Search Results (187)

This study investigates the downstream wake characteristics of a helical hydrokinetic turbine through combined experimental and numerical analyses. A four-bladed helical turbine with a 20 cm rotor diameter and blockage ratio of 53.57% was tested in an open water channel under a flow rate of 180 m³/h, corresponding to a Reynolds number of approximately 90 × 10³. Velocity measurements were collected at 13 downstream cross-sections using an Acoustic Doppler Velocimeter, with each point sampled repeatedly. Standard error analysis was applied to quantify measurement uncertainty. Complementary numerical simulations were conducted in ANSYS Fluent using a steady-state k-ω Shear Stress Transport (SST) turbulence model, with a mesh of 4.7 million elements and mesh independence confirmed. Velocity deficit and turbulence intensity were employed as primary parameters to characterize the wake structure, while the analysis also focused on the recovery of cross-sectional velocity profiles to validate the extent of wake influence. Experimental results revealed a maximum velocity deficit of over 40% in the near-wake region, which gradually decreased with downstream distance, while turbulence intensity exceeded 50% near the rotor and dropped below 10% beyond 4 m. In comparison, numerical findings showed a similar trend but with lower peak velocity deficits of 16.6%. The root mean square error (RMSE) and mean absolute error (MAE) between experimental and numerical mean velocity profiles were calculated as 0.04486 and 0.03241, respectively, demonstrating reasonable agreement between the datasets. Extended simulations up to 30 m indicated that flow profiles began to resemble ambient conditions around 18–20 m. The findings highlight the importance of accurately identifying the downstream distance at which the wake effect fully dissipates, as this is crucial for determining appropriate inter-turbine spacing. The study also discusses potential sources of discrepancies between experimental and numerical results, as well as the limitations of the modeling approach. Full article

The determination of the live body weight of camels (required for their successful breeding) is a rather difficult task due to the problems with handling and restraining these animals. Therefore, the main aim of this study was to predict the ABW of eight indigenous camel (Camelus dromedarius) breeds of Pakistan (Bravhi, Kachi, Kharani, Kohi, Lassi, Makrani, Pishin, and Rodbari). Selected productive (hair production, milk yield per lactation, and lactation length) and reproductive (age of puberty, age at first breeding, gestation period, dry period, and calving interval) traits served as the predictors. Six data mining methods [classification and regression trees (CARTs), chi-square automatic interaction detector (CHAID), exhaustive CHAID (EXCHAID), multivariate adaptive regression splines (MARSs), MLP, and RBF] were applied for ABW prediction. Additionally, hierarchical cluster analysis with Euclidean distance was performed for the phenotypic characterization of the camel breeds. The highest Pearson correlation coefficient between the observed and predicted values (0.84, p < 0.05) was obtained for MLP, which was also characterized by the lowest root-mean-square error (RMSE) (20.86 kg), standard deviation ratio (SD_ratio) (0.54), mean absolute percentage error (MAPE) (2.44%), and mean absolute deviation (MAD) (16.45 kg). The most influential predictor for all the models was the camel breed. The applied methods allowed for the moderately accurate prediction of ABW (average R² equal to 65.0%) and the identification of the most important productive and reproductive traits affecting its value. However, one important limitation of the present study is its relatively small dataset, especially for training the ANN (MLP and RBF). Hence, the obtained preliminary results should be validated on larger datasets in the future. Full article

