Search Results (5,069)

This study aims to explore the relationship between the digital financial literacy of homestay business owners and economic sustainability. A conceptual model is developed by integrating three primary constructs—performance expectancy, effort expectancy, and facilitating conditions—from the unified theory of acceptance and use of technology (UTAUT) with digital financial literacy and FinTech use by homestay business owners. Further, the effect of FinTech use on economic sustainability is examined through the interaction between facilitating conditions and financial inclusion. Data were collected from Southern India, and hypothesized relationships were tested after checking the measurement properties of the survey instrument. The findings indicate that (i) the digital financial literacy of homestay business owners is a precursor to FinTech use, which, in turn, is positively associated with economic sustainability; (ii) digital financial literacy interacting with performance expectancy (first moderator) and effort expectancy (second moderator) significantly influenced FinTech use; and (iii) FinTech use interacting with facilitating conditions (first moderator) and financial inclusion (second moderator) increased economic sustainability. The three-way interactions in this study provide insights into the boundary conditions that increase FinTech use and economic sustainability, particularly in the context of homestay businesses. The proposed digital financial literacy and FinTech adoption model contributes to the information technology adoption research by extending the UTAUT, in which performance expectancy and effort expectancy play a vital role in FinTech adoption by homestay business owners. The three-way model developed and tested, to the best of our knowledge, is the first of its kind in the Indian context and hence makes a pivotal contribution to the advancement of the UTAUT model through its application to homestay business owners. The implications for theory and practice are discussed. Full article

Spirituality and religion are underrecognized components of mental health and substance use (MHSU) care for youth of African descent. This study explores the dual role of spirituality and religion in influencing the help-seeking behaviors, recovery journeys, and care outcomes of youth of African descent aged 18 to 25 residing in Nova Scotia, Canada. Drawing on findings from a Canadian Institutes of Health Research (CIHR) funded community-based study using mixed methods, including interviews, focus groups, and arts-based approaches, the research highlights both the supportive and ambivalent roles spirituality plays in MHSU care. Participants shared themes such as spiritual reflection as a turning point, the assurance of a higher power, the culturally responsive support of faith-informed providers, and the significance of meditation and unseen connection as healing tools. While the narratives of research participants attested to the role of spiritual and religious practices in offering a sense of belonging, hope, and culturally aligned care, they may also reinforce stigma or act as a barrier to accessing formal MHSU services. These themes were derived from qualitative analysis of in-depth interviews with 60 youth participants. Through the lens of Ubuntu and Afrocentric paradigms, this paper calls for a more holistic and culturally inclusive approach to care, one that respects or honors the spiritual and communal lives of youth of African descent. It also reinforces the importance of training MHSU care providers to recognize, respect, and integrate spirituality as a legitimate component of wellness and recovery for help-seeking youth of African descent. Full article

Background: Hygiene and food-safety training is a critical public health strategy for preventing contamination and promoting safe food-handling practices in community settings. This study evaluated the long-term effectiveness of In-person, Online, and Hybrid instructional modes in enhancing hygiene and food-safety competencies among trainees in Ilocos Norte, Philippines. Methods: Using a longitudinal quasi-experimental design, performance was measured at 12, 24, and 36 months across four domains: Personal Health & Hygiene, Food Hazards, Cleaning and Sanitation, and Good Manufacturing Practices. A total of 384 students met all inclusion criteria and completed the full series of evaluations. Descriptive and inferential statistical analyses were employed. Results: Competency scores increased significantly over time in all instructional modes (p < 0.001). Hybrid learners demonstrated the highest early longitudinal gains at 12 months (mean score, M = 20.88), compared with In-person (M = 10.28) and Online (M = 10.57). At 36 months, Online learners achieved the highest performance (M = 19.50), indicating stronger long-term retention. Effect size analysis using eta squared (η²) showed large effects for Cleaning and Sanitation (η² = 0.196), Good Manufacturing Practices (η² = 0.115), and overall performance (η² = 0.138). Standardized Mean Change (SMC) indicated substantial improvement across modes, with Hybrid showing the greatest early change (SMC = 41.76 at 12 months) and Online exhibiting the strongest long-term improvement (SMC = 38.80 at 36 months). Training Efficiency Index (TEI) identified In-person instruction as most efficient (TEI = 30.55), followed by Online (29.49) and Hybrid (19.56). Linear Mixed-Effects Regression confirmed significant main effects of Time (β = 4.82, p < 0.001) and Mode (β = 3.97, p < 0.001), as well as a significant Time × Mode interaction (β = −1.42, p < 0.01). Conclusions: The findings indicate that Hybrid instruction supports rapid early competency gains, while Online instruction yields superior long-term mastery of hygiene and food-safety competencies. These results provide evidence-based guidance for optimizing hygiene training programs in community and public health contexts. Full article

