Search Results (38,013)

Accurate calibration of Manning’s roughness coefficients is essential for reliable river hydraulic modelling, flood prediction, and water resources management, yet conventional calibration methods often struggle with high-dimensional parameter spaces and nonlinear hydraulic interactions. This study proposes and evaluates a hierarchical artificial intelligence framework for the inverse calibration of spatially distributed Manning’s roughness coefficients across three channel zones (left bank, main channel, and right bank), using a 48 km reach of the Tigris River in Baghdad as a case study. A one-dimensional HEC-RAS hydraulic model based on 30 measured cross-sections generated 18,360 simulations by systematically varying Manning’s roughness coefficients (0.02–0.045). Three calibration strategies were evaluated: (i) a simple Gradient Boosting Regression model based on a weighted composite roughness formula, (ii) conventional machine learning models (Random Forest, Gradient Boosting, and Multi-Layer Perceptron), and (iii) a deep learning framework combining a three-layer neural network (64 → 32 → 16 neurons), Differential Evolution optimisation, and cubic spline interpolation. Calibration accuracy increased with model complexity. The deep learning framework achieved the best performance, reducing the root mean square error by 96.6% (from 1.202 to 0.041 m), with R² = 0.992 and negligible bias (−0.004 m). Conventional machine learning models produced spatially variable Manning’s roughness distributions, with the calibrated main-channel roughness (mean n = 0.0512) being 34.0–57.5% higher than the corresponding bank values. The proposed framework provides an effective approach for calibrating spatially distributed roughness coefficients in one-dimensional hydraulic models, with strong potential to improve river hydraulic simulations and support future applications to flood modelling. Full article

Fuel cell systems require high-efficiency DC–DC interfaces capable of regulating rapid voltage variations while respecting the operational constraints of proton-exchange membrane fuel cells (PEMFCs). The floating interleaved boost converter (FIBC) is a strong candidate for this purpose due to its reduced current ripple, improved power sharing, and lower component stress. The design of control strategies for FIBCs supplied by PEMFCs remains challenging because explicitly enforcing fuel cell operational constraints under fast converter dynamics is inherently difficult, particularly when detailed fuel cell models are unavailable or undesirable, as reflected in existing approaches such as classical linear regulators and single-model predictive schemes. Therefore, this paper proposes a multi-model finite control set model-based predictive control (MM-FCS-MPC) strategy for FIBC converters supplied by PEMFCs. The method employs multiple discrete prediction models with cost functions defined by the converter switching mode, integrates a fuel cell-aware reference-generation mechanism to ensure nominal and safe PEMFC operation by enforcing current and power constraints within the predictive framework, and enables fast, accurate output-voltage regulation. Detailed modelling of the FIBC, component sizing, and PEMFC characteristics is provided. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance, with a rise time of approximately 4.4 ms, a $\pm 2\%$ settling time of 10.3 ms, a maximum overshoot of only 0.056%, and a phase delay of approximately 4.26${}^{\circ }$, compared with 9.6${}^{\circ }$ for the conventional PI voltage-tracking baseline. Full article

Water scarcity is a major constraint to wheat production in arid regions, highlighting the need for sustainable approaches to improve crop productivity and irrigation water-use efficiency (IWUE). Although Bacillus-based bioinoculants have been widely investigated, the potential of indigenous strains adapted to arid environments remains underutilized for the developing of site-specific bioinoculants. This study developed an integrated screening strategy combining plant growth-promoting (PGP) traits characterization, greenhouse evaluation, correlation analysis, and multivariate analyses to identify elite indigenous Bacillus strains capable of improving wheat performance under contrasting irrigation regimes. Fifty-one indigenous Bacillus strains representing 19 species, identified by 16S rRNA gene sequencing, were characterized for indole-3-acetic acid (IAA), ammonia (NH₃), siderophore production, and potassium-solubilizing activity and subsequently evaluated in greenhouse conditions under full (FI) and deficit (DI) irrigation. Plant growth was assessed at 85 days after sowing, while yield and yield-related traits were evaluated at physiological maturity (130 DAS). Significant variation was observed among the strains in both PGP traits and their effects on wheat performance. Selected strains increased vegetative growth traits by 22.2–53.7% under FI and 16.4–49.2% under DI, while improving yield-related traits and IWUE by 24.7–52.3% and 14.7–67.2%, respectively, compared with the uninoculated control. Correlation analysis identified IAA production as the PGP trait most strongly associated with wheat growth, grain yield, and IWUE, followed by NH₃ production, whereas siderophore production showed weak associations with most agronomic traits. Hierarchical cluster analysis and principal component analysis consistently identified Bacillus cereus A2, B. pumilus D2 and E3, and B. safensis D5 as the elite strains, while several additional indigenous strains also exhibited considerable potential under both irrigation regimes. This study demonstrates that the integrated screening approach enabled the identification of strain-level differences that could not be adequately captured by individual PGP traits alone, highlighting indigenous Bacillus strains as valuable resources for developing locally adapted bioinoculants to improve wheat productivity, IWUE, and drought resilience in arid and semi-arid agroecosystems. Full article

