering multiple model outputs for reliable climate change impact assessments and adaptation strategies in the region. [ABSTRACT FROM AUTHOR]

Linear latent variable models such as principal component analysis (PCA), independent component analysis (ICA), canonical correlation analysis (CCA), and factor analysis (FA) identify latent directions (or loadings) either ordered or unordered. These data are then projected onto the latent directions to obtain their projected representations (or scores). For example, PCA solvers usually rank principal directions by explaining the most variance to the least variance. In contrast, ICA solvers usually return independent directions unordered and often with single sources spread across multiple directions as multiple sub-sources, severely diminishing their usability and interpretability. This paper proposes a general framework to enhance latent space representations to improve the interpretability of linear latent spaces. Although the concepts in this paper are programming language agnostic, the framework is written in Python. This framework simplifies the process of clustering and ranking of latent vectors to enhance latent information per latent vector and the interpretation of latent vectors. Several innovative enhancements are incorporated, including latent ranking (LR), latent scaling (LS), latent clustering (LC), and latent condensing (LCON). LR ranks latent directions according to a specified scalar metric. LS scales latent directions according to a specified metric. LC automatically clusters latent directions into a specified number of clusters. Lastly, LCON automatically determines the appropriate number of clusters to condense the latent directions for a given metric to enable optimal latent discovery. Additional functionality of the framework includes single-channel and multi-channel data sources and data pre-processing strategies such as Hankelisation to seamlessly expand the applicability of linear latent variable models (LLVMs) to a wider variety of data. The effectiveness of LR, LS, LC, and LCON is shown in two foundational problems crafted with two applied latent variable models, namely, PCA and ICA. [ABSTRACT FROM AUTHOR]

We propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): f(drug) → activity. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with higher activity than any existing ones. Our new formulation for extrapolation is based on learning a bivariate function that predicts the difference in activities of two drugs F(drug1, drug2) → difference in activity, followed by the use of ranking algorithms. This formulation is general and agnostic, suitable for finding samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines , random forests , and Gradient Boosting Machines. We compared the formulation with standard regression on thousands of drug design datasets, gene expression datasets and material property datasets. The test set extrapolation metric was the identification of examples with greater values than the training set, and top-performing examples (within the top 10% of the whole dataset). On this metric our pairwise formulation vastly outperformed standard regression. Its proposed variations also showed a consistent outperformance. Its application in the stock selection problem further confirmed the advantage of this pairwise formulation. [ABSTRACT FROM AUTHOR]

The U.S. News & World Report (U.S. News) law school rankings have impacted the perceptions and behaviors of everyone in the rankings ecosystem for decades. Commentators have almost universally condemned these ordinal rankings, yet they continue to influence the legal education market, often in highly detrimental ways. The influence of these rankings stems from legitimate market demands, for reasons that the psychology of choice literature makes clear. People want (or need) to efficiently acquire and digest information that could help them make consequential decisions. At a time when consumers of law school information did not have such choice-making assistance, U.S. News filled the void. Whether purportedly relevant information comes from U.S. News, different rankers of law schools, or other sources of information, the fundamental problem is that these sources are filling a void that exists because of law schools' inaction. The law school community is the only cohort of entities that can create data about certain relevant considerations that are currently missing, and they are best positioned to both refine the data categories that currently exist and communicate the potential significance of the generated data. Without a more comprehensive product, law school data consumers will continue to rely on incomplete and potentially misleading information, arbitrarily reduced to a single composite score and ordinal rank by commercial enterprises like U.S. News. This Article provides a blueprint for how the law school community can create an alternative product and reclaim legal education. Such a product would add considerable value to all law school data consumers, including prospective students, law professors, law school administrators, and legal employers. This is laudable in its own right, but such a product would also diminish the influence of rankers like U.S. News because its value would make the deficiencies of the current rankings too obvious for the market to ignore. [ABSTRACT FROM AUTHOR]

This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients. [ABSTRACT FROM AUTHOR]

