Search

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]

Our study analyzes the factors associated with the faculty influence. We explore Google Scholar citation counts of 226 faculty at the top 20 doctoral programs in criminology and criminal justice. Our multivariate models show that some of the publication-related factors matter; the number of journal articles, proportion of articles published in prestigious journals, and proportion of books published with prestigious publishers are all positively related to citation counts, while the proportion of solo-authored articles and the number of textbooks published are both negatively related to the citation counts. Among demographic factors, faculty rank, length of years since the Ph.D., and race are related to citation counts, while gender is not. Finally, institutional prestige is positively related to citation counts, while the field of specialization is not. [ABSTRACT FROM AUTHOR]

Patient expectations for orthopedic surgeries, and elective shoulder surgery in particular, have been shown to be important for patient outcomes and satisfaction. Current surveys assessing patient expectations lack clinical applicability and allow patients to list multiple expectations at the highest level of importance. The purpose of this study was to develop and evaluate the use of a novel, rank-based survey assessing the relative importance of patient expectations for shoulder surgery. The Preoperative Rank of Expectations for Shoulder Surgery (PRESS) survey was developed by polling 100 patients regarding their expectations for surgery. The PRESS survey consisted of 8 common expectations for elective shoulder surgery by importance and a 0%-100% scale of expected pain relief and range of motion improvement. After initial development of the PRESS survey, it was administered preoperatively to 316 patients undergoing surgery for shoulder arthritis, rotator cuff tear, subacromial pain syndrome, or glenohumeral instability between August 2020 and April 2021. Patients also completed preoperative outcome measures such as the American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form, Patient-Reported Outcomes Measurement Information System (PROMIS) Upper Extremity Physical Function 7a version 1.0 (PROMIS PF), and PROMIS Pain Interference short form 8a version 2.0 (PROMIS PI) surveys. Patient-reported outcome measure (PROM) surveys were administered 6 months postoperatively. Improvement in range of motion was the expectation most often ranked first for the entire study group (18%), arthritis subgroup (23%), and rotator cuff tear subgroup (19%). Subacromial pain syndrome patients most often ranked improving ability to complete activities of daily living and relieving daytime pain first (19%). Shoulder instability patients most often ranked improving ability to participate in sports first (31%). Patients who ranked improving range of motion or sports highly had better PROMs. Those who ranked relieving pain highly had worse PROMs. Patients with high (>90%) expectations of pain relief had better PROMIS PI scores. Patients with high pain relief expectations in the arthritis and subacromial pain syndrome groups had better PROMs, whereas patients with instability were less satisfied. The novel PRESS survey assesses patient expectations for shoulder surgery in a new, more clinically applicable rank-based format. The responses provided by patients provide actionable information to clinicians and are related to postoperative outcomes. Therefore, the PRESS survey represents a useful tool for guiding discussions between patients and surgeons, as well as aiding in overall patient-centered clinical decision making. [ABSTRACT FROM AUTHOR]

Software is developed using programming languages whose choice is made based on a wide range of criteria, but it should be noted that the programming language selected can affect the quality of the software product. In this paper, we focus on analysing the differences in energy consumption when running certain algorithms that have been developed using different programming languages. Therefore, we focus on the software quality from the perspective of greenability, in concrete in the aspects related to energy efficiency. For this purpose, this study has conducted an empirical investigation about the most suitable programming languages from an energy efficiency perspective using a hardware-based consumption measurement instrument that obtains real data about energy consumption. The study builds upon a previous study in which energy efficiency of PL were ranked using a software-based approach where the energy consumption is an estimation. As a result, no significant differences are obtained between two approaches, in terms of ranking the PL. However, if it is required to have a more realistic knowledge of consumption, it is necessary to use hardware approaches. Furthermore, the hardware approach provides information about the energy consumption of specific DUT hardware components, such as, HDD, graphics card, and processor, and a ranking for each of component is elaborated. This can provide useful information to make a more informed decision on the choice of a PL, depending on several factors, such as the type of algorithms to be implemented, or the effects on power consumption not only in overall, but also depending on specific DUT hardware components. [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]