This study aimed to examine whether managerial changes and their training methodology influence the physical attributes of soccer players and determine if these changes significantly impact the overall physical performance of the team. Twenty-seven male elite-level football players competing in the Eastern Mediterranean region (age: 28.12 ± 5.5 years, height: 179.3 ± 6.25 cm, body mass: 75.8 ± 6.6 kg) participated in this study. To analyze the impact of managerial changes on elite football players’ physical performance, this study evaluated and compared physical attributes during weekly microcycles and official games across three different coaching regimes over an entire season. Data were collected using a 10 Hz GPS tracking technology and included the following external load (EL) parameters: total distance, high metabolic load distance, high-speed running, sprint distance, accelerations, and decelerations. A one-way Analysis of Variance (ANOVA) was utilized to assess differences in physical performance across the three coaching methods. Significant differences were evident in high metabolic load distance during games [F(2,27) = 7.59, p < 0.05]. High-speed running distance also varied significantly across the three coaching regimes, both during training sessions [F(2,27) = 5.89, p < 0.05] and games [F(2,27) = 4.31, p < 0.05]. Furthermore, sprint distance showed significant differences during training [F(2,27) = 4.62, p < 0.05] and games [F(2,27) = 3.37, p < 0.05]. The findings of this study suggest that managerial changes can have a significant effect on the physical performance of soccer players. The results highlight the importance of aligning coaching strategies with physical conditioning techniques for optimizing performance. These findings provide a deeper understanding of the potential benefits and risks associated with managerial changes in professional soccer. Nevertheless, a limitation in this study is that all metrics of EL were interpreted as absolute values rather than relative-based threshold values, which may affect the interpretation of the players’ physical capacities. Full article

The capacity estimation of lithium-ion batteries, serving as an auxiliary power source in fuel cell vessels, is crucial for ensuring system stability and enhancing operational efficiency. Accurate capacity estimation technology not only helps extend battery lifespan but also enhances the energy management and scheduling capabilities of the entire vessel. To address the challenge of accurately estimating lithium-ion battery capacity under complex operating conditions, this study extracts universal health factors from battery data under varied charging and discharging scenarios and combines these with a deep learning model to enhance prediction accuracy. First, battery data from three complex conditions are analyzed, extracting partial charge and discharge data. The distance correlation coefficient calculates the correlation between each factor and the capacity sequence, informing the priority of universal health factors. A TCN-BiGRU model is then developed, with hyperparameters determined by the Kepler optimization algorithm (KOA). Cells from a battery pack under consistent conditions are used for training, while other cells in the same pack serve as the test set. Evaluation metrics include mean absolute error (MAE) and root-mean-square error (RMSE). The testing shows that the MAE and RMSE for full-life capacity estimation remain around 1%, with most cells achieving values under 1%. The results indicate that the proposed method effectively aids in accurate capacity estimation for individual cells in complex operating environments. Full article

The aim of this study was to compare the dimensional accuracy of vinyl polysiloxane impressions differing in terms of curing time (regular-setting (RS) or fast-setting (FS)) in combination with different tray materials (metal (M) and plastic (P)). A typodont reference model simulated a partially edentulous maxilla. Reference points were given by center points of either precision balls welded to specific teeth or finishing-line centers of prepared teeth. These reference points enabled the detection of dimensional deviations between the digitized reference and the scans of the models achieved from the study impressions. Twenty impressions were made for each of the following four test groups: RS-M, RS-P, FS-M and FS-P. Global scan data accuracy was measured by distance and tooth axis deviations from the reference, while local accuracy was determined based on the trueness and precision of the abutment tooth surfaces. Statistical analysis was conducted using ANOVA accompanied by pairwise Tukey post hoc tests (α = 0.05). Most of the distances tended to be underestimated. Global accuracy was favorable; even for long distances, the mean absolute distance deviations were < 100 µm. Local accuracy was excellent for all test groups, with trueness ≤ 11 µm and precision ≤ 9 µm. Within the limitations of this study, all impression and tray materials were suitable to fabricate models with clinically acceptable accuracy. Full article

