Search Results (5,353)

As manufacturing processes evolve towards greater automation and efficiency, the integration of augmented reality (AR) and virtual reality (VR) technologies has emerged as a transformative approach that offers innovative solutions to various challenges in manufacturing applications. This comprehensive review explores the recent technological advancements and applications of AR and VR within the context of manufacturing. This review also encompasses the utilization of AR and VR technologies across different stages of the manufacturing process, including design, prototyping, assembly, training, maintenance, and quality control. Furthermore, this review highlights the recent developments in hardware and software components that have facilitated the adoption of AR and VR in manufacturing environments. This comprehensive literature review identifies the emerging technologies that are driving AR and VR technology toward technological maturity for implementation in manufacturing applications. Finally, this review discusses the major difficulties in implementing AR and VR technologies in the manufacturing sectors. Full article

The modernization of agriculture can help humanity address major challenges such as population growth, climate change, and labor shortages. Semi-autonomous agricultural robots offer clear advantages in automating tasks and improving efficiency. However, in open-field conditions, their autonomy is limited by the size and weight of onboard batteries. Wireless charging is a promising solution to overcome this limitation. This work proposes a methodology for the design, modeling, and experimental validation of a wireless power transfer (WPT) system for battery recharging of agricultural robots. A brief review of WPT technologies is provided, followed by key design considerations, co-simulation, and testing results. The proposed WPT system uses a resonant inductive power transfer topology with series–series (SS) compensation, a high-frequency inverter (85 kHz), and optimized spiral planar coils, enabling medium-range operation under agricultural conditions. The main contribution lies in the first experimental assessment of WPT performance under real agricultural environmental factors such as soil moisture and water presence, combined with electromagnetic safety evaluation and robust component selection for harsh conditions. Results highlight both the potential and limitations of this approach, demonstrating its feasibility and paving the way for future integration with intelligent alignment and adaptive control strategies. Full article

This paper presents a physics-guided machine learning (PGML) approach to model the I-V characteristics of GaN current aperture vertical field effect transistors (CAVET). By adopting the method of transfer learning and the shortcut structure, a physically guided neural network model is established. The shallow neural network with <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier development, this study proposes DySC-MDE, an end-to-end co-designed framework for seizure detection. A novel multi-domain entropy (MDE) representation is constructed at the feature level based on amplitude-sensitive permutation entropy (ASPE), which adopts entropy-based quantifiers to characterize the nonlinear dynamics of EEG signals across diverse domains. Specifically, ASPE is extended into three distinct variants, refined composite multiscale ASPE (RCMASPE), discrete wavelet transform-based hierarchical ASPE (HASPE-DWT), and time-shift multiscale ASPE (TSMASPE), to represent various temporal and spectral dynamics of EEG signals. At the classifier level, a dynamic synapse classifier (DySC) is proposed to align with the structure of the MDE features. Particularly, DySC includes three parallel and specialized processing pathways, each tailored to a specific entropy variant. These outputs are then adaptively fused through a dynamic synaptic gating mechanism, which can enhance the model’s ability to integrate heterogeneous information sources. To fully evaluate the effectiveness of the proposed method, extensive experiments are conducted on two public datasets using cross-validation. For the binary classification task, DySC-MDE achieves an accuracy of 97.50% and 98.93% and an F1-score of 97.58% and 98.87% in the Bonn and CHB-MIT datasets, respectively. Moreover, in the three-class task, the proposed method maintains a high F1-score of 96.83%, revealing its strong discriminative performance and generalization ability across different categories. Consequently, these impressive results demonstrate that the joint optimization of nonlinear dynamic feature representations and structure-aware classifiers can further improve the analysis of complex epileptic EEG signals, which opens a novel direction for robust seizure detection. Full article

Reinforcement learning (RL) is being used more in medical imaging for segmentation, detection, registration, and classification. This survey provides a comprehensive overview of RL techniques applied in this domain, categorizing the literature based on clinical task, imaging modality, learning paradigm, and algorithmic design. We introduce a unified taxonomy that supports reproducibility, highlights design guidance, and identifies underexplored intersections. Furthermore, we examine the integration of Large Language Models (LLMs) for automation and interpretability, and discuss privacy-preserving extensions using Differential Privacy (DP) and Federated Learning (FL). Finally, we address deployment challenges and outline future research directions toward trustworthy and scalable medical RL systems. Full article