Despite the great advances in the field of image classification, the association of ideal approaches that can bring improved results, considering different datasets, is still an open challenge. In this work, a novel approach is presented, based on a combination of compared strategies: feature extraction for early fusion; rankings based on manifold learning for late fusion; and feature augmentation applied in a long short-term memory (LSTM) algorithm. The proposed method aims to investigate the effect of feature fusion (early fusion) and ranking fusion (late fusion) in the final results of image classification. The experimental results showed that the proposed strategies improved the accuracy of results in different tested datasets (such as CIFAR10, Stanford Dogs, Linnaeus 5, Flowers 102, and Flowers 17) using a fusion of features from three convolutional neural networks (CNNs) (ResNet152, VGG16, and DPN92) and its respective generated rankings. The results indicated significant improvements and showed the potential of the approach proposed for image classification. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon (ISM) is a complex and multiscale interacting climate system, which is responsible for major contribution in India's annual rainfall. Understanding the spatial and temporal variability of ISM rainfall is critical for managing water resources, which directly impact and regulate the functioning of India's socio-economic conditions and subsequently, sustenance of over a billion people. This study evaluates the suitability of various gridded precipitation data products with different spatiotemporal resolutions, essential requirement for hydrologic modeling, disaster mitigation, irrigation allocation and agricultural application. Hence, we evaluate the performance of seven gridded datasets in generating time-matched characteristic event occurrences and their respective magnitudes using gauge-based Indian Meteorological Department (IMD) gridded data as reference. We observe that reanalysis datasets underperform compared to satellite and hybrid products in identifying both normal and extreme precipitation events. We develop a performance measure, called 'rank score' that considers deviations from IMD data in magnitude, statistical moments, and rain event detectability for a robust assessment and identifying best-suited dataset. Results indicate that APHRODITE, MSWEP, and CHIRPS (in descending order) are the most suitable data products across India. Additionally, region-specific evaluations provide valuable insights into the applicability of these datasets in different climatic and homogeneous rainfall zones. [ABSTRACT FROM AUTHOR]

In this article, we aim to perform sensitivity analysis of set-valued models and, in particular, to quantify the impact of uncertain inputs on feasible sets, which are key elements in solving a robust optimization problem under constraints. While most sensitivity analysis methods deal with scalar outputs, this article introduces a novel approach to perform sensitivity analysis with set-valued outputs. Our innovative methodology is designed for excursion sets, but is versatile enough to be applied to set-valued simulators, including those found in viability fields, or when working with maps like pollutant concentration maps or flood zone maps. We propose to use the Hilbert-Schmidt Independence Criterion (HSIC) with a kernel designed for set-valued outputs. After setting a probabilistic framework for random sets, a first contribution is the proof that this kernel is characteristic, an essential property in a kernel-based sensitivity analysis context. To measure the contribution of each input, we then propose to use HSIC-ANOVA indices. With these indices, we can identify which inputs should be neglected (screening) and we can rank the others according to their influence (ranking). The estimation of these indices is also adapted to the set-valued outputs. Finally, we test the proposed method on three test cases of excursion sets. [ABSTRACT FROM AUTHOR]

Increased frequency and severity of flooding are linked to global warming. Flooding stress can severely affect crop yields and plant survival, and therefore the livelihoods of farmers. An oil palm breeding trial plot consisting of six progenies in Terengganu, Malaysia, happened to be inundated with flooding within a year after planting and annually for 2 years thereafter. Flooding recurred at the 8th year of planting and several times after that within the 14‐year period of monitoring. Due to the uneven terrain in this trial, palms were exposed to various flood levels and were categorized into two flooding groups. Yields and survivability of the palms were not affected by low‐flood levels, in contrast to the palms in lower areas that experienced higher flood levels. Compared to neighbouring non‐flooded trials, palms exposed to high floodwaters generally experienced lower fruit yields, and reduced vertical growth with poorer canopy cover. However, one of the progenies that exhibited a moderate survival rate in high‐flooded areas outperformed the other progenies in terms of yield in these areas. Generally, young oil palms exposed to low floods were able to survive and produce yields that were comparable to palms in non‐flooded areas, though 10%–20% of the low‐flooded palms produced poor yields in the first 3–5 years. In high‐flooded areas however, oil palm survivability ranged from 45% to 73% during the early 3 years and 46%–89% thereafter. Hence, young palms exposed to recurrent flooding of approximately more than 1 m during their early years are likely to become poor yielders with poor survival rates. Observations from this study may assist oil palm farmers in the management of this crop in the event of unexpected flooding. [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]

This article is devoted to the analysis and development of ranking criteria for microcredit organizations to increase their investment attractiveness. The need to solve problematic issues is associated with the need to minimize risks before the start of the lending process through the correct selection of participants in the credit transaction. This study used the methods of content analysis and interpretation, correlation analysis and regression modeling, ranking, and clustering to assess the factors affecting the effectiveness of microcredit organizations. The most attention is paid to identifying key indicators that help improve the quality of financial services provided and their availability for various categories of borrowers. The results show that factors related to lending volumes and borrower characteristics have a significant impact on the quality of microcredit organizations. Of interest is the interpretation of classical financial indicators of microcredit organizations in the context of the principles of environmental, social, and corporate governance (ESG). The proposed approaches and conclusions can be used to improve the practice of microfinance and develop management and regulation strategies in this area. [ABSTRACT FROM AUTHOR]

