Search Results (13,992)

With the deep integration of artificial intelligence technology into education, AI literacy has emerged as a core competence indispensable for pre-service teachers. However, its formation mechanisms and sustainable cultivation pathways remain to be further explored. This study integrates the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) to construct a theoretical model, in which Individual Innovation (II) and Self-Efficacy (SE) serve as antecedents, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as mediators, Behavioral Intention (BI) as a proximal variable, AI literacy as the outcome variable, gender and major as moderating variables, and grade and AI exposure time as control variables, exploring the influencing factors and mechanisms of pre-service teachers’ AI literacy. Through a questionnaire survey of 778 pre-service teachers, Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) were employed for sequential empirical analysis. The PLS-SEM results reveal that II and SE were significantly and positively associated with AI literacy through the serial mediation of PU, PEOU, and BI. The fsQCA further identified four distinct equifinal configurations associated with high AI literacy: “High-efficacy Practice-Oriented”, “High-Behavioral-Intention-Oriented”, “High-Innovativeness-Oriented”, and “Long-Term-Development-Oriented”. The findings demonstrate that the improvement of pre-service teachers’ AI literacy follows multiple equifinal mechanisms, necessitating a shift beyond the single-training mindset. Accordingly, this study proposes differentiated cultivation pathways, providing theoretical foundations and practical references for normal universities to deliver targeted and sustainable AI literacy training. It also offers empirical evidence and strategic support for the sub-goals of SDG 4 concerning teacher capacity-building and the digital transformation of education, which aim to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Full article

Pumped hydro storage (PHS) systems, increasingly deployed in power systems with large shares of wind and solar generation, can play a key role in managing power system frequency. Variable-speed pumped hydro storage (VS-PHS) systems, in particular, have potential for rapid primary frequency response by enabling the quick release of machine rotor kinetic energy. However, using conventional proportional–integral (PI) control for converters and governors can result in large speed deviations and torque imbalance during fast system transients. This issue is intensified in PHS plants with slow hydraulic response, such as those with long penstocks or slow guide-vane adjustments, potentially violating mechanical operating constraints. This paper develops a model predictive control (MPC) strategy for coordinated governor and converter control, accounting for operational constraints. The proposed approach improves coordination of hydraulic and electrical systems, utilising DC-link storage and proactive guide-vane action for rapid power adjustments. Dynamic simulations using a complex nonlinear plant demonstrate that MPC redistributes energy extraction between the DC-link storage and the rotating mass while respecting their imposed limits. Furthermore, robustness tests indicate that MPC performance is sustained under plant nonlinearities and measurement noise. These results highlight the advantages of predictive control for supporting frequency response in VS-PHS systems. Full article

In recent years, Additive Manufacturing (AM) has transformed the development of new products, enabling more efficient, sustainable, and creative solutions across multiple sectors. Accordingly, this research explores the integration of Selective Laser Melting (SLM) with advanced Computer-Aided (CAx) tools, specifically Generative Design (GD) and Topology Optimisation (TO), to rethink an everyday product. The developed concept, an SLM water tap, highlights the seamless synergy between design and product engineering. Reverse Engineering (RE) was applied to analyse the conventional internal mechanism, which was redesigned in accordance with Design for Additive Manufacturing (DfAM) principles. This approach enabled the integration of the internal cartridge architecture into the tap body as a single metal component, reducing system complexity and part count. TO was applied to key components, achieving a 35% mass reduction without compromising the simulated structural performance of the system. GD was employed to generate optimised internal flow channels, resulting in a numerically simulated flow rate of 4.69 L/min. Integrating CAx tools enabled a customisable product with varied surface textures. This work contributes to the deconstruction of traditional manufacturing paradigms and advances the understanding of AM for functionally relevant product design. Full article

