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Copyright of Scientia Iranica. Transaction E, Industrial Engineering is the property of Scientia Iranica 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.)

• A subset of youth experiences a return of anxiety following CBT. • We developed a remote-delivered single session booster to address return of anxiety. • Clinical response to the booster in three youths was mixed and varied by informant. • Boosters can be an efficient tool to help youth who experience a return of anxiety. There is a subset of youth who, despite responding well to CBT, experience a return of anxiety in the years after treatment ends. Although empirical research and clinical recommendations have described approaches to prevent a return of anxiety following CBT, to our knowledge, no empirical or clinical articles have evaluated approaches to help youth who have experienced such a return. In this article, we take an initial step toward providing clinical guidance on addressing recurrence in youth with anxiety disorders by describing the development of an approach, a booster session delivered remotely using telehealth, and by presenting promising preliminary data in a case series among three youths who experienced a return of anxiety following CBT. Participants were N = 3 youths ages 10 to 15 years (M = 12 years; 33% male, 33% female, 33% nonbinary) who previously completed a full CBT protocol for anxiety disorders as part of outpatient clinical services in an urban area of the southeastern United States. Parent- and youth-report on youth anxiety severity were assessed pretreatment and posttreatment. Clinical response to the booster among these three families was mixed and varied by informant source, with an overall positive response according to parent ratings and a smaller response according to youth ratings. This case series represents an important first step toward addressing the return of anxiety in youth following CBT, and findings provide the impetus for further development and evaluation work of single session boosters. Limitations and implications of the findings are discussed. [ABSTRACT FROM AUTHOR]

Recognising that many workers worldwide often face poverty and exploitation, the International Labour Organisation aims to promote decent and productive work opportunities for all. We conducted a scoping review to get an overall view of current academic literature on decent work and precarious work for samples of blue-collar workers. The studies included in this review, focused on empirical research conducted from 2006 onwards, that primarily investigated decent work or precarious work among semi-skilled and unskilled manual workers. The review team used a data-charting form and collected information from thirty-five articles. Most recent studies (2020–2023) were related to the experiences and livelihoods of non-traditional blue-collar workers namely platform drivers and informal waste pickers. Before 2020, the most commonly studied theme related to the experiences of migrant workers. The findings shed light on the experiences of these workers, particularly in South Africa and India and provide valuable insight into their work experiences for further discussion. [ABSTRACT FROM AUTHOR]

Auxiliary information is an essential component in the field of survey sampling since it enables precise estimation of population parameters like mean, variance, distribution function, and so on, which in turn guarantees the best possible outcomes. In order to estimate the population mean of a study variable, this study makes use of auxiliary information in a two-fold approach. Through a stratified random sampling scheme, we introduce a novel class of estimators that utilize auxiliary information and their corresponding ranks. By conducting a thorough evaluation based on metrics such as mean square error and percentage relative efficiency, these proposed estimators have been shown to be effective in the estimation process. Empirical validation is conducted using a real dataset sourced from the domain of real estate. Exploring the relationship between Assessed Value (X) and Sale Amount (Y) during a five-year period extending from 2017 to 2021 is the primary emphasis of the empirical validation process, which is carried out with the assistance of a real dataset of real estate data. Furthermore, in order to demonstrate that our suggested estimator is superior to conventional unbiased estimators, as well as traditional regression estimators and other estimators that have been considered in the literature, a full simulation analysis is carried out. Our proposed estimator appears to be the most effective choice after being subjected to a comparison study against a variety of preexisting approaches. The findings of this study not only make a significant contribution to the development of the methodology of survey sampling but also offer vital insights for predictive modeling within the real estate sector. [ABSTRACT FROM AUTHOR]

The study explores a long-standing assumption that students learn from peers, and that this learning can be enhanced through carefully designed instruction. Nevertheless, empirical evidence about in-class peer learning designed by the teacher is lacking in how it is enacted and how students respond. We analysed class recordings, and perceptions of teachers and students in 15 Bachelor and Masters' class sessions in humanities, social sciences, and medicine at one European Union university. We found great variation in how teachers designed peer-learning activities. We saw, for instance, how the same strategy can vary in its effectiveness depending on the degree of teacher's tactical thinking and why some designs were more effective than others. By documenting the commonalities and differences across the classes, our study offers an empirical foundation on which to build a more robust understanding of how to recognise and compare manifestations of peer-learning in the classroom. [ABSTRACT FROM AUTHOR]

