Search Results (18,567)

Accurate classification of uranium slag origin and category is essential for nuclear environmental monitoring and safety. This study presents a hybrid framework combining laser-induced breakdown spectroscopy (LIBS), four preprocessing methods, and five machine learning algorithms for rapid uranium slag classification. A total of nine sample categories were collected from three mining areas, with categories defined by their U concentration levels within each origin. Standard normal variate (SNV), Savitzky–Golay smoothing (SG), and their combinations (SNV-SG, SG-SNV) were applied to evaluate preprocessing effects. To address ultra-high-dimensional spectral data (49,242 points per spectrum), principal component analysis (PCA) and random forest (RF) were employed for feature engineering, integrated with support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) classifiers. Hyperparameter optimization via five-fold cross-validation and Bayesian optimization enhanced accuracy and efficiency. RF-based hybrid models consistently outperformed PCA-based counterparts. Remarkably, the RF-LDA model with SNV-SG preprocessing achieved 100% classification accuracy across all test sets with a processing time of only 10.46 s, demonstrating exceptional discriminative power and computational efficiency. These findings establish that combining RF feature selection with advanced machine learning offers a robust solution for LIBS-based nuclear material classification, with significant implications for both nuclear safety and resource management. Full article

Accurate ecological prediction is critical for sustainable environmental management and carbon cycle assessment, yet model development is often constrained by limited datasets and inconsistent preprocessing practices. Reliable litterfall prediction plays a key role in understanding nutrient cycling and supporting sustainable forest ecosystem management. Although gradient boosting models have shown promising performance in ecological applications, structured evaluations integrating preprocessing strategies with synthetic data augmentation remain limited under data-scarce conditions. This study proposes the Hybrid Preprocessing and Augmented Boosting Framework (HPABF), which combines multi-stage preprocessing—including MICE imputation, log transformation, and feature engineering—with synthetic data augmentation to enhance predictive robustness. The framework was evaluated across eight machine learning models using a 968-sample forest ecological dataset. To mitigate data scarcity, 5000 synthetic samples were generated while preserving the statistical distribution and multivariate structure of the original data (91% fidelity). Fractal dimension analysis was further introduced as a geometric validation metric to assess prediction structure and stability beyond conventional performance measures. Within the HPABF, gradient boosting models achieved a 7% improvement over baseline performance (R² = 0.96, MAE = 0.06) under cross-validation strategies designed to reduce overfitting. Training with synthetic data further improved predictive accuracy (R² = 0.98), demonstrating the framework’s effectiveness for data-scarce ecological applications. By improving prediction reliability under limited data conditions, the proposed framework supports more accurate environmental monitoring, informed decision-making, and sustainable management of forest ecosystems. Full article

Landfills are a significant source of atmospheric emissions associated with the decomposition of organic waste; however, conventional monitoring methods typically have limited spatial coverage. This study evaluates the use of an UAV-based system for the spatial characterization of gases associated with biogas emissions at a municipal landfill. A DJI Matrice 350 RTK platform equipped with a Sniffer4D Mini2 multi-gas station and a Zenmuse H20T thermal camera were used. Four flight campaigns were conducted at an altitude of 20 m, with an acquisition frequency of approximately 1 Hz, recording total hydrocarbons (C_xH_y) as an indirect indicator of methane (CH₄), carbon dioxide (CO₂), carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), oxygen (O₂), temperature, and relative humidity. The results showed a marked transition around 13:10 h, characterized by a simultaneous increase in CH₄ equivalent and CO₂, along with a decrease in NO₂, O₃, and SO₂. Furthermore, CH₄ equivalent and CO₂ showed the highest positive correlation among the variables (r = 0.96). Spatial maps generated using ordinary kriging revealed more heterogeneous patterns, while the qualitative thermal orthophoto confirmed the site’s surface variability. Overall, the results demonstrate that the integration of multi-gas sensors and aerial thermography on UAVs is viable for the spatial monitoring of landfills. Full article

Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches. Full article

Kolmogorov–Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications. Full article

This narrative review examines topical anti-inflammatory therapies in veterinary medicine through the lens of the One Health framework, integrating pharmacology, dermatology, ecotoxicology, food safety, and regulatory science. It discusses the interconnected roles of veterinarians, pharmacists, environmental scientists, public health authorities, and regulatory bodies in addressing antimicrobial resistance, environmental contamination, zoonotic transmission, and drug residues in food-producing animals. By emphasising cross-sector collaboration, the review highlights how coordinated strategies can enhance animal welfare, safeguard human health, and reduce ecological burden. The article analyses inflammatory conditions in companion and farm animals and compares systemic versus topical anti-inflammatory approaches. Particular attention is given to corticosteroids, NSAIDs, immunomodulators, pro-resolving lipid mediators, and plant-derived bioactives, alongside advances in vehicles such as lipid nanocarriers and biodegradable film-forming systems designed to minimise systemic absorption and environmental dispersion. Regulatory considerations, residue control, pharmacovigilance gaps, and sustainability-oriented formulation strategies are critically addressed. Topical anti-inflammatory therapies, when rationally designed and monitored under One Health principles, represent a strategic opportunity to improve therapeutic precision while limiting systemic toxicity and ecological impact. Future directions should prioritise translational research, eco-compatible formulation design, and harmonised regulatory frameworks. Full article