To improve the traffic efficiency of connected and automated vehicles (CAVs) in on-ramp merging areas, this study proposes a novel region-level multi-agent reinforcement learning framework, Regionally Coordinated Nash-Advantage Decomposition Deep Q-Network with Conflict-Aware Q Fusion (RC-NashAD-DQN). Unlike existing vehicle-level control methods, which suffer from high computational overhead and poor scalability, our approach abstracts on-ramp and main road areas as region-level control agents, achieving coordinated yet independent decision-making while maintaining control precision and merging efficiency comparable to fine-grained vehicle-level approaches. Each agent adopts a value–advantage decomposition architecture to enhance policy stability and distinguish action values, while sharing state–action information to improve inter-agent awareness. A Nash equilibrium solver is applied to derive joint strategies, and a conflict-aware Q-fusion mechanism is introduced as a regularization term rather than a direct action-selection tool, enabling the system to resolve local conflicts—particularly at region boundaries—without compromising global coordination. This design reduces training complexity, accelerates convergence, and improves robustness against communication imperfections. The framework is evaluated using the SUMO simulator at the Taishan Road interchange on the S1 Yongtaiwen Expressway under heterogeneous traffic conditions involving both passenger cars and container trucks, and is compared with baseline models including C-DRL-VSL and MADDPG. Extensive simulations demonstrate that RC-NashAD-DQN significantly improves average traffic speed by 17.07% and reduces average delay by 12.68 s, outperforming all baselines in efficiency metrics while maintaining robust convergence performance. These improvements enhance cooperation and merging efficiency among vehicles, contributing to sustainable urban mobility and the advancement of intelligent transportation systems. Full article

As automation technologies continue to advance within the automotive industry, urban road traffic is gradually shifting from conventional driving toward fully autonomous. This transition is supported by the progressive integration of partially automated functions, such as Adaptive Cruise Control (ACC) and lane-keeping assistance, which are already implemented in commercial vehicles and increasingly affect both individual driving behavior and overall traffic flow dynamics. The main purpose of this research is to evaluate the impact of automated vehicles presence in a complex signalized intersection under mixed traffic conditions, considering different penetration rates and demand levels. A review of previous modeling approaches from the literature was conducted, highlighting critical aspects to be considered in the design and simulation of road traffic. Field traffic data were collected and used as input for a microsimulation model developed in AIMSUN. A base scenario and a 20% growth scenario were analyzed to assess the impact of AV-ACC penetration, varying the AV-ACC’s rates in traffic composition. The results indicate that increased AV-ACC penetration rates, especially beyond 50%, contribute significantly to improving traffic stability and efficiency. Full article

The aim of the article is to present a comprehensive procedure for processing and evaluating directional data from vehicle license plates, focusing on the specific challenges of areas that are sparsely or not at all equipped with permanently located standard license plate recognition systems. This remains a current issue, especially in smaller towns; it leads to the implementation of short-term directional traffic surveys, often using inexpensive measurement devices, in order to obtain directional traffic data. In this research, a procedure for evaluating license plate recognition data is proposed with a primary focus on its simple adaptability and automation for any subsequent use. The data sources considered are primarily the above-mentioned traffic surveys; however, the proposed evaluation procedure is theoretically transferable to any off-line data obtained from license plate recognition systems. Identifying potential inaccuracies in the data is also an integral part of the evaluation process. The design of the proposed procedure follows the Checkland soft systems methodology and the functionality of the resulting procedure was validated through a case study of a directional survey in Prague. The proposed procedure contributes to greater accuracy of the conclusions drawn from evaluated traffic engineering parameters under non-ideal, but common conditions of smaller cities, not only in the Czech Republic. Full article

