Search Results (3,032)

The transition to electric vehicles (EVs) is widely promoted as a strategy to reduce greenhouse gas emissions from transportation. However, the environmental benefits of EVs depend strongly on the electricity mix used for charging. If charging is predominantly supplied by fossil-fuel-based grid electricity, the resulting carbon dioxide (CO₂) emissions may remain substantial. Renewable-powered charging stations offer a solution, yet their spatial distribution creates a trade-off: if they are located further from the driver than their grid counterparts, then more CO₂ might be emitted along the way. This study developed and validated a framework for quantifying this trade-off. A mathematical model was first constructed, in which charging stations were spatially distributed following a Poisson process, and renewable availability was described by a Gaussian distribution. Emissions were measured in terms of mCO₂, the mass of CO₂ emitted by driving and charging. The model was then tested by comparing it with a MATLAB-based computer simulation incorporating stochastic station distributions and vehicle energy states. Both approaches identified a distinct minimum in the emission–distance curve. The mathematical model located the optimum at 3.737 km, while the computer simulation confirmed the result within an interval of 2.844–4.266 km. These findings prove the existence of an optimal charging distance. The results highlight the value of considering various parameters during EV infrastructure planning and offer practical guidance to reduce life-cycle emissions in sustainable mobility systems. Full article

Designing with reclaimed materials introduces uncertainty as a persistent condition rather than a temporary lack of information. While circular construction increasingly relies on digital infrastructures for traceability and documentation, existing approaches often assume stable and comparable material information. This paper presents ReShelter, a full-scale reclaimed timber demonstrator, as a framework for designing with differentiated uncertainty in circular construction. The project combines heterogeneous timber resources, staged inspection strategies, adaptive design methods, robotic fabrication, and on-site documentation workflows. Four reclaimed timber streams from industrial, demolition, and waste sources, each with distinct levels of geometric complexity and available information, were integrated through source-specific representation and assessment methods. Rather than pursuing complete material characterisation, the workflow establishes fit-for-purpose confidence across design, fabrication, and assembly. Results show how uncertainty can be transformed into actionable constraints through adaptive joint systems, scan-based fabrication, and differentiated data models while retaining architectural flexibility and design intent. During assembly, LiDAR scanning, Gaussian splatting, and semantic annotation were used to document both geometric change and the reasoning behind on-site adaptations. By formalising workflows that valorise reclaimed materials rather than virgin resources, the paper proposes architectural methods that accommodate partial and evolving knowledge, supporting resilient, expressive, and resource-conscious circular building practices. Full article

Magnetic anomaly detection (MAD) is a core technology for the detection of ferromagnetic targets, yet traditional methods such as the Orthogonal basis function (OBF) detector suffer from severe performance degradation in low signal-to-noise ratio (SNR) and Gaussian colored noise environments. To address this issue, this paper proposes a novel neural network architecture based on manual feature extraction, namely the Partitioned Classification Network with 42 features (PCN-42). First, a total of 42 magnetic anomaly features belonging to three categories, i.e., time–frequency features, statistical features, and magnetic moment features, are extracted from magnetic measurement data. Subsequently, each category of features is processed independently by parallel sub-networks followed by feature fusion. Finally, the performance of the proposed method for magnetic anomaly signal detection is verified under colored noise simulation conditions combined with measured geomagnetic noise. In the experiments, the target is modeled as a magnetic dipole with moment 200 $\mathrm{A}\cdot {\mathrm{m}}^2$, closest point of approach (CPA) ranges from 300 to 800 m, and noise conditions span Gaussian colored noise ($\alpha$ = 0.5, 0.8, 1.0) plus measured geomagnetic background. Simulation results demonstrate that compared with the OBF detector, the detection probability of the PCN-42 detector is improved by 45–75 percentage points, reaching over 80% in different magnetic moment directions and approximately 90% at CPA = 450 m. Experiments with measured noise further validate the effectiveness of the proposed method. This method significantly improves the detection probability of magnetic anomaly signals in colored noise environments. MAD serves a broad range of civilian purposes, including mineral exploration, buried pipeline monitoring, archeological prospection, and humanitarian unexploded ordnance (UXO) clearance. Full article

