Search Results (2,728)

Vitamin D₃ plays a pivotal role not only in bone health but also in the functioning of the nervous system, particularly in the context of age-related neurodegenerative diseases such as Alzheimer’s disease, multiple sclerosis, and Parkinson’s disease. Vitamin D₃ deficiency has been associated with cognitive decline, heightened inflammation, and shortened leukocyte telomere length, which may contribute to accelerated cellular aging. Therapeutic interventions involving vitamin D₃ have been reported in selected clinical studies and meta-analyses to potentially enhance cognitive function, decrease amyloid β biomarkers, and prolong telomere length, although heterogeneity remains across study designs and populations. Furthermore, vitamin D₃ has been shown to influence the expression of genes implicated in DNA repair and oxidative stress response, including NRF2, OGG1, MYH, and MTH1. This narrative review synthesizes current knowledge on the molecular mechanisms of vitamin D₃ action in the context of neuroprotection and discusses potential directions for future research, including its possible therapeutic applications in neurodegenerative diseases. Full article

Achieving optimal daylighting in buildings necessitates complex and expensive control systems. This research addresses this challenge by proposing a simple and more practical solution: a parametric louver system based on rotating slats controlled by stepper motors, powered by an Integrated Circuit platform (Arduino board), which can translate the digital figures (the rotation angles) to a physical action. The system automatically adjusts the slats in accordance with solar altitudes and reflects them to specific targets over the ceiling. This ensures a uniform and comfortable distribution of daylight throughout a room. This system was developed using Grasshopper as the parametric software, with future control planned via a user-friendly mobile app through a preliminary prototype. This daylighting system prioritises human visual comfort while targeting a significant 53% reduction in electrical lighting energy consumption. The system aims to enhance occupant well-being to significantly increase energy savings, making it a compelling solution for sustainable building design. Full article

As the core space for students’ daily living and learning, the quality of the indoor wind environment and air quality in dormitory buildings is particularly critical. However, existing studies often neglect natural ventilation optimization under local climatic conditions and the multidimensional evaluation of health benefits, leaving notable gaps in dormitory design. Under the Healthy China Initiative, the indoor wind environment in university dormitories directly impacts students’ health and learning efficiency. This study selects dormitory buildings in Xuzhou as the research object and employs ANSYS FLUENT 2020 software for computational fluid dynamics (CFD) simulations, combined with orthogonal experimental design methods, to systematically investigate and optimize the indoor wind environment with a focus on healthy ventilation standards. The evaluation focused on three key metrics—comfortable wind speed ratio, air age, and CO₂ concentration—considering the effects of building orientation, corridor width, and window geometry, and identifying the optimal parameter combination. After optimization based on the orthogonal experimental design, the proportion of comfortable wind speed zones increased to 44.6%, the mean air age decreased to 258 s, and CO₂ concentration stabilized at 613 ppm. These results demonstrate that the proposed optimization framework can effectively enhance indoor air renewal and pollutant removal, thereby improving both air quality and the health-related performance of dormitory spaces. The novelty of this study lies in integrating regional climate conditions with a coordinated CFD–orthogonal design approach. This enables precise optimization of dormitory ventilation performance and provides locally tailored, actionable evidence for advancing healthy campus design. Full article

