Search Results (2,405)

Urban river networks face significant ecological challenges due to intensive urbanization. Traditional assessment methods focus mainly on individual rivers and overlook cross-scale connections. To fill this research gap, the study refined the Urban Riverscape Conditions-based Assessment for Management Needs (URBAN) framework and developed a dual-scale assessment system covering the entire river network and individual rivers. It evaluates hydrology, geomorphology, ecology, and the waterfront public service dimension. Taking the Qingxi area of Shanghai as a case study, this study integrated multi-source data and adopted field investigations, the analytic hierarchy process (AHP) and principal component analysis (PCA) to collect field data, calculate indicator weights, and extract dominant functional factors. The results show that the overall comprehensive health score of the study area is 59.39, classified as average; the river network scale scores 58.34, and the 21 monitored rivers achieve an average score of 61.80. The assessment identifies clear advantages in hydrological and geomorphological conditions, whereas waterfront public services and river morphological diversity are still deficient. Overall, this system demonstrates good operability and scientific validity, providing practical technical approaches for sustainable urban river network management and supporting refined watershed governance. Full article

The relationship between forest ecosystem services and human health has emerged as a key topic in forest economics and health policy research. This study develops a spatial modelling framework to quantify the health benefits of forest ecosystem services and proposes policy mechanisms to incorporate these benefits into governmental health strategies. Using county-level panel data from 66 administrative units in Zhejiang Province, China, covering the period 2013–2023, we analyse the relationship between forest-mediated air purification services and two population health outcomes: the incidence of respiratory diseases and cardiovascular disease mortality. We employ a Spatial Durbin Model (SDM) to estimate both direct and spatial spillover effects across county boundaries. The findings indicate that forest ecosystem services exert significant negative effects on adverse health outcomes, with spillover effects extending beyond administrative boundaries. The monetised health benefit of forests is estimated at approximately RMB 1108.6 per hectare per year, substantially exceeding current ecological compensation standards and suggesting systematic undervaluation of forest health services. Heterogeneity analysis reveals that health benefits are greater in urbanised regions and among vulnerable population groups, including the elderly. These findings provide an empirical basis for reforming health-oriented ecological compensation mechanisms and offer implications for sustainable land use governance aligned with SDG 3 (Good Health and Well-being) and SDG 15 (Life on Land). Full article

In this study, phenol adsorption from aqueous solutions was investigated using a carbonized adsorbent derived from a 1:1:1 mixture of banana, carrot, and potato peels, representing a major fraction of municipal bio-waste in Serbia. The material (CARB_BCP) was characterized by pH_pzc, SEM, FTIR, and BET analyses. The results indicated a highly porous structure with developed micro- and mesoporosity and a high specific surface area (S_BET = 483 m²/g). FTIR confirmed the formation of a stable aromatic carbon structure, while the high pH_pzc value (10.55) suggested a limited role of electrostatic interactions. Adsorption experiments performed at an initial phenol concentration of 1858 mg/L, room temperature, and an adsorbent dose of 0.1 g achieved a removal efficiency of 20.5%. The Langmuir model provided the best fit, indicating monolayer adsorption, with good agreement between theoretical (≈187 mg/g) and experimental (≈190 mg/g) capacities. Kinetic analysis followed the pseudo-second-order model, suggesting chemisorption as the rate-controlling step. The adsorption mechanism was mainly governed by π–π interactions, hydrophobic effects, and hydrogen bonding. These results demonstrate that CARB_BCP, derived from biodegradable waste, is a promising low-cost adsorbent for wastewater treatment. Full article