Distributed independent pump-controlled systems enhance excavator energy efficiency by reducing throttling losses, but sustained high-load and high-speed operation often forces a single pump into low-efficiency regions. To overcome this limitation, this study proposes a dual-pump load-sharing architecture that enables load sharing and dynamic cooperative operation of two pumps within the high-efficiency region. An adaptive closed-loop GOA-SQP optimization strategy is also developed for real-time pump speed distribution. The proposed strategy combines the global exploration capability of the Grasshopper Optimization Algorithm (GOA) with the rapid local convergence characteristics of Sequential Quadratic Programming (SQP). Unlike conventional serial hybrid optimization methods, a bidirectional efficiency-feedback mechanism is introduced to dynamically coordinate global exploration and local refinement processes in real time. Furthermore, a local perturbation and re-explosion mechanism is incorporated to suppress premature convergence, enhance population diversity, and reduce redundant iterations. Experimental validation on an excavator test platform under four-quadrant conditions shows that the proposed system improves mechanical, volumetric, and overall pump efficiencies by 14.22, 4.57, and 18.79 percentage points, respectively, and reduces total energy consumption by 8.83%. Compared with GOA, SQP, GA-SQP, and PSO-SQP, the proposed GOA-SQP algorithm reduces solution time by 39.15% and improves optimization accuracy by 0.5%. The proposed architecture and optimization strategy offer a novel and effective solution for further improving the energy efficiency of distributed independent pump-controlled excavator systems. Full article

Global urbanization has caused widespread ecological degradation, yet habitat quality in agricultural plains remains understudied. This study addresses this gap by assessing and predicting land use and habitat quality changes in the Northern Anhui Plain from 2000 to 2030 using the PLUS and InVEST models under four scenarios (natural development, farmland protection, economic development, and sustainable development). The optimal parameters-based geographical detector (OPGD) was employed to identify driving factors. Results show that farmland continuously shrank while built-up land expanded, and habitat quality remained low and declined over time, with low-grade areas expanding. All four 2030 scenarios exhibited declines, with the farmland protection scenario yielding the highest habitat quality and the economic development scenario the lowest. The optimal spatial scale was 4 km, and discretization algorithms and break numbers significantly influenced driver analysis. Locational factors had relatively higher explanatory power, though the overall q-statistic was moderately low, indicating limited single-factor explanation. The study reveals the spatiotemporal dynamics and driving mechanisms of habitat quality in this farmland-dominated plain, providing useful insights for spatial planning and policy-making to support sustainable development in agricultural regions. Full article

Concrete remains the most widely used construction material owing to its affordability, local availability, mechanical performance, durability, and versatility. However, its production has a significant environmental impact, and its service life may be reduced under aggressive exposure conditions, affecting both the functionality and cost of structures. This study presents a sustainability assessment based on a harmonized database of previously developed concrete mixtures, rather than a new experimental campaign. Waste-containing mixtures and their corresponding conventional or reference controls were considered to anchor the comparison to reference concretes, rather than only to the minimum values within the analyzed dataset. The objective was to compare the consistency among sustainability index formulations: K_SB and K_SB,C, associated with the Building Material Sustainability Potential (BMSP), and the Empathetic Added Sustainability Index (EASI). These formulations integrate functional and environmental performance, although they differ in their aggregation approach and in the treatment of economic variables, such as cost and eco-cost. The indicators considered include 28-day compressive strength, carbonation rate, global warming potential (GWP), gross energy requirement (GER), natural raw material consumption (NRMC), eco-cost, and material cost. The results show that mixtures with higher replacement levels of natural aggregates by recycled concrete aggregate and partial replacement of cement by locally sourced ashes exhibited the best integrated performance. The 300/100RCAg mixture stood out, with index values close to 3.43, 3.52, and 3.99, up to approximately four times those of the control. Sensitivity and uncertainty analyses showed that the highest-ranked mixtures generally maintained favorable positions across assessment methods and under ±20% input variability, although some alternatives exhibited substantial method-dependent ranking changes. The results demonstrate that local waste materials can improve concrete sustainability; however, their benefits depend on waste type, conditioning requirements, functional performance, and the selected sustainability assessment method. Full article

