Search Results (19,738)

Professional competency frameworks are important for aligning educational outcomes with workforce needs. While multiple frameworks exist across sectors, the underlying research-informed design principles guiding their development remain fragmented. This systematic review synthesizes methodological approaches and proposes research-informed design principles used in developing professional competency frameworks across diverse professions, identifying common patterns and informing future framework design. A systematic review was conducted following PRISMA 2020 and SWiM guidelines. Searches across major academic databases yielded 3656 records. After screening, 47 studies met inclusion criteria. Data extraction focused on methodological processes and development activities. Thematic analysis was used to generate a set of initial design principles: (1) Foundational Inquiry and Evidence Gathering; (2) Consensus-Building and Collaborative Validation; and (3) Framework Development and Iterative Refinement. The development of competency frameworks is inherently cyclical, interdisciplinary, and iterative, blending empirical inquiry with collaborative validation. The identified research-informed design principles offer a transferable blueprint applicable across sectors, from healthcare to education and other industries. Thus, it is strongly recommended that future studies use these initial research-informed design principles to inform competency development. The systematic review has been registered to Open Science Framework (OSF). Full article

With the rapid and ongoing expansion of urban areas, the need for accurate, reliable, and regularly updated topographic maps has become increasingly critical for planning and sustainable development. While traditional aerial photogrammetry—whether analog or digital—has long been the standard for such tasks, it remains costly, time-consuming, and logistically demanding, particularly when large or inaccessible regions are involved. This study proposes an alternative approach based on very-high-resolution satellite imagery, focusing specifically on data acquired from Morocco’s Mohammed VI A and B satellites. The research evaluates the capacity of this satellite imagery to support large-scale topographic mapping, both in terms of geometric accuracy and the ability to identify essential urban features. To validate the results, we conducted a comparative analysis of satellite data with conventional photogrammetric imagery from analog cameras (RMK TOP) and digital sensors (ADS, DMC), using ground control points (GCPs) and differential GPS (DGPS) measurements for calibration and accuracy assessment. The outcomes demonstrate that planimetric accuracy from satellite imagery meets the required standards for mapping at 1:10,000 and 1:5000 scales. However, altimetric accuracy is closer to the upper permissible limits, especially in applications requiring finer detail. While major urban elements such as roads, buildings, and vegetation are well identified, smaller infrastructure components, such as power lines, remain challenging to detect. Despite these limitations, the study highlights the growing potential of satellite imagery as a cost-effective and operationally efficient alternative to traditional methods, particularly in rapidly evolving urban environments where frequent map updates are essential. Integration with GeoAI workflows is identified as a key direction for future research and is not part of the current methodology. Full article

Research on dishonest behavior within organizational contexts has expanded rapidly in recent years. However, the structural organization of dominant explanatory mechanisms within this literature remains insufficiently clarified. This study provides a theory-informed bibliometric analysis focusing on a deliberately selective segment of the workplace dishonesty literature. Rather than attempting an exhaustive census, the study maps a corpus centered on dominant socio-cognitive and organizational explanatory frameworks in order to examine how these mechanisms are positioned, interconnected, and evolving within this theory-filtered segment. To ensure a transparent and reproducible review process, the study was conducted in accordance with the PRISMA 2020 guidelines, which guided the identification, screening, and eligibility assessment of the literature. Drawing on a systematically constructed corpus retrieved from Web of Science and Scopus and covering the period 1989–2025, the bibliometric analysis was conducted using Biblioshiny 4.5.2 on a final dataset of 679 documents. The analysis integrates performance indicators with science-mapping techniques, including keyword co-occurrence networks, thematic mapping, multiple correspondence analysis, thematic evolution, and global citation analysis. The findings indicate that this theory-based subset of the literature has developed steadily over time alongside a clearer structuring of publication outlets. Conceptually, it remains largely organized around a small number of recurring mechanisms, most notably ethical climate and moral disengagement. Thematic analyses suggest a degree of theoretical stabilization alongside diversification within this selected corpus, while factorial mapping suggests recurring contrasts between cognitive, normative, and organizational explanatory logics. From a longitudinal dynamic perspective, the mapped patterns suggest a possible movement toward more context-sensitive and governance-oriented perspectives; however, this should be interpreted as an inferential reading of this selected corpus. Overall, the findings suggest that, within this corpus, unethical workplace behavior is increasingly conceptualized as a context-dependent socio-cognitive phenomenon shaped by justificatory mechanisms, organizational environments, and performance-related pressures. This review contributes to the fields of behavioral ethics and organizational behavior by providing a structured reading of this specific body of work, clarifying its conceptual organization, identifying its main developmental trajectories, and outlining a theoretically grounded future research agenda for this selected body of literature. Full article

