Search Results (8,902)

High-speed electric machines enhance power density and eliminate the need for a gearbox in waste heat recovery microturbine systems. However, existing designs often suffer from high manufacturing costs and complex cooling requirements. This study presents the development, experimental validation, and comparative analysis of three high-speed machine designs. First, a lower-speed induction machine prototype, constructed using standardized components, was tested at an operating speed of 13,000 rpm. This prototype enabled experimental validation of the numerical model used for loss calculations. Experimental results showed total losses of 7.89 kW, closely matching the simulated value of 7.75 kW at an output power of 93.1 kW, i.e., an efficiency of 92.19%. Building on these findings, two smaller machine prototypes were developed: one featuring an induction squirrel-cage rotor and the other employing a surface-mounted permanent magnet rotor topology. Both machines were designed and evaluated using finite element analysis and conjugate heat transfer simulations. Their performance was analyzed under both sinusoidal and pulse-width-modulated voltage supply conditions. At an operating speed of 14,000 rpm, the permanent magnet machine outperformed the induction machine, achieving 63.2 kW of mechanical power and an efficiency of 96.21%, while operating at lower temperatures. In comparison, the induction machine delivered 52.4 kW of mechanical power with an efficiency of 94.64%. The primary novelty and contribution of this work lie in the implementation of a two-pole machine architecture capable of achieving an output power of 100 kW at operating speeds between 20,000 and 25,000 rpm. Compared with similar solutions reported in the literature, the proposed machines feature a simplified bearing arrangement and a more straightforward liquid-cooling system. These characteristics have the potential to reduce manufacturing costs and simplify maintenance during operation. Full article

This work offers an extensive performance evaluation of video-based pothole detection algorithms utilizing a unique dataset of 619 high-resolution movies recorded in South Kalimantan, Indonesia. Seven distinct models were assessed: three multi-frame-based methodologies (Best Frame Selection, Temporal Consistency Loss, and Multi-Frame Ensemble) employing U-Net architectures with temporal modeling, three per-frame models (OneFormer, YOLOv8-seg, and YOLACT), and one fusion ensemble integrating the per-frame models via weighted boxes fusion. The video collection consists of 2 s segments containing 48 frames each, accompanied by ground truth segmentation masks for pothole identification. Results indicate that per-frame models substantially surpass video-based methods, with the fusion ensemble attaining 81% IoU, followed by YOLOv8-seg and OneFormer, each getting 80% IoU. Parameter efficiency investigation indicates that YOLOv8-seg is the most efficient, achieving IoU per million parameters. Full article

Surface heat sources play a critical role in shaping meteorological conditions and permafrost dynamics on the Tibetan Plateau. To better understand surface heat source variability in the permafrost region, multiyear observational data from an isolated permafrost site and a continuous permafrost site were used to analyse the variability. The results indicated that the surface heat source at the isolated permafrost site remained relatively stable, whereas that at the continuous permafrost site increased significantly, at a rate of approximately 2.2 Wm⁻²yr⁻¹. The proportions of sensible and latent heat fluxes in the surface heat source budget varied with the season. Overall, the proportion of latent heat flux was greater than that of sensible heat flux in summer and autumn, whereas the proportion of sensible heat flux was greater than that of latent heat flux in winter and spring. The peak proportion for both fluxes exceeded 80%. A random forest model effectively captured the variations in the surface heat source. And the machine learning simulation indicated that soil temperature, downward shortwave radiation and albedo were identified as major contributors to the surface heat source, collectively accounting for more than 88% of the overall variability at both sites. The impacts of snow cover on the surface heat source varied with intensity. The increasing trend of the surface heat source was closely related to climate warming in autumn and winter. A significant but weak positive correlation was observed between vegetation and the surface heat source. Full article

