Search Results (7,336)

Reduced visibility caused by foggy weather has a significant impact on transportation systems and driving safety, leading to increased accident risks and decreased operational efficiency. Traditional methods rely on expensive physical instruments, limiting their scalability. To address this challenge in a cost-effective manner, we propose a two-stage network for visibility estimation from stereo image inputs. The first stage computes scene depth via stereo matching, while the second stage fuses depth and texture information to estimate metric-scale visibility. Our method produces pixel-wise visibility maps through a physically constrained, progressive supervision strategy, providing rich spatial visibility distributions beyond a single global value. Moreover, it enables the detection of patchy fog, allowing a more comprehensive understanding of complex atmospheric conditions. To facilitate training and evaluation, we propose an automatic fog-aware data generation pipeline that incorporates both synthetically rendered foggy images and real-world captures. Furthermore, we construct a large-scale dataset encompassing diverse scenarios. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both visibility estimation and patchy fog detection. Full article

This paper explores the development and implementation of a brain–computer interface (BCI) system that utilizes electroencephalogram (EEG) signals for real-time monitoring of attention levels to control unmanned aerial vehicles (UAVs). We propose an innovative approach that combines spectral power analysis and machine learning classification techniques to translate cognitive states into precise UAV command signals. This method overcomes the limitations of traditional threshold-based approaches by adapting to individual differences and improving classification accuracy. Through comprehensive testing with 20 participants in both controlled laboratory environments and real-world scenarios, our system achieved an 85% accuracy rate in distinguishing between high and low attention states and successfully mapped these cognitive states to vertical UAV movements. Experimental results demonstrate that our machine learning-based classification method significantly enhances system robustness and adaptability in noisy environments. This research not only advances UAV operability through neural interfaces but also broadens the practical applications of BCI technology in aviation. Our findings contribute to the expanding field of neurotechnology and underscore the potential for neural signal processing and machine learning integration to revolutionize human–machine interaction in industries where dynamic relationships between cognitive states and automated systems are beneficial. Full article

The Personal Care Products (PCP) industry, encompassing cosmetics, hygiene, and personal care items, serves millions of consumers daily and operates under constant pressure for innovation, agility, and sustainability. Within this context, supply chains are viewed as complex and integrated systems, composed of interrelated elements whose interactions determine overall performance and are influenced by external factors. Disruptions—particularly those involving indirect suppliers—can propagate throughout the network, affecting operations, reputation, and business outcomes. Despite the importance of the topic, empirical studies that systematically identify and prioritize these risks in the PCP sector remain scarce, which motivated the conduct of this study. Thus, the aim of this research is to identify, analyze, and evaluate the main supply risks faced by the PCP industry, considering severity, occurrence, and detection capability. Methodologically, the research employed an exploratory multi-case design, carried out in three steps: a literature review to identify key supply chain risks; structured interviews with industry experts to analyze and evaluate these risks; and the application of Gray Relational Analysis (GRA) to aggregate expert judgments and construct a prioritized risk ranking. This combination of qualitative and quantitative techniques provided a detailed foundation for analyzing and interpreting the main risks in the Brazilian PCP sector. The results indicate that indirect supplier failure is the most critical risk, prioritized by 70% of the companies studied. Other significant risks include the inability to meet changes in demand, import issues, lack of supply chain visibility, natural and social disasters, and sustainability or reputational concerns. Consequently, this study contributes to a systemic understanding of risk management in the PCP industry supply chain, providing managers with a practical mapping of critical points and highlighting concrete opportunities to strengthen integration, anticipate disruptions, and enhance operational resilience and performance across the sector. Full article

