Search Results (2,642)

Scientific studies of mine closure and ecosystem management have become very important since the rate of coal mine closures in China has increased rapidly over the last decade. This study first analyzed spatiotemporal changes in land use and ecosystem services value (ESV) during the period 2000–2020 around the Kailuan Mining Area in Tangshan City. The area has a history of over 100 years of continuous mining activities in the region. The analyses used the PLUS model, multi-scenario simulation, and ESV equivalent factor method and multi-source data on land use, mining activities, socioeconomic factors, and climatic conditions. The study then projected land-use changes and spatiotemporal ESV characteristics for the year 2030 for two alternatives: (1) the Current Development Scenario (CDS), representing the current pace of development without mine closure; and (2) the Ecological Restoration Scenario (ERS), representing mine closure and ecological restoration. Key results include: (1) during 2000–2020, cultivated land and construction land were the primary land uses, with the overall trends showing decrease in cultivated, forest, pasture, and unused lands, varying water use areas, and continuously increasing construction land; (2) the revised ESV results show that total ESV declined from 31.27 million USD in 2000 to 25.30 million USD in 2020, a net decrease of 6.19 million USD, mainly because of cropland loss and degradation of forest and grassland; and (3) for 2030, the CDS projected a continued decline in total ESV to 24.30 million USD, whereas the ERS increased total ESV to 26.50 million USD, which is 2.19 million USD higher than the CDS and 1.20 million USD higher than the 2020 baseline. Compared with the CDS, the ERS increased cropland by 13.20 km² and reduced construction land by 10.06 km², indicating that reclaiming subsided water bodies and idle construction land into cropland and restored ecological land can enhance ecosystem services while mitigating subsidence-related risks. The framework can support data-driven post-mining land-use planning and ecological management in resource-based regions. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-model-based detectors; classical detectors are typically trained per dataset and degrade under the distribution shift that is common in operational technology, where attack repertoires evolve faster than retraining cycles. Two foundation-model families are now plausible candidates: open-source Large Language Models (LLMs) and recent tabular foundation models (TabPFN, TabICL) pre-trained for in-context tabular inference. We compare the two families head-to-head, alongside Random Forest and XGBoost classical anchors, across three established industrial security benchmarks (SWaT, HAI, WUSTL-IIoT-2021) under a controlled multi-seed full-holdout protocol with paired McNemar and cross-seed Mann–Whitney tests. The empirical picture is dataset-dependent rather than universal: tabular foundation models establish a strong, previously unreported baseline that is competitive with or superior to classical anchors on every dataset evaluated, while LLMs are complementary detectors with a specific advantage on schemas that carry process-engineering semantics (such as SWaT’s named sensor channels). A per-class analysis on the WUSTL five-class attack taxonomy shows that the two families have structurally different strengths: tabular methods dominate traffic-rich attacks (Denial-of-Service, Reconnaissance), whereas LLMs are competitive on rare attack types (Backdoor, Command Injection). A confidence-gated cascade that escalates only low-confidence tabular decisions to an LLM exceeds either detector alone at a small query budget, and a leave-one-attack-type-out analysis shows that foundation-model detectors generalise to unseen attack families substantially better than the classical anchors. The appropriate detector choice in industrial cyber-physical security is therefore informed by the dataset’s feature schema, the attack-type mix, and the operational cost envelope, rather than by a specific performance metric. Full article

This study investigates the spatiotemporal dynamics of peri-urban expansion and land use transformation in Izmir, Türkiye, over 36 years (1986–2022) using an integrated GIS-based Multi-Criteria Decision Analysis (MCDA) framework. Multi-source datasets, including Landsat imagery, CORINE land cover (CLC) data, demographic statistics, and spatial variables (slope, transportation proximity, and distance to the city center), were combined to delineate urban, peri-urban, and rural zones. Results reveal a substantial percentage increase in urban areas from 2.8% in 1986 to 10.48% in 2022, corresponding to an expansion of approximately 7.6% (≈908.56 km²). In contrast, agricultural land declined by 5.8%, while forest areas experienced a more severe reduction of 19.1%, indicating significant environmental degradation. Population dynamics further support this transformation, with peri-urban districts exhibiting growth rates exceeding the metropolitan core average of 1.8% (1986–2010), followed by a relative slowdown to 0.5% after 2010, accompanied by outward migration-driven expansion. Spatial analysis demonstrates that peri-urban growth is strongly influenced by accessibility and topography, with development concentrated within 30–50 km of the city center and along major transportation corridors (500–1000 m buffers). Land Surface Temperature (LST) analysis indicates increasing urban heat island intensity, with surface temperatures ranging from 12 °C to 46 °C, particularly in densely built inner peri-urban zones. The MCDA-based classification identifies distinct inner and outer peri-urban belts, characterized by contrasting density, land use patterns, and environmental pressures. Overall, the findings highlight that Izmir’s peri-urbanization is a heterogeneous and rapidly evolving process driven by demographic, spatial, and policy-related factors. The study provides a replicable methodological framework and emphasizes the urgent need for integrated, sustainability-oriented planning strategies to mitigate ecological loss and uncontrolled urban sprawl. Full article