A hypothesis was proposed that continuous dual-circuit mixing can be achieved by equipping a feed mixer-distributor with two leveling–mixing finger shafts, which, after lifting the feed mass to a certain height, collect it in the central part of the hopper and divide it into two flows directed toward the end walls of the hopper. In this case, continuous dual-circuit mixing is performed during each rotation of the leveling–mixing shaft. A structural and technological scheme, engineering documentation, and an experimental prototype of the feed mixer-distributor were developed. The machine consists of a 3.0 m³ hopper, two horizontal augers, two leveling–mixing finger shafts, a loading conveyor, and a drive mechanism. Theoretical investigations were carried out, and analytical expressions were obtained to determine the circumferential velocity of the fingers of the leveling–mixing device. This velocity must ensure the movement of the feed mixture without scattering and guarantee the release of the feed mass from the finger surface when the finger rotation angle exceeds 20°. Calculations based on the obtained analytical expressions showed that the critical circumferential velocity of the fingers is 0.866 m/s, while the calculated minimum rotational speed of the finger shaft is 20.7 min⁻¹. Therefore, a rotational speed of approximately 20 min⁻¹ was adopted for the experimental investigations. Experimental studies conducted at different rotational speeds of the leveling–mixing device showed that the optimal rotational speed of the finger shaft is 20 min⁻¹. At this rotational speed, the mixture uniformity exceeded 90%. An analytical expression was also derived to determine the velocity of feed mixture movement along the finger surface. Calculations showed that the optimal velocity ranged from 0.5 to 0.94 m/s. This value corresponds to the rational velocity of feed mixture transportation toward the end walls of the hopper. Laboratory experiments were carried out using the feed mixer-distributor at a leveling–mixing finger shaft rotational speed of n = 20 min⁻¹. The optimal mixing time required to achieve the target mixture uniformity was 5.5 min under the tested operating conditions. Comparative experiments also showed that operation of the feed mixer-distributor without the leveling–mixing device resulted in a 34% higher power consumption than operation with the leveling–mixing device. Full article

Soil salinization limits agricultural production and the management of soil and water resources in arid irrigated regions. Regional prediction remains difficult, while soil salinity is influenced by hydrology, topography, and land cover. This study developed an explainable automated machine learning (AutoML) framework to map surface soil salinity in the Qingtongxia Irrigation District, Ningxia, China. The analysis combined 108 soil samples (0–10 cm), collected in March-April 2024, with 36 candidate input features from Sentinel-2 spectral indices, topography, soil texture, groundwater depth, and climate. Pearson correlation analysis and recursive feature elimination with cross-validation (RFECV) selected 10 key input features. Among the tested models, AutoML achieved the best validation performance, with R² = 0.78, MAE = 1.94 g/kg, and RMSE = 2.65 g/kg. The resulting 30 m prediction map captured broad regional patterns of soil salinity, with predicted values from 0.34 to 22.91 g/kg and higher salinity in northern and northeastern areas. Shapley additive explanations (SHAP) analysis highlighted brightness index, groundwater depth, clay content, elevation, and salinity index I as influential features. These findings suggest that explainable AutoML can help identify regional salinity risk and guide future sampling and model refinement. Full article

Laser marking technology employs a high-energy-density laser beam to irradiate material surfaces along a predefined path. Through photothermal, photochemical, or photomechanical effects, it can generate clear and machine-readable surface marks without physical contact. Previous studies have indicated that this technology has considerable potential for identification, quality and safety management, and traceability in agriculture. This review systematically summarizes the core components of agricultural laser marking systems, including lasers, beam delivery systems, and modulation units. It explains the principal laser–material interaction mechanisms and classifies the resulting marking forms. In addition, it comprehensively examines research progress and applications of laser marking technology in plant systems, animal systems, processed foods, and food packaging. Finally, this review analyzes the key advantages and current challenges of this technology. It also proposes future directions for applying laser marking in agricultural production. Full article

Short-term coastal surface-current forecasts at forecast lead times (τ = 1–12 h) are critical for search and rescue (SAR), pollution response, and vessel routing. From a 2015–2019 archive of hourly CODAR high-frequency radar (HFR) observations off Southwestern Taiwan, we developed grid-point long short-term memory (LSTM) models using historical observations alone, without atmospheric forcing or data assimilation (training 2015–2017, validation 2018, test 2019). Because harmonic tides account for ~19% of the surface-current variance, we tested harmonic detiding under a matched architecture and tuning protocol, comparing a raw-input LSTM with a detided variant (LSTM-HA) that forecasts the detided residual and reconstructs the total current. In the out-of-sample 2019 test year (τ = 12 h), LSTM-HA ranked highest (R = 0.768/0.729 for u/v) and reduced RMSE by ~34% relative to Persistence; both LSTM configurations far exceeded HA-Persistence and tide-free HYCOM, and the LSTM-HA advantage was statistically significant and spatially pervasive. An independent single-drifter Lagrangian proof-of-concept (December 2020) gave 12 h mean separations of 8.52/9.20 km for LSTM/LSTM-HA, comparable to the HFR observations (9.26 km) and below HYCOM. For this tide-influenced focus area, the benefit of LSTM-HA emerges from approximately τ = 3 h and becomes most relevant over τ = 6–12 h. At τ = 1–3 h, the raw-input LSTM performs nearly equivalently while forecasting the total current directly. Broader seasonal validation, including monsoon and typhoon forcing, remains a priority. Full article

