Search Results (52,205)

Soil tillage practices play a key role in controlling soil’s physical properties, water infiltration, and runoff generation, particularly in erosion-prone cropping systems such as maize monocultures. The cultivation of wide-row crops is restricted on erosion-prone land; however, these crops constitute a fundamental basis for livestock feed and represent a key input raw material for biogas plants. This 4-year study evaluated the effects of three tillage practices—conventional ploughing, shallow tillage, and no tillage—on selected soil’s physical and chemical properties and on water infiltration processes in a maize (Zea mays L.) monoculture. Experimental maize stands were established in a field with a silty clay Luvic Chernozem. Field measurements were performed over multiple years and included soil bulk density, macroporosity, cone index, soil organic carbon, soil pH, soil aggregate stability, and water infiltration. Infiltration processes were assessed using a combination of double-ring infiltrometers, rainfall simulation, and dye tracer experiments to characterize water flow patterns under controlled conditions. The results demonstrated that soil tillage significantly influenced the vertical distribution of soil organic carbon and pH, soil aggregate stability, soil compaction, and pore characteristics, with the most pronounced differences observed in the upper soil layers. Soil aggregate stability in the 0–0.10 m layer showed a clear numerical trend, with the highest mean value under ST (0.42) compared with PL (0.28) and no tillage (NT) (0.26). Topsoil Cox was the highest under ST (3.591%) compared with PL (2.838%) and NT (2.634%). Differences among tillage practices were particularly evident during simulated rainfall events, affecting infiltration rates, runoff initiation, and preferential flow patterns. Ring infiltrometer measurements indicated higher infiltration in PL (e.g., 21.1 mm min⁻¹ at minute 1 in PL vs. 11.1/11.9 mm min⁻¹ in ST/NT; 10.9 mm min⁻¹ at minute 10 in PL vs. 5.3/7.6 mm min⁻¹ in ST/NT). However, rainfall simulation showed the highest runoff in PL, including the earliest runoff onset (4.5 min). Despite the soil’s high infiltration capacity due to low bulk density and higher porosity, the decisive factor promoting water infiltration into the soil is the condition of the soil surface, which is influenced by the stability of soil aggregates; this stability was enhanced by the input of organic matter from plant residues. The findings confirm that long-term soil tillage management substantially modifies soil hydraulic behaviour and highlight the importance of tillage system selection for improving soil water infiltration and reducing runoff risk in maize monoculture systems. Full article

Soil and water bioengineering (SWBE) is increasingly used as a nature-based solution for riverbank stabilization and riparian restoration, yet its effectiveness in tropical environments remains constrained by limited field-based evidence of species performance under hydrological disturbance. This study evaluated the establishment success and ecological effectiveness of four native riparian species (Croton urucurana Baill., Sesbania virgata (Cav.) Pers., Iochroma arborescens (L.) J.M.H.Shaw, and Gymnanthes schottiana Müll.Arg.) installed as live cuttings on a riprap structure exposed to recurrent flooding along the Paraopeba River, Brazil. A total of 160 live cuttings were monitored over a 33-month establishment period to assess survival, structural development, spontaneous vegetation recruitment, and changes in soil chemical properties and soil organic carbon stocks. Flooding acted as a dominant ecological filter, causing substantial early mortality, with overall survival declining sharply during a 70-day inundation period that included 58 consecutive days of submergence. Croton urucurana exhibited the highest survival and structural development, reaching median heights exceeding 5 m and cumulative shoot diameters greater than 100 mm after 33 months, whereas Gymnanthes schottiana showed complete mortality within the first year. Vegetation establishment facilitated spontaneous recruitment of native woody species, with 22 individuals recorded in planted sections compared to only 3 in adjacent non-planted areas. Soil organic carbon stocks increased from 38.9 to 60.6 Mg C ha⁻¹ in the 0–40 cm soil profile, indicating rapid soil development. These results demonstrate that SWBE interventions can simultaneously promote riverbank stabilization, vegetation recovery, and soil carbon accumulation. By providing quantitative field-based evidence under realistic hydrological disturbance conditions, this study advances the understanding of species selection and the ecological effectiveness of SWBE interventions in tropical riparian ecosystems. Full article