Interest in research on mathematical argumentation is increasing, leading to growth in the field. However, empirical research on mathematical argumentation in higher education presents challenges in organising a comprehensive overview due to its diversity and variety of underlying themes and tasks. Therefore, the aim of this study is to provide a systematic review of studies that focus on mathematical argumentation in higher education. We conducted a systematic literature review with the guiding questions: "What are the characteristics of tasks used for mathematical argumentation?" and "What themes are being explored in studies on mathematical argumentation in higher education?" The results of this review indicate a strong correlation among mathematical argumentation in higher education, mathematical proof tasks, and instructional design. Additionally, empirical studies conducted between 1995 and 2023 have identified four primary research themes encompassing student abilities, collaboration, designing and teaching, and assessment. Our analysis offers a detailed examination and appraisal of each of these themes and associated tasks, in conjunction with current trends, facilitating a more organized understanding of this multifaceted research discipline. [ABSTRACT FROM AUTHOR]

A consumption-driven economy refers to an economic growth model primarily driven by domestic consumption and is a common goal for the economic growth of various countries. To explore the impact of industrial digitalization on a consumption-driven economy, this paper conducts an empirical study based on data from 31 provinces in China from 2013 to 2021. The empirical test results indicate: first, industrial digitalization significantly promotes the development of a consumption-driven economy, mainly reflected in the improvement of economic foundation and consumption levels, but shows no significant effect on improving the consumption structure. Second, mechanism analysis results show that industrial digitalization can promote the development of a consumption-driven economy by fostering innovation and advancing the industrial structure. Third, heterogeneity analysis results reveal that the promotion effect of industrial digitalization on the consumption-driven economy exists only in eastern and central China but not in western China, indicating that industrial digitalization in underdeveloped areas cannot exert a positive effect on the consumption-driven economy. [ABSTRACT FROM AUTHOR]

Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of "human-machine pairing" in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies. [ABSTRACT FROM AUTHOR]

The interest in Computational Thinking (CT) development among young learners increases with the number of studies located in literature. In this study, a meta-analysis was conducted to address two main objectives: (a) the effectiveness of empirical interventions on the development of CT in children aged of 3–8 years; and (b) the variables that influence the effectiveness of the interventions. Following PRISMA procedures, we identified 17 empirical studies with 34 effect sizes and 1665 participants meeting the inclusion criteria from Web of Science database. Overall, we found a statistically significant large effect size (d =.83 [95% CI: 730,.890]; p <.001) on the CT development of 3–8 years old children, which provides empirical support for having young children to engage in CT experiences. The effect size was significantly influenced by moderating variables including gender, scaffolding, and education level. Intervention length showed a marginally significant effect. Therefore, educators could refer to the significant moderators when designing tailored interventions for CT development in early childhood education while a call for more empirical studies of CT development in young children is proposed. [ABSTRACT FROM AUTHOR]

Using various types of research methods yields breadth and depth in knowledge creation. Replying to calls for more research employing diverse research methods, this study examines research method trends, rigor, and emerging research methods within the field of human resource development (HRD). We created a research method coding scheme to capture the rigor of empirical research among empirical articles published in three specifically selected HRD journals from 2016–2023. Out of 488 selected studies, quantitative (n = 269) and qualitative (n = 185) methodologies were dominantly used in empirical studies with only 7.0% being mixed methods research (n = 34). The study findings provide implications and reflections on (1) the current status of published scholarly research; and (2) the research practices in the field. To move the field of HRD forward as a mature, effective, and evolving discipline, it is imperative to incorporate multiple approaches, including interdisciplinary investigation of emerging research methods, and development of clear guidelines for the rigor of research methods. [ABSTRACT FROM AUTHOR]

Two frequently cited figures by clinical research insiders and observers – the cost of missing a day to generate prescription drug sales and the cost of a day to conduct a clinical trial – are outdated and based on anecdotal evidence. In late 2023, the Tufts Center for the Study of Drug Development conducted empirical research to gather more accurate and granular estimates and to test whether average sales per day have changed over time. 645 drugs launched since 2000, and 409 clinical trial budgets were drawn from commercially available and proprietary data sets and analyzed. The results indicate that a single day equals approximately $500,000 in lost prescription drug or biologic sales, with daily prescription sales for infectious, hematologic, cardiovascular, and gastrointestinal diseases among the highest. The results also show that each year, the average sales per day of prescription drugs and biologics has decreased by approximately $80,000—$100,000. The estimated direct daily cost to conduct a clinical trial is approximately $40,000 per day for phase II and III clinical trials, with those in respiratory, rheumatology, and dermatology having the highest relative daily direct costs. [ABSTRACT FROM AUTHOR]

