Search Results (2,299)

Background: Human exposure to environmental endocrine-disrupting chemicals (EDCs) rarely occurs in isolation, yet most epidemiological research has assessed chemicals individually. PFASs, toxic metals, phthalates, and VOCs are ubiquitous contaminants with well-documented reproductive toxicity. Objective: The aim of this study was to investigate the joint and individual effects of 28 EDCs spanning four chemical classes on six reproductive hormone biomarkers in a nationally representative U.S. population—using an innovative approach that simultaneously characterizes nonlinear mixture effects and chemical interactions across multiple exposure domains. Methods: This cross-sectional study used NHANES 2017–2018 data (n = 9254). Multivariable linear regression and Bayesian Kernel Machine Regression (BKMR) characterized individual and mixture associations, respectively. Missing data were handled using multiple imputations by chained equations. Survey design weights were applied in linear regression models. Results: Linear regression revealed heterogeneous associations across chemical classes and hormones. PFOA was positively associated with SHBG (β = 12.35; 95% CI: 8.33, 16.38) and LH (β = 6.91; 95% CI: 1.44, 12.38), while mercury was inversely associated with estradiol (β = −3.38; 95% CI: −5.12, −1.65). BKMR analyses identified pronounced non-monotonic dose–response relationships and emergent mixture effects not predictable from single-chemical analyses for all six hormones. Posterior inclusion probabilities identified cadmium, PFOA, MEHP, and MBzP as the most influential predictors across hormone endpoints. Conclusions: Concurrent real-world exposure to PFASs, toxic metals, phthalates, and VOCs is associated with measurable, nonlinear alterations in reproductive hormone profiles. Chemical mixture effects cannot be reliably predicted from single-pollutant analyses, underscoring the necessity of mixture-based methodologies in environmental reproductive epidemiology. Prospective studies are needed to establish causal temporality and identify critical windows of susceptibility. Full article

The rapid integration of artificial intelligence (AI) technologies into educational contexts has introduced innovative instructional approaches, particularly within Science, Technology, Engineering, and Mathematics (STEM) education. Although an increasing number of empirical studies have examined AI-supported instruction, existing findings remain heterogeneous, making it difficult to draw firm conclusions about its overall effectiveness. This study aims to systematically synthesize experimental and quasi-experimental research on AI-supported instructional interventions in STEM education, quantify their overall effects on student learning outcomes, and examine potential moderating factors, including educational level, STEM discipline, and intervention duration. A comprehensive systematic literature search was conducted across Web of Science, Scopus, ERIC, ScienceDirect, and Google Scholar, covering studies published between 2005 and 2025. A total of 35 studies meeting predefined inclusion criteria were included in the meta-analysis. Effect sizes were calculated using Hedges’ g, and a Random Effects Model (REM) was employed to account for heterogeneity among studies. Moderator analyses were conducted for educational level, STEM discipline, and intervention duration. Publication bias was assessed using multiple diagnostic methods. The meta-analysis revealed a statistically significant overall positive effect of AI-supported instruction on student learning outcomes in STEM education (g = 0.67, 95% CI [0.49, 0.85], p < 0.001). Moderator analyses indicated that AI interventions were most effective at the high school level. Although Science and Mathematics disciplines showed slightly higher effect sizes, the between-group difference was not statistically significant (Q = 4.85, df = 2, p = 0.088). Regarding intervention duration, the highest effect size was observed in interventions lasting more than one month and up to two months, though no consistent pattern of increasing effectiveness with longer durations was found. Publication bias analyses suggested minimal influence on the overall findings. AI-supported instructional interventions demonstrate a moderately to highly positive impact on student learning outcomes in STEM education. The effectiveness of these interventions varies according to educational level, disciplinary context, and intervention duration. These findings provide robust empirical evidence supporting the pedagogical value of AI in STEM education and offer guidance for educators and policymakers regarding effective implementation. Full article

