Search Results (463)

The increasing number of end-of-life (EOL) products has raised new challenges for sustainable manufacturing, especially when recycling efficiency, structural modularity and worker well-being must be considered simultaneously. From the perspective of symmetry and asymmetry in mechanical product design, this study proposes a Design for human-centric Modular Recycling (DFHMR) approach to improve EOL product recycling while reducing ergonomic risks in disassembly operations. In the proposed framework, functional similarity, structural correspondence and spatial association among components are used to characterize symmetry-oriented modular relationships, whereas asymmetric factors such as disassembly difficulty, carbon emissions, recycling profit and worker-related ergonomic risks are incorporated to describe the heterogeneity of practical recycling processes. A multi-objective optimization model is developed to maximize green disassembly performance and intra-module relevance while minimizing inter-module coupling and human-factor risks. To solve the constrained modular design problem, an enhanced social engineering optimizer (SEO) is introduced to balance global exploration and local exploitation. A turbo reducer case study is conducted to validate the proposed model, and comparative experiments with several multi-objective optimization algorithms demonstrate the effectiveness and robustness of the enhanced SEO. The results indicate that the DFHMR framework can provide decision-makers with a set of balanced modular recycling schemes, offering a practical reference for symmetry-oriented, sustainable and human-centered mechanical design under Industry 5.0. Full article

Magnetic polymeric microspheres are pivotal solid-phase carriers in chemiluminescence enzyme immunoassays (CLEIA). However, their practical clinical application is frequently hindered by non-specific adsorption, irreversible aggregation, and the intrinsic susceptibility of exposed outermost Fe₃O₄ nanoparticles to oxidation. To overcome these critical bottlenecks, we rationally engineered highly original monodisperse P(St-MMA)@Fe₃O₄@P(St-MMA) sandwich-structured microspheres. The bespoke amphiphilic outer shell acts as an impenetrable shield against hydration and oxidation, while maintaining a topologically size-matched mesoporous network (average pore size of 13.11 nm) for optimal antibody anchoring. Strikingly, this architecture ensures exceptional long-term colloidal stability, completely preventing macroscopic agglomeration for over six months in buffer solutions. When evaluated in a carcinoembryonic antigen (CEA), CLEIA, our microspheres achieved an ultra-low limit of detection (LOD) of 0.055 ng·mL⁻¹ and high analytical recovery (93.37–108.25%). In a head-to-head comparison with industry-standard commercial magnetic beads, the engineered microspheres delivered stronger chemiluminescent signals and lower background noise, demonstrating excellent intra-assay (CV < 4.37%) and inter-assay (CV < 10%) precision. This work establishes a scalable, highly stable materials platform that effectively resolves the persistent oxidation limitations, holding immense practical importance for next-generation ultrasensitive clinical in vitro diagnostics. Full article

Wireless sensor networks (WSNs) are fundamental to the Internet of Things (IoT) and are widely used in environmental, industrial, and healthcare applications. However, their operational lifetime is constrained by the limited energy resources of sensor nodes. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol reduces energy consumption through clustering but suffers from random cluster head (CH) selection, leading to uneven energy usage and reduced stability. This study introduces a hybrid optimization approach, LEACH-CSA, which integrates the Crow Search Algorithm (CSA) with LEACH to enhance CH selection and positioning. The proposed method employs CSA’s intelligent search behavior to minimize intra-cluster distances and balance energy consumption across nodes. MATLAB simulations with 100 sensor nodes in a 100 × 100 m² area demonstrate that LEACH-CSA significantly reduces energy consumption and extends network lifetime compared with LEACH and its variants. Furthermore, CSA parameters were optimized using a progressive randomized tuning strategy with 1000, 2000, and 4000 candidate configurations. A comparative evaluation against LEACH-based GA, PSO, GWO, and WOA demonstrated that LEACH-CSA consistently improved the FND metric under different node density and area-scaling scenarios. Full article

Within the trans-regional ecological governance framework of the Yangtze River Delta (YRD), Anhui Province acts as a critical ecological barrier. Yet, intra-provincial disparities in the fiscal pressure of ecological compensation remain underexplored. Drawing on panel data from 16 prefecture-level cities in Anhui (2018–2024), we develop a hierarchy–region dual-dimensional framework. This framework measures fiscal pressure by integrating cost–benefit and opportunity–cost methods. A two-way fixed-effects model exhibits a distinct spatial gradient: fiscal pressure decreases in the order of Southern (19.58%) > Northern (13.45%) > Central Anhui (5.24%). Mechanism tests support the “Triple Systemic Mismatch” as a coherent interpretive lens: fiscal pressure is positively associated with ecological contribution and negatively associated with fiscal capacity and industrial structure. Furthermore, regional integration policy significantly alleviates such fiscal pressure. Accordingly, this study puts forward a three-dimensional sharing mechanism that integrates government coordination, market empowerment, and social participation to support equitable cross-regional governance. Full article

