Search Results (117)

Resource misallocation in data governance is an overlooked source of carbon emissions, since most prioritization methods disregard the environmental cost of computing and human effort. This study proposes a four-layer hybrid intelligence framework (FLHIF) that unifies cloud model uncertainty quantification, social network analysis of data lineage graphs, DeepSeek-V4-driven multi-agent semantic reasoning, and adaptive weight learning via proximal policy optimization (PPO). On a benchmark of 439 data asset instances, FLHIF achieves an NDCG@10 of 0.9500, outperforming all baselines. Beyond ranking accuracy, expert evaluation yields an interpretability score of 4.2/5.0 (ICC > 0.80), indicating that FLHIF’s recommendations are auditable as well as accurate, a requirement of growing importance for AI adoption in regulated governance settings. Holdout stability tests, sensitivity analysis, and zero-shot generalization across finance, healthcare, manufacturing, and government sectors further demonstrate the framework’s robustness across the tested benchmarks. Preliminary simulations indicate that FLHIF can reduce redundant governance operations by 15–22% in a typical enterprise setting, corresponding to an estimated annual reduction of 120–180 kg CO₂-equivalent emissions per medium-sized data platform. FLHIF thus offers a systematic, resource-efficient, explainable, and adaptive approach to sustainable data governance, directly supporting SDG 9.1, SDG 11.3, and SDG 12.2/12.5. Full article

Immersive Virtual Reality (VR) environments are increasingly adopted in clinical psychology and stress-management contexts; however, their therapeutic effectiveness depends on the system’s ability to understand and dynamically respond to users’ affective states. Emotional regulation plays a key role in psychological resilience, directly influencing stress coping mechanisms, cognitive performance, and overall mental well-being. Despite recent advances, automatic recognition of scenario-associated affective conditions in VR remains challenging because head-mounted displays occlude facial features. This study proposes a VR-based serious game for affective training and regulation, in which users interact with goal-oriented scenarios targeting fear, anger, and joy. We introduce a multimodal affective computing model to objectively assess users’ emotional responses by integrating electrocardiogram (ECG) signals and posture-based features extracted through computer vision. An early-fusion architecture combined with a Long Short-Term Memory (LSTM) network captures temporal dependencies in synchronized multimodal data. We established a controlled experimental framework using immersive VR scenarios, enabling the collection of synchronized physiological and behavioral data from a cohort of 20 healthy adult participants. The proposed model was evaluated under a strict Leave-One-Subject-Out (LOSO) cross-validation scheme across independent subjects, achieving a robust inter-subject accuracy of $78.94\%\pm 10.77\%$ and a global macro F1-score of 0.635, demonstrating strong generalization to entirely unseen users without data leakage. Furthermore, the system maintained an outstanding balance in detecting active emotional states (recall > 80.0% for fear, anger, and joy). Additionally, subjective evaluations using the PANAS and SGU questionnaires confirmed the coherence between detected and perceived emotional states, as well as the system’s high usability. The results suggest the potential viability of combining immersive environments and multimodal affective computing to explore the technical feasibility of adaptive frameworks that could eventually translate into healthcare contexts. This work may contribute to the development of intelligent digital health technologies by providing a foundation for responsive VR systems that can monitor emotional regulation and are fully aligned with sustainable well-being ecosystems (SDG 3: Good Health and Well-being and SDG 10: Reduced Inequalities). Full article