This paper addresses the design and analytical synthesis of a corrugated horn antenna with a normalized Gaussian-type variable profile, featuring grooves of variable depth along the inner walls of the waveguide, ranging from ${\mathsf{\lambda }}_{\mathrm{X}}/4$ at the input to ${\mathsf{\lambda }}_{\mathrm{K}\mathrm{u}}/4$ at the aperture. The proposed antenna I is designed to operate simultaneously over the X-band and Ku-band (7–15 GHz), considering the frequency bands commonly employed in satellite communications. The main motivation of this study is to establish a rigorous method for determining the radius of the input waveguide rather than relying on the empirical approaches commonly used in antenna design, such as approximations from $\mathsf{\lambda }/4$ at the center frequency, which do not necessarily ensure adequate impedance matching over the entire operating bandwidth. The theoretical framework of this study is based on the analysis of modal propagation conditions in a circular waveguide, from which an analytical expression is derived for the optimum input radius of the corrugated horn antenna by simultaneously satisfying the propagation conditions associated with the fundamental hybrid HE11 mode. This approach includes a methodology for determining the minimum required length of the smooth circular-waveguide feeding section and evaluating the attenuation constant of evanescent modes below their cutoff frequencies. The geometric synthesis of the antenna profile includes the definition of the normalized Gaussian radius profile and the configuration of the corrugation, for which their depths vary linearly between the limiting values corresponding to the considered frequency bands. The analytical calculations are implemented in the MATLAB computational environment (MATLAB R2021b (MathWorks, Inc., Natick, MA, USA), while the resulting two-dimensional profile is subsequently exported through compatible files to the Ansys HFSS simulation environment (Ansys HFSS Electronics v.17.2), where the resulting three-dimensional structure is analyzed from an electromagnetic perspective. The performance of the proposed corrugated horn antenna is evaluated by comparing two approaches for determining the input radius: the empirical value r_in = $\mathsf{\lambda }/4$ = 7.5 mm and the value obtained through a rigorous minimax criterion balancing the TE₁₁ and TM₁₁ propagation conditions as a function of the waveguide cutoff frequency, r_in = 12.376 mm. The comparison is performed based on the reflection coefficient S11, the antenna gain expressed in dB, and the axial ratio in order to assess whether the proposed antenna provides the circular polarization characteristics required for satellite communication applications. The antenna’s performance is evaluated separately over the uplink and downlink frequency ranges associated with the X-band and Ku-band. The main performance requirements considered in the analysis include an axial ratio below 3 dB in the direction of maximum radiation, a gain greater than 15 dB over each frequency band of interest, and a reflection coefficient below −10 dB over at least 80% of the operating frequency range. Full article

With the rapid expansion of AR/VR-based digital platforms, there is an increasing demand for the automated generation of size-varied 3D shoe assets. However, conventional CAD-based linear scaling and PCA-based global statistical shape models are limited in their ability to capture the non-uniform and locally non-linear deformations observed in insole contours. This study proposes a size-adaptive insole contour generation framework that integrates image-based contour extraction, B-spline parametric representation, and type-specific SVR-based local displacement regression. By decomposing control-point displacements into tangent–normal components, the proposed method directly models non-linear curvature variations associated with size progression without relying on dimensionality reduction. Quantitative evaluations under an eight-fold leave-one-insole-out (LOIO) protocol show that the proposed method achieves a mean Hausdorff distance of 4.73 mm, a Chamfer distance of 1.76 mm, and an IoU of 0.929. It significantly outperforms the no-clustering ablation in both the Hausdorff distance (6.13 mm; $p=0.032$) and IoU ($p=0.033$), as well as the PCA-based kernel ridge regression baseline across all three metrics ($p<0.05$). No statistically significant differences were observed between the proposed method and the ratio scaling, Gaussian process, or thin plate spline baselines ($p>0.05$). PCA-Linear showed a small numerical advantage in the Hausdorff distance (4.16 mm), but the difference was not statistically significant ($p=0.187$). A sensitivity analysis further reveals the existence of a practical control-point density region that balances geometric fidelity and model complexity. This work provides a data-driven parametric approach for standard size-based digital grading automation and establishes a technical foundation for future extension to full 3D shoe mesh generation. Full article

