Search Results (771)

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Background and Objectives: Inflammatory bowel diseases (IBDs) are chronic conditions of the digestive tract, often requiring life-long treatments in order to achieve and maintain remission. However, treatment adherence among patients with IBD can frequently be suboptimal, which can compromise disease control and long-term outcomes. The aim of this study was to analyze the adherence rate and to identify factors that significantly influence treatment adherence in patients with IBD. Materials and Methods: The study employed a cross-sectional design and was conducted at the Fundeni Clinical Institute, a tertiary medical center in Bucharest, Romania. The treatment adherence was assessed using the Medication Adherence Report Scale-5 (MARS-5), with patients scoring greater than 23 considered adherent. Anxiety, depression and perceived stress were assessed using the Depression, Anxiety and Stress Scale-21 (DASS-21). Perceived social support was measured with the Multidimensional Scale of Perceived Social Support (MSPSS), and coping strategies were assessed using the Brief Coping Orientation to Problems Experienced Inventory (Brief COPE Inventory). Results: A total of 188 patients were included in the final analysis. Of these, 99 patients (52.7%) were male and 109 (58.0%) had a diagnosis of Crohn’s disease. The majority of patients (81.9%) were receiving treatment with advanced therapies, including biologics or small molecules. Forty patients were receiving their therapy through more than one route of administration. Optimal adherence was noted in 160 patients (85.1%). Patients treated with advanced therapies (biologics and small molecules) had significantly higher odds of optimal adherence (OR 10.52, 95% CI: 4.3–25.74, p < 0.001), with a rate of adherence of 92.2%. Significantly lower odds of adherence were found for the oral (OR 0.35, 95% CI: 0.14–0.83, p = 0.01) and rectal (OR 0.09, 95% CI: 0.03–0.29, p < 0.001) routes of administration, while the intravenous administration had higher odds of adherence (OR 4.85, 95% CI: 1.02–22.9, p = 0.04) compared to the subcutaneous route. Other factors associated with an improved adherence were being retired (OR 3.5, 95% CI: 1.13–10.8, p = 0.029) and using positive reframing (p = 0.04), planning (p = 0.01) and venting (p = 0.02) as coping strategies; active smoking (OR 0.26, 95% CI: 0.11–0.6, p = 0.002), active disease (OR 0.36, 95% CI: 0.16–0.81, p = 0.014) and behavioral disengagement (p = 0.04) were associated with impaired treatment adherence. No significant differences in adherence were observed between disease phenotypes. Conclusions: The route of administration, smoking status and psychosocial factors, such as perceived stress of social support and coping strategies, may play an important role in influencing treatment adherence in patients with IBD. While the disease phenotype was not associated with differences in adherence, patients with active disease had significantly lower odds of optimal adherence. Full article

The incentive mechanism functions as a core safeguard to ensure the efficient execution of consortium-based Public–Private Partnership (PPP) projects and the realization of value-added outcomes. The heterogeneity of consortium members, their reciprocal preferences, and the collaborative dynamics of the team collectively contribute to the formation of project alliances characterized by resource synergy, complementary advantages, and risk sharing. However, these same factors also contribute to the multi-layered structure of principal–agent relationships and the inherent complexity of incentive pathways and mechanisms in consortium-based PPP settings. Drawing upon the team collaboration effect and reciprocal preferences among consortium members, this study incorporated the member heterogeneity and developed three incentive models for such projects, such as the Dual-Performance (DP) mode, the Total-Performance (TP) mode, and the Individual-Performance (IP) mode. This study examined the conditions under which these incentive modes were established, the relationship between incentive intensity and optimal effort levels of consortium members, and the influence of reciprocal preferences on incentive effectiveness. Further, the selection criteria and appropriate application scenarios for each of the three incentive models were analyzed according to a comparative analysis, thereby putting forward effective suggestions for improving the effort levels of private investors in consortium-based PPP projects. The study results indicate that team synergy effects play an imperative role in improving the optimal effort levels under all three modes, whereas reciprocity preferences exhibit a negative relationship with effort in the DP and TP modes. When reciprocity remains within a moderate range, the DP mode achieves highest aggregate effort levels, whereas the IP mode induces positive incentive effects only under extreme reciprocity conditions. Thus, the application of dual incentive coefficients can enhance operational adaptability and allocative efficiency and governments should establish a multidimensional collaborative incentive for consortium-based PPP projects to strengthen effectiveness and project quality. This comprehensive evaluation provides crucial insights for policymakers, emphasizing the strategic selection of incentive mechanisms to enhance the sustainability and effectiveness of consortium-based PPP Projects. Full article