The assumption space of ID3 algorithm contains all decision trees, and the search space is also a complete assumption space. The current assumptions based on the information gain standard, and uses the information gain to reduce the sensitivity to individual training sample errors. Based on ID3 decision tree algorithm, this paper provides new ideas and methods for the quality optimization of tourist attractions. Firstly, ID3 decision tree algorithm model is constructed based on information entropy theory; Then, based on the literature research and system research methods, the quality evaluation indicator system of tourist attractions is constructed; Finally, based on the mathematical model and evaluation indicator system, the empirical research was carried out, and the data of 17 tourist attractions were collected. The expected research results were obtained according to the data preprocessing, data calculation process, decision tree construction and rule generation process. The research finds that the quality evaluation process of tourist attractions based on ID3 decision tree model, through data preprocessing, data calculation, decision tree construction and decision rules generation, etc. it can be concluded that the "Tourism environment quality" attribute is the most important in this evaluation system, and "Tourism landscape quality" is relatively important. Tourist attractions can improve the work and quality of tourist attractions according to the research results. [ABSTRACT FROM AUTHOR]

E-commerce application usability refers to the design and functional aspects that facilitate user interaction and goal achievement. Well-designed usability of e-commerce applications will lead to usage continuance by users. Therefore, this study tries to identify usability factors and measure the effect of usability on usage continuance. Based on previous studies, the variables hypothesized to be the factors that define usability are learnability, efficiency, memorability, accuracy, emotion, and utility. Utility is a variable that represents the functional aspects of usability. It is described in more detail into information search, interface display, and transaction. Furthermore, this study also examined the relationship between usability, usage continuance, satisfaction, and usefulness. This study’s hypotheses were evaluated using 242 survey responses from e-commerce application users. Structural equation modeling, or SEM, was applied to process the empirical data. Memorability, accuracy, efficiency, and utility were found to be able to describe the usability of e-commerce applications well. This proved that functional quality is also an essential part of usability. Usability was found to influence usage continuance. [ABSTRACT FROM AUTHOR]

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre 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.)

Introduction. People with Type 1 diabetes need to collect data to calculate their own insulin doses, and collect information about potential complications. However, information avoidance may be a barrier to such activities. Method. A preliminary study of information avoidance by younger adults with Type 1 diabetes covered data, information and information channels that they avoid, and adolescence’s effects on diabetes information behaviour. Semi-structured interviews with six younger adults with Type 1 diabetes and five healthcare professionals were used to understand their diabetes information behaviours. Analysis. The understandings were gleaned from thematic analysis of interview transcripts. Results. Younger adults with Type 1 diabetes may only collect blood glucose data, disregarding other relevant data and information. They may also take ‘time off’ from collection, and not gather information about potential complications. Some desire more contact with healthcare professionals. Adolescence may involve significant avoidance of data and information. Conclusions. Observed behaviours speak to ‘utility’ and ‘hedonic drives’. Long gaps between healthcare appointments may exacerbate avoidance, as well as limiting healthcare professionals’ opportunities to advise patients. Younger adults with Type 1 diabetes avoid data and information unless they believe these are needed. Frequency of appointments and routes to information could be improved. [ABSTRACT FROM AUTHOR]

Large language models are capable of generating source code in a zero-shot manner to develop programs that meet user functional requirements. However, when faced with scenarios involving complex business requirements, the generated source code may fail to satisfy user needs. Addressing the challenge of understanding software requirements, we propose a two-stage code generation approach. Initially, the large language model generates pseudocode based on the user's functional requirements, refined through an iterative process with user feedback. Subsequently, the model generates source code based on the finalized pseudocode. We conducted empirical studies on an open code generation dataset, and experimental results with models such as GPT-4, Claude Sonnect 3, and Geminipro 1.5 demonstrate that our method outperforms zero-shot prompt learning in scenarios with complex user requirements, with improvements in PASS@K reaching up to 15%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Media Research is the property of Journal of Media Research 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 Zeitschrift für Pädagogik is the property of Julius Beltz GmbH & Co. KG Beltz Juventa 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.)