The development of sustainable polymer composites reinforced with renewable resources and advanced nanomaterials has attracted considerable attention for multifunctional engineering applications. In this study, an environmentally friendly epoxy-based biocomposite (EBC) reinforced with coconut shell powder (CSP) and graphene nanopowder (GNP) was successfully produced through a casting process. CSP was employed as a bio-based filler, while GNP was incorporated at concentrations ranging from 0 to 0.75 wt.% to improve the overall performance of the composites. The effects of GNP loading on bulk density, tensile strength, elongation at break, Shore D hardness, thermal conductivity, dielectric properties, thermal stability, mechanical and microstructural characteristics were systematically investigated. The results demonstrated that the incorporation of GNP significantly enhanced the multifunctional properties of the improved EBCs. Bulk density increased from 1137.5 to 1143.1 kg m⁻³ with increasing GNP content. The optimum tensile strength of 28.6 MPa and Shore D hardness of 77.4 were achieved at 0.45 wt.% GNP, indicating effective stress transfer and strong interfacial interactions between the epoxy matrix, CSP, and GNP. Thermal conductivity increased from 0.110 to 0.149 W m⁻¹ K⁻¹, while the dielectric constant increased from 3.06 to 4.25 with increasing GNP concentration. Thermogravimetric analysis revealed improved thermal stability and enhanced char formation in graphene-containing composites. FTIR analysis confirmed that graphene acted primarily as a physical reinforcement without altering the fundamental chemical structure of the epoxy network. SEM and EDX investigations demonstrated improved structural compactness, homogeneous filler dispersion, and successful graphene incorporation. The findings indicate that GNP and CSP reinforced EBCs possess significant potential for lightweight structural materials, thermal management systems, dielectric components, and sustainable multifunctional engineering applications. Full article

Accurate crop mapping in middle-to-high latitude agricultural basins is essential for food security, agricultural management, and sustainable land-use planning. However, crop classification in these regions remains challenging because fragmented field patterns, mixed pixels, and overlapping phenological stages often lead to severe spectral confusion among major dryland crops. To address this issue, this study developed a Phenology–Spectral Dual-Constrained Strategy (PS-DCS) by integrating agronomic knowledge with physically constrained spectral features. The proposed framework identified August as the optimal observation window based on crop phenological divergence. Wheat was first extracted using a spectral fingerprint combining the Chlorophyll Index Red Edge (CI_RE) and Redness index. Subsequently, maize and soybean were separated within the non-wheat mask using the B6 red-edge band selected through feature separability analysis. Validation based on Sentinel-2 time-series imagery and 1056 independent field samples collected in 2025 yielded an Overall Accuracy of 95.36% with a Kappa coefficient of 0.928. Compared with RF, XGBoost, and CNN models, PS-DCS maintained competitive classification performance while substantially reducing dependence on large training datasets and complex parameter tuning. Cross-year validation during 2022–2024 further demonstrated stable spatial transferability without threshold recalibration. These results indicate that translating agronomic mechanisms into physically interpretable remote sensing rules provides an effective and transparent framework for high-precision crop mapping and long-term agricultural monitoring in complex agricultural landscapes. Full article

Ethnopharmacological relevance: Dioscorea nipponica Makino is a traditional medicinal plant widely used in East Asia for the treatment of rheumatic disorders, inflammatory diseases, and cardiovascular conditions. Its rhizome has long been applied in clinical practice for relieving pain, promoting blood circulation, and reducing swelling. Aim of the study: This narrative review aims to provide a comprehensive and critical overview of the phytochemical constituents, pharmacological activities, and underlying mechanisms of D. nipponica and to identify current research gaps and future perspectives. Materials and methods: The literature was searched in PubMed, Web of Science, ScienceDirect, CNKI and Wanfang Data from database inception to December 2025. The combined retrieval keywords were set as: (Dioscorea nipponica Makino OR Chuanshanlong) AND (chemical constituents OR steroidal saponins OR flavonoids OR phenols) AND (biological activity OR anti-inflammatory OR cardioprotective OR hepatotoxicity OR clinical application). Both English and Chinese publications were retrieved, and studies written in other languages were excluded. Results: Phytochemical studies have identified diverse secondary metabolites, particularly steroidal saponins, along with diarylheptanoids and phenanthrenes. These compounds exhibit multiple pharmacological activities, including anti-inflammatory, anti-tumor, immunomodulatory, and cardioprotective effects. Mechanistic studies indicate that these activities are mediated through the modulation of key signaling pathways such as NF-κB, PI3K/Akt, AMPK, and the NLRP3 inflammasome. However, current research remains fragmented, with limited integration of chemical composition, molecular targets, and therapeutic outcomes. Conclusions: D. nipponica represents a promising source of bioactive natural products, with steroidal saponins as the major contributors to its pharmacological effects. Future studies should focus on multi-component interactions, pharmacokinetics, quality control, and clinical validation to support its rational development and sustainable utilization. Full article

