Search Results (9,096)

Soil organic carbon (SOC) stock is a key component of terrestrial ecosystems, playing a critical role in climate regulation and ecosystem productivity. Despite its economic importance, the impacts of the expansion of khat (Catha edulis) cultivation at the expense of other land uses and its intensive management practices on the depletion of soil carbon content are overlooked in Eastern Ethiopia. This study aimed to estimate and map SOC stocks using multispectral Sentinel-2 and RapidEye imagery, combined with environmental, soil, and topographic variables, across khat-dominated landscapes in the Haramaya District of Eastern Ethiopia. A total of 88 soil samples were collected and analyzed to quantify SOC stocks. Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms were employed to predict SOC stocks. The dataset was stratified into training (70%) and an independent validation (30%) subset. Model development was performed using five-fold cross-validation on the training dataset, while final performance was assessed on the independent validation set using the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). Laboratory-measured SOC stocks ranged from 24.99 to 65.94 Mg C ha⁻¹, with a mean value of 36.88 Mg C ha⁻¹. The predicted spatial SOC stocks ranged from 30.4 to 50.4 Mg C ha⁻¹ using RapidEye data and from 32.8 to 51.5 Mg C ha⁻¹ using Sentinel-2, with Sentinel-2 producing slightly higher mean estimates. The lowest SOC stocks were consistently observed in bare, grass, and shrub land-use types across both datasets. RF demonstrated superior performance compared with XGBoost, achieving moderate predictive performance for both the RapidEye (R² = 0.56, RMSE = 5.91 Mg C ha⁻¹) and Sentinel-2 (R² = 0.42, RMSE = 6.90 Mg C ha⁻¹) datasets. This result indicates that RF provided greater robustness for SOC stock prediction under the heterogeneous environmental conditions of khat-dominated agricultural landscapes. Topographic and soil-related variables, particularly the Topographic Wetness Index (TWI), land surface temperature (LST), and clay content, were identified as the most influential predictors in both models. Although less consistent, remote sensing indices such as GNDVI, BSI, and NDWI also contributed to SOC prediction. While both sensors proved effective for SOC mapping, a measurable sensor-related effect was observed. The findings demonstrate the effectiveness of integrating multisource remote sensing, environmental, soil, and topographic variables with machine learning for SOC stock mapping in khat-dominated landscapes. This approach provides valuable spatial information to understand SOC variability and support sustainable land management and climate change mitigation strategies in Eastern Ethiopia. Full article

This work presents a numerical investigation of a direct oil cooling system for Axial Flux Permanent Magnet (AFPM) machines. Building upon the authors’ previous study, which established the fundamental fluid dynamic mechanisms governing oil jet impingement on curved coil surfaces, the present research extends the analysis by performing a systematic parametric optimization of nozzle diameter and axial position. A validated CFD model, benchmarked against experimental data from the literature, is employed to quantify the influence of jet momentum, stagnation pressure, and flow attachment on the resulting thermal performance. Nine configurations are simulated at constant coolant mass flow rate, revealing that the nozzle diameter is the dominant parameter: smaller diameters generate higher jet velocities, stronger stagnation regions, and larger jet-induced forces, leading to significantly enhanced heat transfer coefficients and Nusselt numbers. Nozzle height plays a secondary yet relevant role, as higher positions promote a more coherent jet core and improve impingement quality. Among the nine simulated cases, the configuration with D = 3 mm and L = 14 mm achieves the lowest hotspot temperature and the most efficient energetic behavior within the simulated set, with only a modest increase in pumping power. The results confirm that direct oil impingement is highly sensitive to jet momentum and angle of attack and demonstrate that optimized nozzle design can substantially improve the thermal management of high power density AFPM machines. This extended analysis provides quantitative references for nozzle sizing and placement within the simulated operating conditions with enhanced cooling efficiency. Full article