The Sustainable Development Goals (SDGs) constitute an integrated framework for global sustainability, yet progress remains inconsistent across regions and indicators four years before 2030. Identifying the key issues and factors that influence SDG performance is therefore essential for effective implementation and the post-2030 sustainable development agenda. This paper presents a systematic review and thematic synthesis of empirical studies investigating the factors associated with SDG outcomes. Peer-reviewed studies published between 2015 and 2026 were identified across Scopus, Web of Science and PubMed databases and coded according to the SDGs examined, geographic scope, methodological approach and key findings. A total of 43 studies were selected from 684 initial records, covering all 17 SDGs across diverse geographical contexts. Seven cross-cutting themes were identified: governance and institutional capacity; socioeconomic status and cultural barriers; gender equality; education; technology, digital connectivity and innovation; environmental factors; and healthcare infrastructure and access. Theoretically, this review advances sustainable development literature by shifting the paradigm from single-goal evaluations towards an integrated, cross-cutting systems framework. Practically, it highlights the necessity of multi-sectoral financing, institutional capacity building and localised policy interventions to accelerate global progress leading up to and beyond 2030. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article

Across four decades of AI deployment, the same six human challenges (trust calibration, reliance behavior, cognitive engagement, skill retention, accountability, and transparency) recur, yet fragmentation across research communities obscures this continuity and limits knowledge transfer. Functionally similar phenomena are repeatedly relabeled (a jangle fallacy): what aviation researchers call “automation complacency,” decision scientists call “algorithm appreciation,” and LLM researchers describe as “over-reliance.” This systematic review synthesizes 152 papers spanning aviation, healthcare, manufacturing/supply chain, and cross-domain contexts across three AI technology generations: decision support systems, autonomous systems, and large language model (LLM) agents. We introduce the Collaboration Convergence Framework (CCF), a 6 × 3 matrix with solution-maturity indicators that maps each challenge across generations. The framework shows that Gen 3 designers can transfer decades of evidence from automation and decision support research (particularly reliance calibration, cognitive forcing, and skill maintenance) rather than rediscovering them. Cross-generational synthesis also isolates three Gen 3 phenomena without direct precedent in earlier generations: epistemia (attributing genuine knowledge to LLMs based on surface fluency), attribution ambiguity in co-creation, and motivational withdrawal. We distill twelve transferable design principles and propose ten research directions, prioritizing skill-retention interventions and accountability frameworks. These findings carry direct sustainability implications aligned with Industry 5.0: protecting workforce capability under increasing automation (SDG 8), reducing duplicated research effort through cross-generational knowledge reuse (SDG 9), and supporting responsible deployment by treating collaboration risks as predictable rather than novel (SDG 12). The CCF provides conceptual infrastructure for cumulative learning across AI generations and industries. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

The rapid evolution of smart cities demands a transition from reactive healthcare systems to proactive, data-driven health management paradigms that support long-term urban sustainability. Predicting population health status based on lifestyle-related behavioral and physiological factors is critical for enabling early intervention, personalized healthcare, and efficient resource allocation directly contributing to the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-being; SDG 11: Sustainable Cities and Communities). This study proposes an IoT-enabled Explainable Artificial Intelligence (XAI) framework for predictive health scoring as part of sustainable population health management, integrating real-time data acquisition, cloud-based analytics, and interpretable machine learning. To address the limitations of conventional ensemble models particularly the black-box nature and hyperparameter sensitivity of Extreme Gradient Boosting (XGBoost) a Bayesian optimization strategy is employed to automatically fine-tune model parameters, thereby enhancing predictive accuracy and generalization performance. Furthermore, Shapley Additive Explanations (SHAP) are incorporated to provide transparent, interpretable insights into model predictions by quantifying the contribution of individual lifestyle features. Using a publicly available Kaggle dataset (“Health and Lifestyle Data for Regression”), experimental evaluation demonstrates that the proposed Bayesian-Optimized XGBoost model achieves superior performance (Test R² = 0.878, RMSE = 4.983), outperforming ten benchmark models, including standard XGBoost, which exhibits signs of overfitting (Test R² = 0.832). The results further reveal that Body Mass Index (BMI) and diet quality are the most influential factors affecting health scores, providing actionable insights for urban health policymakers. The proposed framework highlights the synergy between IoT, optimization techniques, and explainable AI to develop transparent, reliable, and scalable predictive health systems. This work provides a practical foundation for next-generation smart healthcare applications and decision-support systems, advancing the vision of sustainable, data-driven, and human-centric smart cities. Full article

