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This paper studies a novel robotic warehousing system called the overhead robotic compact storage and retrieval system, which can free up the floor space occupation at a low cost. Bins, as basic storage containers, are stacked on top of each other to form a bin stack. Along overhead tracks, bin-picking robots transport bins between storage/retrieval positions and workstations with the aid of track-changing robots. Little research has been done to study operational policies and performance analysis for this new robotic compact warehousing system. We propose a nested queuing network model that considers two transportation resources and performs reinforcement learning using real data to improve the reshuffling efficiency. We find that reinforcement learning based reshuffling policy greatly reduces the reshuffling distance and saves computation time compared to existing policies. We find that the storage policy of stacks affects the optimal width/length ratio regardless of the system height. Interestingly, we obtain the number of robots that can stabilise the system to avoid an explosion of the order queue; two more robots than that number will produce relatively low throughput times. Compared to an AutoStore system, using our system reduces cost by 30% with a slight increase in throughput time. [ABSTRACT FROM AUTHOR]

Developing accurate and computationally efficient algorithms for system performance metrics calculation is critical to implementing effective control and optimization in manufacturing system operations. In this paper, we propose a recursive decomposition/aggregation-based method for calculating the performance metrics of assembly/disassembly systems with multiple merge/split operations and sub-assemblies. It is assumed that the machines follow the exponential reliability model and the buffers are of finite capacity. To achieve this, we first consider assembly systems with multiple component lines merging at a single assembly operation. By decomposing the system into a set of virtual serial lines, we derive an analytical procedure to approximate the starvation and blockage probabilities of the merge operation, which are used to recursively update the parameters of the virtual serial lines. Then, the performance metrics of the original assembly system are approximated based on the corresponding machines and buffers in these virtual serial lines. Next, we extend the algorithm to assembly/disassembly systems with multiple merge/split operations and sub-assemblies. This is accomplished by identifying the so-called assembly/disassembly units formed based on the virtual serial lines and applying the calculations derived earlier recursively. Simulation experiments are carried out to justify the convergence, computational efficiency, and approximation accuracy of the proposed algorithms. An industrial case study is presented to demonstrate the theoretical methods in practical applications. [ABSTRACT FROM AUTHOR]

Human managers are increasingly challenged by artificial intelligence (AI) technologies in performing managerial functions. We undertook a field experiment that used AI vis-à-vis human managers to perform structured, data-intensive evaluations of employee performance. We generate two sets of insights. First, employees considered AI to be both fairer and more accurate in evaluating their performance than the average human manager. Second, to catch up with AI, human managers' fairness perceived by employees played a first-order role by (a) helping human managers, to a greater extent than those managers' evaluation accuracy, to close the performance gap of the employees evaluated by them compared with that of those evaluated by AI, and (b) constraining the effect of human managers' perceived accuracy of evaluations on employees' performance. Thus, facing the competition from AI, it is all the more important for human managers to treat employees fairly and build positive interpersonal relationships with employees. [ABSTRACT FROM AUTHOR]

PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. APPROACH: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. RESULTS: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. CONCLUSIONS: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks.

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Socially responsible (SR) institutions tend to focus more on the environmental, social, and governance (ESG) performance and less on quantitative signals of value. Consistent with this difference in focus, we find that SR institutions react less to quantitative mispricing signals. Our evidence suggests that the increased focus on ESG may have influenced stock return patterns. Specifically, abnormal returns associated with these mispricing signals are greater for stocks held more by SR institutions. The link between SR ownership and the efficacy of mispricing signals only emerges in recent years with the rise of ESG investing, and is significant only when there are arbitrage-related funding constraints. [ABSTRACT FROM AUTHOR]

In the current time, social sustainability is directed by market turbulence and growing societal and environmental awareness among customers and employees. Manufacturing industries with multi-tier supply chains, especially in developing nations, such as India, are plagued with various social issues, as it employs large manpower. The present study attempts to identify the key social sustainability practices (SSP) and the method to evaluate SSP in multi-tier manufacturing firms. The approach was pilot tested in Indian manufacturing industry. A total of 16 SSP were identified through the process of literature survey and discussions with the domain experts. Furthermore, based on five practitioners' inputs, the relationship between various practices of social sustainability is modelled using Total Interpretive Structural Modelling (TISM). The vagueness in the model and identified relationships are compensated by experts' (practitioners') validation. The practices are classified on the basis of driving and dependence power by adopting the MICMAC analysis. Customer management, information sharing, corporate sustainability reporting and standardisation, and monitoring practices are found to be the most influential practices that drive social sustainability in multi-tier automotive chains. Production managers could adopt these SSP to establish supply chain social sustainability in multi-tier global supply chains and achieve strategic advantage over others. [ABSTRACT FROM AUTHOR]