Water injection development represents the predominant development method for enhancing oil recovery (EOR) efficiency and achieving the balanced utilization of oil reservoirs. In light of the current situation of oilfield water injection technology, a comprehensive overview of the evolution of full-scale water injection technology is given, with particular emphasis on the influence of geological factors, technological advancements, and existing challenges. The principal issues currently encountered include an unequal distribution of layers, the complexity of subdivision, casing deformation, and damage to deep well equipment, which collectively impede the effective implementation of subdivision water injection development technology. The novelty of the research lies in the current development status of full-scale injection wells, which is not only reflected in the depth-scale, but also in the operational difficulty-scale. A thorough exploration of subdivision water injection development technologies has been conducted, and the applicability and limitations of these technologies in diverse reservoir conditions have been evaluated. The proposal is for intelligent injection technology to be adopted for medium–shallow heterogeneous wells, and for ball-pitching plugging profile control technology to be adopted for deep/horizontal/special condition wells. A comparative analysis was conducted to evaluate the characteristics, application scenarios, advantages, and disadvantages of intelligent injection technologies, demonstrating its intelligence, automation, and precision in the practical application. In regard to the ball-pitching plugging profile control technology, the design and performance of the plugging ball, the plugging mechanism, and the application effect were elucidated. Based on the existing challenges in the realm of water injection development, the research prospects for full-scale subdivision water injection development technologies were proposed, and the importance of interdisciplinary cooperation and the integration of artificial intelligence technology were also emphasized. This research would provide a technical foundation for increasing oil displacement efficiency, markedly augmenting EOR, and would also be imperative for improving the economic benefits and alleviating the global oil resource tension. Full article

Fiber-reinforced composites (FRCs) have emerged as transformative alternatives to traditional marine construction materials, owing to their superior corrosion resistance, design flexibility, and strength-to-weight ratio. This review comprehensively examines the current state of FRC technologies in marine deck and underwater applications, with a focus on manufacturing methods, durability challenges, and future innovations. Thermoset polymer composites, particularly those with epoxy and vinyl ester matrices, continue to dominate marine applications due to their mechanical robustness and processing maturity. In contrast, thermoplastic composites such as Polyether Ether Ketone (PEEK) and Polyether Ketone Ketone (PEKK) offer advantages in recyclability and hydrothermal performance but are hindered by higher processing costs. The review evaluates the performance of various fiber types, including glass, carbon, basalt, and aramid, highlighting the trade-offs between cost, mechanical properties, and environmental resistance. Manufacturing processes such as vacuum-assisted resin transfer molding (VARTM) and automated fiber placement (AFP) enable efficient production but face limitations in scalability and in-field repair. Key durability concerns include seawater-induced degradation, moisture absorption, interfacial debonding, galvanic corrosion in FRP–metal hybrids, and biofouling. The paper also explores emerging strategies such as self-healing polymers, nano-enhanced coatings, and hybrid fiber architectures that aim to improve long-term reliability. Finally, it outlines future research directions, including the development of smart composites with embedded structural health monitoring (SHM), bio-based resin systems, and standardized certification protocols to support broader industry adoption. This review aims to guide ongoing research and development efforts toward more sustainable, high-performance marine composite systems. Full article

This work proposes a methodology for the automated sizing of transistors in analog integrated circuits, based on a modular and hierarchical representation of the circuit. The methodology combines structured design techniques and systematic design flow to generate a hierarchy of simplified macromodels that define their specifications locally and are interconnected with other macromodels or transistor-level primitive blocks. These primitive blocks can be described using symbolic models or pre-characterized data from look-up tables (LUTs). The symbolic representation of the system is obtained using Modified Nodal Analysis (MNA), and the exploration of each block is performed using local design spaces constrained by top-level specifications. The methodology is validated through the design of low dropout voltage regulators (LDOs) for DC-DC integrated power systems using open-source tools and three process design kits: Sky130A, GF180MCU, and IHP-SG13G2. Results show that the methodology allows the exploration of several topologies and technologies, demonstrating its versatility and modularity, which are key aspects in analog design. Full article

Java Management Extensions (JMX) are indispensable for managing and administrating Java software solutions, yet when exposed through HTTP bridges such as Jolokia they can radically enlarge an application’s attack surface. This paper presents the first in-depth analysis of CVE-2022-41678, a vulnerability discovered by the authors in Apache ActiveMQ that combines Jolokia’s remote JMX access with Log4J2 management beans to achieve full remote code execution. Using a default installation testbed, we enumerate the Log4J MBeans surfaced by Jolokia, demonstrate arbitrary file read, file write, and server-side request–forgery primitives, and finally to leverage the file write capabilities to obtain a shell, all via authenticated HTTP(S) requests only. The end-to-end exploit chain requires no deserialization gadgets and is unaffected by prior Log4Shell mitigations. We have also automated the entire exploit process via proof-of-concept scripts on a stock ActiveMQ 5.17.1 instance. We discuss the broader security implications for any software exposing JMX-managed or Jolokia-managed Log4J contexts, provide concrete hardening guidelines, and outline design directions for safer remote-management stacks. The findings underscore that even “benign” management beans can become critical when surfaced through ubiquitous HTTP management gateways. Full article