Rice protein peptides, abundant byproducts of rice processing, represent a sustainable source for developing novel nutritional delivery systems. To address the low bioavailability of traditional calcium supplements, this study aimed to fabricate a high-performance calcium-chelating complex (RPP-Ca) and elucidate its functional mechanism. The synthesis process was systematically optimized, yielding a maximum calcium-binding capacity of 93.98 ± 1.99 mg/g under optimal conditions (pH 10, 70 °C, 50 min reaction time, peptide-to-calcium mass ratio of 2:1). Physicochemical characterization utilizing scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) confirmed successful chelation, revealing significant microstructural reorganization and enhanced thermal stability compared to native peptides. Functional validation via in vitro Caco-2 cell models and in vivo calcium-deficient mouse models demonstrated that RPP-Ca significantly promotes intestinal calcium absorption and osteogenesis. Mechanistically, these effects were mediated through the activation of the TRPV6-Calbindin9k signaling axis. These findings underscore the potential of industrial rice protein peptides as an effective and bioavailable calcium fortification ingredient, providing a theoretical basis for the high-value utilization of rice byproducts in functional foods. Full article

Achieving coordinated development between higher education and regional economies is central to sustainable urban growth. This study examines the spatiotemporal coupling coordination between higher education and economic development across 90 cities within seven major coastal urban agglomerations in China over the period 2008–2021. An evaluation index system was constructed across seven dimensions, and three analytical methods were integrated: the entropy weight method for composite index calculation, the coupling coordination degree model for assessing synergistic development, and the panel vector autoregression (PVAR) model for dynamic interaction analysis. A fixed-effects regression model was further applied to identify key driving factors. The results indicate that: (1) higher education levels showed a steady upward trend across all agglomerations, while widening absolute disparities persisted among cities; (2) although a bidirectional Granger-causal relationship exists between higher education and economic development, the interaction is asymmetric along two distinct dimensions: higher education exerts a more statistically robust predictive influence on the economy, while unforecast economic shocks transmit more strongly to higher education than the reverse; this asymmetry manifests in specific urban agglomerations—most notably the Pearl River Delta—as a structural mismatch between economic strength and higher education development; (3) economic development level, government fiscal support, urbanization, and higher education scale are significantly associated with the coupling coordination between the two systems. These findings highlight the structural misalignment between higher education supply and regional economic demand and underscore the need for differentiated, spatially targeted policy interventions to promote sustainable regional development. Policy recommendations are proposed for optimizing higher-education resource allocation, reforming talent cultivation, and strengthening intra-agglomeration coordination mechanisms. Full article

This study develops the Socio-Technical Impact (ST-Impact) model, a sustainability-oriented curriculum design framework for management information systems (MIS) education, aimed at preparing future professionals to design and govern digital systems in socially and environmentally responsible ways. The rapid diffusion of emerging technologies, particularly generative AI, has intensified the need for MIS professionals who can integrate technical competence with social responsibility, ethical reasoning, responsible digital design, and public-value-oriented problem solving—competencies that remain unevenly addressed in existing MIS curricula. To address this gap, the study adopts a design science research approach, conducting a systematic review, reported in accordance with PRISMA 2020, and a design-oriented narrative synthesis of 120 international studies published between 2010 and 2025. Learning activities, student-produced artifacts, and assessment mechanisms were extracted as curriculum design units, while governance-related indicators were coded according to stakeholder requirements, accountability, equity, accessibility, privacy, safety, explainability, and sustainability. The resulting ST-Impact model comprises six iterative modules and a three-layer evaluation system. Its design coherence and practical plausibility are examined through an evidence-to-model traceability mapping, an illustrative comparative analysis of publicly visible curriculum structures, and an adaptable 15-week syllabus architecture. By translating abstract concepts of societal impact, responsible digital design, and digital sustainability into actionable curriculum design elements, this study contributes a literature-grounded foundation for future empirical validation in MIS education. Full article