Community health worker (CHW) and social worker (SW) collaboration is crucial to illness prevention and intervention, yet systems often engage the 2 workforces in silos and miss opportunities for cross-sector alignment. In 2021, a national workgroup of over 2 dozen CHWs, SWs, and public health experts convened to improve CHW/SW collaboration and integration across the United States. The workgroup developed a conceptual framework that describes structural, systemic, and organizational factors that influence CHW/SW collaboration. Best practices include standardized training, delineated roles and scopes of practice, clear workflows, regular communication, a shared system for documentation, and ongoing support or supervision. [ABSTRACT FROM AUTHOR]

• A controlled experiment revealed contextual bias in handwriting examination of China. • Participants disclosed their thoughts and reasoning as they analyzed handwriting samples. • Context management with complementary strategies can be used to combat bias. The impact of contextual bias has been repeatedly demonstrated across forensic domains; however, research on this topic in China is scarce. To examine the prevalence of contextual bias in pattern feature-comparison disciplines, we conducted an experiment involving 24 forensic document examination students. The aim was to determine whether knowledge of different contextual information influenced their forensic decision-making. Participants were divided into different context groups and tasked with examining whether questioned signatures with ambiguous features matched reference signatures. The results of independent-samples t -tests for their decision score data in the two context groups exhibited a statistically significant difference (p < 0.05, Cohen's d > 0.8). Moreover, the submitted forensic reports by participants disclosed a biased evaluation of handwriting features. These findings show how contextual information can bias forensic decision-making in handwriting examination. Context management with complementary strategies such as case triage, cognitive training and decision-making transparency must be implemented to minimize bias in handwriting examination. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to conduct an exploratory research to find out the evolving constructs and variables of digital literacy, as seen by researchers since its inception. This research also includes an empirical study to identify and further analyze the digital literacy dimensions of university students studying fashion design program in Kolkata, India. Design/methodology/approach: The exploratory study is based on a review of extant literature, whereas the empirical study is carried out through a self-assessment survey based on UNESCOs Digital Literacy Global Framework competences after validating their relevance with respect to the fashion and apparel industry. A total of 120 university students studying four years Bachelor of Design (Fashion Design) program were asked to rate their digital literacy competences on a five-point Likert scale, with a self-reported truth response against each statement. The results were analyzed using multivariate statistical tools. Findings: Based on UNESCO competences, it came out that there are eight digital literacy dimensions. ANOVA further confirms that the dimensions requiring higher-order cognition, such as "software management competence" and "digital citizenship competence," increase with progress in the graduate program. However, lower-order competence dimensions remained unchanged over time. Originality/value: The research instrument used for this empirical study, its identified dimensions and the fact that higher-order competence dimensions are enhanced with progression in university education may be helpful for similar research in other fashion-related programs. [ABSTRACT FROM AUTHOR]

Background: This research presents a novel methodology for synthesizing 3D multi-contrast MRI images utilizing the 3D Dual-CycleGAN architecture. The performance of the model is evaluated on different MRI sequences, including T1-weighted (T1W), T1-weighted contrast-enhanced (T1c), T2-weighted (T2W), and FLAIR sequences. Results: Our approach demonstrates proficient learning capabilities in transforming T1W images into target modalities. The proposed framework encompasses a combination of different loss functions including voxel-wise, gradient difference, perceptual, and structural similarity losses. These loss components, along with adversarial and dual cycle-consistency losses, contribute significantly to realistic and accurate syntheses. Evaluation metrics including MAE, PMAE, RMSE, PCC, PSNR, and SSIM are employed to assess the fidelity of synthesized images compared to their ground truth counterparts. Empirical results indicate the effectiveness of the 3D Dual-CycleGAN model in generating T1c images from T1W inputs with minimal average discrepancies (MAE of 2.8 ± 2.61) and strong similarity (SSIM of 0.82 ± 0.28). Furthermore, the synthesis of T2W and FLAIR images yields promising outcomes, demonstrating acceptable average discrepancies (MAE of 3.87 ± 3.32 for T2W and 3.82 ± 3.32 for FLAIR) and reasonable similarities (SSIM of 0.82 ± 0.28 for T2W and 0.80 ± 0.29 for FLAIR) relative to the original images. Conclusions: These findings underscore the efficacy of the 3D Dual-CycleGAN model in generating high-fidelity images, with significant implications for diverse applications in the field of medical imaging. [ABSTRACT FROM AUTHOR]