The Horn of Africa (Kenya, Djibouti, Uganda, Eritrea, Somalia, Ethiopia, South Sudan, and Sudan) faces the highest rates of hunger and malnutrition in the world, exacerbated by conflict and adverse weather conditions. These factors have serious health, educational, social, and economic consequences, especially for children under five and pregnant women. In this context, we analyze each country’s progress toward Sustainable Development Goals (SDGs) 1, 2, 3, and 4, which are closely linked to the eradication of hunger, improved health, and access to quality education. Using comparable data from the United Nations 2030 Agenda up to 2019, the achievement of the SDGs is assessed through a multidimensional approach based on Pena’s P₂ distance method, constructing a composite indicator that allows for robust cross-country comparisons. This method helps identify the key measures needed to prevent future humanitarian crises in the Horn of Africa, including providing urgent assistance to these countries in vital areas such as water, nutrition, education, sanitation, and child and maternal immunization. Factors related to the work of qualified healthcare personnel in treating diseases and improving maternal and neonatal health, as well as facilitating access to basic services such as clean drinking water and sanitation and ensuring girls’ access to primary education, top the rankings in terms of their correlation with greater progress by these countries in achieving these four SDGs, which are crucial for improving the well-being of their populations. Full article

Knowledge sharing (KS) among healthcare professionals is essential for sustaining organisational learning and facilitating the transfer of expertise between experienced and less experienced professionals, thereby supporting workforce stability and retention in healthcare organisations (HCOs). However, despite its importance, high turnover among healthcare professionals remains a significant and persistent challenge in public HCOs, indicating a potential gap in understanding the mechanisms that support workforce stability. To address this gap, this study examines the interplay between work performance (WP), satisfaction with co-workers (CW), KS and turnover intention (TI) among healthcare professionals. Data from 220 respondents were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) within the Input–Process–Output (IPO) framework. The results indicate that CW positively influences KS, while KS has a negative effect on TI, thereby reducing TI. In contrast, WP does not have a statistically significant effect on KS, nor does it indirectly influence TI through KS. Furthermore, although both WP and CW were hypothesised to be predictors of KS, only CW demonstrates a significant indirect effect on TI through KS. Grounded in Social Exchange Theory (SET) and the Knowledge-Based View (KBV), the results highlight the role of KS and interpersonal relationships in supporting sustainable human resource management (SHRM). Although sustainability-related dimensions were not directly measured, the results suggest potential implications for the Sustainable Development Goals (SDGs), particularly SDG 3, SDG 8, and SDG 9. Full article

Ensuring uninterrupted access to essential medicines in public healthcare systems is a persistent challenge with clinical, economic, and environmental implications. Oncology services are particularly vulnerable to stockouts, which compromise therapeutic continuity and increase reliance on urgent procurement with high carbon and waste footprints. This study proposes a risk-informed, data-driven framework for pharmaceutical inventory governance in a high-complexity public oncology hospital in Chile, aligning with sustainability goals and green supply chain principles. Using operational data from 2023–2024, we integrate descriptive analytics, ABC–XYZ segmentation, and a continuous-review (s, Q) policy extended through a Logistic Risk Index (LRI) that consolidates demand variability, supply performance, and clinical-economic criticality. Empirical analysis reveals strong expenditure concentration in AX/AY segments and significant misalignment between institutional and analytically derived parameters. A Monte Carlo simulation N = 1000 runs per scenario) compares baseline, adjusted, and fully risk-informed policies under stochastic demand and lead-time conditions. Results show that the risk-informed configuration reduces stockout exposure by up to 46%, improves fill rates (93.1% → 96.4%), and shortens replenishment delays, while maintaining total logistic cost stability. Critically, urgent orders decrease from 27.4 to 14.8 per year, avoiding an estimated 630 kg CO₂ emissions and 25 kg of packaging waste annually. These findings demonstrate that resilience, efficiency, and sustainability are not competing objectives but can be jointly achieved through integrated analytics and governance. The proposed approach offers a scalable blueprint for public health systems seeking to transition from reactive inventory management toward anticipatory, transparent, and sustainability-oriented decision-making, contributing to SDG 3 (health and well-being) and SDG 12 (responsible consumption and production). Full article

