Search Results (6,164)

Background/Objectives: Menopause, marked by permanent cessation of menstruation, is a universal transition associated with vasomotor, genitourinary, psychological, and metabolic changes. These conditions significantly affect health-related quality of life (HRQoL) and increase the risk of chronic diseases. Despite their impact, timely diagnosis and individualized management are often limited by delayed care, fragmented health systems, and cultural barriers. Methods: This review summarizes current applications of artificial intelligence (AI) in postmenopausal health, focusing on risk prediction, early detection, and personalized treatment. Evidence was compiled from studies using biomarkers, imaging, wearable sensors, electronic health records, natural language processing, and digital health platforms. Results: AI enhances disease prediction and diagnosis, including improved accuracy in breast cancer and osteoporosis screening through imaging analysis, and cardiovascular risk stratification via machine learning models. Wearable devices and natural language processing enable real-time monitoring of underreported symptoms such as hot flushes and mood disorders. Digital technologies further support individualized interventions, including lifestyle modification and optimized medication regimens. By improving access to telemedicine and reducing bias, AI also has the potential to narrow healthcare disparities. Conclusions: AI can transform postmenopausal care from reactive to proactive, offering personalized strategies that improve outcomes and quality of life. However, challenges remain, including algorithmic bias, data privacy, and clinical implementation. Ethical frameworks and interdisciplinary collaboration among clinicians, data scientists, and policymakers are essential for safe and equitable adoption. Full article

Higher Education Institutions (HEI) face increasing expectations to engage in sustainability reporting despite limited resources and heterogeneous practices. This study explores how Joint Sustainability Reports (JSR), built on the EU Voluntary Sustainability Reporting Standard for non-listed SMEs (VSME), can serve as a cooperative and digitally supported framework to enhance transparency, comparability, and efficiency while strengthening universities’ third mission of societal engagement and knowledge transfer. Qualitative interviews with six sustainability experts from German and Austrian universities of applied sciences (UAS) highlight persistent challenges such as data gaps, staffing shortages, and weak strategic anchoring. The findings show that VSME-based JSRs, through shared data collection, centralised coordination, and modular reporting, enable resource and data pooling, standardised indicators, and cross-university synergies. By making social contributions more visible and credible, JSRs reinforce accountability and advance universities’ third mission in fostering community outreach and sustainable development. Full article

This paper addresses the very important and topical issue of the effective and efficient implementation of green and energy transition processes, taking into account social aspects and energy security. Due to climate change and the geopolitical situation, these processes are currently priorities for most countries and regions of the world. The opportunity to achieve success in their implementation lies in the implementation of the Open Innovation concept in a new model developed and presented in this paper. Its essence is an identified group of stakeholders in the processes under study (science, business, state, society, environment) and their specific positions, roles, and relationships. It was also important to analyze the mechanisms of cooperation and interaction between stakeholders, defining key forms and directions, as well as ways of harmonizing them, leading to synergy in innovation processes. A significant stage of the work was also the development of a RACI role and responsibility matrix, which enabled the precise assignment of functions to individual stakeholders in the developed model. Key challenges, barriers (technological, regulatory, organizational, and social), and factors conducive to the coordination of cooperation and interests of the identified stakeholder groups were also identified. To deepen knowledge and better understand the dynamics of this cooperation, a matrix was also developed to assess priorities and their impact on the energy sector within the open innovation model. This tool enables the identification of diverse perspectives in relation to key criteria such as energy security, innovation, social participation, and sustainable development. In addition, a set of indicators (in five key categories of the innovation ecosystem) was developed to enable multidimensional measurement of the effectiveness, efficiency, and scalability of the open innovation model in the energy sector. They also allow for the study of the impact of these factors on the sustainable development, security, and resilience of energy systems. The developed and presented concept of a model of cooperation between stakeholders using the Open Innovation model in the energy industry is universal in nature and can also be used in other sectors. Its application offers broad opportunities to support the management of transformation processes, taking into account the innovative solutions that are necessary for the success of these processes. Full article

