Search Results (5,035)

Educational systems are increasingly required not only to innovate but to sustain innovation over time. While research on Change Laboratory (CL) interventions has extensively examined the development of new models and the emergence of transformative agency, less is known about how such agency is enacted through concrete actions in everyday practice. This study addresses this gap by examining consequential actions as expressions of entrepreneurial agency in the implementation of open work in a kindergarten following a CL intervention. Drawing on semi-structured interviews with 17 staff members, the study adopts a theoretically informed inductive approach to identify types of agentive actions and interpret them in relation to EntreComp competences and activity system components. The findings show that entrepreneurial agency is a distributed and situated process enacted through coordinated material, relational, and organizational actions toward the tools and community, highlighting the importance of environmental reconfiguration and collaboration in sustaining change. The study also shows that agency is unevenly distributed across roles and that newcomers participate differently in the implementation process. Overall, sustaining educational innovation appears to depend less on the design of models than on the collective capacity to continuously enact and transform them in practice. Full article

In sociotechnical transitions, landscape disruptions, such as climate change, exert pressure on incumbent regimes and can trigger the emergence of niche innovations. Renewable energy communities represent one such innovation, increasingly central to European energy policy. This paper applies a critical realist method to examine the energy community niche in Portugal, drawing on a content analysis of the scientific literature and recent Horizon Europe research projects involving Portuguese actors. The analysis reveals three distinct research pathways structuring knowledge production in this niche—technology-driven, socio-governance-oriented, and infrastructure-focused. It also reveals a systemic R&D bias: incumbent actors occupy dual positions—simultaneously at the regime level and within the niche—playing central roles in learning and network formation while exhibiting limited capacity to translate innovation into institutional change and large-scale diffusion. Building on these critical realist findings, we then apply the Strategic Niche Management framework as an evaluative lens, revealing structural misalignments between components of the sociotechnical system. Together, these two analytical steps offer a novel reading of the Portuguese energy community niche, contributing to the theoretical debate on incumbent roles in transition dynamics and identifying concrete shortcomings for future R&D agenda-setting. Full article

To promote the industrial development of reinforced concrete engineering and enhance the construction quality of prefabricated buildings, an innovative partial prefabricated construction method is proposed in this paper, namely the prefabricated reinforced cage–cast in situ concrete (PRC-CISC) structure with an innovative steel bar connection technology. The connection techniques, including direct thread rolling of steel bars and hot-forged sleeves, are adopted. With the design axial compression ratio and the layout of couplers in the reinforcement cage as the main parameters, quasi-static tests are carried out to investigate the failure mode, seismic behavior, and mechanical mechanism of couplers of PRC-CISC columns. The results indicate that all specimens present typical compression–bending failure with plump hysteretic curves, gradual stiffness degradation, good ductility, and energy dissipation capacity. The new couplers can effectively satisfy the seismic performance requirements of PRC-CISC columns. With the increase in axial compression ratio, the bearing capacity rises while ductility decreases, and the stress of longitudinal bars increases. The layout of couplers exerts a controllable influence on the mechanical and deformation performance of specimens. The steel stress in the core stress region of PRC-CISC columns shows a bilinear distribution with stress concentration at both ends of the sleeves, which is related to the material difference in couplers. Finally, two “non-equal” calculation methods (plastic hinge model and fiber model) are established based on experimental results and finite element analysis, forming a systematic calculation theory for the new material–new technology–new structure system. The research provides important references for the engineering application of such structures. Full article

As global education steps into a new era marked by core literacy and sustainable development, teacher literacy has become a critical pillar for fulfilling United Nations Sustainable Development Goal 4 (SDG 4) and advancing Education for Sustainable Development (ESD). Guided by the Educator Spirit and based on the logical framework of dual professional roles and four professional relationships, this study constructs a teacher literacy model for Chinese teachers in the new era, which consists of seven dimensions: disciplinary literacy, general literacy, learning support literacy, holistic education literacy, communication and collaboration literacy, development and improvement literacy, and teacher ethics literacy. Adopting systematic literature review and international comparative research methods, this study integrates mainstream international teacher literacy frameworks issued by the European Union, OECD, UNESCO, the United States and Australia with China’s educational policies and practical experience to establish the proposed model. It further elaborates how the model directs sustainability-oriented teacher education, facilitates transformative teaching approaches, boosts interdisciplinary teaching practice, highlights social justice and global citizenship awareness, and embeds sustainable development principles into curriculum design and teaching practice. This model can effectively tackle prevailing practical dilemmas including teachers’ weakened professional identity, vague professional development paths, unitary evaluation systems, inadequate digital teaching competence, insufficient interdisciplinary integration capacity, deficient ESD literacy and inefficient collaborative education mechanisms. It can systematically support teachers in carrying out sustainability-oriented teaching, innovating curriculum design, conducting transformative teaching and promoting students’ sustainable learning while practicing social justice and educational equity and cultivating global citizenship awareness in educational scenarios. It also provides a theoretical basis and practical guidance for promoting the transition of Chinese teachers toward high-quality, professional and sustainable development, and also offers localized solutions with distinctive Chinese characteristics and universal international implications for the implementation of global ESD initiatives and the achievement of SDG 4. Full article