Soil total phosphorus plays an important role in soil fertility, plant growth, and bioge-ochemical cycles. This study aims to determine the spatial distribution characteristics of soil total phosphorus and identify its main influencing factors in the study area, thereby providing a basis for the scientific management of soil total phosphorus. Here, we conducted a comprehensive analysis by combining classical statistical analysis, ge-ostatistics methods, Pearson correlation analysis, one-way analysis of variance (ANOVA), and structural equation modeling (SEM) to explore the spatial distribution patterns of soil total phosphorus and its influencing factors. The results showed that soil total phosphorus in the study area ranged from 161.00 to 991.00 mg/kg, with an average of 495.71 mg/kg. Spatially, soil total phosphorus exhibited a patchy distribu-tion pattern, with high values primarily concentrated in cultivated areas along rivers and low values mainly located in forested areas in the southeastern and central re-gions. Additionally, the nugget effect of soil total phosphorus was 71.5%, indicating a moderate level of spatial variability. The Pearson correlation analysis revealed that soil total phosphorus content was significantly correlated with multiple factors, including land use types, soil parent material, distance from settlements, slope, and soil pH. Based on these findings, we employed ANOVA to analyze the impacts of various fac-tors. The results indicated that soil total phosphorus content showed significant differences under the influence of different factors. Subsequently, we further explored in depth the action paths through which these factors affect soil total phosphorus us-ing SEM. The SEM results showed that the absolute values of the total effects of the influencing factors on soil total phosphorus, ranked from highest to lowest, were as follows: land use types (0.499) > soil parent material (0.240) > distance from settle-ments (0.178) > slope (0.161) > elevation (0.127) > soil pH (0.114) > normalized differ-ence vegetation index (0.103). These findings provide a scientific foundation for the effective management of soil total phosphorus in similar study areas. Full article

Accurate and efficient acquisition of the acoustic reflection properties of sediments with different grain sizes is key for sediment substrate classification and the construction of seafloor acoustic scattering models. To accurately measure surface sediments on the seafloor, an in-depth investigation of the acoustic properties of sediments with different grain sizes at different measurement distances is an indispensable prerequisite. While previous studies have extensively explored the acoustic reflection properties of sediments in mid- and low-frequency bands (e.g., 6–85 kHz), research on high-frequency reflectivity (95–125 kHz) remains limited. Existing equipment often suffers from large beam angles (e.g., >10°), leading to challenges in standardising laboratory measurements. To this end, we developed a technique using a high-frequency submersible sub-bottom profiler (HF-SSBP) to measure the high-frequency reflection intensity of homogeneous sediments screened by grain size. To ensure stable measurements of the high-frequency reflection intensity, we conducted experiments using standard acrylic plates. This demonstrates the dependability of the HF-SSBP and determines the absolute measurement error of the HF-SSBP. Variations in radiofrequency reflection intensity across different sediment types with different grain sizes in a frequency range of 95–125 kHz were investigated. The reflectance amplitude was measured and the reflectance coefficients were calculated for six uniform sediments with different grain sizes ranging from 0.1–0.3 to 2.0–2.5 mm. The scattering intensity of the six sediments with a uniform grain size distribution at the same measurement distance varies to some extent. There is variation in the intensity of acoustic wave reflections for different grain sizes, but some of the differences are not statistically significant. The dispersion coefficients of the acoustic reflection intensities for all sediments, except for those with a grain size of 1.0–1.5 mm, are less than 5% at different measurement distances. These coefficients are almost independent of the detection distance. Full article

In transmission lines, the discharge characteristics of long air gaps significantly influence the design of external insulation. Existing machine learning models for predicting breakdown voltage are typically limited to single gaps and do not account for the combined effects of complex factors. To address this issue, this paper proposes a novel prediction model based on the Improved Sparrow Search Algorithm-optimized XGBoost (ISSA-XGBoost). Initially, a comprehensive dataset of 46-dimensional electric field eigenvalues was extracted for each gap using finite element simulation software and MATLAB. Subsequently, the model incorporated a comprehensive set of input variables, including electric field eigenvalues, gap distance, waveform and polarity of the switching impulse voltage, temperature, relative humidity, and atmospheric pressure. After training, the ISSA-XGBoost model achieved a Mean Absolute Percentage Error (MAPE) of 7.85%, a Root Mean Squared Error (RMSE) of 56.92, and a Coefficient of Determination (R²) of 0.9938, indicating high prediction accuracy. In addition, the ISSA-XGBoost model was compared with traditional machine learning models and other optimization algorithms. These comparisons further substantiated the efficacy and superiority of the ISSA-XGBoost model. Notably, the model demonstrated exceptional performance in terms of predictive accuracy under extreme atmospheric conditions. Full article

