Search Results (5,212)

Diversity, equity, and inclusion, most commonly known as DEI, serves as a framework for practices that promote the fair treatment and full participation of all members of a community. Culturally responsive teaching and critical pedagogy are commonly associated with DEI as part of the larger strategy to validate and inspire learners while improving their self-efficacy and using education to challenge oppressive systems. While DEI is becoming increasingly better known in higher education, Historically Black Colleges or Universities (HBCUs) are heralded in the literature as a model for this work. Nevertheless, there is relatively limited empirical research exploring facets of DEI and culturally responsive teaching on HBCU and other minority-serving institutions’ campuses. This paper examines the campus of an HBCU located in the Mid-Atlantic United States, with an institutional commitment to diversity, equity, and inclusion, via a comprehensive DEI climate study that included separate surveys of students and faculty/staff. More specifically, the study explored whether all community members consider the campus inclusive, whether all community members experience a culture of belonging, whether adequate resources and supports exist for all campus members to succeed, whether faculty exhibit culturally responsive teaching practices, and whether the perceptions of faculty and staff differ from those of students. The purpose of the study was to help address the gap in the DEI literature exploring the practices of minority-serving institutions. According to the results, participants found the University to be an inclusive place, expressing strong satisfaction with the campus climate and experience. Further, when the presence of culturally responsive teaching practices was explored, strong evidence was indicated. Possible areas for improvement include greater supports and resources for LGBTQIA+, Indigenous, and disabled community members. Full article

To better understand how teaching quality has been conceptualized and measured within the sub-field of mathematics education, we conducted a systematic review of 24 journals to identify instruments that have been used to measure mathematics teaching quality; which instruments have interpretation and use statements; and the validity, reliability, and fairness evidence for each instrument. We found 47 instruments with validity, reliability, and fairness evidence. These instruments primarily captured teachers’ enactment of specific teaching practices through classroom observations or student questionnaires. Some instruments captured approximations of practice through teacher questionnaires or interviews. Only two instruments presented an integrated interpretation and use argument (IUA) framework, although eleven included at least one component of an IUA framework. We found that measure developers were most likely to present reliability evidence and evidence related to test content, internal structure, and relations to other variables. They were least likely to present evidence related to response processes, consequences of testing, or fairness. These findings suggest that although there are many instruments of mathematics teaching quality, instrument developers still have considerable work to do in collecting and presenting validity and fairness evidence for these instruments. Full article

This paper establishes dividend-based representations for two influence assessments. First, we define a system of min-dividends derived from the minimal-influence evaluation via a unique linear decomposition using unanimity-type spanning models. Building on this, we further construct a pair of internal and external min-dividends satisfying Completeness and Balancedness conditions, through which we express the stable min-value as the net difference of internal gains and external losses. We then demonstrate that the minimal self-stable value can be represented as accumulated average min-dividends across all coalitions they have participated in. Furthermore, the proposed expression also is adopted to analyze the stability of the minimal self-stable value. These results extend the classical notion of dividends into a minimal-influence-based framework with potential applications in fair resource allocation and responsibility apportionment. Full article

The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over the past 25 years. Through a comprehensive search on Scopus and backward reference searches via Google Scholar, 87 of the most relevant papers were identified. The investigation focused on the application context, ML techniques employed, data sources, and the implications of contextual nuances for safety analysis outcomes. ML techniques have been effective for post-accident analysis, predictive, and real-time incident detection across diverse aviation scenarios. Supervised, unsupervised, and semi-supervised learning methods, including neural networks, decision trees, support vector machines, and deep learning models, have all been applied for analyzing accidents, identifying patterns, and forecasting potential incidents. Notably, data sources such as the Aviation Safety Reporting System (ASRS) and the National Transportation Safety Board (NTSB) datasets were the most used. Transparency, fairness, and bias mitigation emerge as critical factors that shape the credibility and acceptance of ML-based safety research in aviation. The review revealed seven recommended future research directions: (1) interpretable AI; (2) real-time prediction; (3) hybrid models; (4) handling of unbalanced datasets; (5) privacy and data security; (6) human–machine interface for safety professionals; (7) regulatory implications. These directions provide a blueprint for further ML-based aviation safety research. This review underscores the role of ML applications in shaping aviation safety practices, thereby enhancing safety for all stakeholders. It serves as a constructive and cautionary guide for researchers, practitioners, and decision-makers, emphasizing the value of ML when used appropriately to transform aviation safety to be more data-driven and proactive. Full article