In safety‐critical systems engineering, regulations such as Automotive SPICE, ISO26262, or ED‐109A mandate software quality assurance measures to provide evidence that the developed system is high quality. The constraints that define quality assurance conditions during the engineering life cycle are often non‐trivial. This paper addresses the challenges, engineers face who are unfamiliar with the precise constraints of various projects (e.g., when newly joining a company or switching between departments). Understanding how to fulfill a constraint is a time‐consuming and challenging task as an engineer needs to determine the most suitable option (out of potentially many) to fulfill a constraint violation. To this end, we propose a guidance action ranking framework to provide engineers with the most relevant guidance actions. Our primary ranking algorithm analyzes in the background the actions that engineers have made in the past to resolve a constraint violation without requiring explicit feedback from them. We evaluated our framework on two real‐world data sets: an open‐source drone management and an industrial air traffic control software system. Concretely, we replay past engineering activities and measured whether, in the case of a constraint violation, our suggested guidance actions were indeed selected by the engineer. The evaluation results revealed that learning from prior guidance actions effectively identifies the most appropriate guidance actions (ranked top 1 or 2) when compared to ranking algorithms based on action simplicity and artifact property change frequency. Specifically, we achieve a median MRR of 0.95 for the first case study and 0.94 for the second case study: an improvement of 80% and 100% over the baseline. Additionally, we observed that the simplicity of a guidance action does not reliably indicate its suitability for fulfilling a constraint, whereas learning from prior change operation property out‐performed simplicity‐based ranking but did not surpass guidance frequency‐based ranking. [ABSTRACT FROM AUTHOR]

The handling of missing attribute values remains a challenging and problematic issue in data analysis. Imputation techniques are key procedures used to deal with missing attribute values. However, although these methods are widely used, they cause data bias. Rough set theory, a unique mathematical tool for decision making under uncertainty, overcomes this problem by properly adjusting the relationships. Rough sets are often preferred in both classification and sorting problems. The aim of sorting problems is to sort the objects in the decision table (DT) from best to worst and/or to select the best one. For this purpose, it is necessary to obtain a pairwise comparison table (PCT) from the DT. However, in the presence of missing values, the transformation from DT to PCT is not feasible because there are no ranking methods in the literature for sorting problems based on rough sets. To address this limitation, this paper presents a way to transform from DT to PCT and introduces a generalization of the relation belonging to the "do not care" type of missing values in the dominance-based rough set approach (DRSA) to the decision support tool jRank. We also adapted the DomLem algorithm to enable it to work in PCT with missing values. We applied our method step by step to a decision table with 11 objects and investigated the effect of missing values. The experimental results showed that our proposed approach captures the semantics of 'do not care' type missing values. [ABSTRACT FROM AUTHOR]

The 21st century has been accompanied by significant global changes that confound traditional diplomacy. This article focuses on modern diplomacy and the four main challenge areas it faces. These challenges necessitate diplomatic adaptation to remain relevant and effective. To achieve this modernization, the author suggests the implementation of a newly created hierarchy for the selection, training, deployment, ranking, and promotion of a more specialized diplomatic corps. This process is envisioned to occur within four streams of specialization, each with its own type of expert diplomat. This new diplomatic tool is called the Specialist Diplomatic Hierarchy (SDH). [ABSTRACT FROM AUTHOR]

Summary: The dynamic ranking, due to its increasing importance in many applications, is becoming crucial, especially with the collection of voluminous time‐dependent data. One such application is sports statistics, where dynamic ranking aids in forecasting the performance of competitive teams, drawing on historical and current data. Despite its usefulness, predicting and inferring rankings pose challenges in environments necessitating time‐dependent modelling. This paper introduces a spectral ranker called Kernel Rank Centrality, designed to rank items based on pairwise comparisons over time. The ranker operates via kernel smoothing in the Bradley–Terry model, utilising a Markov chain model. Unlike the maximum likelihood approach, the spectral ranker is nonparametric, demands fewer model assumptions and computations and allows for real‐time ranking. We establish the asymptotic distribution of the ranker by applying an innovative group inverse technique, resulting in a uniform and precise entrywise expansion. This result allows us to devise a new inferential method for predictive inference, previously unavailable in existing approaches. Our numerical examples showcase the ranker's utility in predictive accuracy and constructing an uncertainty measure for prediction, leveraging data from the National Basketball Association (NBA). The results underscore our method's potential compared with the gold standard in sports, the Arpad Elo rating system. [ABSTRACT FROM AUTHOR]