Ultrasonic Impact Treatment (UIT), a prevalent surface-strengthening technology for welded structures, combines mechanical shock and ultrasonic vibration to induce plastic deformation and beneficial residual compressive stress at weld toes, effectively enhancing welded joint fatigue performance. This study adopts a full-process numerical simulation approach, integrating the finite element software ABAQUS and FE-SAFE fatigue-life prediction platform to investigate QSTE700TM high-strength automotive steel butt joints. Considering welding-induced initial residual stress, ABAQUS simulates the welding and subsequent UIT processes; explicit dynamic analysis reveals residual stress evolution, with pre- and post-UIT stress-distribution comparisons. The post-UIT residual stress field is input into a static tensile model to obtain load-stress distributions, which are then imported into FE-SAFE with S-N curves for fatigue-life prediction. Simulation results align well with experimental data: UIT improves the fatigue limit of welded specimens by 31.3% and unwelded ones by 42.9%. Additionally, optical and scanning electron microscopes observe fatigue fracture morphologies to further clarify UIT’s fatigue-enhancement mechanism. Full article

The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this paper, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with a passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. When the system detects an unfamiliar individual, it sends out alert notifications and emails to the control room with the captured picture using IoT. A web interface has also been set up to control the robot from a distance through Wi-Fi connection. The proposed face recognition method is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrate the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results indicate that the algorithm can identify human faces with an accuracy of 98.63%. Full article

Greek oregano (Origanum vulgare ssp. hirtum) is an important aromatic and medicinal crop grown in Greece, often on marginal lands. Effective weed management is essential for sustainable production, but the use of herbicides raises concerns about potential pesticide residues. Therefore, this study was conducted to evaluate the residue levels of metribuzin + pendimethalin applied and incorporated pre-planting, as well metribuzin + cycloxydim and glyphosate applied post-emergence in oregano crop grown over a three-year period in the Agrinio location in Greece. Herbicide residue analysis in the edible part of the oregano plants was performed using two validated protocols, i.e., QuEChERS and QuPPe coupled with LC-MS/MS. The analytical methods demonstrated high sensitivity, with limits of quantification (LOQ) at 0.01 mg/kg and recovery rates ranging from 71% to 102%. These results indicated that the application of the above herbicides in oregano crop grown under Greek field conditions resulted in no detectable residues above the established LOQs, strongly supporting the potential safe use of these herbicides in oregano crop and their possible use for regulatory assessments and consumer safety assurance. Full article

Accurate and real-time monitoring of root soil water content (RSWC) is key in optimizing field irrigation decisions and enhancing crop water productivity. However, relying only on the vegetation index as the input to the inversion model may result in lower inversion accuracy due to the canopy spectral saturation effect. To break through the limitation of a single data source, this study constructed an integrated network model (ATT-LSTM) incorporating the attention mechanism based on the long and short-term memory network (LSTM) to enhance the inversion performance by integrating heterogeneous data from multiple sources. The experiment used canopy spectral data based on UAV remote sensing and weather station monitoring data as input features. A control group was set up for cross-validation to realize the accurate inversion of RSWC in kiwifruit plants. The results show that the coefficient of determination (R²) of the ATT-LSTM model on the test set reaches 0.868. This study confirms that the multi-source data fusion framework effectively overcomes vegetation index saturation, improves rhizosphere moisture monitoring accuracy, supports precision irrigation decisions in kiwifruit orchards, and provides a reference for smart agriculture water management optimization. Full article