This paper examines the social and political consequences of parenting with a conviction for a sexual offence in contemporary Britain. We argue that the systems governing people labelled “sex offenders” operate in ways that exceed what Michel Foucault described as biopolitical governance. While biopolitical frameworks have often been interpreted as oriented toward the optimisation and management of life, including through practices of rehabilitation and reintegration, contemporary punishment bureaucracies frequently foreclose these possibilities in practice. For many parents, redemption is not simply delayed but structurally denied, leaving their citizenship permanently uncertain. Drawing on collaborative, reflexive phenomenology, we develop the concept of cleft identity to describe this condition. Parenting is typically understood as a key site of responsible citizenship, centred on the care and protection of life. Yet parents with sexual offence convictions remain subject to ongoing surveillance, disclosure and stigma, marking them as permanently suspect. They are therefore required to perform the responsibilities of “good” parenting while simultaneously treated as moral outsiders. We argue that this tension produces a form of suspended citizenship in which stigma operates not simply as social reaction but as a mechanism of governance. The paper develops this argument through a theoretically driven, collaborative phenomenological case study intended for analytic illumination rather than empirical generalisation. Full article

Polylactic acid (PLA) scaffolds with triply periodic minimal surface (TPMS) structures have become ideal scaffolds in the field of bone defect repair due to their good designability, connectivity, biocompatibility, and degradability. However, it is currently difficult to obtain the scaffold degradation rate and osteogenic efficacy from in vivo experiments, making it challenging to provide recommendations for scaffold design. In this study, an algorithm to construct a TPMS scaffold–interfacial layer–tissue three-phase composite model was developed using polylactic acid hydrolysis and bone remodeling as the governing equations to simulate scaffold degradation and tissue osteogenesis behavior under an external mechanical stimulus. This method is based on a numerical calculation framework that can more closely simulate the in vivo environment, characterizing the changes in the overall macroscopic mechanical properties of tissue under the influence of scaffold degradation and tissue osteogenesis. The results confirmed the accelerating effect of mechanical stimulation on scaffold degradation and its promoting effect on new bone formation. Under 10% compressive loading, the Schwarz P representative volume element (RVE) lost 33% of its apparent modulus within initial days, while the lidinoid RVE, despite showing a much higher initial modulus, dropped to only 20% of its initial value over the same period. In addition, the mechanical performance of the fused TPMS RVE was not simply linear, even though the surface equations are combined linearly. These results provide a new method for pre-designing scaffold structures based on numerical simulation results using the finite element simulation. Full article

Designing ultra-high-performance concrete (UHPC) mixtures requires balancing multiple, often conflicting, performance criteria, particularly mechanical strength and rheological behavior. However, the limited availability of publicly accessible datasets containing synchronized multi-property measurements, together with cross-source heterogeneity, poses a major challenge for robust data-driven modeling under small-sample conditions. To address this issue, this study proposes an integrated framework combining cross-source data harmonization, Variational Autoencoder (VAE)-based latent-space augmentation, multi-output deep learning, interpretability analysis, and Genetic Algorithm (GA)-driven inverse design. A dataset comprising 139 valid UHPC records was curated from 22 peer-reviewed studies and expanded to 2780 samples through VAE-based augmentation. Using the augmented dataset, a multi-output deep neural network was developed to jointly predict compressive strength, flexural strength, yield stress, and plastic viscosity. On the independent test set, the model achieved R² values of 0.8601, 0.9212, 0.8464, and 0.6603, respectively. Comparative benchmarks and augmentation ablation analyses further showed that VAE-based augmentation consistently improved predictive performance and generalization, especially under small-sample conditions. SHAP and partial dependence analyses identified curing age, steel fiber content, water-to-binder ratio, and superplasticizer dosage as the dominant factors governing UHPC performance. Finally, the trained surrogate model was coupled with a GA for multi-objective inverse optimization, and experimental validation of three candidate mixtures confirmed good agreement between predicted and measured values. This study provides a transparent and engineering-oriented methodology for the integrated prediction, interpretation, and optimization of UHPC mixtures. Full article

