Search Results (2,694)

As global resource depletion and environmental challenges continue to intensify, the circular economy has emerged as a critical strategy for firms pursuing sustainable development. This study integrates the perspectives of circular economy, the resource-based view (RBV), and stakeholder theory, and incorporates a system dynamics approach to construct a causal feedback model of circular economy systems. First, through a comprehensive literature review and systems thinking, this study develops a causal loop diagram (CLD) that captures the dynamic interactions among key elements, including firms, resources, design, products, consumers, recycling, and waste, thereby illustrating the underlying mechanisms of circular economy operations. Subsequently, the CLD is transformed into a structural equation model (SEM), and empirical analysis is conducted using 134 valid questionnaire responses. The results indicate that significant and positive causal relationships exist among the constructs. In particular, resource-based design advantage is identified as the core driving factor of the system, influencing waste reduction through circular recycling and resource circulation mechanisms. Moreover, the interaction between reinforcing feedback loops and balancing feedback loops forms a dynamic equilibrium within the circular economy system. The findings not only validate the theoretical framework of circular economy systems but also provide practical implications for firms in terms of resource allocation, product design, and recycling management, thereby facilitating resource circulation and sustainable development. Full article

This study presents a multivocal literature review (MLR) on the implementation of Shared Autonomous Vehicles (SAVs), a relatively new concept in urban mobility that merges autonomous driving with shared transportation. The purpose of this review is to analyse the feasibility, challenges and potential impacts of SAV deployment by aggregating and synthesising insights from the academic literature and grey sources. The review addresses factors influencing deployment, including social acceptance, environmental impact, business models, policy frameworks, needs and barriers, and lessons from existing pilot programmes. The findings reveal that successful SAV implementation depends on combining technology, regulation and infrastructure. Public trust and perception of safety, cost and convenience can also significantly influence the adoption of this technology, as well as potential sustainability benefits (like reduced emissions and fewer private vehicles). Case studies from cities like Phoenix, San Francisco and Singapore show promising results but also context-specific challenges. This study concludes that future research should apply these insights to specific cities, where urban layouts and public transport reliance demand customised approaches to successfully deploy SAVs. Full article

The integration of digital technologies is increasingly recognized as a critical enabler of lean practices in prefabricated construction projects. However, a systematic understanding of the underlying factors that drive this lean–digital transformation remains limited. To address the gap, this study identified 18 factors through an in-depth review of 30 papers and a follow-up questionnaire survey. The factors are divided into five dimensions, i.e., organizational, social, technological, economic and environmental, according to an extended framework of the Socio-Technical Systems (STS) and Technology–Organization–Environment (TOE). These 18 factors were then analyzed via a back propagation (BP) neural network model. The empirical data were collected from 148 practitioners across 11 regions in China where PC industrialization, digital technology adoption, and lean-related practices are relatively mature. These regions were selected because digitalization-driven lean practices are more observable in such contexts, allowing the BP model to capture the comprehensive contribution of key factors more effectively. The findings reveal that the effective implementation of the smart lean practices via digitalization is primarily driven by a systematic process, where greater attention should be directed toward simulation-based process optimization, robust information management, integrated design and construction, lean management systems, and the workers’ digital skills. Although the empirical evidence is derived from relatively mature PC and digital construction markets in China, the identified factors provide reference insights for broader PC projects including less mature regions to make effective measures to improve lean implementation. This study contributes to the existing knowledge body of lean in PC by extending the theories of STS and TOE to advance the understanding of digital drivers. Additionally, the results serve as a reference for stakeholders by informing strategic priorities such as resource allocation for workforce development, advancing the realization of smart lean prefabricated construction. Full article

