Search Results (2,438)

The equitable allocation of carbon emission responsibility is fundamental to advancing China’s industrial decarbonization, achieving its dual-carbon goals, and realizing regional sustainable development. However, prevailing interprovincial carbon accounting frameworks often neglect the coupled dynamics of economic benefits, energy flows, and ecological capacity, leading to systematic misattribution of industrial carbon footprint transfers. Here, we develop an integrated analytical framework combining multi-regional input–output (MRIO) modeling and net primary productivity (NPP) assessment to comprehensively quantify industrial carbon footprints and their transfers across 30 Chinese provinces. By embedding both the benefit principle (aligning responsibility with trade-generated economic gains) and the energy flow principle (accounting for interprovincial energy trade), we construct a dual-adjustment mechanism that rectifies spatial and sectoral imbalances in traditional accounting. Our results reveal pronounced east-to-west industrial carbon footprint transfers, with resource-rich provinces (e.g., Inner Mongolia, Xinjiang) disproportionately burdened by external consumption, impacting the balance of sustainable development in these regions. Implementing benefit and energy flow adjustments redistributes responsibility more fairly: high-benefit, energy-importing provinces (e.g., Shanghai, Jiangsu, Beijing) assume greater carbon obligations, while energy-exporting, resource-dependent regions see reduced responsibilities. This approach narrows the gap between production- and consumption-based accounting, offering a scientifically robust, policy-relevant pathway to balance regional development and environmental accountability. The proposed framework provides actionable insights for designing carbon compensation mechanisms and formulating equitable decarbonization policies in China and other economies facing similar regional disparities. Full article

Rural communities face persistent transport disadvantages due to low population density, limited-service availability, and high operational costs, restricting access to essential services and exacerbating social inequality. Autonomous public transport systems offer a transformative solution by enabling flexible, cost-effective, and inclusive mobility options. This study investigates the operational determinants for autonomous road-based transit systems in rural and peri-urban South-East Queensland (SEQ), employing a structured survey of 273 residents and analytical approaches, including General Additive Model (GAM) and Extreme Gradient Boosting (XGBoost). The findings indicate that small shuttles suit flexible, non-routine trips, with leisure travelers showing the highest importance (Gain = 0.473) and university precincts demonstrating substantial influence (Gain = 0.253), both confirmed as significant predictors by GAM (EDF = 0.964 and EDF = 0.909, respectively). Minibus shuttles enhance first-mile and last-mile connectivity, driven primarily by leisure travelers (Gain = 0.275) and tourists (Gain = 0.199), with shopping trips identified as a significant non-linear predictor by GAM (EDF = 1.819). Standard-sized buses are optimal for high-capacity transport, particularly for school children (Gain = 0.427) and school trips (Gain = 0.148), with GAM confirming their significance (EDF = 1.963 and EDF = 0.834, respectively), demonstrating strong predictive accuracy. Hybrid models integrating autonomous and conventional buses are preferred over complete replacement, with autonomous taxis raising equity concerns for low-income individuals (Gain = 0.047, indicating limited positive influence). Integration with Mobility-as-a-Service platforms demonstrates strong, particularly for special events (Gain = 0.290) and leisure travelers (Gain = 0.252). These insights guide policymakers in designing autonomous road-based transit systems to improve rural connectivity and quality of life. Full article

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation. Full article

This study investigates the flexural behavior of reinforced concrete (RC) beams strengthened with externally bonded Carbon Fiber Reinforced Polymer (CFRP) or steel plate (SP), with partial debonding between internal steel reinforcement and surrounding concrete. A finite element model was developed using ABAQUS (v2021) and validated against existing experimental data by others. A total of 296 beam models were analyzed to assess the effects of shear span-to-depth ratio ($a_v/d$), reinforcement ratio ($\rho$), debonding degree ($\lambda$), strengthening material type (CFRP/SP), and material thickness ($t$) on residual flexural strength. Based on the finite element analysis (FEA) results, analytical models were proposed using a dimensionless parameter $\mathsf{\Psi }$, defined as the ratio of equivalent plastic region length to neutral axis depth. Analytical models were developed in IBM SPSS Statistics (Version 30) and showed strong agreement with FEA results. The findings provide insight into the influence of reinforcement debonding on structural behavior and support improved prediction of residual flexural capacity in strengthened RC beams with partially unbonded reinforcement. Full article