(1) Background: Attending universities in foreign countries is a great challenge for international students, especially when adapting to a new culture and meeting specific university requirements. In this context, the present study investigates the relationship between students’ personal (i.e., psychological capital) and social resources (i.e., teachers’ support, perceived support of family, friends, and significant others), the satisfaction and frustration of their psychological needs (i.e., autonomy, competence, and relatedness), and how these factors contribute to their overall well-being (i.e., academic engagement and burnout). (2) Methods: A sample of 185 international students enrolled at a medical university in Romania completed six questionnaires. Stepwise linear regression analysis was conducted to verify the study’s hypotheses. (3) Results: The results indicated that students’ engagement was positively associated with psychological capital, teachers and family support, and autonomy need satisfaction. Also, autonomy needs’ satisfaction was negatively related to burnout, while autonomy and relatedness needs’ frustrations were positively associated. (4) Conclusions: Based on these findings, specific strategies were proposed to significantly enhance international students’ well-being in the medical university environment. Full article

Background/Objectives: Anxiety disorders are among the most common mental disorders and are often associated with significant discomfort and impaired functioning. One of the more frequent forms is social anxiety disorder, which is characterized by excessive fear of social evaluation and the avoidance of social situations. The aim of this study was to assess the potential of role-playing games (RPGs) as an alternative form of support for people with social anxiety disorder. Methods: Thirty participants aged 18–28 with a non-generalized form of social anxiety were qualified for the study and assigned to two conditions differing in session frequency (once a week vs. once every two weeks). Participants were assigned to groups based on the order of registration for the study. As the recruitment was open to the public and participants registered voluntarily, the assignment process was not strictly random and may have been influenced by self-selection factors. The intervention lasted 3 months and included elements of social exposure and social skills training within a structured RPG scenario. The study lasted from March to November 2024 at the Faculty of Social Sciences of the University of Szczecin. Standardized tools (LSAS, ISSB) were used to measure social anxiety and received social support before and after the intervention. Statistical analyses were performed using the JASP statistical program version 0.17.2. Results: The results indicate a statistically significant reduction in anxiety and avoidance in all groups, with a greater effect observed in the once-a-week group (Cohens’s d = 0.94). At the same time, an increase in perceived social support was noted, especially in the biweekly condition. The greatest changes were observed in the total support score, while specific components (emotional, informational, instrumental) showed differentiated dynamics depending on frequency. Conclusions: The findings suggest that RPG-based interventions may serve as a preliminarily effective and engaging form of support for individuals with social anxiety, contributing to symptom reduction and improved functioning in social contexts. Full article

Purpose: Gastrointestinal (GI) illness demands precise and efficient diagnostics, yet conventional approaches (e.g., endoscopy and histopathology) are time-consuming and prone to reader variability. This work presents GID-Xpert, a deep learning framework designed to improve feature learning, accuracy, and interpretability for GI disease classification. Methods: GID-Xpert integrates a hierarchical, multi-stage attention-driven mixture of experts with dynamic routing. The architecture couples spatial–channel attention mechanisms with specialized expert blocks; a routing module adaptively selects expert paths to enhance representation quality and reduce redundancy. The model is trained and evaluated on three benchmark datasets—WCEBleedGen, GastroEndoNet, and the King Abdulaziz University Hospital Capsule (KAUHC) dataset. Comparative experiments against state-of-the-art baselines and ablation studies (removing attention, expert blocks, and routing) are conducted to quantify the contribution of each component. Results: GID-Xpert achieves superior performance with 100% accuracy on WCEBleedGen, 99.98% on KAUHC, and 75.32% on GastroEndoNet. Comparative evaluations show consistent improvements over contemporary models, while ablations confirm the additive benefits of spatial–channel attention, expert specialization, and dynamic routing. The design also yields reduced computational cost and improved explanation quality via attention-driven reasoning. Conclusion: By unifying attention, expert specialization, and dynamic routing, GID-Xpert delivers accurate, computationally efficient, and more interpretable GI disease classification. These findings support GID-Xpert as a credible diagnostic aid and a strong foundation for future extensions toward broader GI pathologies and clinical integration. Full article