Background: Thyroid cancer (TC) represents the most common endocrine malignancy worldwide, with incidence increasing rapidly across diverse geographic regions. However, population-based evidence from North Africa remains limited, and comprehensive longitudinal analyses examining sex-specific incidence patterns, histological subtypes, and trends in tumor extension are lacking in Tunisia. Aim: This study aimed to (i) quantify TC incidence trends by sex and age group, (ii) characterize histological subtype-specific temporal patterns and tumor extension at diagnosis in northern Tunisia between 2000 and 2018, and (iii) to address projections in incidence by sex until 2040. Methods: A retrospective, population-based registry study was conducted using data from the Northern Tunisia Cancer Registry (NTCR), covering 11 governorates with a population of 5,233,700 in 2018. All primary invasive TC cases diagnosed between 2000 and 2018 were included (n = 3639). Age-standardized incidence rates (ASIRs) were calculated using the WHO standard population. Temporal trends were assessed using Joinpoint regression to estimate average annual percentage change (AAPC) with 95% confidence intervals. Projections of TC incidence to 2040 were generated using Bayesian autoregressive age–period–cohort models. Results: TC incidence increased significantly between 2000 and 2018, with overall ASIR rising from 2.8 to 5.0 per 100,000 person-years (AAPC = 3.8%, p < 0.001). In males, ASIR increased from 0.9 to 2.4 (AAPC = 3.0%, p < 0.001), while in females it rose from 3.7 to 7.8 (AAPC = 4.3%, 95% CI: 3.0–5.7; p < 0.001). The increase was predominantly driven by papillary thyroid carcinoma (PTC) (AAPC = 6.4% in males; 5.8% in females; both p < 0.001), whereas follicular thyroid carcinoma (FTC) remained stable. Notably, the proportion of metastatic cases decreased significantly in females (AAPC = −7.2%, p = 0.033), and the proportion of regionally advanced disease decreased in males (AAPC = −5.0%, p = 0.034). Conclusions: This population-based study demonstrates a sustained rise in TC incidence in northern Tunisia, disproportionately affecting women and largely driven by papillary histology. The concurrent increase in TC incidence alongside a reduction in regional and metastatic extension at diagnosis occurred. These findings have important implications for cancer prevention and control, highlighting the need for risk-adapted screening strategies and rationalized diagnostic practices. Full article

Student well-being, encompassing mental, social, cognitive, and behavioral domains, is increasingly compromised by academic stress, social isolation, and sedentary lifestyles. Horticulture-based interventions (HBIs), involving plant-based activities, have shown potential in promoting holistic health across populations. Nevertheless, no systematic review has synthesized global evidence for its effects on students. This systematic review aimed to evaluate HBI’s impact on students’ well-being, synthesizing global evidence to inform educational and therapeutic practices. This systematic review was registered in PROSPERO (CRD420251250712). Following PRISMA guidelines, we searched PubMed, Web of Science, MEDLINE, EMBASE, and APA PsycInfo from inception to 30 June 2025. Keywords were used to search for related articles. Fifteen studies (n > 2000 students, aged 5–18 years) from South Korea (n = 8), Taiwan (n = 3), Chinese Mainland (n = 1), Hong Kong, China (n = 1), Italy (n = 1), and the United States (n = 1) were included for analysis. Results showed that HBI has the potential to enhance emotional/psychological well-being (e.g., stress reduction, mood improvement), social well-being (e.g., peer relations, social skills), cognitive and education benefits (e.g., attention, academic attitudes), and physical and overall health benefits (e.g., physical activity, quality of life). HBI may contribute to multifaceted student well-being, particularly emotional and social domains. This systematic review provides a reference for educators to integrate horticultural programs into the curriculum. Government and school policies may consider funding school gardens. Future randomized controlled trials with diverse populations are needed to address limitations like small samples and geographic bias. Full article