Artificial intelligence (AI) is reshaping tourism businesses by improving decision making, service personalization, operational efficiency, and data-driven management. Beyond these organizational benefits, AI may also strengthen firms’ capacity to cope with market volatility, demand shocks, cost pressures, and other sources of financial fragility. This study provides a systematic literature review and bibliometric analysis of 146 Web of Science articles on AI in tourism published between 2019 and 2023. Following a structured screening process, it identifies the intellectual structure, thematic evolution, and main performance-related drivers associated with AI adoption. The findings show a rapidly expanding field centered on business performance, information technology, big data, robotics, and AI-enabled service innovation. The literature suggests that AI contributes to resilience by enhancing forecasting, resource allocation, customer management, and organizational adaptability under uncertainty. However, explicitly financial perspectives—such as financial vulnerability, resilience, liquidity, solvency, and risk management—remain underdeveloped. This study contributes by reframing AI in tourism as a potential resilience-building capability rather than only a tool for service innovation. Its main limitations are the reliance on Web of Science and a fixed 2019–2023 bibliometric corpus, which future research should extend. Full article

The digital economy has changed the way agricultural production is organized and how rural households access markets, jobs, and information. Yet it remains unclear whether these changes translate into higher income for rural residents, especially in major agricultural regions. This study examines the income effect of digital infrastructure development by using the rollout of the Broadband China policy as a quasi-natural experiment. The analysis draws on panel data for 77 prefecture-level administrative units in the Yellow River Basin, one of China’s major agricultural regions, from 2009 to 2021. A staggered difference in differences model is used to estimate the policy effect. The results show that digital infrastructure development significantly increases rural residents’ income. Under the log income specification, the baseline coefficient indicates an average income increase of about 8.33%. The mechanism analysis shows that innovation capacity and nonfarm employment both serve as positive partial transmission channels, with innovation capacity explaining a larger share of the total effect. The heterogeneity results suggest that the income effect is stronger in regions with higher GDP and larger population size. These findings indicate that digital infrastructure can support rural income growth when it is linked with local innovation capacity, employment opportunities outside agriculture, and rural development policies suited to local conditions. Full article

Low-carbon and smart agriculture talent cultivation requires structural conditions that vary widely across countries. This study develops the Agricultural Talent Cultivation Readiness Index (ATCRI) as a proxy-based structural diagnostic tool for approximating the multi-dimensional enabling conditions and bottlenecks that shape whether SDG-linked agricultural education transformation can be operationalized at scale. ATCRI covers 160 countries across four interdependent dimensions: Education and Research, Digital/Energy/Enabling Infrastructure, Green Transition Pressure, and Innovation/Institutional Capacity. Results indicate a highly uneven global distribution: high transition pressure does not automatically translate into high readiness, with 17 countries exhibiting a pressure–capacity mismatch. China ranks 21st globally, showing a hybrid profile in which education and innovation capacity are strong while digital delivery infrastructure remains a relative bottleneck. Survey evidence from Chinese crop science students is consistent with this interpretation, revealing elevated practice-oriented reform demand where macro-level structural gaps are sharpest. ATCRI is intended as a diagnostic framework for identifying structural bottlenecks, not as a definitive measure of educational quality or reform outcomes. Full article