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article

This study investigates the shear behavior of concrete elements reinforced with both traditional steel reinforcement and macro-synthetic fibers, with an emphasis on evaluating the predictive capabilities of current shear design provisions. A review of available experimental data, involving 52 beams and 8 panel specimens, revealed limitations in both quantity and consistency, hindering the formulation of robust design recommendations. To address this, an extensive parametric numerical study was conducted using the VecTor2 nonlinear finite element program, incorporating a recently developed modeling approach for PFRC shear response. A total of 288 simulations were carried out to explore the influence of fiber content, transverse reinforcement ratio, and concrete compressive strength, particularly in ranges not previously captured by experimental programs. The performance of existing design codes, including ACI, CSA, EC2, AASHTO, and the Fib Model Code, was assessed against both experimental data and the enriched parametric dataset. The Fib Model Code demonstrated the most reliable and consistent predictions, maintaining close alignment with reference strengths across all fiber contents, reinforcement ratios, and concrete strengths. AASHTO provisions performed moderately well, showing generally conservative and stable predictions, though some underestimation occurred for beams with higher shear reinforcement. In contrast, ACI and CSA models were consistently conservative, especially at higher concrete strengths, potentially leading to uneconomical designs. EC2 models exhibited the highest variability and least reliability, particularly in the presence of fibers, indicating limited applicability without modification. The results highlight that most conventional codes do not fully account for the synergistic action between fibers and transverse steel reinforcement, and that their reliability deteriorates for high-strength PFRC. These findings have practical implications for the design of PFRC elements, suggesting that the Fib Model Code may be the most suitable for current applications, whereas other provisions may require recalibration or modification. Future research should focus on expanding experimental datasets and developing unified design models that explicitly consider fiber–steel interactions, concrete strength, and fiber distribution. Full article

Buried pipelines in permafrost areas are affected by harsh environments, especially those with defects and damages, which are prone to failure or even leakage accidents. However, current research is limited to single-factor analysis and fails to comprehensively consider the interaction relationships among temperature fields, moisture fields, and stress fields. Therefore, based on the thermodynamic equilibrium equation and the ice–water phase transition theory, this paper constructs the temperature field equation including the latent heat of phase transition, the water field equation considering the migration of unfrozen water, and the elastoplastic stress field equation. A numerical model of the heat–water–force three-field coupling is established to systematically study the influence laws of key parameters such as burial depth, water content, pipe diameter, and wall thickness on the strain distribution of pipelines with defects. The numerical simulation results show that the moisture content has the most significant influence on the stress of pipelines. Pipelines with defects are more prone to damage under the action of freeze–thaw cycles. Based on data analysis, the safety criteria for pipelines were designed, the strain response surface function of pipelines was constructed, and the simulation was verified through experiments. It was concluded that the response surface function has good predictability, with a prediction accuracy of over 90%. Full article

This study presents a novel integrative review of 329 review articles on sustainable buildings from 2015 to 2025, combining quantitative bibliometrics with qualitative insights to map the field’s evolution and pinpoint critical future pathways. Seven core research themes were identified in this study: (1) material and advanced construction technologies, (2) energy efficiency and renewable energy systems, (3) digitalization and smart technologies, (4) policy, standards, and certification, (5) sustainable design and optimization, (6) stakeholder and socio-economic factors, (7) other (cross-cutting) topics. Key findings reveal a surge in publications post-2020, driven by global net-zero commitments, with China, Australia, and Hong Kong leading research output. Innovations in low-carbon materials (e.g., hemp concrete, geopolymers), artificial intelligent (AI)-driven energy optimization, and digital tools (e.g., building information modeling (BIM), internet of things (IoT)) dominate recent advancements. However, challenges persist, including policy fragmentation, scalability barriers for sustainable materials, and socio-economic disparities in green building adoption. The study proposes a unique future research framework emphasizing nanotechnology-enhanced materials, interpretable AI models, harmonized global standards, and inclusive stakeholder engagement. This review provides actionable recommendations to bridge gaps between technological innovation, policy frameworks, and practical implementation in sustainable construction. Full article

This paper explores how digital crowdsourcing platforms communicate sustainability-oriented innovation and mobilise stakeholder engagement. Through a directed content analysis of three platforms (OpenIDEO, San Francisco, CA, USA; Enel Innovation Hub, Rome, Italy; and InnoCentive, Waltham, MA, USA). The study examines communication strategies, participation models, and alignment with the United Nations Sustainable Development Goals (SDGs). Results show that communication is not neutral but functions as a governance mechanism shaping who participates, how innovation is framed, and what outcomes emerge. OpenIDEO fosters inclusive co-creation and SDG alignment, Enel Innovation Hub highlights technical readiness and energy transition, and InnoCentive relies on rewards and competition. Word-frequency analysis confirms these emphases, while interpretation through Motivation Crowding Theory, Social Exchange Theory, and Transaction Cost Theory explains how motivational framing, legitimacy signals, and participation structures affect engagement. The study contributes to research on open innovation and platform studies by demonstrating the constitutive role of communication in enabling or constraining sustainable collective action. Practical implications are outlined for platform designers, marketers, and policymakers seeking to align digital infrastructures with systemic sustainability goals. Full article