Higher education is often positioned as a pathway to upward social mobility, yet access to highly selective universities (HSUs) remains limited, with first-generation college (FGC) students from low-income and ethnoracially minoritized backgrounds disproportionately constrained by structural barriers. This study applies an asset-based lens to examine how a cross-generational team of six Latine FGC affiliates of an HSU (i.e., alumni, doctoral students, professor) resiliently persisted in their educational and professional journeys, leveraging cultural and social capital. Employing Chicana/Latina feminist methodology and dialogic inquiry, we engaged in pláticas to critically reflect on factors that shaped our life trajectories. Findings reveal that social mobility was negotiated collectively rather than individually, highlighting tensions between personal advancement and commitments to family and community. We also consider the role of structured happenstance in pivotal encounters (e.g., being recognized by mentors, recruited by scholarship programs) that appeared serendipitous but were situated within systems where opportunity is inequitably distributed. Structured happenstance exposes the precariousness of such pathways and systemic gaps in FGC student support, challenging the notion that access to elite, capital-rich institutions is the product of merit alone. Our narratives offer a nuanced portrait of how FGC students navigate social mobility across the life course. Full article

Background: Therapeutic plasma exchange (TPE) is an established treatment for immune-mediated neurological diseases in adults, but pediatric-specific data remain limited. This retrospective single-center study investigates the safety, complication profile, and clinical outcomes of TPE in children with pediatric neuroimmunological disorders (PNID). Methods: Medical records of pediatric patients who underwent TPE at the Medical University of Vienna between April 2006 and October 2022 were reviewed. Inclusion criteria required TPE initiation before the age of 18 years. Data collected included diagnoses, pre-TPE therapy, TPE characteristics, complications and clinical outcomes based on retrospective documentation. Results: A total of 53 patients (60% female, median age 13 years) were included and underwent 378 TPE procedures. Most common diagnoses were pediatric-onset multiple sclerosis (23%) and autoimmune encephalitis (19%). TPE was preceded by corticosteroids and/or intravenous immunoglobulin in 83% of patients. Complications occurred in 81% of patients and 23% of procedures and were predominantly rated mild to moderate (CTCAE I–II), including nausea, hypotension, and catheter-related issues. Severe complications (CTCAE III–IV) occurred in 11% of patients; no deaths were reported. Clinical improvement was documented in 84% of patients, with 42% showing significant improvement. Conclusions: TPE is a generally well-tolerated and effective treatment in PNID, with a high rate of clinical improvement and predominantly mild complications. The higher reported complication rate compared to other studies likely reflects more comprehensive documentation of minor adverse events. These findings support the use of TPE in PNID, particularly in cases refractory to first-line therapies. Standardized reporting of outcomes and complications is essential to improve comparability across studies and guide future clinical practice. Full article

Background/Objectives: Identifying social determinants of health (SDOH) is important for effective clinical care. The ICD-10 introduced diagnostic categories to describe patients’ adverse SDOH, but these codes are infrequently used across health systems, presenting challenges to implement data-driven healthcare. This study illustrates SDOH code utilization within a setting that is recognized as one of the largest integrated healthcare systems across the United States. Methods: Real-world clinical data were used with ICD-10 SDOH records obtained from 13,523 participants randomized into the Diuretic Comparison Project, a pragmatic trial conducted within the Veterans Affairs (VA) Health Care System between 2016 and 2022. SDOH code utilization was assessed across study years and among the specialized outpatient clinics. Results: A total of 29,305 SDOH records were identified, and 99.2% were from outpatient encounters. Social, mental, and housing care services generated the most SDOH records. Moreover, 3894 (28.8%) participants had at least one SDOH record during the 6-year period. Particular, 6.9% of participants had a record in the first year, and this increased to 7.6%, 8.1%, 8.7%, 9.6%, 10.3% in consecutive years. Conclusions: Our results suggest that SDOH code utilization has continued to improve within the VA, but SDOH assessments may not occur annually or be performed systematically within an integrated health setting. Much work is needed to develop universal screening tools and mandate routine SDOH evaluations. Nevertheless, a persistent increase in the counts of ICD-10 SDOH records shows a positive movement towards systematic documentation, supporting service providers to efficiently identify patients with adverse SDOH. Full article

