Search Results (17,550)

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Benthic foraminifera, single-cell marine organisms found worldwide, represent an important component of seabed ecosystems. Due to their sensitivity to environmental pollution, they are often used as bioindicators, providing an efficient tool in toxicity studies. Among the pollutants affecting marine coastal and estuarine environments, persistent flame retardants, such as polybrominated diphenyl ethers (PBDEs), are frequently found. Low-level exposure to BDE-47, a PBDE congener, is known to affect organismal development. In this framework, this study aims to assess the effects of BDE-47 exposure on benthic foraminifera from coastal marine environments. Foraminifera specimens belonging to the symbiont-bearing Peneroplidae family were sampled and exposed to two different BDE-47 concentrations for up to 48 h. Vitality indicators such as changes in pseudopodial activity, movement, reproduction, loss of symbiont algae, and occasional mortality events were monitored during the experiment. Exposure to BDE-47 induced alterations in pseudopodial activity, movement, reproduction, and symbiont retention, with the progressive loss of vitality and limited mortality at increasing exposure levels, highlighting the sensitivity of this species to BDE-47. These findings suggest the harmful repercussions of PBDE pollution on marine coastal ecosystems, affecting benthic organisms and potentially contributing to biomagnification processes within the food web, with possible implications for human health. Full article

Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring and exploration. However, harsh underwater conditions and frequent topology changes caused by node and sink mobility pose significant challenges for reliable routing. Conventional routing protocols that depend on global route reconstruction and static paths generate excessive control overhead and degrade performance in large-scale underwater environments. In this paper, we propose an energy-efficient virtual cell-based mobile-sink adaptive routing (VC-MAR) protocol for UWSNs. The sensing field is logically partitioned into a three-dimensional grid of virtual cells, where a cell-gateway is elected in each cell to construct a low-overhead routing backbone. To support sink mobility, VC-MAR introduces a localized route-adjustment mechanism that updates only the affected backbone segments rather than reconstructing the entire routing structure. By confining routing updates to neighboring cells influenced by sink movement, the proposed protocol significantly reduces control packet exchanges while ensuring stable and reliable data delivery. Simulation results show that the proposed VC-MAR improves the packet delivery ratio by up to 20% and reduces routing control overhead by about 34% compared with traditional grid-based routing methods. These results confirm the suitability of VC-MAR for dynamic and realistic underwater sensing scenarios. Full article

Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM_2.5 and r = 0.86 for PM₁₀, and low error levels (RMSE ≈ 2.1–2.2 µg/m³). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency < 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems. Full article

In response to increasing environmental protection requirements and the rapid advancement of analytical technologies, particular attention is being paid to the development of environmentally friendly, cost-effective, and sensitive measurement tools. One of the key challenges in modern analytics is the effective monitoring of inorganic nitrogen in the natural environment. In this review, we present a variety of ammonium and nitrate ion-selective electrodes. The review includes the years 2020–2026. It was divided into sections based on sensitivity to a given ion, as well as into subsections based on application: monitoring of soil and aquatic environments, the use of sensors for the determination of specific ions in a wide range of samples, and electrode designs that were used only in laboratory studies. A total of 64 nitrate electrodes and 22 ammonium electrodes were analyzed. Comparisons were made based on electrode type, type of internal contact, materials used to enhance ion-to-electron conductivity, and type of ionophore. Additionally, analytical parameters were compiled, including sensitivity, detection limit, linearity range, potential stability, operating pH range, and actual application scenarios. This review allows for an assessment of current trends and may serve as a basis for the design of new potentiometric sensors. Full article