Underreamed anchor bolts, as an emerging anchoring element in geotechnical engineering, operate via a fundamentally distinct load transfer mechanism compared with conventional friction type anchors. The accurate and reliable prediction of their ultimate bearing capacity constitutes a pivotal technological impediment to their broader engineering adoption. Firstly, this paper systematically elucidates the constituent mechanisms of underreamed anchor resistance and their progressive load transfer trajectory. Subsequently, in situ full-scale pull-out experiments are leveraged to decompose the load–displacement response throughout its entire evolution. The multi-stage development law and the underlying mechanisms governing the evolution of anchorage characteristics are thereby elucidated. Based on the experimental dataset, a three-dimensional elasto-plastic numerical model is rigorously established. The model delineates, at high resolution, the failure mechanism of surrounding soil mass and the spatiotemporal evolution of its three-dimensional displacement field. A definitive critical displacement criterion for the attainment of the ultimate bearing capacity of underreamed anchors is established. Consequently, analytical models for the ultimate side frictional stress and end-bearing capacity at the limit state are advanced, effectively circumventing the parametric uncertainties inherent in extant empirical formulations. Ultimately, characteristic parameters of the elasto-plastic branch of the load–displacement curve are extracted. An ultimate bearing capacity prognostic framework, founded on an optimized hyperbolic model, is established. Its superior calibration fidelity to the evolving load–displacement response and its demonstrable engineering applicability are rigorously substantiated. Full article

Transition to electric vehicles has accelerated in diverse consumer sectors all over the world. Electric School Buses (ESBs) are a particular area of interest due to their environmental and financial potential benefits, including Vehicle-to-Grid (V2G) synergies. Storing electricity in times of lower demand to supply the grid at optimal times can provide significant sustainability benefits, among them a reduction in new generation capacity and financial revenue for battery owners. ESBs, with their high-capacity batteries, have significant potential to supply the grid in V2G systems. There are more than half a million school buses in the US, with a wide geographical distribution, which have significant idle times during school days and holidays. This presents very attractive investment possibilities, providing school districts with additional revenue and supplying local communities with sustainable electricity at high-demand times. This study develops a framework to financially evaluate sustainability of ESB V2G schemes in the US. It applies data analytics, GIS, and Business Intelligence to integrate and assess publicly available data to provide stakeholders with decision-making tools in selecting optimal locations and operation times for these projects. Results indicate that revenue for these projects is significant in most schools, with some locations generating very high revenue potential. Geospatial analysis for most locations and time frames indicates very promising results, with schools potentially receiving significant income from these systems. The framework provides, therefore, relevant information for stakeholders to make sustainable decisions on the development of these projects. Full article

Poly(3-thienylboronic acid) (PThBA) has recently been suggested as a conducting polymer with affinity for saccharides. In this study, thin films of this compound were deposited onto gold electrodes. The system obtained was studied as a possible chemical sensor. The measurements were performed by impedance spectroscopy using potassium ferro/ferricyanide as a redox mediator. The thickness of the polymer and the deposition of the adhesive sublayer were optimized to achieve a compromise between the blocking of defects in the polymer layer and the unnecessary increase in the internal resistance of this conductometric sensor. A comparative study of the influence of fructose, glucose, and sorbitol on transversal polymer resistance was conducted. The binding constants for these saccharides were extracted from the concentration dependencies of sensor conductance. Among them, sorbitol showed the highest affinity with a binding constant up to ~15,000 L·mol⁻¹, followed by fructose (~8700 L·mol⁻¹) and glucose (~4500 L·mol⁻¹). In order to exclude the contribution of the analyte tautomers on the obtained binding constants, measurements of ethylene glycol were also performed. The effects of pH and the redox state of PThBA on its affinity properties were studied, revealing higher affinities at alkaline pH and in oxidized state of the chemosensitive polymer. The developed system has the capacity to be applied in chemical sensors and virtual sensor arrays with electrical affinity control. Full article

