Search Results (1,238)

Artificial Intelligence (AI) has become a key enabler in soil science and agriculture, supporting advanced modeling, monitoring, and decision-making processes. This systematic review synthesizes recent developments in AI-based soil characterization and agricultural applications, with emphasis on soil physicochemical properties, digital soil mapping, irrigation management, and crop yield prediction. Following the PRISMA 2020 framework, a structured search of the Scopus database identified 196 eligible studies published between 2018 and 2026. The reviewed literature reveals a clear transition toward data-driven approaches, with machine learning and deep learning models dominating recent research. Random Forest, Support Vector Machines, gradient boosting methods, artificial neural networks, Convolutional Neural Networks, and Long Short-Term Memory architectures were the most frequently reported techniques. The primary data sources included in situ sensors, laboratory measurements, remote sensing imagery, and environmental covariates, often integrated through multi-source data fusion frameworks. The results indicate that tree-based ensemble models provide robust performance across diverse soil properties, whereas deep learning models are particularly effective for spatiotemporal prediction and remote sensing applications. AI-driven systems are increasingly used to support precision agriculture through irrigation optimization, crop yield forecasting, digital soil mapping, and soil health monitoring. However, challenges remain regarding data quality and availability, model transferability across regions, and the limited interpretability of complex models. The findings highlight current research trends, methodological challenges, and future opportunities for the development of reliable and scalable AI-driven soil and agricultural systems. Full article

Mass spectrometry and high-throughput sequencing have been introduced into clinical bacteriology. We characterized strain IMB-1, previously isolated from the cerebrospinal fluid of a child, as Pseudoxanthomonas kaohsiungensis and analyzed its biological properties, resistance phenotype, and complete genome. The IMB-1 strain displayed amylolytic, weak lipolytic activities, and it exhibited a phenotypic resistance profile only for aminoglycosides. The dDDH calculation based on the complete genome sequence showed that strain IMB-1 was closely grouped with the type strain P. kaohsiungensis DSM 17583, and the dDDH (d4) value was 70.1%. A comparative pan-genome analysis was performed for four P. kaohsiungensis genomes, revealing a substantial shared core genome. The IMB-1 genome contained 508 unique gene clusters, representing the largest strain-specific gene set among the analyzed genomes, suggesting genomic plasticity and adaptation to the host-associated environment. Genome annotation revealed genes responsible for antibiotic, disinfecting agent, and antiseptic resistance. Gene clusters exhibiting the potential to form biofilms, adhere to the epithelial surface, and exhibit resistance to stress factors were identified. Our study demonstrates that strain IMB-1 is a potential opportunistic pathogen with significant pathogenic potential. The application of high-resolution whole-genome sequencing data in public health for pathogen identification and monitoring can improve the accuracy of infection source determination, reduce the scale and burden of outbreaks, and identify and quantify antimicrobial resistance in pathogens. Full article

Outlier detection in time series is traditionally formulated as the identification of rare or extreme observations with respect to global statistical properties. While effective for stationary processes, this perspective becomes insufficient in complex and non-stationary systems, where atypical behavior may manifest as disruptions of stable relationships rather than numerical extremeness. This paper proposes a rule-based framework for detecting structural outliers in non-stationary time series. Regular system behavior is represented by an interpretable set of deterministic IF–THEN rules describing stable relational patterns between features. Each rule defines a logical context and an admissible range of a diagnostic quantity, estimated nonparametrically from historical observations satisfying the rule condition. For a given observation, the set of active rules is identified and a structural inconsistency score is computed as the fraction of violated rule consequences. Additionally, observations lacking support from high-frequency contexts are treated as candidates for structural atypicality. The method is deterministic and avoids the need for explicit probabilistic modeling or iterative parameter learning, which simplifies interpretation and implementation. The framework is illustrated on daily EUR/USD data (2010–2022) using technical indicators (EMA, RSI) and absolute log-returns as the diagnostic measure. Results provide evidence that structurally atypical events can be identified even when global statistical thresholds remain unviolated, suggesting the practical relevance of relational analysis for non-stationary time series monitoring contexts. Full article

