Search Results (977)

Green innovation is a crucial aspect of an enterprise’s core competitiveness and long-term sustainable development, garnering significant attention from both academic scholars and industry practitioners. However, while existing research has primarily focused on green innovation at the organizational level, the mechanisms driving green innovation behaviors at the individual level have not been thoroughly explored in the literature. This study is grounded in the classic Job Demands–Resources (JD-R) theoretical framework and highlights the interplay between job demands (such as environmental ethics and corporate environmental strategies) and job resources (such as green human resource management practices and green transformational leadership). It also integrates individual-level characteristics, specifically green mindfulness and connectedness to nature, to construct a multidimensional interactive model aimed at uncovering the complex mechanisms driving employees’ green innovation. To achieve this, the study employs fuzzy-set qualitative comparative analysis (fsQCA). The findings suggest that no single condition is necessary for employee green innovation. However, connectedness to nature consistently appears across all core configurations, indicating a prominent “enabling” effect. This suggests that employee green innovation is an active and proactive form of environmentally responsible behavior, largely driven by individuals’ emotional affinity with nature. Additionally, connectedness to nature serves as a foundational source of intrinsic motivation for environmental awareness and acts as a catalyst across multiple pathways. Configurational analysis reveals an equifinal pattern, identifying three distinct motivational pathways: (1) Self-motivation Combined with Resource Support; (2) Self-motivation Combined with Job Demands; and (3) Triple Interaction of Demand, Resources, and Individuals. This study possesses both theoretical and practical significance in systematically examining green innovation behaviors at the individual level. Full article

Background: Chronic non-cancer pain (CNCP) is an increasing global health concern and a multidimensional condition shaped by biological, psychological, social, and cultural factors, with impacts on functioning, quality of life, and healthcare. However, evidence remains fragmented, limiting integrated understanding and care. Objective: This study aimed to synthesize and critically analyze existing evidence on the biological, psychological, social, and cultural dimensions characterizing individuals with CNCP, and their impact on functionality, quality of life, and healthcare. Methodology: An integrative review was conducted following the Whittemore and Knafl framework. Searches were performed in Medline, Cumulative Index of Nursing and Allied Literature Complete (CINAHL), PsycINFO, Scopus, Web of Science, and grey literature in English and Spanish, without time restrictions. Studies were screened using predefined eligibility criteria and appraised with Joanna Briggs Institute tools. Data were systematically extracted and synthesized using thematic analysis to identify key attributes of people living with CNCP. Quantitative findings were summarized descriptively and mapped to thematic domains, while qualitative data were analyzed interpretively. Both evidence streams were integrated through convergent thematic synthesis. Results: Forty-four studies were included, predominantly cross-sectional and observational. Five themes emerged: biological aspects; functioning and quality of life; psychological and mental factors; social support and peer relationships; and social and gender determinants. CNCP was consistently associated with multimorbidity, sleep disturbance, psychological distress, and maladaptive coping, contributing to reduced functional capacity, greater disability, poorer quality of life, and increased healthcare utilization. Socioeconomic disadvantages and environmental constraints were linked to higher pain burden, whereas resilience and social support emerged as protective factors mitigating functional and psychosocial impact. Conclusions: Evidence largely concentrates on biomedical, functional, and psychological dimensions, whereas social determinants and healthcare quality remain comparatively underexplored. Broadening these perspectives is essential to inform public health strategies and support multidisciplinary, equitable care for individuals living with CNCP. Full article

Understanding how ecological dominance aligns with culturally and economically valued plant use is critical for participatory forest management. This study integrated vegetation structure and ethnobotanical valuation to assess angiosperm importance across three forest strata (Mixed Deciduous Forest (MDF), Dry Dipterocarp Forest site 1 (DDF1), and Dry Dipterocarp Forest site 2 (DDF2)) within the Ngao Model Forest, Northern Thailand. Fifteen 10 × 10 m vegetation plots (five per forest stratum) were surveyed to calculate the Importance Value Index (IVI), and 198 semi-structured interviews were conducted to derive the Use Value Index (UVI) and a standardized Socio-Economic Value Index (SEVI). A total of 112 angiosperm species were recorded across forest types, with strong structural dominance by dipterocarps in DDF sites and greater compositional heterogeneity in MDF. Spearman rank correlation analysis supported the working hypothesis that ecological dominance is only weakly associated with cultural and socio-economic importance. IVI showed weak but significant positive correlations with UVI (ρ = 0.288, p < 0.05) and SEVI (ρ = 0.300, p < 0.05), indicating partial but limited alignment between structural abundance and livelihood value. Several species with moderate or low IVI exhibited disproportionately high UVI and SEVI scores, reflecting their importance in food, medicinal, and commercial use categories. Conversely, certain canopy dominants showed limited ethnobotanical significance. These findings demonstrate that ecological abundance alone is an insufficient proxy for community-defined species value. Integrating structural, cultural, and socio-economic indices provides a more comprehensive framework for identifying priority species in community-managed forest systems. The IVI–UVI–SEVI comparative approach offers practical insights for model forest governance by distinguishing ecological dominants, multipurpose livelihood species, and culturally significant taxa occurring outside forest interiors. This multidimensional valuation framework strengthens participatory forest management and biodiversity prioritization in heterogeneous tropical landscapes. Full article

