Search Results (447)

Due to dust, haze and uneven lighting conditions, images captured in coal mines frequently suffer severe quality degradation. Traditional dehazing methods typically overlook color characteristics and employ single algorithms, and deep-learning-based approaches require substantial training data and demand high hardware specifications, which restricts their dehazing performance and efficiency. This research proposes an efficient image dehazing framework. This method integrates bright and dark channel information to derive contrast feature values based on their linear differences. These values reflect dust concentration levels in the environment. By incorporating dust sensor data, the adaptive scaling coefficient and dust compensation terms are established. The adaptive scaling coefficient serves as a dynamic pixel selection ratio during ambient light estimation, effectively preserving the brightest pixel points. The global color mean functions as the criterion for determining image color characteristics, distinguishing between color images and low-light grayscale images to enable different dehazing approaches. This process achieves state verification and information complementarity between visual perception and dust measurement. The weighted fusion of bright and dark channels yields more accurate estimation for ambient light and transmission. Additionally, a weighted guided filter is designed with dust compensation terms incorporated. Ablation studies were conducted to validate the effectiveness of this method in enhancing image features. Finally, comparative experiments were performed using a self-constructed coal mine hazy image dataset, along with SOTS-indoor and SOTS-outdoor datasets. Experimental results demonstrate that, compared with other state-of-the-art methods, this method effectively removes haze while restoring image features and details, exhibiting superior stability, adaptability, and computational efficiency. Full article

Aluminum alloy has been increasingly widely used in the construction field due to its green advantages of light weight, easy processability, high corrosion resistance, and recyclability, which conforms to the concept of green energy conservation and sustainable development in modern architecture. To improve its performance, carbon fiber-reinforced polymer (CFRP) was used to reinforce aluminum alloy pipes. A total of 22 groups of specimens with different lengths, thicknesses, and CFRP configurations were constructed to study their mechanical properties under axial compression. The experimental results show that CFRP reinforcement can effectively inhibit the lateral deformation and delay the global buckling of aluminum alloy pipes, among which the three-segment and full-coverage reinforcement have significant effects; the combination of aluminum and CFRP can transform direct failure into progressive failure and improve bearing capacity. This composite material not only has an excellent high strength-to-weight ratio and durability, but also can reduce structural self-weight. Full article

Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the Daliuta Mine, this study proposes a multi-source remote sensing monitoring technology system, which aims to improve the accuracy of surface deformation in the mining area. At the mining area scale, optimised Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology utilised 168 Sentinel-1A image scenes to generate 789 interferometric image pairs. This extracted the long-term surface deformation field of the Daliuta mining area, revealing the spatiotemporal evolution patterns of surface subsidence under repeated mining activities. To further enhance monitoring accuracy and reliability, this study proposed a Satellite Aerial-Prior Weighting (SA-PW) method. This approach integrated satellite-based time-series InSAR, aerial Pixel Offset Tracking (POT), and unmanned aerial vehicle light detection and ranging (UAV LiDAR) data through a dynamic priority weighting model. This enabled the synergistic inversion of high-precision surface deformation parameters for repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR alone, the SA-PW method achieved a 10% improvement in surface deformation parameter accuracy. By constructing a dynamic priority-weighted model, this approach systematically integrated multi-source data to achieve collaborative inversion of high-precision surface deformation parameters in repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR and UAV LiDAR methods, SA-PW data fusion yielded higher accuracy, with MAE and RMSE values of 60 mm and 90 mm on the A line, and 57 mm and 83 mm on the H line, respectively. This method facilitates the establishment of integrated air–space–ground real-time monitoring networks for mining areas, enables subsidence hazard early warning and management, identifies key zones for ecological restoration, explores synergistic mechanisms between repeated mining and ecological rehabilitation, and promotes safe and green mining operations and development. Full article