This study focuses on a novel lightweight technology for manufacturing variable-axial fiber-reinforced polymer components. In the presented approach, channels following the load flow are implemented in an additively manufactured basic structure and impregnated continuous fiber bundles are pulled through these component-integrated cavities. Improved channel cross-section geometries to enhance the mechanical performance are proposed and evaluated. The hypothesis posits that increasing the surface area of the internal channels significantly reduces shear stresses between the polymer basic structure and the integrated continuous fiber composite. A series of experiments, including analytical, numerical, and microscopic analyses, were conducted to evaluate the mechanical properties of the composites formed, focusing on Young’s modulus and tensile strength. In addition, an important insight into the failure mechanism of the novel fiber composite is provided. The results demonstrate a clear correlation between the channel geometry and mechanical performance, indicating that optimized designs can effectively reduce shear stress, thus improving load-bearing capacities. The findings reveal that while fiber volume content influences the impregnation quality, an optimal balance must be achieved to enhance mechanical properties. This research contributes to the advancement of production technologies for lightweight components through additive manufacturing and the development of new types of composite materials applicable in various engineering fields. Full article

Effective protein immobilization is a critical step in biosensor development, as it ensures the stability, functionality, and orientation of biomolecules on the sensor surface. Here, we present a novel affinity-based terpolymer coating designed to enhance protein immobilization for biosensor applications. The novelty lies in the incorporation of nitrilotriacetic acid (NTA) ligands directly into the polymeric chains, facilitating histidine-tagged protein oriented binding through a robust metal-chelating interaction. To validate the system, magnetic microbeads coated with the polymer were tested for their ability to bind native and His-tagged proteins. The results demonstrated the superior binding capacity, enhanced stability, and reversibility of the interactions compared to traditional coatings, which immobilize proteins through nucleophile reactions with amine residues. Moreover, enzyme immobilization tests confirmed that the polymer preserves enzymatic activity, highlighting its potential for biosensor applications requiring functional biomolecules. This innovative polymeric coating offers a fast, versatile, and scalable solution for next-generation biosensor platforms, paving the way for improved sensitivity, reliability, and accessibility in diagnostic and analytical technologies. Full article

China faces the persistent challenge of uneven spatiotemporal water resource distribution, constraining economic and social development while exacerbating regional disparities. Achieving co-evolution between water source systems and water use systems is thus a critical proposition in water resources management. Based on synergetics theory, this study takes Henan Province, a typical water-scarce social–ecological system, as the research object, and constructs a quantitative analysis framework for supply–demand bidirectional synergy. It systematically reveals the evolution patterns of water resource systems under the mutual feedback mechanism between water sources and water use. Findings indicate that between 2012 and 2022, the synergy degree of Henan’s water resource system increased by nearly 40%, exhibiting significant spatiotemporal differentiation: spatially “lower north, higher south”, and dynamically shifting from demand-constrained to supply-optimized. Specifically, the water source system’s order degree showed a “higher northwest, lower southeast” spatial pattern. Since the operation of the South-to-North Water Diversion Middle Route Project, the provincial average order degree increased significantly (annual growth rate of 0.01 units), though with distinct regional disparities. The water use system’s order degree also exhibited “lower north, higher south” pattern but achieved greater growth (annual growth rate of 0.03 units), with narrowing north–south gaps driven by improved management efficiency and technological capacity. This study innovatively integrates water source systems and water use systems into a unified analytical framework, systematically elucidating the intrinsic evolution mechanisms of water resource systems from the perspective of supply–demand mutual feedback. It provides theoretical and methodological support for advancing systematic water resource governance. Full article

Urban climate readiness requires multi-dimensional implementation strategies that operate effectively across both spatial scales and time horizons. This article introduces a multi-scale, phased model designed to support integrated climate action by distinguishing between metropolitan and building levels and addressing three core domains: physical resilience, decarbonization, and social/community engagement. The framework conceptualizes metropolitan and building scales as analytically distinct but operationally linked, allowing strategies to reflect the different systems, stakeholders, and capacities at each level. It also outlines a three-phase progression—Initial (assessment and goal setting), Readiness (planning and implementation), and Steady-State (monitoring and iterative adjustment)—to support staged, adaptive deployment. Each phase includes sample metrics and SMART goals that can be tailored to local context and tracked over time. By integrating theoretical insights with practical implementation tools, the framework offers a flexible yet rigorous approach for advancing urban sustainability. It emphasizes the importance of aligning technical interventions with institutional capacity and community participation to enhance effectiveness and equity. This model contributes to both planning theory and applied sustainability efforts by providing a structured pathway for cities to enhance climate readiness across systems and scales. Full article

