Search Results (17,478)

Tropical forests are major contributors to the global carbon budget but are affected by disturbances such as hurricanes, which cause extensive yet spatially variable tree damage and mortality. High-resolution maps of forest aboveground biomass (AGB) and its temporal change aid in quantifying disturbance impacts, assessing resilience, and supporting forest management. This study presents wall-to-wall, high-resolution mapping of pre- and post-hurricane AGB and AGB change across Puerto Rico. The maps represent forest AGB measured 0–2 years before and after two major hurricanes (Irma and Maria), as well as longer-term conditions up to four years post-disturbance. AGB was modeled using Random Forest (RF) algorithms that integrated Forest Inventory and Analysis (FIA) plot data with canopy height and cover derived from discrete-return LiDAR, multi-temporal satellite imagery, and additional geospatial predictors. Model performance was evaluated using a 10% holdout dataset. Predicted versus observed regressions yielded, at 10 m and 90 m spatial resolutions, respectively, r = 0.75 and 0.79 with model residual mean standard deviation (RMSD) = 87.7 and 39.2 Mg ha⁻¹ for pre-hurricane AGB, and r = 0.77 and 0.74 with RMSD = 69.7 and 58.1 Mg ha⁻¹ for post-hurricane AGB. AGB change models at 10 m and 90 m resolutions yielded r = 0.58 and 0.73 with RMSD = 17.0 and 18.7 Mg ha⁻¹, respectively. Ten-fold cross-validation produced stronger correlations and reduced RMSD values. Frequency distributions of mapped pixels of forest AGB and AGB change, in comparison with previously published maps and island-wide field-based estimates, indicate that, although hurricane-driven biomass reductions of up to 20% were recorded in field data, patterns consistent with longer-term recovery from historical deforestation are evident within four years after the hurricanes. The 10 m maps capture fine-scale heterogeneity in canopy damage and regrowth, whereas the 90 m maps emphasize broader regional patterns. This integrated framework provides a transferable approach for monitoring forest structure and biomass dynamics in disturbance-prone tropical ecosystems. Full article

Turnout locomotor activity is a potentially informative indicator of health and welfare in older horses, yet objective field data in seniors remain limited. We examined whether a brief turnout recording could detect cross-sectional associations between chronological age and locomotor activity in senior horses in this study setting. In this single-site observational study, 28 senior Selle Français horses (17–35 years) contributed 122 paddock sessions (2 h each), with total distance and mean speed quantified using a Polar Team Pro sensor. Associations with age were assessed using linear mixed-effects models adjusted for temperature and precipitation. Age was decomposed into between-horse and within-horse components to separate cross-sectional from within-horse information. Raw (untransformed) total distance ranged from 148 m to 3994 m (median 1128 m; IQR 638–1779 m; mean 1292 ± 834 m). Log-transformed total distance was negatively associated with age (β = −0.062 per year; 95% CI −0.094 to −0.032; p < 0.001), driven by the between-horse component (β = −0.063; q = 0.003). The within-horse estimate was imprecise and not statistically supported (p = 0.75). Mean speed showed a similar pattern, with a significant between-horse association (β = −0.060; q = 0.003) and an imprecise within-horse estimate (p = 0.87). These findings suggest that brief paddock actimetry may help characterize between-horse heterogeneity and support group-level welfare monitoring. However, the present dataset does not allow robust inference about within-horse ageing trajectories or individual-level biological ageing. Larger multi-site cohorts with denser follow-up and external validation are needed before individual trajectories or clinical interpretation can be established. Full article

