Search Results (3,417)

Bridges are a crucial part of infrastructure, but many are in urgent need of maintenance. Digital methods like bim and Digital Twinning can support this process but depend on digital models that are often missing for existing structures. Automating the reconstruction of these models from existing documentation, such as construction drawings, is essential to accelerate digital adoption. Addressing a key step in the reconstruction process, this paper presents an end-to-end pipeline for extracting bridge cross-sections from drawings. First, the YOLOv8 network locates and classifies the cross-sections within the drawing. The results are then processed by the segmentation model sam, which generates pixel-wise masks without requiring task-specific training data. This eliminates the need for manual mask annotation and enables straightforward adaptation to different cross-section types, making the approach broadly applicable in practice. Finally, a global optimization algorithm fits parametric templates to the masks, minimizing a custom loss function to extract geometric parameters. The pipeline is evaluated on 33 real-world drawings and achieves a median parameter deviation of −2.2 cm and 2.4 cm, with an average standard deviation of 35.4 cm. Full article

In this study, we develop a game-theoretic optimization framework to analyze competing vessels’ technology choices between shore power (SP) and low-sulfur fuel oil (LSFO) within a maritime supply chain which is regulated by a cap-and-trade mechanism. Using a Stackelberg game approach, we construct two models—one port-led and the other vessel-led—to derive closed-form equilibrium for pricing, service quantities, profits, emissions, and social welfare. The results reveal three key findings. First, the leader in either Stackelberg structure always achieves higher profits, while total supply chain profits remain identical across power structures. Second, at low carbon prices, LSFO-equipped vessels provide more services and earn higher profits due to cost advantages. As the carbon price rises—which directly incentivizes emission reduction and accelerates maritime decarbonization—SP becomes more attractive and eventually dominates in profitability despite higher initial investment. Notably, although SP has lower unit emissions, its total emissions may surpass those of LSFO at certain carbon-price thresholds because the SP-equipped vessel optimally expands output. Third, intensified competition reduces service quantities, profits, and emissions, with a more substantial reduction effect on LSFO vessels. Overall, our results provide mathematically grounded insights for optimizing low-carbon technology adoption in maritime transport and offer actionable policy implications for carbon pricing that balance environmental objectives and supply chain efficiency. This research contributes specifically to the United Nations’ Sustainable Development Goals (SDGs), specifically SDG 13 (Climate Action) and SDG 9 (Industry, Innovation and Infrastructure). Full article

Forest ecosystems provide essential ecological functions in the context of accelerating climate change. However, evaluating their conservation values and conditions remains challenging due to conceptual and methodological ambiguities. In particular, ecosystem integrity and ecosystem authenticity are often conflated in vegetation-based assessments, despite representing distinct dimensions of ecosystem condition. This study advances vegetation-based assessments by explicitly decoupling ecosystem integrity from ecosystem authenticity, while integrating spatial completeness, vegetation patterns and quality, and successional–disturbance attributes into a unified operational framework for reserve-level diagnosis and comparison. The resulting indices enable managers to distinguish boundary-driven limitations of landscape integrity from internal vegetation conditions that persist in near-natural states, thus enhancing interpretability for conservation planning in the context of climate change. Using standardized forest resource survey data and spatial analysis, we constructed two composite indices: Forest Ecosystem Integrity (FEI) and Forest Ecosystem Authenticity (FEA). These indices were applied to two adjacent cold-temperate forest nature reserves, Hanma and Huzhong, in the Greater Khingan Mountains of northeastern China, as well as to a merged spatial scenario. The results demonstrate consistently high ecosystem authenticity (>90%) across all study areas, indicating strong naturalness and successional maturity. In contrast, ecosystem integrity remains moderate (63–69%), primarily constrained by the low spatial completeness of conservation units. The spatial integration of the two reserves significantly improved ecosystem integrity without compromising authenticity, highlighting the role of boundary configuration in conservation effectiveness. By operationalizing integrity and authenticity as complementary yet distinct dimensions, this study provides a reproducible framework for evaluating forest ecosystem conditions and offers practical insights for the design of protected area networks and adaptive management in cold-temperate forest regions. Full article