The excessive use of herbicides in agricultural fields has emerged as a critical environmental concern. This study innovatively synthesized a ZIF-8@TPPa composite through a solvothermal method for the efficient removal of herbicides from aqueous environment. The material exhibited remarkable adsorption capacities for butachlor (232.56 mg/g), anilofos (188.68 mg/g), and pendimethalin (285.71 mg/g), along with excellent acid–base stability (pH 3–9), strong anti-ion interference capability, and good reusability (adsorption efficiency >80% after five cycles). The adsorption processes were well-described by the two isotherm models and the pseudo-second-order model, indicating that the dominant mechanism is a synergistic effect between monolayer chemical adsorption and multilayer physical adsorption, primarily driven by π-π stacking, hydrogen bonding, and coordination. The material maintained outstanding adsorption efficiency (>85%) in real water samples (tap water, seawater, and river water). This study not only provides a sustainable and effective strategy for herbicide remediation from aqueous environment but also expands the practical applications of MOF@COF in aqueous environment. Full article

Acorns (Quercus spp.) are an underutilized forest resource with recognized nutritional and bioactive potential, making them promising candidates for the development of sustainable plant-based functional foods. This study aimed to valorize acorns through the formulation of two novel acorn-based products, a plant-based beverage, and a pudding, and to assess their nutritional properties, sensory acceptability, and, for the beverage, refrigerated shelf-life stability. The beverage was optimized as a neutral-flavored milk alternative, using sodium alginate as a natural clean-label stabilizer to enhance emulsion stability and physicochemical properties. The final formulation exhibited low energy density and a lipid profile rich in monounsaturated fatty acids, contributing to its nutritional and functional value. Throughout 63 days of storage at 4 °C, sodium alginate effectively prevented phase separation and supported the retention of antioxidant capacity, as evidenced by stable ferric reducing antioxidant power (FRAP) and total phenolic content, although ABTS radical scavenging activity declined over time. No microbial growth was detected during storage, confirming the adequacy of the applied thermal treatment and aseptic filling procedures applied. The acorn-based pudding, developed by adapting a traditional egg-based recipe, functioned as a proof of concept illustrating the technological versatility of acorns across distinct plant-based matrices, exhibiting a nutritional profile comparable to commercial counterparts and high consumer acceptability. Overall, this work demonstrates the technological feasibility and versatility of incorporating acorns into plant-based food matrices, supporting their potential as sustainable ingredients for the development of innovative value-added foods and contributing to the valorization of forest resources. Full article

Cement–soil mixing piles commonly face the problem of insufficient pile quality during on-site construction, and traditional measures such as increasing grouting pressure or enhancing mixing intensity are difficult to resolve effectively. The development of flowable solidified soil technology offers a new path for innovating soil pile reinforcement techniques. Based on an in situ test, this research proposes and introduces a new technology for pre-mixed fluid-solidified soil piles (PSPs). This technique effectively improves pile quality and significantly enhances pile bearing capacity by pre-mixing flowable solidified soil and then grouting it after pre-drilling holes with a screw drill. The results show that reinforcement of soil piles using the pre-mixed flowable solidified soil and pre-drilled grouting process has significantly improved pile quality, with better core sample integrity and uniformity. The results indicate that the characteristic bearing capacity of the uniform-section PSP is 252 kPa, meeting the design requirement of 130 kPa. The ultimate bearing capacity of the uniform-section PSP is 177% higher than that of the uniform-section CMP. In addition, the ultimate bearing capacity of the PSP after variable-section treatment is 153% higher than that of the uniform-section PSP. Finally, new design recommendations have been proposed, specifically calculation formulas for the load-bearing capacity and settlement of composite foundations based on current standards. Full article

Industrial transformation in resource-based regions (RBRs) is a global challenge. Shanxi is a typical resource-based province in China. The long-term exploitation of coal resources has posed huge challenges to its ecological protection and high-quality development. Breaking away from the single-city perspective, this study focuses on the regional scale and comparative analysis and attempts to construct a novel three-dimensional analytical framework, namely, “industrial characteristics, industrial layout, and industrial policies”, to explore the industrial transformation path of typical RBRs. The results indicate the following: (1) Shanxi does not have obvious advantages in terms of resource endowment, with a severely heavy industrial structure and strategic emerging industries still in the initial stage of development. At the national strategic level, it is still necessary to strengthen the application of the “pioneer and pilot” policies and mechanisms for innovation. (2) In the context of high-quality development, Shanxi needs to clarify the industrial transformation orientation. For agriculture, the focus should be placed on characteristic and efficient development. For industrial development, priority should be given to upgrading advantageous industries and cultivating emerging industries. For the tertiary industry, it is necessary to form a development pattern of “new producer services + characteristic tourism”. In terms of regional development layout, Shanxi should establish a macro-pattern to promote inter-regional coordinated development. (3) In the new period, Shanxi should accelerate the construction of transportation systems to improve the convenience of inter-regional cooperation. It is essential to increase investment in education and scientific research so as to enhance the overall social innovation capacity. Meanwhile, differentiated regional development policies should be adequately supplied to drive the high-quality evolution of local industries. Focusing on the regional scale, the new logical analysis paradigm can provide theoretical references for RBRs to clarify the direction of industrial transformation and formulate transformation policies. Full article