Green technology transfer is a vital pathway for optimizing innovation resources and advancing regional low-carbon transformation. Using green patent transfer data from 92 cities in China’s five major urban agglomerations during 2006–2023, this study employs two-way fixed-effects and mediation models to examine the spatiotemporal evolution of green technology transfer and its impact on carbon emission efficiency. The results show that: (1) Green technology transfer has expanded steadily. While local transfers remain dominant, inter-city transfers are rising, and the spatial pattern has evolved into a core–periphery structure with gradient diffusion and multi-center linkage. (2) Such transfer significantly improves carbon emission efficiency, with heterogeneous effects across regions. The Yangtze River Delta, Pearl River Delta, Middle Yangtze, and Chengdu–Chongqing agglomerations show the strongest effects. Within these regions, intra-agglomeration and inter-city transfers produce greater emission-reduction outcomes than intra-city transfers. (3) Green technology transfer indirectly improves carbon emission efficiency by upgrading industrial structures, strengthening urban innovation capacity, and enhancing resource allocation efficiency. This study explores the multidimensional mechanisms through which green technology transfer influences carbon emission efficiency at the urban-agglomeration scale and provides empirical evidence for optimizing regional green technology transfer patterns, promoting collaborative low-carbon and high-quality development, and supporting China’s dual-carbon goals. Full article

Against the background of increasing complexity in global aquatic food supply chains and rising trade risks, the structural stability and vulnerability of ready-to-eat Aquatic Products trade networks have become increasingly prominent. This study applies complex network analysis to examine the evolution and vulnerability of the overall trade network and its subnetworks. The results show that (1) the overall trade network exhibits significant small-world characteristics; (2) the core countries in the network are China, the United States, Thailand, France, and Spain, which play dominant roles; (3) the community structure of the overall network is clearer, with stronger intra-community trade links; (4) all subnetworks also exhibit small-world properties, and different categories of ready-to-eat Aquatic Products have different core countries; (5) vulnerability analysis shows that the network is sensitive to targeted attacks, non-fish subnetworks are more vulnerable, while fish subnetworks are more stable. These findings provide practical guidance for procurement decisions, supply chain configuration, and risk management in the ready-to-eat Aquatic Products industry. Full article

Nighttime light (NTL) satellite data provide an effective proxy for analyzing urbanization, tourism development, industrial activity, and population dynamics. Based on these premises, the present study investigates the spatiotemporal behavior of Nighttime Light Dynamics across 107 Italian provinces from 2014 to 2022 using VIIRS Day/Night Band composites processed in Google Earth Engine (GEE). A comprehensive framework combining descriptive statistics, seasonal analysis, correlation assessment, time-series clustering, and Emerging Hotspot Analysis (EHA) was applied to characterize spatial patterns, temporal trends, and joint spatiotemporal dynamics. The results reveal pronounced spatial heterogeneity, with higher and more stable Nighttime Light Dynamics concentrated in Northern and Central Italy, while Southern regions exhibit lower intensity and greater temporal variability. Seasonal analysis shows that summer contributes more strongly to intra-annual Nighttime Light Dynamics dispersion, whereas winter illumination patterns are rather uniform. A strongly positive relationship between Nighttime Light Dynamics and population density was observed at national and regional scales (R² = 0.71), confirming the reliability of Nighttime Light Dynamics as an honest demographic proxy. Time-series clustering and EHA further identify central locations, stable urban cores, transitional regions, and areas experiencing intensifying (or diminishing) illumination trends. Overall, the study highlights the value of integrating spatiotemporal analytics with Nighttime Light Dynamics data to support evidence-based regional planning and sustainable development strategies aimed at addressing spatial inequalities across Italy and, more generally, advanced economies. Full article

The safety of workers in industrial settings is ensured through the correct use of Personal Protective Equipment (PPE). The use of such equipment can be monitored using Deep Learning (DL). Federated Machine Learning (FML) is a technique that can be used in this context to preserve the privacy of sensitive information and provide explainability for the models adopted. Explainability techniques are an essential resource for interpreting the classification performed by the model. In this regard, this study aims to evaluate, through the adoption of specific similarity indices, the robustness and consistency of the explainability algorithms adopted to identify the areas of the images that are decisive for PPE classification. The dataset consists of 1600 real images representing work environments, in which staff are portrayed both with and without Personal Protective Equipment; specifically, there are workers wearing helmets, workers wearing reflective vests, workers wearing both devices and, finally, workers without any PPE. SSIM, VIF and SCC are the most relevant indices involved in the study. In the experimental phase, their mean values stand at 0.99, 0.96 and 0.96 for the intra-client study, and 0.96, 0.91 and 0.71 in the inter-client analysis. Full article