High-voltage bushings, the fault-prone key electrical components of transformers, are critical for real-time and high-accuracy fault monitoring and management. Intelligent fault detection via infrared images is plagued by low classification accuracy due to massive interference from similar tubular objects and small target characteristics. This study proposes a lightweight deep learning model, MTrip–YOLO, an improved YOLO11n integrated with Mamba-2 (Structured State Space Duality, SSD) and Triplet Attention, to achieve efficient fault monitoring in complex backgrounds. The training and validation dataset comprises open-source images, on-site data from a substation, and field-collected infrared images, categorized into four types: normal bushings, poor contact, oil shortage, and high dielectric loss faults. Mamba-2 captures the long-range global context of infrared features with its linear-complexity long-range modeling capability to enhance feature extraction, while Triplet Attention suppresses complex background radiation noise through cross-dimensional interaction without dimensionality reduction, enabling the model to focus on small targets and accurately classify bushings from morphologically similar strip-shaped objects. Experimental results show that MTrip–YOLO achieves a top mAP50 of 91.6% and a minimal parameter count of 1.9 M, outperforming Faster R-CNN, RT-DETR, and YOLO26n across all evaluated metrics and being potentially suitable for edge deployment on UAV-mounted or handheld infrared platforms, pending hardware validation on embedded computing devices. Ablation experiments verify the independent contributions of Mamba-2 (0.8027% mAP50 improvement) and Triplet Attention (0.89327% mAP50 improvement), with a synergistic effect from their combination. MTrip–YOLO provides a potential edge-deployable solution for high-voltage bushing fault monitoring, offering important application value for the intelligent operation and maintenance of substations. Full article

Hyperspectral (HS) analysis was used to measure the dynamics of Fusarium head blight (FHB) disease severity on panicles of three oat cultivars, ‘Husky’, ‘Ivory’, and ‘Lelde’, under greenhouse conditions. Inoculation with Fusarium spp. spore material was conducted (i) on the seeds and (ii) plants at the mid-flowering stage (BBCH 65). Disease development on oat panicles was assessed visually, and imaged with an HS camera from the end of the flowering stage (BBCH 69) to the early–middle ripe stage (BBCH 83–85). To verify that FHB symptoms were caused by Fusarium spp. pathogens, a microbiological test was performed. At the end of the trial, mycotoxin analysis of the kernels was conducted. The collected HS data from diseased and control plant panicles were used to estimate the head blight index (HBI). A Python-based software was developed to assess HBI at the pixel level. Both visual assessment and HS analysis confirmed statistically significant differences in disease severity between all treatment options. The highest disease severity results were obtained in the last disease assessment run (BBCH 83–85) for the inoculated head treatment. Microbiological test results confirmed that FHB symptoms in oat kernels were mostly caused by F. sporotrichioides. The correlation coefficient between the visually assessed FHB disease severity results and HS analysis results was 0.969. The correlation coefficient between T-2/HT-2 mycotoxins and HS disease severity results was 0.971, which suggests the potential for using HS analysis in field monitoring for mycotoxin content detection. Full article

Bearings serve as vital components in rotating machinery. Fault diagnosis of bearings constitutes an essential area within mechanical health monitoring. However, most existing methods rely solely on single-modal data or employ a single signal-to-image conversion technique, leading to insufficient information dimensionality and inadequate feature representation, which ultimately limits diagnostic accuracy. To address these challenges, this paper proposes a bearing fault diagnosis method (GADF-MTF-RP-VGG16, GMRVGG) based on tri-modal image feature fusion. Specifically, three image conversion techniques—Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP)—are utilized to first convert 1D vibration signals into 2D images. Subsequently, shallow to deep features are extracted and fused through the VGG16 backbone network. Finally, fault diagnosis is achieved by integrating a fully connected classifier layer. The proposed methodology was comprehensively validated on both the Case Western Reserve University (CWRU) and the University of Ottawa datasets, which were augmented with severe 6 dB Gaussian white noise and 6 dB pink noise to simulate complex industrial environments. Under these harsh conditions, the proposed method achieved superior overall accuracies (up to 96.9% on the CWRU dataset and consistently 95.8% on the Ottawa dataset), significantly surpassing conventional single-modal approaches. This effectively addresses the limitations of insufficient feature dimensionality and inadequate representation, establishing a highly reliable and robust solution for intelligent bearing fault diagnosis. Full article