Sustainable machining of composite materials requires optimizing conflicting responses influenced by limited experimental datasets, trade-offs, nonlinear process variables, and response variability. This study proposes a hybrid framework (Gaussian Process Regression—Method based on the Removal Effects of Criteria—Criteria Reliability—Momentum-Based Optimization Algorithm: GPR–MEREC-CR–MOA) to address these challenges in turning composite materials (PA66, PA66 + GF30, and PA66 + MoS₂). The GPR model learns from small datasets to capture nonlinear relationships between machining variables (workpiece material, tool approach angle, tool nose radius, cutting speed, feed rate, depth of cut) and performance characteristics (surface roughness, cutting force, vibration, tool wear rate, temperature, sound pressure level, specific cutting energy, and material removal rate). The MEREC-CR method considers experimental dispersion and response variability to enhance the robustness of the multi-response aggregation model. The weighted responses determined by MEREC were optimized by exploring the operating ranges of machining variables using MOA. The GPR model accurately predicts eight performance characteristics (R² ≥ 0.973). The GPR–MEREC-CR–MOA model identified optimal conditions for PA66 + MoS₂ and composite material (tool angle = 93°, nose radius = 0.40 mm, cutting speed = 200 m/min, feed rate = 0.300 mm/rev, depth of cut = 1.08 mm), resulting in a composite performance index (CPI) of 0.9265 and a 30.2% improvement over the best experimental datasets from Taguchi L₂₇ design. The tool wear rate, specific cutting energy, and vibration have a significant impact on overall machining performance. Feed rate has the strongest influence on CPI, as confirmed by Partial Rank Correlation Coefficients analysis. Monte Carlo-driven uncertainty analysis validates the optimal solution with a 95% confidence level for CPI between 0.8859 and 0.9451. External validation with nine independent cases confirmed the GPR model’s strong generalizability (R² = 0.811–0.998). Benchmarking showed that MOA achieves solution quality comparable to GA, PSO, and GWO while reducing computational time by 66–86%, making it suitable for real-time optimization. The proposed hybrid framework provides an alternative data-driven decision support approach for evaluating sustainable machining parameters using limited experimental datasets of polymer composites. Full article

Ultra-high-temperature ceramic (UHTC) coatings offer a promising route to extending cutting tool service life under severe conditions. This work evaluates the tribological behaviour and wear regime transitions of ZrB₂–SiC/ZrO₂ coatings, deposited by physical vapour deposition (PVD) onto ASSAB-17 high-speed steel tool bits, during dry turning of Ti-6Al-4V. Structural, microstructural, mechanical, and tribological characterisation was performed by X-ray diffraction (XRD), scanning electron microscopy (SEM), nanoindentation, and pin-on-disc testing under three pressure–velocity (PV) severity levels, with worn surfaces analysed by SEM and profilometry. The coating exhibited a nanostructured ZrB₂/β-SiC/t-ZrO₂ architecture with a hardness (H) of 24 ± 3 GPa, a hardness-to-reduced-elastic-modulus ratio (H/Er) of 0.100, and an elastic resistance to plastic deformation (H³/Er²) of 0.240 GPa. Three tribological regimes were identified: running-in, steady-state sliding, and progressive degradation, with the highest severity (PV = 6.0 N·m/s) triggering degradation beyond approximately 620 m, a more than one-order-of-magnitude rise in wear rate, and the only case exceeding the tool-life criterion of maximum flank wear (VBmax = 0.30 mm) according to ISO 3685. The main advantage of the proposed approach is that it condenses tool-life-relevant behaviour into a single, easily measurable severity parameter, the PV product, directly applicable to coating design and the selection of safe machining-condition windows. The overall behaviour is consistent with a mechanism governed by the stability and regeneration capacity of a protective tribofilm. As the composition of this layer was not directly characterised, this mechanism is proposed as a phenomenological interpretation, from which a PV threshold is derived as a design criterion for UHTC coatings. Full article

In the manufacturing industry, cutting force is the primary factor affecting bamboo milling. Excessive cutting force can increase tool wear and affect processing quality, processing energy consumption, and processing stability. Cutting parameters have significant impacts on cutting forces. The cutting force is also one of the important indexes for evaluating machining performance. This article uses response surface methodology to study the influence of different cutting parameters on the cutting force during the bamboo milling process. Moreover, the importance of cutting parameters to cutting forces was determined by variance analysis, corresponding mathematical models were established, and the interaction between cutting parameters and cutting forces was analyzed to optimize the bamboo milling process. On this basis, the cutting parameter combination corresponding to the optimal milling process under experimental conditions was determined and experimentally verified. The prediction accuracy was high and the test optimization was good. Therefore, the proposed method can be used to predict and optimize the actual cutting force, providing a scientific basis for high-quality processing of bamboo milling. Full article

