Search Results (5,922)

Traditional analyses of ship roll damping and added moment of inertia rely on free roll decay and forced roll tests, but acquiring linear (small angles) and nonlinear (large angles) relationships demands extensive computational cases and parameter fitting, limiting efficiency. To address this, this study couples Support Vector Machine (SVM) and Neural Ordinary Differential Equation (NODE) networks: SVM solves for added moment of inertia, linear damping, and nonlinear damping, while NODE constructs a complete model for the roll motion equation. Using the DTMB5415 hull form, Computational Fluid Dynamics (CFD) simulations of forced roll build a “time-angle-moment” sample space, and the coupled model learns and predicts free roll decay under different initial angles. The results show that SVM effectively determines roll damping and added moment of inertia from constant-amplitude variable-frequency and constant-frequency variable-amplitude data, reducing required cases significantly. NODE’s simulation of free roll decay validates coefficient accuracy. Within a certain angle range, the SVM-NODE model meets rapid roll motion analysis needs, providing an innovative method for ship roll research and engineering. Full article

As an efficient drag reduction technique, ventilated cavity technology demonstrates significant application in underwater launch systems. This study employs numerical simulations to systematically examine the ventilated cavity flow characteristics and cavity–rudder interaction mechanisms for a rudder-equipped axisymmetric body. Numerical simulation predicts the gas leakage behavior, cavity geometry, and internal flow structure. The results indicate that the development of the ventilated cavity proceeds through three distinct stages: rapid growth, slow development, and quasi-periodic shedding. During this process, local high pressure at the leading edge of the rudder suppresses cavity growth, while cavity shedding is associated with re-entrant jet effects. Under the influence of the ventilated cavity, the overall load on the entire body and the local load on the rudder exhibit consistent patterns: F_x > F_y > F_z ≈ 0 and T_z > T_x ≈ T_y ≈ 0, with F_y and T_z fluctuating the most violently. The shedding cavity clusters are primarily concentrated at the rudder root during the quasi-periodic shedding stage. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for intelligent, scalable, and interoperable charging infrastructure. Traditional EV charging networks based on the Open Charge Point Protocol (OCPP) face challenges related to dynamic load management, cybersecurity, and efficient integration with renewable energy sources. This paper presents a novel AI-driven framework that integrates federated learning, predictive analytics, and real-time control within OCPP-compliant networks to enhance performance and sustainability. The proposed system utilizes edge AI modules at charging stations, supported by a central aggregator that employs federated learning to preserve data privacy while enabling network-wide optimization. A case study involving simulated smart charging stations demonstrates significant improvements, including an 18% reduction in peak load demand, a 29% increase in forecasting accuracy (MAPE of 8.5%), a 10% decrease in average charging wait times, and a 12% increase in on-site solar energy utilization. The framework’s compatibility with OCPP and related standards (e.g., IEC 61851, ISO 15118) ensures ease of deployment on existing infrastructure. These results indicate that the proposed AI-OCPP integration provides a scalable and intelligent foundation for next-generation EV charging networks that align with the goals of sustainable transportation and smart grid evolution. Full article

Accurately predicting future activities in egocentric (first-person) videos is a challenging yet essential task, requiring robust object recognition and reliable forecasting of action patterns. However, the limited number of observable frames in such videos often lacks critical semantic context, making long-term predictions particularly difficult. Traditional approaches, especially those based on recurrent neural networks, tend to suffer from cumulative error propagation over extended time steps, leading to degraded performance. To address these challenges, this paper introduces a novel framework, Virtual Frame-Augmented Guided Forecasting (VFGF), designed specifically for long-term egocentric activity prediction. The VFGF framework enhances semantic continuity by generating and incorporating virtual frames into the observable sequence. These synthetic frames fill the temporal and contextual gaps caused by rapid changes in activity or environmental conditions. In addition, we propose a Feature Guidance Module that integrates anticipated activity-relevant features into the recursive prediction process, guiding the model toward more accurate and contextually coherent inferences. Extensive experiments on the EPIC-Kitchens dataset demonstrate that VFGF, with its interpolation-based temporal smoothing and feature-guided strategies, significantly improves long-term activity prediction accuracy. Specifically, VFGF achieves a state-of-the-art Top-5 accuracy of 44.11% at a 0.25 s prediction horizon. Moreover, it maintains competitive performance across a range of long-term forecasting intervals, highlighting its robustness and establishing a strong foundation for future research in egocentric activity prediction. Full article