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

Hydrogen-compatible elastomeric seals are critical for the reliability and safety of high-pressure hydrogen infrastructure. However, hydrogen exposure can alter the mechanical response and surface condition of elastomeric materials through coupled transport–mechanical interactions. This study presents a comparative experimental and data-driven investigation of the pressure-dependent degradation behavior of ethylene propylene diene monomer (EPDM), nitrile butadiene rubber (NBR), and fluorocarbon elastomer (FKM) O-ring seals following 192 h exposure to hydrogen pressures ranging from 800 to 7000 psi at room temperature. Tensile testing was performed directly on complete O-ring geometries, and descriptor-based analysis was used to quantify peak-response behavior, energy absorption, stiffness evolution, and normalized deformation characteristics. Multivariate statistical methods, principal component analysis (PCA), clustering analysis, and Random Forest regression were applied to identify material-specific degradation patterns. NBR maintained the highest overall load-bearing capability and stiffness-related response across the investigated pressure range, whereas EPDM exhibited more compliant and non-monotonic deformation behavior. FKM showed the strongest pressure sensitivity, with substantial increases in force- and stiffness-related descriptors at elevated hydrogen pressures. Optical image analysis revealed pronounced increases in defect density and defect area fraction for NBR, while FKM exhibited comparatively stable surface-state behavior. PCA and clustering analyses identified distinct material-dependent degradation trajectories, and Random Forest regression achieved an R² value of 0.888 for energy-absorption prediction. The results demonstrate that hydrogen-induced degradation emerges through coupled interactions among stiffness evolution, deformation progression, energy absorption, and surface-state changes, providing a comparative framework for assessing elastomer performance in hydrogen environments. Full article

Pectinases are a group of enzymes widely applied in agri-food processes. This study aimed to isolate and characterize pectinase-producing yeasts and yeast-like fungi from soil and humus samples collected in a Moroccan argan forest, a region characterized by arid to semi-arid climatic conditions, with emphasis on screening and evaluating their pectinolytic activity. Among nine isolated strains, four exhibited detectable pectinolytic activity on pectin agar medium. Two promising isolates were molecularly identified by ITS region sequencing as Coniochaeta pulveracea PX765016 and Coniochaeta ligniaria PX765017. Notably, C. pulveracea PX765016 showed the highest pectinolytic potential, with a pectinolytic degradation index of 4.2 on pectin agar. This strain also exhibited maximal pectinase production after 96 h of submerged fermentation in YEPD medium under optimized conditions of pH 4, 30–35 °C, and 0.5% (w/v) pectin. The crude enzyme obtained under these conditions exhibited a specific activity of 559.90 ± 11.62 U/mg. The enzyme was subsequently subjected to sequential purification comprising ammonium sulfate precipitation, dialysis, and gel filtration chromatography on a Sephadex G-100 column, yielding a 2.99-fold purification with a final recovery of 14%. The purified enzyme exhibited optimal activity at pH 6.0 and 40–55 °C, with a reaction time of 20 min. Kinetic analysis of pectin hydrolysis revealed a Michaelis–Menten constant (Km) of 7.33 mg pectin per mL and a maximum reaction velocity (Vmax) of 1666.7 U/mg. To the best of our knowledge, this is the first report of pectinase production by a member of the genus Coniochaeta, and the first characterization of pectinase activity from C. pulveracea. Full article

Uniaxial compressive strength (UCS) is a fundamental parameter for rock engineering design and stability assessment, but direct laboratory testing is costly, time-consuming, and often difficult for weak or fractured rocks. To improve predictive accuracy while preserving mechanical interpretability, this study proposes a Gated Empirical-Law LightGBM model (GEL-LightGBM). The framework embeds three representative rock-strength priors, including point-load strength, multi-index strength, and porosity-degradation relationships, as empirical-law experts. A sample-adaptive gating mechanism dynamically assigns its contributions for different rock states, while a controlled residual corrector captures nonlinear deviations between empirical estimates and measured UCS. Using 344 published rock-mechanics samples, porosity, Schmidt rebound hardness, P-wave velocity, and point-load strength index were used as predictors. GEL-LightGBM outperformed LightGBM, XGBoost, random forest, MLP, CNN, SVR, and BPNN, achieving a testing R2 of 0.9790 and an RMSE of 7.5623 MPa. SHAP analysis identified porosity as the dominant factor, contributing 49.0%, followed by rebound hardness (32.2%) and P-wave velocity (17.2%). The strongest interaction occurred between porosity and rebound hardness (2.31 MPa). These findings indicate that GEL-LightGBM provides accurate, stable, and physically interpretable UCS prediction for heterogeneous rock datasets. Full article