Monitoring complex river networks in Myanmar is challenging due to the high spatial heterogeneity and fragmentation of surface water bodies. Accurate identification of surface water resources is therefore essential for water resource management and for improving preparedness against climate change–induced hydrological hazards. To address this problem, this study proposes an adaptive interwoven deep learning–based segmentation framework that jointly utilizes multispectral reflectance information and topographic elevation data to enhance the extraction of fragmented water bodies. The framework is designed to coordinate feature interaction across spectral, spatial, and topographic dimensions by integrating channel-wise feature recalibration and attention-guided feature modulation within the encoding–decoding architecture. Experimental results demonstrate that the proposed method outperforms several traditional water index–based approaches, conventional machine learning algorithms and deep learning models. Across five independent training runs, the proposed framework achieves an average precision of 91.0%, recall of 93.5%, and F1 score of 92.3% (95% confidence interval: 91.8–93.0), demonstrating stable performance for fragmented water-body extraction. Cross-site experiments across three within-country study areas further demonstrate the spatial transferability and robustness of the proposed framework across diverse hydrological conditions within Myanmar. Overall, the proposed approach provides a reliable solution for fragmented water body extraction under heterogeneous hydrological conditions within Myanmar. Full article

The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud–edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack surface, making intelligent intrusion detection essential for protecting sensitive medical data and ensuring resilient clinical operations. Existing reviews examine specific aspects of AI-powered intrusion detection but rarely provide a deployment-oriented synthesis linking technical performance with operational and clinical requirements. This review critically examines Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDSs) for IoMT across six analytical dimensions: detection performance, explainability, privacy preservation, computational efficiency, benchmarking practices, and cross-dataset generalization. This structured narrative review adopted the PRISMA 2020 framework to ensure transparent record identification, screening, and reporting, with evidence synthesized qualitatively rather than through quantitative meta-analysis. A total of 5127 records published between 2021 and 2026 were screened, resulting in 24 primary studies supported by 115 complementary studies. The findings show that machine learning, deep learning, hybrid AI, Explainable Artificial Intelligence (XAI), Federated Learning (FL), blockchain-assisted security, and edge intelligence have significantly advanced IoMT intrusion detection. However, despite benchmark accuracies often exceeding 95%, deployment remains constrained by dataset dependency, weak cross-dataset generalization, computational overhead, limited explainability, fragmented benchmarking, and insufficient operational validation. This review identifies deployment readiness, rather than predictive accuracy alone, as the principal challenge for next-generation healthcare cybersecurity and provides a practical framework for developing trustworthy, interoperable, privacy-preserving, and deployment-ready IoMT cybersecurity architectures supported by standardized evaluation protocols. Full article

Thin film printed electronics, such as flexible circuits and sensor sheets, require non-contact inspection to detect defects and edge degradation. The present paper presents a laser-enhanced machine vision framework for detecting and analyzing the edges of printed conductive tracks using Canny edge detection and Otsu thresholding. Using a coherent laser source, Otsu’s method enhances contrast at the ink–substrate interface, enabling robust segmentation of edge lines. Canny operator is applied to thresholded images to extract precise edge profiles. Multiple printed tracks are analyzed to calculate four lateral edge roughness values (Ra). As a result, the values are 40.43 µm, 40.09 µm, 50.26 µm and 40.94 µm. The results show that the suggested method can detect and qualify variations in edge parameters. Printed electronics are produced using an inline inspection and quality control system based on non-contact, high-resolution, and scalable technologies. Full article

Pseudocereals such as quinoa, amaranth, and buckwheat have attracted increasing attention as ingredients for extruded snack products because of their nutritional value, gluten-free status, and content of bioactive compounds. The quality of extruded products is governed by complex interactions among processing variables, including barrel temperature, screw speed, feed moisture content, and formulation characteristics. As a result, mathematical modelling has become an important tool for predicting product properties and identifying suitable processing conditions. This review summarizes modelling approaches applied to extruded food products with a focus on pseudocereal extrusion. Particular emphasis is placed on response surface methodology (RSM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and hybrid optimisation strategies. Published studies indicate that RSM remains the most commonly used approach because of its simplicity and interpretability, while ANN-based models generally provide much higher predictive accuracy when strong nonlinear relationships are present. The widespread use of small experimental datasets and limited external validation remains a major challenge for the practical implementation of advanced machine-learning models. This review examines the strengths and limitations of current modelling approaches and discusses future opportunities for integrating predictive models with digital manufacturing frameworks. Full article