Artificial intelligence (AI)-enabled unmanned aerial vehicles (UAVs) are rapidly emerging as transformative technologies for sustainable healthcare logistics, particularly in remote and infrastructure-constrained regions. Despite growing implementation, the environmental, social, and governance (ESG) implications of these systems remain insufficiently synthesized in the literature. This study conducts a PRISMA-ScR-guided Systematic Review of 37 peer-reviewed studies selected from 333 records across six major scientific databases (2015–2026). The analysis reveals a sharp acceleration of research after 2021, with over 80% of publications produced between 2021 and 2024, indicating increasing global interest in AI-supported autonomous medical logistics. Evidence demonstrates that AI-enabled drones can substantially reduce delivery times; expand access to blood, vaccines, and essential medicines; and enhance emergency response capacity in rural and disaster-affected environments. From a sustainability perspective, AI-driven route optimization and autonomous navigation may reduce transport-related emissions, supporting climate-responsive healthcare supply chains. However, large-scale deployment remains constrained by regulatory fragmentation, cybersecurity risks, operational limitations, and challenges with social acceptance. This review proposes an ESG-oriented framework linking technological innovation, ethical governance, and equitable healthcare access while identifying key research gaps in lifecycle sustainability assessment, cost-effectiveness modeling, and real-world implementation aligned with the Sustainable Development Goals (SDGs). Full article

Background: Service quality and patient satisfaction are key indicators of healthcare performance, yet disparities remain between public hospitals and private clinics in Peru. Understanding these differences is essential for improving patient-centered care and advancing Sustainable Development Goal 3 (Good Health and Well-Being). The study examined how perceived service quality relates to user satisfaction in Trujillo’s private and public institutions. Methods: A cross-sectional study was conducted involving 480 users from public and private healthcare institutions. Service quality was assessed using the SERVQUAL model, and user satisfaction was measured with a validated Likert-scale instrument. Data did not follow a normal distribution (Kolmogorov–Smirnov test); thus, nonparametric statistics were applied. A two-step cluster analysis was additionally performed to identify user profiles based on the five quality dimensions of quality. Results: Participants from both health centers exhibited a range of sociodemographic profiles with regard to age, gender and income. Private clinics reported high levels of perceived service quality (95.8%) and user satisfaction (89.3%), whereas the public hospital showed moderate ratings in both dimensions. In the public setting, empathy and tangible elements emerged as significant predictors of satisfaction, while in private clinics, these same dimensions exhibited negative associations. The cluster analysis identified two distinct user profiles, with tangibles and reliability being the most influential predictors. Conclusions: Significant differences were observed between private and public institutions. Although service quality was positively associated with satisfaction, its explanatory power was limited, suggesting the influence of additional unmeasured factors. This study opens avenues for future research on how differentiated strategies can be scaled and adapted to strengthen public healthcare delivery in Peru, ensuring alignment with equitable and patient-centered care principles promoted by SDG 3. Full article

