Social dilemmas describe conflict situations between immediate self-interest and longer-term collective interests. In these situations, it is better that all players work together to attain a common goal, but individuals may threaten the best payoff of the group by free-riding. Human behavior in a pandemic is one example of a social dilemma but wait-and-see games and relying on herd immunity to get a free ride generates a threat of continuing the pandemic. This study aims to use probabilistic incentives given by a third party as a mechanism to inhibit free-riding behavior by promoting cooperation in the volunteer dilemma game. For more realistic human behavior simulation, we use an agent-based model of network topology. When the parameters of the problem change gradually, an abrupt jump in the cooperation rate may happen and lead to a significant shift in the outcome. Catastrophe theory is a valuable approach to survey these nonlinear changes. This study tries to give some managerial insights to the decision-makers to find the minimum level of necessary effort in which the cooperation dominates the defection.

This article introduces an innovative fuzzy-based approach for developing a comprehensive portfolio optimization model that effectively accounts for inherent uncertainty while incorporating the investor's unique perspective on the dynamic stock market. The multi-objective optimization framework employs Conditional Drawdown at Risk to enhance investor flexibility in determining risk tolerance and optimal investment strategies tailored to specific needs. The research is notable for its pioneering use of intelligent methods to systematically collect valuable data from the Tehran Stock Exchange under fuzzy uncertainty. It incorporates important constraints such as cardinality and ceiling and floor limits for each investment period, allowing for a detailed analysis of various stock market scenarios and potential future outcomes. A case study is conducted with 25 diverse assets from the top five industry groups based on profit per share, from which five shares are thoughtfully selected to effectively demonstrate the model's unique effectiveness. The analysis rigorously assesses the model's performance in real-world conditions, highlighting the importance of accurately understanding the current stock market outlook and trends. To validate the model, the research compares results with a portfolio constructed under similar conditions of certainty and risk. The findings indicate that portfolios created under certainty yield significantly higher values, suggesting that successful portfolio construction is heavily influenced by the prevailing market conditions experienced by investors.

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

Portfolio optimization aims to minimize risk and maximize return on investment by determining the best combination of securities and proportions. The variance in portfolio optimization models is typically used for a measure of risk. Over the last few decades, portfolio optimization utilizing a variety of risk measures has grown significantly, and many studies have been conducted. Therefore, this paper provides a systematic review of risk measures for portfolio optimization using bibliometric analysis and maps to analyze the evolution and trends of 682 articles published between 2000 and 2022. Throughout this analysis, communication networks among articles, authors, sources, countries, and keywords are explored. Furthermore, a classification of risks and risk measures were presented to demonstrate a comprehensive overview of the field, and the top 50 papers were analyzed to determine which risk measures were most often used in recent studies.

The industry has expressed significant concern regarding the issue of parallel machines and the influence of learning and deterioration. This research investigates non-identical parallel machine scheduling, taking into account the simultaneous consideration of learning effects, deterioration, and past-sequence-dependent setup times. Due to the existence of uncertain parameters in real-world scenarios, the processing times and due dates are assumed to be triangular fuzzy numbers. A fuzzy nonlinear mathematical model with two objective functions is presented and solved using the fuzzy Chance Constraint Programming approach. The two objectives are the summation of earliness and tardiness, as well as makespan. To achieve an efficient near-optimal Pareto front for the problem, a hybrid NSGA-II and VNS multi-objective meta-heuristic is proposed and the results are discussed. Finally, the augmented ε-constraint method is utilized to address issues with small dimensions. The computational analysis demonstrates the effectiveness of this proposed algorithm in tackling problems, especially those with substantial dimensions.

Due to the increasing technological advances in all fields, a considerable amount of data has been collected to be processed for different purposes. Data mining is the process of determining and an-alyzing hidden information from different perspectives to obtain useful knowledge. Data mining can have many various applications, one of them is in medical diagnosis. Today, many diseases are regarded as dangerous and deadly. Heart disease, breast cancer, and diabetes are among the most dangerous ones. This paper investigates 168 articles associated with the implementation of data mining for diagnosing such diseases. The study concentrates on 85 selected papers which have received more attention between 1997 and 2018. All algorithms, data mining models, and evaluation methods are thoroughly reviewed with special consideration. The study attempts to determine the most efficient data mining methods used for medical diagnosing purposes. Also, one of the other significant results of this study is the detection of research gaps in the application of data mining in health care.