Photocatalysis is regarded as a potent alternative to conventional treatment methods with low removal efficiency (e.g., activated sludge process) against refractory/recalcitrant antibiotics in wastewater. Here, the potential utility of bismuth oxyiodide (BiOI)-based materials has been evaluated as visible-light–driven photocatalysts (bandgap energy < 2.0 eV) with unique optical structure for the enhanced treatment of tetracyclines (TCs) as a representative antibiotic product. The effects of key modification strategies (e.g., elemental doping and heterojunction formation) on the photocatalytic activity of BiOI-based photocatalysts are assessed against TCs in terms of the key performance metrics such as quantum efficiency (QE) and space-time yield (STY). This review also highlights the challenges and promising opportunities for the photocatalytic application of BiO-based materials against TCs in real effluents. Accordingly, zinc-doped BiOI is recognized to have the maximum efficiency with QE of 2.95 × 10−5 molecules photon−1 and STY of 2.95 × 10−7 molecules photon−1 mg−1. This review is expected to help deepen the exploration path to the construction of efficient BiOI-based systems based on their performance evaluation along with the discussions on their perspectives. [ABSTRACT FROM AUTHOR]

Introduction: Under the label of 'Responsible Research and Innovation (RRI)', increasing efforts have been made over the past decade to make research and innovation (R&I) more open and inclusive to improve the way we solve today's complex challenges. However, no commonly accepted norms or standards on the practical implementation of RRI currently exist. One of today's challenges is how to facilitate its implementation within science education. This article aims to contribute to the necessary conceptual and practical grounding of RRI in STEAM (science, technology, engineering, the arts and mathematics) education by developing a framework of RRI competencies and by gaining insight into secondary school STEAM educators' practices and perspectives on teaching RRI competencies. It adds to an emerging research field in which science education contributes to solving real-world problems by focusing on competencies needed to participate in RRI processes. Methodology: After developing a Comprehensive Framework of RRI Competencies in STEAM education, we engaged secondary school teachers (n=40) involved in innovation within the education project Xplore Health (XH). Participants were recruited through purposive and homogenous sampling from three cities in Spain (Barcelona, Granada and A Coruña). Through one focus group in each city, we analysed teachers' perspectives on how well RRI competencies in the Framework were covered using the XH's educational resources and on how that coverage could be improved. Results: The results show that teachers encounter important limitations in addressing competencies within all the RRI process requirements: reflexivity, anticipation, inclusion and responsiveness. They suggested learning methods and actions for improving that coverage in secondary STEAM education. Discussion and conclusions: We conclude that the Comprehensive Framework of RRI Competencies helps teachers reflect on how to innovate in STEAM education and that Community-Based Participatory Research facilitates the development of most of the competencies teachers considered difficult to apply with the other approaches. [ABSTRACT FROM AUTHOR]

Background and Purpose: As a growing number of instructional units have been developed to promote young children's scientific and engineering practices (SEPs), understanding how to evaluate and assess children's SEPs is imperative. However, paper-and-pencil assessments would not be suitable for young children because of their limited reading and writing skills. To explore the assessments for SEPs available for young children aged 3–8 years, this study reviewed assessments of young children's SEPs reported in empirical studies, and analysed the characteristics of these assessments to delineate how young children's SEPs have been measured. Methods: We followed the procedures of a systematic review proposed by Zawacki-Richter et al. (2020). The EBSCOhost database was used to gather empirical studies in education and psychology. A total of 46 articles published from 2003 to 2020 met the inclusion criteria and were reviewed. Findings: The findings indicated that of the eight SEPs suggested by the National Research Council (2012), Analysing and interpreting data was assessed the most, followed by Using mathematics and computational thinking, Constructing explanations and designing solutions, and Planning and carrying out investigations. A majority of assessments were designed for children of 4, 5, and 6 years old and used paper-based visualizations and real objects to present the tasks and items. Additionally, due to the verbal or performance nature of the SEPs, the assessments collected different types of data as evidence to evaluate children's SEPs. Performance-based assessments were the most common, followed by multiple-choice, ranking, and oral responses. Conclusion: The findings of the reviewed assessments revealed a variety of performance expectations of SEPs and suggested that some SEPs are measurable and developmentally appropriate for young children. Also, the availability of assessments is uneven in different types of SEPs, and more assessments for information communication and modelling practices are needed. [ABSTRACT FROM AUTHOR]

Leadership in administration does not operate in a vacuum. Every decision comes with consequences for those tasked to make them, and foreseen career consequences, which often materialize during performance evaluations, may support or constrain administrators' leadership. This article explores how and why administrators factor in foreseen evaluation career outcomes when making leadership decisions, drawing from the qualitative investigation of 14 Canadian university decanal reappointments. The study asked 13 reappointed and one non-reappointed deans how they experienced and made sense of their reappointments. The study found that performance evaluations allow organizational agents to bypass normal role negotiation and that foreseen career consequences associated with evaluations play a role in leadership decisions. The effect of performance evaluations is contingent on personal, role, and evaluation characteristics. In the case of decanal reappointments, the short-term effect on individual leadership was modest. However, by limiting deans' effective in-role time, reappointments had an important long-term constraining effect on their consecutive and collective leadership. [ABSTRACT FROM AUTHOR]