Dendrobium officinale is a valuable Chinese medicinal herb, but distinguishing it from other Dendrobium species after processing is challenging, leading to low classification accuracy and time-consuming analysis. This study proposes a rapid classification model based on near-infrared hyperspectral imaging (NIR-HSI), incorporating data preprocessing and feature wavelength selection. Five Dendrobium species—D. officinale, D. aphyllum, D. chrysanthum, D. fimbriatum, and D. thyrsiflorum—were used. Spectral preprocessing techniques like normalization and smoothing were applied, and Support Vector Machine (SVM) models were constructed. Normalization improved both accuracy and stability, with the full-spectrum Normalize-SVM model achieving 97% accuracy for calibration and 88% for prediction. D. chrysotoxum performed best, with all metrics reaching 100%, while D. aphyllum had poor classification (40% recall and 51.74% F1 score). To improve efficiency and performance, feature wavelength selection was performed using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA). The CARS-Normalize-SVM model yielded the best results: 98% accuracy for calibration and 96% for prediction, improving by 1% and 8%, respectively. D. aphyllum’s classification also improved significantly, with a 100% recall rate and 95.24% F1 score. These findings highlight hyperspectral imaging’s potential for rapid Dendrobium species identification, supporting future quality control and market supervision. Full article

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery. Full article

This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm³ and a coefficient of determination (R²) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures. Full article

A critical challenge in engine research lies in minimizing harmful emissions while optimizing the efficiency of internal combustion engines. Dual-fuel engines, operating with methanol and diesel, offer a promising alternative, but their combustion modeling remains complex due to the intricate thermochemical interactions involved. This study proposes a predictive framework that combines validated CFD simulations with deep learning techniques to estimate key combustion and emission parameters in a methanol–diesel dual-fuel engine. A three-dimensional CFD model was developed to simulate turbulent combustion, methanol injection, and pollutant formation, using the RNG k-ε turbulence model. A temporal dataset consisting of 1370 samples was generated, covering the compression, combustion, and early expansion phases—critical regions influencing both emissions and in-cylinder pressure dynamics. The optimal configuration identified involved a 63° spray injection angle and a 25% methanol proportion. A Gated Recurrent Unit (GRU) neural network, consisting of 256 neurons, a Tanh activation function, and a dropout rate of 0.2, was trained on this dataset. The model accurately predicted in-cylinder pressure, temperature, NOx emissions, and impact-related parameters, achieving a Pearson correlation coefficient of ρ = 0.997. This approach highlights the potential of combining CFD and deep learning for rapid and reliable prediction of engine behavior. It contributes to the development of more efficient, cleaner, and robust design strategies for future dual-fuel combustion systems. Full article

Human rhinovirus (RV) is the most frequent cause of the common cold, as well as severe exacerbations of chronic obstructive pulmonary disease (COPD) and asthma. Currently, there are no effective and accurate diagnostic tools or antiviral therapies. MicroRNAs (miRNAs) are small, non-coding sections of RNA involved in the regulation of gene expression and have been shown to be associated with different pathologies. However, the precise role of miRNAs in RV infection is not yet well established. Also, no unified computational framework exists to specifically link miRNA expression with functional gene targets during RV infection. This study aimed to first analyse the impact of RV16 on miRNA expression across the viral life cycle to identify a small panel with altered expression. We then developed a novel bioinformatics pipeline that integrated time-resolved miRNA profiling with multi-database gene-phenotype mapping to identify diagnostic biomarkers and their regulatory networks. Our in-house Python-based tool, combining mirDIP, miRDB and VarElect APIs, predicted seven genes (EZH2, RARG, PTPN13, OLFML3, STAG2, SMARCA2 and CD40LG) implicated in antiviral responses and specifically targeted by RV16 and regulated by our miRNAs. This method therefore offers a scalable approach to interrogate miRNA-gene interactions for viral infections, with potential applications in rapid diagnostics and therapeutic target discovery. Full article