To reveal the differences in stratum response among different environmental sections and the influences of key construction parameters on deep soil deformation during river-crossing double-line shield tunneling, the paper takes the East Genshan Road River-Crossing Tunnel as the engineering case, and systematically investigates the stratum responses of the onshore and riverbed sections as well as the effects of construction parameters via field monitoring, measured construction parameter data and three-dimensional finite element simulation based on ABAQUS. The simulation results suggest that, compared with the onshore section, the riverbed section may present larger cumulative displacement, more intense deep soil response and a wider influence range of transverse settlement under the investigated high-water-pressure and saturated soft-soil conditions. These differences are more reasonably interpreted as the combined effects of burial depth, stratum composition, mechanical properties, hydraulic boundary conditions, surface boundary constraints and overburden conditions. Among these factors, the high-water-pressure and saturated soft-soil environment may contribute to the enhanced disturbance diffusion and cumulative deformation response observed in the riverbed section. The longitudinal displacement evolution of the riverbed section presents obvious stratified transmission characteristics, and its transverse settlement trough shows a typical double-peak W-shaped distribution with larger peak values, wider trough profile and slower far-field attenuation. The single-factor parametric analysis suggests that, within the investigated parameter ranges, cutterhead torque produced the largest absolute settlement variation, followed by total shield thrust and tunneling speed. The results of this study can provide a reference basis for settlement control and construction parameter optimization of river-crossing double-line shield tunneling in high-water-pressure and saturated soft soil strata. Full article

Shear wave elastography (SWE) is an increasingly investigated ultrasound-based technique in musculoskeletal imaging that provides quantitative information on tissue stiffness and biomechanical properties. This narrative review aims to summarize the basic principles, technical considerations, current clinical applications, limitations, and future perspectives of SWE in musculoskeletal imaging. Unlike conventional grayscale and Doppler ultrasonography, which mainly assess morphology and vascularity, SWE may provide additional functional information in major musculoskeletal tissues, including tendons and ligaments, skeletal muscles, peripheral nerves, fibrocartilaginous structures, plantar fascia, and selected soft tissue lesions. Current evidence suggests potential roles for SWE in detecting early biomechanical alterations, assessing disease severity, differentiating symptomatic from asymptomatic tissues, and monitoring response to treatment or rehabilitation. However, musculoskeletal tissues are anisotropic, viscoelastic, and position-dependent; as a result, SWE measurements are influenced by acquisition-related factors, tissue biomechanics, positioning and loading conditions, region of interest (ROI) placement, tissue depth, and device-related variability. For this reason, SWE findings should not be interpreted as standalone diagnostic criteria but should be considered together with clinical findings, conventional ultrasonography, MRI, electrophysiology, histopathology, and patient-centered outcomes when appropriate. This review highlights the need for tissue-specific measurement protocols, standardized reporting, normative reference data, inter-vendor harmonization, and longitudinal validation against clinically meaningful outcomes before SWE can be more reliably integrated into routine musculoskeletal imaging and rehabilitation practice. Full article

Epilepsy can be effectively controlled with appropriately selected antiepileptic drugs and carefully titrated dosage regimens. Although lamotrigine exhibits favorable pharmacokinetic properties following oral administration, fluctuations in plasma concentration may still occur due to interindividual variability, irregular dosing, and pharmacokinetic interactions. In this study, a subcutaneous implant capable of monitoring plasma lamotrigine levels and adjusting drug delivery accordingly was developed to maintain stable therapeutic concentrations. The proposed system combines intermittent drug release with continuous concentration monitoring using an enzymatic biosensor. A pharmacokinetic model based on first-order absorption and elimination kinetics was implemented in MATLAB/Simulink using clinical lamotrigine concentration data obtained from patients receiving chronic therapy. In the closed-loop configuration, biosensor measurements were used as feedback for a proportional–integral (PI) controller that adjusted the implant release rate in real time. System performance was evaluated using in silico simulations. The open-loop system produced rapid concentration peaks (Cmax ≈ 0.06 mmol/L) followed by a decline below the therapeutic threshold within approximately 80 min. In contrast, the closed-loop system achieved lower peak concentrations (Cmax ≈ 0.045 mmol/L) and maintained plasma concentrations within the therapeutic range of 0.02–0.03 mmol/L with reduced fluctuations. These findings support further investigation of biosensor-guided closed-loop lamotrigine delivery systems. Full article