Background: Formal and informal social support networks are crucial for mental well-being, providing a sense of personal security and safety, especially during times of crisis. Aims: The aim of this study is to examine women’s perceptions of their own experiences alongside their perceptions of their partners’ experiences in the relationship between social support and the sense of danger within intimate relationships, based solely on women’s self-reports. It compares couples experiencing high-intensity conflict (particularly related to separation) with couples from the general population, and explores how the distribution of social support, whether received by the woman, the man, both, or neither is associated with feelings of danger. Methods: The sample comprised 165 women from two subsamples: 70 women from the general population and 95 women engaged in high-intensity intimate conflict, who were undergoing separation proceedings. Data were collected via self-report questionnaires, which included the Multidimensional Scale of Perceived Social Support and a custom questionnaire for Sense of Danger from the Partner. No direct data were collected from male partners; women provided both their own reports and their perceptions of their partner’s experiences. Repeated measures analysis was performed to examine the sense of danger as perceived for both themselves and their partners. Results: The analysis shows that the higher the level of social support, the weaker the sense of danger reported by women for themselves and for their partners. It also reveals that the sense of danger (both self-reported and attributed to the partner) is stronger among couples engaged in high-intensity conflict than among those in the general population, and that women report a stronger sense of danger for themselves than they attribute to their men. Importantly, when social support is provided to only one partner, it is associated with a higher sense of danger in the other partner. Conclusions: Social support has been associated with lower reported feelings of danger in intimate relationships. However, when support is given to only one partner, it may increase the other partner’s sense of danger. These findings highlight the need for balanced support for both partners in order to reduce tension and promote a greater sense of safety during times of conflict. Full article

Accurate prediction of water quality involves early identification of future pollutant concentrations and water quality indicators, which is an important prerequisite for optimizing water environment management. Although deep learning algorithms have demonstrated considerable potential in predicting water quality parameters, their broader adoption remains hindered by limited interpretability. This study proposes an interpretable deep learning framework integrating an artificial neural network (ANN) model with Shapley additive explanations (SHAP) analysis to predict spatiotemporal variations in water quality and identify key influencing factors. A case study was conducted in the Poyang Lake Basin, utilizing multi-dimensional datasets encompassing topographic, meteorological, socioeconomic, and land use variables. Results indicated that the ANN model exhibited strong predictive performance for dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), ammonia nitrogen (NH₃N), and turbidity (Turb), achieving R² values ranging from 0.47 to 0.77. Incorporating land use and socioeconomic factors enhanced prediction accuracy by 37.8–246.7% compared to models using only meteorological data. SHAP analysis revealed differences in the dominant factors influencing various water quality parameters. Specifically, cropland area, forest cover, air temperature, and slope in each sub-basin were identified as the most important variables affecting water quality parameters in the case area. These findings provide scientific support for the intelligent management of the regional water environment. Full article

The excessive emission of cadmium (Cd²⁺) poses a serious threat to the aquatic environment due to its high toxicity and bioaccumulation potential. This study constructed three types of vertical-subsurface-flow constructed wetlands configured with iron–carbon–zeolite composite substrates, including an iron–carbon–zeolite constructed wetland (TF-CW), a zeolite–iron–carbon constructed wetland (FT-CW), and an iron–carbon–zeolite mixed constructed wetland (H-CW), to investigate the purification performance and mechanisms of constructed wetlands for cadmium-containing wastewater (0~6 mg/L). The results demonstrated that iron–carbon–zeolite composite substrates significantly enhanced Cd²⁺ removal efficiency (>99%) through synergistic redox-adsorption mechanisms, where the iron–carbon substrate layer dominated Fe-Cd co-precipitation, while the zeolite layer achieved short-term cadmium retention through ion-exchange adsorption. FT-CW exhibited superior NH₄⁺-N removal efficiency (77.66%~92.23%) compared with TF-CW (71.45%~88.05%), while iron–carbon micro-electrolysis effectively inhibited NO₃⁻-N accumulation (<0.1 mg/L). Under cadmium stress, Typha primarily accumulated cadmium through its root systems (>85%) and alleviated oxidative damage by dynamically regulating antioxidative enzyme activity, with the superoxide dismutase (SOD) peak occurring at 3 mg/L Cd²⁺ treatment. Microbial community analysis revealed that iron–carbon substrates promoted the relative abundance of Bacteroidota and Patescibacteria as well as the enrichment of Saccharimonadales, Thauera, and Rhodocyclaceae (genera), enhancing system stability. This study confirms that iron–carbon–zeolite CWs provide an efficient and sustainable technological pathway for heavy metal-contaminated water remediation through multidimensional mechanisms of “chemical immobilization–plant enrichment–microbial metabolism”. Full article