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article

Increasing external uncertainty, supply disruptions, and market volatility have made resilience enhancement increasingly important for sustainable agricultural supply chains. While existing studies mainly examine agricultural supply chain resilience from macro or operational perspectives, limited attention has been paid to how firms’ strategic AI investment reshapes organizational resilience under external shocks. Using panel data on Chinese agricultural-related listed firms from 2010 to 2024, this study examines whether and how strategic AI investment enhances supply chain resilience. Empirical results show that strategic AI investment significantly improves both dimensions of supply chain resilience, namely resistance capacity and recovery capacity. Mechanism analyses indicate that this effect mainly operates through supply diversification, technological innovation, and information transparency. Further analyses reveal heterogeneous effects across supply chain positions, ownership structures, and regional digital development environments. In addition, compatibility analyses show that strategic AI investment not only strengthens supply chain resilience but also improves operational efficiency, R&D investment intensity, and financial stability. Overall, this study highlights strategic AI investment as an important organizational capability for strengthening agricultural supply chain resilience under increasing external uncertainty. Full article

Next-generation vaccines are being developed to elicit durable and cross-protective immune responses against diverse pathogens, particularly those targeting the respiratory and enteric systems. By strategically engaging T cell-centric antigen design, mucosal immune engagement, and induction of trained innate immunity, these innovative platforms are expected to reshape the paradigm of immunoprophylaxis and to offer promising avenues for enhanced protection against complex infectious diseases. Conventional antibody-based vaccines, though effective against many infections, often lack the capacity to induce durable or cross-protective immunity at mucosal surfaces. Advances in antigen design, delivery platforms, and adjuvant technologies now facilitate precise activation of tissue-resident memory T cells and enhancement of mucosal secretory IgA responses, thereby achieving sterilizing immunity at barrier surfaces while reinforcing systemic immune protection. Advanced delivery platforms, including lipid nanoparticles, viral vectors, and nano or liposomal carriers, further refine antigen presentation, enhancing stability, targeting, and overall immunogenicity. Concurrently, progress in understanding trained innate immunity highlights opportunities to induce broad, non-antigen-specific protection through epigenetic and metabolic reprogramming of innate cells. The integration of these adaptive and innate mechanisms may enhance early pathogen control, limits transmission, and strengthens defense against variant and antimicrobial-resistant pathogens across diverse populations. However, translating these immunological insights into safe, scalable, and globally accessible vaccines remains a major challenge. This review explores the emerging conceptual framework of next-generation vaccines that demonstrate partial integration of these axes in preclinical models, though human translation and functional synergy require Phase II validation. It highlights progress toward next-generation vaccines leveraging integrated adaptive and innate immune reprogramming for superior protection against respiratory and enteric pathogens. Full article

This study proposes an innovative methodology that integrates finite element simulation, machine learning, and interpretable model analysis to predict the vertical bearing capacity of piles in cold saline environments. Initially, Python scripts are developed to drive the ABAQUS platform, and LHS (Latin Hypercube Sampling) is employed to generate random parameter combinations to construct a multi-dimensional ML (machine learning) database. Six ML models, including XGBoost and LightGBM, are developed with hyperparameters optimized by cross-validation and grid search. Model performance is evaluated by five metrics (R², MSE, RMSE, MAE, and MAPE). Finally, parametric sensitivity is analyzed by the SHAP (SHapley Additive exPlanations) method. The study demonstrates that: (1) the XGBoost and LightGBM models achieve optimal performance on the test set, and the generalization ability significantly exceeds other models; (2) pile diameter is the primary factor influencing vertical bearing capacity, and corrosion depth exhibits higher sensitivity than corrosion thickness; and (3) the bearing capacity of the pile is predicted by using the automated parametric modeling method based on Python (3.8)-ABAQUS (2022). The automated modeling and prediction framework may serve as a reference for pile design in similarly complex environments. Full article