Faculty productivity and influence constitute an important aspect of doctoral program quality. Literature on faculty productivity of Criminology and Criminal Justice (CCJ) Ph.D. programs has not yet properly weighted the impact of journal publications in a standardized and objective manner, limiting their analyses to select CCJ journals or the CCJ discipline. This is problematic because CCJ is an interdisciplinary field, and many criminologists publish in highly influential non-CCJ journals. While past assessments have largely neglected citation-based data, the present study analyzes scholarly productivity for the years 2015 to 2021 using a more comprehensive and inclusive method for assessing influence. Based on this study's productivity measure, the top programs are Florida State University, Pennsylvania State University, Arizona State University, the University of Pennsylvania, and the University of Cincinnati. Findings are situated with prior research and the latest U.S News and World Report rankings. [ABSTRACT FROM AUTHOR]

Unlike prior studies that have examined the research productivity to develop various rank-order metrics of particular scholars or institutions, we seek to determine ways in which criminology and criminal justice faculty at the top 21 doctoral programs publish their research. To account for a potential diversity in faculty publication productivity, our study considers various outlets for faculty publications—journal articles, books, and book chapters—and develops composite profiles of publication productivity. Our intention is not to rank faculty and determine academic stars, but to distinguish groups of faculty similar in their patterns of faculty productivity. We analyze the publications of over 90% of all tenured and tenure-track faculty employed by the top 21 U.S. doctoral programs in criminology and criminal justice. Our latent profile analysis yielded five distinct profiles of productivity among the faculty in the top doctoral programs. Our study reaffirms the need for the use of multidimensional measures of publication productivity. [ABSTRACT FROM AUTHOR]

The Arkansas Department of Transportation (ARDOT) Maintenance Division is concerned about the use of certain aggregates in asphalt mixture because of their durability and performance issues. Good performance of the pavements can be ensured by selecting good-quality aggregates along with asphalt binders that can produce compatible aggregate–binder systems. The primary goal of this research was to determine proper asphalt binders and aggregates for the construction of highways in Arkansas. Three performance grade (PG) asphalt binders, namely PG 64-22, PG 70-22, and PG 76-22, prepared from two different crude sources, were investigated. This study also included four types of ARDOT-commissioned aggregates, namely gravel, sandstone, limestone, and dolomite, from four quarries throughout Arkansas. Selected properties of asphalt binders and aggregates (e.g., physical, mechanical, and chemical) were evaluated by performing a series of routine laboratory tests. To evaluate the performance of the asphalt mixtures, work of cohesion, work of adhesion, and boiling resistance tests were conducted. These aggregates, binders, and aggregate–binder systems were ranked based on the properties obtained from laboratory tests. In general, any combination of dolomite or limestone with a stiffer binder such as PG 76-22 or PG 70-22 is expected to be superior to an asphalt mixture with either sandstone or gravel. The outcomes of this research will help highway agencies and pavement professionals in selecting suitable asphalt binders and aggregates for long-lasting pavement construction. [ABSTRACT FROM AUTHOR]

The article focuses on enhancing contextual ranking models by proposing data augmentation methods to improve ranking performance effectively and robustly. It mentions by utilizing supervised and unsupervised augmentation schemes, along with contrastive losses adapted for ranking tasks, the study demonstrates significant performance improvements, particularly in sample efficiency and robustness across in-domain and out-of-domain benchmarks.

The article focuses on addressing the inefficiency of dual-encoder-based dense retrieval models in information retrieval (IR) by proposing Fast-Forward indexes, which leverage semantic matching capabilities for efficient re-ranking. By enhancing computational efficiency and optimizing memory footprint, Fast-Forward indexes achieve low latency and competitive results in IR tasks without requiring hardware acceleration like GPUs.

The circular economy is important in implementing EU Green Deal targets, climate change mitigation commitments, and pursuing sustainable development. A circular economy allows the reduction of waste and environmental damages linked to waste to save natural resources and mitigate climate change. The transition from linear to circular economy models provides many economic and environmental benefits and is a priority for most countries, especially developed ones. There is a need to measure and monitor the circular economy's progress. In this context, indicators can be useful tools to assess progress achieved by countries in pursuing circular economy development. The paper developed an indicators framework to measure circular economy development progress in the EU and applies the developed framework for Visegrad countries. The Visegrad Group (V4) includes four Central European countries, Poland, the Czech Republic, Slovakia, and Hungary, linked by similar geographical and geopolitical situations and common history and culture. MCDM tool ARAS was applied to ranking four Visegrad countries in 2010 and 2021 based on the progress achieved in circular economy development. [ABSTRACT FROM AUTHOR]