Integration of LiDAR and thermal sensing has become increasingly important in robotics, infrastructure diagnostics, environmental monitoring, and autonomous perception systems. LiDAR sensors provide accurate three-dimensional geometric information but do not directly capture thermal properties of observed objects, whereas thermal cameras provide temperature distributions without explicit spatial structure. Fusion of both sensing modalities enables thermally augmented 3D scene reconstruction and spatial localization of temperature anomalies. This paper presents a practical LiDAR–thermal fusion framework for three-dimensional localization of heat sources using an Ouster OS1 LiDAR sensor and a FLIR A70 thermal camera. The proposed framework includes intrinsic thermal-camera calibration, extrinsic LiDAR–thermal calibration, multimodal data synchronization, projection of LiDAR points onto the thermal image plane, and assignment of temperature values to spatial points. Additionally, a dedicated thermally distinguishable calibration target is proposed to enable reliable multimodal feature extraction under low-contrast LWIR imaging conditions. The developed framework was experimentally validated using real radiometric thermal data and LiDAR point clouds acquired under laboratory conditions. Quantitative evaluation demonstrated reprojection errors below 1 pixel and a mean hottest-point localisation error of approximately 4.1 cm at a distance of 12.3 m. The results confirm that accurate spatial localisation of thermal anomalies can be achieved using a geometry-based multimodal fusion approach without relying on computationally expensive learning-based methods. The proposed framework emphasises practical deployment, deterministic calibration, and applicability in scenarios where limited training data or constrained computational resources make learning-based approaches difficult to apply. The proposed system may be applied to building energy diagnostics, industrial inspection, technical infrastructure monitoring, and robotic perception systems that require reliable spatial localisation of heat sources under real measurement conditions. Full article

The rate control algorithm is designed for natural video by default in video-coding standards. However, computer-generated screen content video (SCV) is very different from natural video captured by a camera, with many different statistical characteristics, such as sharp edges, thin lines, and flat area. This will lead to a difference in the focus of the human visual system (HVS) when viewing on-screen content video. Especially in various sensor data visualization applications such as intelligent display terminals, industrial monitoring and human–computer interaction interfaces, screen content video carries key information collected and reconstructed by image sensors, vision sensors and multimodal sensors. Its edge structures and local details directly affect the interpretation accuracy and application reliability of sensor information. Therefore, it is crucial to investigate perceptual rate control methods that integrate both video content characteristics and human visual perception properties, which possesses substantial theoretical and practical significance. In this paper, we propose a perceptual rate control algorithm for screen content video based on just-noticeable distortion (JND) which is established on the edge profile reconstruction with tolerable variations. First of all, target bit rate allocation for the frame level and CTU level is based on a perceptual weight which is calculated on the JND factor and reconstruction edge character. Secondly, under the constraint of the JND model, an intra rate-distortion (RD) model is established under the constraint of the JND model. The similarity between reference frames and reconstructed frames is taken as feedback in this model. Finally, the proposed rate control algorithm (JND–perceptual rate control (PRC)) is integrated to the existing rate control framework in High-Efficiency Video Coding–Screen Content Coding (HEVC-SCC) for improving the coding efficiency. The experimental results show that the proposed algorithm achieves better bit control precision than the platform, as well as improves the R-D performance of screen content video. In particular, compared with the HEVC-SCC reference software, the coding performance is improved by 3.09 dB on average, the bit rate is saved by 26.51% on average, and the average bit rate mismatch is within 1.159%. Full article

The increasing convergence of Operational Technology (OT) and Information Technology (IT) within the Industrial Internet of Things (IIoT) brings about remarkable improvements in monitoring and automation. However, it also exposes industrial systems to large-scale Distributed Denial of Service (DDoS) attacks. Edge-based defences are essential in satisfying low-latency demands and data sovereignty rules, yet they must function under severe resource limitations and adapt to shifting traffic characteristics without cloud assistance. In this work, we introduce a lightweight hybrid deep learning architecture that fuses a Convolutional Neural Network (CNN) with a Convolutional Block Attention Module (CBAM) and a Multi-Layer Perceptron (MLP) in a single detector. A sequential transfer learning scheme is adopted, including a feature projection layer that handles differences in input dimensionality. The model is pre-trained on the CIC-DDoS2019 dataset, then adapted to the more recent CICIoT23 dataset. Evaluations are performed on both datasets while preserving their natural class imbalance. We provide extensive ablation and variance analysis under identical experimental conditions. The proposed method achieves 99.52% accuracy on CICIoT23 while maintaining 99.65% recall, which is a crucial property for critical systems. Real-time measurements on a CPU-only testbed show an average inference latency of 0.013 ms, inference-only throughput exceeding 93,000 packets/s, and end-to-end batch throughput of approximately 38,000 packets/s. The solution demonstrates effective domain adaptation, sub-millisecond latency, and suitability for resource-constrained IIoT edge gateways. Full article