Accurate detection and segmentation of young ‘Yuluxiang’ pear fruits at the fruit thinning stage are crucial for the development of intelligent fruit thinning robots. To address the challenges in recognition and segmentation of young ‘Yuluxiang’ pears in natural environments characterized by occlusion, overlap, and small targets, this paper proposes an improved instance segmentation model based on YOLOv8n-seg, named YOLOv8n-DSW. Firstly, the C2f modules were optimized by introducing DualConv to construct C2f-Dual modules, which enhanced feature extraction capability while reducing the number of parameters. Secondly, a Spatial-Channel Synergistic Attention (SCSA) mechanism was embedded ahead of the small-object detection head to improve detection accuracy for small targets. Finally, the original CIoU loss function was replaced with the WIoU v3 loss function to accelerate model convergence and improve accuracy. Deployment on a Firefly ROC-RK3588S-PC development board confirmed the model’s suitability for edge devices. Experimental results demonstrated that YOLOv8n-DSW achieved excellent performance. The mAP₅₀, mAP₇₅, and mAP_50:95 for detection reached 95.6%, 83.2%, and 70.3%, respectively, and those for segmentation were 94.8%, 78.2%, and 65.3%. The proposed model outperformed its baseline, YOLOv8n-seg, as well as other classic models such as YOLOv5n-seg, YOLOv11n-seg, and YOLOv12n-seg. These results demonstrate that YOLOv8n-DSW provides accurate and efficient segmentation of young ‘Yuluxiang’ pear fruits. Full article

We develop a novel Steffensen-type iterative solver to solve nonlinear scalar equations without requiring derivatives. A two-parameter one-step scheme without memory is first introduced and analyzed. Its optimal quadratic convergence is then established. To enhance the convergence rate without additional functional evaluations, we extend the scheme by incorporating memory through adaptively updated accelerator parameters. These parameters are approximated by Newton interpolation polynomials constructed from previously computed values, yielding a derivative-free method with R-rate of convergence of approximately 3.56155. A dynamical system analysis based on attraction basins demonstrates enlarged convergence regions compared to Steffensen-type methods without memory. Numerical experiments further confirm the accuracy of the proposed scheme for solving nonlinear equations. Full article

With accelerating urbanisation, extreme rainfall events have become increasingly frequent, leading to rising urban flooding risks that threaten city operation and infrastructure safety. The rapid expansion of impervious surfaces reduces infiltration capacity and accelerates runoff responses, making cities more vulnerable to short-duration, high-intensity storms. Although the SWMM is widely used for urban stormwater simulation, its application is often constrained by the lack of detailed drainage network data, such as pipe diameters, slopes, and node connectivity. To address this limitation, this study focuses on the main built-up area within the Second Ring Expressway of Chengdu, Sichuan Province, in southwestern China. As a regional core city, Chengdu frequently experiences intense short-duration rainfall during the rainy season, and the coexistence of rapid urbanisation with ageing drainage infrastructure further elevates flood risk. Accordingly, a technical framework of “open road data substitution–automated modelling–SWMM-based assessment” is proposed. Leveraging the spatial correspondence between road layouts and drainage pathways, open road data are used to construct a virtual drainage system. Combined with DEM and land-use data, Python-based automation enables sub-catchment delineation, parameter extraction, and network topology generation, achieving efficient large-scale modelling. Design storms of multiple return periods are generated based on Chengdu’s revised rainfall intensity formula, while socioeconomic indicators such as population density and infrastructure exposure are normalised and weighted using the entropy method to develop a comprehensive flood-risk assessment. Results indicate that the virtual drainage network effectively compensates for missing pipe data at the macro scale, and high-risk zones are mainly concentrated in densely populated and highly urbanised older districts. Overall, the proposed method successfully captures urban flood-risk patterns under data-scarce conditions and provides a practical approach for large-city flood-risk management. Full article