The cutting variables of end milling are positively correlated with the machined surface roughness, but their mechanism of action remains unclear. Since the cutting tool used in end milling is a three-dimensional solid, under the action of horizontal cutting force, there exist both axial tensile strain and axial compressive strain in the cutting tool at the same time, thus putting forward an assumption about the formation mechanism of machined surface roughness: cutting variables affect the axial tensile strain of the cutting edge through the horizontal cutting force, and cause the cutting-edge tip to vertically wedge into the machined surface, thereby influencing the unevenness of the processed surface. Based on the cutting-tool deflection model of a two-segment cantilever beam and the horizontal cutting force formula, the mathematical expression for the axial strain at the sharp tip of the cutting edge was derived. Groove milling and half-groove milling experiments were done, and the experimental results show that the roughness Ra value in the central area is significantly higher than that in the cut-in area, with its maximum average value being 1.32 times that of the cut-in area. The surface roughness rises following the rise in depth of machining and feed per cutting tooth, but this relationship is significant. The laboratory findings are consistent with the theoretical analysis results, indicating that the assumption about the formation mechanism of surface roughness should be reasonable, and the surface roughness in the central area is greater than that in the cutting tool cut-in area. The research results provide a kind of new insight into the formation mechanism of machined surface roughness, which can serve as a reference for relevant research and cutting practices. Full article

Inconel 718 exhibits poor machinability due to its high strength and low thermal conductivity, which induce severe thermo-mechanical loads. Conventional cooling strategies struggle to concurrently regulate heat dissipation and interface lubrication. This paper proposes a machining method based on Active Thermal Conductive Media (ATCM), which simultaneously exerts the Rehbinder mechanochemical effect and solid-phase enhanced heat transfer effect by pre-coating a liquid graphene film on the workpiece surface. Orthogonal turning tests were conducted using a K313 carbide tool at a cutting speed of 30 m/min, cutting width of 2 mm, and undeformed chip thickness of 0.1 mm. The cutting force, cutting temperature, cutting power, and tool wear characteristics under six machining conditions—dry cutting, flood cutting, Minimum Quantity Lubrication (MQL), Cryogenic MQL (CMQL), Nanofluid MQL (NMQL), and ATCM-assisted cutting—are systematically compared. The results show that ATCM achieves a 21.6% reduction in cutting force, a 20% reduction in cutting temperature, and a 34.9% reduction in cutting power through the synergistic coupling effect of reduced heat generation and enhanced heat dissipation, with adhesive wear and diffusion wear of the cutting tool significantly suppressed. Full article

Integratingartificial intelligence (AI), the Internet of Things (IoT), and Building Information Modeling (BIM) holds considerable promise for modernizing construction management, yet a unified real-time framework connecting these technologies for heavy civil earthmoving remains lacking. This paper presents BIM-iDT, a BIM-Integrated Digital Twin framework that couples multi-source IoT sensing with an IFC-based BIM model to enable intelligent fleet coordination and automated progress control. The research follows a design-science methodology comprising framework formulation, modular development, field deployment, and multi-project validation. The framework comprises a heterogeneous sensor fusion layer aligning GPS, IMU, fuel-consumption, and LiDAR data within the BIM coordinate system; a spatio-temporal graph attention network (ST-GAT) that recognizes equipment states and predicts short-horizon productivity by modeling fleet-level spatial dependencies; a temporal point cloud differencing module that quantifies cut/fill volumes against BIM design surfaces; and a constrained multi-objective evolutionary optimizer (CMOEO) that generates Pareto-optimal dispatch plans balancing fuel, cycle time, utilization, and schedule adherence. Validation on a highway project with instrumented machines shows that ST-GAT achieves a macro-averaged F1 of 0.943, volume MAPE stays below 3%, and CMOEO reduces fuel consumption by 12.6% and cycle time by 9.3% while maintaining schedule adherence above 96%, yielding an estimated 168-ton CO₂ emission reduction. End-to-end latency averages 600 ms, satisfying real-time requirements. Cross-project transfer experiments on a secondary dam construction site further confirm framework generalizability, establishing BIM-iDT as a scalable paradigm for AI-and-IoT-enabled smart construction in infrastructure engineering. Full article