Researchers have argued that social‐comparative rating formats hold important psychometric advantages over traditional absolute ratings. We asked 152 participants to observe and assess the videotaped performance of individuals completing a task using a social‐comparative (Relative Percentile Method; RPM) and absolute rating (Behaviorally Anchored Rating Scale; BARS) formats. After collecting expert ratings on the same set of videos, we calculated accuracy indices and leniency. Additionally, we collected rater perceptions of accuracy and fairness for both formats. Our study revealed that the BARS was perceived as more accurate and fairer than the RPM. However, the RPM was found to be better at combating rater leniency. We discuss the implications of these findings. Practitioner points: Some scholars suggest social‐comparative ratings offer psychometric advantages over traditional absolute ratings, improving their value in real‐world organizations.Our study scrutinizes whether the Relative Percentile Method (RPM) yields more accurate and less lenient ratings than traditional Behaviorally Anchored Rating Scales (BARS).We probed whether raters perceived the RPM as fairer and more accurate than the BARS.We conducted a lab experiment where raters evaluated the performance of a series of targets using the RPM and BARS and calculated accuracy and leniency metrics.We found that raters perceived the BARS as fairer and more accurate than the RPM.We present evidence that there are trade‐offs to using social‐comparative ratings in the workplace. [ABSTRACT FROM AUTHOR]

Shadow education has burgeoned into a global phenomenon, and over the last 30 years, a substantial body of literature has emerged to explore this trend. Drawing on bibliometric data from 377 articles retrieved from the Web of Science (WOS), this study investigates the evolving landscape of shadow education research. It examines historical publication trends through performance analysis, intellectual sources and evolving themes using co‐citation analysis, complemented by co‐word analysis. Major authors and collaboration dynamics are discerned through co‐authorship analysis, contributing to a comprehensive understanding of collaborative networks. Additionally, the study constructs an evolution path for shadow education research published in journals, employing citation analysis supplemented by social network analysis methods. In conclusion, the study not only illuminates the historical trajectory of shadow education as a concept but also poses pertinent questions and offers recommendations for future research. Our research significantly contributes to the comprehension of how shadow education has evolved from a nascent concept into a global research discourse. [ABSTRACT FROM AUTHOR]

Background: According to Dweck's mindset theory, implicit beliefs (a.k.a. mindset) have an organizing function, bringing together mindset, achievement goals and effort beliefs in a broader meaning system. Two commonly described meaning systems are a growth‐mindset meaning system with mastery goals and positive effort beliefs, and a fixed‐mindset meaning system with performance goals and negative effort beliefs. Aims: Because of assumed heterogeneity within these two meaning systems, we aim to (1) examine multiple‐mindset profiles based on mindset, achievement goals and effort beliefs, by using a data‐driven person‐oriented approach, and (2) relate these different profiles to several outcome measures (academic achievement, motivation and school burnout symptoms). Sample: Self‐report questionnaire data were collected from 724 students (11.0–14.7 y.o.; 46.7% girl; 53.3% boy; Mage = 12.8 y.o.). Methods: Latent profile analysis was conducted using mindset, achievement goals and effort beliefs. Results: Four profiles were revealed: one fixed‐mindset profile and three growth‐mindset profiles, which differed in their performance goal levels (low, moderate and high). Growth‐mindset students with low‐ or moderate‐performance goals had more advantageous outcomes, for example, higher math grades and lower school burnout symptoms, compared to growth‐mindset students with high‐performance goals. Fixed‐mindset students had the least advantageous outcomes, for example, lower grades, less intrinsic motivation and more school burnout symptoms. Conclusions: Our study emphasizes the importance of taking a holistic approach when examining mindset meaning systems, revealing the importance of the level of performance goals and including multiple academic outcomes. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) enables the concurrent transmission of numerous user signals, ameliorating spectral efficiency by superimposing these signals. NOMA users utilize energy harvesting to efficiently utilize available energy sources. The energy harvesting process is non-linear in general. Additionally, innumerable antennas are used at the NOMA transmitter and power source to facilitate efficient energy transfer and information transmission with antenna selection (AS). Also, wireless channels cause path loss, fading, and shadowing effects, which directly influence harvested energy and communication reliability. Accordingly, the work assesses the outage performance and throughput of NOMA, examining realistic aspects like non-linear energy harvesting (nlEH), AS, multiple antennas, and κ - μ shadowed fading. The findings indicate a considerable performance degradation due to nlEH. Moreover, appropriate parameter selection is crucial for preventing complete outages in NOMA networks and achieving optimal performance. In addition, the performance of NOMA networks meliorates with an accreting number of antennas. Furthermore, the AS criterion dramatically impacts performance difference between NOMA users. [ABSTRACT FROM AUTHOR]