Chinese bayberry (Myrica rubra) is an extensively popular fruit with unique flavors and high nutritional values. A precise and efficient method for assessing external quality traits of Chinese bayberry is critical for machine automatic sorting to ensure the quality and increase economic value. Here, we developed an automatic evaluation flow based on deep learning detection and a multi-trait scoring framework for postharvest quality grading of Chinese bayberry. Firstly, the YOLOv11 framework was applied to detect the Chinese bayberry fruits, and the network consists of a CSPDarknet-based backbone with C3k2 modules, an SPPF block, a bidirectional PANet neck, and three decoupled detection heads. The experimental results showed a precision of 0.9991 and a recall of 1.0000 for the waxberry class (mAP@0.5 = 0.9950, mAP@0.5:0.95 = 0.8632). Furthermore, the quality scoring model was built on a modified AlexNet backbone that provided more stable training and better accuracy compared to the other seven CNN models. The R², MAE, and RMSE showed the close alignment between predicted and reference values, revealing reliable estimations of our six fruit-quality traits, including radius, color depth, color class, color uniformity, surface injury area ratio, and plumpness. This self-developed flow provides a comprehensive and effective assessment method to improve fruit quality and economic value, which further facilitates automated agricultural production. Full article

Micro curved-surface parts are widely used in the aerospace, defense, biomedical, and automotive industries, and their growing adoption imposes increasingly stringent performance requirements. Five-axis micro-milling can achieve precision machining of parts with complex shapes. In the micro-milling process, the cutting force is a critical parameter, as it is the main factor causing machining deformation, vibration, and tool wear. Therefore, this study develops a prediction model for five-axis micro-milling forces in the machining of micro complex curved-surface parts. First, four coordinate systems were established for the five-axis milling process, and the transformation relationships among them were derived. A cutter–workpiece engagement (CWE) extraction method based on solid modeling was also introduced. Then, an instantaneous undeformed chip thickness (IUCT) model was established, taking into account tool runout, elastic recovery of the machined surface, minimum chip thickness, and the local radius of the micro ball-end mill. On this basis, a five-axis micro-milling force prediction model was developed. Finally, five-axis micro-milling experiments were conducted on a micro-impeller and a micro-spherical part, and the cutting forces at different cutter location (CL) points were measured. For the micro-impeller blade, the average percentage errors in the X, Y, and Z directions at all selected CL points were below 11.2%; for the micro-spherical part, the corresponding errors were below 14.4%. These results show good agreement between the predicted and measured values, verifying the effectiveness of the proposed model. Full article

Soil salinization reduces crop productivity and threatens agricultural sustainability in arid regions. Reliable estimation of farmland soil salinity is therefore essential for salinization monitoring and land management. During the non-growing season, limited crop cover increases soil surface exposure. Existing studies have mainly relied on optical indices, although SAR features and terrain variables have also been used to improve estimation accuracy. However, climatic and soil texture variables have not been fully considered after the harvest of crops in farmland. In addition, traditional machine learning methods have difficulty effectively learning the complex nonlinear relationship between multi-source variables and soil salinity. Therefore, this research proposed a method to estimate soil salinity by integrating multi-source remote sensing data with a deep learning model. This study focused on farmland in the Wei-Ku Oasis of northwestern China during the non-growing season. Six variable combination scenarios were constructed using Sentinel-1/2 data and environmental covariates, including terrain, land surface temperature (LST), and soil texture. Support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models were developed to estimate soil salinity. The results showed that: (1) integrating optical indices, SAR features, terrain variables, LST, and soil texture achieved the highest estimation accuracy; (2) the CNN showed better overall estimation performance than the traditional machine learning models across 50 random-split experiments (R² = 0.68 and RMSE = 1.24 dS/m); and (3) optical indices contributed most to the SVR and RF models, whereas environmental variables contributed most to the CNN model in this study. This study proposed a soil salinity estimation framework that integrates multi-source remote sensing data with a deep learning model during the non-growing season. It provides new data support and technical support for soil salinity estimation of farmland in arid regions during the non-growing season. Full article