Efficient development of bottom-water reservoirs is seriously affected by low recovery due to the rapid rise in water content in horizontal wells. In order to cope with this problem, a number of water control devices (including ICD and AICD) have been installed in horizontal wellbores in recent years. These are used in conjunction with packers to achieve the effect of balancing the fluid production profile and controlling water in sections. As an alternative to packers, the method of horizontal-well gravel packing has been widely used. This technique utilizes the permeability of gravel to block axial flow in the annulus of the horizontal wellbore, and uses water control devices for the purpose of sectional flow restriction. In this paper, a coupled method of numerical simulation of the production dynamics of gravel-packed water-control completions in horizontal wells in bottom-water reservoirs is proposed, which can consider multi-phase flows in porous media, in layers packed with gravel particles, and in water control devices simultaneously. In order to obtain the blocking capacity of the layer packed with gravel, we built an experimental setup of the same size as the borehole and annulus of a horizontal well, tested the permeability of the layer using Darcy’s law, and applied it to a coupled numerical simulation model. After comparison with actual well examples, it was proved that the coupled numerical simulation model has good accuracy, and can be used to carry out production predictions for gravel-packed water-control completions in horizontal wells in bottom-water reservoirs. The study also provides field engineers with a design tool for parameter optimization using a different water control method. Full article

Monitoring soil heavy metal contamination in urban land to protect human health requires rapid and low-cost methods. Visible and infrared (vis-NIR) spectroscopy shows strong promise for monitoring metals such as copper (Cu). However, an important question is how “spectrally nearby” samples influence Cu estimation models. This study investigates that issue in depth. We collected 250 soil samples from Shenzhen City, China (the world’s tenth-largest city). During building the model, we selected spectrally nearby samples for each validation sample, varying the number of neighbors from 20 to 200 by adding one sample at a time. Results show that, compared with the traditional method, incorporating nearby samples substantially improved Cu prediction: the coefficient of determination in prediction (${\mathrm{R}}_{\mathrm{p}}^2$) increased from 0.75 to 0.92, and the root mean square error of prediction (RMSEP) decreased from 8.56 to 4.50 mg·kg⁻¹. The optimal number of nearby samples was 125, representing 62.25% of the dataset. And the performance followed an L-shape curve as the number of neighbors increased—rapid improvement at first, then stabilization. We conclude that using spectrally nearby samples is an effective way to improve vis-NIR Cu estimation models. The optimal number of neighbors should balance model accuracy, robustness, and complexity. Full article

Rapid and non-destructive acquisition of soil nutrient information is crucial for precision fertilization and soil quality monitoring. This study aims to establish a Raman spectroscopy-based framework for predicting key soil fertility indicators, including alkali-hydrolyzable nitrogen (AN), total nitrogen (TN), total phosphorus (TP), and organic matter (OM). The framework systematically integrates three typical spectral preprocessing methods (Standard Normal Variate transformation (SNV), Savitzky–Golay first derivative (SG_D1), and wavelet transform (Wavelet)), three feature selection strategies (Recursive Feature Elimination, XGBoost importance, and Random Forest importance), and 14 mainstream regression models to construct a multi-combination modeling system. Model performance was evaluated using five-fold cross-validation, with 80% of samples used for training and 20% for validation in each fold. Preprocessed Raman spectral features served as input variables, while the corresponding nutrient contents were used as outputs. Results showed significant differences in prediction performance across various combinations of preprocessing methods and regression algorithms for the four soil nutrient indicators. For AN prediction, the combination of Raw_SNV preprocessing with ElasticNet and BayesianRidge models achieved the best performance, with Test R² values of 0.713 and 0.721, and corresponding Test NRMSE as low as 0.092. For OM prediction, the same Raw_SNV preprocessing with ElasticNet and BayesianRidge also performed well, yielding Test R² values of 0.825 and 0.832, and Test NRMSE of 0.100 and 0.098, respectively. In TN prediction, both ElasticNet and BayesianRidge under Raw_SNV preprocessing achieved consistent Test R² of 0.74 and Test NRMSE around 0.20, indicating stable reliability. For TP prediction, the BayesianRidge model with Raw_SNV preprocessing outperformed all others with a Test R² of 0.71 and Test NRMSE of just 0.089, followed closely by ElasticNet (Test R² = 0.70, Test NRMSE = 0.092). Overall, the Raw_SNV preprocessing method demonstrated superior performance compared to SG_D1_SNV and Wavelet_SNV. Both BayesianRidge and ElasticNet consistently achieved high R² and low NRMSE across multiple targets, showcasing strong generalization and robustness, making them optimal model choices for Raman spectroscopy-based soil nutrient prediction. This study demonstrates that Raman spectroscopy, when combined with appropriate preprocessing and modeling techniques, can effectively predict soil organic matter and nitrogen in specific soil types under laboratory conditions. These results provide initial methodological insights for future development of intelligent soil nutrient diagnostics. Full article