Despite the prevalence and importance of multiple goals for organizations, research on how organizations respond to performance on multiple goals continues to be limited and has examined only search intensity as the focal response, ignoring that search may occur in different locations. We extend the research on multiple goals by developing and testing novel theory on the relationship between performance feedback on multiple goals and the locus of search. Drawing upon the behavioral theory of the firm and using panel data from global pharmaceutical firms, we first show that when performance is below aspirations on a primary goal, a firm's propensity to engage in distal search increases along both the technological (i.e., familiar vs. unfamiliar search) and the organizational dimension (i.e., internal vs. external search). However, building on more recent literature that points to the need to consider multiple goals of unequal importance and, specifically, the self-enhancement perspective, we argue and find that performance on a secondary goal modifies this pattern, particularly when performance on a primary goal is unsatisfactory. Under feedback inconsistency, where performance on a primary goal is low but performance on a secondary goal is high, decision-makers decrease distal search to both unfamiliar technological areas and areas external to the organization. Our theory and findings highlight the importance of performance feedback regarding multiple goals in regulating the key locus of search choices and extend research on self-enhancement and learning from performance feedback. [ABSTRACT FROM AUTHOR]

Along with the popularity of Olympic Games, evaluating and ranking participating nations has become one of the hottest issues in recent years. Data envelopment analysis (DEA) is extensively used for participating nations' performance evaluation, while the prior literature mainly addresses the maximum efficiency by ignoring the weights diversity, which might lead to unreasonable and biased results. By considering the fixed-sum medals constraint, this paper proposes an integrated approach of stochastic multicriteria acceptability analysis (SMAA) and DEA for evaluating the participating nations. The proposed approach addresses the weights diversity and multiple equilibrium efficient frontiers by investigating the overall feasible space, and two strategies are considered to guarantee the fairness concern for calculating the efficiencies. Besides, the proposed approach provides both rank acceptability and holistic acceptability index to obtain the complete ranking from the best to the worst and to get the relative ranking relationship in the overall feasible space. The proposed approach can provide more reasonable performance measurements since it explores different rank positions and improves the whole satisfaction of all participating nations. Finally, the proposed approach is applied to empirically analyze the participating nations in the Winter Olympic Games from 2010 to 2022 and some valuable analytical results are provided. [ABSTRACT FROM AUTHOR]

Banks are the engine of providing financial resources in the economy and have special importance for governments. Accordingly, presenting the factors and components affecting financing restrictions and changes in the corporate debt bonds yield, risk management, as well as changes in their stock prices, in terms of bank management and from a macroeconomic perspective, can be considered as the leader of the economy and government. On the other hand, the proper management of banking challenges and the improvement of factors affecting the efficiency and effectiveness of banking resources can move the economic cycle in the right direction, considering that the banks’ primary activity is to grant loans and provide them to applicants. Accordingly, the researcher addressed the effect of the ability to manage political relations and financial crises on performance improvement. Therefore, a combination of Data Envelopment Analysis (DEA) and Two-Stage least squares (2SLS) regression analysis was used. This study is descriptive research with the correlational and post-event approach. In this way, the information about 11 banks in a period of 9 years (2014- 2023) was analyzed. The results showed that the improvement of ability level and proficiency in bank management will lead to the improvement of financing constraints, changes in corporate bond yield, and the growth rate of stock and non-shared incomes. The management authority not only affects credit risks, credit risk management and liquidity risk but also does not affect the stock price drop. [ABSTRACT FROM AUTHOR]

Healthcare services supply chain performance evaluation in hospitals that deal with society’s well-being has significant importance on their performance improvement. The purpose of this research was to evaluate healthcare services supply chain performance using a neutrosophic multiple attribute decision-making technique in Tehran’s hospitals. Comprehensive performance evaluation was conducted by applying both objective attributes that focused on the outcome along subjective attributes that were based on the judgment of evaluators. In this regard, neutrosophic logic has been deployed to face uncertainties in the expert’s judgment for determining the priority of attributes over each other explained via linguistic variables in the form of trapezoidal neutrosophic numbers. Eigenvector-power as one of the multiple attribute decision-making techniques concerned with evaluating and choosing the best option among the available ones based on diverse and conflicting attributes was used to ascertain attributes’ importance in addition to guaranteeing obtainment of the largest eigenvalue of the characteristic polynomial, has led to reduction of calculations. Neutrosophic algebraic operations embedded in the eigenvector-power technique after efficiency confirmation of the technique was acquired. In order to gap analysis, a paired t-test was exploited to discover the existence of differences between the current and desired performance of attributes. Then, attributes’ weighted performance gaps were calculated by multiplying the weight of each attribute by its performance gap which smoothed attributes’ performance gap criticality definition by applying quartiles. “Staff job satisfaction” and “response to demands” attributes were categorized as very highly critical, “treatment branding” and “proportion of service with cost” attributes were classified as highly critical, “technological comfort” and “stakeholder’s interests” attributes were grouped as moderately critical and “access” and “treatment plan fulfilment” attributes were relegated as low critical, respectively according to the weighted performance gap. [ABSTRACT FROM AUTHOR]