This article addresses real-time local conflict resolution for a self-planning UAV operating in a three-dimensional dynamic environment with surrounding UAVs. To this end, we develop an SBA–DWA hybrid planning framework in which an improved sticky bacteria algorithm (SBA) is embedded in the dynamic window approach (DWA) to enhance real-time velocity selection for the self-planning UAV. First, a chemotaxis operator with projection is developed to strictly constrain bacterial positions within the convex dynamic window. Furthermore, an anisotropic Gaussian adhesion potential field is proposed to adaptively guide the current population search using historical optimal velocity commands, achieving cross-step memory transfer. Then, a dynamic pruning mechanism is designed to ensure that historical memory does not lead UAVs into infeasible or hazardous regions. The proposed scheme guarantees that the single-step planning latency satisfies stringent real-time requirements. Comparative simulation results demonstrate that the proposed method reduces path length by approximately 30% and planning time by approximately 31% compared with the standard DWA, while achieving a larger minimum inter-vehicle clearance in dense dynamic scenarios. 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

Physical sensor deployments in ground-based environmental monitoring networks are frequently constrained by high installation costs, hardware failures, and limited spatial coverage, resulting in incomplete observational datasets and degraded sensing capacity across monitoring stations. Data-driven virtual sensing offers a cost-effective alternative by estimating target environmental parameters through machine learning models trained on correlated sensor measurements, reducing dependency on dense physical infrastructure. This paper presents VS-DCFF, an applied virtual sensing framework for dual-target estimation of near-surface air temperature and relative humidity from ground-based sensor network data. VS-DCFF integrates: (i) a deterministic pipeline applying temporal encoding, rolling-window statistics, and mutual information-based feature selection to capture a linear trend/seasonal component and to select features predictive of the residual signal; (ii) a probabilistic pipeline employing a Gaussian copula model to generate statistically consistent synthetic residual samples preserving inter-variable dependencies; and (iii) an early feature-level fusion strategy feeding a copula-augmented XGBoost residual-boosting stage, whose output is combined with the linear trend component for the final prediction. Under a strict chronological evaluation protocol, VS-DCFF is benchmarked against persistence, linear and ensemble regression baselines, and a same-protocol re-implementation of the statistical core of the VSG-SGL framework, and achieves near-surface air temperature $\mathrm{RMSE}=0.7791{}^{\circ }$C, $R^2=0.9735$, and relative humidity $\mathrm{RMSE}=3.7747\%$, $R^2=0.9701$, outperforming all tested baselines. The framework is further validated through leave-one-station-out spatial generalization, robustness evaluation under simulated sensor faults and target-history loss, copula-variant and synthetic-data fidelity diagnostics, and a lightweight edge-deployment ablation. All findings, including cases where tested extensions such as spatial context features did not yield a robust improvement, are reported transparently. Results indicate that the proposed architecture provides a computationally efficient, extensively validated approach to dual-target environmental virtual sensing under realistic deployment conditions. Full article

Hyperspectral retrieval of nitrogen-related vegetation variables has undergone rapid methodological advances driven by the emergence of protein-sensitive radiative transfer models (RTMs), modern machine learning (ML), and operational imaging spectroscopy. This review synthesizes recent developments in hyperspectral retrieval of nitrogen-related vegetation variables across leaf and canopy scales, with particular emphasis on advances reported between 2020 and 2026. We examine the evolution from classical parametric regression and nonlinear ML approaches towards physically based RTM inversion and hybrid RTM–ML frameworks that integrate the complementary strengths of physical modeling and statistical learning. Particular attention is given to protein-sensitive RTMs, advanced ML approaches, and uncertainty-aware retrieval. Recent developments highlight the potential of hybrid RTM–ML frameworks to combine physical consistency with computationally efficient statistical learning, while probabilistic methods such as Gaussian Process Regression provide additional capabilities for uncertainty characterization. The review further discusses the transition from experimental studies to operational applications enabled by airborne and satellite imaging spectroscopy, including PRISMA, EnMAP, and forthcoming missions such as CHIME. Remaining challenges include the inherently ill-posed nature of nitrogen retrieval, limited and insufficiently representative calibration data, uncertainty characterization, and generalization across sensors, species, and ecosystems. Overall, the reviewed evidence points towards increasingly integrated retrieval frameworks, while emphasizing that robust transferability and operational implementation remain dependent on representative data, physical realism, and rigorous uncertainty assessment. Full article