This research introduces a system designed to predict three-dimensional airspace occupancy over Colombia using historical Automatic Dependent Surveillance-Broadcast (ADS-B) data and machine learning techniques. The goal is to support proactive air traffic management by estimating future aircraft positions—specifically their latitude, longitude, and flight level. To achieve this, four predictive models were developed and tested: K-Nearest Neighbors (KNN), Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Among them, the LSTM model delivered the most accurate results, with a Mean Absolute Error (MAE) of 312.59, a Root Mean Squared Error (RMSE) of 1187.43, and a coefficient of determination (R²) of 0.7523. Compared to the baseline models (KNN, Random Forest, XGBoost), these values represent an improvement of approximately 91% in MAE, 83% in RMSE, and an eighteen-fold increase in R², demonstrating the substantial advantage of the LSTM approach. These metrics indicate a significant improvement over the other models, particularly in capturing temporal patterns and adjusting to evolving traffic conditions. The strength of the LSTM approach lies in its ability to model sequential data and adapt to dynamic environments—making it especially suitable for supporting future Trajectory-Based Operations (TBO). The results confirm that predicting airspace occupancy in three dimensions using historical data are not only possible but can yield reliable and actionable insights. Looking ahead, the integration of hybrid neural network architectures and their deployment in real-time systems offer promising directions to enhance both accuracy and operational value. Full article

Investigating the properties of red clay under the action of dry–wet cycles is crucial for mitigating geological disasters and promoting the sustainable development of geotechnical engineering infrastructure. In this paper, red clay from the Yuanmou dry-hot valley in Yunnan Province was selected as the research subject. The investigation focused on examining the effects of dry–wet cycles on its permeability and shear strength. Samples were prepared by controlling the initial moisture content (8%, 11%, 14%, 17%, and 20% for permeability tests; 11%, 14%, and 17% for strength tests) and initial dry density (1.65 g/cm³, 1.70 g/cm³, 1.75 g/cm³, and 1.80 g/cm³). We conducted variable-head permeability tests and direct shear tests on samples undergoing 1–5 dry–wet cycles. The results demonstrated that (1) the saturated moisture content decreased with the increasing number of dry–wet cycles, with the first cycle showing the most significant decrease (decreasing by approximately 15–25% depending on initial conditions). (2) The permeability coefficient decreased continuously with the number of cycles, exhibiting a transition behavior around the optimum moisture content (14%). Samples with lower initial moisture content (8–14%) showed higher permeability reduction (up to 40% decrease) compared to those with higher initial moisture content (14–20%). (3) The dry–wet cycles lead to a significant attenuation of the shear strength, and the first cycle has the largest reduction. The shear strength parameters of red clay exhibit distinct attenuation patterns. The cohesion decreased exponentially with the number of cycles (total attenuation ≈55–60%), and the internal friction angle decreased linearly (total attenuation ≈20–25%). The total attenuation of cohesion was much larger than the internal friction angle. (4) The degradation mechanism is essentially a multi-scale coupling process of cementation dissolution, pore collapse, and fracture expansion of red clay internal structure. These findings provide critical insights for sustainable engineering design and disaster prevention in regions with similar soil conditions, contributing to the resilience and longevity of infrastructure under changing climatic conditions. Full article