This study explores how higher mathematics education can be reoriented towards greater sustainability, thereby better preparing students to meet the challenges of the future and supporting their sustainable employability. An interpretative phenomenological analysis was conducted to explore the lived experiences of university mathematics teachers (N = 6) integrating sustainability principles into their teaching practice. Data were collected through interviews, which revealed five thematic areas: responsibility for contributing to a sustainable future, pedagogical contradictions, ways of promoting sustainability, finding community and transdisciplinarity. These themes formed the basis of strategic principles including multi-level integration, methodological and content support, professional community development and transdisciplinarity embedded in a non-linear, cyclical implementation model. Results show that effective integration requires a combination of individual motivation with systemic institutional support, access to structured resources, and collaboration across institutions and disciplines. The proposed framework not only aligns mathematics education with the Sustainable Development Goals (SDGs) but also enhances students’ ability to apply mathematical tools to solve complex real-world problems, contributing to their long-term professional sustainability and adaptation to different educational contexts. Full article

Background and Objectives: Quantifying nasal proportions is central to facial plastic and reconstructive surgery, yet manual measurements are time-consuming and variable. We sought to develop a simple, reproducible deep learning pipeline that localizes the nose in a single frontal photograph and automatically computes the two-dimensional, photograph-derived apparent nasal index (aNI)—width/height × 100—enabling classification into five standard anthropometric categories. Materials and Methods: From CelebA we curated 29,998 high-quality near-frontal images (training 20,998; validation 5999; test 3001). Nose masks were manually annotated with the VGG Image Annotator and rasterized to binary masks. Ground-truth aNI was computed from the mask’s axis-aligned bounding box. A lightweight one-class YOLOv8n detector was trained to localize the nose; predicted aNI was computed from the detected bounding box. Performance was assessed on the held-out test set using detection coverage and mAP, agreement metrics between detector- and mask-based aNI (MAE, RMSE, R²; Bland–Altman), and five-class classification metrics (accuracy, macro-F1). Results: The detector returned at least one accepted nose box in 3000/3001 test images (99.97% coverage). Agreement with ground truth was strong: MAE 3.04 nasal index units (95% CI 2.95–3.14), RMSE 4.05, and R² 0.819. Bland–Altman analysis showed a small negative bias (−0.40, 95% CI −0.54 to −0.26) with limits of agreement −8.30 to 7.50 (95% CIs −8.54 to −8.05 and 7.25 to 7.74). After excluding out-of-range cases (<40.0), five-class classification on n = 2976 images achieved macro-F1 0.705 (95% CI 0.608–0.772) and 80.7% accuracy; errors were predominantly adjacent-class swaps, consistent with the small aNI error. Additional analyses confirmed strong ordinal agreement (weighted κ = 0.71 linear, 0.78 quadratic; Spearman ρ = 0.76) and near-perfect adjacent-class accuracy (0.999); performance remained stable when thresholds were shifted ±2 NI units and across sex and age subgroups. Conclusions: A compact detector can deliver near-universal nose localization and accurate automatic estimation of the nasal index from a single photograph, enabling reliable five-class categorization without manual measurements. The approach is fast, reproducible, and promising as a calibrated decision-support adjunct for surgical planning, outcomes tracking, and large-scale morphometric research. Full article