With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article

To address cryodamage in Culter alburnus sperm, this study evaluated the effects of trehalose supplementation in a conventional cryomedium (D-15 + 10% ethylene glycol). Six experimental groups were established: fresh sperm (G1), a conventional cryomedium (G2), groups supplemented with 10, 100, or 200 mmol/L trehalose (G3–G5), and a control group with extender only (G6). The group with 100 mmol/L trehalose (G4) was associated with improved post-thaw motility parameters (activation rate, movement time, and lifespan) and higher antioxidant (superoxide dismutase and catalase) and energy metabolism (ATPase, succinate dehydrogenase, lactate dehydrogenase) enzyme activities. Ultrastructural damage in G4 included partial plasma membrane rupture and mitochondrial swelling, while G6 exhibited additional damage features including membrane disintegration, mitochondrial disruption, and flagellar fracture. Proteomic analysis revealed that, compared to G1, G4 exhibited higher abundance of proteins (e.g., Histone H2A, cytochrome c oxidase, profilin) involved in structural integrity and energy homeostasis, whereas G6 showed signatures of oxidative stress and metabolic dysfunction (lower abundance of NADH dehydrogenase and higher abundance of calcium-transporting ATPase and glutathione S-transferase). In conclusion, 100 mmol/L trehalose was associated with improved cryopreservation outcomes, and the proteins identified provide a basis for further investigation. This approach offers a framework for refining germplasm conservation strategies in aquaculture. Full article

This research develops a new low-cost method for continuous flow monitoring in open channels. Flow is calculated using a standard 1D hydraulic model that integrates surveyed cross-sections and water level measurements at the boundaries of a studied reach, from which the name Boundary Water Level Method (BWLM) is derived. By implementing low-cost ultrasonic sensors for water level measurement, the method gains advantage for application on smaller channels, which are often not included in national hydrological monitoring networks due to limited budgets. New and innovative monitoring methods in hydrology are a necessary alternative to increasing the monitoring budgets, especially for continuous, real-time flow monitoring. Like any novel method, it requires validation under the intended environmental conditions, especially when designed primarily for ungauged channels. Validation was conducted on two test-sites by comparing the BWLM discharge and the discharge from official hydrological stations, with an error of up to 15%. BWLM provides reliable discharges using estimated hydraulic roughness values based on the literature and experience. Sensitivity analysis of the estimated hydraulic roughness coefficient demonstrated a substantial influence on the resulting discharge values. This has to be considered when implementing the method in unstudied basins. Full article

Diameter at Breast Height (DBH) is a key parameter in forest measurement. However, existing research has mostly focused on improving the accuracy of individual technologies, lacking a systematic synthesis of the evolutionary logic of measurement techniques and a standardized selection framework for forestry applications. To this end, this paper constructs a multi-level classification framework based on measurement platforms and technical principles, establishes for the first time a five-dimensional comprehensive evaluation system (covering accuracy, efficiency, cost, environmental adaptability, and automation) along with a hierarchical technology decision tree, and systematically analyzes the application logic of multi-source fusion technologies across three levels: ground-based, near-ground mobile, and aerial. The review indicates that traditional contact-based measurement has limited efficiency; modern remote sensing technologies (photogrammetry and LiDAR) offer significant advantages in automation and accuracy, but still face challenges such as high equipment costs, complex data processing, and poor environmental adaptability. Multi-source fusion and machine learning are key methods to overcome the limitations of single sensors and improve the robustness of DBH estimation. Finally, it is anticipated that with decreasing sensor costs and the advancement of intelligent algorithms, DBH measurement will continue to evolve toward automation, intelligence, and engineering practicality, providing technical support for large-scale, long-term, and repeatable forest monitoring. Full article

Smart feedlots are increasingly adopting Precision Livestock Farming technologies to enable continuous, individual-animal monitoring and more proactive management in intensive beef production systems. This narrative review synthesises evidence from approximately 350 academic publications, of which 117 are formally cited, complemented by industry deployments and the authors’ experience in smart feedlot system development. We cover enabling digital infrastructure (power, sensing networks, wireless connectivity, and gateways), animal identification and sensing (RFID, automated weighing, wearables, and pen-side sensors), machine vision (RGB, thermal, and multispectral imaging from fixed and mobile platforms), and AI-based analytics and decision support for health, welfare, performance, and environmental management. Across the literature, key components have progressed beyond proof-of-concept toward operation under commercial constraints. Reported outcomes include reduced reliance on routine pen-rider observation and yard handling, earlier triage of emerging morbidity risk and behavioural change, and more standardised welfare auditing. Vision-based methods are repeatedly validated against trained human scorers in both on-farm and abattoir contexts, while automated weighing and image-based liveweight estimation support higher-frequency growth monitoring with low single-digit percentage error in representative studies. Precision feeding and targeted supplementation are associated with improved feed utilisation and reduced resource wastage, although effectiveness and adoption vary across animal classes and production stages. We identify priorities for robust, scalable deployment: resilient communications in harsh environments, appropriate edge–cloud partitioning under intermittent connectivity, and interoperable multi-sensor data fusion to deliver trustworthy alerts and actionable insights. Persistent barriers remain cost, durability, maintenance burden, integration and interoperability, data governance, and workforce capability. Full article

