Search Results (65)

Corporate Social Responsibility (CSR) has evolved from philanthropy to a strategic capability, but its role in recovering economies remains underexplored. This study examines how CSR strategies affect consumer perceptions in the Greek retail sector, where firms face fragile trust and constrained resources. Using survey data from 322 consumers, the research tested three drivers of CSR effectiveness: strategic integration into core business and community engagement, emphasis on sustainability-oriented initiatives, and localization to cultural and stakeholder expectations. Data were analyzed through exploratory factor analysis to validate the constructs, followed by hierarchical multiple regression to assess their influence on perceived CSR performance. Results showed that CSR embedded transparently into strategy had strong effects on consumers’ overall evaluations of CSR effectiveness, sustainability-oriented actions emerged as the most powerful predictor of perceived CSR performance, and localized initiatives enhanced trust and authenticity by signaling responsiveness to community needs. Perceived CSR performance was conceptualized as an integrative construct, capturing outcomes such as brand equity, consumer engagement, and loyalty in a unified evaluative measure. These findings suggest that CSR is a credible driver of consumer value even during economic recovery and that its effectiveness depends on authenticity, environmental relevance, and cultural fit. The study offers theoretical contributions by contextualizing CSR in fragile markets and provides practical guidance for retailers seeking resilience through responsible, strategically aligned practices. Full article

The increasing quantity and complexity of code in vehicles have imposed a heavy burden on traditional integration testing methods. However, applying new testing methods, such as eliminating redundant test cases, prioritizing based on risk, and optimizing test matching, requires the testing team to possess sufficient information, which entails communication and time costs. In this study, an information management framework is developed for the integration testing phase of automotive software to assess the importance and acquisition difficulty of specific information. The framework mainly includes three core parts: (1) classifying 37 types of test-related information into five hierarchical levels (requirement level, architecture level, function level, component level, and source code level) based on risk theory; (2) designing a scale to evaluate the difficulty of information usage from three dimensions (acquisition, transmission, and evaluation); (3) providing an operational guide for integration testing departments to match information with testing strategies. This framework assists enterprises in making wiser decisions regarding testing methods and provides guidance for future collaboration between original equipment manufacturers and suppliers. Full article

In recent years, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed to tackle constrained multi-objective optimization problems (CMOPs). However, most of them still struggle to achieve a good balance among convergence, diversity, and feasibility. To address this issue, we develop an adaptive constraint-boundary learning-based two-stage dual-population evolutionary algorithm for CMOPs, referred to as CL-TDEA. The evolutionary process of CL-TDEA is divided into two stages. In the first stage, two populations cooperate weakly through environmental selection to enhance the exploration ability of CL-TDEA under constraints. In particular, the auxiliary population employs an adaptive constraint-boundary learning mechanism to learn the constraint boundary, which in turn enables the main population to more effectively explore the constrained search space and cross infeasible regions. In the second stage, the cooperation between the two populations drives the search toward the complete constrained Pareto front (CPF) through mating selection. Here, the auxiliary population provides additional guidance to the main population, helping it escape locally feasible but suboptimal regions by means of the proposed cascaded multi-criteria hierarchical ranking strategy. Extensive experiments on 54 test problems from four benchmark suites and three real-world applications demonstrate that the proposed CL-TDEA exhibits superior performance and stronger competitiveness compared with several state-of-the-art methods. Full article

This study aimed to systematically characterize the volatile organic compound (VOC) profiles of safflower (Carthamus tinctorius L.) from eight major production regions, providing a scientific basis for quality evaluation and geographical traceability. VOC profiling was conducted using gas chromatography–ion mobility spectrometry (GC–IMS), and regional differences were assessed through multivariate statistical analyses, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS–DA), Euclidean distance, and hierarchical clustering. Key differential compounds were identified by variable importance in projection (VIP) and relative odor activity value (ROAV) analyses, with aldehydes and esters emerging as the primary contributors to the discrimination of samples across regions. VOC fingerprints of safflower were further established, and a combined VIP–ROAV strategy was proposed for the screening of characteristic compounds. These findings provide a reliable reference for safflower quality control and offer practical guidance for its geographical authentication in the food industry. Full article