This work presents a physics-guided parameter estimation framework for cold spray additive manufacturing (CSAM), focusing on simulating and validating deposit profiles across diverse process conditions. The proposed model employs a two-zone flow representation: quasi-constant velocity near the nozzle exit followed by an exponentially decaying free jet to capture particle acceleration and impact dynamics. The framework employs a comprehensive approach by numerically integrating drag-dominated particle trajectories to predict deposit formation with high accuracy. This physics-based framework incorporates both operational and geometric parameters to ensure robust prediction capabilities. Operational parameters include spray angle, standoff distance, traverse speed, and powder feed rate, while geometric factors encompass nozzle design characteristics such as exit diameter and divergence angle. Validation is performed using 36 experimentally measured profiles of commercially pure titanium powder. The simulator shows excellent agreement with the experimental data, achieving a global root mean square error (RMSE) of 0.048 mm and a coefficient of determination $R^2=0.991$, improving the mean absolute error by more than 40% relative to a neural network-based approach. Sensitivity analyses reveal that nozzle geometry, feed rate, and critical velocity strongly modulate the amplitude and shape of the deposit. Notably, decreasing the nozzle exit diameter or divergence angle significantly increases local deposition rates, while increasing the standoff distance dampens particle velocities, thereby reducing deposit height. Although the partial differential equation (PDE)-based framework entails a moderate increase in computational time—about 50 s per run, roughly 2.5 times longer than simpler empirical models—this remains practical for most process design and optimization tasks. Beyond its accuracy, the PDE-based simulation framework’s principal advantage lies in its minimal reliance on sampling data. It can readily be adapted to new materials or untested process parameters, making it a powerful predictive tool in cold spray process design. This study underscores the simulator’s potential for guiding parameter selection, improving process reliability and offering deeper physical insights into cold spray deposit formation. Full article

This study’s primary goal is to use computer vision and ultra-wideband (UWB) localisation techniques to automatically mark numerals in cow photos. In order to accomplish this, we created a UWB-based cow localisation system that involves installing tags on cow heads and placing several base stations throughout the farm. The system can determine the distance between each base station and the cow using wireless communication technology, which allows it to determine the cow’s current location coordinates. The study employed a neural network to train and optimise the ranging data gathered in the 1–20 m range in order to solve the issue of significant ranging errors in conventional UWB positioning systems. The experimental data indicates that the UWB positioning system’s unoptimized range error has an absolute mean of 0.18 m and a standard deviation of 0.047. However, when using a neural network-trained model, the ranging error is much decreased, with an absolute mean of 0.038 m and a standard deviation of 0.0079. The average root mean square error (RMSE) of the positioning coordinates is decreased to 0.043 m following the positioning computation utilising the optimised range data, greatly increasing the positioning accuracy. This study used the conventional camera shooting method for image acquisition. Following image acquisition, the system extracts the cow’s coordinate information from the image using a perspective transformation method. This allows for accurate cow identification and number labelling when compared to the location coordinates. According to the trial findings, this plan, which integrates computer vision and UWB positioning technologies, achieves high-precision cow labelling and placement in the optimised system and greatly raises the degree of automation and precise management in the farming process. This technology has many potential applications, particularly in the administration and surveillance of big dairy farms, and it offers a strong technical basis for precision farming. Full article

Sustainable building construction encounters challenges stemming from escalating expenses and time delays associated with geotechnical assessments. Developing and optimizing geotechnical soil maps (SMs) using existing data across heterogeneous geotechnical formations offer strategic and dynamic solutions. This strategic approach facilitates economical and prompt site evaluations, and offers preliminary ground models, enhancing efficient and sustainable building foundation design. In this framework, this paper aimed to develop SMs for the first time in the rapidly growing district of Gujrat using the optimal interpolation technique (OIT). The subsurface conditions were evaluated using the standard penetration test (SPT) N-values and soil classification including seismic wave velocity to account for seismic effects. Among the different geostatistical and geospatial models, the inverse distance weighting (IDW) model based on an optimized spatial analyst approach yielded the minimum error and a higher association with the field data for the understudy region. Overall, the optimized IDW technique yielded root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC) ranges between 0.57 and 0.98. Furthermore, analytical depth-dependent models were developed using SPT-N values to assess the bearing capacity, demonstrating the association of R² > 0.95. Moreover, the study area was divided into three geotechnical zones based on the average SPT-N values. Comprehensive validation of different strata evaluation based on the optimal IDW for the SPT-N and soil type-based SMs revealed that the RMSE and MAE ranged between 0.36–1.65 and 0.30–0.59, while the CC ranged between 0.93 and 0.98 at multiple depths. The allowable bearing capacity (ABC) for spread footings was determined by evaluating the shear, settlement, and seismic factors. The study offers insights into regional variations in geotechnical formations along with shallow foundation design guidelines for practitioners and researchers working with similar soil conditions. Full article