Cities are artificial ecosystems that suffer most from environmental issues and climate change. Urban Heat Island (UHI) effects represent an increasing challenge, especially for compact Mediterranean cities characterized by high population density and extensive impervious surfaces. This study assessed localized microclimatic conditions within the small Maltese coastal town of Isla through a 15-day summer field monitoring campaign. Air temperature, relative humidity, and wind speed were measured across urban locations characterized by different levels of vegetation coverage and thermal vulnerability. The analysis combined descriptive statistics, Mann–Whitney U testing, and Multiple Linear Regression (MLR) models. In addition, site-specific Nature-based Solutions (NbS) scenarios were proposed as context-sensitive strategies to support urban heat mitigation and climate resilience. The results highlighted distinct microclimatic responses between the sites investigated. In particular, the MLR analysis suggested that non-vegetated areas were more sensitive to short-term atmospheric variability associated with wind speed and relative humidity fluctuations. These findings suggest that urban vegetation may contribute not only to localized cooling, but also to increased microclimatic stability within compact Mediterranean urban environments. Full article

Pollution of rivers and large water bodies, including reservoirs, by wastewater from various sources is one of the most critical issues in the Donetsk region, requiring continuous monitoring and assessment of surface water quality. The research aims to assess the state of the Kalmius River under anthropogenic pressure, as well as to find correlations between the species composition, photosynthetic activity of phytoplankton, and the degree of water pollution. This study presents the results of biomonitoring of the Kalmius River and its tributaries within Donetsk City, which are under intense anthropogenic pressure. Pollution of the river channel by phenol, anionic surfactants, Ferrum ions, chlorides, and sulfates was identified. Based on the combinatorial pollution index, the water in the Kalmius River and its tributaries can be classified as polluted. The pigment composition of water samples was analyzed, and the species composition of river phytoplankton was determined. Dominant species include Chlorella vulgaris Beij., Dictyosphaerium pulchellum H.C.Wood, Scenedesmus quadricauda Brébisson, and Oscillatoria agardhii M.A.Gomont. Photosynthetic activity of the river’s algal flora was assessed based on chlorophyll fluorescence induction curves of natural phytoplankton. A correlation was established between surface water pollution levels and changes in the photosynthetic apparatus of microalgae cells. A strong negative correlation was found between the content of nitrate nitrogen in the aquatic environment and the photosynthetic activity, pigment composition, and abundance of the main dominant forms of phytoplankton, particularly the microalgae of the genus Cyclotella. The data obtained shows that the Kalmius River’s pollution has a significant impact on phytoplankton biodiversity, leading to the growth of cyanobacteria species. Full article

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure. Full article

Accurate landslide susceptibility assessment remains challenging in mountainous regions with complex terrain, heterogeneous geology, and clustered landslide inventories. This study develops a slope–unit–based landslide susceptibility assessment framework for Tongren County, Qinghai Province, China, using a landslide inventory of 217 events, multi–source environmental data, Certainty Factor (CF)–based conditioning–factor analysis, and machine learning models. Eighteen conditioning factors derived from remote sensing, geological survey, and meteorological datasets were extracted at the slope–unit scale, and their collinearity was evaluated using Pearson’s correlation and the Variance Inflation Factor (VIF). Eight models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), AdaBoost, Decision Tree (DT), XGBoost, K–Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—were evaluated under a 70:30 train/test split. The results show clear performance differences among the tested models: SVM achieved the best overall balance between discrimination and landslide detection (AUC = 0.9489; recall = 0.879). The tested CNN baseline showed relatively weak performance under the current slope–unit–based tabular–data setting. Susceptibility zoning results showed that high– and very–high–susceptibility zones were mainly concentrated along the Longwu River and its tributaries, where middle–elevation dissected terrain, weak lithological materials, river–valley erosion, and human engineering activities spatially coincide. These results provide a practical basis for slope monitoring and land–use planning in Tongren County. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article

Mechanical engineering systems must sense, inspect, and navigate constrained environments and operate adaptively under uncertainty—requirements that map structurally onto capabilities achieved by biological systems through distributed sensing, morphology-driven locomotion, multimodal perception, and decentralised control. Biomimetics can therefore be interpreted not merely as a source of design inspiration but also as a functional engineering framework relevant to industrial monitoring, inspection, maintenance, and autonomous operation. This study presents a PRISMA 2020-guided systematic mapping review of the biomimetics literature explicitly relevant to mechanical-engineering functions over the decade 2016–2026. A Scopus corpus of 11,114 records was screened through a two-stage abstract-level process. After deduplication and broad relevance filtering, a stricter eligibility audit retained 505 studies assignable to five predefined functional clusters: robotics and access (235 records; 46.5%), mechanical surfaces and tribology (141; 27.9%), sensing and monitoring (106; 21.0%), vision and inspection (14; 2.8%), and control and computation (9; 1.8%). Publication output accelerated markedly after 2022, with 2025 yielding the highest annual count. The principal gap identified is not a shortage of biomimetic concepts, but their limited consolidation into deployable industrial inspection and maintenance architectures. A translational taxonomy connecting biological principles, engineering abstractions, enabling technologies, and mechanical use cases is proposed as an interpretive structuring tool for future research prioritisation and technology-readiness discussion. Full article