Adopting the event study method, this paper examines how the stock market reacts to the public release of environmental performance rankings of heavy polluters. Specifically, we explore the impact of the public release of the 100 best and 100 worst companies in terms of environmental performance on the stock prices of companies involved, and the potential moderation of ultimate controllers. We find that the stock prices of the companies on the lists were negatively affected by the event. However, stock price changes are not significantly related to the relative rankings on the list. Furthermore, companies whose ultimate controllers possess a larger control–ownership wedge experienced a less severe fall in stock prices than did their counterparts. This study sheds light on the nuances of the financial implications of negative environmental publicity. [ABSTRACT FROM AUTHOR]

The number of mobile applications ("Apps") has grown significantly in recent years. App Stores rank/recommend Apps based on factors such as average star ratings and the number of installs. Such rankings do not focus on the internal artifacts of Apps (e.g., security vulnerabilities). If internal artifacts are ignored, users may fail to estimate the potential risks associated with installing Apps. In this research, we present a framework called E-SERS (Enhanced Security-related and Evidence-based Ranking Scheme) for comparing Android Apps that offer similar functionalities. E-SERS uses internal and external artifacts of Apps in the ranking process. E-SERS is a significant enhancement of our past evidence-based ranking framework called SERS. We have evaluated E-SERS on publicly accessible Apps from the Google Play Store and compared our rankings with prevalent ranking techniques. Our experiments demonstrate that E-SERS, leveraging its holistic approach, excels in identifying malicious Apps and consistently outperforms existing alternatives in ranking accuracy. By emphasizing comprehensive assessment, E-SERS empowers users, particularly those less experienced with technology, to make informed decisions and avoid potentially harmful Apps. This contribution addresses a critical gap in current App-ranking methodologies, enhancing the safety and security of today's technologically dependent society. [ABSTRACT FROM AUTHOR]

Apples are a healthy and environmentally friendly snack, but the consumption of apples in many countries, including Norway, has decreased in the last fifteen years. This trend has a potential negative impact on public health and the environment. In this paper, we use a consumer survey and a random effect ordered logistic regression model to find out what is most important for people when they buy apples. Ten different values are considered and ranked according to individual's importance. We find that taste and safety are the most important values for the average individual's choice of apples, while the appearance and type of apple rank third. The least important values are wrapping size and regional origin. Everyday apple eaters are more concerned about pesticide use and less concerned about price than the average consumers. Furthermore, compared to the average consumer, those with low levels of trust in apple producers and authorities care more about pesticides and environmentally friendly production methods, while consumers with a high level of trust care less about these aspects. Our results indicate that to increase the consumption of Norwegian apples, it is important to maintain or improve their reputation as being tasty and safe to eat. [ABSTRACT FROM AUTHOR]

Smart cities and information and communications technology is a rapidly growing field in both research and real-world implementation, but it is one that is still new and with many different ideas. Unfortunately, there is less cooperation and knowledge sharing across the field, and research often fails to move into real-world applications, which holds it back from becoming fully realized. This paper aims to provide an overview of the current state of smart cities, its definitions, technologies, and technical dimensions, from architectural design to standards and data handling, and how they are handled in the real world and its impact on society. Additionally, it examines important smart city projects, their applications, and ranking systems. This text aims to forecast the future of the field, its impact, the challenges it faces, and what should be addressed to help it reach its full potential. [ABSTRACT FROM AUTHOR]

A social planner wants to rank people. Utilitarianism implies that a natural ranking which orders people from most to least sensitive to their rank is the best ranking. But people's feelings toward the social planner, like blame or gratitude, can depend both on their assigned rank, and on the alternatives they might have been assigned. As a result, the social planner needs to do pairwise comparisons and there may not be a best ranking due to intransitive cycles. This paper shows the possibility of the coexistence of a best choice and non-transitivity. It illustrates how to tell when a best ranking exists, and that when it exists, it is indeed the natural ranking. When this best does not exist, an alternative second-best group ranking strategy is proposed. [ABSTRACT FROM AUTHOR]

Since independence is the most important characteristic of an auditor, we want to expand and develop this characteristic in auditing institutions, so our goal is to provide a practical and practical solution to maintain independence in auditing institutions. We collected and summarized. The experts stated that out of 34 characteristics of auditor independence, except 8 cases are applicable in audit institutions. If these features are not collected in one place and are dispersed and individually under the title of auditor independence, we will rarely see the independence of audit institutions. Currently, the only place where the characteristics of independence can be summarized and considered under a practical and operational model is in the ranking of audit institutions, which is done by the public accountants community. If these characteristics and criteria are taken into account and used in a practical way in the ranking of auditing institutions, we can increase and maintain the independence of auditing institutions. The ranking of auditing institutions can be more appropriate by considering the independence characteristics and bringing fair distribution of work. [ABSTRACT FROM AUTHOR]