Human hands play a central role in manipulation, communication, and physical interaction, making their accurate digital reconstruction a long-standing challenge in computer vision and graphics. Reliable modeling of hand pose, hand shape, and interaction is essential for applications ranging from immersive virtual and augmented reality to robotics, activity understanding, and human–machine interfaces. Over the past decade, research has evolved from isolated single-hand pose estimation toward increasingly holistic frameworks that jointly reconstruct two hands and the objects they manipulate. This review provides a comprehensive overview of hand reconstruction methods, tracing the progression from classical model-based approaches and early learning-driven pipelines to modern systems capable of two-hand interaction modeling and category-agnostic hand–object reconstruction. We structure the surveyed literature according to fundamental algorithmic paradigms, encompassing model-based formulations, convolutional and graph-based learning methods, transformer-based architectures, and emerging neural implicit and Gaussian representations. This review is tailored for researchers new to the field and follows a chronological and conceptual organization that elucidates how key design choices have shaped current capabilities and limitations. We analyze persistent challenges that hinder the widespread adoption of hand reconstruction methods in practical applications, including articulation complexity, generalization to unseen objects, and computational efficiency. Finally, this paper provides a forward-looking perspective on emerging research directions, highlighting trends toward real-time, category-agnostic, and semantically meaningful hand reconstruction systems for future human-centered computing. Full article

Forecasting power consumption is essential for intelligent power management in IoT-enabled smart environments, where heterogeneous behaviors appear from diverse building usages. University campuses are considered environments that share similarities with smart cities, making them suitable for power dynamics analysis. We build upon an IoT-based Advanced Metering Infrastructure (AMI) we deployed at our Engineering School’s Campus (INPT, Morocco), and an optimized XGBoost pipeline enhanced via Genetic Algorithms. Limited modeling granularity is addressed in heterogeneous consumption patterns. We propose and justify a cluster-aware approach partitioning data $\left(D\right)$ into K regimes such that $D={\bigcup }_{c=1}^K{C}_c$. Each cluster is treated as a homogeneous behavioral profile and modeled using a GA-XGBoost model, enabling an intermediate granularity between global and meter-level learning. Experiments on real-world campus AMI data show that our proposed GA-XGBoost model consistently outperforms SVR and LSTM baselines across all clusters. In addition, cluster-specific models further improve performance compared to a single GA-XGBoost model trained without clustering, achieving a 48.42% improvement in MASE. Overall, beyond improving forecasting accuracy, cross-cluster generalization shows performance degradation and distributional shift when models are transferred across clusters, while residual diagnostics reveal differences in variance, temporal dependence, and non-Gaussianity. Full article

Accurate diagnosis of micro short-circuits (MSCs) is essential for ensuring the safety of lithium-ion batteries used in electric vertical take-off and landing (eVTOL) aircraft. Unlike conventional electric vehicles, eVTOL batteries normally operate under high-rate discharge conditions, where strong polarization and rapid voltage variations are apt to mask the weak signatures of MSCs. To address this challenge, this study proposes an MSC diagnosis method based on multiscale voltage residual analysis. A battery model is first established to characterize the normal response under high-rate discharge, and the discrepancy between the measured and estimated terminal voltages is used to construct the model residual. Features describing the overall voltage evolution, residual statistical distribution, and multiscale residual fluctuations are then extracted. Specifically, the shadow region integral area and voltage–capacity slope are used to characterize the global voltage trajectory, while the residual mean and kurtosis quantify the systematic deviation and non-Gaussian fluctuation of the residual. Wavelet decomposition is further applied to capture the low- and high-frequency residual characteristics. After feature reduction, eight representative features are retained to establish the diagnostic model. Experimental validation under high-rate discharge conditions demonstrates that the proposed method can effectively identify MSCs despite interference from abnormal aging, thereby reducing the false alarms caused by feature similarity. This study provides a reliable approach for micro short-circuit diagnosis of eVTOL lithium-ion batteries under strong polarization and highly dynamic operating conditions. Full article