Introduction: Chronic primary low back pain (CPLBP) is a prevalent condition in primary care and a leading cause of disability and absenteeism worldwide. Multidimensional approaches may be necessary to achieve physical and mental health benefits in individuals with CPLBP. Objective: The BACKFIT randomized controlled trial aims to evaluate the effectiveness of a multidimensional intervention—combining supervised exercise and mindfulness—on pain, physical fitness, mental health, and functional outcomes in individuals with CPLBP. Hypothesis: Both the supervised exercise program focused on motor control and trunk muscle strength (IG1) and the multidimensional intervention combining supervised exercise with mindfulness training (IG2) are expected to produce significant health improvements in individuals with CPLBP. It is further hypothesized that IG2 will yield greater improvements compared to IG1, both immediately post-intervention and at the two-month follow-up. Design: Randomized controlled trial. Setting: Virgen de las Nieves University Hospital, Granada (Spain). Participants: 105 individuals. Inclusion criteria: Previously diagnosed with CPLBP, aged ≥18 and ≤65 years, able to read and understand the informed consent, and able to walk, move, and communicate without external assistance. Exclusion criteria: serious lumbar structural disorders, acute or terminal illness, physical injury, mental illness, and medical prescriptions that prevent participation in the study. Intervention: Individuals will be randomly assigned to a supervised physical exercise group (2 days per week, 45 min per session), a multidimensional intervention group (same as supervised physical exercise group, and mindfulness 1 day per week, 2.5 h per session) or an active control group (usual care, 2 days per week, 45 min per session). The intervention will last 8 weeks. Main Outcome Measures: Primary outcome: pain threshold, perceived acute pain, and disability due to pain. Secondary measures: body composition, muscular fitness, gait parameters, device-measured physical activity and sedentary behavior, self-reported sedentary behavior, quality of life, pain catastrophizing, mental health, sleep duration and quality, and central sensitization. The groups will undergo pre-intervention, post-intervention, and a 2-month follow-up after a detraining period. Statistical Analysis: Both per-protocol and intention-to-treat approaches (≥70% attendance) will be used. Program effects will be assessed via one-way ANCOVA for between-group changes in primary and secondary outcomes. Conclusions: Given the complex nature of CPLBP, multidimensional approaches are recommended. If effective, this intervention may provide low-cost alternatives for health professionals. Full article

Amid the accelerating global transition toward a low-carbon and intelligent economy, the issues of resource misallocation and mounting environmental pressures in agriculture have become increasingly prominent, posing significant bottlenecks to the modernization of the sector. As a novel factor of production, agricultural and rural big data theoretically offer new avenues for facilitating a green transformation in agriculture. However, institutional constraints have hindered its full potential. Drawing on provincial panel data from 2011 to 2022, this study treats the big data policy pilot as a quasi-natural experiment and employs a difference-in-differences (DID) approach to comprehensively analyze its mechanisms and actual effects on high-quality agricultural development. An indicator system encompassing five dimensions—innovation, coordination, greenness, openness, and sharing—is constructed, and the entropy method is used to measure the level of high-quality agricultural development. Multiple empirical strategies, including parallel trend tests, are utilized to ensure the robustness of the findings. The results indicate that high-quality agricultural development exhibits significant regional gradients and periodic leaps. The implementation of the big data policy in 2016 marked a crucial turning point, yielding a significant positive effect on agricultural development. Notably, pronounced heterogeneity exists regarding regional distribution, major grain-producing areas, and development stages. The policy’s impact primarily operates through pathways of openness and sharing, although some mechanisms remain to be improved. Accordingly, this paper recommends differentiated regional policies and enhanced targeted support, thereby providing theoretical and practical policy guidance for optimizing big data policy design, promoting high-quality agricultural development, and advancing rural revitalization. For policymakers, these findings clarify the priorities for differentiated interventions and offer empirical evidence for optimizing the spatial allocation of big data policy pilots and strengthening open and shared development mechanisms. This, in turn, can improve the precision of agricultural policy and accelerate the green transformation and revitalization of rural areas. Compared to existing literature, the distinct contribution of this study lies in its pioneering use of big data policy pilots as a quasi-natural experiment. The research systematically constructs a multidimensional indicator system to measure high-quality agricultural development, elucidates the heterogeneous effects and specific pathways of policy intervention, and addresses gaps in the empirical assessment and mechanism analysis of agricultural big data policies. Full article