Data platforms with large dimensions, despite the opportunities they create, create many computational challenges. One of the problems of data with large dimensions is that most of the time, all the characteristics of the data are not important and vital to finding the knowledge that is hidden in them. These features can have a negative effect on the performance of the classification system. An important technique to overcome this problem is feature selection. During the feature selection process, a subset of primary features is selected by removing irrelevant and redundant features. In this article, a hierarchical algorithm based on the coverage solution will be presented, which selects effective features by using relationships between features and clustering techniques. This new method is named GCPSO, which is based on the optimization algorithm and selects the appropriate features by using the feature clustering technique. The feature clustering method presented in this article is different from previous algorithms. In this method, instead of using traditional clustering models, final clusters are formed by using the graphic structure of features and relationships between features. The UCI database has been used to evaluate the proposed method due to its extensive characteristics. The efficiency of the proposed model has also been compared with the feature selection methods based on the coverage solution that uses evolutionary algorithms in the feature selection process. The obtained results indicate that the proposed method has performed well in terms of choosing the optimal subset and classification accuracy on all data sets and in comparison with other methods. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic has led to a global shutdown of sporting activities. While professional sports competitions restarted in mid-2020, spectators were usually not allowed. This paper investigates the effect of absent fans and reduced social pressure on performance in professional darts – a setting where individual player performances can be well observed. Considering almost five years of tournament data, we use Bayesian multilevel models to investigate potential heterogeneity across players concerning reduced social pressure. For our analysis, we consider the two main performance measures in darts: the three-dart average and the checkout performance. Our results indicate that the effect of reduced social pressure on performance varies substantially across players. We further find experienced players to be less affected by social pressure compared to relatively inexperienced players. [ABSTRACT FROM AUTHOR]

The intricacy of the underwater setting makes it difficult for optical lenses to capture clear underwater photos without haze and colour distortion. Some studies use domain adaptation and transfer learning to address this issue, they aim to reduce the latent mismatch between composition and real-world data, making the space of latent data difficult to read and impractical to control. The background light is a crucial component of the decaying paradigm that directly impacts how well images are enhanced. Thus, to improve the quality of the images over the underwater, new deep-learning techniques are being designed in this paper. Here, the Adaptive Trans-ResUnet++ with Attention Mechanism-based model performs the real-time underwater image enhancement process. In addition, a novel Random Enhanced Artificial Gorilla Troops Optimizer algorithm model is used for optimising the parameters over the given model to further enhance the given model’s performance. A diverse quantitative and qualitative validation is also carried out to learn the enhancement of underwater image quality. The enhanced underwater image may be also useful in the underwater object detection process. Thus, the enhanced images obtained from the developed model are compared with the existing techniques to confirm the efficacy of the suggested underwater image enhancement process. [ABSTRACT FROM AUTHOR]

Objective Design Methods Results Conclusion Relevance to Clinical Practice Patient or Public Contribution To establish and validate a set of best nursing practice indicators for medical groups in Chinese general hospitals based on value‐based healthcare (VBHC) theory.A modified Delphi method.This study engaged experienced clinical nurses from public hospitals and multidisciplinary experts in operations management. Through a literature review and structured brainstorming, a comprehensive framework for assessing best nursing practices in Chinese general hospital medical groups was developed. A modified Delphi method was then employed to establish an indicator framework, followed by the Combined Empowerment‐MABAC method to weight and validate the indicators using empirical data collected between June 2023 and October 2023. The CREDES checklist guided the reporting of this study.Sixteen experts, each with at least 10 years of experience in nurse management from nine healthcare organizations, participated in two rounds of consultation. The experts' responses and suggestions for rewording, deleting and adding items were incorporated into each round. Of the 34 proposed indicators, 25 were approved, covering healthcare service capacity, efficiency, quality and safety, patient experience and cost.The Delphi survey reached a consensus on necessary actions to improve nursing performance. The developed indicator system provides a foundational framework for standardizing the monitoring of care quality and performance assessment in Chinese clinical healthcare groups, with broad applicability.This study provides a reliable basis for developing a nursing performance assessment database, offering crucial insights for measuring quality of care and improving patient value.No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous studies have primarily focused on the educational benefits of Hour of Code games, there has been limited research on their support methods. Therefore, this paper aims to address this gap with an empirical study of the experience of 104 students who played the game for one hour. Student progress was tracked by an administering teacher and after the game session, a questionnaire was administered to collect data on the participant's perceptions of the support system, interface efficiency, and overall experience with Hour of Code. The results of the study reveal significant problems with the aforementioned systems, which apply not only to Minecraft Adventurer but also to several other similar serious games. Additionally, the findings showed a correlation between the utilization of the support system and student performance, indicating that student's comprehension of the support system significantly influences their learning outcomes. This paper concludes by providing potential solutions to address the identified insufficiencies, offering valuable insights for future researchers and game developers on the design and evaluation of serious games for educational purposes. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network's edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. Scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author's knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded. [ABSTRACT FROM AUTHOR]