More and more spaceborne infrared hyperspectral atmospheric observations are assimilated into data assimilation systems. The key to bias correction (BC) of these instruments depends on selecting predictors. However, it is difficult to find a set of predictors that are highly correlated with the O-B biases in all FY-3E/HIRAS-II channels, due to its multi-channel characteristics. A machine learning model XGBoost (EXtreme Gradient Boosting) BC scheme for FY-3E/HIRAS-II is established in this article. The selected predictors include model skin temperature, model total column water vapor, 1000–300 hPa thickness, 200–50 hPa thickness, scan position, observed brightness temperature (BT) and simulated BT. The method is also compared with the operational static BC and the variational BC, to validate its effect. The two-week data assimilation experiments show that the XGBoost BC is the most effective among the three BC schemes. The mean and standard deviation of O-B in all channels are the smallest after BC, and the effective observations through quality control are the largest, followed by the static BC. The static BC and variational BC are performed based on linear regression, which may lead to a small loss of valid observations in some channels that are weakly correlated with the predictor, whereas machine learning algorithms can search for the nonlinear correlation between biases and predictors. Compared with ERA5, both temperature- and humidity-analysis fields based on XGBoost BC are closest to ERA5 at all levels, and the root mean square errors do not change much over time. Full article

The use of antibiotics as growth promoters in livestock production has contributed to the emergence and spread of antimicrobial resistance (AMR), posing a significant global public health threat specifically from the projected mortality burden. Although many countries have restricted the non-therapeutic use of antibiotics, practical and effective alternatives are still required to maintain livestock productivity. This scoping review examines the current evidence on non-antibiotic compounds evaluated as growth-promoting agents in livestock production. The primary objective of this search was to generate a comprehensive list of commonly applied alternatives to antibiotics used as growth promoters in livestock systems. A search was conducted in the CAB Abstracts, Web of Science Core Collection, and AGRICOLA databases. Prior to the scoping review, an initial list of alternatives to antibiotic components was generated through a screening of selected scientific sources and subsequently verified using Google Scholar for the period 2010–2025. This list included brief descriptions of each component, which were used to inform the keyword strategy for the scoping review. Eligible studies were screened in accordance with PRISMA-ScR guidelines, and data were extracted on compound type, livestock species, geographic region, and reported performance outcomes. The alternatives identified included probiotics and prebiotics, phytogenic compounds and essential oils, enzymes and organic acids, vaccines and immunostimulants, bacteriophages, and competitive exclusion products. A total of 1230 records were retrieved and imported into Zotero for reference management. After removal of duplicate records using Zotero’s built-in deduplication tool, 377 unique records remained for screening. Overall, these compounds demonstrated variable effects on feed efficiency, weight gain, and gut health. However, most studies were limited in scale, duration, and methodological consistency. As a result, comprehensive comparative trials and large-scale field evaluations are needed to support evidence-based policy recommendations and the sustainable implementation of alternatives to antibiotics in livestock production systems. Our findings identified six major categories that represent the most frequently reported alternatives to antibiotic growth promoters. Although probiotics, phytogenic, and organic acids were the most extensively studied, substantial heterogeneity in trial design, dosage, and production systems limited meaningful cross-comparisons. In addition, most studies focused on poultry and swine, with comparatively fewer investigations involving ruminant species. This scoping review was not intended to evaluate the efficacy or practical applicability of these alternatives; such assessments require further standardized and extensive studies before recommendations for their widespread application can be made. Full article

Fenton-like reactions are broadly employed in advanced oxidation processes (AOPs) for the degradation of organic pollutants through catalytic peroxide activation. In this study, NiO was precipitated on a wrinkled SiO₂ to improve catalytic performance. The kinetics and reaction mechanism between the catalyst and S₂O₈²⁻ were studied via following the degradation of methylene blue as a model organic pollutant. The findings indicate that the excellent catalytic activity of Ni/SiO₂ and highly active sulfate radicals enhanced the degradation of methylene blue for environmental remediation. Moreover, the cost-effectiveness, and eco-friendly characteristics of the prepared Ni@SiO₂-persulfate system represents a substantial advancement in the field of catalytic oxidation, contributing valuable knowledge to the development of next-generation catalytic systems. Full article