To address the technical bottlenecks of strong background radiation interference and weak target signals in mid-wave infrared (MWIR) hyperspectral mineral detection over complex terrain, this paper proposes a “full-link background radiation suppression” methodological framework. A coupled illumination-terrain-atmosphere-sensor radiative transfer model is constructed to systematically quantify how multidimensional parameters—such as observation geometry, surface temperature, elevation, aerosol optical depth, and water vapor content—influence the target background radiation contrast. The findings reveal that daytime observation, lower surface temperature, higher altitude, dry atmosphere, and moderate solar and observation zenith angles are key factors for maximizing the signal-to-noise ratio. Comprehensive optimization analysis demonstrates that observations during midday in autumn and winter achieve optimal performance, with the target background relative contrast potentially enhanced by up to 6.29 times compared to unfavorable conditions such as summer nights. This work elucidates the physical mechanisms governing MWIR hyperspectral detection efficacy in complex scenarios, provides direct parameter-optimization strategies for intelligent mission planning of spaceborne imaging systems, and holds significant value for advancing mineral remote sensing from “passive acquisition” to “cognitive detection”. Full article

Urban depopulation has become an increasingly visible phenomenon worldwide, affecting cities of different sizes and economic structures. This article develops a typology of U.S. depopulating cities beyond the Rust Belt’s iconic industrial cities, which dominate academic literature, to include a wider range of shrinking settlements in the shadows. The analysis is based on a dataset of U.S. census places constructed from decennial census population data (1990–2020) combined with employment structure indicators and spatial classification variables identifying metropolitan position and industrial specialization. Using 1990–2020 population change and three explanatory dimensions—city size, industrial heritage, and peripheral location—the analysis identified 1082 places that lost at least 10% of their population. Logistic regression showed manufacturing and mining reliance, small size, and remoteness as significant predictors of depopulation. Based on these factors, settlements are divided into seven types, from large urban centers to small peripheral towns with fewer than 5000 people. The overwhelming predominance of small towns (97%) in the sample highlights their distinct development challenges and questions the narrative of decline focused solely on larger industrial cities. By situating American trajectories within the broader shrinking cities discourse, the findings demonstrate the value of typology as a methodological tool for identifying intra-group heterogeneity, capturing regional differences, and establishing a more reliable basis for comparative urban studies. Ultimately, the study shows that urban decline in the United States is not exclusively a Rust Belt phenomenon, but a multidimensional process encompassing different scales, sectors, and geographies. Full article

Aiming at the challenge of simultaneously controlling ride comfort and wheel grounding performance for mining dump trucks, this paper proposes a multi-dimensional synergistic optimization control (MDSOC) strategy based on model predictive control (MPC) for active hydro-pneumatic suspension. First, an accurate hydro-pneumatic suspension and hinged mining truck full-vehicle-dynamics model is established, and the model accuracy is validated through actual vehicle testing. Subsequently, an MDSOC-MPC for active hydro-pneumatic suspension is constructed to minimize the mean square root of the three-axis acceleration of the body, pitch angle, roll angle, and wheel dynamic tire load. Comparative analysis is performed with traditional single-MPC longitudinal, lateral, and vertical control, and the simulation results showed: under emergency braking conditions, the root mean square (RMS) value of the pitch angle is reduced by 18.2%; under single and double-shift conditions, the RMS values of the roll angle are reduced by 40.4% and 30%, respectively; under D-class random road, the RMS values of the longitudinal, lateral, and vertical body acceleration are significantly reduced by 22%, 21.5%, and 21.2%, respectively, while the RMS values of pitch angle and roll angle are reduced by 22.5%, and 20.2%, respectively, systematically improving riding comfort, vehicle wheel contact, and driving safety. This study provides a theoretical basis and feasible engineering methods for the active control of hydro-pneumatic suspension systems in heavy engineering vehicles. Full article