Artificial intelligence (AI) is increasingly embedded in students’ learning practices, yet little is known about how AI engagement evolves from an external technological aid into an agentic component of self-regulated learning. This study applies psychological network analysis to examine the structural relations among students’ knowledge of AI, perceived value and perceived cost of AI, intention to use AI, and three core self-regulated learning processes—forethought, performance control, and self-reflection—across different levels of AI use frequency. The study was conducted on a sample of 673 university students and early-career graduates. Networks were estimated using EBICglasso for the full sample and separately for low-, moderate-, and high-frequency AI users. Across all models, a stable two-system organization emerged, consisting of an AI appraisal subsystem (knowledge, value, cost, intention) and a self-regulation subsystem (forethought, performance control, self-reflection). However, the connectivity between these subsystems differed systematically by usage frequency. Among low-frequency users, perceived cost was more prominently positioned within the appraisal subsystem, suggesting that cost-related concerns may be more salient in lower-frequency use contexts. In contrast, in the moderate- and high-frequency groups, performance control appeared more centrally positioned at the interface between appraisal and self-regulation, suggesting stronger alignment between AI-related appraisals and performance-level regulatory processes in these groups. Students’ knowledge of AI displayed context-dependent structural roles across networks, consistent with a variable relational position across use-frequency groups. Overall, the findings suggest that AI appraisal and self-regulated learning form partially distinct but interconnected subsystems, and that their configuration may vary across AI use-frequency groups. Because subgroup comparisons were descriptive and formal stability analyses were not conducted, these findings should be interpreted as exploratory. The results do not support causal or developmental inference and require replication using bootstrapped stability analyses and formal network comparison procedures. Full article

Singular Design Foresight (SDF) is proposed as a foundational methodological framework for advancing Design Foresight (DF) toward a more explicit, traceable, and evaluable scientific discipline. The framework formalizes DF as a structured cycle in which qualitative foresight inputs—such as signals, trends, and expert interpretations—are progressively transformed into analyzable representations that support decision closure under conditions of structural uncertainty. SDF combines an expert-defined conceptual universe with semantic projections to relate textual and contextual evidence to anticipatory constructs, enabling the generation of traceable indicators and structured configurations of viable futures. Within this architecture, the Stakeholder Viability Principle (SVP) functions as a filtering mechanism that delimits relevant futures according to continuity, agency, and axiological coherence, while Social Singularity captures context-specific critical transitions that shape when and why decision closure becomes necessary. The framework is organized in alignment with Design Science Research (DSR), adopting an evaluation logic centered on validity, utility, and attribution. Rather than presenting conclusive system-level validation, the article synthesizes summative evidence from previously published studies on semantic projections, singularity detection, and mixed expert–corpus foresight applications to support the plausibility, internal coherence, and operational feasibility of the proposed framework, while delimiting full integrated validation as a future research objective. SDF does not aim to provide deterministic prediction; instead, it enables auditable anticipatory representations and justified closure under uncertainty. In this sense, the framework is compatible with forecasting understood as the production of evaluable anticipations under explicit assumptions, while preserving the interpretive and situated character of strategic decision-making. Full article

Writing-intensive courses help undergraduate students develop disciplinary knowledge and communication skills, yet many students, particularly first-generation college students and those writing in a second language, enter these courses with low confidence and high writing anxiety. Writing self-efficacy, or students’ beliefs about their ability to succeed as writers, is associated with motivation and academic success, but less is known about how instructional practices shape its development. This qualitative study examined how students experienced instructional practices in a writing-intensive public health course and how these experiences influenced writing self-efficacy. Data were collected through a focus group with six undergraduate students and analyzed using a deductive thematic approach guided by Bandura’s four sources of self-efficacy. Students identified scaffolded assignments, opportunities for revision, and explanatory feedback as key facilitators of writing self-efficacy. Supportive classroom relationships, including proactive instructor outreach and consistent feedback, also appeared to foster confidence. Barriers included linguistic challenges, limited academic role models, and negative experiences with writing support services. These findings suggest writing self-efficacy may develop through the interaction of structured instructional practices and supportive classroom environments. Full article