The low-carbon transition (LCT) of manufacturing clusters is a critical pathway to addressing bottlenecks in global climate governance and promoting sustainable economic development in developing countries. Accurately measuring the level of this transition and clarifying its dynamic trends are of great significance. Drawing on the economic rationale of a low-carbon economy, this study constructs a comprehensive evaluation indicator system and employs the entropy-weighted CRITIC-grey relational TOPSIS method to measure the LCT levels of China’s four major industrial bases from 2013 to 2023. Combined with convergence analysis, the Theil index, mechanism analysis, and policy scenario simulation, it systematically analyzes the characteristics of disparities and the underlying mechanisms. The study’s results show that low-carbon technology is the core driver of the LCT of the four major industrial bases. The LCT levels of the four major industrial bases have generally increased, with some bases exhibiting a catch-up effect internally. The overall disparity among the four major industrial bases has widened, primarily driven by intra-base differences. Specifically, the Beijing–Tianjin–Tangshan industrial base displays polarization characteristics, while the Central-Southern Liaoning industrial base shows a relatively low-level equilibrium. The transition of resource-based cities lags, mainly constrained by rigid industrial structures and insufficient investment in technology. Industrial structure optimization plays a certain role in improving resource-based regions, whereas technological innovation has a more pronounced effect in developed regions. This study constructs a comprehensive analytical framework of “measurement–evolution–mechanism–simulation,” which refines the quantitative evaluation system for the LCT of manufacturing clusters. The findings provide empirical support for formulating differentiated low-carbon policies for manufacturing clusters and optimizing coordinated emission reduction pathways, while also offering a reference paradigm for similar research in other developing countries. Full article

In recent years, local governments in China have actively promoted new district development, encouraging a transition from monocentric to polycentric urban form. Whether and how this spatial restructuring is reflected in government-mediated land grant prices, however, remains unsettled. Using LandScan population grids and Exploratory Spatial Data Analysis (ESDA), this paper measures the polycentric spatial structure of 283 prefecture-level cities in China. We merge this measure with city-level land transaction data to examine how polycentricity affects overall urban land prices as well as industrial, residential, and commercial land prices. The results show that a more polycentric urban structure significantly increases comprehensive land prices. Across land-use categories, the effect is smallest for industrial land and stronger for residential and commercial land. Further analysis shows that land-finance dependence moderates the effect of polycentric urban spatial structure on land prices, and this moderating effect differs across land-use types. In addition, the positive effect of polycentricity is weaker in larger cities. Spatial econometric estimates further suggest that local polycentricity raises land prices in neighboring cities, implying the presence of positive spillovers across urban areas. The paper contributes to the literature on urban spatial structure by linking intra-urban polycentricity to land price and by showing that these effects extend beyond city boundaries. Full article

With the increasing demand for intelligent fault monitoring, acoustic-based diagnosis has emerged as a promising solution for industrial applications such as pipeline leakage and electrical equipment fault detection. However, complex working conditions and domain shifts significantly degrade model performance, especially when unseen target domain data is unavailable. To address this, we propose an amplitude-phase collaborative augmentation network named AP-CANet tailored for acoustic fault diagnosis. Specifically, the network adaptively aligns amplitude and phase features across multiple source domains and performs label-consistent sample augmentation to enrich data diversity while preserving semantic consistency. A frequency–spatial interaction module further integrates global spectral information with local temporal details to improve feature discriminability. Moreover, we introduce a manifold triplet loss that scales shortest path distances in the feature manifold, encouraging the model to better capture subtle distinctions among hard samples and improving intra-class compactness and inter-class separability. We evaluate the proposed method on two publicly available datasets: the Pipeline Leak Acoustic Dataset (GPLA-12) and the Electrical Sound Dataset (MIMII-DG). Experimental results demonstrate superior performance under domain-shift scenarios, highlighting the method’s potential for scalable and low-cost acoustic fault diagnosis in real-world industrial environments. Full article

The critical role of green consumption in mitigating carbon emissions is widely acknowledged. As a prerequisite for green consumption, consumer green awareness (CGA) plays a pivotal role in advancing sustainable development. This study constructs a comprehensive indicator system for CGA from the three dimensions of “antecedent-behavior-outcome” and measures the CGA levels of 30 provinces in China from 2014 to 2022. Using the Theil index, kernel density estimation, Moran’s I, and Markov chain methods, we analyze its spatiotemporal evolution characteristics. Furthermore, spatial econometric models are applied to explore its driving factors. The results show that China’s CGA exhibits sustained growth during the study period, but regional disparities are widening, driven by inter-regional rather than intra-regional differences. Moreover, China’s CGA gradually demonstrates the long-tailed and multimodal distribution, accompanied by emerging spatial clustering effects. In terms of transition dynamics, CGA demonstrates a short-term “gradient lock”, which is substantially alleviated when spatial spillover effects are incorporated. Additionally, we find that economic development, the advancement of emerging industries, accelerated urbanization, emphasis on education, and policy guidance significantly promote CGA, while overconsumption inhibits CGA. Among these factors, economic development, informatization, e-commerce, education, and policy guidance show significant spillover effects. Full article