Coastal urban environments are increasingly exposed to natural hazards, including storm surges, tsunamis, coastal erosion, and flooding, which threaten lives, livelihoods, and infrastructure. Despite their widespread use, conventional hard and soft engineering measures have often proved insufficient to address the escalating risks posed by climate change and rapid urbanisation. This study explores the potential of Nature-Inspired Solutions (NiS) as a complementary pathway to advance resilience in architecture, urban design, and planning. Unlike Nature-Based Solutions that utilise existing ecosystems directly, NiS draw design principles from both biotic and abiotic natural systems, offering innovative models for resilient settlements, coastal infrastructure, and adaptive urban planning. Using a mixed-methods approach that includes systematic and narrative reviews, semi-structured expert interviews, analysis of urban development plans, a panel discussion, and expert brainstorming, this research examines how natural coastal systems inform design interventions. Sri Lanka was selected as the primary case study context due to its exceptional coastal vulnerability, significant climate adaptation policy gaps, and status as a small island developing state representative of the coastal challenges faced by similar contexts globally. Furthermore, Sri Lanka was selected as the case study in accordance with the original research proposal submitted to the University of Huddersfield, which identified the country as a suitable context due to its significant vulnerability to coastal hazards, as outlined above. Field investigations in the Lunawa coastal area documented community-based adaptive practices emerging from multi-generational environmental observation. Analysis reveals how dune morphologies, root structures, living shorelines, and rock pool formations translate into architectural and engineering applications. Findings identify critical implementation challenges, including context-specific requirements, technical knowledge gaps, insufficient policy frameworks, limited practitioner awareness, and uncertainties about economic feasibility, as well as key enablers such as demonstrated ecological effectiveness and the potential of multifunctional infrastructure. The study demonstrates that embedding NiS into risk-informed planning and resilient urban design contributes to climate change adaptation, ecological sustainability, and inclusive governance, while highlighting persistent barriers that require strategic intervention. By bridging ecological wisdom and architectural innovation, NiS offers transformative opportunities to reimagine resilient coastal cities and communities facing escalating climate-induced hazards. Full article

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article

The inclusion of the cement industry into China’s national carbon emissions trading system in 2025 has fundamentally altered the compliance environment for high-emission enterprises, transforming carbon allowances from passive regulatory instruments into dynamic assets whose management directly affects financial performance. We develop a multi-scenario carbon asset management decision model tailored to the intensity-based benchmarking mechanism adopted by the national market. The model centres on the quota surplus-deficit variable EA4, which is computed from enterprise-level emission intensity relative to the industry benchmark, and decomposes the management problem into sequential selling and buying subproblems linked by coupled decision boundaries. A systematic parameter framework is constructed, and the model is applied to two cement enterprises—Enterprise A, a leading producer with a clear allowance surplus, and Enterprise B, a mid-tier producer operating near the benchmark boundary—through historical backtesting over the 2024–2025 period. Three principal findings emerge. First, the intensity benchmarking mechanism creates a dual-leverage effect whereby a 1.4% improvement in emission intensity (from 0.8112 to 0.8000 t/t) increases the quota surplus by 27%, a nonlinearity not captured by conventional compliance-cost models. Second, the model-driven strategy outperforms traditional experience-based approaches by 36.8% (baseline scenario, +95.20 vs. +69.58 MRMB) and 37.3% (risk scenario, −44.55 vs. −71.08 MRMB), with the improvement rate remaining consistent across both enterprises, suggesting that trading timing outweighs instrument selection in determining compliance cost outcomes. Third, dynamic CEA–CCER allocation captures an incremental 2.33 MRMB through the exploitation of a transient price inversion, a gain invisible to single-instrument strategies. Sensitivity analysis confirms that the relative advantage is robust to carbon price variations (±30%) and CCER offset caps (2–10%), while emission intensity and carry-over allowances represent the most consequential parameters for strategy direction, with EA4 crossing zero near the industry benchmark (I ≈ 0.85). The framework provides actionable decision support for cement and other high-emission enterprises navigating the unified carbon market, and contributes a quantitative methodology to the emerging field of environmental management accounting. This study contributes to Sustainable Development Goal 13 (Climate Action), Goal 7 (Affordable and Clean Energy), and Goal 9 (Industry, Innovation, and Infrastructure) by providing operational tools for decarbonisation in carbon-intensive industries. Full article