Health chatbots have been growing into a necessary tool for dealing with risky and important contexts, such as medical and health information seeking. Meanwhile, trust towards chatbots influences people’s willingness to embrace technology and use it consistently. Thus, it is important to explore the mechanism of forming trust towards the health chatbots. The TAM has been introduced to explain the mechanism. This study extends the TAM framework by incorporating perceived risk and self-efficacy to develop an expanded model that explains the mechanisms underlying trust formation in health chatbots, applying a survey and investigating 480 Chinese chatbot users on the Credamo. The findings show that perceived risk reduces trust both directly and indirectly through perceived usefulness, perceived ease of use, and self-efficacy. Both parallel and serial mediation pathways were supported. These results offer a more complete insight into trust formation in high-risk AI contexts and provide practical guidance for chatbot design and governance in health communication. Full article

Across modern Asia and many other regions, artificial intelligence is transforming religious life in diverse and profound ways. Robot priests chant sutras at Japanese Buddhist temples, AI-powered apps offer personalised coaching in Quranic recitation to millions of Muslims, and bereaved families consult algorithm-generated avatars of the deceased in China. They are neither merely tools for instrumental use nor channels for transmitting pre-existing religious authority. Instead, they create new forms of religious content, new types of spiritual encounters for religious users, and new structures of authority. This paper argues that understanding these phenomena requires theoretical innovation beyond simply applying existing concepts to new domains. Drawing on Actor–Network Theory, algorithmic culture studies, and scholarship on Asian religious traditions, the paper proposes the theoretical framework of generative charisma, theorising how AI systems gain religious authority through three interconnected mechanisms: captivation by generation, intimacy trust through personalisation, and oscillating enchantment. It also highlights accountability as a structural issue that needs critical discussion regarding governance. The paper demonstrates the framework’s usefulness by examining AI recitation coaching in Islamic practice and AI grief avatars in Chinese Buddhist mourning, showing its relevance across different religious traditions and technological forms. Full article

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the evolving and adaptive strategies employed by fraudsters. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for detecting fraudulent activities in large-scale financial datasets. This paper presents a comprehensive survey of ML/DL approaches for financial fraud detection. The survey systematically reviews existing research across multiple methodological paradigms, including classical supervised learning, anomaly detection, graph-based methods, deep neural networks, multimodal architectures, and cost-sensitive learning frameworks. Particular emphasis is placed on emerging techniques such as graph neural networks, transformer-based architectures, and federated learning approaches designed to address privacy and scalability challenges. In addition to reviewing model architectures, this work analyzes key challenges inherent to fraud detection systems, including extreme class imbalance, concept drift, adversarial behavior, data privacy constraints, and real-time deployment requirements. Furthermore, the survey examines evaluation methodologies, highlighting the limitations of commonly used metrics and discussing more realistic evaluation strategies that incorporate operational costs and risk management considerations. This paper also provides a structured taxonomy of fraud detection methods, comparative analyses of commonly used datasets, and a synthesis of current research trends. Finally, open challenges and promising research directions are identified, including adaptive learning systems, interpretable Artificial Intelligence models, graph-based behavioral modeling, and privacy-preserving collaborative fraud detection frameworks. Full article

Since the early 2000s, there has been rising research interest in using tangible user interfaces (TUIs) in geospatial education, terrain modeling and analysis, landscape design and planning, and collaborative decision making. Many of these systems explicitly model geospatial data and allow users to interact with complex computational workflows by direct manipulation of a shared tangible interface. However, prior research has largely examined these systems within disciplinary silos and with a wide variety of terminology, limiting synthesis and cross-domain applicability. To address this gap, we define a unifying term, geocomputational tangible user interfaces (G-TUIs), and establish a set of criteria for identifying such systems. We then conduct a systematic literature review to examine the types of technologies (interfaces, sensors, software) used and how these systems are applied across different fields. We find G-TUIs are most commonly applied in educational and urban or landscape design contexts, yet empirical evidence evaluating their effectiveness remains limited. We highlight the potential for these systems in participatory approaches to social–environmental challenges and provide four case studies from our own work that demonstrate how geocomputational TUIs can be impactful and purposeful in education, participatory science, and stakeholder collaboration. We conclude by highlighting current research directions, challenges, and future research opportunities. Full article