Quantifying the effects of land-use changes on soil fertility is essential for agricultural planning, yet long-term analyses combining field and remote sensing data remain scarce in Hungary. This systematic review followed PRISMA 2020 guidelines to assess arable land fertility trends between 2000 and 2020. A comprehensive search of WoS, Scopus, and Google Scholar identified 202 records, with 106 studies meeting inclusion criteria. Eligibility required empirical soil data collected from Hungarian arable lands. Among these, 17% reported declines in SOC, 13% indicated nutrient depletion, 36% observed stable or lost fertility, and 34% documented improvements. Regarding monitoring methods, 41% relied solely on field sampling, 44% applied GIS or spatial analyses, and 15% incorporated remote sensing indices such as NDVI. Evidence revealed spatial–temporal heterogeneity: fertility declines occurred in intensively cultivated regions, while western Transdanubia showed stability. Trends were linked to land-use intensification and intermittent reductions in agricultural area. Integration of remote sensing indices, such as NDVI, with field observations enhanced detection of spatial and temporal patterns. These findings underscore the need for harmonised monitoring frameworks, precision agriculture tools, and predictive modelling to support sustainable soil management. Identifying fertility-decline zones informs policy aligned with the EU Soil Strategy 2030 and supports Hungary’s agricultural resilience. Full article

Background noise and intensive sample preparation frequently compromise the field screening of Bacillus anthracis. Addressing these analytical bottlenecks, we constructed a giant magnetoresistive (GMR) biosensor incorporating geometrically tailored trapezoidal magnetic flux concentrators (MFCs). 3D finite element magnetic simulations directed the MFC topology to mitigate edge saturation, reconciling central magnetic gain with spatial uniformity. The resulting platform demonstrated a 100-fold sensitivity improvement over recent electrochemical methods, achieving a limit of detection (LOD) of 10 CFU/mL in standard buffers, with the entire testing process completed within 40 min. Direct target quantification remained viable in heterogeneous matrices—muddy water, whole milk, and apple cider—circumventing tedious pretreatment. This geometric and magnetic optimization yields a pragmatic sensing architecture tailored for on-site biodefense monitoring. Full article

Alternate Wetting and Drying (AWD) is a proven water-saving irrigation technique that reduces irrigation water use and methane emissions from rice cultivation. The emission reduction achievable through AWD irrigation practices represents a significant opportunity for credits generation, particularly for the major rice-producing countries. To capitalize on this opportunity, a scalable, reliable, and cost-effective information system for AWD irrigation monitoring, reporting, and verification (MRV) is urgently needed. However, most existing MRV systems depend on manual data collection or software systems driven by field-based observation. Satellite remote sensing, derived from different tools and techniques, has achieved considerable traction in agriculture monitoring. This study attempts to develop a remote sensing and Internet of Things (IoT)-based system for large-scale AWD irrigation detection and monitoring as a potential tool for the MRV system. IoT sensor-based water level measurement, L-band PALSAR-2 full polarimetric data, and intensive field survey data were integrated and analyzed. Three study sites in the Naogaon District of Bangladesh, one of the major rice-growing regions, were selected as the study area. The PALSAR-2 full-polarimetric data were collected, radiometrically and geometrically corrected, and converted into the backscattered coefficient (Sigma-naught) value. Using the full-polarimetric channel of VV, VH, HH, and HV, the Freeman–Durden three-component decomposition, surface scattering, double-bounce, and volume scattering were constructed to assess the irrigation water condition of the rice paddy field. IoT sensors data, field survey data, and three-component data on 8 different dates and a total of 704 fields during the rice growing period were subsequently analyzed and cross-calibrated. The results showed that surface scattering and double bounce are more sensitive to irrigation water status, while volume scattering primarily responds to plant height changes. By leveraging the backscatter characteristics of these three components, a Random Forest classifier was applied to classify AWD and non-AWD irrigated paddy fields. Classification accuracy achieve 94% in early crop growth stages and declined to 80% during dense canopy stages. These findings offer a reliable and scalable approach to documenting water regime management with direct applicability to carbon emissions reduction verification and carbon credits claims. Full article