Following our earlier work on reduced beam section (RBS) connections, this study examines a new set of corrugated-web cell configurations for steel moment-resisting connections. A finite element model, validated against four experimental investigations reported in the literature, was used herein to assess the effects of cell geometry, transverse depth, and position relative to the column face on load-carrying capacity, deformation capacity, and damage evolution. The results show that the proposed configurations alter the stress distribution and shift the plastic hinge away from the beam–column joint, thereby decreasing the stress concentration in critical areas. Structural performance was highly sensitive to the interaction between cell geometry and its position along the beam. The curved cell configuration performed best when located near the column face, whereas the angle-shaped and trapezoidal configurations performed better farther from the column face, with the angle cell configuration giving the best overall response among all four. Comparatively lower efficiency was observed with the double curved configuration. To gain further insight into the viability of the proposed approach, the optimal designs derived from simplified beam analyses were then incorporated into a validated two-story steel sub-frame model. These results confirm that the selected geometric properties remain effective at a larger structural scale, preserving the beneficial effects observed in the beam-level analysis. These results demonstrate the potential of the proposed system to improve steel moment-resisting connections and provide a basis for further research on optimizing more advanced connection design techniques. Full article

This paper presents a novel fault-tolerant model predictive control (FTMPC) framework for fuel cell hybrid electric vehicles (FCHEVs) used for postal delivery applications. The main contribution of the proposed FTMPC is the adaptive adjustment of the model predictive control cost function weights based on fault severity. The proposed reformulation incorporates fault characterization across the diverse degradation mechanisms while maintaining reliable vehicle operation. The FTMPC approach dynamically adapts cost function weights and system constraints based on the fault severity index. The resulting control strategy provides fault-aware power allocation between the fuel cell and battery while accounting for the specified operating and safety constraints. To isolate the contribution of the proposed health-dependent adaptation mechanism, a controlled ablation study was performed against a structurally identical fixed-MPC controller under the same vehicle model, driving cycle, initial conditions, prediction and control horizons, solver configuration, and fault scenarios. The adaptive FTMPC achieved a 10.6956% reduction in direct hydrogen consumption relative to the fixed-MPC baseline. Because differences in terminal battery state of charge (SoC) can influence comparisons based solely on hydrogen consumption, a charge-corrected hydrogen-equivalent metric was also evaluated; using this more conservative metric, the adaptive FTMPC retained a 2.7276% improvement. The final quadratic programming implementation achieved a 100% successful optimization rate in the validation run with no fallback-controller activation, while the maximum soft-constraint slack remained on the order of 10−9. Additional sensitivity analyses were conducted to evaluate the influence of relevant vehicle and operating conditions on energy consumption and battery utilization. These results provide direct quantitative evidence of the contribution of the proposed fault-adaptive mechanism and demonstrate its numerical feasibility for FCHEV energy management, while the limitations of the present simulation-based validation are explicitly acknowledged. Full article

To address the limitations of traditional analytical models in characterising multi-core coupling effects and the excessive computational cost of finite element simulations, this paper presents a high-precision analytical model for a novel T-II composite-core eddy current testing (ECT) probe using the Truncated Region Eigenfunction Expansion (TREE) method. The analytical expressions of coil impedance are derived by partitioning the solution domain into ten subdomains under an axisymmetric cylindrical coordinate system, with rigorous satisfaction of electromagnetic continuity at all material interfaces. Numerical cross-validation against 2D and 3D Finite Element Method (FEM) simulations under idealised modelling assumptions across the frequency range of 100 Hz to 10 kHz shows that the proposed TREE model yields relative errors below 2% for both coil resistance and reactance. Notably, the proposed approach requires significantly less computation time than 2D and 3D FEM. Further parametric analysis confirms that the proposed T-II composite-core probe delivers superior electromagnetic performance compared to conventional single-core probes, including intensified subsurface eddy current densities and improved magnetic field redistribution. This work overcomes the inherent limitations of single-core ECT analytical models, establishes a robust theoretical paradigm to interpret the distinctive electromagnetic field advantages of composite-core probes, and provides solid support for the structural optimisation of multi-core ECT sensors. Full article