We argue for the application of bibliometric indices to quantify the long-term uncertainty of outcome in sports. The Euclidean index is proposed to reward quality over quantity, while the rectangle index can be an appropriate measure of core performance. Their differences are highlighted through an axiomatic analysis and several examples. Our approach also requires a weighting scheme to compare different achievements. The methodology is illustrated by studying the knockout stage of the UEFA Champions League in the 20 seasons played between 2003 and 2023: club and country performances as well as three types of competitive balance are considered. Measuring competition at the level of national associations is a novelty. All results are remarkably robust concerning the bibliometric index and the assigned weights. Since the performances of national associations are more stable than the results of individual clubs, it would be better to build the seeding in the UEFA Champions League group stage upon association coefficients adjusted for league finishing positions rather than club coefficients. [ABSTRACT FROM AUTHOR]

Goodhart's Law describes the phenomenon where a measure becomes a target, leading to behaviors aimed at optimizing that measure rather than achieving the true underlying goals. In the context of university rankings, excessive focus on attaining higher ranks can incentivize universities to manipulate metrics rather than genuinely improving educational quality. Analyzing this behavioral shift, research highlights the negative impacts of Goodhart's Law in higher education, particularly in Vietnam. Instead of enhancing teaching and research quality, universities often prioritize ranking advancement through opaque methods. This competitive race results in high costs for students and families without commensurate educational benefits. Addressing these issues requires raising awareness, establishing new evaluation approaches, and promoting genuine educational practices.

Objective: To rank diagnostic features of childhood pulmonary tuberculosis; and to determine the effect of working in tuberculosis Directly Observed Treatment Short Course (DOTS) facilities on the ranking of these features by medical doctors. Methods: A cross sectional descriptive study, using structured questionnaires to collect data from medical doctors whose daily routine included attending to sick children in 34 selected children outpatient clinics and TB DOTS centers in southeastern Nigeria. Results: Approximately, one quarter (25.3% or 56 of 221) of respondents worked in Directly Observed Treatment Short course (DOTS) clinics, while three quarters (74.7% or 165 of 221) worked in nonDOTSclinics. Majority of the respondents (69.7%) ranked chronic persistent cough (1), 42.5 % ranked weight loss and failure to thrive (2), another 27.7% ranked weight loss and failure to thrive (3), while 17.6% and 21.7% ranked History of contact with adult index case and radiographic abnormalities, (4) and (5), respectively. Conclusions: The study found that the percentage of doctors working in DOTS clinics who ranked weight loss and failure to thrive (2) was statistically and significantly higher than those of non-DOTS respondents. The most important symptoms/signs on which medical doctors based their diagnosis of childhood pulmonary tuberculosis include cough, weight loss and failure to thrive, history of contact with adult with smear positive pulmonary tuberculosis, and r adiogr aphicabnormalities consistent with active tuberculosis. There was statistically significant difference between the ranking of weight loss and failure to thrive by doctors working in DOTS clinics and their counterparts in non DOTS clinics. This study showed a decline in the percentage of ranking in both DOTS and Non DOTS respondents as they moved from the first to the fifth.

Water distribution networks (WDNs) are critical infrastructures prone to vulnerabilities which lead to failures. Identifying vulnerable components, especially multiple pipe failure combinations, is crucial for effective management and ensuring high reliability. Hydraulic simulations are commonly used for analysing the criticality of WDN, but are time-consuming and highly data-reliant, limiting the number of testable combinations. To address these limitations and constraints, a graph-based method is proposed to quantify the impact magnitude of multiple pipe failure scenarios on performance, enabling the identification of critical combinations. The proposed graph-based approach utilizes structural and topological characteristics of WDNs as well as spatial demand distribution to replicate hydraulic behaviour. The accuracy of the approach is assessed by testing it on three case studies with various pipe failure combinations, and the results are compared with hydraulic analyses. The results demonstrate a strong correlation (Spearman coefficient > 0.75) between graph-based ranking and state-of-the-art hydraulic-based ranking. Additionally, the method exhibits a significant computational gain factor of greater than 30 compared with the hydraulic-based method, rendering it valuable for actively exploring a wide range of critical pipe failure combinations and devising countermeasures. Furthermore, a hybrid-based method that integrates both the graph and hydraulic-based methods is proposed for enhanced accuracy and robust assessments. HIGHLIGHTS An innovative graph-based ranking method for fast and accurate pipe criticality analysis is developed.; The results show a high correlation with the hydraulic-based ranking method with a computational gain factor of more than 30.; A hybrid-based ranking method is presented, combining the advantages of two approaches to address the limitations of the graph-based ranking method and robust assessment of the impact.;