Background: Migraine (MIG) management guidelines support a comprehensive approach combining medication, therapeutic patient education (TPE), behavioral strategies, lifestyle changes, diet, and physical activity (PA). Objective: To present an innovative interdisciplinary outpatient model for individuals with MIG, focusing on PA, sedentary behavior, eating habits (EH), metabolic health, temporomandibular disorders, and postural dysfunctions. Design: A randomized controlled trial will enroll 200 adults with MIG over two years. Inclusion criteria are chronic MIG (≥15 attacks/month for ≥3 months) or high-frequency episodic MIG (8–14 attacks/month), physical inactivity, and independent walking ability. Exclusion criteria include contraindications to PA and lack of informed consent. Participants will be randomized to standard care (SC) or an intervention group receiving TPE plus three months of supervised exercise (EXE). All participants will receive an informational brochure with nutritional tips (included in SC) and undergo: (1) neurological examination, (2) validated questionnaires, (3) kinesiological and postural assessment, and (4) gnathological evaluation. The primary outcome is change in monthly MIG frequency at 6 and 12 months; additional outcomes include disability, quality of life, and intensity of MIG, PA levels, sedentary behavior, medication use, EH, functional capabilities, postural parameters, and temporomandibular disorder-related variables. Results: Hypothetically, the intervention may reduce monthly MIG frequency by approximately 15–20% relative to baseline. Improvements may also occur in disability, quality of life, medication use, lifestyle behaviors, and psychological and cardiometabolic parameters. Conclusions: This trial will evaluate whether adding supervised EXE and TPE to SC may improve MIG outcomes compared with SC alone, supporting a comprehensive management strategy. Full article

Against the backdrop of the “Dual Carbon” strategy and global shipping digitalization, data elements have emerged as the key enabling factor and predictive correlate of coastal port smartness. Using panel data for seven coastal provinces/municipalities and eight coastal ports in China from 2017 to 2024, this paper constructs a “base-supply-flow-use” data element development index (DEDI) and a “WSR” coastal port smartness index (CPSI), employing VHSD-EM dynamic model, random forest algorithm, and partial effect model to examine the association patterns, nonlinear responses, and differentiated enhancement pathways between data elements and port smartness. Findings reveal: (1) CPSI and DEDI exhibit a high positive correlation with narrowing regional disparities; (2) CPSI shows stepwise spatial differentiation, with Shanghai and Ningbo-Zhoushan Ports leading, while Guangdong demonstrates “data advancement but smartness lag”; (3) in the random forest model, the predictive contribution of DEDI to CPSI is 13.586%, which ranks behind digital inclusive finance and openness level but is higher than regional economic strength and innovation output. The combined predictive contribution of the DEDI main effect and its interaction terms reaches 32.567%; (4) the univariate partial effect of DEDI on predicted CPSI followed a three-stage nonlinear pattern of initial accumulation, accelerated improvement around a threshold of DEDI ≈ 0.215, and diminishing marginal effects at higher levels; and (5) the joint partial effects of DEDI with digital inclusive finance, economic development, fiscal transportation expenditure, and innovation output showed clear dimensional and regional heterogeneity. Accordingly, four policy pathways are proposed: constructing a full-chain data element system, enabling synergistic empowerment of data and supporting elements, formulating regionally differentiated catch-up strategies, and strengthening the dual-wheel support of digital inclusive finance and opening-up—all aimed at advancing the development of world-class ports. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic neurological disorder affecting more than 2.8 million individuals worldwide and is commonly associated with cognitive deficits that compromise independence and quality of life. In recent years, virtual reality (VR) has emerged as an innovative rehabilitation strategy, offering immersive and engaging environments that promote neuroplasticity and enhance patient motivation. To evaluate the effectiveness of immersive and non-immersive VR-based interventions in improving cognitive performance among adults diagnosed with MS. Methods: A systematic review was conducted following the Cochrane Handbook for Systematic Reviews of Interventions and PRISMA 2020 guidelines (PROSPERO registration: CRD420251103762). Comprehensive searches were carried out across seven international databases up to October 2025, including only randomized controlled trials assessing cognitive outcomes after VR-based rehabilitation programs. Results: From 1948 records screened, 13 studies comprising 649 participants met the inclusion criteria. Intervention durations ranged between 6 and 17 weeks, with sessions lasting 30–60 min. The interventions involved treadmill training with VR, exergaming, and cognitive stimulation protocols. Most studies demonstrated significant improvements in processing speed, visuospatial and verbal memory, and executive functioning (p < 0.05). Adherence rates were above 80%, and no serious adverse events were reported. Conclusions: VR-based rehabilitation appears to be a safe, feasible, and effective approach for enhancing cognitive abilities in individuals with MS, particularly in processing speed and visuospatial memory. Nonetheless, the heterogeneity of methodologies underscores the need for standardized intervention frameworks and large-scale multicenter randomized trials to establish optimal parameters and confirm sustained long-term benefits. Full article