Considering rising global uncertainties and intensifying resource and environmental pressures, it has become an inevitable trend to add more ecologically green factors to the traditional industrial chain resilience system and build a system of green industrial chain resilience (GICR). To address the inherent tension between security and green goals, this study develops a novel two-dimensional analytical framework encompassing fracture repair capacity and development regeneration capacity. This framework provides the theoretical foundation for constructing a pioneering city-level evaluation system for GICR. Employing this system and a suite of spatial econometric methods, we empirically analyze the spatiotemporal evolution, convergence, and driving mechanisms of GICR across 245 Chinese cities. The main findings are threefold. First, the proposed framework effectively captures the complexity of GICR, revealing an overall upward trend but significantly widening regional disparities, with a persistent core-periphery spatial pattern. Second, convergence analysis uncovers a club convergence dynamic nationwide, characterized by a notable “high-level equilibrium lock-in” in the advanced eastern region, in contrast to the catch-up convergence observed in central, western, and northeastern China. Third, geographical detector analysis identifies talent agglomeration as the paramount driver, with its interaction with other factors producing nonlinear enhancement effects. These findings underscore that enhancing GICR requires regionally differentiated strategies: policies must break the innovation lock-in in the east, embed resilience standards into industrial transfer in the central and western regions, and prioritize talent as the core lever for synergistic capacity building. Full article

Background: Hypoplastic left heart syndrome (HLHS) is defined as “a spectrum of congenital cardiovascular malformations with normally aligned great arteries without a common atrioventricular junction, characterized by underdevelopment of the left heart with significant hypoplasia of the left ventricle including atresia, stenosis, or hypoplasia of the aortic or mitral valve, or both valves, and hypoplasia of the ascending aorta and aortic arch”. Without treatment, HLHS is usually lethal in the neonate. Many hypotheses have been advanced to explain the etiology of HLHS; however, no single theory appears to fully explain the phenotypic variability seen in HLHS. Furthermore, many of these theories offer no explanations regarding the precipitating events which lead to the development of HLHS. Objective: This review considers and critically evaluates the strengths and weaknesses of the leading theories proposed to explain the pathogenesis of HLHS—including hemodynamic disturbances, primary myocardial structural defects, valvar malformations, and genetic or epigenetic alterations that may provoke developmental and anatomic abnormalities. After presenting each model, we propose a novel, comprehensive, and data-driven framework which may assist researchers in developing models for the pathogenesis of the various subtypes of HLHS. Methods: Key findings from human fetal imaging, histopathology, genetic studies, and animal models were considered, as well as the hypothetical contribution of each in observed HLHS phenotypes. The rationales for these findings as causal factors initiating individual HLHS patterns, as well as how they might contribute to HLHS in general, were critically analyzed. Results: The flow theory is strongly supported by animal models and in utero interventions that demonstrate the impact of altered hemodynamics on cardiac morphogenesis. However, the flow theory fails to identify initial causes of disturbed flow or related histological features of HLHS like endocardial fibroelastosis. The myocardial and valve-first models suggest an important role in developmental defects, but do not necessarily have a strong experimental basis that provides explanations for how they mediate HLHS. Genetic studies in patients with HLHS have identified several candidate causal mutations. However, such genetic causes of HLHS exhibit incomplete phenotypic penetrance and clinical impact. A multifactorial framework attempts to integrate these diverse mechanisms and may provide the most coherent explanation that can accommodate the heterogeneity and variable presentation of HLHS. Such a framework may identify multiple forces that drive disease but does not provide useful pathways for future research about HLHS. Conclusions: No single hypothesis has fully explained how HLHS is initiated, progresses, and presents with the clinical conditions that are encountered by cardiac surgeons and cardiologists. The most current models suggest that the spectrum of HLHS reflects acomplex interaction between genetic susceptibility, flow-dependent cardiac remodeling, and environmental factors in utero. A multifactorial model integrates these diverse mechanisms and may provide the most coherent explanation for the various phenotypic variations in HLHS. Based on our analysis of the most current data and the strengths and weaknesses of the current theoretical frameworks, we propose a novel research strategy aimed at identifying specific cardiac progenitor cell populations whose dysregulation may represent a unifying explanation for the etiology of the various phenotypes of HLHS. Based on the arguments made throughout this manuscript that evaluate the various genetic, structural, and hemodynamic models of initiation of disease, we believe that the significant phenotypic variability across the spectrum of HLHS (i.e., the different anatomic subtypes for “classic” HLHS) most likely reflects different underlying etiologies and mechanisms. At the very least, it is very likely that the timing of the insult is critical in determining anatomic subtype. Based on the published data and the arguments within this manuscript, it seems naive to think that there is a single unifying mechanism explain all forms of HLHLS. Full article