Underground structures are becoming increasingly vital components of modern transportation networks and urban systems, making their structural integrity a critical factor for safety and operational reliability. However, despite considerable progress in Structural Health Monitoring (SHM), the application of data-driven and vibration-based strategies to underground infrastructures remains an open and under-explored field, often because of limited data availability. Population-Based Structural Health Monitoring (PBSHM) offers a promising pathway to overcome this challenge by leveraging transfer learning to share diagnostic knowledge among similar structures. This study investigates the feasibility of extending the PBSHM paradigm to underground infrastructures, with a particular focus on a metro tunnel application. Through dynamic finite element simulations, relevant vibration features are identified, and damage detection strategies based on transmissibilities and cross-correlation functions are evaluated. The numerical results show that transmissibility-based indicators enable accurate damage localisation along the tunnel lining, even under noisy conditions. In contrast, cross-correlation features exhibit more limited performance in some configurations. Building on this evidence, the transmissibility-based damage indicator is subsequently embedded within the PBSHM framework and used as a transferable feature between tunnel models, achieving reliable damage detection in a second tunnel with heterogeneous characteristics, with F1 scores exceeding 80% for all considered damage severities and above 94% for the most critical case, thereby highlighting the potential of knowledge transfer for large-scale underground networks. Full article

The escalating economic losses in agriculture due to deer intrusion, estimated to be in the hundreds of millions of dollars annually in the U.S., highlight the inadequacy of traditional mitigation strategies such as hunting, fencing, use of repellents, and scare tactics. This underscores a critical need for intelligent, autonomous solutions capable of real-time deer detection and deterrence. But the progress in this field is impeded by a significant gap in the literature, mainly the lack of a domain-specific, practical dataset and limited studies on the viability of deer detection systems on edge devices. To address this gap, this study presents a comprehensive evaluation of state-of-the-art deep learning models for deer detection in challenging real-world scenarios. We introduce a curated, publicly available dataset of 3095 annotated images with bounding box annotation of deer. Then, we provide an extensive comparative analysis of 12 model variants across four recent YOLO architectures (v8 to v11). Finally, we evaluated their performance on two representative edge computing platforms, the CPU-based Raspberry Pi 5 and the GPU-accelerated NVIDIA Jetson AGX Xavier, to assess feasibility for real-world field deployment. To ensure a standardized comparison, we established a framework-agnostic deployment pipeline using universal Open Neural Network Exchange (ONNX) runtimes. Results show that the real-time detection performance is not feasible on Raspberry Pi using universal runtimes, suggesting that while framework-agnostic runtimes facilitate portability, low-power CPU deployment requires hardware-specific optimization to achieve real-time thresholds. Conversely, NVIDIA Jetson provides greater than 30 frames per second (FPS) with ‘s’ and ‘n’ series models. This study also reveals that smaller, architecturally advanced models such as YOLOv11n, YOLOv8s, and YOLOv9s offer the optimal balance of high accuracy (Average Precision (AP) > 0.85) and computational efficiency (Inference Time < 34 milliseconds). Full article

This paper presents the numerical results of a turbulent vortex in wave–current boundary layers, based on Large Eddy Simulations. Rough wall flow problems have always been a research hotspot in the field of fluid mechanics. The turbulent vortex structure within wave–current boundary layers is of great significance for the study of flow characteristics. However, little is known about turbulent vortices in combined wave–current flows. The purpose of this paper is to investigate the differences in the average velocity profile when waves are superimposed on turbulence compared to when waves and turbulence exist independently, and to demonstrate the evolution process of the turbulent vortex structure formed when waves are superimposed on turbulence. The study adopted rough wall simulations and verified the computational results. The findings indicate that under rough wall conditions, stronger secondary flows and turbulent vortex structures are formed within the boundary layer, and an increase in roughness enhances the turbulence intensity within the boundary layer. Additionally, the impact of wall height on the flow structure cannot be overlooked. This paper also presents the evolution process of the turbulent vortex structure within wave–current boundary layers, providing new insights for the study of rough wall flow-related issues. For the interaction of waves and turbulence under rough wall conditions, high-precision numerical discretization schemes are adopted to construct a bottom boundary layer numerical model. This is achieved by summarizing the progress of existing conclusions, understanding the research progress of numerical simulation in the wave–current boundary layer, constructing high-precision numerical discretization schemes, establishing a physical model of the studied problem and abstracting it into a mechanical model, establishing the entire geometric shape and its spatial influence area, performing spatial grid division, adding the initial conditions required for the solution, and selecting the LES algorithm. Full article