Tourism is a key driver of Portugal’s economy, with the WTTC projecting it to contribute EUR 56.4 billion (21.1% of GDP) by 2033. However, the sector has proven highly vulnerable to external shocks, such as the 2008 financial crisis, Brexit, and the pandemic, which have disrupted demand patterns and exposed structural weaknesses. It is essential to understand these impacts at a regional level in order to design more resilient and sustainable tourism strategies. This study examines how major crises have shaped tourism in Portugal’s NUTS II regions, focusing particularly on overnight stays, and assesses the implications for sustainable development and regional policy. Quarterly data from the National Statistics Institute (INE) covering 2004/2024 are used. We apply ARIMA and SARIMA models to account for seasonality and autocorrelation, and evaluate the accuracy of our forecasts using Mean Absolute Percentage Error (MAPE) and Theil’s U statistics. Structural breaks are considered to capture the effects of crises. The findings show that crises have significantly altered tourism patterns, with a shift towards less crowded and more remote destinations. This reflects vulnerabilities and opportunities for sustainability-oriented tourism. The study offers policymakers actionable guidance by aligning its results with the United Nations Sustainable Development Goals (SDGs), particularly those related to economic resilience (SDG 8), innovation and infrastructure (SDG 9), and partnerships for sustainable governance (SDG 17). This work is original in combining long-term regional data with robust forecasting techniques to provide innovative insights for scientific research and practical policy planning. Full article

Conventional academic warning systems in higher education often rely on end-of-semester grades, which severely limits opportunities for timely intervention. To address this, our interdisciplinary study developed and validated a comprehensive socio-technical framework that integrates social-cognitive theory with learning analytics. The framework combines educational data mining with culturally responsive, personalized interventions tailored to a non-Western context. A two-phase mixed-methods design was employed: first, predictive models were built using Learning Management System (LMS) data from 2,856 students across 64 courses. Second, a quasi-experimental trial (n = 48) was conducted to evaluate intervention efficacy. Historical academic performance, attendance, and assignment submission patterns were the strongest predictors, achieving a Balanced Area Under the Curve (AUC) of 0.85. The intervention, specifically adapted to Confucian educational values, yielded remarkable results: 73% of at-risk students achieved passing grades, with a large effect size for academic improvement (Cohen’s d = 0.91). These findings empirically validate a complete prediction–intervention–evaluation cycle, demonstrating how algorithmic predictions can be effectively integrated with culturally informed human support networks. This study advances socio-technical systems theory in education by bridging computer science, psychology, and educational research. It offers an actionable model for designing ethical and effective early warning systems that balance technological innovation with human-centered pedagogical values. Full article

In language classes, speaking skills are often taken for granted, and not enough attention is paid to developing these skills in a targeted way. In our study, the speaking skills of third-grade students (N = 46) are developed in integrated Lithuanian and English lessons through the analysis of visual material. Visual material is an aid and a means for expanding students’ vocabulary and developing their ability to express their thoughts verbally. The students are aged 9–10 years old. The aim of the study was to investigate the development of third-grade students’ speaking skills using visual material analysis in two languages. The Action Research was conducted in a school in one of Lithuania’s major cities. During the Action Research, students completed mind maps and analyzed visual material by answering questions in two languages. The questions were designed to cover different groups of thinking skills (knowledge and understanding, drawing conclusions, interpretation, and evaluation). The students spoke their prepared answers to the questions. The accuracy and correctness of the answers, English pronunciation, and the ability to speak in complete sentences were evaluated. Full article

Background/Problem: Sarcopenic obesity (SO) is characterized by significant muscle loss combined with obesity, and it is mostly prevalent among older adults. Consequences include a heightened incidence of falls and a greater susceptibility to non-communicable diseases. Thailand currently lacks a care model for SO in older adults. Objective/Purpose: This study utilizes participatory-action research (PAR) to develop a care model for sarcopenic obesity in Thailand. Design and Methodology: In-depth interviews with 25 older adults with SO and focus group discussions with 12 stakeholders were conducted to develop a preliminary care model. An action research spiral process was utilized with 15 older adults with SO over 16 weeks. Findings: We developed a culturally sensitive care model for SO in older adults. This study demonstrates that a participatory-action research (PAR) method for behavior transformation, highlighting health awareness and SO literacy, is crucial for behavior change. Conclusions and Implications: The behavior change process using transformative behaviors facilitated internal changes. This approach helps individuals to understand interconnected factors through personal experiences, leading to profound understanding and readiness for deep, continuous, and meaningful behavioral changes. Full article

This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm. Full article