Volatile organic compound (VOC) emissions exhibit strong process-level heterogeneity, yet regional source characterization still commonly relies on sector-average profiles, introducing substantial uncertainty into source identification and control prioritization. In this study, process-resolved VOC source profiles were established for five representative industrial sectors in Deyang, a typical industrial city in the Chengdu–Chongqing region, including pharmaceutical manufacturing, industrial coating, chemical industry, food manufacturing, and the textile industry. A total of 19 organized emission samples were collected from 9 enterprises, and 123 VOC species were quantified. These measured profiles were further integrated with literature-derived profiles and a bottom-up emission inventory to construct an emission-weighted regional composite source profile for 17 major industrial sectors. An emission-based hydroxyl radical (OH) reactivity-weighted framework was then introduced to compare mass-dominant and chemically dominant VOC sources. The results showed pronounced process- and sector-specific differences in composition. Pharmaceutical manufacturing was mainly dominated by oxygenated VOCs (OVOCs), industrial coating by low-carbon halocarbons, the chemical industry by methanol and reactive low-carbon compounds, food manufacturing by alkenes and OVOCs, and the textile industry by light alkanes. At the regional scale, industrial VOC emissions were dominated by OVOCs (35.67%), followed by alkanes (19.01%) and aromatics (15.99%). Ethyl acetate, 1,4-dioxane, 1,1,2,2-tetrachloroethane, and m/p-xylene were identified as the most abundant species. However, OH reactivity was largely dominated by alkenes, and substantial discrepancies were observed between emission contribution and OH-reactivity-weighted contribution across sectors. In particular, the chemical industry contributed 21.10 ± 8.43% of reactive organic gas emissions but 28.82 ± 11.61% of OH-weighted emissions, whereas printing contributed 13.55 ± 13.42% of mass emissions but only 7.66 ± 13.08% of OH-weighted emissions. These findings demonstrate that regional VOC management should move beyond bulk mass reduction and prioritize high-reactivity sectors and process units to maximize O₃ mitigation benefits. Full article

Although Non-Wood Forest Products can offer interesting economic opportunities for rural communities, little is known about their commercial harvesters. Our work aims to shed light on the labour profiles, their accessibility to new entrants, and attractiveness for future green jobs. Through in-depth interviews, we explored the five-capitals profile of commercial resin, cork, mastic foliage, chestnut, pine nut, and wild mushroom harvesters in Spain. We found either freelance harvesters or entrepreneurs with a small gang. Our data show a typical male collector, who started the activity through his social networks (Social Capital), and whose origin depends on the product and Spanish region. Some commercial female harvesters were found in mushroom, chestnut and resin harvesting. Social constructs around the masculinization of these activities may explain their limited attractiveness for women. The ratio of non-Spanish commercial harvesters correlates with the weight of migrants in the analysed regions. Only a subgroup of resin harvesters devotes most of their year to this single activity. The rest complement NWFP income with a main forestry (cork and pinenut) or non-forestry occupation (mushroom, chestnut and mastic). For the latter products, access to Natural Capital was found to be crucial for job progress, as non-landowners require administrative and/or negotiation capacities to secure harvesting permits. Human Capital differs across NWFPs, from simpler skills such as recognising marketable produce and handling easy tools (mushroom, chestnuts, pine nut ground gathering and mastic), to complex abilities needed to balance efficiency with minimising tree damage (in resin tapping, pinenut shaking, and cork extraction). Such specialised tools and machinery (Built Capital) typically act as a barrier to entry and advancement. These profiles are expected to help decision-makers to design instruments promoting and regulating commercial harvesting, and tackle their risks: local landowners in allocating harvesting rights to external collectors; regional policymakers as competent authorities in forest legislation; and state-level administration concerning cultural, fiscal and labour-permit aspects. Full article