This paper explores the consistent alignment of mediated intergroup contact and persuasive communication in prejudice reduction. Through a systematic review of literature, it provides a comprehensive overview of indirect mediated contact research (parasocial/vicarious contact) within the immigrant minority outgroup context. The study draws insights, particularly in message design, from persuasive communication for anti-immigrant prejudice reduction. Utilizing O'Keefe's [2016a. Communicator factors. In D. J. O'Keefe (Ed.), Persuasion: Theory and research (Third edition, pp. 130–157). SAGE] classification (message variables, recipient variables, and communicator variables), the review illuminates prevalent variables in current mediated contact studies and identifies opportunities and challenges for future research. The findings underscore the need for a more rigorous application of variables and geographical expansion in mediated contact empirical research. These results are discussed along with implications and recommendations for future research. [ABSTRACT FROM AUTHOR]

This Article is the first to empirically examine whether firms draft well-organized online contracts that consumers can easily read, navigate, and analyze. "Messy Contracts," as this Article dubs them, are contracts that lack organizational signals in the form of a table of contents and informative headings. Analyzing the sign-in-wrap agreements employed by the most popular 100 websites--such as Google, Facebook, LinkedIn, Tik-Tok, Walmart, Twitter, Instagram, YouTube, and Amazon--this Article finds that businesses routinely draft messy contracts. The importance of this finding goes beyond what initially meets the eye. Messy contracts (1) reduce consumers' comprehension; (2) impose excessive cognitive costs on consumers, consumer advocates, watchdog organizations, and adjudicators who wish to read or navigate the contract or part of it; (3) undermine consumers' ability to remember the contracts' content, and (4) deter consumers from reading contracts. Furthermore, messy contracts indicate that competition over contract terms is lacking and that firms can utilize the features of the online environment to draft consumer contracts without accounting for consumers' needs more generally. Alarmingly, messy contracts may also denote that term ignorance may drive consumers' assent. In all, messy contracts facilitate exploitation, often harming the most vulnerable consumers. Part II of this Article provides the theoretical background to messy contracts. It contextualizes the problem of messy contracts, linking them to the 'no-reading' problem and delineating their social costs. Part III contains the empirical test of this Article. It discusses the sample contracts, the empirical methodology, and our results. Against this background, Part IV considers the normative implications of messy contracts, suggesting ex ante regulatory measures and ex post judicial scrutiny. It also acknowledges the limitations of our proposals and replies to key critiques. [ABSTRACT FROM AUTHOR]

This article aims to highlight the influencing factors on omni-channel consumer attitudes towards virtual shopping channels, providing the literature with a new conceptual model that studies the use of technology by omni-channel consumers. The research hypotheses were established based on the literature review, and a conceptual model was defined. Quantitative research was carried out on an emerging market through the survey technique to verify the relations between the investigated concepts. In total, 307 responses from Millennials and Generation Z members were analyzed using structural equations modeling in SmartPLS. The results show that both channel and consumer characteristics, alongside their media contexts, influence the attitude and willingness to access and use retail channels. To keep up with constantly changing consumer needs, companies are advised to continually analyze the target market and implement any necessary measures. The paper expands the studies investigating the behavior of technology users, enhancing the UTAUT2 model-based literature. [ABSTRACT FROM AUTHOR]

This study designed a survey questionnaire encompassing three levels: research institutions, research environment, and research talents, with 19 specific measurement indicators. The questionnaire was distributed to research personnel from over 20 research institutes, universities and related enterprises in China. Based on 537 valid responses, a structural equation model (SEM) was constructed to analyze the relational effects of the driving factors. The findings are as follows: (1) The innovative capability of research talents is a composite outcome of the interaction between multiple "external-internal" factors. Effective organizational support from research institutions and a favorable research environment, both soft and hard, are indispensable elements in enhancing this capability. (2) A key factor in enhancing innovation capability is the proper resolution of effective supply and scientific utilization of research resources by research institutions, along with the improvement of research satisfaction. (3) Research institutions should establish an integrated research mechanism that combines "environment-organization-talent" to achieve the generation and effective transformation of high-quality research outputs. [ABSTRACT FROM AUTHOR]

