Search Results (158,816)

This study demonstrates the power of combining unmanned aerial vehicle (UAV) imagery and deep learning (DL) for monitoring forest regeneration, specifically focusing on young white pine (Pinus strobus). Using high-resolution three-band RGB and five-band multispectral orthomosaics derived from UAV flights, 20 DL object-detection models were evaluated within ArcGIS Pro 3.4 software (Esri Inc., Redlands, CA, USA). The models were tested across study sites in St. Lawrence County, NY, to assess performance on three distinct size classes of white pine, each stratified into low, medium, and high density areas. The Faster R-CNN (F-RCNN) model, particularly when trained with image rotation and no augmentation, significantly outperformed others, achieving an average precision of 0.88 across both imagery types. Subsequent confusion matrix analysis yielded 91% and 90% overall accuracy in medium and high-density white pine blocks, respectively. These findings validate the use of UAV-DL systems as an accurate and efficient tool for operational white pine regeneration assessment, reducing the need for labor-intensive fieldwork. Full article

Machine vision based on artificial intelligence technology is being actively utilized to reduce defect rates in the photovoltaic module production process. This study aims to propose a machine vision approach using EfficientNet-B0 for defective photovoltaic module detection. In particular, the proposed approach is applied to the electroluminescence (EL) operation, which identifies microcracks in PV modules by using polarization current. The proposed approach extracts low-level structures and local brightness variations, such as busbars, fingers, and cell boundaries, from a single convolutional block. Furthermore, the mobile inverted bottleneck convolution (MBConv) block progressively transforms defect patterns—such as microcracks and dark spots—that appear at various shooting angles into high-level feature representations. The converted image is then processed using global average pooling (GAP), Dropout, and a final fully connected layer (Dense) to calculate the probability of a defective module. A sigmoid activation function is then used to determine whether a PV module is defective. Experiments show that the proposed Efficient-B0-based methodology can stably achieve defect detection accuracy comparable to AlexNet and GoogLeNet, despite its relatively small number of parameters and fast processing speed. Therefore, this study will contribute to increasing the efficiency of EL operation in industrial fields and improving the productivity of PV modules. Full article

Big data has significantly transformed data processing and analytics across various domains. However, ensuring security and data confidentiality in distributed platforms such as Hadoop remains a challenging task. Distributed environments face major security issues, particularly in the management and protection of large-scale data. In this article, we focus on the cost of secure information transmission, implementation complexity, and scalability. Furthermore, we address the confidentiality of information stored in Hadoop by analyzing different AES encryption modes and examining their potential to enhance Hadoop security. At the application layer, we operate within our Hadoop environment using an extended, secure, and widely used MQTT protocol for large-scale data communication. This approach is based on implementing MQTT with TLS, and before connecting, we add a hash verification of the data nodes’ identities and send the JWT. This protocol uses TCP at the transport layer for underlying transmission. The advantage of TCP lies in its reliability and small header size, making it particularly suitable for big data environments. This work proposes a triple-layer protection framework. The first layer is the assessment of the performance of existing AES encryption modes (CTR, CBC, and GCM) with different key sizes to optimize data confidentiality and processing efficiency in large-scale Hadoop deployments. Afterwards, we propose evaluating the integrity of DataNodes using a novel verification mechanism that employs SHA-3-256 hashing to authenticate nodes and prevent unauthorized access during cluster initialization. At the third tier, the integrity of data blocks within Hadoop is ensured using SHA-3-256. Through extensive performance testing and security validation, we demonstrate integration. Full article

This paper proposes a unified energy management, fault detection, and fault-tolerant control (EMS–FDI–FTC) framework for Hybrid Hydrogen Electric Vehicles (HHEVs) integrating a fuel cell (FC), battery (Bat), and supercapacitor (SC). While such multi-source architectures enable high-efficiency propulsion under dynamic driving conditions, actuator and state faults such as FC voltage sag, Bat internal resistance increase, and SC capacitance degradation can compromise safety, availability, and component lifetime. The proposed framework converts real-world GPS-recorded vehicle speed profiles into route-aware traction power demand and combines interpretable model-based indicators with an AI-based fault detection and classification module. Based on the diagnosis outcome, a fault-tolerant supervisory strategy performs online power reallocation among the FC, Bat, and SC while enforcing operational constraints. Validation is conducted in a MATLAB-based software-in-the-loop (SIL) environment using three urban driving routes collected from on-road measurements in Tunisia with injected ground-truth faults. The results demonstrate reliable fault classification performance and effective service continuity during fault intervals, supplying over 94% of the demanded energy across all routes, with energy-not-served remaining below 0.02 kWh. In addition, processor-in-the-loop (PIL) implementation on an STM32F407VG controller confirms real-time feasibility with a 10 Hz supervisory sampling rate and execution time margins compatible with embedded automotive deployment. Overall, the proposed closed-loop framework provides a practical route-aware diagnosis-to-control solution for robust and fault-resilient HHEV operation under realistic driving variability. All energy and efficiency indicators reported in this study are derived from control-oriented component models and are intended for consistent comparative evaluation across routes and operating scenarios, rather than absolute representation of a specific commercial vehicle. Full article