The effectiveness of waste recycling systems in developing contexts is constrained not only by limited infrastructure but also by insufficient behavioral engagement. This study advances the understanding of recycling behavior by proposing and testing an integrated framework that investigated the combined effects of environmental concern, recycling knowledge, and moral norms, as well as their indirect effects through attitudes toward recycling. Using a cross-sectional survey administered on 516 university students in Libya, the study employed regression and mediation analyses. The results demonstrate that recycling knowledge is the most significant predictor of attitudes toward recycling, while attitudes serve as a critical mechanism driving recycling behavior. Environmental concern and moral norms were found to exert significant direct and partially mediated effects, revealing multiple pathways through which these factors influence recycling behavior. These findings provide empirical support for a multidimensional explanation of recycling behavior in which cognitive, normative, and attitudinal factors jointly shape outcomes. This study provides evidence from an underexplored context and extends existing models of pro-environmental behavior and highlights the role of knowledge-driven and attitude-based interventions. The findings provide actionable insights for designing behavior-focused interventions to enhance recycling participation and improve sustainable waste management outcomes in developing regions. Full article

This study examines how vulnerability is addressed within ESG-related research and identifies the governance limitations that hinder its effective integration into sustainability frameworks. Although ESG has become a dominant mechanism for sustainability assessment and climate-related governance, vulnerability remains insufficiently incorporated as a core analytical dimension. To address this gap, the study combines a systematic literature review with a bibliometric analysis based on 829 publications indexed in the Scopus database between 1996 and 2026. Using Bibliometrix v.5.3.0 and VOSviewer v.1.6.20, the study analyzes the evolution, conceptual structure, thematic development, and intellectual organization of the field. The findings reveal that ESG-related research is rapidly expanding and is strongly associated with themes such as climate change, resilience, adaptation, and sustainability. However, vulnerability remains comparatively underdeveloped and weakly integrated within the dominant knowledge structure. The results further demonstrate that existing ESG approaches prioritize generalized and risk-based performance metrics while overlooking the spatial, social, and institutional dimensions that shape exposure and adaptive capacity at the local level. The study contributes to the literature by highlighting vulnerability as a governance blind spot within ESG systems and emphasizes the need for more integrated, place-based, and context-sensitive sustainability frameworks capable of supporting resilience and climate adaptation. Full article

Aquatic ecosystems are increasingly impacted by anthropogenic pressures, including nutrient over-enrichment, industrial discharge and physical habitat alteration, resulting from industrialization, urbanization, agricultural intensification and population growth. In recent years, microalgae have been extensively studied in engineered and controlled systems for their potential role in aquatic ecosystem restoration, owing to their capacity to assimilate nutrients, sequester contaminants and interact with microbial consortia, alongside valuable biomass generation. While most of the existing studies are based on ex situ systems, such as high-rate algal ponds, wastewater treatment reactors, algal–bacterial granular sludge, constructed wetlands and aquaculture effluent treatment units, these processes provide mechanistic insights relevant to aquatic ecosystem restoration. Hence, this review critically evaluates the emerging role of microalgae in aquatic ecosystem restoration, mainly through two implementation pathways, namely ex situ engineered systems and in situ applications, based on the current state of the art in microalgae-driven processes, with particular emphasis on nutrient uptake pathways and mechanisms. Furthermore, key challenges and future directions associated with the translational potential of microalgae-based approaches, including field-scale validation, ecological performance assessment, operational stability and regulatory integration, essential for real-world aquatic ecosystem restoration, are discussed. Despite the limitations in direct field-scale restoration, microalgae-based strategies are promising and sustainable platforms for aquatic ecosystem rehabilitation, which align with Sustainable Development Goals 6 and 14. Full article