Background: Pediatric emergencies are high-risk clinical situations requiring timely, accurate, and coordinated interventions. Nurses play a pivotal role in early recognition and management of acute pediatric conditions; however, the rarity and complexity of these events often limit clinical exposure and preparedness. Continuous professional education is therefore essential to ensure patient safety and high-quality care. Objective: This systematic review aimed to synthesize evidence on innovative continuing education strategies for nurses involved in pediatric emergency care, with a primary focus on studies evaluating educational effectiveness and a secondary contextual focus on studies describing training needs, perceived barriers, preparedness, and implementation conditions. Methods: The review was conducted according to PRISMA guidelines. The protocol was registered in PROSPERO (ID CRD420251120993). A comprehensive search of PubMed, CINAHL Complete, Scopus, and the Cochrane Library identified studies published between 2015 and August 2025. Primary intervention studies were used to assess educational effectiveness, whereas descriptive, observational, qualitative, and review-based evidence was retained as contextual evidence. Methodological quality was assessed using Joanna Briggs Institute (JBI) tools. Results: Forty-nine studies met the inclusion criteria, including randomized controlled trials, quasi-experimental studies, observational and cohort studies, and integrative or narrative reviews. Educational interventions mainly involved simulation-based training, blended learning, telesimulation, digital education, and structured training programs. Intervention studies suggested improvements in knowledge, technical skills, self-efficacy, and team performance, while contextual studies highlighted training needs, perceived barriers, preparedness, and implementation challenges. However, the evidence was limited by methodological heterogeneity, frequent reliance on self-reported outcomes, and limited long-term follow-up. Conclusions: Simulation-based, blended, and telesimulation-based educational strategies may be associated with short-term improvements in nurses’ preparedness and educational outcomes in pediatric emergency care. However, conclusions regarding effectiveness should be interpreted cautiously because of methodological heterogeneity, reliance on subjective outcomes, and limited evidence on long-term clinical and patient-safety outcomes. Full article

Waste cooking oils (WCO) are large-scale residual streams from domestic and industrial food processing. Their improper disposal poses severe environmental risks, yet their integration into the oleochemical sector offers a strategic opportunity for the green transition by substituting fossil-based feedstocks. This systematic review provides a comprehensive assessment of WCO valorization as a sustainable precursor for high-value products, specifically biopolymers, bioplasticizers, and biolubricants. The study followed the PRISMA 2020 guidelines, searching PubMed, Scopus, and MDPI databases (up to September 2025). The search strategy utilized combinations of keywords present in the title. Inclusion criteria focused on peer-reviewed chemical and biotechnological conversion pathways published in English within the last decade. Studies addressing biofuel production, patents, and review were excluded. Screening, data extraction, and qualitative risk of bias assessment, centered on experimental reproducibility and reporting transparency, were performed independently by multiple reviewers. From an initial pool of 2637 records, 87 studies met the eligibility criteria. The analysis reveals that polyhydroxyalkanoates (PHAs) represent the most extensively researched pathway, followed by WCO-derived epoxides and innovative biolubricant formulations. While several studies report high conversion yields under optimized conditions, the transition from bench-scale to industrial implementation remains hindered by the heterogeneous composition of WCO and a lack of standardized pre-treatment protocols. WCO valorization shows transformative potential for the circular economy, offering a dual benefit of waste mitigation and sustainable material synthesis. However, future research must address scalability challenges and feedstock variability. This review identifies emerging trends and provides a roadmap for the industrial adoption of WCO-based processes in the framework of clean technologies. Full article

This paper proposes a sociological reinterpretation of the concept of sustainability, understood as a cultural dispositive capable of shaping habitus, social representations, and models of action. From a culturalist perspective, sustainability is analysed as a process of social construction grounded in the internalisation of values, knowledge, and practices that contribute to the formation of responsible citizenship. Within this theoretical framework, the school assumes a strategic role in processes of sustainability education by fostering the ethical, collaborative, and inclusive competences required to address contemporary socio-environmental transformations. The paper presents the Edumat+ design protocol, developed within the framework of the Erasmus+ programme, aimed at experimenting with innovative methodologies for digital education in primary schools through the integration of STEAM approaches, with reference to coding, educational robotics, and information design. The protocol involved the development of infographic mats and digital learning activities focused on themes of environmental sustainability. The findings highlight how the integration of digital education, visual storytelling, and collaborative learning can contribute to the construction of inclusive and participatory educational environments capable of supporting processes of sustainable citizenship from primary education onwards. Although the activation of such pathways is consistent with recent European policies promoting the integration of digital technologies and STEAM approaches within schools, particularly through initiatives focused on teacher education and the acquisition of technologies and software, the widespread dissemination of the project still requires further governmental support, especially for the development and dissemination of the project outputs. Full article