Objetivo: Analisar o desempenho dos municípios brasileiros nas dimensões do Ranking da Qualidade da Informação Contábil e Fiscal no Siconfi (Ranking) e avaliar a percepção dos profissionais contábeis sobre o tema. Metodologia: Para analisar o desempenho dos municípios nas dimensões do Ranking, foi efetuada coleta de dados no sítio da Secretaria do Tesouro Nacional (https://www.tesourotransparente.gov.br/visualizacao/Ranking-da-qualidade-da-informacao-contabil-e-fiscal-estadual) relativos aos exercícios de 2020 a 2023. Para avaliar a percepção dos profissionais contábeis municipais sobre o tema, foi aplicado questionário estruturado com quatro perguntas, elaboradas com o objetivo de capturar a percepção desses profissionais sobre a existência do Ranking, o conhecimento deles quanto aos dados relativos aos municípios em que trabalhavam e eventuais medidas tomadas pelo município para melhorar a nota atribuída pela STN. Os Municípios foram contatados pela equipe de Call Center da Confederação Nacional de Municípios, devidamente instruída pela equipe de pesquisa. Resultados: A pesquisa mostra que a Dimensão IV (que compara a Declaração de Contas Anuais, Matriz de Saldos Contábeis e relatórios da Lei de Responsabilidade Fiscal) apresentou o menor percentual de acertos no triênio, revelando problemas na consistência dos dados quando as informações entre relatórios diferentes são comparadas. Com a realização do questionário foi colhido como achado que 40% dos respondentes desconheciam o Ranking e mesmo entre os que não o conhecia 46% não realizaram ações para melhorar a colocação do município no Ranking, revelando o desinteresse dos profissionais ou dos gestores municipais sobre a sua importância no contexto de indicadores da qualidade da informação contábil. Contribuições do Estudo: Este artigo indica a necessidade de ampliar as pesquisas empíricas sobre a qualidade dos dados contábeis municipais, buscando aperfeiçoar as questões apresentadas no questionário realizado, incluindo, por exemplo, itens que avaliam a melhoria no sistema contábil.

Abstract This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

Existing research suggests that elite clubs exist in venture capital markets, but a standard for determining their size and composition is lacking. This paper addresses this challenge by using the weighted k-means sorting algorithm to construct a research framework for elite clubs. Validating the framework with investment events data from China’s venture capital market (2001–2018), intriguing findings emerge. The ranking of Venture Capitalists (VCs) follows a power-law distribution, providing evidence for elite clubs’ existence. The analysis identifies a turning point in the score curve, serving as a valuable indicator for club boundaries. Elite clubs demonstrate relatively high stability, maintaining advantages and elite status in future competitions. Empirical validation confirms the proposed framework’s superior stability compared to existing methods. Importantly, elite club members outperform non-elites significantly. This paper effectively identifies elite clubs in the Chinese venture capital market, helping other VCs recognize potential partners, access high-quality information, and enhance investment performance.

Community based question and answer websites; such as, Yahoo Answers, Stack Overflow, Stack Exchange and Quora are briskly generating content in the form of questions and answers posted by users. These online discussion forums allow the users to append and edit the content easily. Therefore, a huge amount of content has accumulated over the time, which is beneficial for the search engine users for addressing their queries to the related content. On the other hand, the task of maintaining the answer quality and ranking of retrieved results has always been a challenging task for the websites that accumulate user's generated data. Here, the problem of ranking the answers based on the user's satisfaction is investigated. The best answers based on their ranking are identified. Three techniques are proposed, which are Relevance Features (REL_FS), Readability Features (RED_FS), and Weighted Answer Rank WEIGHTED ANS_RANK. REL_FS performs a summation of relevance features, RES_FS sums the readability features and WEIGHTED ANS_RANK performs a summation of REL_FS and RED_FS to deal with the task. Proposed methods compute the rank of answers according to both the relevance and readability of answers. Experimental results show proposed methods ability of providing effective ranking of answers for the selection of best answers. Moreover, it also measures the consistency of ranked answers with the help of a detailed correlational analysis. [ABSTRACT FROM AUTHOR]