Traditional Software-Defined Wide Area Network (SD-WAN) solutions lack adaptive load-balancing mechanisms, leading to inefficient traffic distribution, increased latency, and performance degradation. This paper presents an Application-Aware Dynamic Load Balancing (AADLB) framework designed for open-source SD-WAN environments. The proposed solution enables dynamic traffic routing based on real-time network performance indicators, including CPU utilization, memory usage, connection delay, and packet loss, while considering application-specific requirements. Unlike conventional load-balancing methods, such as Weighted Round Robin (WRR), Weighted Fair Queuing (WFQ), Priority Queuing (PQ), and Deficit Round Robin (DRR), AADLB continuously updates traffic weights based on application requirements and network conditions, ensuring optimal resource allocation and improved Quality of Service (QoS). The AADLB framework leverages a heuristic-based dynamic weight assignment algorithm to redistribute traffic in a multi-cloud environment, mitigating congestion and enhancing system responsiveness. Experimental results demonstrate that compared to these traditional algorithms, the proposed AADLB framework improved CPU utilization by an average of 8.40%, enhanced CPU stability by 76.66%, increased RAM utilization stability by 6.97%, slightly reduced average latency by 2.58%, and significantly enhanced latency consistency by 16.74%. These improvements enhance SD-WAN scalability, optimize bandwidth usage, and reduce operational costs. Our findings highlight the potential of application-aware dynamic load balancing in SD-WAN, offering a cost-effective and scalable alternative to proprietary solutions. Full article

The aquarium trade is one of the main pathways for the introduction of non-native freshwater species. Such species include crayfish, which are valued ornamental animals commonly kept in aquaria. Some crayfish have been released into the environment, becoming invasive alien species. Due to the threat they pose to biodiversity and related ecosystem services, they have been subject to legal restrictions as invasive alien species of Union concern. In Poland, examples of species that have entered aquatic ecosystems this way include red swamp crayfish Procambarus clarkii and the marbled crayfish Procambarus virginalis. Given the highly developed aquarium pet trade in Poland, a detailed analysis of the availability of crayfish in the pet trade was conducted. This study examines the presence and sale of crayfish at locations at zoological trade fairs, shops, and online marketplaces in Poland. Additionally, pricing, the volume of imported crayfish, and their welfare in the year 2024 are presented. Crayfish were recorded in all surveyed locations. In shops and zoological trade fairs, five crayfish species were recorded, including the invasive alien species P. clarkii and C. destructor. Online advertising platforms featured 15 crayfish species, of which four were invasive alien species. Cambarellus patcuarensis was the most commonly sold species in all examined places. Crayfish were often kept in poor condition. Furthermore, many sellers probably disguised the species of crayfish being sold illegally. This work shows that trade in invasive alien crayfish species is still widespread in Poland. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