Serverless computing has become very popular in recent years due to its flexibility and cost efficiency. Serverless computing is a cloud computing model that allows developers to write and deploy code without managing the underlying infrastructure. Serverless computing has been welcomed due to its scalability, affordability, and ease of use. However, memory orchestration in serverless computing can be challenging due to the ephemeral nature of serverless functions, which are short-lived and stateless. This article has reviewed memory orchestration mechanisms and then presented a classification of memory orchestration mechanisms in serverless computing, which are classified into three main methods: Machine learning-based approach, Heuristic-based approach, and framework-based approach. The advantages and disadvantages of each mechanism as well as the challenges and performance metrics affecting their effectiveness have been investigated. Each memory orchestration approach, whether heuristic-based, framework-based, or machine learning-based, has its own advantages and disadvantages. Different mechanisms are suitable for different use cases. Therefore, it is important to carefully evaluate the trade-offs between performance, cost, and complexity when choosing a memory orchestration mechanism. Finally, the paper identifies several future research directions for memory orchestration in serverless computing, which include developing memory orchestration mechanisms and dynamic memory management, integrating memory orchestration mechanisms with other resource management mechanisms, security, memory sharing, compression, pricing, and investigating the relationships between memory orchestration with cost and performance in serverless computing. [ABSTRACT FROM AUTHOR]

Simple Summary: HER2-positive breast cancer occurs in 15–30% of cases and has a poor prognosis. Digital image analysis of HER2 is promising, but its implementation in real clinical practice remains unclear. This systematic review evaluates the effectiveness of digital image analysis algorithms for HER2 in breast cancer and their performance, with a focus on testing them in real-world clinical settings. The authors aim to assess the applicability of these algorithms in practical clinical scenarios. By analyzing 25 papers from the period 2013–2024 and emphasizing mostly deep learning approaches, the review underscores the importance of standardized evaluation criteria, study designs tailored for clinical applications, and clinical validation. While direct evidence of clinical application was not found, the findings aim to guide future research and the implementation of digital image analysis in breast cancer diagnosis within clinical settings, potentially impacting the research community by advancing algorithmic applications in real clinical practice. This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer. [ABSTRACT FROM AUTHOR]

The FinRay soft gripper achieves passive enveloping grasping through its functional flexible structure, adapting to the contact configuration of the object to be grasped. However, variations in beam position and thickness lead to different behaviors, making it important to research the relationship between structure and force. Conventional research using FEM simulations has tested various virtual FinRay models but replicating phenomena such as buckling and slipping has been challenging. While hardware-based methods that involve installing sensors on the gripper and the object to analyze their states have been attempted, no studies have focused on the tangential contact force related to slipping. Therefore, we developed a 16-way object contact force measurement device incorporating two-axis force sensors into each of the 16 segmented objects and compared the normal and tangential components of the enveloping grasping force of the FinRay soft gripper under two types of contact friction conditions. In the first experiment, the proposed device was compared with a device containing a six-axis force sensor in one segmented object, confirming that the proposed device has no issues with measurement performance. In the second experiment, comparisons of the proposed device were made under various conditions: two contact friction states, three object contact positions, and two object motion states. The results demonstrated that the proposed device could decompose and analyze the grasping force into its normal and tangential components for each segmented object. Moreover, low friction conditions result in a wide contact area with lower tangential frictional force and a uniform normal pushing force, achieving effective enveloping grasping. [ABSTRACT FROM AUTHOR]

In this work, historical simulations of CMIP6 GCMs are evaluated with respect to the ERA5 reanalysis dataset, in order to examine their ability to assess human thermal load in Europe in the winter season. The period of 1981–2010 is chosen for the analysis, and thermal load is expressed via the clothing resistance index (rcl index; expressed in clo). It is found that the GCMs are able to reproduce the areal differences of thermal load satisfactorily, the spatial correlation with the reanalysis is greater than 0.95 in all cases. The effects of the main geographical constraints (latitude, continentality and elevation) are shown by all GCM simulations, as rcl index values are greater at higher latitudes, away from the ocean and in mountainous areas, although GCMs only capture major mountains (the Caucasus, the Armenian Highlands, the Scandinavian Mountains, the Alps). The root‐mean‐square error (RMSE) is around 0.2 clo in all cases, GCMs generally perform better in homogenous lowland areas, while results are less accurate in highlands and mountains owing to the coarse horizontal resolution of GCMs (~1°). The smallest errors occur over central and western Europe and the Mediterranean region, while results tend to be less accurate over the northeastern part of Europe. Biases in the estimation of heat deficit can mainly be attributed to biases in temperature, but biases in wind speed and atmospheric downward radiation seem to be important factors as well. [ABSTRACT FROM AUTHOR]