Traffic congestion poses a critical challenge to urban transport systems, substantially increasing energy consumption and environmental impacts. This study investigates the mechanisms driving transport energy intensity by linking traffic microdynamics with macroscopic fuel consumption patterns, with particular emphasis on the role of traffic flow destabilization. The research is based on a case study of a complex urban intersection in Szczecin (Poland), integrating field observations, traffic microsimulation using the Eclipse SUMO (Simulation of Urban MObility), and energy modeling based on the HBEFA (Handbook Emission Factors for Road Transport) 4.2 methodology. The study provides empirical evidence that traffic flow destabilization constitutes a primary mechanism driving fuel consumption, independent of traffic volume, with implications transferable to other intersections in terms of underlying processes. Empirical traffic data collected during peak periods were used to calibrate the simulation model, and the resulting dataset was analyzed using a general linear model (GLM) to assess the effects of speed and vehicle type on fuel consumption. The results indicate that vehicle speed is the dominant factor influencing fuel consumption (η²p = 0.60), significantly outweighing the effect of vehicle type (η²p = 0.15). Vehicle speed emerged as the dominant determinant of fuel consumption, while vehicle type had a secondary but statistically significant effect. Results reveal a strong, near-linear relationship between time loss and fuel consumption, indicating that congestion-induced delay is a key proxy for energy intensity. These findings demonstrate that energy consumption is primarily driven by traffic flow instability rather than traffic volume alone, highlighting the potential of traffic management strategies aimed at stabilizing flow conditions, where even minor infrastructural interventions can substantially improve energy efficiency in urban transport systems. Full article

The accurate estimation of State of Health (SoH) for lithium-ion batteries in real-world electric vehicles (EVs) is critical for ensuring safety, reliability, optimal energy management, and lifecycle sustainability. Unlike laboratory-controlled conditions, real-world EV batteries operate under highly dynamic loads, irregular charging behaviors, diverse environmental conditions, and user-dependent driving patterns. This review provides a comprehensive and structured overview of recent progress in SoH estimation for real-world EV applications. The fundamentals of battery aging mechanisms are summarized, with a clarification of key SoH definitions, metrics, and influencing factors under practical operating conditions. Subsequently, existing methodologies are systematically categorized into physics-based models, data-driven approaches, hybrid/model-assisted frameworks, and uncertainty-aware probabilistic methods, with a focus on their strengths and limitations in real-world deployment. Key challenges, including domain shift, computational constraints, explainability, thermal variability, and data heterogeneity, are critically and systematically analyzed. Finally, future research directions are outlined, emphasizing transfer learning, foundation models, physics-informed AI, self-supervised learning, digital twins, and the need for standardized benchmarks. This review aims to provide researchers and practitioners with a clear roadmap toward reliable, scalable, and trustworthy SoH estimation for next-generation intelligent battery management systems in electric vehicles. Full article