In this article, I argue that the state creates legal advice deserts in immigration and asylum by designing law and policy which drive up legal need, driving down provision through unfavourable conditions for providers, and by placing people in need into areas from which they have no realistic prospect of accessing legal advice and representation. I draw on frameworks of spatial justice and of demand to analyse the impact of the legislative and policy developments in the Special Issue’s focal period of 2022–24 on legal aid in each of the UK’s three legal aid systems: England and Wales, Scotland, and Northern Ireland. The legislative changes included introducing new stages into asylum law, which created new legal needs. Policy changes drove a wholesale geographical shift in demand as all local authorities in the UK (except Scilly) now host people in the asylum process. The changes depended upon the involvement of legal aid lawyers in order to be workable, but the marketised model of legal aid provision in England and Wales, and the low-paid laissez faire model in Northern Ireland, are fundamentally incompatible with that demand. I conclude by arguing that legal aid cannot be an afterthought. Asylum policy should be shaped to reduce failure demand, while legal aid policy should be funded and designed so as to pay for the necessary provision, with interventions to remove the spatial inequalities in access to (legal) justice. Full article

This study presents a novel and interpretable, deployment-ready framework for predicting cybersecurity incidents through item-level behavioral, cognitive, and dispositional indicators. Based on survey data from 453 professionals across countries and sectors, we developed 72 logistic regression models across twelve self-reported incident outcomes—from account lockouts to full device compromise—within six analytically stratified layers (Education, IT, Hungary, UK, USA, and full sample). Drawing on five theoretically grounded domains—cybersecurity behavior, digital literacy, personality traits, risk rationalization, and work–life boundary blurring—our models preserve the full granularity of individual responses rather than relying on aggregated scores, offering rare transparency and interpretability for real-world applications. This approach reveals how stratified models, despite smaller sample sizes, often outperform general ones by capturing behavioral and contextual specificity. Moderately prevalent outcomes (e.g., suspicious logins, multiple mild incidents) yielded the most robust predictions, while rare-event models, though occasionally high in “Area Under the Receiver Operating Characteristic Curve” (AUC), suffered from overfitting under cross-validation. Beyond model construction, we introduce threshold calibration and fairness-aware integration of demographic variables, enabling ethically grounded deployment in diverse organizational contexts. By unifying theoretical depth, item-level precision, multilayer stratification, and operational guidance, this study establishes a scalable blueprint for human-centric cybersecurity. It bridges the gap between behavioral science and risk analytics, offering the tools and insights needed to detect, predict, and mitigate user-level threats in increasingly blurred digital environments. Full article

This study highlights the role of forensic psychology in supporting juvenile offenders within the Portuguese Justice System. Forensic psychologists ensure that legal proceedings are developmentally appropriate and psychologically informed, helping minors to understand and participate in the process. Data from the Victims Information and Assistance Office (GIAV) show that, between 2020 and 2025, 87 juvenile offenders (54 boys and 33 girls) were supported, with theft and drug trafficking being the most common crimes. Girls were more often involved in theft, and boys were more often involved in drug trafficking. Forensic psychology adds critical value by promoting rehabilitation, safeguarding rights, and contributing to fair, proportionate, and context-sensitive decisions. Full article

Simulated (or measured) photoabsorption spectra often provide the first indication of how matter interacts with light when irradiated by some radiation source. In addition to the direct, often slowly varying photoabsorption cross-section as a function of the incident photon frequency, such spectra typically exhibit numerous resonances and edges arising from the interaction of the radiation field with the subvalence or even inner-shell electrons. Broadly speaking, these resonances reflect photoexcitation, with its subsequent fluorescence, or the autoionization of bound electrons. Here, a (relativistic) cascade model is developed for estimating the photoabsorption of (many) atoms and multiply charged ions with a complex shell structure across the periodic table. This model helps distinguish between level- and shell-resolved, as well as total photoabsorption, cross-sections, starting from admixtures of selected initial-level populations. Examples are shown for the photoabsorption of C${}^{+}$ ions near the $1s-2p$ excitation threshold and for Xe${}^{2+}$ ions in the photon energy range from 10 to 200 eV. While the accuracy and resolution of the predicted photoabsortion spectra remain limited due to the additive treatment of resonances and because of missing electronic correlations in the representation of the levels involved, the present implementation is suitable for ions with quite different open-shell structures and may support smart surveys of resonances along different isoelectronic sequences. Full article