The superposition principle in quantum mechanics enables the encoding of an entire solution space within a single quantum state. By employing quantum routines such as amplitude amplification or the Quantum Approximate Optimization Algorithm (QAOA), this solution space can be explored in a computationally efficient manner to identify optimal or near-optimal solutions. In this article, we propose quantum circuits that operate on binary data representations to address a central task in prototype-based classification and representation learning, namely the so-called winner determination, which realizes the nearest prototype principle. We investigate quantum search algorithms to identify the closest prototype during prediction, as well as quantum optimization schemes for prototype selection in the training phase. For these algorithms, we design oracles based on arithmetic circuits that leverage quantum parallelism to apply mathematical operations simultaneously to multiple inputs. Furthermore, we introduce an oracle for prototype selection, integrated into a learning routine, which obviates the need for formulating the task as a binary optimization problem and thereby reduces the number of required auxiliary variables. All proposed oracles are implemented using the Python 3-based quantum machine learning framework PennyLane and empirically validated on synthetic benchmark datasets. Full article

This paper focuses on examining the impact of introducing sound scattering in room acoustic modelling, using a ray tracing approach. A parametric study is conducted on a simplified shoebox auditorium, isolating distinct factors, such as the average absorption of the room, room geometry, volume of the space or the introduction of prismatic-shape diffusers. Diffusion is considered by assigning a scattering coefficient (s) to the surfaces, except in the analysis of a prismatic diffuser, which is modelled using a geometric approach. Changes in the reverberation time are analyzed alongside their corresponding just noticeable differences (JNDs). It was found that sound scattering can reduce reverberation time, especially in rooms with parallel walls, but only when sufficient and well-distributed sound absorption is present. Geometric modifications that remove parallelism reduce flutter echoes and can decrease reliance on scattering. Volume scaling of a room has negligible perceptual influence on sound scattering, whereas modifying room proportions offers a stronger influence on reverberation perception. Prismatic diffusers provide efficient geometric diffusion, achieving outcomes comparable to flat surfaces assigned with medium sound scattering coefficients. Full article

Rose leaf diseases severely reduce yield and product quality, and traditional disease monitoring relies on manual visual inspection by experts, which is inefficient for large-scale cultivation. However, deploying accurate and lightweight detectors in field environments remains challenging due to two main obstacles. First, models trained under controlled laboratory conditions suffer performance degradation due to domain shift when deployed in complex field environments. Second, the computational capacity of hardware deployable in the field is often limited. To address these problems, this study proposes a practical knowledge distillation approach based on transferable feature representations from a pre-trained teacher model, rather than on complex distillation architecture. A high-capacity YOLOv12-L teacher, pre-trained on laboratory images, guided the training of a compact YOLOv12-N student using field images. The distilled YOLOv12-N student model achieved an mAP@50 of 81.1% on field test set, representing a 3.5% improvement over the baseline YOLOv12-N model, while maintaining a highly efficient architecture of only 2.56 million parameters and 6.3 GFLOPs. Several ablation studies confirm the core contribution of this work, namely that the performance gains in lightweight detection stem primarily from the transfer of the teacher model’s feature representations, rather than from modifications to the distillation algorithm or student model’s architecture, thus clarifying the importance of high quality feature transfer in cross-domain agricultural vision tasks. This approach provides a generalizable and efficient solution for real-time rose leaf disease detection in precision agriculture. Full article