Marine macroalgae, microalgae, and associated microorganisms are increasingly recognised as valuable sources of bioactive compounds with applications across biotechnology and health. The environmental and ecological conditions they inhabit shape their metabolite diversity, leading to the production of high-value compounds such as sulphated polysaccharides, lipids, pigments, phenolics, and peptides. These compounds exhibit conserved biological activities that underpin potent antioxidant, anti-inflammatory, cytotoxic, and pro-regenerative effects with strong potential for translation. Although external factors drive rich metabolite diversity, continual variation can also lead to translational constraints including heavy-metal accumulation, inconsistency in extract composition, and regulatory complexity. This review examines the environmental drivers of metabolite diversity and the functional potential of bioactives derived from marine algae. We focus on their translational application within four areas of growing interest: nutraceuticals, cosmetics, regenerative medicine, and oncology, where emerging evidence suggests their promise as next-generation bioactive ingredients and therapeutic leads. In addition, insights from Irish and Welsh Small and Medium Enterprises (SMEs) are collated to identify key bottlenecks in commercialisation and the requirements for effective marine biodiscovery pipelines. We consider the importance of controlled cultivation, standardised analytics, preclinical testing platforms, and collaborative innovation ecosystems and highlight the need for coordinated scientific, technical, and regulatory advances to unlock the full translational potential of marine-derived compounds. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

Soybean (Glycine max) is a vital food and oil crop in China, yet its yield and quality are severely threatened by piercing–sucking damage caused by Riptortus pedestris (Hemiptera: Alydidae) to soybean pods. Under global climate warming and expanded soybean cultivation, temperature has become a key environmental factor driving the spread of and aggravated damage caused by R. pedestris. We investigated the effects of temperature (32, 36, 40, 42, and 44 °C) and exposure duration (1–4 h) on the energy substances and antioxidant enzyme activities in adult R. pedestris. These two factors also had significant effects on the pest’s energy substances and antioxidant defense. Under short-term high-temperature stress, the water loss rate and fat, total sugar, and glycogen contents increased significantly, while protein content showed a fluctuating upward trend, with distinct sexual differences in these responses; the water loss and energy substance levels within the lethal high-temperature range, around 44 °C, were generally higher than those in the sublethal range (36–42 °C). R. pedestris showed physiological changes consistent with enhanced heat tolerance and adaptability, including water balance regulation, carbohydrate and lipid accumulation, and modulation of protein synthesis and degradation. In the sublethal high-temperature range, antioxidant enzyme activity patterns were altered, and SOD activity was increased; meanwhile, the MDA content also rose, and POD and CAT activities decreased. In the lethal high-temperature range, the overall antioxidant enzyme activities were lower than in the suitable temperature range, with the POD activities and MDA content still rising. These results suggest that the dynamic adjustment of antioxidant enzyme activities may contribute to alleviating oxidative damage and rapid adaptation to temperature-induced oxidative stress in R. pedestris. These findings indicate that R. pedestris possesses physiological plasticity to cope with sublethal heat stress through metabolic reallocation and antioxidant defense activation, but extreme temperatures cause severe physiological disruption. This study provides insights into the thermal biology and heat resistance mechanisms of this pest under climate warming scenarios. Full article

As the inner driving factor of space vitality, environmental perception can be expressed in many ways. Given the current lack of in-depth research on related perceptions, the study integrated theoretical origin and empirical study methods to clarify the role that preference played as the common foundation of different expression ways of environmental perception. The study also explored the interaction mechanism of different preference expression ways in the “quality-to-vitality” pathway and significant environmental characteristics of them, so as to realize the transformation from landscape design to urban vitality. Key findings indicate that: (1) Three environmental preference expressions—emotion, satisfaction, and behavioral preference—collectively lend credence to a significant chain mediation pathway (“emotion → satisfaction → behavioral preference”) in the quality-to-vitality process; (2) Pedestrian safety infrastructure (e.g., traffic barricades, well-maintained pavements) could ensure perceived security and walking activities; (3) Cultural/recreational facilities mean complementary legibility-enhancing elements (appropriate spatial enclosure, pleasant color schemes, architectural coherence) to evoke positive affect; (4) Streetscape diversity and visual interest might mitigate monotony induced by excessive block length, serving as vital vitality catalysts in some degree. Full article