We investigate three related problems concerning sampling minorities in attributed networks. This is guided by a general attributed network model which can incorporate several levels of homophily and heterophily, and whose degree and Page-rank distributions have known properties. The first problem investigates sampling schemes that favor the representation of the minority over majority nodes and give preference to "more popular" minority nodes (i.e. higher degree/Page-rank) for a given homophily scenario. We show that (in-)degree and Page-rank sampling schemes increase the probability of sampling a minority node. The second problem concerns the relative ranking of minorities compared to majorities in degree and Page-rank based sampling schemes for several homophily and heterophily scenarios. We provide analytical conditions for the minority nodes to rank higher as a function of the model parameters for the degree based samplings and investigate the problem numerically for Page-rank based sampling schemes. The third problem considers subgraph sampling schemes and the bias of the proportion of minority nodes in top ranked degree nodes in several homophily and heterophily scenarios. Finally, the results and findings obtained from the sampling analysis are assessed on real-world networks. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the modification of mechanical, rheological, and sensory properties of chickpea pastes and gels by incorporating other ingredients (olive oil or quinoa flour), to develop plant‐based alternatives that meet consumer demands for healthy, natural, and enjoyable food products. The pastes and gels were made with different amounts of chickpea flour (9% and 12%, respectively). For each product, a first set of products with different oil content and a second set with quinoa flour (either added or replaced) were produced. The viscoelastic properties of the pastes and the mechanical properties of the gels were measured. Sensory evaluation and preference assessment were carried out with 100 participants using ranking tests. The study found remarkable differences in rheological, mechanical, and sensory properties of chickpea products upon the inclusion of oil and quinoa flour. The addition of oil increased the viscosity and decreased the elastic contribution to the viscoelasticity of the pastes, while it improved the firmness and plasticity in gels. It also increased the creaminess and preference of both pastes and gels. Replacing chickpea with quinoa flour resulted in less viscous pastes and gels with less firmness and more plasticity. In terms of sensory properties, the use of quinoa as a replacement ingredient resulted in less lumpiness in the chickpea paste and less consistency and more creaminess in both the pastes and gels, which had a positive effect on preference. The addition of quinoa increased the viscosity of pastes and the firmness and stiffness of gels. It increased the consistency and creaminess of both pastes and gels. Quinoa flour and/or olive oil are suitable ingredients in the formulation of chickpea‐based products. They contribute to the structure of the system, providing different textural properties that improve acceptance. [ABSTRACT FROM AUTHOR]

The security of the global Certification Authority (CA) system has recently been compromised as a result of attacks on the Public Key Infrastructure (PKI). Although the CA/Browser (CA/B) Forum publishes compliance requirements for CAs, there are no guarantees that even a commercially successful CA is complying with these recommendations. In this paper, we propose the first systematic CA ranking mechanism that ranks CAs in terms of their adherence to the CA/B Forum and X.509 certificate standards. Unfortunately, there is no consolidated and widely accepted parameter to rank the CAs so we have proposed formula-based rating models and introduced different ranking techniques like Direct, Bayesian, and MarkovChain Ranking. These rankings are applied to a comprehensive dataset of X.509 trust chains gathered during the time period of 2020 to 2023. Our proposed ranking scheme can serve as a criterion for both consumers and enterprises for selecting and prioritizing CAs based on performance as well as adherence to the certificate standards. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was responsible for the cancellation of both the men's and women's 2020 National Collegiate Athletic Association (NCAA) Division I basketball tournaments. Starting from the point when the Division I tournaments and unfinished conference tournaments were canceled, we deliver closed-form probabilities for each team of making the Division I tournaments, had they not been canceled, under a simplified method for tournament selection. We also determine probabilities of a team winning March Madness, given a tournament bracket. Our calculations make use of conformal win probabilities derived from conformal predictive distributions. We compare these conformal win probabilities to those generated through linear and logistic regression on college basketball data spanning the 2011–2012 and 2022–2023 seasons, as well as to other publicly available win probability methods. Conformal win probabilities are shown to be well calibrated, while requiring fewer distributional assumptions than most alternative methods. [ABSTRACT FROM AUTHOR]