Autonomous commercial vehicles often mount multiple LiDARs to enlarge their field of view, but conventional calibration is labor-intensive and prone to drift during long-term operation. We present an online self-calibration method that combines a ground plane motion constraint with a virtual RGB–D projection, mapping 3D point clouds to 2D feature/depth images to reduce feature extraction cost while preserving 3D structure. Motion consistency across consecutive frames enables a reduced-dimension hand–eye formulation. Within this formulation, the estimation integrates geometric constraints on $SE\left(3\right)$ using Lagrange multiplier aggregation and quasi-Newton refinement. This approach highlights key aspects of identifiability, conditioning, and convergence. An online monitor evaluates plane alignment and LiDAR–INS odometry consistency to detect degradation and trigger recalibration. Tests on a commercial vehicle with six LiDARs and on nuScenes demonstrate accuracy comparable to offline, target-based methods while supporting practical online use. On the vehicle, maximum errors are 6.058 cm (translation) and 4.768° (rotation); on nuScenes, 2.916 cm and 5.386°. The approach streamlines calibration, enables online monitoring, and remains robust in real-world settings. Full article

Ensuring hygiene compliance in regulated environments—such as food processing facilities, hospitals, and public indoor spaces—requires reliable detection of personal protective equipment (PPE) usage, including gloves, face masks, and hairnets. Manual inspection is labor-intensive and unsuitable for continuous, real-time enforcement. This study benchmarks three lightweight object detection models—YOLOv8n, YOLOv10n, and YOLOv12n—for automated PPE compliance monitoring using a large curated dataset of over 31,000 annotated images. The dataset spans seven classes representing both compliant and non-compliant conditions: glove, no_glove, mask, no_mask, incorrect_mask, hairnet, and no_hairnet. All evaluations were conducted using both detection accuracy metrics (mAP@50, mAP@50–95, precision, recall) and deployment-relevant efficiency metrics (inference speed, model size, GFLOPs). Among the three models, YOLOv10n achieved the highest mAP@50 (85.7%) while maintaining competitive efficiency, indicating strong suitability for resource-constrained IoT-integrated deployments. YOLOv8n provided the highest localization accuracy at stricter thresholds (mAP@50–95), while YOLOv12n favored ultra-lightweight operation at the cost of reduced accuracy. The results provide practical guidance for selecting nano-scale detection models in real-time hygiene compliance systems and contribute a reproducible, deployment-aware evaluation framework for computer vision in hygiene-critical settings. Full article

Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article

Subterranean ecosystems represent some of the most unique and fragile habitats on Earth, yet they remain poorly understood and highly vulnerable to human-induced disturbances. Despite their ecological significance, these systems are rarely integrated into conservation planning, and surface-level protected areas alone are insufficient to safeguard their biodiversity. In southeastern Brazil, a karst landscape spanning approximately 1200 km², recognized as the region with the highest cave density in South America (approximately 2600 caves), is under increasing pressure from urban expansion, agriculture, and mining, all of which threaten the ecological integrity of subterranean habitats. This study sought to identify caves of high conservation priority by integrating species richness of non-troglobitic invertebrates, occurrence of troglobitic species, presence of endemic troglobitic taxa, and the degree of anthropogenic impacts, using spatial algebra and polygon-based mapping approaches. Agriculture and exotic forestry plantations (54%) and mining operations (15%) were identified as the most prevalent disturbances. A total of 32 troglobitic species were recorded, occurring in 63% of the 105 surveyed caves. Notably, seven caves alone harbor 25% of the region’s known cave invertebrate diversity and encompass 50% of its cave-restricted species. The findings highlight the global significance of this spot of subterranean biodiversity and reinforce the urgent need for targeted conservation measures. Without immediate action to mitigate unsustainable land use and resource exploitation, the persistence of these highly specialized communities is at imminent risk. Full article