Artificial sweeteners have emerged as contaminants of increasing concern due to their widespread consumption, environmental persistence, and resistance to conventional wastewater treatment. This review provides an integrated assessment of global research trends and the environmental behavior of major artificial sweeteners, including sucralose, acesulfame potassium, saccharin, and aspartame. Bibliometric analysis of SCOPUS-indexed publications reveals rapid growth in research since 2010, with key themes focusing on environmental occurrence, treatment technologies, and ecotoxicological effects. These compounds are frequently detected in wastewater effluents, surface waters, groundwater, and even drinking water systems, driven by their high solubility and limited biodegradability. Their persistence raises concerns regarding ecological impacts, including potential alterations to microbial communities and aquatic organisms. In addition, emerging evidence suggests potential human health implications, including gut microbiota disruption, metabolic effects, and risks associated with chronic low-dose exposure, although these remain poorly understood. The performance of existing treatment technologies, including biological processes, adsorption, advanced oxidation, and membrane filtration, is critically evaluated, highlighting limitations in complete removal and in the formation of transformation products. Future research should prioritize sustainable treatment strategies, comprehensive risk assessment, and improved monitoring frameworks to better address both environmental and human health risks associated with artificial sweeteners. Full article

Background: Antimicrobial resistance is a One Health problem driven by the intricate interactions across human, animal, and environmental interfaces that enable microbial exchange and movement of mobile genetic elements encoding resistance and virulence. This study investigated the genomic epidemiology of ESBL and non-ESBL Escherichia coli across One Health interfaces in Oman. Methods: This prospective cross-sectional study analyzed 295 non-duplicate Escherichia coli isolates derived from 104 clinical, 173 animal [diseased (123) and healthy (50)], 14 sewage and four water sources. Antimicrobial susceptibility testing was performed phenotypically, and a representative subset of 50 ESBL and non-ESBL Escherichia coli from the three interfaces underwent whole genome sequencing to determine MLST, phylogroups, resistance genes, virulence determinants and plasmid replicons. Results: ESBL prevalence was highest in human isolates (73%), followed by sewage (28.6%) and animals (16.3% diseased; 8% healthy). blaCTX-M-15 predominated in humans, whereas blaCTX-M-55 dominated in animals and sewage, suggesting ecological partitioning with partial overlap. Quinolone resistance was lowest in the animal interface. Sewage isolates harbored the most complex resistome, including rmtB and plasmid-mediated quinolone resistance genes. MLST analysis revealed high diversity in human isolates, including globally recognized ExPEC lineages (ST10, ST38, ST73, ST127, ST131), while ST224 dominated in animals with evidence of possible spillover to humans. ST167 was confined to sewage, consistent with environmental maintenance of high-risk clones. Phylogroup structuring showed predominance of A, B2 and D among human isolates and A, B1, and E among animal and sewage isolates. Virulence profiling demonstrated broader virulome diversity in humans, but shared core determinants (fimH, sitA, traT) across all domains. IncFIB(AP001918) was the dominant plasmid replicon, particularly among ESBL isolates, underscoring its role in horizontal gene dissemination. Alarmingly, mutation in pmrB (V161G) was identified in a healthy animal isolate, pointing to a need for greater colistin restriction in animal husbandry. Conclusions: This study highlights plasmid-mediated resistance and shared virulence determinants linking reservoirs; although AMR profile was quite distinct across the three interfaces, human isolates demonstrated greater resistance than animal isolates, suggesting healthcare-driven AMR in Oman. Continued integrated genomic surveillance is essential to monitor gene flow and inform coordinated antimicrobial stewardship strategies. Full article

We investigated the biodiversity and spatiotemporal dynamics of benthic hydroids at two contrasting eastern South Adriatic sites: exposed, oligotrophic Lokrum Island and sheltered, nutrient-enriched Bistrina Bay. A total of 54 hydroid taxa were recorded, with substantially higher richness at Lokrum (42 taxa) than at Bistrina (24 taxa). Assemblage composition differed markedly between sites, confirming that local environmental conditions are a primary determinant of community structure, while shallow sublittoral assemblages showed the greatest temporal variability due to seasonally short-lived athecate species. The shared seasonal partitioning at both sites suggests that temperature-mediated life-cycle timing is a key structuring mechanism, and the sharp summer decline in richness underscores the need for multi-seasonal sampling. Laboratory observations of Stylactis inermis from Torre del Serpe near Otranto revealed notable life-cycle plasticity, with detached short-lived eumedusoids reverting to a sessile stolonal stage. This trait may promote persistence under fluctuating conditions while reducing field detectability. Together, these results provide the first seasonal, depth-stratified ecological baseline for monitoring eastern South Adriatic benthic communities under environmental and anthropogenic change. Full article