As unique geographical entities, island cities boast abundant ecological resources and profound cultural values, serving as critical hubs for maintaining ecosystem services in coastal transition zones. Ensuring the stability of ecosystem services is strategically significant for sustainable urban development, while the construction of Ecosystem Service Networks (ESNs) has emerged as a core strategy to enhance ecological functionality and mitigate systemic risks. Based on current research gaps, this study focuses on three key questions: (1) How to construct a Composite Ecosystem Service Index (CESI) for island cities? (2) How to identify the Ecosystem Service Networks (ESNs) of island-type cities? (3) How to optimize the ecosystem service networks of island cities? This study selects Pingtan Island as a representative case, innovatively integrating the InVEST and MaxEnt models to conduct a comprehensive assessment of ecological and cultural services. By employing Principal Component Analysis (PCA), a Composite Ecosystem Service Index (CESI) was established. The research follows a systematic technical approach to construct and optimize the ESN: landscape connectivity indices were applied to identify ecological source areas based on CESI outcomes; multidimensional resistance factors were integrated into the Minimum Cumulative Resistance (MCR) model to develop the foundational ecological network; gradient buffer zone analysis and circuit theory were sequentially employed to refine the network structure and evaluate ecological efficacy. Key findings reveal: (1) Landscape connectivity analysis scientifically delineated 20 ecologically valuable source areas; (2) The coupled MCR model and circuit theory established a hierarchical ESN comprising 45 corridors (12 Level-1, 14 Level-2, and 19 Level-3), identifying 5.75 km² of ecological pinch points, 7.17 km² of ecological barriers, and 84 critical nodes—primarily concentrated in cultivated areas; (3) Buffer zone gradient analysis confirmed 30 m as the optimal corridor width for multi-scale planning; (4) Circuit theory optimization significantly enhanced network current density (1.653→8.224), demonstrating a leapfrog improvement in ecological service efficiency. The proposed “assessment–construction–optimization” integrated methodology establishes an innovative paradigm for deep integration of ecosystem services with urban spatial planning. These findings provide practical spatial guidance for island city planning, supporting corridor design, conservation prioritization, and targeted restoration, thereby enhancing ecosystem service efficiency, biodiversity protection, and resilience against coastal ecosystem fragmentation. Full article

Coastal ecosystems, located at the interface of terrestrial and marine environments, provide significant ecological functions and resource value. Coastal salt pans, as critical coastal resources with significant implications for coastal ecosystem health and resource management, have attracted extensive research attention. However, current studies on the extraction of spatiotemporal patterns of coastal salt pans remain relatively limited and superficial. This study takes coastal salt pans in Laizhou Bay as a case study, proposing a hierarchical classification method—Salt Pan Feature-Enhanced Fusion Image Random Forest (SPFEFI-RF)—based on multi-index synergy guidance and deep-shallow feature fusion, achieving high-precision extraction of coastal salt pans. First, a Modified Water Index (MWI) and Salt Pan Crystallization Index (SCI) were constructed from image spectral features, specifically targeting the extraction of evaporation ponds. Concurrently, a salt pan sample dataset was developed for the DeepLabv3+ (DL) method to extract deep semantic features and perform multi-scale feature fusion. Subsequently, a three-channel fusion strategy—R(MWI)-G(SCI)-B(DL)—was employed to produce the Salt Pan Feature-Enhanced Fusion Image (SPFEFI), enhancing distinctions between salt pans and background land cover. Finally, the Random Forest (RF) classifier using shallow spectral features was applied to extract salt pan information, further optimized by spatial domain denoising techniques. Results indicate that the SPFEFI-RF approach effectively extracts coastal salt pan features, achieving an overall accuracy of 92.29% and a spatial consistency of 85.14% with ground-truth data. The SPFEFI-RF method provides advanced technical support for high-precision extraction of global coastal salt pan spatiotemporal characteristics, optimizing coastal zone management decisions and promoting the sustainable development of coastal ecosystems and resources. Full article

For the motion control of ships in confined and curved waterways, from broad coastal channels to narrow river bends, conventional methods often struggle to ensure both tracking accuracy and navigational safety. A key deficiency is the inability of standard algorithms to incorporate the nuanced principles of good seamanship. To address this, a novel, hierarchical adaptive control framework is proposed. The core novelty of this framework lies in its versatile and adaptive guidance rules, which embed maritime practice into the control loop for different navigating scenarios. In general maritime channels with wind and current, these rules function to ensure robust, high-fidelity route tracking. For the most challenging inland river curved channels, it is further enhanced to generate a strategic, non-centerline trajectory that replicates the crucial inland navigational practice of “holding high and taking low”. This is complemented by a reinforcement learning-based strategy at the control layer, which performs real-time tuning of PID gains to adapt to the vessel’s dynamics. The framework’s dual capabilities were systematically validated. The core adaptive algorithms proved effective for robust control in curved channels under wind and current disturbances. Furthermore, the full framework, including the seamanship-informed strategy, demonstrated superior performance in the most complex inland river scenarios. Compared to a conventional controller, the proposed method reduced the peak cross-track error by over 40% and increased the minimum safety margin from the bank by more than 49% under a strong 3 m/s cross-current. An effective solution for motion control is thus provided, bridging the gap between modern control theory and the context-dependent expertise of practical pilotage. Full article