Accurate and reliable positioning is essential for unmanned marine vehicles (UMVs), especially in complex maritime environments. Existing algorithms often underutilize historical information, struggle with nonlinear dynamics, and lack adaptability in the GNSS Measurement Noise Covariance, leading to degraded performance. This study proposes an enhanced Factor Graph Optimization (FGO) method integrated with an adaptive neuro-fuzzy inference system (ANFIS) to overcome these challenges. First, an improved GNSS/Dead Reckoning (DR) factor graph is built using refined error models to enhance baseline accuracy. Second, a marginalization factor is introduced utilizing a sliding window and the Schur complement method to retain informative historical data while reducing computational load, thereby improving stability and field performance. Third, an ANFIS-based adaptive GNSS factor dynamically updates the GNSS Measurement Noise Covariance Matrix (GMNCM) to strengthen robustness under variable maritime conditions. Simulation and field tests demonstrate significant improvements: the proposed method achieves 29.1%, 26.5%, and 9.9% higher accuracy than EKF, UKF, and conventional FGO, respctively. Under GNSS interruptions, EKF and UKF diverge with errors exceeding 500 m, while FGO limits drift to 20 m. The proposed ANFIS–FGO shows the smallest fluctuations and fastest recovery, confirming its strong resilience and practical applicability for UMV navigation. Full article

This study investigates the mechanical performance and blast resistance of high-performance aramid, carbon, and ultra-high molecular weight polyethylene (UHMWPE) fiber fabrics, responding to the need for lightweight and flexible materials in anti-explosion containers for aviation and critical infrastructure. The experimental methodology integrated quasi-static and dynamic tensile tests to characterize the strain-rate effect, followed by near-field air blast tests on both single-material and hybrid multi-ply fabric specimens to analyze their dynamic response, failure modes, and overpressure attenuation. Key findings revealed that carbon fabric exhibited high stiffness but was strain-rate insensitive and susceptible to brittle perforation failure, whereas aramid and UHMWPE fabrics demonstrated strain-rate sensitivity, with UHMWPE showing superior ductility and energy absorption. The hybrid multi-ply configuration (A-C-U sequence) achieved the least amount of failure, effectively utilizing the wave impedance of aramid fabric for initial shock reflection, high stiffness of carbon fabric for stress homogenization, and plasticity of UHMWPE fabric for energy dissipation. Additionally, all fabrics attenuated peak overpressure by over 80%, with enhancement observed for increased thickness. The study concludes that the strategic layering of different fabrics creates a synergistic effect, mitigating the weaknesses of individual fabrics and establishing an effective design paradigm for advanced blast-resistant structures, further enhancing the protective performance. Full article

Particulate matter (PM) emissions are common in technological processes, and effective mitigation requires gas pre-treatment before high-efficiency filtration to reduce fine and ultrafine PM that are particularly dangerous to the human health. This study evaluates a multichannel electric field manipulator (agglomerator) as a flow pre-treatment stage and investigates the aerodynamic conditions that govern particle–gas flow distribution and variation in trajectories and dynamics at different flow rates. These factors provide meaningful assumptions about the possible behavior of particles in the flow, and they are critical for optimizing an agglomeration and its intensity. Such phenomena can have an impact on the probability of agglomeration in the manipulator channels, i.e., the adherence of small particles into larger ones, and this allows for improving the design and operating conditions of the apparatus. Gas flow velocities and pressure were analyzed experimentally at various cross-sectional points in the inlet and outlet ducts at inflow rates of 3.4 L/s and 50 L/s. The static inlet pressure of the manipulator ranged from 8 Pa to 178 Pa. This study provides new insights into flow pre-treatment using the electric field mechanism in a multichannel modular apparatus and provides a reasonable understanding of the necessary characteristics of gas flow distribution to support subsequent improvements targeting higher agglomeration. Full article