This study presents a mechanics based analytical framework for predicting the flexural–shear capacity of reinforced concrete (RC) beams strengthened with Engineered Cementitious Composites (ECCs) and a hybrid ECC–GFRP near surface mounted (NSM) system. Building upon previously reported experimental observations, the present work aims to establish rational prediction models capable of capturing the interaction between flexural and shear mechanisms in strengthened beams. The analytical approach integrates sectional analysis for flexural capacity with a modified truss analogy for shear resistance, explicitly incorporating the strain hardening tensile contribution of ECC and the tensile and confinement effects of GFRP reinforcement. An interaction based failure criterion is subsequently employed to identify the governing failure mode under combined flexural shear actions. The proposed model is validated against experimental results obtained from twenty seven beam specimens with varying flexural and shear reinforcement ratios and strengthening configurations. The predicted ultimate loads show good agreement with experimental values, with an average deviation within ±10%. The analytical framework accurately captures the transition between flexural dominated, combined flexural–shear, and diagonal tension failures observed experimentally. Results demonstrate that ECC significantly enhances ductility and shear crack control, while the hybrid ECC–GFRP system provides substantial strength enhancement with a controlled shift in failure mode. Overall, the developed analytical models offer a reliable and computationally efficient tool for predicting the flexural–shear capacity and failure behavior of ECC and hybrid ECC–GFRP-strengthened RC beams, supporting performance based design and practical strengthening applications. Full article

The increasing demand for sustainable and lightweight structural systems has motivated the development of alternative materials for wind turbine tower applications, where conventional steel structures are associated with high material consumption and environmental impact. In this study, a novel steel-reinforced recycled thermoplastic composite system is proposed as an alternative structural solution. To enable the design and practical application of such composite systems, the mechanical properties of the recycled thermoplastic matrix were experimentally characterized. Compression and tensile tests revealed average yield strengths of approximately 32 MPa in compression and 7.8 MPa in tension. To account for the environmental conditions encountered in field applications, the temperature-dependent mechanical behavior of the material was investigated. Since the critical mechanical response of the thermoplastic matrix in the composite system is governed by compression rather than tension, the study was limited to compression tests under elevated temperatures. The results show that the compressive yield strength decreases to approximately 31 MPa at 55 °C. An analytical model based on the transformed-section approach was also developed to predict the flexural behavior of the composite section and was validated through three-point bending tests, with an analytically predicted yield load of approximately 31.5 kN showing good agreement with experimental results. To assess structural applicability at a larger scale, a full-scale composite wind turbine tower was designed and manufactured, and its dynamic performance was evaluated through field measurements under natural wind loading conditions. The results indicate that the composite tower exhibits comparable dynamic behavior to a conventional steel tower, with a first natural frequency of approximately 3.08 Hz compared to 2.89 Hz for the steel tower, along with enhanced damping characteristics. These findings demonstrate that steel-reinforced recycled thermoplastic composites offer a promising and sustainable alternative for wind turbine tower applications, with potential for broader use in structural systems. Full article

Land trusteeship is an innovative agricultural management model that connects smallholder farmers with modern agriculture. It promotes large-scale agricultural operations, but still faces the impacts of conventional natural disasters. Although agricultural disaster insurance serves as a critical mechanism for farmers to mitigate these natural risks, its risk-mitigation potential remains underutilized due to the persistent challenge of low insurance participation rates. This study develops a decision-making model for farmers’ purchase of agricultural disaster insurance under land trusteeship, drawing on protection motivation theory, market failure theory, and quasi-public goods theory. Using structural equation modeling, we empirically analyze survey data from 319 land-trusteed farmers to uncover the mechanisms and pathways influencing their insurance purchase decisions. The results indicate that: (1) Vulnerability and severity are positively associated with protection motivation through perceived response efficacy and self-efficacy, and protection motivation is directly associated with purchase decisions; (2) Government support has both direct and indirect effects on purchase behavior; and (3) Individual and household characteristics are significantly associated with purchase decisions, with pure farmers, Type I part-time farmers, and farmers with larger landholdings tending to purchase agricultural disaster insurance more often. Full article