In urban underground construction, Earth Pressure Balance (EPB) tunneling faces complex geological uncertainties and dynamic operational risks. Traditional safety management approaches often struggle under such conditions. This paper proposes an integrated safety resilience assessment framework for EPB tunneling that combines an entropy-weighted TOPSIS method, the Analytic Network Process (ANP), and an extension cloud model to capture interdependencies and uncertainties. A hierarchical indicator system with four primary dimensions (stability, redundancy, efficiency, and fitness) is constructed. The entropy-TOPSIS algorithm provides objective initial weights and scenario ranking, while ANP models the feedback relationships among criteria. The extension cloud model quantifies fuzziness in expert judgments and converts qualitative assessments into probabilistic resilience ratings. The methodology is applied to a case study of the EPB shield tunnel section of Jinan Metro Line 6 (China). The section’s resilience is classified as a medium level, which agrees with expert evaluation. The results demonstrate that the proposed approach yields accurate and robust safety resilience evaluations, supporting data-driven decision-making. This framework offers a quantitative tool for resilience-based safety management of shield tunneling projects, providing guidance for shifting from traditional risk control toward a resilience-enhancement strategy. Full article

Government-invested Engineering, Procurement, and Construction (EPC) projects often encounter challenges, such as ambiguous value-added pathways and undefined key driving mechanisms, which impede efficiency improvements during implementation. To systematically elucidate the value-added pathways and core driving mechanisms in these projects, this study identified and distilled 20 critical influencing factors across four dimensions—contract cost, organization, technology, and environment—through a combination of a literature review, case analysis, and a questionnaire survey yielding 68 valid responses. Employing a DEMATEL–TAISM–MICMAC hybrid model, the research conducted an in-depth analysis: the DEMATEL method quantified the interdependencies among factors and identified key causal elements; a TAISM-directed topological hierarchy diagram was constructed to clearly delineate the hierarchical transmission pathways; and the MICMAC model was utilized for driver–dependency analysis, classifying factor roles and providing cross-validation from three analytical perspectives. The results indicate that S₁₂ (collaborative participation in early planning and design phases) exhibits the highest causal influence and serves as the core driving factor, while S₁ (detailed and explicit contractual clauses) and S₁₂ are positioned at the root level of the hierarchical model, functioning as foundational independent factors that regulate the entire system. The value-added pathways are characterized by a hierarchical transmission logic of “root level → transitional level → direct level”. Based on these findings, the study proposes a system optimization strategy of “strengthening the root level, optimizing the transitional level, and safeguarding the direct level”, thereby offering both theoretical insights and practical guidance for enhancing the value-added efficiency of government-invested EPC projects. Full article

The growth of bamboo shoots during the rapid growth phase critically determines overall bamboo height development. While exogenous hormones and sugars promote plant growth, their interactions with environmental factors and regional variations remain unclear. This study examined moso bamboo (Phyllostachys edulis) from Anhui and Hubei provinces using random forest and Bayesian hierarchical models to analyze direct and interactive effects of auxin, gibberellin, sucrose, auxin transport inhibitors, mTOR signaling pathway inhibitors, and environmental factors on shoot height. Results identified mean temperature, minimum temperature, precipitation, and subsurface runoff as key environmental drivers. Regional adaptations were evident: Anhui bamboo showed positive correlations with temperature factors, while Hubei bamboo exhibited negative correlations. Subsurface runoff consistently promoted growth, whereas precipitation negatively impacted development. Gibberellin and auxin treatments significantly enhanced bamboo responsiveness to favorable environmental conditions, while inhibitor treatments reduced these responses. This research elucidates complex interactions among exogenous hormones, sugars, and environmental factors affecting bamboo shoot growth. The findings reveal distinct regional adaptation patterns and demonstrate how hormone treatments can modulate environmental responsiveness. These insights provide theoretical foundations and practical guidance for optimizing regional bamboo forest management strategies and improving yield outcomes. Full article