In dynamic and information-rich contexts, systems must be capable of making instantaneous, context-aware decisions. Such scenarios require optimization methods that are both fast and flexible. This paper introduces an innovative hardware-based intelligent optimization framework, deployed on FPGAs, designed to support autonomous decisions in real-time systems. In contrast to conventional methods based on a single chaotic map, our scheme brings together six separate chaotic generators in simultaneous operation, orchestrated by an adaptive voting system based on past results. The system, in conjunction with the Secretary Bird Optimization Algorithm (SBOA), constantly adjusts its optimization approach according to the changing profile of the objective function. This delivers first-rate, timely solutions with improved convergence, resistance to local minima, and a high degree of adaptability to a variety of decision-making contexts. Simulations carried out on reference standards and engineering problems have demonstrated the scalability, responsiveness, and efficiency of the proposed model. These characteristics make it particularly suitable for use in embedded intelligence applications in sectors such as intelligent production, robotics, and IoT-based infrastructures. The suggested solution was tested using post-synthesis simulations on Vivado 2022.2 and experimented on three concrete engineering challenges: welded beam design, pressure equipment design, and tension/compression spring refinement. In each situation, the adaptive selection process dynamically determined the most suitable chaotic map, such as the logistics map for the Welded Beam Design Problem (WBDP) and the Tent map for the Pressure Vessel Design Problem (PVDP). This led to ideal results that exceed both conventional static methods and recent references in the literature. The post-synthesis results on the Nexys 4 DDR (Artix-7 XC7A100T, Digilent Inc., Pullman, WA, USA) show that the initial Q16.16 implementation exceeded the device resources (128% LUTs and 100% DSPs), whereas the optimized Q4.8 representation achieved feasible deployment with 80% LUT utilization, 72% DSP usage, and 3% FF occupancy. This adjustment reduced resource consumption by more than 25% while maintaining sufficient computational accuracy. Full article

We introduce a novel technique for computing the periods of $(d,k)$-IETs based on Rauzy induction $\mathcal{R}$. Specifically, we establish a connection between the set of periods of an interval exchange transformation (IET) T and those of the IET $T^{\prime }$ obtained either by applying the Rauzy operator $\mathcal{R}$ to T or by considering the Poincaré first return map. Rauzy matrices play a central role in this correspondence whenever T lies in the domain of $\mathcal{R}$ (Theorem 4). Furthermore, Theorem 6 addresses the case when T is not in the domain of $\mathcal{R}$, while Theorem 5 deals with IETs having associated reducible permutations. As an application, we characterize the set of periods of oriented 3-IETs (Theorem 8), and we also propose a general framework for studying the periods of $(d,k)$-IETs. Our approach provides a systematic method for determining the periods of non-transitive IETs. In general, given an IET with d discontinuities, if Rauzy induction allows us to descend to another IET whose periodic components are already known, then the main theorems of this paper can be applied to recover the set of periods of the original IET. This method has been also applied to obtain the set of periods of all $(2,k)$-IETs and some $(3,k)$-IETs, $k\ge 1$. Several open problems are presented at the end of the paper. Full article

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

The complex environment along railway lines, characterized by low imaging quality, strong background interference, and densely distributed small objects, causes existing detection models to suffer from low accuracy in practical applications. To tackle these challenges, this study aims to develop a robust and lightweight insulator detection model specifically optimized for these challenging railway scenarios. To this end, we release a dedicated comprehensive dataset named complexRailway that covers typical railway scenarios to address the limitations of existing insulator datasets, such as the lack of small-scale objects in high-interference backgrounds. On this basis, we present CutP5-LargeKernelAttention-SIoU (C5LS), an improved YOLOv8 variant with three key improvements: (1) optimized YOLOv8’s detection head by removing the P5 branch to improve feature extraction for small- and medium-sized targets while reducing computational redundancy, (2) integrating a lightweight Large Separable Kernel Attention (LSKA) module to expand the receptive field and improve contextual modeling, (3) and replacing CIoU with SIoU loss to refine localization accuracy and accelerate convergence. Experimental results demonstrate that it reaches 94.7% in mAP@0.5 and 65.5% in mAP@0.5–0.95, outperforming the baseline model by 1.9% and 3.5%, respectively. With an inference speed of 104 FPS and a model size of 13.9 MB, the model balances high precision and lightweight deployment. By providing stable and accurate insulator detection, C5LS not only offers reliable spatial positioning basis for subsequent defect identification but also builds an efficient and feasible intelligent monitoring solution for these failure-prone insulators, thereby effectively enhancing the operational safety and maintenance efficiency of the railway power system. Full article