This study examines how managerial leadership styles influence the perceived effectiveness of compensation systems and employee performance. While prior research on organizational control has focused on optimizing compensation structures, it often neglects the role of managers within these systems. Drawing on survey data from a large international bank in Serbia, the study finds that transformational leadership enhances employees’ perceptions of compensation system effectiveness. Furthermore, managers who rely more extensively on relational contracts foster greater intrinsic motivation and perceptions of fairness, thereby increasing system effectiveness. The study also reveals that managerial performance evaluations significantly affect employee productivity—but only when the compensation system is perceived as effective. This research contributes to the literature on leadership by highlighting the substantial impact of leadership styles on the use and outcomes of relational contracts within organizations. Full article

Background: Syndromic forms of obesity are uncommon, complicated illnesses that include early-onset obesity along with other clinical characteristics such as organ-specific abnormalities, dysmorphic symptoms, and intellectual incapacity. These syndromes frequently have a strong genetic foundation, involving copy number variations, monogenic mutations, and chromosomal abnormalities. Methods: Using terms like “syndromic obesity,” “genetic diagnosis,” and “monogenic obesity,” a comprehensive literature search was conducted to find articles published between 2000 and 2025 in PubMed, Scopus, and Web of Science. Peer-reviewed research addressing the clinical, molecular, or genetic aspects of syndromic obesity were among the inclusion criteria. Conference abstracts, non-English publications, and research without genetic validation were among the exclusion criteria. The whole genetic, clinical, diagnostic, and therapeutic domains were thematically synthesized to create a thorough, fact-based story. Research using chromosomal microarray analysis (CMA), whole-exome sequencing (WES), next-generation sequencing (NGS), and new long-read sequencing platforms was highlighted. Results: Despite the fact that molecular diagnostics, especially NGS and CMA, have made tremendous progress in identifying pathogenic variants, between 30 and 40 percent of instances of syndromic obesity are still genetically unexplained. One significant issue is the variation in phenotype across people with the same mutation, which suggests the impact of environmental modifiers and epigenetic variables. In addition, differences in access to genetic testing, particularly in areas with limited resources, can make it difficult to diagnose patients in a timely manner. Additionally, recent research emphasizes the possible contribution of gene–environment interactions, gut microbiota, and multi-omic integration to modifying disease expression. Conclusions: Syndromic obesity is still poorly understood in a variety of groups despite significant advancements in technology. Multi-layered genomic investigations, functional genomic integration, and standardized diagnostic frameworks are necessary to close existing gaps. The development of tailored treatment plans, such as gene editing and focused pharmaceutical therapies as well as fair access to cutting-edge diagnostics are essential to improving outcomes for people with syndromic obesity. Full article

Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors. Full article

This study investigates how the integration of artificial intelligence (AI) transforms job practices within a leading European infrastructure company. Grounded in the Feeling Economy framework, the research explores the shift in task composition following AI implementation, focusing on the emergence of new roles, required competencies, and the ongoing reconfiguration of work. Using a qualitative, single-case study methodology, data were collected through semi-structured interviews with ten employees and company documentation. Thematic analysis revealed five key dimensions: the reconfiguration of job tasks, the improvement of efficiency and quality, psychological and adaptation challenges, the need for AI-related competencies, and concerns about dehumanisation. Findings show that AI systems increasingly assume repetitive and analytical tasks, enabling workers to focus on strategic, empathetic, and creative responsibilities. However, psychological resistance, fears of job displacement, and a perceived erosion of human interaction present implementation barriers. The study provides theoretical contributions by empirically extending the Feeling Economy and task modularisation frameworks. It also offers managerial insights into workforce adaptation, training needs, and the importance of ethical and emotionally intelligent AI integration. Additionally, this study highlights that the Feeling Economy must address AI’s epistemic risks, emphasising fairness, transparency, and participatory governance as essential for trustworthy, emotionally intelligent, and sustainable AI systems. Full article