Background and Aim: Large language models (LLMs) demonstrate significant potential in assisting with medical image interpretation. However, the diagnostic accuracy of general-purpose LLMs on image-based internal medicine cases and the added value of brief clinical history remain unclear. This study evaluated three general-purpose LLMs (ChatGPT, Gemini, and DeepSeek) on expert-curated cases to quantify diagnostic accuracy with image-only input versus image plus brief clinical context. Methods: We conducted a comparative evaluation using 138 expert-curated cases from Harrison’s Visual Case Challenge. Each case was presented to the models in two distinct phases: Phase 1 (image only) and Phase 2 (image plus a brief clinical history). The primary endpoint was top-1 diagnostic accuracy for the textbook diagnosis, comparing performance with versus without a brief clinical history. Secondary/Exploratory analyses compared models and assessed agreement between model-generated differential lists and the textbook differential. Statistical analysis included Wilson 95% confidence intervals, McNemar’s tests, Cochran’s Q with Benjamini–Hochberg correction, and Wilcoxon signed-rank tests. Results: The inclusion of clinical history substantially improved diagnostic accuracy for all models. ChatGPT’s accuracy increased from 50.7% in Phase 1 to 80.4% in Phase 2. Gemini’s accuracy improved from 39.9% to 72.5%, and DeepSeek’s accuracy rose from 30.4% to 75.4%. In Phase 2, diagnostic accuracy reached at least 65% across most disease nature and organ system categories. However, agreement with the reference differential diagnoses remained modest, with average overlap rates of 6.99% for ChatGPT, 36.39% for Gemini, and 32.74% for DeepSeek. Conclusions: The provision of brief clinical history significantly enhances the diagnostic accuracy of large language models on visual internal medicine cases. In this benchmark, performance differences between models were smaller in Phase 2 than in Phase 1. While diagnostic precision improves markedly, the models’ ability to generate comprehensive differential diagnoses that align with expert consensus is still limited. These findings underscore the utility of context-aware, multimodal LLMs for educational support and structured diagnostic practice in supervised settings while also highlighting the need for more sophisticated, semantics-sensitive benchmarks for evaluating diagnostic reasoning. Full article

Smallholder agricultural systems in tropical and subtropical regions are threatened by climate change. This systematic review of 218 peer-reviewed studies (2000–2024) synthesizes evidence on agroforestry’s role as a climate-smart economic strategy across Africa, Asia, and Latin America. Using a PRISMA-guided approach, we evaluated carbon sequestration pathways, biophysical adaptation benefits, and socioeconomic outcomes. Findings indicate that agroforestry systems can sequester 0.5–5 Mg C ha⁻¹ yr⁻¹ in biomass and soils. The results show that agroforestry has the potential to improve above- and below-ground carbon stocks, moderate microclimates, decrease erosion and improve functional biodiversity. The results, however, differ greatly depending on the type of system, ecology and practice. The socioeconomic advantages can be diversification of income and stability of the yield, and adoption is limited due to barriers related to the economy, lack of security in tenure, information asymmetry, and insufficient policy encouragement. We find that agroforestry is a multifunctional and climate resistant land-use approach, but the potential that agroforestry has cannot be fulfilled without context-specific policies, better extension services and inclusive carbon financing schemes. Full article

This paper examines how colonial and neoliberal logics have influenced the ideas of self-management, democracy, and participation and how a decolonial perspective might reinterpret them. Although democracy and participation are celebrated in mainstream development discourse, they frequently serve as technologies of control that uphold market rationalities and dependency. The paper presents a conceptual model for comprehending how political and organisational practices in the Global South are both resisted by and limited by these dynamics, drawing on the framework of the colonial matrix of power. With reference to grassroots movements like Abahlali base Mjondolo, which represent alternative democratic logics based on collective self-management and epistemic justice, South Africa is used as a focal case. How gaps in the global architecture of dominance create opportunities for pluriversal futures is further demonstrated by comparative observations from Latin America and other Global South contexts. By (i) exposing the limitations of institutionalised participatory frameworks, (ii) highlighting radical democracy at the grassroots level, and (iii) describing the structural and epistemic prerequisites for significant change, the paper adds to discussions on decolonial political economy. By doing this, it reinterprets participation as a fight for liberating alternatives outside of colonial modernity rather than as inclusion within the status quo. Full article