Objective: To develop and validate a multidimensional nomogram for predicting 28-day all-cause mortality in patients with post-traumatic acute respiratory distress syndrome (ARDS). Methods: A retrospective analysis was conducted on 667 post-traumatic ARDS patients from the MIMIC-IV database, divided into training (n = 466) and validation (n = 201) cohorts (7:3). LASSO regression combined with the Boruta algorithm was used to screen variables and construct a nomogram. Model performance was evaluated by AUROC, calibration curves, and decision curve analysis (DCA) with SHAP analysis to identify core predictors. Results: Ten variables (e.g., lactate, platelet transfusion units, D-dimer) were selected and used to construct the nomogram model. The nomogram showed superior discriminative ability (AUROC = 0.848 in training set, 0.846 in validation set) compared with SOFA, APACHE II scores, and machine learning models (XGBoost, random forest). Calibration curves confirmed good agreement between predicted and actual risks, and DCA indicated better clinical net benefit. SHAP analysis identified lactate and platelet transfusion units as core risk factors and albumin and base excess trauma as protective factors. Conclusions: The nomogram has excellent predictive efficacy and interpretability, providing a reliable tool for clinical intervention in post-traumatic ARDS patients. Full article

Background/Objectives: Quality of life is conceptualized as a multidimensional construct encompassing subjective well-being, health, and social functioning. Evidence suggests that engagement in physical activity contributes to higher quality of life scores among older adults, indicating that structured exercise programs can positively influence both physical and psychological domains in this population. The present study examined the impact of an 18-week structured physical exercise program on the quality of life of older adults, assessed through the World Health Organization Quality of Life-BREF (WHOQOL-BREF) instrument, which consists of four domains: physical health, psychological, social relationships and environment. A total of 86 participants were allocated to three groups: individual exercise (n = 31), collaborative exercise (n = 32), and a control group (n = 23). Quality of life was evaluated before and after the intervention using the WHOQOL-BREF. Results: Correlation analysis indicated strong relationships among the WHOQOL-BREF domains, both before and after the program. Repeated-measures analysis revealed no significant Group × Time interaction effects for any WHOQOL-BREF domain. A significant main effect of Time was observed for the Environment domain, indicating a small overall decrease across all groups during the study period. Conclusions: The structured exercise protocol did not lead to greater changes in quality of life compared to the control condition. Perceived environmental quality of life showed a small overall decrease over time across participants. Full article

Background and Objectives: Uterine fibroids are common benign tumors that frequently require surgical management, particularly myomectomy, in women of reproductive age. Metabolic dysfunction and insulin resistance have been implicated in fibroid biology; however, the clinical relevance of glycated hemoglobin (HbA1c) in predicting myomectomy requirement remains unclear. This study aimed to evaluate the predictive role of HbA1c for myomectomy requirement in women with uterine fibroids using conventional statistical analyses and machine learning-based models under real-world clinical decision-making conditions. Materials and Methods: This study evaluated data from a retrospective multicenter cohort comprising 618 women with a diagnosis of uterine fibroids. Patients were stratified according to myomectomy status (performed vs. not performed). Comparative analyses, univariate and multivariate logistic regression, and machine learning modeling were conducted using demographic, laboratory, hormonal, and fibroid-related variables. A total of 155 machine learning models were trained, and the top 20 models with the highest accuracy were evaluated. Blinded concordance analysis was conducted on 50 independent, anonymized cases evaluated by a gynecologist who was blinded to the study data. Results: Patients undergoing myomectomy (38.5%) had significantly higher HbA1c levels than non-surgical patients (5.57 ± 0.32 vs. 5.03 ± 0.61, p < 0.001). HbA1c showed a strong association with myomectomy requirement in univariate analysis (OR 0.026, 95% CI 0.012–0.055) but lost significance in multivariate models, while ferritin remained independently associated. Machine learning models incorporating HbA1c, ferritin, hormonal, and fibroid parameters achieved accuracies between 0.99 and 1.00. Blinded concordance analysis demonstrated 94% concordance between model predictions and expert clinical judgment. Conclusions: HbA1c is a valuable integrative marker in predicting myomectomy requirement when evaluated within multidimensional machine learning frameworks, although its independent effect is confounded by iron-related parameters. These findings support the use of HbA1c as part of a comprehensive decision-support approach in uterine fibroid management. Full article