Digital tools in aluminum alloy processing are computational, sensing-based, and data-driven methods used to understand, predict, monitor, optimize, and control how aluminum alloys are transformed into components. They support decisions across casting, deformation processing, heat treatment, welding, surface engineering, and additive manufacturing by linking processing conditions with geometry, microstructure, defects, properties, and service performance. In technical use, the term includes finite element method (FEM), computational fluid dynamics (CFD), CALculation of PHAse Diagrams (CALPHAD), microstructure models, machine-learning regressors, surrogate models, nondestructive digital inspection, image-analysis tools, and digital twins. These tools are most effective when they establish links among controllable processing variables, underlying metallurgical mechanisms, measurable quality indicators, and service-relevant performance outcomes. Full article

Soccer is a complex sport with significant physical, physiological, psychological, technical, and tactical demands on players. This review presents an ergonomics-based model of soccer performance, emphasizing that no single component operates in isolation. Building on the foundational ergonomic framework, this review integrates contemporary evidence on training load monitoring, ecological dynamics, and cognitive-perceptual performance dimensions not systematically addressed in prior frameworks. Elite outfield players cover 9–14 km·h⁻¹ per match, with high-speed running (19.8–24.8 km·h⁻¹) making up about 20% of total distance and sprinting (>25 km·h⁻¹) around 2%. These outputs vary by playing position, tactical formation, possession dynamics, and environmental conditions. Longitudinal data from the English Premier League indicate a 35% increase in high-speed running over the past decade, suggesting intensifying physical demands. Physiological responses, including average heart rates of 156–175 bpm, reflect the aerobic and anaerobic demands on players. The review also examines benchmarks like VO₂max, sprint velocity, and anthropometry, highlighting their utility and limitations as performance indicators. Regarding training load management, the review evaluates frameworks such as the Acute:Chronic ratio and high-speed running exposure protocols, noting limitations and risks of over-relying on external load metrics. Periodization approaches, including tactical periodization, are discussed for integrating physical, technical, tactical, and psychological components in training. The proposed ergonomic model conceptualizes elite soccer performance as an emergent property of interacting physical, physiological, tactical, psychological, and environmental subsystems, with direct implications for training design, selection, and load management. Selection decisions should consider cognitive and perceptual competencies like decision-making, anticipation, and situational awareness, alongside physical and physiological profiles, aligned with the team’s game model. Full article

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

Accurate bathymetric information for inland lakes is essential for water resource management, ecological monitoring, and environmental research. However, the accuracy and robustness of remote sensing-based bathymetric retrieval are often constrained by the complex optical properties of inland waters and the limited representation of conventional inversion features. To address these challenges, this study systematically compared the performance of a multiband logarithmic ratio model and three machine learning models, including Random Forest (RF), XGBoost, and AdaBoost, for inland lake bathymetric retrieval. Furthermore, a synergistic retrieval framework integrating chlorophyll-a concentration (Chla) and a Water Optical Index (WOI) was proposed. The results show that: (1) The overall accuracy of the Random Forest, XGBoost, and AdaBoost models constructed with the integration of chlorophyll-a concentration and WOI ($R^2=0.93$, $0.93$, and $0.91$; MAE $=0.06$ m, $0.07$ m, and $0.12$ m; RMSE $=0.14$ m, $0.14$ m, and $0.16$ m) outperforms that of models using only multispectral band information ($R^2=0.93$, $0.91$, and $0.82$; MAE $=0.06$ m, $0.07$ m, and $0.14$ m; RMSE $=0.14$ m, $0.16$ m, and $0.22$ m). Moreover, all these machine learning models significantly outperform the traditional numerical model ($R^2=0.27$; MAE $=0.29$ m; RMSE $=0.45$ m), with the Random Forest model achieving the best overall performance. This indicates that the proposed method offers higher applicability and retrieval accuracy in complex inland lake environments. (2) The optimal Random Forest model integrating chlorophyll-a concentration and WOI achieved high-precision bathymetric inversion for inland lakes ($R^2=0.93$, MAE $=0.06$ m, RMSE $=0.14$ m). Based on the three-dimensional bathymetry derived from this model, the estimated lake storage capacity was $1072.11\times {10}^4$ m³, compared with a measured volume of $1094.27\times {10}^4$ m³, yielding a relative error of $-2.03\%$. This result provides reliable and highly accurate data to support water resource management. Full article