Dry matter intake (DMI) in dairy cows is a critical indicator of nutrient intake from feed, serving as the cornerstone of precision feeding practices, playing a critical role in improving production efficiency and enhancing the quality of dairy products. To address the high costs of traditional measurement methods and the structural complexity and large parameter counts of neural network models, this study proposes a Stacking ensemble learning model to assess DMI, with model parameters optimized using the Tuna Swarm Optimization (TSO) algorithm to enhance assessment accuracy, taking cow body weight, lying duration, lying times, rumination duration, foraging duration, walking steps, and the concentrate-to-roughage feed ratio as input variables. To further improve TSO’s search efficiency and spatial exploration, this study introduces Sine–Logistic chaotic mapping, Levy flight, and Gaussian random walk strategy to optimize the TSO algorithm, developing the improved Tuna Swarm Optimization (ITSO). ITSO-optimized Stacking model achieved superior performance in DMI assessment, with an accuracy of 95.84%, significantly outperforming SVR, RF, DT, GBR, ETR, and AdaBoost models. This study provides a robust tool for precision feeding, contributing to optimizing cow feeding strategies, improving farm efficiency, and supporting sustainable dairy farming practices. Full article

5G systems have delivered on their promise of seamless connectivity and efficiency improvements since their global rollout began in 2020. However, maintaining subscriber identity privacy on the network remains a critical challenge. The 3GPP specifications define numerous identifiers associated with the subscriber and their activity, all of which are critical to the operations of cellular networks. While the introduction of the Subscription Concealed Identifier (SUCI) protects users across the air interface, the 5G Core Network (CN) continues to operate largely on the basis of the Subscription Permanent Identifier (SUPI)—the 5G-equivalent to the IMSI from prior generations—for functions such as authentication, billing, session management, emergency services, and lawful interception. Furthermore, the SUPI relies solely on the transport layer’s encryption for protection from malicious observation and tracking of the SUPI across activities. The crucial role of the largely unprotected SUPI and other closely related identifiers creates a high-value target for insider threats, malware campaigns, and data exfiltration, effectively rendering the Mobile Network Operator (MNO) a single point of failure for identity privacy. In this paper, we analyze the architectural vulnerabilities of identity persistence within the CN, challenging the legacy “honest-but-curious” trust model. To quantify the extent of subscriber identities being utilized and exchange within various API calls in the CN, we conducted a study of the occurrence of SUPI as a parameter throughout the collection of 5G SBI (Service-Based Interface) Core VNF (Virtual Network Function) API (Application Programming Interface) schemas. Our extensive analysis of the 3GPP specifications for 3GPP Release 18 revealed a total of 4284 distinct parameter names being used across all API calls, with a total of 171,466 occurrences across the API schema. More importantly, it revealed a highly skewed distribution in which subscriber identity plays a pivotal role. Specifically, the “supi” parameter ranks 57th with 397 occurrences. We found that SUPI occurs both as a direct parameter (“supi”) and within 72 other parameter names that contain subscriber identifiers as defined in 3GPP TS 23.003. For these 73 parameter names, we identified a total of 8757 occurrences. At over 5.11% of all parameter occurrences, this constitutes a disproportionately large share of total references. We also detail scenarios where subscriber privacy can be compromised by internal actors and review future privacy-preserving frameworks that aim to decouple subscriber identity from network operations. By suggesting a shift towards a zero-trust model for CN architecture and providing subscribers with greater control over their identity management, this work also offers a potential roadmap for mitigating insider threats in current deployments and influencing specific standardization and regulatory requirements for future 6G and Beyond-6G networks. Full article

Topology optimization using density-based approaches often requires high-resolution meshes to achieve reliable compliance evaluation and robustness against mesh dependency. However, increasing the problem sizes—especially in 3D—results in prohibitively expensive computation times. Coarse-mesh approaches significantly accelerate runtimes; however, they also introduce discretization errors that can guide the optimizer towards incorrect topology families if left unregulated. To address this issue, a multifidelity framework with acceptance control was developed that enables runtime verification and explicitly manages the optimizer state. The main idea is to use coarse discretizations to generate new design proposals and transfer candidate designs to fine discretizations at periodic intervals for verification. Proposals are then accepted or rejected using a best-referenced criterion; if verification fails, the optimizer reverts to the best verified state. The proposed framework balances fine-discretization accountability with coarse-discretization efficiency through configurable verification schedules and a cleanup phase. The framework is evaluated on standard 2D and 3D structural benchmark problems with deterministic load perturbations, and performance is assessed in terms of final verified compliance, wall-clock runtime, acceptance rate, and gray fraction. Full article