Climate-driven changes in ocean temperatures, currents, or plankton dynamics may disrupt pelagic forage fish recruitment. Being responsive to such impacts enables fisheries management to ensure continued sustainable harvest of forage species. We conducted a management strategy evaluation to assess the robustness of current and alternative Pacific sardine harvest control rules under a variety of recruitment scenarios representing potential projections of future climate conditions in the California Current. The current environmentally informed control rule modifies the harvest rate for the northern sardine subpopulation based on average sea surface temperatures measured during California Cooperative Oceanic Fisheries Investigations field cruises. This rule prioritizes catch at intermediate biomass levels but may increase variability in catch and closure frequency compared to alternative control rules, especially if recruitment is unrelated to ocean temperatures. Fishing at maximum sustainable yield and using dynamically estimated reference points reduced the frequency of biomass falling below 150 000 mt by up to 17%, while using survey index-based biomass estimates resulted in a 14% higher risk of delayed fishery closure during stock declines than when using assessment-based estimates. [ABSTRACT FROM AUTHOR]

The relative importance of key performance factors is poorly studied in high-level climbers. This study aimed to (1) determine the role of forearm muscle strength and endurance in high-level rock climbing, (2) and provide suitable dynamometric test parameters for muscle endurance assessment in high-level climbers. Six higher elite (redpoint Fr.9a) and eight elite/advanced (redpoint Fr.8a+) climbers performed one finger flexor maximal strength test and three finger endurance tests: a 30 s all-out test and a continuous and intermittent test at 60% of maximal voluntary contraction. Higher elite climbers had higher (p < 0.05) maximal strength, all-out test average force and continuous test force-time integral (all relative to body mass) than the elite/advanced climbers. These parameters correlated significantly (p < 0.05) with climbing performance, which has not been observed so far for continuous test scores. Unlike in previous research, intermittent test force-time integral neither distinguished climbing ability groups nor correlated significantly with climbing performance. Nevertheless, regression models comprising of intermittent and maximal strength test scores more strongly determined climbing performance than models including continuous or all-out test scores. Intermittent muscle endurance is among the performance factors in difficult rock climbing but appears to be less important than maintaining high forces during sustained muscle contractions. [ABSTRACT FROM AUTHOR]

Subjected to the coupling action of multiple hazards in hydraulic engineering, hydraulic tunnels may be corroded and damaged to varying degrees during their service lives, which will decrease the seismic performance of these structures. However, the research and seismic design of significant hydraulic engineering projects focus on investigating the structural response based on the design material parameters, which may overestimate the seismic capacity of structures during their service lives. In this paper, research is performed to identify the effect of hydro-chemo-mechanical corrosion on the seismic performance of hydraulic tunnels with different burial depths. A plastic damage model of time-varying concrete degradation induced by the hydro-chemo-mechanical effect is first determined and implemented, and the endurance time acceleration records are generated in MATLAB. Then, a study of the endurance time relationship of hydro-chemomechanical corrosion-affected hydraulic tunnels, considering the fluid-structure-surrounding rock interaction systems throughout the service period, is undertaken to directly associate the structural response with the predefined evaluation index. Moreover, this research constructs 3D time-varying fragility surfaces considering the hydro-chemo-mechanical effect and seismic intensity. The results show that the relative displacement of hydro-chemo-mechanical corrosion-affected hydraulic tunnels is larger than that of nonaffected hydraulic tunnels. Hydro-chemo-mechanical effect-induced material deterioration will lead to an increase in the cumulative damage (crack) area and damage degree of hydraulic tunnels. Additionally, the seismic fragility analysis shows that the longer the service time of hydro-chemo-mechanical corrosion-affected hydraulic tunnels, the more likely they are to collapse. Hence, attention should be given to improving the aseismic capacity of hydro-chemo-mechanical corrosion-affected hydraulic tunnels in future seismic design and performance assessments. [ABSTRACT FROM AUTHOR]

Higher academic performance is almost universally considered a good thing, and most quantitative studies show that performance is positively, although weakly, related to mental health. Simultaneously, however, qualitative studies consistently find that high-performing students and students attending high-performing schools report high levels of stress and other mental health problems. This study investigates a simple explanation for this puzzle – that the relationship between performance and mental health is not linear and is conditional on the performance culture of the school. Data on almost 5000 Swedish students from the Programme for International Student Assessment were used. The results show that the relationship between performance and mental health is generally not linear and that intermediate-performing boys have the best mental health, while both low- and high-performing girls and boys alike have poorer mental health. Although inconclusive, the results also suggest that low-performing students may be vulnerable to a strong school performance culture. [ABSTRACT FROM AUTHOR]