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR (R² = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR (R² = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF (R² = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability. Full article

Accurate river flow forecasting is vital for flood warning and water resource management, yet hourly-scale prediction in small catchments remains underexplored despite its importance for rapid response flood control. To address this gap, this study proposes an enhanced temporal attention module xLSTM (TAM-xLSTM) model that combines temporal feature extraction with timestep-level attention to better capture dynamic variations and dependencies. Case studies in the Qiandongnan region demonstrate that TAM-xLSTM substantially outperforms baseline models during wet season forecasting at Panghai Station, reducing RMSE by 9.6%, MAE by 24.1%, and Theil’s U by 6.6%, while increasing NSE by 4.8%. These results highlight the model’s ability to improve short-term river flow prediction in complex mountainous terrain and its potential to support effective flood warning and water resource management. Full article

As an indigenous goat breed unique to southern China, Leizhou Black Goats (LZBGs) are highly valued for their rapid growth, high reproductive performance, and superior meat quality. However, their offspring frequently exhibit symptoms of muscle atrophy and malnutrition, suggesting potential genetic defects underlying these adverse phenotypes. As a unique extracellular matrix component, collagen Q (COLQ) is specifically enriched within the synaptic basal lamina at vertebrate neuromuscular junctions (NMJs), where it anchors acetylcholinesterase (AChE) to facilitate efficient acetylcholine hydrolysis, ensuring precise neuromuscular signaling. The current investigation sought to characterize the spectrum of genetic polymorphisms within the COLQ gene and assess their correlation with key production traits, including growth performance and meat quality parameters, in the LZBG population. Previously, through whole-genome sequencing and transcriptome sequencing analyses of an LZBG population, we identified four SNPs in the COLQ gene, namely, two missense mutations (SNP1: p.238A/S and SNP3: p.47G/S), one intronic variant (SNP2), and one synonymous mutation (SNP4: p.101P/P). Population genetic analysis revealed strong linkage disequilibrium between SNP1 and SNP2. Computational modeling of protein structures predicted that the identified missense mutations may lead to alterations in protein conformation. Association analyses demonstrated significant correlations of SNP1 and SNP3 with growth and meat quality traits (p < 0.05), where SNP3 reduced COLQ expression by 0.64-fold in homozygotes. Association analysis revealed that both SNP1 and SNP3 showed significant correlations with growth and meat quality traits in LZBGs (p < 0.05). Notably, SNP3 (p.47G/S) was found to regulate COLQ gene expression, reducing its levels by 0.64-fold in homozygous individuals, suggesting its potential as a genetic marker for selecting goats with superior growth performance and muscular development characteristics. The identified genetic variants establish a foundation for marker-assisted selection in LZBG breeding programs with particular relevance to growth performance enhancement, while also advancing the understanding of COLQ’s functional mechanisms in muscle development. Full article