Fermentation, one of the oldest biotransformation processes, has become a key element of contemporary sustainable biotechnology. In modern food systems, it enables the simultaneous resolution of environmental, nutritional, and economic challenges by converting agricultural and food residues into high-value-added products, such as bioactive compounds, organic acids, biofuels, enzymes, and proteins. Consistent with the concept of a circular bioeconomy, fermentation supports resource recycling, waste minimization, and greenhouse gas reduction, contributing to the achievement of selected United Nations Sustainable Development Goals (SDGs). The importance of fermentation extends beyond its environmental aspects—fermented foods and postbiotics support the modulation of the gut microbiome, strengthen immunity, and can act as a preventative measure against metabolic and inflammatory conditions. Simultaneously, the dynamic development of precision fermentation and synthetic biology enables the design of microorganisms that produce specific food ingredients without the use of animals or traditional agriculture, paving the way for more responsible production and consumption. This review presents the categories of organic residues valorized through fermentation, explains their role in circular food and healthcare systems, and identifies key technological and regulatory barriers limiting the scaling of this approach. Collectively, fermentation emerges as a biotechnology platform with significant transformative potential for future sustainable food systems. Full article

Background/Objectives: This article presents the feasibility and preliminary outcomes of an international cooperation project between the University of Valencia (Spain) and the University and health authorities of Pinar del Río (Cuba), designed to implement and evaluate an innovative rehabilitation protocol. Aligned with the United Nations Sustainable Development Goals (SDGs 3, 4, and 10), the initiative aims to implement a low-cost, evidence-based rehabilitation program combining mirror-neuron stimulation via Virtual Walking and therapeutic exercise. Methods: The program included multidisciplinary meetings and both digital and on-site training for healthcare professionals, caregivers, and educators, aimed at strengthening local capacities in evidence-based practice. The transferred protocol consisted of Virtual Walking (10 min) and therapeutic exercise (30 min), implemented three times per week, for eight weeks. Outcomes assessed included gait speed and endurance (10-Minute Walking Test, 6-Minute Walking Test), lower limb function (Timed Up and Go Test), frailty status (Fried criteria), pain (Visual Analog Scale), and satisfaction with the training program. Pre-post comparisons were conducted using the Wilcoxon signed-rank test for continuous data. Results: The program was successfully implemented in two polyclinics with high levels of participant satisfaction. Eleven patients completed the program, showing significant improvements in gait endurance (p < 0.05), while lower limb function and pain did not change significantly. Noteworthily, severe infrastructural and connectivity limitations were found. Overall, results demonstrate the feasibility, adaptability, and acceptability of the proposed protocol, which integrates technological innovation, clinical training, and community engagement to promote health quality and equity. Conclusions: This project provides a replicable framework for rehabilitation initiatives in low-resource settings and demonstrates the potential to achieve meaningful clinical results. Full article

Smallholder rice farmers in sub-Saharan Africa face persistent livelihood challenges due to declining returns from monocropping, limited diversification opportunities, and vulnerability to climate and market shocks. This study integrated the Drivers–Pressures–State–Impact–Response (DPSIR) framework with the sustainable livelihood approach to evaluate how the transition from rice monocropping to integrated rice–fish farming influences productivity, profitability, and household welfare in Nigeria’s leading rice-producing region. Using a mixed-methods, three-year panel (2021–2023) of 228 households across three communities in Kebbi State, descriptive statistics, regression models, and thematic analyses were combined to assess changes in livelihood capitals, system pressures, and response mechanisms. Adoption of rice–fish systems was associated with substantial improvements: 96.1% of farmers reported increased income, 56.3% improved food security, and 30.6% greater dietary diversity. Regression analyses confirmed that access to more land (p < 0.001 for healthcare and education; p = 0.011 for social status), labor affordability (p < 0.001), and farm size (p < 0.05) were consistent predictors of gains in healthcare, education, and social status, while pesticide and herbicide use negatively affected food access and wellbeing (p < 0.05). The DPSIR assessment revealed that rice–fish integration altered the state of rice production systems through reductions in input-related pressures and generated positive livelihood impacts. The results align with Sustainable Development Goals (SDGs) related to poverty reduction, food and nutrition security, sustainable production, and biodiversity conservation, and provide the first large-scale, longitudinal evidence from West Africa that integrated rice–fish systems support food security, income diversification, and sustainable resource management. Full article