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts. Full article

Hispanic breast cancer survivors reported worse quality of life, and fatalism is considered one of the mediators for this disparity. This study aimed to identify the factors associated with fatalism within a diverse Hispanic population. Hispanic origin was self-reported, and the Multidimensional Fatalism Measure questionnaire, a validated tool that measures fatalism across multiple dimensions, was used to assess fatalism. A total of 390 women, consisting of 210 Puerto Ricans, 34 Colombians, 29 Dominicans, 25 Cubans, 24 Venezuelans, 22 Mexicans, and 46 individuals of other Hispanic backgrounds, completed the fatalism assessment. The mean fatalism score was 16.4 (95% CI = 15.8–17.0), characterized by a high internal locus of control and strong religious beliefs, along with moderate beliefs in luck and a low external locus of control. The higher fatalism scores were reported in Dominican, Mexican, and Venezuelan groups, while Colombians reported the lowest score. Multivariable analysis showed that Colombians (β = −4.0), individuals with higher household incomes (β = −2.3 for USD 20,000–USD 75,000, β = −2.4 for ≥75,000), higher education levels (β = −1.9), and those using English more frequently at home (β = −2.0) reported lower fatalism compared to their reference group. To enhance the quality of life for these survivors, culturally tailored interventions should focus on improving perceived control and mitigating fatalism. Full article

The rapid retrieval and precise acquisition of land use approval information are crucial for enhancing the efficiency and quality of land use approval, as well as for promoting the intelligent transformation of land use approval processes. As an advanced retrieval method, question-answering (Q&A) technology has become a core technical support for addressing current issues such as low approval efficiency and difficulty in obtaining information. However, existing Q&A technologies suffer from significant hallucination problems and limitations in considering spatiotemporal factors in the land use approval domain. To effectively address these issues, this study proposes a spatiotemporal–semantic coupling intelligent Q&A method for land use approval based on knowledge graphs (KGs) and intelligent agent technology, aiming to enhance the efficiency and quality of land use approval. Firstly, a land use approval knowledge graph (LUAKG) is constructed, systematically integrating domain knowledge such as policy clauses, legal regulations, and approval procedures. Then, by combining large language models (LLMs) and intelligent agent technology, a spatiotemporal–semantic coupling Q&A framework is designed. Through the use of spatiotemporal analysis tools, this framework can comprehensively consider spatial, temporal, and semantic factors when handling land approval tasks, enabling dynamic decision-making and precise reasoning. The research results show that, compared to traditional Q&A based on LLMs and Q&A based on retrieval-enhanced generation (RAG), the proposed method improves accuracy by 16% and 9% in general knowledge Q&A tasks. In the project review Q&A task, F1 scores and accuracy increase by 2% and 9%, respectively, compared to RAG-QA. Particularly, under the spatiotemporal–semantic multidimensional analysis, the improvement in F1 score and accuracy ranges from 2 to 6% and 7 to 10%, respectively. Full article