Herpesvirus infections belong to major pathogens in the human population. This study aimed at evaluating diagnostic data for eight human herpesviruses, based on datasets derived from a large European tertiary care center. Specifically, we analyzed 118,692 herpesvirus submittals to the Diagnostic Division of the Virological Institute, University Hospital Erlangen (UKER), Germany, between July 2014 and June 2024. Our points of focus were the following: (i) the frequencies of herpesvirus diagnostic results with positivity rates, (ii) departments representing main sample submitters, (iii) the specific importance of intensive care units (ICUs), (iv) the COVID-19 pandemic period, and (v) distinct properties of sample types. Overall, we are stating the highest frequencies of diagnostic assessment for herpes simplex virus (HSV), human cytomegalovirus (HCMV), and Epstein–Barr virus (EBV) infections, pointing to their dominant relevance for clinical practice. Notably, HCMV submittals (46.6% of total), together with EBV (26.2%) and HSV (15.7), accounted for almost 90% of all herpesviral diagnostic samples during this period. Within these key groups, HCMV, EBV and HSV showed positivity rates of 14.5%, 35.0%, and 18.5%, respectively. Concerning a main input of sample submittals, two departments were predominant in our center, i.e., the Departments of Haematology–Oncology and Anaesthesiology. These included patients under multifold types of treatment associated with an increased risk of herpesvirus reactivation or primary infection. Furthermore, another high portion of submittals was noted for ICUs and external sources. In addition, a numerical, transient increase in herpesvirus diagnostic submittals, from various sources, was shown for the COVID-19 pandemic years (mostly 2021) as compared to other periods. Combined, these data underlined the importance of clinical monitoring of herpesvirus infections, particularly for high-risk patients, and the steady need of improvements in preventive measures, therapeutic options, and safe diagnostic tools. Full article

Background: Oxidative stress contributes significantly to premature skin aging and inflammatory dermatological conditions. While plant-derived antioxidants have demonstrated considerable promise in topical applications, Bougainvillea glabra Choisy remains underexplored in standardized pharmaceutical dosage form development despite its documented phytochemical richness. Objective: This study aimed to develop, standardize, and characterize topical cold cream formulations incorporating B. glabra ethanolic leaf extract, with HPTLC-based quantification of marker compounds, validated antioxidant assessment, and preliminary dermal safety evaluation. Methods: The ethanolic leaf extract was prepared by maceration and characterized by preliminary phytochemical screening and HPTLC fingerprinting with quantitative densitometric analysis of quercetin and pinitol. Three cold cream formulations were developed at 10% (F1), 20% (F2), and 30% (w/w) (F3) extract loading. Formulations were evaluated for organoleptic properties, pH, homogeneity, spreadability, and viscosity. Antioxidant activity was assessed using a validated methanol extraction procedure followed by DPPH radical scavenging and potassium permanganate reduction assays. Ex vivo skin permeation was evaluated using Franz diffusion cells with freshly excised goat skin. Accelerated stability was conducted at 40 ± 2 °C/75 ± 5% RH for 90 days with HPTLC-based marker retention monitoring. Primary dermal safety was assessed in Wistar albino rats (n = 6) following OECD Test Guideline 404. Results: Quantitative HPTLC confirmed quercetin (4.82 ± 0.14 mg/g dry extract) and pinitol (2.31 ± 0.09 mg/g) as marker compounds, with linearly increasing content across F1–F3. All formulations demonstrated acceptable physicochemical properties (pH 5.7–5.9, viscosity 440,000–460,000 cP, spreadability 11.8 ± 0.3 cm·g/s). F3 exhibited the highest DPPH scavenging activity (56.68 ± 1.05%) with IC₅₀ of 1.3 ± 0.1% w/v, demonstrating a 3.2-fold improvement over F1. Extraction recovery from the cream matrix was 96.4–97.1%, validating the antioxidant data. Ex vivo quercetin permeation through goat skin reached 51.3 ± 2.8 μg/cm² at 24 h for F3, following Higuchi diffusion kinetics (R² > 0.99). No dermal irritation was observed (Primary Irritation Index = 0). Accelerated stability confirmed ≥98.3% retention of both marker compounds and antioxidant activity after 90 days. Conclusions: B. glabra leaf extract was successfully incorporated into a physicochemically stable, non-irritating cold cream with demonstrated dose-dependent antioxidant efficacy and cutaneous delivery capability. The study establishes preliminary dermal safety and in vitro antioxidant efficacy warranting further controlled clinical evaluation. Full article