Additive manufacturing offers transformative opportunities but faces barriers due to costly, imported filaments. This study at Caraga State University, Cabadbaran Campus, developed a prototype automated filament extrusion system using recycled thermoplastics, specifically polypropylene (PP) and PET, to address material scarcity and plastic waste. Employing a developmental–descriptive design, the system integrated heating, extrusion, spooling, and microcontroller-based controls. Results confirmed functional capability, producing filaments with acceptable dimensional consistency, though challenges in accuracy and flexibility remain. The project advances sustainable, affordable 3D printing, supports circular economy principles, enhances technical education, and empowers local innovators toward inclusive, environmentally responsible manufacturing. Full article

Workplace inclusion and the sustainable well-being of people with disabilities represent a critical social and technological challenge. This paper presents the exploratory insights of a study aimed at identifying both interaction needs for workplace technologies and socio-organizational conditions that support inclusive and sustainable employment. The study adopts a co-creation methodology based on the Design Thinking approach through the implementation of four co-creation events with workers with disabilities and key stakeholders. The findings reveal persistent barriers at structural, cultural, and operational levels, including the lack of dedicated support roles, limited awareness of disability diversity, and the persistence of non-accessible environments, tools, and work processes. From a technological perspective, the study identifies key interaction needs for inclusive workplace design across five dimensions: usability, accessibility, robustness, accuracy, and personalization. While these requirements manifest differently across disability types, several cross-cutting needs emerge, including accessible and comprehensible information, adaptive and customizable systems, reliable assistive technologies, and inclusive organizational practices. The results conceptualize workplace inclusion as a socio-technical ecosystem in which technological innovation, organizational arrangements, and accessibility culture co-evolve. The paper contributes to research and practice by providing empirically grounded design implications for developing inclusive workplace technologies that promote autonomy, participation, and long-term well-being. Full article

Renewable energy sources, such as solar thermal and photovoltaic, geothermal, biomass, hydropower, and wind, offer significant sustainability advantages. Yet the sector still faces difficulties in several areas that tend to reduce the efficiency of these new energy forms. Some of these challenges include inconsistent electricity supply, the diffuse nature of renewable energy sources, which makes them difficult to exploit, and the inconsistent and unpredictable nature of electricity supply, which has repercussions for renewable energy markets. Although Industry 4.0 is inherently energy-intensive, its positive contribution to renewable energy systems may outweigh its costs. Consequently, this study conducts a scoping review on the role of digital technologies in renewable energy systems. It focuses on open-access conference papers, journal articles, and book chapters published between 2020 and 2026, selected from scientific platforms and databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. A multi-stage screening process and a summary sheet for a set of 89 selected articles were produced to extract the necessary information. The results show that Industry 4.0 influences renewable energy systems at the design and installation stage in predictive maintenance, efficient management, and energy security. Meanwhile, Industry 4.0 in renewable energy systems still faces negative externalities that can be categorised as political, financial, infrastructural, environmental, human, security, and technological. To address these challenges, which tend to become entangled in cycles of negative reinforcement, the paper suggests defining standardised, clear, strict, and stable frameworks at the political, legal, regulatory, and environmental levels to overcome most challenges associated with the digital transformation of renewable energy. The study also recommends flexible, inclusive strategic planning that accounts for the digital maturity of the renewable energy system. From these perspectives, the study contributes to the literature by addressing the role of Industry 4.0 technologies in renewable energy systems from a strategic and coordinated perspective, from both human and technological standpoints. It also offers managerial and policy implications by supporting innovation in renewable energy systems on the one hand and contributing to policy and regulatory decision-making that favour their growth on the other. Full article

Although South Africa has an extensive water infrastructure, it continues to face significant water scarcity due to its semi-arid climate, increasing urbanisation, ageing infrastructure, and pollution. These challenges, coupled with climate change and increasing water demand, have led to inefficiencies across the water value chain, particularly in rural areas. This review paper evaluates the current adoption of predictive analytics in South Africa’s water management system through a systematic literature review. It identifies the current applications, implementation gaps, and key system components that are suitable candidates to enhance efficiency, resource planning, and long-term sustainability in the sector. The findings show that while predictive models are being applied in urban systems for demand forecasting and proactive maintenance, only 15% of the reviewed studies address their actual adoption in rural or under-resourced contexts. This underscores the need for more inclusive development strategies to ensure equitable water service delivery. Although strides have been made in research and innovation, a major barrier is the slow transition from research to operational deployment, which hinders the full realisation of these technologies’ benefits that are essential for water supply sustainability and availability. Full article