This paper explores the role of the leading universities in the genesis and the evolution of the academic field of bibliometrics. A recent bibliometric methodology is applied to publications in bibliometrics from the major universities of the Academic (Shanghai) Ranking of World Universities and Times Higher Education rankings. The results of this study challenge the traditional notion that prestigious universities are the main catalysts for innovation, emphasizing the substantial contributions made by second-tier and third-tier institutions schools. The study confirms the thesis of Fassin (2022) that—as for innovation in business—innovation in scientific knowledge does not necessarily originate from the top universities. It also corroborates the crucial role of the 'idea champion' or 'concept advocates'. It are a few individuals who have started in different universities developing metrics and applying bibliometrics in their field, often solely and sometimes as a sideline to their major research areas. That makes the science of sciences a unique phenomenon in recent science. [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]

Value addition is crucial for enhancing the market position of any agricultural product, improving its quality and branding, boosting income, and creating more employment opportunities. Social media marketing has become an essential tool for microenterprises, offering an affordable means to reach larger audiences and engage with customers. This study examines the obstacles when using social media to market value-added products at the microlevel by agripreneurs. These challenges include a lack of digital literacy, mistrust of social media, language barriers, supply chain complexity, seasonal demand fluctuations, limited internet access, restricted budgets, and time constraints. Employing a mixed-methods approach that combines quantitative surveys and qualitative interviews with microenterprise owners, the study identifies critical issues and proposes using the Garrett ranking and recommendations using word frequency in NVivo software. Training programs, suitable platform selection, and increased awareness among agripreneurs are some recommendations for using social media for marketing. This study provides valuable insights for policymakers and practitioners aiming to support this vital sector of the economy. [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]

Characterization of soil infiltration is crucial for designing and assessing hydrological processes. The study aimed to characterize the infiltration across various soil types (ranging from fine to moderately coarse in texture), agricultural land uses (including paddy fields, fallow lands, and upland crops) and major soil groups in India using Kostiakov, Philip, Horton, Holtan, Green and Ampt, and modified Kostiakov models. The mean basic infiltration rate was maximum in medium-texture soil (7.0 cm h− 1), followed by moderately fine (4.6 cm h− 1), fine (3.7 cm h− 1), and moderately coarse (3.2 cm h− 1) soils. Under paddy, fallow land uses and upland crops, the values of basic infiltration rate were 2.5, 4.1 and 5.3 cm h− 1, respectively. Across major soil groups, a significant variations in basic infiltration rates were observed among different soil types. Particularly, the highest infiltration rate was recorded in brown hill soil (8.1 cm h− 1); conversely, the lowest infiltration rate was in black soil (3.2 cm h− 1), attributed to the predominance of montmorillonite clay. By using rank-based multicriteria decision making, the Philip model was found to be the overall best-performing model. The Kostiakov model came in close second phase. Moreover, depending on the kind of land cover, Philip model outperformed other models, while Kostiakov fared better in upland crops and paddy fields. Additionally, Philip's model also performed better than other infiltration models across a range of soil textures. The study emphasized the significane of choosing suitable models to appropriately quantify the infiltration across a range of hydrological and agricultural contexts by highlighting the effects of soil texture, land use, and soil type on infiltration rates. [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]

Abstract The Indian summer monsoon (ISM) is a complex and multiscale interacting climate system, which is responsible for major contribution in India’s annual rainfall. Understanding the spatial and temporal variability of ISM rainfall is critical for managing water resources, which directly impact and regulate the functioning of India’s socio-economic conditions and subsequently, sustenance of over a billion people. This study evaluates the suitability of various gridded precipitation data products with different spatiotemporal resolutions, essential requirement for hydrologic modeling, disaster mitigation, irrigation allocation and agricultural application. Hence, we evaluate the performance of seven gridded datasets in generating time-matched characteristic event occurrences and their respective magnitudes using gauge-based Indian Meteorological Department (IMD) gridded data as reference. We observe that reanalysis datasets underperform compared to satellite and hybrid products in identifying both normal and extreme precipitation events. We develop a performance measure, called ‘rank score’ that considers deviations from IMD data in magnitude, statistical moments, and rain event detectability for a robust assessment and identifying best-suited dataset. Results indicate that APHRODITE, MSWEP, and CHIRPS (in descending order) are the most suitable data products across India. Additionally, region-specific evaluations provide valuable insights into the applicability of these datasets in different climatic and homogeneous rainfall zones.