Under complex lighting conditions, particularly in low-light environments, general object detectors often suffer from degraded detection performance due to insufficient brightness, severe noise, and loss of discriminative details. This issue is especially critical in underground mining scenarios, where weak illumination, complex backgrounds, dust interference, and frequent small or partially occluded targets make reliable visual perception highly challenging. To address this issue, we propose an Illumination-Aware Detection Network (IADNet) for object detection in low-light environments. Specifically, an Illumination Modeling Subnetwork (IMS) is designed to extract illumination-aware and degradation-aware auxiliary features from low-light images. Within the IMS, an Adaptive Weighted Downsampling (AWD) layer is introduced to reduce noise interference during feature downsampling and enhance illumination-aware representation learning. Furthermore, a Global Feature Enhancement Module (GFEM) is incorporated to strengthen global context modeling and improve feature representation capability in complex scenes. In addition, an extra contrastive loss is introduced to constrain the optimization of the IMS, and weighting factors are employed to balance the detection loss and the contrastive loss during training. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of the proposed method. On the public ExDark dataset, IADNet achieves an mAP@50 of 80.3%, outperforming the baseline YOLO11m by 3.4 percentage points. On the self-constructed mining low-light dataset Lowlight_Mine, the proposed method achieves 92.3% Precision, 82.0% Recall, 89.3% mAP@50, and 57.8% mAP@50:95, showing favorable performance in object detection tasks under mining-related low-light scenarios. On the DARK FACE dataset, IADNet achieves 54.6% mAP@50 and 31.2% mAP@50:95, further indicating its robustness under real low-light conditions. On the synthetic low-light Dark_VOC dataset, IADNet attains an mAP@50 of 91.6%, and on the normal-light VOC dataset, it achieves an mAP@50 of 93.0%, suggesting that the proposed method maintains stable detection performance under the evaluated illumination conditions. These results indicate that IADNet improves low-light object detection performance and provides a useful experimental reference for object detection tasks in mining-related low-light scenarios. Full article

Photocatalytic oxygen reduction to hydrogen peroxide (H₂O₂) offers a promising route for sustainable chemical synthesis, yet the efficiency of carbon nitride-based photocatalysts is often limited by narrow light absorption and rapid charge recombination. Low-molecular-weight carbon nitride exhibits a favorable reduction potential but suffers from poor visible-light utilization, while π-conjugation extension and heteroatom doping are effective yet rarely combined within a single oligomeric framework. In this work, we report a low-temperature (400 °C) one-step copolymerization approach employing urea and terephthalonitrile to construct an oxygen-doped, benzene-bridged carbon nitride oligomer (O-B-CNO). Comprehensive characterization confirms the successful integration of both benzene rings and oxygen dopants into the oligomer backbone, with the former enhancing structural stability and the latter introducing active sites. The extended conjugation and oxygen incorporation synergistically modulate the electronic structure, leading to a narrowed bandgap, improved visible-light harvesting, and suppressed charge recombination. As a result, O-B-CNO delivers a photocatalytic H₂O₂ yield of approximately 3000 μM under visible-light irradiation, a 10-fold enhancement over the pristine oligomer, with optimal activity at neutral pH via the two-electron oxygen reduction pathway. The enhanced performance stems from the complementary functions of the two modifications: benzene rings promote electron delocalization and charge transport, while oxygen dopants serve as selective active centers for oxygen reduction. This work demonstrates a viable molecular engineering strategy for developing efficient carbon nitride photocatalysts for H₂O₂ production. Full article

Micro- and nano-container-based self-healing coatings have emerged as a promising strategy for the long-term conservation of cultural heritage artifacts, including metals, stone, organic matter, and construction materials. These coatings incorporate microcapsules or nanocapsules with tailored shell and core materials, enabling autonomous release of healing agents or corrosion inhibitors in response to damage. For metallic artifacts, benzotriazole@mesoporous silica nanoparticles (BTA@MSN) microcapsules achieve selective pH-responsive release, reaching 77% at pH 9.0 and 42% at pH 5.0, effectively mitigating localized corrosion. Temperature-adaptive poly(methyl methacrylate-co-methacrylic acid) (PMMA-MA)/MgO microcapsules exhibit controlled rupture rates, with a 75% reduction at elevated temperatures, enhancing crack repair efficiency by approximately 5%. Organic artifacts, such as wooden or paper manuscripts, benefit from clove oil nanocapsules, which increase tensile strength by 43.5% and fracture toughness by 101.9%, with only 2.91% weight loss over 7 days compared to 33.1% for unencapsulated oil. Advanced fabrication methods—including microfluidics, Pickering emulsions, and multi-core systems—enable high encapsulation efficiency (up to 73.5%), uniform particle size, and repeatable healing. Multi-stimuli responsiveness (pH, temperature, light, magnetic fields) and biobased, environmentally friendly materials further enhance adaptability and sustainability. In this review, “self-healing” is defined broadly to include both physical crack repair and autonomous restoration of protective functions. Overall, self-healing micro/nanocapsule coatings provide a highly controllable, efficient, and durable solution for active heritage protection, representing a shift from passive to intelligent conservation strategies. Furthermore, a systematic comparison of different capsule systems is provided to clarify their respective advantages and limitations. Overall, hybrid systems exhibit the most balanced performance, while inorganic nanocontainers offer superior stability and controlled release, and polymeric capsules enable rapid healing but limited reusability. Full article