Understanding the environmental drivers underlying the spatial heterogeneity of soil organic carbon (SOC) in mountainous regions remains a major challenge in digital soil mapping. This study investigated the spatial distribution and driving mechanisms of SOC contents in a typical subtropical mountainous area using an integrated modeling and interpretation framework based on 132 soil samples. The SOC content in Yangshan County ranged from 3.33 to 50.00 g kg⁻¹, with a coefficient of variation of 48.64%, indicating a moderate level of variability across the study area. Six mainstream modeling approaches were compared, including multiple linear regression (MLR), geographically weighted regression (GWR), Cubist, eXtreme Gradient Boosting (XGBoost), random forest (RF), and a hybrid RF-GWR model. The results showed that RF outperformed traditional linear methods and other machine learning approaches, achieving an R² of 0.45 and RMSE of 7.78 g kg⁻¹, while the hybrid model further improved prediction accuracy (R² = 0.48). Then, spatial mapping revealed a clear elevational gradient, with higher SOC values concentrated in forested mountainous areas in the north and lower values distributed across low-elevation cultivated and disturbed zones. SHAP analysis identified intrinsic soil properties, particularly total nitrogen (TN) and cation-exchange capacity (CEC), as dominant controls on SOC contents. When extended to prediction datasets, relative humidity (RH) and mean annual precipitation (MAP) showed greater importance on SOC, suggesting an amplification of climatic factors at the broader scale. Subsequently, hotspot analysis of GeoShapley components further revealed the spatial differentiations in group indicators, with overall contributions ranked as soil physicochemical properties (36.4%) > geographic conditions (21.1%) > climate (17.4%) > organisms (12.9%) > parent material (12.1%). Soil properties formed clustered hotspots overlaid on carbonate-dominated areas, while geographic conditions and climate primarily acted as spatial modulators, generating localized zones of intensified or weakened influence across the landscape. The integrated framework proposed in this study has potential applicability across broader regions. These findings provided a scientific basis for the localized interpretation of environmental drivers of SOC and offered valuable support for region-specific land management and sustainable decision-making. Full article

In the context of the rapid growth of the digital economy and the continued implementation of China’s “dual carbon” strategy, clarifying the interactive relationships among the digital economy, low-carbon logistics, and the ecological environment is crucial for promoting sustainable regional development and green transformation. Based on the theoretical mechanisms underlying the coordinated development of these three systems, this study constructs a comprehensive evaluation index system for the Digital Economy–Low-Carbon Logistics–Ecological Environment (DLE) system. The entropy weighting method, a modified coupling coordination model, kernel density estimation, spatial autocorrelation analysis, and the barrier model are integrated to investigate the spatiotemporal evolution and driving mechanisms of coupling coordination among the three systems. The results indicate that (1) the development levels of the digital economy, low-carbon logistics, and the ecological environment have generally increased, although their evolutionary trajectories differ across stages. The digital economy shows the most rapid improvement, low-carbon logistics maintains steady progress, and the ecological environment exhibits gradual optimization. (2) From a temporal perspective, the overall coupling coordination of the national DLE system has shown a fluctuating upward trend, with the coordination type gradually evolving from a near-coordination stage to an initial coordination stage, though it remains at a low-to-medium coordination level overall. (3) From a spatial perspective, the coupling coordination degree presents a stable gradient pattern, with higher levels in eastern China, intermediate levels in central China, and lower levels in western China. Medium- and high-coordination areas are gradually extending from coastal regions to inland areas, while regional disparities remain evident. (4) The spatial autocorrelation results reveal significant positive spatial clustering at the provincial level. Both high-value and low-value clusters show a certain degree of stability, indicating clear spatial spillover effects. (5) An analysis of constraining factors reveals that insufficient scale of digital economic development and innovation application capabilities, constraints on ecological and environmental resource carrying capacity and governance, as well as low operational efficiency and delayed transformation of low-carbon logistics, are the primary types of obstacles hindering the coordinated improvement of the three systems. These findings provide empirical evidence and policy implications for leveraging the digital economy to facilitate low-carbon logistics transformation and enhance coordinated regional sustainability. Full article