High-entropy alloys (HEAs) present a fundamental design paradox: their exceptional properties arise from complex, high-dimensional composition–process–microstructure–property (CPMP) relationships, yet the knowledge needed to navigate this space is fragmented across a vast and unstructured literature. Large language models (LLMs) offer a transformative interface to this complexity. By extracting structured facts from text, they can convert dispersed and heterogeneous evidence (i.e., findings scattered across many studies and reported with inconsistent test protocols or characterization standards) into queryable knowledge graphs. Through code generation and tool composition, they can automate simulation pipelines, surrogate model construction, and inverse design workflows. This review analyzes how LLMs can augment key stages of HEA research—from intelligent literature mining and multimodal data integration (using LLMs to automatically extract and structure data from texts and to combine information across text, images, and other data sources) to model-driven design and closed-loop experimentation—illustrated by emerging case studies. We propose concrete evaluation protocols that measure direct scientific utility, including knowledge-graph completeness, workflow setup efficiency, and experimental validation hit rates. We also confront practical limitations: data sparsity and noise, model hallucination, domain bias (where models may exhibit superior predictive performance for specific, well-represented alloy systems over others due to imbalances in training data), and the imperative for reproducible infrastructure. We argue that domain-specialized LLMs, embedded within grounded, verifiable research systems, can not only accelerate HEA discovery but also standardize the representation, sharing, and reuse of community knowledge. Full article

With the continuous acceleration of the green transformation of sports stadiums, green sports stadiums characterized by low-carbon and sustainable attributes provide consumers with green consumption options in the context of sports consumption. By constructing a structural equation model, this study examines the effects of environmental awe and environmental guilt on green sports stadium consumption intention, as well as the parallel mediating role of compensatory consumption psychology. The results show that, first, environmental awe and environmental guilt have significant positive effects on green sports stadium consumption intention; second, environmental awe and environmental guilt exert positive effects on compensatory consumption psychology, including symbolic, enhancement, emotional restorative, and resilience dimensions; third, the parallel mediation analysis reveals that significant parallel mediating effects are observed only among avoidance-oriented mediators, whereas such effects are not confirmed among approach-oriented mediators. This study aims to provide theoretical references for further exploring the compensatory consumption mechanisms of green sports stadiums, promoting consumers’ intentions toward green sports stadium consumption, and supporting the operation of green sports stadiums. Full article

The windings of aerospace motors are fabricated using enameled wires; with polyimide (PI) serving as the primary material for their insulating enamel coatings, thermal aging is the predominant factor contributing to insulation failure in enameled wires. The prolonged natural aging process of enameled wires, coupled with the complexity and sluggish variation rates of dielectric parameters used for aging monitoring, presents significant challenges in developing a universal method for assessing insulation performance. To address this challenge, our study determined accelerated aging conditions based on the Arrhenius law, fabricated twisted-pair specimens, and implemented a step-stress aging protocol, in order to monitor the insulation capacitance (IC) and dielectric dissipation factor (tan δ) of the sample. Finally, a two-parameter Weibull distribution plot was established to characterize the relationship between service life and failure probability. Initial-value normalization combined with B-spline interpolation was employed to construct IC–life correlation curves. A novel method for monitoring PI-enameled wire insulation life using IC variation rate was proposed and experimentally validated, providing a methodological framework for lifespan prediction of aerospace motor windings. Finally, a two-parameter Weibull distribution plot was established to characterize the relationship between service life and failure probability. Initial-value normalization combined with B-spline interpolation was employed to construct IC–life correlation curves. The rationality of the method using IC change rate to monitor the insulation lifetime of polyimide-enameled wire was verified, the lifetime assessment of aviation motor stator windings was achieved by monitoring corresponding dielectric parameters, and a reference standard for the maintenance and support of aviation equipment was provided. Full article