Space robots operate in unconventional environments, which places specific demands on their mechanical, actuation, and control systems. They need to address a variety of challenges in future space exploitation and exploration, such as in-orbit deployment, active debris removal, or servicing operations. Using robots for such applications presents a unique challenge, as a high level of autonomy is required, and the manipulator’s motion affects the position and orientation of the spacecraft. The article presents basic theoretical statements regarding redundancy in space robotics. Various methods for overcoming difficulties in designing, using, and controlling a space robot are considered. Specialized control algorithms based on the null space of the Jacobian matrix and zero reaction maneuvers (ZRMs) are discussed. The review is limited to space robots with one or more arms and does not include mobile and humanoid robots. Furthermore, the primary motion planning algorithms for these systems are evaluated. Redundant space robots are categorized based on their degrees of freedom, number of arms, operational efficiency, primary objectives, and application areas and the most commonly used algorithms for planning movements. The advantages and disadvantages of both redundant and hyper-redundant space robots are analyzed. The objective of this review is to provide a comprehensive overview of the current state and prospects for the development of redundant robots for operation in space conditions. Full article

Efficient and accurate characterization of lithium-ion battery packs is critical for both first-life applications and second-life reuse. Electrochemical Impedance Spectroscopy (EIS) provides detailed insight into internal electrochemical processes, but full-spectrum measurements are time-consuming, especially at low frequencies. This work presents a methodology combining cell-level equivalent circuit modeling, integrated gradients sensitivity analysis, and D-optimal frequency selection to reduce the number of measurement points while preserving parameter identifiability. Individual 16s5p LFP cells were characterized using full-spectrum EIS at 10 °C, and the resulting equivalent circuit models were scaled to the pack level. Integrated gradients were used to quantify the frequency-dependent influence of each parameter on the real and imaginary parts of the impedance, identifying the regions containing the most information. Using the per-frequency Jacobian and the Fisher Information Matrix, a D-optimal frequency selection was performed to demonstrate that a reduced set of measurements is sufficient to estimate key parameters reliably. The results show that variations in parameters due to aging are accurately captured using the reduced frequency set, validating the approach for fast, accurate, and traceable characterization at the pack level. The proposed methodology highlights a systematic strategy for frequency selection, enabling faster EIS measurements, maintaining sensitivity to aging and degradation mechanisms, and supporting standardized and sustainable evaluation of lithium-ion batteries. Full article

Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing. Full article

The use of high-strength reinforcing steel is an effective way to improve the flexural efficiency of reinforced concrete beams. However, the flexural behaviour of beams reinforced with 630 MPa grade longitudinal rebars in combination with normal-strength concrete is still not fully understood, especially with regard to serviceability performance. In this study, the flexural performance of simply supported RC beams reinforced with HRB500, HRB600 and HRB630 longitudinal rebars and cast with C60 steel-fibre-reinforced concrete was investigated through a combined experimental and numerical approach. Six beams were tested under four-point bending to examine cracking patterns, deflection development and ultimate flexural capacity. A three-dimensional nonlinear finite element model based on the Concrete Damage Plasticity model in ABAQUS was then established and calibrated against the test data. Using the validated numerical model, a parametric study was carried out to investigate the influence of steel grade, tensile reinforcement ratio on flexural stiffness and ductility. Test results indicate that, for the same reinforcement ratio, the ultimate moment capacity of HRB630 beams is about 8% higher than that of HRB600 beams and about 25% higher than that of HRB500 beams, while a ductile flexural failure mode governed by yielding of tension reinforcement is still maintained. The study also shows that for HRB630 beams, deflection predictions need to account for the higher steel stress level and the deterioration of tension stiffening effects. In general, the results demonstrate that HRB630 high-strength rebars can be safely and efficiently used in flexural members when the tensile reinforcement ratio is kept within the under-reinforced range and steel-fibre-reinforced concrete is adopted to improve cracking and deflection performance. Full article

This article describes a non-isolated boost DC–DC configuration that uses a two-winding coupled inductor (CI) together with two synchronous power switches to acquire ultra-high voltage conversion at relatively low duty cycles. The proposed structure combines a quadratic gain stage with the coupled inductor to realize a substantial output voltage boost. The overall conversion ratio can be flexibly adjusted through two independent design factors: the duty cycle of the switches and the turns ratio of the coupled inductor providing additional degrees of freedom for optimization. The main merits of the converter are its very high voltage gain (VG), reduced voltage stress (VS) on the active switches, continuous input current, common ground between input and output, soft-switching operation for diodes D₃ and D₄, and the possibility of using a synchronized gate-drive scheme. The paper thoroughly examines the operating intervals, steady-state behavior, design procedure, and efficiency performance, and also develops a dynamic model for control-oriented analysis. To highlight its strengths, the proposed topology is systematically compared with several existing high-gain converters. Finally, experimental outcomes obtained from a 400-W laboratory prototype operating at 50 kHz confirm the feasibility and effectiveness of the proposed converter in achieving high voltage gain, reduced device voltage stress, and high efficiency under practical operating conditions. Full article