Surface-enhanced Raman scattering (SERS) offers exceptional sensitivity for trace contaminant detection; conventional noble-metal substrates suffer from high cost, signal irreproducibility, and poor chemical stability. While semiconductor alternatives are promising, their performance is fundamentally limited by sluggish interfacial charge-transfer kinetics under static band alignment. To overcome these limitations, we introduced a new strategy centred on a high carrier generation rate (HCGR). By integrating TiOPc, a material that exhibits strong Ti–O bond polarisation and a high HCGR, with atomically thin MoS₂, we constructed a hybrid platform that drives efficient charge transfer via HCGR-enabled kinetic pumping, surpassing traditional thermodynamic band engineering. This HCGR-driven efficient CT mechanism primarily amplifies SERS through enhanced chemical mechanisms (CM) with minor electromagnetic contributions, achieving an enhancement factor (EF) of 10⁷. The platform can detect methylene blue (MB) and rhodamine 6G (R6G) at concentrations as low as 10⁻¹⁴ M and 10⁻¹³ M, respectively, demonstrating excellent repeatability (RSD = 7.2%) and stability over 60 days. Additionally, efficient CT accelerated MB photodegradation under UV light, achieving complete decomposition within 80 min. The practical applicability of the platform is evidenced by detecting Hg²⁺ (LOD: 10⁻¹¹ M) and malachite green in tap/lake water (LODs: 10⁻¹² M/10⁻¹⁰ M). This work establishes HCGR-driven efficient CT as the next generation of semiconductor SERS platforms. It provides a scalable route toward low-cost, reusable sensors for real-time, in situ monitoring of industrial effluents and the dynamic pollutant degradation of pollutants in environmental monitoring. Full article

The Yellow River Delta, a young alluvial plain in China, is experiencing severe land subsidence that threatens its ecological security and sustainable development. However, the driving mechanisms of this subsidence exhibit strong spatial heterogeneity, which traditional global models fail to capture. This study integrates high-precision subsidence measurements from Sentinel-1A imagery and SBAS-InSAR technology (2017–2023) with multi-source environmental factors (topography, geology, land use, precipitation) to propose a Spatially Adaptive Ensemble Learning Model with feature selection (SA-GSE). The model concatenates predictions from base learners (CatBoost, XGBoost, Random Forest) with spatial features (e.g., distance to salt pans, local topographic variance) to form meta-features, which are then input into a multilayer perceptron meta-learner. Through 5-fold spatial cross-validation, SA-GSE learns spatially dynamic base-model weights, implicitly adapting to regional variations in subsidence drivers. The model achieves an R² of 0.7810 and RMSE of 40.55 mm/yr on the test set, outperforming individual base models and ordinary stacking. Residual spatial autocorrelation is substantially reduced, with SA-GSE yielding the lowest Moran’s I (0.0334, p = 0.206) among all evaluated models, confirming effective capture of spatial heterogeneity. Driving force analysis reveals that distance to salt pans is the most important predictor (permutation importance: 0.4456), underscoring the dominant role of brine extraction-induced aquifer compaction. Lagged precipitation importance (0.3191) exceeds that of current precipitation (0.2453), indicating a recharge lag effect. SHAP interaction analysis uncovers a nonlinear “precipitation decoupling” mechanism in salt pan areas, where high precipitation paradoxically exacerbates subsidence. The resultant map of predicted subsidence rates highlights elevated rate zones in the northern salt pans and along the Guangli River. While the map does not represent a full risk assessment—as it does not include exposure or vulnerability—it provides a spatially explicit estimate of hazard likelihood. This ensemble framework yields novel perspectives on subsidence drivers in heterogeneous regions and can support land subsidence prevention and groundwater management planning. Full article