Heart disease is one of the foremost reasons for death globally. Machine learning (ML) can be used to predict heart diseases early, which can help improve patient outcomes. This research proposes a novel machine learning method for predicting heart disease using a combination of Grey Wolf Optimization (GWO) and stacked ensemble techniques. GWO is a metaheuristic algorithm that can be used to optimize the parameters of machine-learning models. The stacked ensemble technique is a combination of multiple machine learning models to improve the overall accuracy of the prediction. The model proposed was evaluated using a dataset of heart patients. The results showed that the model achieved a 93% accuracy, which was significantly higher compared to traditional machine learning methods. The proposed method also had a higher precision of 91%, sensitivity of 95.3%, F1 score of 92.9%, and Matthew coefficient of 0.83, less in Log_Loss 2.87 than the traditional methods. The results of this research suggest that the proposed model is a promising new approach for predicting heart diseases. This method is more accurate and reliable than traditional methods and has the potential to improve patient outcomes. [ABSTRACT FROM AUTHOR]

In recent years, the interaction of a human operator with teleoperated robotic systems has been much improved. One of the factors influencing this improvement is the addition of force feedback to complement the visual feedback provided by traditional graphical user interfaces. However, the users of these systems performing tasks in isolated and safe environments are often inexperienced and occasional users. In addition, there is no common framework to assess the usability of these systems, due to the heterogeneity of applications and tasks, and therefore, there is a need for new usability assessment methods that are not domain specific. This study addresses this issue by proposing a measure of usability that includes five variables: user efficiency, user effectiveness, mental workload, perceived usefulness, and perceived ease of use. The empirical analysis shows that the integration of haptic feedback improves the usability of these systems for non-expert users, even though the differences are not statistically significant; further, the results suggest that mental workload is higher when haptic feedback is added. The analysis also reveals significant differences between participants depending on gender. [ABSTRACT FROM AUTHOR]

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There are multiple scenarios in machine learning where the data used presents a heavy bias towards one of the classes. Evaluating the performance of machine learning models in such imbalanced scenarios proves to be difficult and challenging, as one of the classes is poorly represented in the data, and this class is often more relevant to the end-user. An abundance of performance metrics have been devised and commonly used in order to solve these specific problems, however, there is often a lack of common agreement on which metric is best and which to use in specific imbalanced scenarios. In this study, we experimentally study the impact of choosing one metric over another in the evaluation of a classifier for binary classification, as well as the effect of data characteristics such as class imbalance and noise on those metrics. Based on our extensive empirical analysis, we provide a set of easy-to-follow guidelines for which performance metric is best to use depending on the context of the problem. Specifically, we highlight the importance of using multiple different metrics which are fundamentally different in imbalanced domains, we also display results on why the usage of Davis' interpolation of the area under the precision-recall curve and the Matthews Correlation Coefficient metrics should be preferred over other similar metrics, as well as why the usage of geometric mean and F 1 score should be avoided in scenarios likely to present noise on the labels. [ABSTRACT FROM AUTHOR]

Machine learning models' performance in simulating monthly rainfall–runoff in subtropical regions has not been sufficiently investigated. In this study, we evaluate the performance of six widely used machine learning models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), LASSO Regression (LR), Extreme Gradient Boosting (XGB), and the Light Gradient Boosting Machine (LGBM), against a rainfall–runoff model (WAPABA model) in simulating monthly streamflow across three subtropical sub-basins of the Pearl River Basin (PRB). The results indicate that LSTM generally demonstrates superior capability in simulating monthly streamflow than the other five machine learning models. Using the streamflow of the previous month as an input variable improves the performance of all the machine learning models. When compared with the WAPABA model, LSTM demonstrates better performance in two of the three sub-basins. For simulations in wet seasons, LSTM shows slightly better performance than the WAPABA model. Overall, this study confirms the suitability of machine learning methods in rainfall–runoff modeling at the monthly scale in subtropical basins and proposes an effective strategy for improving their performance. [ABSTRACT FROM AUTHOR]

Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions. [ABSTRACT FROM AUTHOR]

With the advance of climate change and the local effects of human activity, it has become of utmost importance to sense spatially extended natural and artificial physical phenomena to predict, monitor, and mitigate hazardous events. Wireless sensor networks are suitable for observing such phenomena, for example, wildfires, floods or landslides, without human supervision. This is due to affordable devices, independent power sources, wireless communication, and a broad range of sensors. During normal operation a few, while during the occurrence of an event a multitude of devices can fail. This leads to further disconnected devices, degrading the network's sensing capabilities. The communication requirements of such applications are difficult to fulfil with general routing protocols. The monitored event is rare compared to the network's lifetime, while its occurrence results in multiple, gradual node failures, still demanding the network to perform reliably. Available routing protocols fail to address every aspect of such application, thus the authors propose the Reliable Resilient Multipath Routing Protocol, designed to construct multiple disjoint paths from each device to a distinguished one, called the sink. The protocol employs proactive and reactive network management techniques to increase connection redundancy and maintain connectivity during failures. To verify the proposed protocol end‐to‐end, we evaluated the supported parameters, performed comparative simulations with routing algorithms known from the literature, and provided estimates of a realistic deployment. [ABSTRACT FROM AUTHOR]