Discrete manufacturing environments face increasing challenges in managing work-in-process (WIP) inventory due to growing product customization and demand volatility. While Value Stream Mapping (VSM) has been widely used for process improvement, traditional approaches lack the ability to dynamically control WIP levels while optimizing multiple performance dimensions simultaneously. This research addresses this gap by developing an integrated framework that synergizes Multi-Dimensional Value Stream Mapping (MD-VSM) with multi-objective optimization, functioning as a specialized digital twin for dynamic WIP control. The framework employs a four-layer architecture that connects real-time data collection, multi-dimensional modeling, dynamic WIP monitoring, and execution control through closed-loop feedback mechanisms. A mixed-integer optimization model is used to balance time, cost, and quality objectives. Validation using a high-fidelity simulation, parameterized with real-world industrial data, demonstrates that the proposed approach yielded up to a 31% reduction in inventory costs while maintaining production throughput and showed a 42% faster recovery from equipment failures compared to traditional methods. Furthermore, a comprehensive sensitivity analysis confirms the framework’s robustness. The system demonstrated stable performance even when key operational parameters, such as WIP upper limits and buffer capacity coefficients, were varied by up to ±30%, underscoring its reliability for real-world deployment. These findings provide manufacturers with a validated methodology for enhancing operational efficiency and production flexibility, advancing the integration of lean principles with data-driven, digital twin-based control systems. Full article

As a typical cold region, Northeast China is characterized by its unique climate, hydrological conditions, and land systems, which collectively shape the diversity and complexity of regional ecosystem services (ESs). This review systematically examines research on ESs in Northeast China from 1997 to 2025, with particular emphasis on recent advances in service classification and spatiotemporal patterns, trade-offs and synergies among ESs, the identification of driving mechanisms, regulatory pathways, and policy effectiveness. The findings reveal obvious spatial heterogeneity and distinct stage-wise changing patterns in ESs across the region, with particularly pronounced trade-offs between food production and regulating services. The primary driving factors are concentrated in natural and human activities dimensions, whereas region-specific variables and policy-related drivers remain underexplored. Current research predominantly employs methods such as correlation analysis and geographically weighted regression; however, the capacity to uncover causal mechanisms and nonlinear interactions remains limited. Future research should strengthen the simulation of ecological processes in cold regions, improve the balance between ES supply and demand, improve policy scenario assessments, and develop dynamic feedback mechanisms. Compared with previous studies focusing on single services or regions, this review provides a multidimensional perspective by synthesizing multiple ES categories, integrating spatiotemporal comparative analysis, and incorporating modeling strategies specific to cold-region dynamics. These efforts will help shift ES research beyond static description toward more systematic regulation and management, providing both theoretical support and practical guidance for sustainable development and ecological governance in Northeast China. Full article

Energy poverty is a multidimensional phenomenon that impairs access to basic energy services and threatens social well-being, particularly in disadvantaged rural communities. This study investigates the extent and drivers of household energy poverty in a Hungarian village through a survey-based analysis (N = 257) conducted in early 2025. The sample is not nationally representative, however, it reflects approximately 20% of the total village population (1331 inhabitants). This study aims to identify vulnerable household profiles, explore correlations between socio-economic and housing factors and perceived thermal comfort, and compare the effectiveness of multiple measurement indicators the 10% rule, low income high cost, 2M, and M/2. We employ descriptive statistics, Pearson correlation, Fuzzy C-Means clustering, and linear regression, revealing that over half of the sample is energy poor according to the 10% rule, while the LIHC method identifies 29%. Our regression results confirm that cluster membership significantly influences perceived comfort levels (R² = 0.063, p = 0.002). We conclude that single-indicator approaches are insufficient to capture the nuanced realities of rural energy poverty, therefore, we recommend the development of a rural energy poverty index. Such a tool could help identify affected households and support the formulation of context-sensitive, evidence-based energy and social policy interventions. Full article

The high sedimentation rate of Rawapening Lake is both an environmental challenge and a potential resource. Seedlings currently rely on single-use plastic polybags, which contribute significantly to plastic waste. The use of mineral soil as a growing medium can accelerate natural resource depletion. This study aims to evaluate the feasibility and sustainability of utilizing lake sediment as an alternative seedling media through soil block technology. An integrated Life Cycle Assessment was conducted to quantify the environmental impacts, and Multidimensional Scaling was applied to assess sustainability across environmental, technological, economic, social, and institutional dimensions. Field data from ten seedling producers using soil blocks and ten using polybags were analyzed. The results showed that soil block media reduced Global Warming Potential by 48% compared to polybags, increased phosphorus and organic matter content, and was more financially efficient, with an increase in productivity of 90.24% and a revenue cost ratio of 24.56%. Sustainability analysis classified the innovation as moderately sustainable, with the highest scores in the environmental and technological dimensions. Institutional support was identified as a limiting factor. These findings suggest that sediment-based soil block media are a viable, lower-impact alternative for seedling production, although scaling up will require policy and institutional support. Full article