Object detection in underground coal mines is plagued by critical challenges, including low illumination, high dust-induced noise, extensive target scale variation, frequent occlusion, and fragmented target feature representation, which commonly result in severe missed detections and insufficient detection confidence. To tackle these bottlenecks, this study proposes DLR-YOLO, a high-performance lightweight object detector built upon the YOLOv11n baseline, with three core optimized modules. Specifically, a dynamic multi-scale global perception enhancement module (DMGPEM) is embedded in the backbone to realize adaptive multi-scale feature extraction under low-light conditions; a lightweight cross-attention (LCA) module is integrated into the neck to achieve efficient fusion of shallow detail features and deep semantic features while suppressing dust-related noise; and a Reparameterized stem (RepStem) module is developed for initial feature extraction to minimize critical information loss during downsampling. Experimental results on our self-collected and annotated in-house underground coal mine dataset demonstrate that DLR-YOLO achieves 94.4% mAP@50 and 66.7% mAP@50–95, corresponding to 3.5 and 5.7 percentage point improvements over the YOLOv11n baseline, respectively. Ablation studies further validate the independent effectiveness of each proposed module. Meanwhile, the detector maintains a lightweight architecture with only 2.7M parameters and 6.6 GFLOPs, and reaches an inference speed of 157.1 FPS, outperforming several state-of-the-art real-time detectors, including YOLOv12, YOLOv13, and RT-DETR, on the same dataset. These findings confirm that DLR-YOLO provides a robust, high-performance technical foundation for real-time safety monitoring systems in complex underground coal mine environments. Full article

Background: Wastewater-based epidemiology (WBE) is a valuable population-level surveillance tool for monitoring SARS-CoV-2 circulation. However, evidence on optimal viral concentration approaches in confined institutional settings such as prisons remains limited. This study aimed to compare the performance of Direct Capture (DC) and Electronegative Membrane Filtration (EMF) for SARS-CoV-2 RNA detection in wastewater from a prison facility in Selangor, Malaysia. Methods: Composite wastewater samples collected over 18 weeks (April–August 2023; n = 50) were analysed by RT-dPCR targeting the N1 and N2 gene regions, with concentrations normalized to pepper mild mottle virus (PMMoV). DC consistently outperformed EMF across both gene targets. Median concentrations obtained using DC were 11.09 × 10³ copies L⁻¹ (N1) and 3.43 × 10³ copies L⁻¹ (N2), compared with 0.70 × 10³ copies L⁻¹ (N1) and 0.48 × 10³ copies L⁻¹ (N2) using EMF. Detection frequencies were higher with DC (N1: 94%, N2: 84%) than with EMF (N1: 88%, N2: 76%). Paired statistical analysis confirmed significant differences between methods (N1: p = 2.3 × 10⁻⁷; N2: p = 9.4 × 10⁻⁵), and Bland–Altman analysis demonstrated systematic underestimation by EMF (mean bias −1.15 log₁₀ for N1; −0.87 log₁₀ for N2), indicating that the methods are not analytically interchangeable. Conclusions: Normalization reduced absolute SARS-CoV-2 RNA concentrations while preserving temporal trends, supporting its use to improve comparability across sampling periods. Overall, these findings demonstrate that DC combined with N1 detection provides a more sensitive and reliable approach for SARS-CoV-2 WBE in confined settings, underscoring the importance of methodological optimization to strengthen early-warning capacity in high-risk environments. Full article