Thermal energy storage (TES) technologies are essential for renewable energy integration, industrial waste heat recovery, grid flexibility, and improved energy efficiency. Despite advances in sensible heat thermal energy storage (SHTES), latent heat thermal energy storage (LHTES), and thermochemical energy storage (TCES), practical deployment remains constrained by inadequate heat transfer rates, which limit charging and discharging processes, reduce storage utilization, and increase system size and cost. Conventional heat-transfer enhancement approaches, including fins, embedded heat exchangers, conductive additives, porous structures, and flow intensification techniques often introduce trade-offs related to manufacturability, complexity, durability, and energy consumption. Additive manufacturing (AM) has emerged as a promising approach for overcoming these limitations by enabling precise control of internal geometry, porosity, surface-area-to-volume ratio, and fluid pathways. Through the fabrication of architected structures, lattice networks, triply periodic minimal surface (TPMS) geometries, and multifunctional heat-transfer architectures, AM enables geometry-driven optimization of thermal performance that is difficult to achieve using conventional manufacturing methods. These capabilities support the development of compact TES systems with enhanced heat transfer, improved thermal uniformity, and increased energy utilization. This review examines additive manufacturing technologies relevant to TES applications, including powder bed fusion, directed energy deposition, material extrusion, vat photopolymerization, and binder jetting. The relationships among manufacturing processes, material selection, and thermal performance are discussed across SHTES, LHTES, and TCES systems. Particular emphasis is placed on AM-enabled heat-transfer enhancement strategies, phase change material (PCM)-integrated structures, architected thermal networks, embedded heat exchangers, and computational design methodologies such as topology optimization. Current challenges involving material compatibility, scalability, cost, and long-term durability are also evaluated. The review highlights how additive manufacturing is transforming TES design from a material-centered paradigm toward geometry-enabled thermal engineering, creating new opportunities for next-generation energy storage systems. Full article

Colles fracture is a common type of wrist fracture, typically resulting from falling onto an outstretched hand. Postoperative patients often require long-term self-rehabilitation to restore wrist function. However, traditional rehabilitation relies on medical personnel and lacks real-time feedback and progress tracking, which may lead to poor rehabilitation or even deterioration of the condition. This article proposes an intelligent wearable system based on edge computing to enhance the efficiency of self-rehabilitation, improve system portability, and ensure comprehensive recording of rehabilitation data. The system performs real-time data processing and feedback to assist patients and healthcare providers in monitoring rehabilitation progress and optimizing recovery outcomes. The proposed system integrates an STM32 microcontroller, NanoEdge AI, and a 9-axis inertial sensor to detect and evaluate the accuracy of hand rehabilitation movements, ensuring the precision of self-rehabilitation. All rehabilitation movements are schemed under the guidance of professional physicians to ensure correctness and minimize the risk of secondary injuries caused by improper rehabilitation. Data such as motion records, training duration, and count are transmitted via Bluetooth to the Local database for further analysis. A custom web interface allows healthcare providers to monitor and analyze collected data. This approach improves diagnostic accuracy, supports personalized rehabilitation recommendations, and improves treatment outcomes and patient recovery success rates. Full article

Spatial transferability remains a major limitation in erosion modelling across environmentally heterogeneous catchments. This study evaluated climatic, vegetation, and morphometric predictors and compared a linear model, Extreme Gradient Boosting (XGBoost), and a hybrid stacking ensemble using 93 catchment–year records from three semi-arid Mediterranean catchments in southern Spain. The methodological contribution lies in jointly evaluating hierarchical variance partitioning and targeted predictor interactions, comparing the linear and XGBoost models under leave-one-catchment-out (LOCO) validation, and examining an internally fitted stacking ensemble based on pooled LOCO base-model predictions. Precipitation was the strongest statistical predictor of the Universal Soil Loss Equation (USLE)-derived annual erosion estimates. Targeted linear interactions did not consistently improve LOCO performance, and the estimated catchment-level variance approached zero after inclusion of the environmental predictors. Under pooled LOCO validation, the linear model yielded a squared Pearson correlation of 0.39, and root mean square error (RMSE) of 47.9 t ha⁻¹ yr⁻¹. XGBoost reduced RMSE to 27.9 t ha⁻¹ yr⁻¹, while the squared correlation was 0.35. he internally fitted stacking meta-model yielded an apparent RMSE of 23.6 t ha⁻¹ yr⁻¹ and a squared correlation of 0.53 on the same pooled predictions used for meta-model estimation. Transferability was catchment-dependent and poorest for Casasola. Because the response was model-derived, only three catchments were available, and the meta-learner lacked an outer spatial validation layer, the stacking results represent an internal comparison rather than definitive evidence of regional transferability. Lower prediction error did not necessarily imply broader spatial generalization or improved process understanding. Full article