Urban micro-tidal canals frequently suffer from severe hypoxia due to restricted hydrodynamic exchange and untreated discharges. Field monitoring during a 2022 mass fish mortality event at the Dongsam tidal canal revealed that during the ‘tidal window gap’—a hydraulic stagnation period required for passive tidal flushing—bottom-layer dissolved oxygen (DO) plummeted to a lethal 0.44 mg/L. To address the limitations of passive tidal exchange, this study proposes a conceptual hybrid water purification framework integrating active ecological interventions: wall-mounted spiral flow aeration for continuous oxygenation and vertical bio-curtains for pollutant interception. By synergizing fluid mechanics with ecological engineering, core design parameters were systematically derived: an effective mixing width (W_eff = 2.2 h), longitudinal spacing (L_s = 13.6 × W_eff ), an optimal root immersion ratio (D_r/h = 0.6), and climate-adaptive planting densities (ρ_p ≈ 2–32 plants/m²). Additionally, a corrosion-resistant FRP guide rail system was incorporated to facilitate autonomous adaptation to tidal fluctuations. The framework was conceptualized through a prototype design for the Dongsam canal and subsequently scaled to 15 international micro-tidal canals across diverse climatic zones. The optimized bilateral staggered configuration established a continuous 528 m² ecological refuge, ensuring DO levels recover above the critical 3 mg/L threshold. Ultimately, this research presents a comprehensive methodological framework and a flexible engineering toolkit to guide water quality and ecological resilience enhancements in shallow urban waterways worldwide. Full article

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement. Full article

Accurate identification of fly species composition and their responses to attractants is critical for risk assessment and targeted vector control. To evaluate the efficacy of different attractants in surveillance and their species-specific trapping biases, a standardized field study was conducted from June to September 2021 across seven representative cities in China’s major zoogeographical regions: Xining, Ürümqi, Yanji, Beijing, Chongqing, Kunming, and Sanya. Cage traps baited with either fish offal or sugar–vinegar solution were deployed, supplemented by hand-net collection. A total of 134 traps were set, yielding 2132 flies belonging to 21 species. Fish offal captured 1961 flies (91.9%), significantly more than the 101 flies (4.7%) caught with sugar–vinegar solution (χ² = 1582.3, p < 0.001). Lucilia sericata was the dominant species (885 individuals, 41.51%), followed by L. cuprina (178, 8.35%), Sarcophaga portschinskyi (127, 5.96%), and Sarcophaga africa (100, 4.70%). High-risk taxa (Calliphoridae and Sarcophagidae) were almost exclusively attracted to fish offal. Our findings demonstrate that protein-based baits, such as fish offal, are substantially more effective than traditional sugar–vinegar solutions for capturing epidemiologically relevant fly species across diverse ecological zones in China. We recommend prioritizing proteinaceous attractants in national fly surveillance programs and advocate for routine species-level identification to enable risk-informed vector monitoring. Full article

Pasture ecosystems in the arid regions of Kazakhstan are highly vulnerable to the combined effects of climatic variability and increasing grazing pressure, while long-term spatial assessments of degradation remain limited. This study develops an integrative remote sensing-based framework for assessing pasture degradation in Atyrau Oblast by combining long-term NDVI time series (2000–2023) with grazing pressure indicators ($K_{sust}$ and LIPS), field observations, and climatic data. The results show that 49.3% of pasturelands are degraded, with statistically significant negative NDVI trends observed across most administrative districts. Areas experiencing pasture overload ($K_{sust}$ > 1.2) spatially coincide with persistent vegetation decline, and significant negative relationships between NDVI and livestock numbers are identified in several districts. The analysis also reveals spatial heterogeneity and lagged responses of vegetation dynamics to grazing pressure under varying climatic conditions. The proposed approach provides a novel integrative framework that links spectral vegetation indicators with climate-adjusted grazing metrics, enabling the identification of degradation hotspots and supporting spatially differentiated pasture management. This framework can be applied in regional land monitoring systems to improve decision-making for sustainable rangeland use under climate change. Full article