This study investigates the mechanical behaviour of timber–concrete composite (TCC) floor members with an innovative adhesive connection reinforced by granite chips, glass fibre yarn net in the epoxy adhesive layer, and polypropylene (PP) fibres in the concrete layer. Laboratory tests involved three groups of specimens subjected to three-point bending over a span of 500 mm with specimen lengths of 550 mm. Group A specimens exhibited crack initiation at approximately 8 kN and partial disintegration at an average load of 11.17 kN, with maximum vertical displacements ranging from 1.7 to 2.5 mm at 8 kN load, increasing rapidly to 4.3 to 5 mm post-cracking. The addition of reinforcing fibres decreased the brittleness of the adhesive connection and improved load-bearing capacity. Finite element modeling using the newly developed Verisim4D software (2025 v 0.6) and analytical micromechanics approaches demonstrated satisfactory accuracy in predicting the composite behavior. This research highlights the potential of reinforcing the adhesive layer and concrete with fibres to enhance the ductility and durability of TCC members under flexural loading. Full article

This study focuses on the evolution of the Internet Water Army on social media, identifying a novel form known as artificial-intelligence-generated-content-enhanced social bots (AESBs), and compares their structural influence with traditional social bots in the context of public opinion guidance. Based on 3 years of real-world data from Weibo, this study develops a comprehensive framework integrating bot account detection, AESB content identification, and quantitative assessments of opinion guidance. A large-scale opinion propagation network is constructed to examine the structural roles of traditional social bots and AESB across three analytical levels: the node, community, and overall network. The results reveal substantial differences between AESB and traditional social bots. Social bots play a limited guiding role but help maintain network connectivity. In contrast, AESBs produce highly consistent and human-like content that demonstrates a significant capacity to reinforce topic focus, amplify emotional homogeneity, and deepen diffusion pathways, indicating a shift toward strategic content manipulation. These results suggest that AESBs are not merely passive generators but active agents of structural opinion control, capable of combining human mimicry with machine-level efficiency. This study advances theoretical understanding of IWA manipulation mechanisms, provides a replicable methodological approach, and offers practical implications for platform governance. Full article

Desertification threatens the sustainability of dryland ecosystems, yet many existing monitoring frameworks rely on static maps, coarse spatial resolution, or lack temporal forecasting capacity. To address these limitations, this study introduces PyGEE-ST-MEDALUS, a novel spatiotemporal framework combining the full MEDALUS desertification model with deep learning (CNN, LSTM, DeepMLP) and machine learning (RF, XGBoost, SVM) techniques on the Google Earth Engine (GEE) platform. Applied across Tebessa Province, Algeria (2001–2028), the framework integrates MODIS and Sentinel-1/-2 data to compute four core indices—climatic, soil, vegetation, and land management quality—and create the Desertification Sensitivity Index (DSI). Unlike prior studies that focus on static or spatial-only MEDALUS implementations, PyGEE-ST-MEDALUS introduces scalable, time-series forecasting, yielding superior predictive performance (R² ≈ 0.96; RMSE < 0.03). Over 71% of the region was classified as having high to very high sensitivity, driven by declining vegetation and thermal stress. Comparative analysis confirms that this study advances the state-of-the-art by integrating interpretable AI, near-real-time satellite analytics, and full MEDALUS indicators into one cloud-based pipeline. These contributions make PyGEE-ST-MEDALUS a transferable, efficient decision-support tool for identifying degradation hotspots, supporting early warning systems, and enabling evidence-based land management in dryland regions. Full article

In metallurgy, the quenching process often induces changes in certain material properties, such as hardness and ductility, through the rapid cooling of a workpiece in water, gas, oil, polymer, air, or other fluids. Given that lithium-ion batteries operate under relatively benign conditions, conventional rapid cooling does not significantly affect the property variations in their internal electrode materials during normal use. However, thermal runaway presents an exception due to its dramatic temperature fluctuations from room temperature to several hundred degrees Celsius. In this study, we investigated NCM811 cathodes in 18,650 batteries subjected to transient thermal runaway followed by rapid cooling using several advanced analytical techniques. The results reveal a phenomenon characterized by intergranular cracking within NCM811 cathode materials when exposed to rapid cooling during transient thermal runaway. Furthermore, lithium-ion cells utilizing reused NCM-182.4 electrodes in fresh electrolyte demonstrate a reversible capacity of 231.4 mAh/g after 30 cycles at 0.1 C, highlighting the potential for reusing NCM811 cathodes in the lithium-ion battery recycling process. These findings not only illustrate that NCM811 particles may experience intergranular cracking when subjected to rapid cooling during transient thermal runaway, but also the rapidly cooled NCM811 electrodes exhibit potential for reuse. Full article