A Healthcare Recommender System (HRS) is a personalized decision-support system designed to recommend healthcare-related services, providers, information, advice, diagnoses, treatments, or lifestyle tips to users based on users’ preferences, characteristics, or individualized health data. Healthcare recommendation systems rely on multiple data sources. Among these sources, user reviews and comments on online healthcare platforms and social media serve as a valuable source to provide direct and often experience-based information about users’ satisfaction and opinions regarding healthcare services. Therefore, this study focuses on exploiting sentiment information extracted from user-generated reviews to enhance healthcare service recommendations. We propose a novel framework that integrates sentiment analysis on the Yelp dataset using DistilBERT, a lightweight transformer-based language model. The proposed framework incorporates Neural Collaborative Filtering (NCF) for the recommendation process. It utilizes Singular Value Decomposition (SVD) to address sparsity issues in user–item interaction data, thereby maintaining reliable performance even with limited data availability. The proposed approach achieved a Mean Absolute Error (MAE) of 0.34, a Root Mean Square Error (RMSE) of 0.66, and an Area Under the Curve (AUC) of 0.92. It also demonstrated strong ranking performance, achieving a Recall@10 of 0.74, demonstrating its effectiveness and accuracy in recommendation tasks. Compared with the rating-based, without-SVD, and without-NCF variants, which achieved RMSE values of 0.97, 1.80, and 1.14, respectively, the proposed model consistently achieved better performance. These results highlight the contribution of sentiment analysis, SVD-based interaction augmentation, and NCF to the recommendation performance. These results are promising and confirm the potential of the proposed approach for improving the reliability and performance of recommender systems. Full article

This study presents a leakage-proof hybrid machine learning framework for early flood risk forecasting using multimodal spatiotemporal tabular data. An event-based dataset covering Kazakhstan from 2001 to 2021 was created by integrating topographic characteristics, hydrometeorological variables, long-term surface water dynamics, and remotely sensed spectral indices. To ensure realistic assessment, only pre-event observations were used, and event-based temporal data separation was employed to prevent leakage between the training and test subsets. The proposed Remote Sensing Adaptive Linear Opinion Pool Machine Learning (RS-ALOP-ML) framework combines multiple logistic regression experts with different regularization strengths through an Adaptive Linear Opinion Pool (ALOP) probabilistic fusion strategy, thereby preserving interpretability while improving forecasting robustness. The proposed framework was evaluated for four independent forecast horizons (T + 1, T + 7, T + 14, and T + 30 days) and compared with traditional machine learning algorithms and state-of-the-art tabular deep learning models, including Random Forest, ExtraTrees, XGBoost, LightGBM, CatBoost, MLP, FT-Transformer, TabNet, and Process Wide&Deep. Experimental results demonstrate that the proposed hybrid approach consistently achieves competitive or superior forecasting performance while maintaining computational efficiency and transparent probabilistic outputs. The study highlights that carefully designed hybrid machine learning architectures, combined with leakage-safe evaluation protocols, provide a robust foundation for multimodal environmental forecasting and decision support applications. Full article

Background/Objectives: Acute kidney injury (AKI) is a common complication across the cancer care continuum and can compromise kidney function, delay or interrupt anticancer therapy, and worsen renal and oncologic outcomes. This state-of-the-art narrative review examines conventional clinical risk scores and artificial intelligence (AI)- and machine learning (ML)-based approaches to AKI prediction in oncology and identifies priorities for clinical translation. Methods: We conducted iterative searches of PubMed and Google Scholar through July 2026 and screened reference lists of relevant primary studies and reviews. We prioritized oncology-specific model development and validation studies and selectively included conventional scores and observational evidence where dedicated prediction models were unavailable. Results: Prediction evidence is most developed in hospitalized cancer populations, cisplatin exposure, contrast-enhanced computed tomography, immune checkpoint inhibitor therapy, and selected oncologic surgical procedures. Hematopoietic stem cell transplantation includes an early conventional risk score, whereas evidence for CAR T-cell therapy and targeted therapies remains predominantly observational. Most studies are retrospective, and independent external validation, calibration assessment, fairness evaluation, and prospective workflow implementation remain uncommon. Reported performance cannot be compared directly across studies because outcome definitions, prediction windows, populations, and validation strategies differ. Conclusions: AI- and ML-based AKI prediction may support precision onco-nephrology, but no oncology-specific model has yet demonstrated improved outcomes in a prospective interventional study. Clinical progress will require standardized outcomes, treatment-aware longitudinal data, rigorous external validation, and prediction tools linked to evidence-based response pathways. These advances may ultimately enable precision onco-nephrology by supporting proactive kidney protection while preserving optimal cancer treatment. Full article