The aim of this paper is to investigate dynamic adjustment of the in situ stress field and the stability of main faults in the Zhangbei strong seismic region. Firstly, we utilized in situ stress measurement and monitoring data to discuss the dynamic adjustment process of the in situ stress field. Subsequently, the Fault Slip Potential (FSP) v.1.0 software package was employed to calculate the fault slip potential of the main faults. Finally, the potential hazard of fault activity was assessed. The conclusions are as follows. (1) Since November 2015, the in situ stress field has been primarily influenced by NEE compressive tectonic action, with a slight enhancement in the near SN compressive tectonic action. (2) In the initial stage, NE-trending faults exhibited the highest stress accumulation levels, with near-EW-trending faults the lowest. Influenced by the enhanced near-SN-trending compressive action, as of 19 October 2020, near-EW-trending faults displayed the highest stress accumulation, followed by NW-trending faults, with NE-trending faults showing the least accumulation. (3) From November 2015 to October 2020, the in situ stress field was in a continuous accumulation process. Using the Shangyi–Pingquan fault as a boundary, fault activity in the southern part of the strong seismic region is more hazardous than that in the northern part. Full article

The key to implementing IPM of wireworms effectively is to associate feasible, reliable and affordable sampling methods with well-defined damage thresholds. As wireworms live underground, they cannot be observed directly, thus estimating population levels can be challenging. Soil sampling to ascertain larval density is very time-consuming, and although the use of bait traps is much more time-effective, it is unclear how wireworm numbers in bait traps are associated with wireworm density. The work described herein was conducted between 1993 and 1999 in two regions of Northern Italy: Veneto and Piedmont. The experimental protocol involved placing soil bait traps in a 15–30 m × 10 m grid in selected cultivated fields and taking a soil sample 3 m from the location of each bait trap. The number of monitoring points ranged from 12 to 48 per site. Both trap contents and soil cores were put in funnels to dry out, forcing the wireworms to move and fall into a vial, according to the Berlese method. A moderate association was found between the number of wireworms (Agriotes brevis, A. sordidus and A. ustulatus) caught by the bait traps and by soil sampling, indicating a potential for reciprocal estimation between methods. In other words, the number of bait-trap catches can be estimated by soil sampling (e.g., when bait traps cannot be used due to low temperatures or when growing plants cover a field) and vice versa. The potential of bait traps for catching wireworms was shown to be 5 to 25 times higher than the potential of soil sampling. The threshold values for soil samples, which were derived from the original bait-trap values, range between 15 and 20 larvae/m². Full article

Colonoscopy is the gold standard for diagnosing and monitoring gastrointestinal diseases. However, bowel preparation, rather than the procedure itself, appears to be the main driver of transient gut microbiota disruption. Available evidence suggests that microbiota alterations after bowel preparation and colonoscopy may persist for days to weeks and may be associated with changes in barrier function, microbial metabolism, and symptom burden in susceptible individuals. This review summarizes current knowledge on the mechanisms underlying microbial disruption induced by bowel preparation, including loss of diversity, shifts in key taxa, impairment of metabolic pathways, and alterations in immunomodulatory metabolites. It also discusses potential clinical consequences and highlights nutritional strategies that may support microbiota recovery, including dietary fiber, polyphenols, and microbiota-targeted approaches. This review also highlights current research gaps and the need for well-designed clinical studies in this field. Full article