This paper proposes a location error-tolerant data embedding technique for display-to-camera (D2C) communication systems. The method is designed to enable robust data transmission while maintaining high image fidelity, facilitating simultaneous digital content display and data communication. To address the common issue of alignment and localization inaccuracies in D2C systems, the proposed approach defines specific regions of interest to ensure robustness against object detection model location errors. The architecture employs an expanding and contracting network structure for the encoder to achieve seamless data integration, while the decoder utilizes a computationally efficient “thin” structure for rapid data extraction. To improve performance in diverse environments, various distortion models were integrated into the system training. The system’s effectiveness was evaluated by measuring the bit error rate under conditions of Gaussian noise, blur, simulated localization errors, and real-world distortions. Image quality was validated using peak signal-to-noise ratio and the structural similarity index measure. The results indicate that the proposed technique maintains superior image quality and achieves reliable data recovery even in the presence of significant localization errors. These findings suggest that the approach provides a stable and effective solution for practical mobile-based D2C communication. Full article

During cherry-tomato-bunch harvesting, clustered fruits, thin stems, and ambiguous fruit–stem junctions cause missed detections and errors in picking-point localization. To address these challenges, we propose TSP-Net, a structure-aware and geometry-constrained detection network built on YOLOv11n, together with an ROI heat-map regression module for picking-point localization. TSP-Net integrates the Truss-aware Multi-scale Ghost Cross Stage Partial (TMG-CSP) module into the backbone to strengthen structural features of fruit edges, small stems, and clusters. It introduces the Truss-oriented Axial-Local Attention (TALA) module during feature fusion to capture fruit-arrangement direction and local occlusion boundaries. We also design Physics-informed Truss Consistency (PTC) Loss to regularize detections by enforcing consistency with fruit-cluster morphology through constraints on center distribution, inter-fruit spacing, and scale continuity. For picking-point localization, local ROIs are extracted from detection outputs, Gaussian heat maps are predicted, and continuous coordinates are decoded using soft-argmax. In the controlled evaluation, TSP-Net attains precision 81.81%, recall 73.21%, mAP@50 79.53%, and mAP@50:95 64.83%, which exceed the corresponding YOLOv11n results by 0.83, 0.32, 1.29, and 1.03 percentage points, respectively. The model size is reduced from 5.23 MB to 5.08 MB, the parameter count falls from 2.59 M to 2.49 M, and computational cost declines from 6.4 to 6.0 GFLOPs. On the common valid-ROI test set, the proposed heat-map localization yields a mean localization error of 47.2 px, a median error of 24.2 px, and PCK@20, PCK@50, and PCK@100 of 45.1%, 68.4%, and 85.5%, respectively. Under identical ROI conditions, this heat-map method reduces the mean localization error by 11.3 px and raises PCK@20 by 14.9 percentage points compared with direct coordinate regression. In summary, the proposed approach enhances cherry-tomato-bunch detection and 2D picking-point candidate localization while meeting lightweight constraints, offering a structure-aware visual perception solution for greenhouse cherry-tomato harvesting. Full article

Hydrogels are hydrated three-dimensional polymeric networks with biomedical potential because they can encapsulate therapeutic agents and provide localised, sustained, or stimulus-responsive drug delivery. Their performance is determined by interacting variables, including polymer composition, synthesis route, crosslinking chemistry, drug loading, swelling, degradation, and the biological microenvironment. This multidimensional design space often makes hydrogel development slow and dependent on trial-and-error experimentation. This review examines the data-driven development of biomedical hydrogels for controlled drug delivery, focusing on clinical applications and emerging machine-learning approaches that support material selection, formulation design, synthesis optimisation, and release prediction. The review first discusses natural and synthetic hydrogels, including alginate, chitosan, gelatin-based systems, hyaluronic acid, and polyethylene glycol, with emphasis on how their physicochemical properties influence biocompatibility, synthesis flexibility, and drug-release behaviour. Key applications are then considered, including wound healing, cancer therapy, glucose-responsive insulin delivery, and inflammatory disease management. Particular attention is given to injectable and stimuli-responsive hydrogels, where formulation conditions and synthesis parameters can be tuned to improve localisation, therapeutic exposure, and release control. The review evaluates machine-learning methods, including random forest, gradient boosting, artificial neural networks, Gaussian process regression, and active learning, for predicting hydrogel properties, modelling release profiles, optimizing synthesis and formulation variables, and prioritizing experimental candidates. Finally, translational challenges are addressed, including small non-standardised datasets, limited external validation, weak in vitro-clinical correlations, material safety, explainability, reproducibility, scalability, and regulatory requirements. By integrating clinical, materials, synthesis, and machine-learning perspectives, this review highlights opportunities for developing safer and clinically relevant hydrogel-based drug-delivery systems. Full article