Securing coordination between AI agent and teammates (human players or AI agents) in contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot Coordination. The issue of cooperative incompatibility becomes particularly prominent when an AI agent is unsuccessful in synchronizing with certain previously unknown partners. Traditional algorithms have aimed to collaborate with partners by optimizing fixed objectives within a population, fostering diversity in strategies and behaviors. However, these techniques may lead to learning loss and an inability to cooperate with specific strategies within the population, a phenomenon named cooperative incompatibility in learning. In order to solve cooperative incompatibility in learning and effectively address the problem in the context of ZSC, we introduce the Cooperative Open-ended LEarning (COLE) framework, which formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy. We present two practical algorithms, specifically COLESV and COLER, which incorporate insights from game theory and graph theory. We also show that COLE could effectively overcome the cooperative incompatibility from theoretical and empirical analysis. Subsequently, we created an online Overcooked human-AI experiment platform, the COLE platform, which enables easy customization of questionnaires, model weights, and other aspects. Utilizing the COLE platform, we enlist 130 participants for human experiments. Our findings reveal a preference for our approach over state-of-the-art methods using a variety of subjective metrics. Moreover, objective experimental outcomes in the Overcooked game environment indicate that our method surpasses existing ones when coordinating with previously unencountered AI agents and the human proxy model. Our code and demo are publicly available at https://sites.google.com/view/cole-2023. [ABSTRACT FROM AUTHOR]

Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an end-to-end system. This approach shows significant potential to revolutionize combinatorial decision-making in real-world applications that operate under uncertainty, where estimating unknown parameters within decision models is a major challenge. This paper presents a comprehensive review of DFL, providing an in-depth analysis of both gradient-based and gradient-free techniques used to combine ML and constrained optimization. It evaluates the strengths and limitations of these techniques and includes an extensive empirical evaluation of eleven methods across seven problems. The survey also offers insights into recent advancements and future research directions in DFL. [ABSTRACT FROM AUTHOR]

Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensemble detectors based on label feedback allows us to quickly discover true anomalies. This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also present empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup. [ABSTRACT FROM AUTHOR]

Financial literacy plays an important role in the investment behavior of households and companies within an economy. The increase in financial literacy might be influenced by the government's actions, as well as by the public events organized by financial institutions. This study aims to analyze the level of financial literacy within Romania by using a questionnaire. Obtaining 117 responses from 22 different fields of activity, the main findings show that respondents present a more advanced financial literacy level than those presented in previous empirical studies conducted so far in Romania. This might be explained by the high number of seminars offered free by financial institutions lately. Nevertheless, the media promotes constantly the importance of possessing financial knowledge. These results have significant implications for investment behavior. Those who are characterized by a higher level of financial knowledge are more inclined to save money or to invest in stock shares or bonds. [ABSTRACT FROM AUTHOR]

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre 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 aim of this paper is to investigate the possible association among profitability evaluated ROA and ROE and capital structure, represented by the debt ratio and debt to equity ratio with the share price of Jordanian banks. The chosen sample consists of 7 traditional banks operating in Jordan, and panel data analysis is utilized to examine the period spanning from 2015 to 2019. The model employed to analyze data is the fixed effect model. This study relied on (ROA, ROE, DR and DER) as independent variables, whereas the share price as dependent variable. The results reached reveal that a negative influence of debt ratio exists on Jordanian banks' share price, while positively impacted by ROA and ROE, however, DER has insignificant influence on share price that traded by Jordanian banks. The outcomes presented above align with the prevailing literature reviews included in this study, confirming the theoretical discussions concerning the interplay between capital structure, profitability, and share price. [ABSTRACT FROM AUTHOR]

This research aims to investigate the impact of adopting fintech solutions in HR. The efficiency of HR processes plays a vital role in the overall effectiveness of human resources management. The findings indicated that the efficiency of HR processes impact on adoption of Fintech solutions. Also, the findings confirm that employee experience significantly impacts adoption of Fintech solutions. in addition, the findings revealed a substantial impact of data security and Privacy on Adoption of Fintech Solutions. Moreover, the findings highlight the potential of fintech to improve the efficiency of HR processes. By leveraging fintech solutions, organizations can automate manual tasks, streamline workflows, and enhance data accuracy, leading to time and cost savings. Secondly, the integration of fintech in HR has the potential to enhance the employee experience. [ABSTRACT FROM AUTHOR]

During the realization of the work, we encountered obstacles and a lack of literature, both in the theoretical and empirical aspects. Also, during the review of the literature, we also encountered a lack of local research in terms of addressing these problems by researchers of different profiles, which belong to the topic that will be analyzed in this study. The purpose of the paper is to review and analyze the empirical literature related to the impact of financial statements on management decision-making, but empirical research in this field of study is relatively few, so in general empirical research is lacking, which makes it more difficult further expanding the research in this paper. The aim was to evaluate the relevance of accounting practice with accounting rules based on the theoretical framework based on the review of empirical research. [ABSTRACT FROM AUTHOR]