As a Chinese national key protected medicinal fern naturally occurring in forest understories, Cibotium barometz faces severe threats of wild population degradation, while standardized large-scale artificial breeding technology for conservation purposes remains immature. To establish an efficient spore-based conservation propagation system for this endangered forest fern, this study quantified the independent and interactive effects of spore storage temperature, storage duration and sowing density on spore germination, gametophyte growth and sporophyte seedling establishment. Spores were preserved under four gradient temperature treatments with sequential sampling at multiple storage durations, followed by sowing trials with a series of density gradients; germination rate, seedling establishment rate and gametophyte–sporophyte conversion rate were dynamically recorded and statistically analyzed. The results demonstrated that appropriately extended storage significantly shortened the germination phase and simultaneously elevated both spore germination and sporophyte seedling formation rates. Among all temperature treatments, storage at −4 °C achieved the maximum germination and seedling establishment capacity, whereas ultra-low-temperature cryopreservation at −196 °C greatly promoted gametophyte–sporophyte conversion rate. The optimal sowing density balancing growth space and survival rate was determined to be 30 spores per cm². The complete dynamic developmental traits covering the full spore propagation life cycle of C. barometz were systematically summarized in this work. Our findings supply reliable technical parameters to standardize spore breeding protocols, and offer critical support for ex situ conservation, wild forest population restoration and sustainable resource utilization of C. barometz. Full article

This study investigated the impact of recreational use on trails in the Atlantic Forest (Ubatuba Municipality, São Paulo State, Brazil) using physical, chemical and geochemical indicators. Five trails with different morphological characteristics were selected, and paired samples were collected from the trail surface (TR) and trail-side slope (TA). The statistical approach combined local analyses for each trail with global clustering (n = 19) using Student’s t-test, along with multivariate modeling through Principal Component Analysis (PCA) and Pearson correlation. The analysis included physical attributes (bulk density, particle size and porosity), chemical attributes (pH, organic matter and macronutrients) and geochemical compositions (major oxides and trace elements determined by XRF). The overall results reveal systematic compaction in the trail surface (TR), with bulk density increasing from 1.32 g/cm³ (TA) to 1.37 g/cm³ (TR) (p = 0.038), and total porosity decreasing from 47.26% to 45.34% (p = 0.016). In contrast, the geochemical oxide composition (SiO₂, Al₂O₃, Fe₂O₃) remained stable (p > 0.05), indicating the resilience of the mineral matrix. However, significant local dynamics (p < 0.05) in K₂O and MgO were observed in more preserved trails, associated with surface compaction and fragmentation of the litter layer, and phosphorus showed strong dependence on organic matter (r = 0.85). Multivariate analysis indicates that degradation is predominantly physical and micromorphological at the local scale, with bulk density and porosity being the most sensitive indicators for environmental monitoring. Full article

For stable operation of autonomous surface ships, real-time anomaly detection of engine conditions must be coupled with an operational framework that sustains model performance in dynamic maritime environments. This study proposes an autonomous maintenance system that combines a subsystem-level condition-based maintenance (CBM) model with a dedicated MLOps framework. The main engine is decomposed into multiple functional component units, each governed by an independent diagnostic pipeline that applies a hybrid algorithm combining an attention LSTM autoencoder with an isolation forest to capture subtle anomalies. Although this hybrid attains higher precision than conventional single models, it remains sensitive to operating environment shifts. To address this, we develop an onboard MLOps pipeline that monitors distributional shifts in real-time sensor data and executes an autonomous maintenance mechanism, retraining and redeploying models on local data when performance degradation is anticipated. A dual-monitoring rule set based on a standardized deviation score and its smoothed change rate is used to discriminate abrupt mechanical anomalies from gradual drift. Experiments on a fault simulation testbed indicate that, under data drift, the system can recover detection reliability and adapt to changing engine conditions, providing a technical basis for the self-sustaining reliability of autonomous surface ships. Full article