Reliability and maintenance serve as pivotal factors in safeguarding safety, enhancing efficiency, optimizing costs, and fostering sustainable development. They permeate all facets of industry, daily life, and society, thereby constituting a crucial foundation for achieving long-term, stable development. The rapid evolution of data-driven methods and artificial intelligence (AI) has revolutionized reliability and maintenance practices, driving a shift from reactive to predictive maintenance (PdM) and ultimately intelligent maintenance strategies. Unlike existing reviews that focus on single technologies or tasks, this paper adopts a system-level integration perspective to construct a closed-loop framework connecting data-driven reliability analysis, maintenance optimization, and intelligent decision-making. It further elucidates the integrated logic between prediction and decision-making through formalized mechanisms. This article systematically reviews the research progress and practical applications of data-driven methods and AI in reliability and maintenance. First, it classifies and summarizes data-driven reliability analysis methods based on existing literature. Second, a reliability-oriented maintenance optimization framework is proposed, comprehensively integrating economic, reliability, resource efficiency, and multi-objective collaboration considerations, while analyzing the characteristics of diverse maintenance systems. Furthermore, the innovative applications and performance advantages of AI algorithms in complex system maintenance are synthesized, and a comparative analysis of the applicability of different methods across various operational scenarios is conducted. And conducted a multidimensional comparison of the applicability scenarios for different methods from an engineering selection perspective. In addition, this review examines the current status and challenges of applying data-driven and AI technologies across multiple real industrial settings and identifies common obstacles encountered during project implementation. We further elucidate the research positioning of this work and provide a comparative discussion with existing review articles. Finally, the article conducts a bibliometric analysis to map the research landscape, provides quantitative support for the development trends in the field. Limitations in this field are also discussed. Full article

Traditional talent identification systems often rely on static assessments and overlook the dynamic nature of long-term development. To address this limitation, this study proposes a biomimetic predictive framework inspired by crayfish behavioral ecology. The Crayfish Optimization Algorithm (COA), derived from adaptive foraging and competition mechanisms observed in crayfish, is enhanced through a Thinking Innovation Strategy (TIS) to form TISCOA for hyperparameter optimization of a Gradient Boosting Decision Tree model. Using a five-year longitudinal dataset of 160 elite mathematical students, the framework models Professional Achievement in Mathematics (PAM) from multidimensional baseline indicators. Comparative experiments with multiple metaheuristic optimizers show that the proposed approach achieves stable generalization performance within the examined cohort. Feature attribution analysis indicates that non-cognitive factors, particularly Emotion Regulation, contribute substantially to long-term outcomes, while temporal variables such as the Latency Period further shape developmental trajectories. Residual analysis highlights heterogeneous patterns that may reflect unobserved contextual influences. Overall, the study demonstrates how a biologically inspired optimization mechanism can support interpretable and stability-oriented longitudinal prediction in small-sample educational settings. Full article

Water sustainability plays a critical role in achieving the Sustainable Development Goals (SDGs), as it influences human well-being, ecosystem integrity, and long-term development pathways. Over the past three decades, a substantial body of research has emerged on water sustainability; however, there remains a limited synthesis of how sustainability has been assessed, how assessment approaches have evolved, and the extent to which they align with the multidimensional intent of the SDGs. This study addresses the gap by combining a systematic review conducted using the PRISMA framework and bibliometric analysis from 1995 to 2025. The results show a marked acceleration in research output after 2015 following the formal adoption of the SDGs, with concentrations in a small number of countries and research hubs. Water sustainability assessment is mainly shaped by technically oriented indicator-based frameworks that emphasise water availability, water quality, and management performance. While these approaches have enabled comparability and methodological consistency, they often provide a partial representation of sustainability with limited integration of governance processes, social equity, cultural contexts, indigenous knowledge, and ecosystem services. The findings highlight the need for assessment approaches that go beyond technical metrics to more integrative and context-sensitive frameworks that can inform policy, support adaptive decisions, and reflect the interconnected nature of sustainable development. Full article