With the rapid expansion of rural tourism in China, high-quality development has become a key concern for academics and policymakers. Existing studies have focused primarily on economic and industrial growth, with limited attention paid to development quality from the perspective of resident well-being. Using panel data from 30 Chinese provinces from 2012 to 2022, this study establishes a multidimensional evaluation framework for high-quality rural tourism. We employed the entropy weight method, Theil index, and quadratic assignment procedure analysis to examine its level, regional differences, and driving factors. The findings revealed that: (1) the overall level of rural tourism development remained relatively low but rose steadily from 0.064 (2012) to 0.150 (2022) (134.38% cumulative growth), driven by supply-side improvements and demand-side expansion. (2) Pronounced regional inequalities existed: eastern provinces had higher overall levels but larger internal gaps, whereas central/western provinces had lower overall levels but smaller internal differences, with intra-regional disparities accounting for over 66% of the national inequality. (3) The tourism market and transportation were universal key drivers, but the underlying mechanisms differed: the ecological environment exerted greater influence in the east, while public services and living standards were more critical in the central/western regions. By incorporating resident well-being into a systemic analytical framework, this study reconceptualizes high-quality rural tourism as an adaptive socio-ecological system shaped by multilevel interactions among the economy, society, and the environment. The results provide empirical evidence and systemic governance insights for promoting balanced and sustainable rural tourism development. Full article

The marble industry relies on proprietary commercial names rather than objective visual categories, creating market inefficiencies for stakeholders who select stones based on appearance. Supervised classification perpetuates this problem by replicating inconsistent commercial labels instead of discovering intrinsic visual structure. We propose an unsupervised pipeline combining a two-stage training strategy: A pure self-supervised pretraining followed by cluster-aware fine-tuning of a DINO Vision Transformer, with empirically selected dimensionality reduction and agglomerative hierarchical clustering. Systematic ablation studies on 1480 marble images spanning 10 commercial varieties validate each design choice: cluster-aware training at k = 10 yields geometrically improved embeddings over the self-supervised baseline (mean Silhouette Score 0.693 ± 0.053 vs. 0.660 ± 0.030; mean Davies–Bouldin Index 0.386 ± 0.075 vs. 0.569 ± 0.012; N = 9 independent evaluations across 3 data partitions × 3 training initializations). The resulting taxonomy reveals three phenomena invisible to commercial classification: cross-category merging of visually indistinguishable stones carrying different market names, intra-category splitting of heterogeneous sub-populations within single varieties, and coherent grouping where commercial and visual boundaries coincide, with all three confirmed in every independent run. We further demonstrate that standard extrinsic metrics are misaligned with unsupervised taxonomy objectives when reference labels encode the inconsistencies the method aims to resolve. Validating this methodology across diverse stone types, larger datasets, and varied acquisition conditions represents a natural and necessary next step toward establishing its cross-domain generalizability. Full article

The latest H266/VVC standard has received numerous praises for its excellent compression efficiency. However, its extremely high computational complexity has become a hindrance to the VVC adaptation industry ecosystem, while also increasing the difficulty of hardware design and application costs. To address this issue, we designed an efficient intra-coding scheme based on neural networks, which consists of three parts: Firstly, we designed a neural network-based reverse prediction algorithm that uniquely utilizes the CNN’s prediction results for lower-level blocks to determine the QTMT partitioning of upper-level blocks, cleverly solving the adaptation problem of existing models to complex VVC partitioning patterns—a decision-making logic that has not been fully explored. Secondly, we designed a pruning algorithm, which is the first to dynamically couple the real-time RDO cost of BT segmentation with the TT segmentation direction, achieving adaptive decision-making. Finally, we designed a complexity pre-screening module. On the basis of analyzing whether the CU texture is smooth, this module designs four sets of adaptive thresholds for non-square CUs introduced in VVC. These thresholds can dynamically adjust local and global thresholds based on CU size, enabling size sensitive texture evaluation to determine whether the current block needs further partitioning. The experimental results show that, compared with traditional VTM4.0, our method reduces the average encoding time by 49.21%, while the BD-BR increase is 1.61%, and the BD-PSNR decreases by 0.06 dB, fully demonstrating its superiority and performance balance. Full article