The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. The straight path distances from the ignition point grid to each grid on fire line in Sentinel-2 imageries for each forest fire were used as the target variables. We obtained the values of 11 independent variables for each pathway, including wind speed component, Temperature, Relative Humidity, Elevation, Slope, Aspect, Degree of Relief, Normalized Difference Vegetation Index, Vegetation Type, Fire Duration, and Gross Domestic Product reflecting human intervention capacity for fires. The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. The hyper-parameters of each model were optimized using grid search method. After analyzing the validation results of models which showed temperature as a non-significant variable, the training and validation process of models above was repeated after excluding temperature. The results show that RF is the optimal model with 49.55 m for root mean square error (RMSE), 29.19 m for mean absolute error (MAE) and 0.9823 for coefficient of determination (R²). This study used the RF model to construct the shape of burned areas by predicting lengths of all straight path distances from the ignition point to the fire line. The study can dynamically capture the development of forest fire scenes. Full article

The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r²) of 0.98, a confidence interval (CI) of −0.14 to −0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m³ and 14.09 ± 2.10 m³, respectively, with an MAE of 0.57 m³, an RMSE of 0.61 m³, an r² value of 0.97, and a CI of −0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r² values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and −0.18 for tree spacing and 0.01, −0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards. Full article

The self-heating temperature of the cement composite mixed with multi-walled carbon nanotubes (MWCNT–cement composite) is influenced by several factors, including the concentration of nano-material. However, conducting experiments to measure this temperature is time-consuming and expensive. Additionally, there are challenges in elucidating the correlations between the various influencing factors of the MWCNT–cement composite and its self-heating temperature. This study utilizes machine learning (ML) to predict the self-heating temperature of the MWCNT–cement composite and identify the correlation with influencing factors. ML techniques, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM), were employed. These ML models were optimized through hyperparameter tuning and k-fold cross-validation. The predictive performance of each model was evaluated using R², mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) metrics. All ML models exhibited high predictive performance, with the GBM model demonstrating the best thermal prediction capability, achieving an R² value of 0.9795. Subsequently, the GBM model was used to analyze the major factors affecting the self-heating temperature of the MWCNT–cement composite. The analysis revealed that the concentration of MWCNTs, the amount of voltage, and the outdoor temperature are significant factors determining the self-heating temperature. Furthermore, it was found that the self-heating temperature of the MWCNT–cement composite increases as the concentration of MWCNTs and the amount of voltage increase and as the distance of the mesh decreases. Full article

This paper proposes a novel path-following method for autonomous ships that optimizes overall performance by balancing course changes and convergence to the original route. The proposed method extends the line-of-sight (LOS) guidance law by dynamically adjusting key parameters based on the ship’s cross-track error (XTE) and the distance of new course considering maneuvering characteristics. By incorporating these maneuvering characteristics, the method enables more precise adjustments during course changes, improving overall path-following performance. Simulation results showed that the proposed method outperformed three existing methods, including the traditional LOS guidance law, by minimizing overshoot and maintaining reasonable XTE during larger course changes. It achieved the lowest mean absolute cross-track error (MAE) while also significantly reducing the total time required to follow the path, highlighting its superior accuracy and efficiency in path following. These outcomes highlight the method’s potential to enhance significantly the path-following capabilities of autonomous vessels, contributing to greater efficiency and accuracy in pre-determined route navigation. Full article