In the optical fiber Fabry–Perot (FP) multi-dimensional acceleration sensing system, multi-dimensional acceleration measurement is realized based on a single optical path, resulting in the existence of multi-channel interference signals in the spectrum, and the traditional cavity length demodulation algorithm cannot achieve efficient separation of aliasing signals and high-precision demodulation of FP cavity length. To solve this problem, an adaptive filtering–multiple peaks–cooperative least squares algorithm (AF-MP-LS) is proposed for cavity length demodulation of optical fiber FP multi-dimensional accelerometer. The adaptive Gaussian filter is used to dynamically adjust the parameters according to the frequency difference in the aliasing optical signal, and the interference spectra of each channel are efficiently separated. The multiple peaks–least squares method is used to demodulate the separated signals, improve the demodulation resolution, and solve the problem of limited dynamic range of spectral signals. Furthermore, based on the multiplexing structure, a complementary correction method utilizing ‘triple-interferometric’ information—derived from the FP cavities and the auxiliary Michelson interference component—is proposed to improve the demodulation accuracy and stability of the system. The performance of the proposed method was verified through simulations, multi-angle vibration experiments and comparative algorithm analysis. The experimental results show that this algorithm can accurately demodulate multi-dimensional signals under different tilt angles of vibration excitation. Particularly, after compensating for the triple interference information, the mean square error (MSE) of the demodulated acceleration decreased by 0.0044 g, and the accuracy increased by 70.9% compared to before correction. Full article

Urban innovation capacity is increasingly critical to city development, and quantitative assessments of innovative cities’ innovation capability can be achieved via the composite index method, which fully integrates multidimensional indicators. This study develops a hierarchical data envelopment analysis (H-DEA) method to establish a composite index evaluation model for innovation capacity, which features flexible and objective two-level indicators—an advantage that avoids subjective weight assignment and adapts well to the hierarchical structure of innovation evaluation indicators. The proposed H-DEA model is applied to evaluate 67 innovative cities in China, yielding composite scores and rankings that are further compared with those from the traditional weighting method. Sensitivity analysis is conducted by adjusting different upper and lower bounds of the H-DEA model to verify its robustness. Additionally, these 67 cities are divided into four regions, with region-specific weights assigned to the evaluation indicators in the model. The results show that the eastern region has the highest average innovation capacity (0.3783), where technological innovation (weight 0.27) serves as a key driving force; the western region has the lowest average innovation capacity (0.3235), and its innovative cities should prioritize improving outcome transformation capacity (weight 0.1357). Overall, technological innovation receives the highest average weight (0.2422), while outcome transformation capacity gets the lowest (0.1647). Full article

Special Economic Zones (SEZs) have increasingly been adopted worldwide as policy instruments for industrialization, export promotion, and employment creation. However, despite their rapid expansion, the long-term sustainability of SEZ projects remains uneven, particularly in emerging economies such as Kenya, where several zones continue to operate below expected performance levels. Existing studies largely emphasize financial viability while paying limited attention to how governance and institutional factors jointly influence multidimensional sustainability outcomes. This study therefore examines the combined influence of project finance structuring, public sector participation, and institutional capacity on the sustainability of SEZ projects in Kenya. In this study, sustainability is conceptualized through the triple bottom line dimensions of economic, social, and environmental sustainability. The study adopted a cross-sectional research design and collected primary data from stakeholders across SEZ projects using structured questionnaires administered to project managers, government officials, and community representatives. Reliability and validity of measurement instruments were confirmed through Cronbach’s alpha and factor analysis, while diagnostic tests verified compliance with regression assumptions. Data were analyzed using descriptive statistics, Pearson correlation, and multiple linear regression techniques. Descriptive findings indicate moderate overall project sustainability, with economic sustainability recording relatively stronger outcomes compared to social and environmental sustainability, suggesting uneven progress across sustainability dimensions. Regression results show that public sector participation emerged as the strongest predictor of SEZ projects’ sustainability, followed by institutional capacity, while project finance structuring demonstrated only a moderate relationship and became statistically insignificant when public sector participation and institutional factors were jointly considered. Collectively, the integrated model explained approximately 76.5% of the variation in SEZ projects’ sustainability. The study concludes that sustainable SEZ projects in Kenya depends less on project finance structuring alone and more on strong institutional systems and proactive public sector participation capable of balancing economic growth with social and environmental objectives. The findings contribute to policy and practice by emphasizing a shift from finance-centric SEZ projects development toward integrated governance frameworks that promote inclusive and environmentally responsible industrialization. Full article