Our objective was to conduct a scoping review and narrative synthesis of qualitative studies that examined experiences of partners of cancer patients. We searched MEDLINE, Embase, CINAHL, PsycINFO, and Scopus for qualitative studies involving adult (≥18 years) partners (e.g., in a romantic relationship) of patients diagnosed with cancer and published in English. We extracted findings from included studies, along with key study and participant characteristics, and applied a narrative summary approach, a process that allowed us to identify synthesized themes across studies. Our search identified 15,729 records, of which 159 met the inclusion criteria. Included studies primarily collected data through interviews, with participants being predominantly female and middle-aged (55.2 ± 8.3 years). Partners were commonly in relationships with patients diagnosed with breast, genital–urinary, or gastrointestinal cancer. Our synthesis identified four conceptual themes—transformation of relationship dynamics and roles, distress and burden, coping strategies, and unmet needs and support gaps—which reflect the emotional, relational, and practical challenges partners navigate throughout the cancer trajectory. These findings highlight the need to better recognize and support the role of partners within the cancer care landscape as their wellbeing impacts care and experiences of patients with cancer. Full article

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed teaching, learning, and research practices within higher education institutions (HEIs). Although a growing body of literature has examined the application of AI in higher education, existing studies remain fragmented, often focusing on isolated tools or outcomes, with limited synthesis of best practices, core functionalities, and implementation challenges across diverse contexts. To address this gap, this systematic review aims to comprehensively examine the best practices, functionalities, and challenges associated with the integration of AI in HEIs. A comprehensive literature search was conducted across major academic databases, including Google Scholar, Scopus, Taylor & Francis, and Web of Science, resulting in the inclusion of 35 peer-reviewed studies published between 2014 and 2024. The findings suggest that effective AI integration is supported by best practices, including promoting student engagement and interaction, providing language support, facilitating collaborative projects, and fostering creativity and idea generation. Key AI functionalities identified include adaptive learning systems that personalize educational experiences, predictive analytics for identifying at-risk students, and automated grading tools that improve assessment efficiency and accuracy. Despite these benefits, significant challenges persist, including limited knowledge and skills, ethical concerns, inadequate infrastructure, insufficient institutional and management support, data privacy risks, inequitable access to technology, and the absence of standardized evaluation metrics. This review provides evidence-based insights to inform educators, institutional leaders, and policymakers on strategies for leveraging AI to enhance teaching, learning, and research in higher education. Full article

A new family of stochastic $\eta$-power log-logistic diffusion processes was introduced and defined based on the classical log-logistic diffusion model. The probabilistic characteristics of the proposed process were derived through an analysis of the associated stochastic differential equation (SDE), including its explicit expressions, the transition probability density function, and the conditional and non-conditional mean functions. The statistical inference of the model was studied, and parameter estimation was performed using the maximum likelihood method based on discrete sampling paths. The proposed probabilistic and statistical framework was applied to data on individuals using the Internet in the United States to assess the practical performance of the model. The empirical results demonstrated that the inclusion of a power in the process improved the goodness of fit compared with the classical formulation, providing better agreement with the observed data. Finally, a small Monte Carlo experiment was performed to examine the robustness of the estimation procedure. Full article

Monoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application. In this study, a combinatorial protein engineering strategy was employed to overcome these limitations. We integrated fusion protein technology with rational design to enhance both the functional expression and catalytic efficiency of AOL. Firstly, the superfolder green fluorescent protein (sfGFP) was fused to the N-terminus of AOL. The results indicated that the sfGFP fusion tag significantly improved the solubility and stability of the enzyme, preventing the formation of inclusion bodies. The fusion protein sfGFP-AOL exhibited a MAG conversion rate of approximately 65%, confirming the positive impact of the fusion tag on enzyme developability. To further boost catalytic performance, site-directed mutagenesis was performed based on structural analysis. Among the variants, the mutant sfGFP-Y92Q emerged as the most potent candidate. In the MAG conversion, sfGFP-Y92Q achieved a conversion rate of 98%, which was not only significantly higher than that of sfGFP-AOL but also outperformed the widely used commercial immobilized lipase, Novozym 435 (~54%). Structural modeling and docking analysis revealed that the Y92Q mutation optimized the geometry of the active site. The substitution of Tyrosine with Glutamine at position 92 likely enlarged the substrate-binding pocket and altered the local electrostatic environment, thereby relieving steric hindrance and facilitating the access of the bulky MAG substrate to the catalytic center. In conclusion, this work demonstrates that the synergistic application of sfGFP fusion and rational point mutation (Y92Q) can dramatically transform the catalytic properties of AOL. The engineered sfGFP-Y92Q variant serves as a robust and highly efficient biocatalyst for MAG degradation. Its superior performance compared to commercial standards suggests immense potential for cost-effective applications in the bio-manufacturing of high-purity fatty acids and biodiesel, offering a greener alternative to traditional chemical processes. Full article