This paper presents a scientometric and analytical examination of machine learning applications in human factors research using a Scopus indexed dataset of 1,708 publications from 2019 to 2025. Keyword cooccurrence analysis was conducted using VOSviewer with a minimum occurrence threshold of 10, resulting in a network of 75 keywords grouped into eight thematic clusters. The thematic structure indicates a dominant empirical stream focused on cognitive state assessment, especially mental workload and fatigue detection using physiological signals such as electroencephalography and deep learning methods. Complementary clusters emphasize human AI interaction, explainable AI, decision support, automation and human reliability, affective computing, ergonomics in Industry 4.0/5.0 contexts, and healthcare oriented clinical decision support and patient safety. Overlay analysis shows recent growth in generative AI, large language models, fairness, and human centered AI concepts in 2024-2025. An analytical synthesis identifies major gaps, including limited human-in-the-loop validation, weak cross domain generalizability, insufficient longitudinal evaluation of human AI collaboration, and emerging explainability work that remains under validated empirically. The results provide a structured map of the field and a focused research agenda for responsible, transparent, and effective human centered intelligent systems.

Lung cancer remains a major global health burden, and deep learning, especially convolutional neural networks, has become a widely used approach for CT-based detection and diagnostic support. This scientometric study maps how this research area has developed and how its main themes and collaborations are organized, using a workflow that combines bibliometrix (R) with VOSviewer. From a broader Scopus query result set, an exported core subset of 200 journal articles published between 2017 and 2025 was selected for in-depth bibliometric analysis, spanning 139 sources and including 1,659 cited references. The citation overview reports 7,167 total citations across 175 cited documents, corresponding to a corpus h-index of 42. Thematic mapping and keyword co-occurrence analyses show a stable CT-centric backbone that connects most studies, alongside faster-growing streams focused on model generalization and optimization, and smaller niche lines such as 3D CNN–driven lung nodule detection. Collaboration indicators point to moderate internationalization, with 43 multi-country publications (21.5%). Overall, the field is consolidating around CT-based deep learning pipelines, while a key gap remains in translation-ready evidence, multi-center external validation, robustness to protocol heterogeneity, and more consistent reporting practices to support reproducibility and clinical adoption.

The widespread application of quantitative measurements in evaluating research, though very attractive for its supposed objectivity and expediency, has on the other hand given rise to an intricate web of ethical issues and biases in the system. The current review not only critiques but strategically moves through a thorough system analysis revealing the limitations of metrics and their sociotechnical implications. Our first step is to map out the ethics involved and thereby set up rules for the proper use of metrics. The next stage is to look into the bias aspect of metrics and the various forms of bias such as issues of location and language, unfairness among different fields, and the ongoing divide between the genders. The whole matter of metric malpractice—gaming, manipulation, and the detrimental over-optimization of research integrity—are some of the things that we have extensively discussed in this paper. Likewise, we have raised the emerging trend's ethical implications, namely, altmetrics, visualization, and algorithmic evaluation, taking into account their capability of both widening influence and introducing additional types of bias. Alongside this, we provide a picture of recent empirical findings about the status of research ethics and the level of support from institutions. Lastly, we bring together a progressive agenda for change, which includes institutional accountability, the shaping of reflexive evaluation methodologies, and the essential incorporation of qualitative, expert opinion. We express that a major change in mentality is necessary—one that will place metrics in a supportive role in a holistic, qualitative, and ethically-grounded research evaluation ecosystem.

Therapeutic recovery services are considered essential towards restoration of functional independence and the quality of life across Canada. Greater needs and constraining opportunities have begun emphasizing assessing the relative efficiency of all rehabilitation centers to help with evidence-based policy and funding decisions. Thus, this article subject’s throughput, functional outcome, and budget allocations for ten Canadian provinces to analysis using DEA to provide a comparative look at service delivery and resource utilization. The results disclose that Prince Edward Island demonstrates the highest efficiency in utilizing rehabilitation budgets, followed closely by Nova Scotia, Manitoba, and Alberta. These provinces provide strong throughput and functional gains in spite of modest funding levels. In contrast, Quebec shows lower relative efficiency, suggesting potential gaps in resource deployment or care coordination. These results underscore the relevant importance of strategic investment and outcome-driven planning in rehabilitation policy, giving actionable insights for provincial health authorities and national benchmarking efforts.