Accurate interwell connectivity characterization is critical for fine-grained waterflood optimization and reservoir management, often requiring integrated analysis across multiple disciplines and methods. Among these, injection–production response analysis stands as the most cost-effective and widely adopted approach. However, it remains highly subjective, heavily reliant on senior engineers’ decades of accumulated experience, and prohibitively labor-intensive for large-scale oilfields with hundreds of wells. With the exponential growth of production data in modern oilfields, manual analysis has become the bottleneck restricting the timeliness of reservoir management decisions. While signal processing techniques offer a promising path to automation, general-purpose algorithms fail to incorporate fundamental reservoir fluid flow laws, resulting in insufficient accuracy for practical engineering applications. To address this gap, we propose a novel connectivity identification method that mimics expert analysis logic by focusing on large-amplitude fluctuation segments rather than full-curve matching. Using curve slope as the core metric, cosine similarity quantifies trend consistency, while Root Mean Square Error (RMSE) measures amplitude proximity. Three targeted strategies enhance accuracy: key region screening with segmented matching, outlier removal accounting for time-varying lags, and multi-index weighted fusion. Validated on synthetic and mature carbonate waterflood field cases, the method improves the identification performance over benchmark Normalized Cross-Correlation (NCC) and Capacitance-Resistance Model (CRM) methods by more than 12% in both cases. It retains the reliability of traditional response analysis while achieving full automation, and can help estimate the timing of preferential flow path formation, requiring only routine production data to provide valuable reference for timely field development decision-making. Full article

Hydrogen embrittlement (HE) is a critical degradation mechanism in high-strength offshore fasteners, where early-stage hydrogen-induced cracking (HIC) is difficult to detect using conventional inspection methods, due to its subsurface and time-dependent nature. This study presents an automatic acoustic emission (AE) signal classification framework for identifying hydrogen-related damage mechanisms in high-strength offshore bolts subjected to in situ electrochemical hydrogen charging under cyclic loading. Fatigue experiments were performed on modified property class 10.9 steel bolts using a bespoke axial fatigue rig integrated with localized hydrogen charging and multi-channel AE monitoring. Baseline fatigue experiments performed under uncharged conditions were additionally used to compare hydrogen-assisted and non-hydrogen-assisted AE activity. AE data was analysed using a structured framework incorporating signal filtering, feature extraction, principal component analysis (PCA), and Gaussian mixture model (GMM) clustering. To improve signal discrimination, spectral and temporal energy-distribution features, supported by continuous wavelet transform analysis, including partial-power and energy-ratio parameters, were introduced. The proposed framework enabled separation of AE signals associated with hydrogen evolution, plastic deformation, hydrogen-induced cracking, and brittle fracture. Comparison with manually classified datasets demonstrated strong agreement between automatic and physically interpreted signal clusters, while scanning electron microscopy (SEM) supported the presence of hydrogen-assisted brittle-fracture features associated with HIC-related AE activity. The introduction of spectral and temporal energy-distribution features improved cluster separability under in situ hydrogen-charged conditions. The results demonstrate that physically informed feature engineering combined with automatic clustering provides a promising proof-of-concept approach for mechanism-informed identification of hydrogen-assisted AE activity in high-strength steel fasteners. Full article

Also known as spherical or omnidirectional video, 360-degree video has become increasingly prevalent in autonomous vehicles (AVs), virtual reality (VR), augmented reality (AR), and immersive media applications. However, its compression poses unique challenges due to the projection from a spherical surface onto a 2D plane. Common projections like Equirectangular Projection (ERP) introduce significant geometric distortions, causing straight-line motions on the sphere to appear as curved trajectories in the 2D domain. Standard motion estimation in codecs like H.266 (Versatile Video Coding, VVC) relies on translational (linear) motion vectors, leading to poor prediction accuracy, large residual errors, and inflated bitrates for 360-degree video content. This paper proposes the implementation of Spherical Motion Vector (SMV) mapping in the pre-encoder stage. By performing motion vector calculation directly on the spherical coordinate system (θ, φ) before mapping to the 2D pixel grid (x, y), SMV enables accurate tracking of object motion across projection boundaries and warped regions. This approach minimizes residual data and improves overall compression efficiency. This paper details the mathematical foundations, integration with H.266, implementation considerations, and simulated performance gains. The proposed method builds on prior work in rotational and geodesic motion models while introducing pre-encoder spherical preprocessing for broader compatibility. Full article