Predicting the anthropogenic economic pollutant emissions index helps balance economic growth and environmental protection. In this study, a set of constructed features was derived using provincial-level basic data from 1995 to 2023. By constructing a weighted ensemble strategy incorporating Extreme Gradient Boosting, light gradient boosting machine, Random Forest, and Multi-Layer Perceptron, and integrating it with the Autoregressive Integrated Moving Average model and Shapley Additive Explanations, an anthropogenic economic pollutant emission index (AS_GPI) forecasting model was finally established, with basic and constructed features employed as its inputs (AFEA-AG). The AS_GPI forecasting model (EA-AG) was developed without constructed features, with all other settings consistent with the AFEA-AG model for comparison. Results show that the proposed model achieves high forecasting accuracy for AS_GPI across four typical pollutants, with R² values exceeding 0.95 for the AS_GPI_NOx and AS_GPI_PM_2.5. The mean absolute percentage error was as low as 0.0744. The forecasting model with constructed features yielded lower errors and higher stability in its prediction results compared with the one without such features. Feature contribution analysis revealed differing key contributors, with AS_GPI lagged values and economic-related characteristics among underlying variables exhibiting strong predictive importance. The 2024 projection results indicated certain disparities in pollution control effectiveness between key and non-key regions. Further analysis of historical and predicted data revealed a nationwide decline in AS_GPI between 1995 and 2024. Beijing and Shanghai achieved notable environmental quality improvements through anthropogenic emission reductions. The coefficient of variation values of different AS_GPIs reveal spatial heterogeneity and differences. Nationwide efforts should prioritize the control of anthropogenic NMVOC and NOx emissions. This framework provides a prediction method that offers certain reference for the development of the economy and the environment. Full article

Polyurethane buffers serve as critical safety protection devices for elevators, with their integrity directly impacting the effectiveness of protective functions during accidents. Current buffer inspections primarily rely on manual patrols, suffering from low inspection frequency, high subjectivity, and significant detection difficulties. To enhance the intelligence and real-time capability of buffer fault detection, this paper proposes a visual fault detection system for elevator buffers based on image enhancement. The system first designs a Hierarchical Fusion Enhancement Module, which effectively suppresses elastic artifacts and significantly enhances crack edge saliency through illumination correction, texture-sensitive guided filtering, and direction-frequency complementary enhancement. It then proposes a gradient-direction texture feature extractor that integrates a gradient-magnitude-weighted Grey-Level Co-occurrence Matrix with a completed local ternary pattern to construct strongly discriminative texture prior features. Finally, a Texture Fusion-Enhanced YOLO detector is developed, which incorporates texture features into the backbone network via a learnable mapping mechanism to achieve early alignment of texture knowledge with depth features. Experimental results indicate that under low-light and complex background conditions, the system achieves a detection accuracy (mAP@0.5) of 0.903 and an F1 Score of 0.891, showing competitive accuracy and robustness within the tested scenarios. Full article

Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank’s System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. Full article

Building reliable vehicle detection models for intelligent transportation systems calls for large, well-annotated datasets—yet gathering and labelling real traffic data remains both costly and labour-intensive. This paper introduces Traffic Synth, an automated pipeline that generates synthetic training datasets by altering real traffic camera images rather than constructing entirely artificial scenes. The system begins by detecting vehicles through instance segmentation and removing them from the frame. It then places new vehicles directly into the cleared regions using diffusion-based inpainting, all while retaining the original road layout, lighting, and camera perspective. Doing so preserves the realistic scene context while broadening the visual variety of vehicles in the dataset. To ensure that the resulting traffic looks physically plausible, we incorporate a lane-aware prompting mechanism that matches each vehicle’s orientation to the direction of travel as seen from the camera. The system further draws on a weighted vehicle brand database that mirrors the makes and colours commonly found on European roads to better match actual deployment conditions. Class-specific mask processing—involving anisotropic scaling and relative dilation—rounds out the pipeline by improving generation quality across different vehicle size categories. The final output is a set of images with automatically generated annotations in a standard object detection format. Full article