Purpose: Analyze the performance of Brazilian municipalities in the dimensions of the Accounting and Fiscal Information Quality Ranking in Siconfi (Ranking) and evaluate the perception of accounting professionals on the topic. Methodology: To analyze the performance of municipalities in the Ranking dimensions, data was collected on the website of the National Treasury Secretariat (https://www.tesourotransparente.gov.br/visualizacao/Ranking-da-qualidade-da-informacaocontabil-e-fiscal-state) relating to the years 2020 to 2023. To evaluate the perception of municipal accounting professionals on the topic, a structured questionnaire was applied with four questions, designed with the aim of capturing the perception of these professionals about the existence of the Ranking, the their knowledge of data relating to the municipalities in which they worked and any measures taken by the municipality to improve the score given by the STN. The Municipalities were contacted by the Call Center team of the National Confederation of Municipalities, duly instructed by the research team. Results: The research shows that Dimension IV (which compares the Declaration of Annual Accounts, the Accounting Balances Matrix and reports from the Fiscal Responsibility Law) presented the lowest percentage of correct answers in the three-year period, revealing problems in the consistency of this data when the information between different reports are compared. With the completion of the questionnaire, it was found that 40% of respondents were unaware of the Ranking and even among those who did not know it, 46% did not take actions to improve the municipality's placement in the Ranking, revealing the lack of interest of professionals or municipal managers about its importance in the context of accounting information quality indicators. Contributions of the Study: This article indicates the need to expand empirical research on the quality of municipal accounting data, seeking to improve the questions presented in the questionnaire, for example, by adding items that evaluate the improvement in the accounting system. [ABSTRACT FROM AUTHOR]

The relation between objectivity and subjectivity has been explored for many centuries in many aspects of fields. Most of the approaches used to study their relationship focus on the qualitative research, which is very difficult to resolve a decision problem regarding the closeness of their relation. In this article, we devise a non-parametric statistical mechanism to resolve such decision problem. The mechanism utilises the notion of granular knowledge which is regarded as the product of subjective perception toward objective notions. To measure the degree of conversion, we associate the perception with three metrics, which in turn derive three norms. These norms would measure the degree of the conversion from objectivity to subjectivity. Then by taking the set of Bell partitions as our sample space, and three norms as our statistics, we could then construct their non-parametric distributions. Based on these distributions, we could then conduct the statistical testing whether the subjectivity of a given notion (or target) is significantly related to the objectivity of that notion (or target). [ABSTRACT FROM AUTHOR]

In this present work, a new approach for ranking generalized trapezoidal fuzzy numbers has been introduced. This new proposed method is based on a left and right deviation and fuzzyness measure of the generalized trapezoidal fuzzy numbers. Some propositions have been introduced corresponding to the new proposed method. The proposed method has been compared with the different existing methods of ranking generalized trapezoidal fuzzy numbers. Also, it has been shown that the proposed technique succeeds in overcoming the drawbacks of the existing methods. The proposed method has been applied to a risk analysis problem in the selection of a production house by a retailer. [ABSTRACT FROM AUTHOR]

In this work, we systematize several implementations of the Gray code over an alphabet with m ≥ 2 elements, which we present in C code so that they can be used directly after copying from the text. We consider two variants—reflected and modular (or shifted) m-ary Gray codes. For both variants, we present the ranking and unranking functions, as well as algorithms for generating only a part of the code, more precisely the codewords between two given vectors. Finally, we give algorithms that generate a maximal set of non-proportional vectors of length n over the given alphabet in a Gray code. [ABSTRACT FROM AUTHOR]

This paper shows how the leading Higher Education Institutions (HEIs) in the UI GreenMetric World University Ranking communicate their environmental sustainability policies through their websites. Specifically, this paper analyses the presence of sustainability‐related sustainable development goals on the websites of the top 100 universities in the UI GreenMetric World University Ranking (2021). In the area of university social responsibility, sustainability is less commonly communicated, hence the need to delve deeper into the presence of these messages, carrying out a descriptive study using content analysis as a form of data extraction. The conclusion is that neither position in the ranking nor geographic area are decisive for developing better communication about sustainability. In terms of content, sustainability features on the websites are in three key dimensions of the communication of HEIs worldwide (Climate change, Efficiency of sustainable cities and communities and Striving for peace and justice). Thus, the areas for improvement are around the positioning of this sustainability‐related content on their home pages, improving location and format (especially video). Moreover, reinforcing reputational messages, through plans or programmes, as well as improving engagement working on the multi‐directionality to other social media, is required. [ABSTRACT FROM AUTHOR]