To address the pervasive engineering challenges of rigid interference and subpar machining efficiency encountered during the complex freeform surface polishing of P20 mold steel, this study proposes and fabricates a structurally designed, five-petal composite compliant polishing tool via fused granulation fabrication (FGF). The tool structurally integrates a passive thermoplastic polyurethane (TPU) compliant buffer layer with an active PA66/diamond micro-cutting functional layer, achieving monolithic precision assembly through dual-temperature-zone 3D printing. Tensile mechanical characterization (n = 6) reveals that the composite interface attains an average ultimate tensile strength (UTS) of 59.39 ± 15.41 MPa (with a peak of 78.90 MPa) and an average elongation at break of 27.42 ± 7.41%, demonstrating exceptional structural robustness and fracture toughness under heavy-load abrasive machining conditions. During adaptive polishing validations on complex convex topographies and deep concave mold cavities, the compliant tool effectively compensated for normal vector spatial errors intrinsic to three-axis CNC machining via passive geometric adaptation. Topographical evaluations suggest a ductile-regime, differential asperity planarization material removal paradigm, which is attributed to the macroscopic 3D elastic deformation of the tool synergized with the proposed compliance of the polymer matrix. Following high-intensity sequential polishing regimens, the original macroscopic milling striations were substantially reduced. Quantitative profilometric analysis reveals that the average surface roughness of the convex profiles decreased from an initial 13.33 µm to 7.42 µm, while that of the restrictive deep concave features was reduced from 10.84 µm to 4.11 µm. Ultimately, this technological framework circumvents the traditional reliance on capital-intensive, six-degree-of-freedom robotic platforms, providing a scalable automated polishing protocol compatible with standard CNC systems for the cost-effective surface planarization of precision molds. Full article

Ultrashort Pulse Laser (USPL) ablation is a versatile manufacturing process, but predicting its outcomes across different materials often requires extensive and costly experimentation. This work provides a machine learning framework that leverages transfer learning to bridge the gap between simulation and experimental data, enabling accurate prediction of material behavior during USPL ablation under data-scarce conditions. We generated a high-fidelity computational dataset using the LS-PLUME^® simulator for Stainless Steel 316 (SS 316), and then complemented with targeted experimental studies on SS 316, Silicon (Si) and Aluminum (Al) to capture real-world deviations. A model pre-trained on the simulation data was successfully adapted to the experimental domain, effectively absorbing systematic deviations and extending its predictive capability to new materials with minimal experimental data. Our transfer learning framework bridged the simulation-to-experiment gap using minimal data, successfully fine-tuning a base model trained on 3075 samples with just 49 experimental points for Si and 46 for Al with mean percentage errors under 5%, thus demonstrating high data efficiency for industrial laser surface texturing. Furthermore, the application of explainable artificial intelligence revealed that the model predictions are more sensitive to peak fluence and the number of passes, with SS 316 exhibiting higher overall sensitivity to input parameter variations than Si and Al, thus providing actionable physical and process-level insight relevant for industrial optimization. Full article