Cognition in older adults may be influenced by environmental factors; however, the pathways linking environmental exposures and cognition remain unclear. The aim of this narrative review is to synthesize evidence on the association between the environment and cognition in older adults, integrating biological, environmental, and behavioral elements. Systematic reviews and original studies addressing this topic were identified in Web of Science, PubMed, and Scopus. The primary neural processes associated with maintaining cognition during aging are neuronal plasticity and compensatory scaffolding. Participation in intellectually stimulating activities, physical exercise, and a healthy diet; mitigation of chronic stress; reduction in the severity of depressive symptoms; and buffering against the adverse effects of air pollution are proposed as plausible pathways that may mediate the relationship between neural processes and the environment. In this context, environmental factors that affect cognition can be classified at three levels: (i) micro-level (family and home): social interaction with family members and indoor pollution; (ii) meso-level (community and services): social interaction, land-use diversity, transportation systems, environmental design, and urban green spaces; and (iii) macro-level (society in general and public policies): social representations of old age and aging (positive aging vs. ageism) and public policies aimed at improving pathways related to cognitive maintenance. Overall, the environment may influence cognition in older adults; however, the available studies show methodological and conceptual heterogeneity, inconsistent findings, and important gaps in knowledge. Full article

Some adolescents prefer parents as their attachment figures, while others prefer peers, such as romantic partners and friends. However, how these attachment preferences influence (mal)adjustment is unclear. This study aimed to examine the longitudinal associations between adolescents’ preferences for attachment figures and their (mal)adjustment. We recruited 215 Czech adolescents (M_age = 14.02 in the 1st year, SD = 2.05, ranging between 11 and 18 years; girls = 54%) and utilized data from the adolescents’ reports of their attachment preferences in the 1st year of this project. In addition, adolescents, parents, and teachers reported adolescents’ (mal)adjustment over two years. The results showed that adolescents’ attachment preferences for mothers were longitudinally associated with lower parent-reported externalizing problems. On the other hand, attachment preferences for peers predicted lower teacher-reported internalizing problems. The findings suggest that attachment preferences for parents were linked to some more favorable adjustment outcomes, and that attachment preferences for peers may be more positively associated with adjustment when accompanied by attachment preferences for parents. Full article

Accurate thermal characterization of building insulation materials is essential for reliable energy performance assessment, regulatory compliance, and the development of high-performance envelopes. On one hand, the growing adoption of innovative insulating products, such as nanoporous materials, aerogel-based composites, bio-based panels, and thin insulating coatings, helps to enhance buildings’ energy efficiency by means of sustainable raw materials. On the other hand, conventional measurement techniques encounter significant challenges, due to their heterogeneity, reduced thickness, and unconventional geometries. In this study, an intra-laboratory comparison of three widely used methods for thermal conductivity determination is presented: the Transient Plane Source (TPS, Hot Disk) method, the Guarded Hot Plate (GHP) method, and the Heat Flow Meter (HFM) method. A total of twelve insulating materials, spanning super-insulating cores, insulating renders, bio-based panels, and nanocomposite coatings, were experimentally characterized under controlled laboratory conditions. A view on the analyzed insulating materials’ cradle-to-grave environmental impact is also given, to enhance the users’ awareness for the highly informed choice. The results highlight systematic differences between transient and steady-state approaches, with TPS measurements generally exhibiting larger deviations for materials characterized by surface roughness, limited thickness, or strong internal heterogeneity. In contrast, GHP and HFM methods show closer agreement when specimen geometry and stabilization requirements are satisfied. The influence of contact resistance, probing depth, specimen preparation, and uncertainty propagation is critically analyzed for each technique. The study provides practical insights into the applicability limits of commonly used thermal characterization methods and emphasizes the importance of selecting measurement techniques in relation to material morphology and testing constraints. These findings support more reliable thermal property assessment of emerging insulation materials and contribute to improved consistency between laboratory measurements and energy performance evaluations for buildings. Full article