In modern conditions, the challenges caused by the growth of uncertainty lead to the formation of new conceptual views on the competitiveness of countries and the high importance of their practical application, which is especially relevant for countries on the path of integration into the European Union. This article delves into the issue of competitiveness within countries' socioeconomic systems, considering the influence of contemporary confrontational globalization. It argues that the concept of global sustainable competitiveness, along with its corresponding model, is fitting for addressing these challenges. The research is based on empirical methods, such as panel regression and structural equation modeling. This model concept can be used as a methodological framework in the process of building policies increasing competitiveness of the country. The results of the study are interesting for the systems representation of the problem of sustainability of the countries on the way to the EU membership, for the formation of the appropriate mechanisms for rise of promoting their sustainable competitiveness in the face of modern globalization. [ABSTRACT FROM AUTHOR]

With the continuous advancement of educational technology, interactive mobile and adaptive learning platforms are playing an increasingly important role in the field of education. This is particularly evident in the development of educational design skills among education students, as these platforms showcase their unique value. Educational design capability is a crucial skill for education students, directly related to the quality of designing and implementing future teaching activities. Traditional methods of education often fail to fully consider individual student differences, resulting in inadequate cultivation of personalized capabilities. This study aims to explore and achieve precise cultivation of educational design capabilities in education students through interactive, mobile, and adaptive learning platforms. This paper first reviews the current application of interactive mobile and adaptive learning platforms to cultivate educational design capabilities. It highlights deficiencies in existing research methods related to personalized matching and recommendation system design. To address these deficiencies, this study proposes a new set of adaptive matching methods. These methods include capability characteristic recognition based on competitive advantage thinking, the construction of individual strength models, as well as matching calculation, and decision-making scheme optimization using the projection decision method and the Hungarian method. Additionally, the study designs a learning project recommendation algorithm based on explicit ratings to enhance the accuracy and personalization of learning project recommendations. The application of these methods not only enhances the educational design capabilities of education students but also provides new theoretical support and practical guidance for the development of interactive, mobile, and adaptive learning platforms. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present research related to the analysis and evaluation of the applicability of project maturity models in organizations conducting research projects, and to propose a project maturity model that could be used in such organizations. The subject of the mixed research undertaken was how to measure the project maturity of organizations conducting research projects (using university as an example). The following research steps were performed: a literature review, empirical research, and a synthesis of the obtained knowledge. [ABSTRACT FROM AUTHOR]

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Objective This study investigated the efficacy of telerehabilitation (TR) in school-based Occupational Therapy (OT) for children with Specific Learning Disorder (SLD), focusing on occupational competence and parental satisfaction, aiming to contribute empirical insights to the discourse on the educational well-being of this population. Materials & Methods The study adopted a Randomized Controlled Trial (RCT) design involving 31 children diagnosed with SLD, implementing TR and inperson interventions alongside a control group. Outcome measures included the School Self-Concept Inventory, Child Occupational Self-Assessment (COSA), and Canadian Occupational Performance Measurement (COMP), analyzed using descriptive and inferential statistics (ANOVA, post hoc tests). Results Both TR and in-person interventions exhibited significant enhancements in academic self-efficacy (F=23.96, p<0.001, Partial η²=0.461), occupational competence (F=70.59, p<0.001, Partial η ²=0.716), and parent satisfaction (F=17.03, p<0.001, Partial η²=0.378) compared to the control group. Notably, no significant differences emerged between the TR and in-person groups, emphasizing their comparable effectiveness in improving outcomes. Conclusion In conclusion, the study demonstrated the efficacy of TR and inperson interventions in school-based OT for children with SLD. The cohesive outcomes in academic self-efficacy, occupational competence, and parental satisfaction highlight TR as a versatile modality. This research, grounded in robust methodology, encourages further exploration of TR's transformative role in enhancing the holistic well-being of children with SLDs. [ABSTRACT FROM AUTHOR]

Copyright of GWP: Gesellschaft Wirtschaft Politik is the property of Verlag Barbara Budrich GmbH 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.)