Background: Accurate recognition of upper-limb force levels is important for wearable movement monitoring and rehabilitation engineering, yet the value of combining surface electromyography (sEMG) and mechanomyography (MMG) for shoulder force classification remains incompletely characterized. Methods: Ten healthy adults performed right shoulder abduction at four maximum voluntary contraction (MVC)-normalized force levels of approximately 10%, 30%, 60%, and 90% MVC. Signals were synchronously collected from the middle deltoid at 1000 Hz and segmented using 500 ms windows with a 150 ms stride. The evaluated classifiers were logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), support vector machine with a radial basis function kernel (SVM-RBF), random forest (RF), extremely randomized trees (ET), histogram-based gradient boosting decision tree (HGBDT), and multi-layer perceptron (MLP). Models were evaluated using group-aware five-fold cross-validation at the action-trial level. Results: The dataset contained 12,231 windows from 30 action-trial groups. HGBDT achieved the best performance, with an accuracy of $0.904\pm 0.023$, macro-F1 score of $0.911\pm 0.018$, quadratic weighted Cohen’s kappa of $0.910\pm 0.038$, and mean absolute grade error of $0.137\pm 0.042$. Fusion increased macro-F1 from $0.821\pm 0.030$ for sEMG-only and $0.771\pm 0.015$ for MMG-only to $0.911\pm 0.018$. Conclusions: These internally validated findings support the complementary value of sEMG and MMG for MVC-normalized shoulder force-level classification in healthy adults. Subject-independent and patient-level validation is required before clinical rehabilitation use. Full article

Flood risks are escalating under climate change, necessitating advanced methods to improve runoff prediction and multivariate flood-risk assessment. In this study, a physics–XGBoost-based Bayesian model averaging with bivariate copulas (PXB-BVC) framework was developed by integrating the Soil and Water Assessment Tool (SWAT), the Hydrologiska Byråns Vattenbalansavdelning (HBV) model, Extreme Gradient Boosting (XGBoost), Bayesian model averaging (BMA), and bivariate copulas. Spatially detailed underlying surface parameters including 30 m land-use data derived from the 2000 China land-use remote sensing monitoring data were pre-processed and reclassified using ArcGIS to support spatially explicit hydrological simulation. The framework was applied to the Xiangxi River Basin (XXRB), China, under four general circulation models and three shared socioeconomic pathways. PXB-BVC improved daily runoff simulation by combining process-based hydrological information with nonlinear machine learning correction, achieving Nash–Sutcliffe efficiency (NSE) values of 0.95 during calibration and 0.89 during validation. Future runoff generally increased from the near-term to the late-century period, with stronger changes under SSP585 and Sen slopes reaching up to 0.46 m³ s⁻¹ yr⁻¹, although the magnitude and significance of trends varied among GCMs. The dependence structures among flood peak, flood volume, and flood duration showed non-stationary behavior under future climate forcing, with Kendall’s tau for peak–volume pairs mostly ranging from 0.6 to 0.8. The revised bivariate return-period analysis further indicates that inferred flood-risk changes depend on the joint risk definition. Under SSP245 and ACCESS-ESM1–5, OR-type joint return periods show that representative near-future 50-year events may become more frequent in 2061–2100, whereas AND-type return periods show weaker and less uniform changes among flood-characteristic pairs. Conditional probability analysis also indicates enhanced compound risk under high-emission conditions: given an extreme peak flow, the probability of accompanying high flood volume increases from 0.23 to 0.56, while the probability of prolonged duration increases from 0.18 to 0.45. These results demonstrate that the PXB-BVC framework can support non-stationary multivariate flood-risk assessment and provide useful information for climate-resilient water-resource management and infrastructure planning. Full article

Urban heat islands (UHIs) significantly influence microclimatic conditions, energy consumption, and public health. This research leverages ensemble models and correlation analysis based on Landsat 5-8 satellite data to forecast LST and explore its environmental relationships. This study employed the XGBoost machine learning algorithm to model seasonal LST dynamics in three rapidly urbanizing Arkansas cities, including Fort Smith, Little Rock, and Northwest Arkansas, using Landsat imagery from 2001 to 2021. The results show significant increases in urban heat, particularly in the summer, with Fort Smith seeing an increase in the area classified in higher-temperature bins (35–45 °C) from approximately 33% in 2001 to more than 83% by 2021. Model validation showed high predictive performance (R² = 0.74–0.78, RMSE ≤1.46 °C), indicating reliable project-based estimation of spatial LST variability for 2026 and 2031. The results revealed a substantial intensification of built-up area expansion, to 9.8% by 2026 and 20.7% by 2031, accompanied by cropland reductions of 13.2% and 25.5%, respectively. This rapid urban growth is projected to elevate summer LSTs above 45 °C across more than 700 km² combined, and winter LSTs to ≥25 °C across nearly 125 km² in the region by 2031. The integration of Landsat time series data and machine learning provide valuable insights for urban planners and policymakers, underscoring the critical importance of targeted climate-resilient strategies and sustainable urban development practices. Full article