Abstract With the ability of servers to remotely control and manage a mobile robot, mobile robots are becoming more widespread as a form of remote communication and human-robot interaction. Controlling these robots, however, can be challenging because of their power consumption, delays, or the challenge of selecting the right robot for a certain task. This paper introduces a novel methodology for enhancing the efficacy of a mobile robotic network. The key two contributions of our suggested methodology are: I: A recommended strategy that eliminates the unwieldy robots before selecting the ideal robot to satisfy the task. II: A suggested procedure that uses a fuzzy algorithm to schedule the robots that need to be recharged. Since multiple robots may need to be recharged at once, this process aims to manage and control the recharging of robots in order to avoid conflicts or crowding. The suggested approach aims to preserve the charging capacity, physical resources (e.g. Hardware components), and battery life of the robots by loading the application onto a remote server node instead of individual robots. Furthermore, our solution makes use of fog servers to speed up data transfers between smart devices and the cloud, it is also used to move processing from remote cloud servers closer to the robots, improving on-site access to location-based services and real-time interaction. Simulation results showed that, our method achieved a 2.4% improvement in average accuracy and a 2.2% enhancement in average power usage over the most recent methods in the same comparable settings.

t The roots of the modern corporation are to be found in the Dutch Republic and British Empire. In the last decades, more and more countries of the world have organized their economies around corporations, either public or private, either multinationals or transnationals. The large business corporation has become not only the main organization of the market economy but also one of the most representative institutions of society. Today’s large American corporations dominate most of the markets in which they operate either internal or external. The paper aims to present and briefly analyse the ranking of the most profitable American corporations in the period 2019-2022. To reach this goal the author used a qualitative research method. The results of the study concluded that the hierarchy of the most profitable American corporations in the period 2019-2022 suffered small changes at its top. In 2019, Berkshire Hathaway, a conglomerate holding company, dominated the ranking whereas Apple, a colossus from the IT&C sector, obtained the highest profit the following three consecutive years. The paper provides a theoretical framework for a better understanding of the American corporations. Also, it presents and analyse the evolution of the ten most profitable American corporations in the period 2019-2022.

Event planning, handled by the event planner, involves organizing and coordinating events. The growing number of event planning companies has created a competitive environment where all strive to be the top choice for event managers. Ranking event planners can be subjective, as it depends on specific event requirements. Therefore, it is a crucial stage in selecting the best event planner since many criteria need to be considered. This study aims to identify event planner contractors and rank the alternatives by applying Consistent Fuzzy Linguistic Preference Relations (CFLPR). CFLPR is chosen because it reduces the number of comparisons, streamlines the process, and prevents inconsistencies, especially with large amounts of data. As hierarchy levels increase, there are more comparisons to make, leading to inconsistent decisions. CFLPR is also used to address the event planner selection issue comprehensively, improving the consistency of Fuzzy AHP and handling fuzzy judgments. This method constructs matrices of fuzzy preference relations using linguistic variables. This study considered 10 criteria to rank 3 alternatives Through CFLPR execution, Persona Fiza Bridal emerges as the top choice, followed by Butik Pengantin Seriheza and Nwahyu Butik Pengantin & Kecantikan. These rankings are based on consistent fuzzy linguistic preference relations, with the highest value assigned the top ranking and the lowest the last. Persona Fiza Bridal is thus recommended as the ideal event planner for Rio Event Management.

We suggest a novel approach to determining the centrality measures for directed signed networks, based on the notion of social balance. We postulate that along with the existing positive connections, the structure of positive and negative connections can be used to determine potential secondary connections, respectively, weak social ties between pairs of individuals who are, e.g., either friends with the same person or under threat from the same person. This kind of connection agrees perfectly with the theory of social balance. Given the structure of primary and secondary connections, the centrality is measured using an eigenvector-based scheme. The suggested approach is applied to the classical example of the social network of monks in a monastery, and the results show a good agreement with the available ground truth. • A novel approach to evaluate centrality scores for directed signed networks. • The scheme is particularly suitable for the application to social networks. • Important insights into the role of positive and negative connections. • An application demonstrates a good agreement with the available ground truth. [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]

Objective: In this paper, a mathematical modeling is presented to determine efficient locations for deployment of support forces using data envelopment analysis. Methodology: The proposed model has the possibility to first change the manageable inputs in order to improve the outputs according to the principle of managerial accessibility and also if it is not possible to reduce the unmanageable inputs according to the principle of natural accessibility, it keeps them at least at the existing level. Therefore, the most important innovation and contribution of the present research is the modeling of the positioning of the support forces to support the ground forces in future battles, which is created by using data envelopment analysis and the simultaneous use of natural and managerial accessibility principles. Findings: The intended model has been used to evaluate 25 potential locations that are ready to provide ground support services to help local forces in the disputed area so that the fire of war ends in favor of local forces. Finally, by providing a sensitivity analysis on the manageable input values, the maximum possible changes on the value of this type of input for each of the locations are specified. Originality: The results of this research are of great importance considering the conditions raised in making and the effectiveness of commanders' decisions in order to locate support forces in future battles.

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.

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.