In crisis times, traditional models of water quality assessment and water resources management lose their effectiveness. In the current conditions of local climate change, accidental pollution, emergencies or military operations, there is an urgent need to transition from traditional descriptive hydrochemical monitoring toward intelligent analysis of spatial-temporal datasets. In this paper, the integrated approach combining spatial cluster analysis, GIS-based visualization, and machine learning is proposed for assessing the surface water quality under conditions of limited and incomplete hydrochemical monitoring data. A geospatial assessment of nutrient impacts on surface water quality was conducted using 192 hydrochemical observations collected during the 2024–2025 monitoring period at eight state monitoring stations located in the basins of the Teteriv, Uzh, Irsha, Ubort, Sluch, Hnylopiat, and Voznia rivers, Polissia, Ukraine. Permutation feature importance analysis based on the Random Forest model showed that nitrate concentration accounted for approximately 75% of the total relative importance, whereas phosphate concentration contributed approximately 14%, indicating that these variables were the most informative predictors among the investigated hydrochemical parameters. The latter parameters are associated with dissolved oxygen variability among the analyzed hydrochemical parameters. According to the results of this study, three interpretable groups of monitoring stations were formed: Cluster 1, representing moderate water quality with increased nutrient pressure, Cluster 2, representing comparatively favourable background conditions, and Cluster 3, representing a nitrate-dominated hydrochemical type. The Random Forest model demonstrated limited predictive performance (R² = 0.154), indicating that nutrient-related variables alone explain only a small proportion of dissolved oxygen variability. Hence, additional factors, including hydrological conditions, water temperature, organic matter decomposition, biological productivity, and catchment-specific characteristics, also play an important role in shaping oxygen dynamics. The spatial visualization of cluster membership showed that geographical location alone does not fully determine the surface water quality patterns in Ukrainian Polissia. Instead, the local catchment characteristics and land-use conditions appear to exert a stronger influence on the formation of nutrient-related water quality differences. The authors propose to employ the spatial cluster analysis and machine learning as a basic supporting tool for the transition from retrospective interpretation of hydrochemical monitoring data to predictive and adaptive water resources management. The integration of geospatial analysis and machine learning provides a practical decision-support framework for the early detection of anomalies, identification of potential pollution sources, and prioritization of river sub-basins for implementing nature-based solutions. Full article

High-quality and high-efficiency machining of film cooling holes (FCHs) is critical for enhancing the performance of gas turbines and aero-engines. Nanosecond fiber lasers offer advantages such as high stability, good beam quality, and ease of integration. However, when machining FCHs, issues including low drilling efficiency and significant thermal effects severely limit their industrial applications. In this study, a dual-rotary trepanning system was developed based on a nanosecond fiber laser, a galvanometer, and a five-axis cradle machine. High-quality FCHs with a diameter of 0.6 mm were efficiently machined in a 3-mm-thick DD6 superalloy plate within only 6.5 s. Compared with the method using machine tool rotation alone, the average recast layer thickness on the inner wall was reduced by 62.1% to 6.7 μm, and the average surface roughness was reduced by 61.1% to 0.35 μm. These improvements are primarily attributed to the galvanometer speed being two orders of magnitude higher than that of the machine tool, which significantly reduces the laser pulse overlap rate and the thermal accumulation effect. Moreover, the kerf widened by the galvanometer rotation allows the ablation products to expand more fully and be expelled efficiently, thereby reducing impact, scratching, and debris adhesion on the inner wall and improving the drilling efficiency. Furthermore, 10 × 10 FCH arrays were machined on both vertical and inclined plates, demonstrating high consistency and stability, indicating the potential for industrial applications in the field of FCH machining. Full article

Abrasive Water Jet Machining (AWJM) is increasingly used for post-weld surface modification of dissimilar metallic joints; however, the depth-dependent surface response of welded stainless–carbon steel joints remains insufficiently quantified. In this study, four dissimilar welded systems, TIG 316, TIG 309, ARC 316, and ARC 309, were systematically investigated to elucidate the combined influence of welding technology, filler composition, and jet parameters on surface integrity. Surface roughness was evaluated at multiple jet-penetration depths using amplitude (R_a, R_q, R_t, R_z) and statistical (R_sk, R_ku) descriptors. The results reveal three distinct hydrodynamic erosion regimes governing texture evolution. Duplex welds (TIG 309 and ARC 309) exhibited highly stable erosion behavior, with R_a confined to 1.91–2.99 µm, low roughness gradients (ΔR_a/Δdepth = 0.012–0.015 µm·mm⁻¹), and near-Gaussian surface statistics (R_sk ≈ 0, R_ku ≈ 3–4). In contrast, austenitic welds (TIG 316 and ARC 316) showed pronounced depth-dependent instability, with R_a increasing up to 4.54 µm and the normalized roughness ratio R_z/R_a reaching 5.69 in TIG 316 near the jet exit. Strong inter-parameter correlations in duplex welds (r ≥ 0.94) confirm uniform erosion kinetics, whereas weakened correlations in austenitic systems (r ≈ 0.70–0.83) reflect jet-energy dissipation. These findings establish a mechanistically grounded AWJM performance window for achieving R_a ≤ 3 µm in dissimilar welded steels. Full article