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article

This study presents an integrated framework that couples three-dimensional geotechnical ground modeling with a HAZUS-based urban seismic vulnerability assessment for Seoul, Korea. Over 63,000 boreholes, in situ seismic tests, and building inventory records were compiled into a unified relational database following rigorous multi-stage quality control. A multi-parameter N–Vs regression model was calibrated to supplement missing shear-wave velocity (Vs) data, reducing prediction errors by more than 20% relative to conventional empirical equations. Based on the quality-controlled Vs dataset, a high-resolution three-dimensional Vs–ground model was constructed to represent subsurface heterogeneity and associated uncertainty across the metropolitan area. The building inventory, comprising approximately 700,000 structures, was standardized according to the HAZUS structural taxonomy and mapped to Korean seismic design eras, enabling a Seoul-adapted vulnerability assessment in which exposure characterization and seismic demand are localized. Site-specific ground-motion amplification and response spectra derived from the 3D ground model were used to modify the spectral acceleration input to the HAZUS fragility functions. Results reveal pronounced spatial variability in site conditions, with northern mountainous zones corresponding primarily to NEHRP Site Class B, central districts to Class C, and southern alluvial basins to Classes D–E, producing amplification differences of up to 1.7 under identical input spectral accelerations. High-risk zones such as Gangnam, Songpa, and Yeouido exhibit concentrated expected damage where thick alluvial deposits coincide with dense stocks of mid-rise reinforced-concrete buildings. Overall, the study demonstrates that integrating high-resolution 3D geotechnical ground models with HAZUS-based fragility analysis provides a physically consistent and data-driven basis for urban-scale seismic risk assessment and resilience planning. Full article

To meet the challenge of microbial contamination of food, smart packaging materials with active controlled-release functions have become a research hotspot. In this study, a humidity-responsive antimicrobial composite film was constructed by introducing cinnamaldehyde@β-cyclodextrin inclusion complexes (CIN@β-CD ICs) into a konjac glucomannan/polyvinyl alcohol/lithium chloride (KGM/PVA/LiCl) matrix. Characterization results showed that the CIN@β-CD ICs formed a dense structure through hydrogen bonding, which enhanced the thermal stability, mechanical strength (tensile strength: 20.83 MPa) and surface hydrophilicity (water contact angle < 60°) of the film. The film acted as a humidity-triggered release system for CIN, enabling controlled antimicrobial delivery: at high humidity (98% RH), the film rapidly swelled and accelerated the release of CIN, with a cumulative release rate of 87.29% over 7 days, whereas the release slowed significantly at low humidity (43% RH). The antimicrobial activity of the released CIN was strongly influenced by ambient humidity, with the effect enhanced under high humidity conditions. It is noteworthy that the film containing 0.2% ICs exhibited the optimal antimicrobial performance among the formulations studied. This study elucidates a mechanism for humidity-triggered release through multicomponent synergism, which provides a feasible strategy for the design of environmentally friendly, smart packaging materials with high antimicrobial activity. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Against the backdrop of accelerating global digitalization and mounting climate pressures, enabling digital-economy growth while simultaneously controlling carbon emissions has become a critical challenge for China. This study constructs a Digital Economy Development Index (DEI) and a Carbon Emissions Index (CEI) to examine the spatiotemporal evolution and spatial heterogeneity of coordinated development between the digital economy and carbon emissions. We employ global and local Moran’s I, a spatial Markov chain model, and kernel density estimation to investigate spatiotemporal autocorrelation, interregional transition patterns, and the dynamic evolution of the coupling coordination degree over 2011–2022. The results indicate that China’s eastern region performs notably better in achieving coordinated development, maintaining persistently higher coupling coordination levels. In contrast, the central and western regions face substantial challenges; in particular, low-value areas exhibit considerable potential to transition toward higher-value states, suggesting substantial room for improvement. The spatiotemporal analysis further reveals pronounced regional disparities and provides a scientific basis for policymaking aimed at advancing green and low-carbon development strategies tailored to regional characteristics. Full article

Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Eğirdir–Karacaören (EKB) and Beyşehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da⁻¹ in EKB and 75.04 kg da⁻¹ in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da⁻¹. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography. Full article