Solar energy has emerged as a promising alternative to fossil fuels for mitigating climate change; however, efficient photovoltaic (PV) operation requires continuous monitoring and accurate energy forecasting. This study proposes an intelligent IoT-based photovoltaic monitoring and short-term energy prediction system integrating real-time sensing, solar tracking, and machine learning techniques. A small-scale experimental prototype based on a 10 W photovoltaic panel was implemented to collect real-time data, including voltage, current, temperature, humidity, ultraviolet radiation, and dust accumulation during a 30-day monitoring period under outdoor conditions. The acquired data were transmitted through an IoT architecture based on the Arduino Uno and ESP32, programmed using Arduino IDE, and integrated with the Blynk cloud platform for real-time monitoring and analysis. To evaluate predictive performance, Random Forest, XGBoost, and LSTM models were trained and compared for photovoltaic energy forecasting. Experimental results showed that XGBoost achieved the best predictive performance, obtaining the lowest error values (MAE = 0.00077, RMSE = 0.001103) and the highest coefficient of determination (R² = 0.918), outperforming the other evaluated models. In addition, the proposed system enabled effective remote monitoring and degradation analysis associated with environmental conditions. The results demonstrate the potential of integrating IoT and machine learning for accurate short-term photovoltaic energy forecasting in small-scale experimental environments. Nevertheless, further long-term and large-scale validation is required to evaluate system robustness under operating conditions. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article

Introduction: The global ornamental fish trade is a rapidly expanding sector and a major pathway for the introduction of non-native species, particularly in freshwater ecosystems in developed countries. The introduction of non-native species can result in a range of ecological impacts, including predation, competition, hybridization, and disease transmission, often leading to ecosystem degradation and biotic homogenization. Therefore, it represents a clear ecological risk, especially serious in freshwater systems with a high endemism rate, such as the Iberian Peninsula. The occurrence of ornamental non-native species in the Iberian Peninsula has been common, yet little has been done to describe the overall ornamental fish trade as a first step to evaluate invasion risk. Objective: This study characterizes the import dynamics of ornamental freshwater fish in Portugal between 2020 and 2024 and evaluates its potential role as a pathway for species introductions. Methodology: Data were obtained from the Institute for Nature Conservation and Forests database, including information on species composition, quantities, sizes, prices, and countries of origin. A total of 431 records were analyzed, resulting in 27,689 validated entries of imported freshwater fish, which were taxonomically verified and filtered to retain only freshwater species. Results: A total of 666 species from 88 families were identified, with an average of 380 species imported annually, reflecting high taxonomic diversity. Import volumes increased from approximately 1.25 million individuals in 2020 to 1.75 million in 2024, while total import value nearly doubled from €300,000 to €600,000. Imports were predominantly from five Southeast Asian countries, particularly Indonesia and Vietnam, and largely supported by aquaculture production (88%). A stable core of highly traded species, including Carassius auratus, Poecilia reticulata, and Paracheirodon innesi, suggests a sustained and very high propagule pressure, while some species variability was observed on yearly basis, suggesting the importance of monitoring programs on actual imports. Conclusions: Overall, the ornamental fish trade represents a significant and growing pathway for biological invasions in Portugal. The combination of increasing trade volume, high species diversity, and persistent dominance of key taxa highlights the need for improved monitoring, regulatory frameworks, and public awareness to mitigate ecological risks. Full article

African soils face increasing levels of metal pollution due to industrialization, artisanal mining activities, improper waste management, and enhanced agricultural productivity. However, unlike many organic pollutants, heavy metals do not degrade naturally and therefore persist in environmental systems for prolonged periods. Heavy metals accumulate over many decades in the soil and bioaccumulate through the food chain causing severe health complications such as cancer, kidney problems, and neurological impairment. This paper reviews the current literature on the origin, prevalence, and behavior of the main pollutants Pb, Cd, Cr, As, Hg, and Cu. The major phytoremediation methods including phytoextraction, rhizofiltration, phytostabilization, and phytovolatilization are highlighted alongside in planta screening methods for hyperaccumulating plants including Berkheya coddii (Ni) and Haumaniastrum robertii (Co). The paper evaluates various enhancement techniques such as the use of chelators, Rhizobium inoculations, and genetic modifications. The significance of these approaches in tropical and subtropical climates is discussed. The paper suggests a holistic framework involving empirical kinetic modeling, geospatial machine learning (random forest, kriging), and molecular omics in prediction modeling. Major hurdles in such predictions include lack of field-based verification of the models, biotechnology safety of genetically modified (GM) organisms, and inadequate regulations. Future perspectives emphasize community-driven phytomining, biomass recycling, and resilient phytoremediation solutions. Full article