Background: Binge eating disorder (BED) frequently arises during the transitional age (18–25 years), a critical developmental period characterized by challenges in autonomy, identity formation, and interpersonal functioning. This study investigated psychopathological predictors of BED risk in this age group, with particular focus on parental bonding, attachment style, body dissatisfaction, alexithymia, and depressive symptoms. Methods: A total of 287 participants aged 18–25 years completed the Binge Eating Scale (BES), Beck Depression Inventory-II (BDI-II), Body Shape Questionnaire (BSQ), Toronto Alexithymia Scale (TAS-20), Attachment Style Questionnaire (ASQ), and Parental Bonding Instrument (PBI). Sociodemographic information and body mass index (BMI) were also collected. Results: Compared with non-BED risk groups, individuals at risk of BED exhibited significantly higher BMI, greater alexithymia, higher body dissatisfaction, more insecure attachment patterns, and lower recalled paternal and maternal care. Hierarchical binary logistic regression revealed that the final model explained 56.1% of the variance (Nagelkerke R²) and correctly classified 92.1% of cases. Significant predictors of BED included body dissatisfaction, elevated BMI, low maternal care, and low maternal protection. Conclusions: This study is the first to examine BED risk factors specifically during the transitional age. Findings indicate that body image dissatisfaction, higher BMI, and inadequate maternal emotional care and protection are salient predictors at this life stage. Preventive interventions should integrate parental psychoeducation, nutritional guidance, and therapeutic strategies addressing both eating disorder symptoms and attachment-related difficulties to reduce BED onset and improve psychosocial outcomes in emerging adults. Full article

This study endeavors to elucidate the mechanisms and pathways through which the digital economy shapes agricultural green development, providing theoretical underpinnings and practical guidance for the green transformation of regional agriculture. (1) Using panel data from 18 prefecture-level cities in Sichuan Province (2013–2022), a comprehensive evaluation index system for agricultural green development was formulated. Fixed-effects, mediating-effects, and threshold-effects models were employed to systematically analyze the direct effects, transmission pathways, and nonlinear characteristics of the digital economy on agricultural green development. (2) The fixed-effects model shows that the digital economy markedly propels agricultural green development in Sichuan Province. The mediating-effects model verifies two transmission pathways: “digital economy → technological progression → agricultural green development” and “digital economy → industrial structure upgrading → agricultural green development”. The threshold-effects model suggests that when the digital economy is in the low-threshold interval, it exerts a suppressive impact on agricultural green development; however, once the threshold is surpassed, its promoting effect strengthens significantly. (3) The results demonstrate the following findings: First, the digital economy exerts a significant positive effect on agricultural green development. Second, this promoting effect exhibits significant nonlinear characteristics that vary with the level of digital economy development. Third, the impact manifests remarkable regional heterogeneity, necessitating context-specific development strategies. (4) Five optimization recommendations are proposed: promote the categorized development of agricultural digital technologies and industrial upgrading; advance digital infrastructure and technology adaptation in phases; design differentiated regional policies; establish a hierarchical and classified long-term guarantee mechanism; and strengthen the “industry-university-research-application” collaborative innovation and dynamic monitoring system. Full article

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

This study examines how a visitor-centered approach enhances engagement, participation, and intangible heritage transmission to support cultural sustainability in ethnographic museums. We conducted online and on-site behavioral observations, questionnaire surveys, and in-depth interviews at the She Ethnic Minority Museum to identify gaps in current visitor experience design. We combined the Analytic Hierarchy Process (AHP) with the Contextual Model of Learning (POE) and Emotional Experience Theory (EET) to develop a hierarchical evaluation model. The model comprises one goal layer, three criterion layers (Experience, Participation, Transmission), and twelve sub-criteria, each evaluated across People, Object, and Environment dimensions. Quantitative weighting revealed that participation exerts the greatest influence, followed by transmission and experience. Findings indicate that targeted interventions promoting active participation most effectively foster emotional resonance and heritage transmission, while strategies supporting intergenerational engagement and immersive experiences also play a significant role. We recommend prioritizing small-scale, low-cost participatory initiatives and integrating online and offline community engagement to establish a participatory chain where engagement leads to meaningful experiences and sustained cultural transmission. These insights offer practical guidance for museum practitioners and policymakers seeking to enhance visitor experiences and ensure the long-term preservation and vibrancy of ethnic minority cultural heritage. Full article

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article