Prompt engineering (PE) has emerged as a transformative paradigm in software engineering (SE), leveraging large language models (LLMs) to support a wide range of SE tasks, including code generation, bug detection, and software traceability. This study conducts a systematic literature review (SLR) combined with a co-citation network analysis of 42 peer-reviewed journal articles to map key research themes, commonly applied PE methods, and evaluation metrics in the SE domain. The results reveal four prominent research clusters: manual prompt crafting, retrieval-augmented generation, chain-of-thought prompting, and automated prompt tuning. These approaches demonstrate notable progress, often matching or surpassing traditional fine-tuning methods in terms of adaptability and computational efficiency. Interdisciplinary collaboration among experts in AI, machine learning, and software engineering is identified as a key driver of innovation. However, several research gaps remain, including the absence of standardized evaluation protocols, sensitivity to prompt brittleness, and challenges in scalability across diverse SE applications. To address these issues, a modular prompt engineering framework is proposed, integrating human-in-the-loop design, automated prompt optimization, and version control mechanisms. Additionally, a conceptual pipeline is introduced to support domain adaptation and cross-domain generalization. Finally, a strategic research roadmap is presented, emphasizing future work on interpretability, fairness, and collaborative development platforms. This study offers a comprehensive foundation and practical insights to advance prompt engineering research tailored to the complex and evolving needs of software engineering. Full article

Deep learning has significantly advanced medical image analysis by enabling accurate, automated diagnosis across diverse clinical tasks such as lesion classification and disease detection. However, the practical deployment of these systems is still hindered by two major challenges: the limited availability of expert-annotated data and substantial domain shifts caused by variations in imaging devices, acquisition protocols, and patient populations. Although recent semi-supervised domain generalization (SSDG) approaches attempt to address these challenges, they often suffer from two key limitations: (i) reliance on computationally expensive uncertainty modeling techniques such as Monte Carlo dropout, and (ii) inflexible shared-head classifiers that fail to capture domain-specific variability across heterogeneous imaging styles. To overcome these limitations, we propose MultiStyle-SSDG, a unified semi-supervised domain generalization framework designed to improve model generalization in low-label scenarios. Our method introduces a multi-style ensemble pseudo-labeling strategy guided by entropy-based filtering, incorporates prototype-based conformity and semantic alignment to regularize the feature space, and employs a domain-specific multi-head classifier fused through attention-weighted prediction. Additionally, we introduce a dual-level neural-style transfer pipeline that simulates realistic domain shifts while preserving diagnostic semantics. We validated our framework on the ISIC2019 skin lesion classification benchmark using 5% and 10% labeled data. MultiStyle-SSDG consistently outperformed recent state-of-the-art methods such as FixMatch, StyleMatch, and UPLM, achieving statistically significant improvements in classification accuracy under simulated domain shifts including style, background, and corruption. Specifically, our method achieved 78.6% accuracy with 5% labeled data and 80.3% with 10% labeled data on ISIC2019, surpassing FixMatch by 4.9–5.3 percentage points and UPLM by 2.1–2.4 points. Ablation studies further confirmed the individual contributions of each component, and t-SNE visualizations illustrate enhanced intra-class compactness and cross-domain feature consistency. These results demonstrate that our style-aware, modular framework offers a robust and scalable solution for generalizable computer-aided diagnosis in real-world medical imaging settings. Full article

Arduino-based sensor systems are gaining widespread adoption in modern technological applications due to their accessibility, low-cost components, diverse sensor compatibility, high reliability, and user-friendly programming. Because of these advantages, such a system was selected to monitor and control microclimate parameters in a small-scale experimental greenhouse. The greenhouse will cultivate several vegetable species in soils with varying zeolite concentrations. The aim of this paper is to present the design and prototype development of a sensor system capable of tracking key environmental parameters, including temperature, humidity, atmospheric pressure, and soil moisture, while also enabling automated irrigation. Full article