Large infrastructure projects frequently experience schedule slippage and cost escalation; however, time–cost planning still relies on expert-assigned activity parameters that fail to reflect the variability induced by construction modes, resource supply, and on-site conditions. This study focuses on activity-level multi-mode time–cost trade-off planning and its dynamic correction during project execution. The proposed methodology is intended for project-level short-term operational scheduling and rolling re-scheduling within a finite project execution horizon, rather than long-term strategic or portfolio-level scheduling. A predict–optimize–update framework is proposed, where light gradient boosting machine (LightGBM) is employed to predict the duration and direct cost of activity–mode pairs using unified features extracted from BIM/IFC records, schedule-resource ledgers, and cost-settlement data, covering engineering quantities, mode and resource decisions, and contextual factors. These predicted parameters are then fed into a time-indexed bi-objective mixed-integer linear program (MILP), which minimizes both project makespan and total cost (including indirect cost) to generate an interpretable Pareto frontier via a weighted-sum approach. Meanwhile, real-time monitoring updates refresh the predictors and re-solve the remaining project network to ensure dynamic adaptability. Validated on a desensitized proprietary enterprise multi-source dataset comprising 25 completed infrastructure projects and 5258 activity–mode samples, the proposed method achieves a mean absolute error (MAE) of 2.7 days and a coefficient of determination (R²) of 0.89 for duration prediction, as well as an MAE of 7.4 × 10⁴ CNY and an R² of 0.91 for direct-cost prediction. The generated Pareto set exhibits a diminishing return trend: as the project duration is relaxed from 101 to 146 days, the total cost decreases from 45.10 to 40.27 million CNY. A weather-triggered update case demonstrates that the completion forecast is revised from 133 to 128 days, with the total cost reduced from 53.05 to 52.75 million CNY. This framework enables explainable schedule–cost co-control, thereby effectively aiding decision-making for the planning and control of large infrastructure projects. Full article

Cultivated land fragmentation has become a critical constraint on regional agricultural sustainable development. Revealing its spatial patterns and driving mechanisms is of great significance for optimizing the utilization and management of cultivated land resources and enhancing regional agricultural productivity. This study focuses on the main grain-producing areas in the middle and lower reaches of the Yangtze River Basin. It constructs a Cultivated Land Fragmentation Index (CLFI) using an integrated method that combines landscape index analysis with an entropy-weighted approach, based on 2023 land-use data. The optimal analytical grain size and extent were determined before employing geographic detectors to identify dominant factors influencing cultivated land fragmentation. The key findings include the following: (1) The appropriate spatial resolution for fragmentation analysis was identified as 330 m, with an optimal analysis extent of 8910 m. (2) CLFI values ranged from 0.001 to 0.973, exhibiting significant spatial heterogeneity. The central plains and northeastern regions demonstrated low fragmentation levels and better contiguous cultivated land distribution, while the western and peripheral areas showed higher fragmentation. A provincial-scale comparison revealed that Jiangxi Province had the highest fragmentation level (0.255), whereas Jiangsu Province had the lowest (0.146). The topographic gradient analysis indicated a decreasing trend from the Guizhou Plateau (0.503) to the North China Plain (0.125), with plateaus and basins showing significantly higher fragmentation than hilly and plain regions. (3) Dominant controlling factors varied among provinces: In provinces with greater topographic relief (Anhui, Hubei, Hunan, Jiangxi), natural factors like elevation, slope gradient, and NDVI primarily controlled fragmentation patterns; in contrast, socioeconomic factors such as nighttime light intensity dominated in Jiangsu Province, characterized by flat terrain and high urbanization. Multi-factor interactions generally enhanced explanatory power regarding spatial patterns, confirming that cultivated land fragmentation is a result of comprehensive multi-factor interactions. This study reveals the spatial distribution characteristics of cultivated land fragmentation at the pixel scale in the study region, providing theoretical foundations and decision-making references for the efficient utilization of cultivated land resources and rural land system reforms. Full article

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article