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice. Full article

The escalating challenges of climate change, soil degradation, and the need to ensure global food security are driving the transition towards more sustainable agricultural practices. Biostimulants, a diverse category of substances and microorganisms, have emerged as promising tools to enhance crop resilience, improve nutrient use efficiency (NUE), and support sustainable intensification. However, their widespread adoption is hampered by significant variability in efficacy and a lack of consensus on their optimal use. This comprehensive review synthesizes current scientific knowledge to critically evaluate the performance of biostimulants within sustainable agricultural systems. It aims to move beyond isolated case studies to provide a holistic analysis of their modes of action, efficacy under stress, and interactions with the environment. The analysis confirms that biostimulant efficacy is inherently context-dependent, governed by a complex interplay of biological, environmental, and management factors. Performance variability is explained by four core principles: the Limiting Factor Principle, the Biological Competition Axiom, the Stress Gradient Hypothesis, and the Formulation and Viability Imperative. A significant disconnect exists between promising controlled-environment studies and variable field results, highlighting the danger of extrapolating data without accounting for real-world agroecosystem complexity. Biostimulants are not universal solutions but are sophisticated tools whose value is realized through context-specific application. Their successful integration requires a precision-based approach aligned with specific agronomic challenges. We recommend that growers adopt diagnostic tools and on-farm trials, while producers must provide transparent multi-location field data and invest in advanced formulations. Future research must prioritize field validation, mechanistic studies using omics tools, and the development of crop-specific protocols and industry-wide standards to fully unlock the potential of biostimulants for building resilient and productive agricultural systems. Full article

Continuous monitoring of electrical parameters is essential for understanding energy consumption, assessing power quality, and analyzing load behavior. This paper presents a dataset comprising measurements of three-phase voltages and currents, active and reactive power (per phase and total), power factor, and system frequency. The data was collected between April and December 2024 in the low-voltage system of a university laboratory, using high-accuracy power analyzers installed at the point of common coupling. Measurements were recorded every 10 min, generating 79 files with 432 records each, for a total of approximately 34,128 entries. To ensure data quality, the values were validated, erroneous entries removed, and consistency verified using power triangle relationships. The curated dataset is provided in tabular (CSV) format, with each record including a timestamp, three-phase voltages, three-phase currents, active and reactive power (per phase and total), power factor (per phase and global), and system frequency. This dataset offers a comprehensive characterization of electrical behavior in a university laboratory over a nine-month period. It is openly available for reuse and can support research in power system analysis, renewable energy integration, demand forecasting, energy efficiency, and the development of machine learning models for smart energy applications. Full article

Background/Objectives: Prediabetes (PreDM) is a heterogeneous condition, impacting hundreds of millions worldwide, associated with a substantially high risk of Type 2 Diabetes Mellitus (T2DM) and cardiovascular complications. Early identification of subgroups within the PreDM population may support tailored prevention strategies. Methods: We conducted a cross-sectional study using data from annual health check-ups of 419 university staff (aged 27–69) in Kazakhstan. Latent Class Analysis (LCA) was applied to identify subgroups of individuals with PreDM based on cardiovascular risk factors. Differences in glucose metabolism markers (fasting glucose, OGTT, HOMA-IR, HOMA-β) were compared across identified classes. Results: PreDM prevalence was 43.4%. LCA revealed four distinct classes: Class 1: healthy, low-risk individuals; Class 2: overweight with moderate metabolic risk; Class 3: older, overweight individuals with high cardio-metabolic risk; and Class 4: obese, middle-aged to older individuals with very high cardio-metabolic risk. Significant differences were found in glucose metabolism profiles across the classes. IFG predominated in Class 1 (95%), while Classes 3 and 4 had higher rates of β-cell dysfunction and combined IFG/IGT patterns. HOMA-β differed significantly between classes (p  <  0.001), while HOMA-IR did not. Conclusions: PreDM is highly prevalent in this working-age Kazakh population and demonstrates marked heterogeneity. Based on easily obtainable cardiovascular risk factors, we have identified four subgroups with distinct glucose profiles that may inform personalized interventions. These distinct subgroups may require differentiated prevention strategies, moving beyond a one-size-fits-all approach. Full article