Penetration rate effects and partial drainage can govern piezocone (CPTu) response in intermediate permeability geomaterials, but field testing at a fixed standard rate limits systematic evaluation. This study presents the development and laboratory validation of a miniature piezocone system and testing framework to investigate rate-dependent penetration response in laboratory-prepared silty sand. Baseline dry and flooded specimens were tested using a triaxial-based configuration at penetration velocities of 9.6, 0.28, 0.10, and 0.03 mm/s, including selected holding periods for dissipation. A dedicated servo-controlled penetration system was then implemented for slurry-prepared specimens, enabling continuous constant-velocity penetration over a wider velocity range (0.004–15 mm/s). Cone resistance was interpreted using normalized net resistance (Q) and normalized velocity (V_h), and pore pressure using normalized excess pore pressure (Δu₂/σ′_v0). The results show a monotonic rate dependency, with Q increasing as V_h decreases, while Δu₂/σ′_v0 progressively decreases toward zero at intermediate-to-low V_h; at the lowest rates, pore-pressure readings were affected by instrument signal limitations. A hyperbolic-cosine backbone fitted to the normalized response provided good agreement for resistance (R² = 0.99, RMSE = 3.41) and more limited agreement for pore pressure (R² = 0.30, RMSE = 0.23). The drainage transition for the tested material occurs in an interval of approximately V_h ≈ 0.3~30. The study provides a reproducible laboratory approach—combining miniature instrumentation, controlled specimen preparation, and variable-rate penetration—to generate normalized drainage-transition trends for rate-effect investigations in intermediate geomaterials. Full article

This study develops a stochastic degradation-based reliability framework for mechanical systems subject to interacting operational stresses and imperfect maintenance. The degradation dynamics are formulated in cumulative damage space and modeled using a geometric Itô diffusion process, in which the drift term incorporates a multiplicative degradation kernel representing the combined influence of load, speed, misalignment, and environmental exposure. Imperfect maintenance is represented through a continuous attenuation functional embedded within the drift structure, allowing maintenance actions to reduce degradation growth without restoring the system to an as-good-as-new condition. Using a logarithmic transformation, the multiplicative stochastic differential equation is converted into an additive diffusion process, enabling analytical treatment via Itô’s lemma. A closed-form reliability expression is then obtained through first-passage analysis, yielding a lognormal survival function governed directly by the degradation dynamics. Numerical evaluation demonstrates physically consistent wear-out behavior and confirms the stability of the derived reliability formulation. The model further enables reliability-based maintenance optimization through preventive replacement analysis. Sensitivity results indicate that system reliability is strongly influenced by the degradation growth parameter governing the stochastic drift. The proposed framework provides a mathematically tractable connection between stochastic degradation modeling, reliability theory, and maintenance optimization. Beyond its application to conveyor belt systems, the formulation offers a general analytical structure for reliability assessment of degrading engineering systems governed by multiplicative stochastic dynamics. Full article

Background: Artificial intelligence (AI) is transitioning from experimental pilots to core public health functions such as disease surveillance, resource planning, and analysis of social and structural determinants of health. Yet, health data collection and stewardship remain fragmented across the globe; some jurisdictions still rely on paper-based systems, while others operate noninteroperable digital systems that can exacerbate inequities. Treating health data as a global good therefore requires governance that enables innovation while protecting rights, safety, and trust. This study aims to develop a conceptual meso-level capability framework that translates responsible AI principles into organizational practices for public health agencies. Methods: We developed the framework using a targeted narrative synthesis of contemporary governance guidance and documented early implementation experiences, purposively selected to represent major strands of current practice and debate. A structured expert panel consultation (n = 9) was subsequently conducted to assess the face validity and content validity of the proposed framework domains. Results: We propose the Public Health Responsible AI Capability (PH-RAIC) framework, which adapts principles of transparency, accountability, fairness, ethics, and safety to institutional realities faced by public health agencies. PH-RAIC identifies four interdependent capability domains: (1) strategic governance and alignment; (2) data and infrastructure stewardship; (3) participatory design, equity, and public engagement; and (4) lifecycle oversight, learning, and decommissioning. All four domains achieved Content Validity Index (CVI) values ≥ 0.85 in the expert panel consultation. The framework is presented as a conceptual, meso-level model that has undergone preliminary expert validation but requires further empirical testing in real-world agency settings. Conclusions: PH-RAIC links these domains to example practices, diagnostic questions, and illustrative measurement indicators to help agencies navigate efficiency–equity trade-offs and strengthen legitimacy and accountability in AI-enabled public health systems. It offers a validated conceptual basis for future empirical testing and operational readiness tools. Full article