Teacher education represents a global strategic priority for improving educational systems and fostering inclusive, high-quality processes. Recent studies highlight the need for systematic and replicable education models capable of addressing the challenges of contemporary complexity and bridging the gap between theory and practice. Teaching professionalism is increasingly recognized as a key driver of change, requiring a balance of pedagogical, relational, and technological competences, along with strong reflective capacity. Within this framework, practicum programs play a crucial role for the development of professional identity and authentic teaching skills. Methods: This contribution adopts a theoretical–argumentative approach grounded in a critical analysis of the international scientific literature on teacher education, with specific focus on the role of practicums. The aim is to present the model implemented by Pegaso University in the context of practicum activities within initial teacher education programs to outline an interpretative framework and provide pedagogical reflections in light of the results arising from critical reflection and systematic monitoring (not covered in this specific contribution) of the effectiveness of the model implemented in the first two training cycles (academic years 23–24 and 24–25), with the involvement of 5 regions and a total of 2834 teachers in the first cycle and 10 regions and a total of 5551 teachers in the second cycle. Convenience sampling based on a non-probabilistic method was adopted, using the entire sample of teachers admitted to the training program who met the requirements of Article 7 of the Decree of the President of the Council of Ministers (DPCM). Results: This paper outlines the theoretical and methodological trajectories of the model, offering interpretative frameworks and pedagogical reflections in light of the outcomes achieved during the initial implementation phase. Conclusions: In accordance with recent national and European regulatory frameworks, the Pegaso teaching model is presented as an example of good practice for initial teacher education. It aims to foster a reflective, situated, and responsible teaching professionalism, moving beyond traditional approaches toward a continuous and transformative learning process. Full article

Access to health care is a fundamental human right established in various legal frameworks worldwide. However, increasing evidence indicates that individuals with physical disabilities in rural Ethiopia continue to face barriers and disparities in accessing health care, leading to unmet needs and worsening health. Guided by Critical Disability Theory and Intersectionality Theory, this instrumental case study explored the barriers to accessing primary health care (PHC) for adults with physical disabilities in rural Ethiopia and examined how disability intersects with poverty, gender, and rurality to shape exclusion. Following purposeful sampling, we conducted 14 interviews and one focus group discussion (n = 7) with adults with physical disabilities, supplemented by PHC facility infrastructure observation. Using reflexive thematic analysis, we identified intersecting barriers across four domains: physical and environmental barriers, socioeconomic marginalization, socio-cultural stigma and attitudinal barriers, and systemic weaknesses in health service delivery. This revealed that disability-related barriers are rooted in structural ableism and intersecting inequities, underscoring the urgent need for inclusive policies and disability-inclusive practices. Such practices include enforcing accessibility standards, expanding community-based health insurance to cover disability-specific needs, supporting transportation, and providing disability-specific training for health professionals. Implementing these practices is essential to advancing disability rights and ensuring equitable health access in Ethiopia. Full article

Students with learning disabilities (LD) often struggle to develop deep, transferable conceptual understanding in mathematics due to cognitive and processing challenges, underscoring the relevance of instruction grounded in strong teacher pedagogical content knowledge (PCK). This issue is critical given widening post-pandemic achievement gaps and increased expectations for conceptual understanding in inclusive classrooms. Although many studies document effective mathematics interventions for students with LD, relatively few examine how teachers’ PCK functions in these classrooms. In contrast, general education research highlights the importance of PCK for conceptual learning. This manuscript bridges these studies by examining how insights from broader PCK research may inform instruction for students with LD. This manuscript presents a practice-based conceptual synthesis of research on mathematics teachers’ PCK, integrating findings from special education and mathematics intervention literature with classroom vignettes and practitioner examples. The synthesis highlights how core PCK components—content knowledge, understanding of student thinking and misconceptions, and instructional strategies—may support early conceptual understanding in students with LD, emphasizing multiple representations, error analysis, and routines that promote generalization through distributed practice. Implications for practice, professional development, and future research are discussed, offering practice-informed pathways to support inclusive mathematics instruction for students with LD. Full article

Workplace weight stigma is a form of discrimination affecting equality, health, and careers, yet occupational research remains fragmented. This gender-sensitive systematic review synthesizes evidence on workplace weight stigma among adult workers and job applicants since 2000. Following PRISMA procedures, we searched psychological, medical, sociological, and economic databases, identifying 25 included studies examining work outcomes. The corpus includes experimental vignette and correspondence studies, surveys, and qualitative designs, predominantly from high-income Western countries. Higher body weight is consistently associated with disadvantages across the employment life cycle: reduced callbacks and hiring, lower wages and wage growth, fewer promotions, and negative performance evaluations. Penalties are systematically stronger for women; intersectional analyses remain rare. Weight-based teasing, unfair treatment, and stereotype threat are linked to poorer self-rated health, psychological distress, burnout, reduced work ability, lower job satisfaction and commitment, and stronger turnover intentions. Organizational-level evidence is indirect but suggests detrimental effects on engagement and citizenship behaviors. Findings support conceptualizing workplace weight stigma as both a psychosocial hazard and a structural driver of labor-market inequality, underscoring the need for size-inclusive HR practices, leadership, and occupational risk-prevention policies. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article