JSON Web Token (JWT) is a simple, compact way to share claims in a space-constrained environment. JWT is part of the interoperable JSON-based identity suite. Many libraries that provide JWT-based authentication and authorization exist. While the JWT standard is secure, some implementations still need to be made. This research paper delves into a comprehensive analysis of the prominent Python libraries utilized for JWT authentication. By meticulously examining these libraries, we aim to provide an in-depth understanding of their features and capabilities. Our investigation encompasses an enumeration of the distinct signing algorithms that are supported by each of these JWT Python libraries. To ensure the robustness and security of these libraries, we employ a multifaceted approach that utilizes various statistical application Security Testing (SAST) tools. These tools play a pivotal role in our assessment by not only evaluating the adherence of the codebase to the PEP8 standard but also by meticulously scanning for common security vulnerabilities and bugs that could potentially compromise the integrity of the authentication process. Our research goes beyond mere identification; we meticulously analyze each warning generated by the SAST tools, emphasizing those warnings that hold the most tremendous significance regarding potential security risks. Furthermore, our investigation extends to gauging the popularity and adoption of each library. To achieve this, we leverage GitHub statistics and harness the power of the Sourcegraph code search utility. By delving into these metrics, we gain a comprehensive view of the community's engagement, usage trends, and overall traction of each library. In summary, this paper thoroughly explores the landscape of JWT authentication in Python, encompassing library evaluation, security assessment, warning analysis, and popularity metrics. [ABSTRACT FROM AUTHOR]

Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users' understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users' understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants' interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the factors affecting the size of nongovernmental organizations (NGOs) from a multinational perspective. Empirical analyses were conducted through econometric modeling, and the data of the 17 countries from 2003 to 2019 were used. The data was obtained from the World Bank and OECD database. We used advanced panel data estimation techniques (CUP-FM and CUP-BC) to obtain long-run coefficients. GDP per capita, general government final consumption expenditure, adult education at tertiary level, and Gini index growth declined grants by private agencies and NGOs' contribution to GDP (GinGDP) initially but after a threshold level, GinGDP increased with growth at later stages of these variables. Urban population growth, employment, and foreign population have reducing effect on the size of NGOs. The size of NGOs has bidirectional causality relationship between GDP per capita, adult population at tertiary education level, employment to population ratio, and Gini index; and unidirectional causality between urban population growth, general government final consumption expenditure, and foreign population. [ABSTRACT FROM AUTHOR]

In the realm of big data, where datasets of immense scale pose processing challenges, distributed processing platforms like open-source Apache Spark have emerged to address these issues. Spark's internal configuration parameters exert varying impacts on execution times based on job characteristics, making manual optimization daunting. The core focus of this study lies in optimizing Spark's internal configurations, with specific attention directed towards three types of workloads: Iterative-intensive, Memory-intensive, and CPU-intensive. Employing Grid Search, Random Search, and Evolutionary Optimization algorithms yields substantial execution time reductions: 23.24% with Grid Search, 19.71% with Random Search, and 23.06% with Evolutionary Optimization. Notably, Evolutionary Optimization achieves optimal configurations approximately 29% faster than Grid Search. While Random Search and Evolutionary Optimization share similar time requirements, Random Search's execution time reduction for a given Spark workload is relatively lower. This research sheds light on algorithmic configuration tuning intricacies and its influence on Spark workload execution times, contributing to the exploration of optimizing big data processing platforms. [ABSTRACT FROM AUTHOR]

A small but growing share of sociological research recognizes the importance of perceptions of the future for explaining social outcomes. This article, taking sociology of knowledge perspective, provides a systematic study of sociological scholarship dealing empirically with perceptions of the future. It is based on a qualitatively driven mixed methods analysis of 571 sociological research articles published between 1950 and 2019 and distinguishes between three clusters of scholarship: in the first cluster authors consider future orientations as either dependent or independent variable; in the second cluster they analyze the multifaceted ways in which actors imagine the future; and in the third cluster they are interested in the politically contested construction and diffusion of future orientations. By investigating these clusters, the article demonstrates how sociologists pay attention to future orientations in a broad spectrum of sociological fields, using a variety of methods and asking a wide set of questions about assessments of the future. The future is increasingly a realm of sociological analysis. We show how this field of research is structured and has developed over time. Elaborating on different facets of research that so far operate largely in isolation from each other, we aim to contribute to the development of the field. Sociology could benefit from more systematically integrating perceptions of the future -- as they are reflected in actor expectations, aspirations, and future beliefs -- into the discipline's empirical investigations and explanatory models and from integrating the existing knowledge on these issues better. [ABSTRACT FROM AUTHOR]