Wire laser cladding (WLC) of bronze on stainless steel offers a promising approach for combining the structural strength of steel with the superior tribological and corrosion properties of copper alloys. In this study, the influence of key process parameters, including wire preheating current, deposition speed, laser power, and wire feed speed on melt pool temperature and clad geometry was investigated using response surface methodology (RSM). Experiments were performed using a robot-assisted coaxial wire feeding laser cladding system, and real-time thermal monitoring was conducted using an infrared camera. The results showed that defect-free bronze clads with good metallurgical bonding and limited dilution were achieved across the investigated parameter range. Statistical analysis revealed that melt pool temperature is primarily governed by laser power and deposition speed, with a significant interaction between these parameters. Clad height was mainly influenced by wire feed speed and deposition speed, whereas clad width was controlled by laser power and deposition speed. The side angle was affected by deposition speed, laser power, and wire feed speed, reflecting the balance between vertical buildup and lateral spreading. Overall, the study demonstrates that stable and high-quality clads can be achieved by properly balancing energy input and material supply. The developed models provide valuable insight for optimizing process parameters in wire laser cladding of bronze on stainless steel. Full article

Against the backdrop of deep integration of technological innovation and industrial innovation, this study constructs a theoretical model incorporating market and government behavior. Utilizing panel data from 284 prefecture-level cities across China from 2013 to 2023, it empirically analyzes the roles and mechanisms of efficient market and proactive government in facilitating the deep integration. Findings indicate that the market’s “push–pull mechanism” promotes the deep integration of technological and industrial innovation, enhancing output performance. However, market mechanism exhibits diminishing marginal output performance: beyond a certain threshold, the force to drive further performance improvements weakens. The government’s role can positively influence the market mechanism’s ability to drive deep integration of technological innovation and industrial innovation, and growth in output performance. In coordinating government and market efforts, the following principles should be observed: as market mechanism matures, gradually enhance technological innovation by allocating human capital to research activities, particularly basic research; formulate industrial policies that progressively prioritize science and technology innovation activities; and advance the development of public goods, such as technology transfer and commercialization platforms. To advance the deep integration of technological and industrial innovation, policy implications include consistently leveraging market mechanisms, coordinating government and market efforts to design policies for capital and talent mobility, and promoting the construction of conversion platforms like concept-validation centers and science incubators. Full article

Sports non-fungible tokens (NFTs) have rapidly emerged as tradable digital goods within platform-mediated marketplaces, reshaping how sports organizations, athletes, and brands design fan experiences and monetize digital assets. To consolidate fragmented scholarship and clarify the concept space, this study conducts a systematic quantitative literature review combined with thematic analysis, following PRISMA 2020 and a SPIDER-guided review logic. Searches across six major databases (Web of Science, Scopus, ScienceDirect, PubMed, IEEE Xplore, ProQuest) plus Google Scholar (2017–March 2025) yielded 40 peer-reviewed studies that met predefined inclusion criteria and passed quality appraisal. Results show a sharp growth of sports-NFT research from 2021 to 2024, with strong inter-disciplinary convergence spanning sports marketing, information systems, computer science, and law. Integrating findings through a consumer-value lens, we inductively propose a five-type taxonomy—collectible, empowerment, identity/authentication, physical-asset linked, and virtual-interaction NFTs—each associated with distinct value mechanisms and e-commerce functionalities. The thematic synthesis further identifies four dominant research streams (industry digitalization, consumer psychology/behavior, legal–regulatory issues, and digital marketing), while revealing gaps in theory operationalization, method diversity (e.g., limited experiments/longitudinal designs), cross-context generalizability, and governance/sustainability. The study contributes to marketing and management scholarship by positioning sports NFTs as emerging technologies that reorganize customer engagement, brand-community building, and governance in platform-mediated sport markets, and it offers a research agenda for measuring consumer, brand, and organizational effects. Full article