Meteorological conditions are a key factor affecting airport site selection and operational efficiency. To overcome the limitations of traditional methods in evaluating the joint impact of multiple meteorological elements, this paper aims to develop an airport site selection decision support method based on case-based reasoning (CBR) and multi-meteorological element clustering. Firstly, we propose a universal framework: utilizing K-means clustering to extract typical weather scenarios from historical meteorological data; subsequently, using Zhengzhou Xinzheng International Airport as a case study, a quantitative mapping relationship was established between these weather scenarios and flight operation efficiency (such as delay rate and cancellation rate) to calibrate and validate the model; finally, by calculating the frequency of occurrence of various weather scenarios at candidate sites, the future operational efficiency can be inferred, providing a ranking basis for site selection decisions. The results indicate that low-cloud-base weather has the greatest impact on flight takeoff performance, while good weather has a relatively small impact on flights. This method can effectively and quickly rank the advantages and disadvantages of all candidate airports, providing a reference for airport construction. Full article

3D point cloud object tracking plays a critical role in fields such as autonomous driving and robotics, making the security of these models essential. Adversarial attacks are a key approach for studying the robustness and security of tracking models. However, research on the generalization of adversarial attacks for 3D point-cloud-tracking models is limited, and the frequency-domain information of the point cloud’s geometric structure is often overlooked. This frequency information is closely related to the generalization of 3D point-cloud-tracking models. To address these limitations, this paper proposes a novel adversarial method for 3D point cloud object tracking, utilizing frequency-domain attacks based on the importance of frequency bands. The attack operates in the frequency domain, targeting the low-frequency components of the point cloud within the search area. To make the attack more targeted, the paper introduces a frequency band importance saliency map, which reflects the significance of sub-frequency bands for tracking and uses this importance as attack weights to enhance the attack’s effectiveness. The proposed attack method was evaluated on mainstream 3D point-cloud-tracking models, and the adversarial examples generated from white-box attacks were transferred to other black-box tracking models. Experiments show that the proposed attack method reduces both the average success rate and precision of tracking, proving the effectiveness of the proposed adversarial attack. Furthermore, when the white-box adversarial samples were transferred to the black-box model, the tracking metrics also decreased, verifying the transferability of the attack method. Full article

Unlike traditional control systems that rely on physical input devices, facial gesture-based interaction offers a contactless and intuitive method for operating autonomous systems. Recent advances in computer vision and deep learning have enabled the use of facial expressions and movements for command recognition in human–robot interaction. In this work, we propose a lightweight, real-time facial gesture recognition method, GhostNet-BiLSTM-Attention (GBA), which integrates GhostNet and BiLSTM with an attention mechanism, is trained on the FaceGest dataset, and is integrated with a 3D robot simulation in Unity. The system is designed to recognize predefined facial gestures such as head tilts, eye blinks, and mouth movements with high accuracy and low inference latency. Recognized gestures are mapped to specific robot commands and transmitted to a Unity-based simulation environment via socket communication across machines. This framework enables smooth and immersive robot control without the need for conventional controllers or sensors. Real-time evaluation demonstrates the system’s robustness and responsiveness under varied user and lighting conditions, achieving a classification accuracy of 99.13% on the FaceGest dataset. The GBA holds strong potential for applications in assistive robotics, contactless teleoperation, and immersive human–robot interfaces. Full article

Aim: This review aims to validate the Experience-Based Onboarding Model (EBOM) and refine it into an improved adaptive onboarding model, OnMod. Methods: In this review, autoethnography is combined with a Multivocal Literature Review (MLR) that combines white and gray literature sources. Evidence is mapped to entities and semantic relations and assessed using predefined decision rules. Main findings: The validation of the model confirms the core EBOM entities and semantic relations. It also introduces several new or renamed entities or semantic relations that close the feedback loop and yield the refined OnMod model. Implications: The theoretical contribution is reflected in the application of autoethnography in combination with the MLR, where it represents a good basis for the development of an onboarding model. In industrial practice, the presented OnMod model can be used by mentors and managers as a guide for improving operational and daily activities, as well as for the development of onboarding strategies in IT and software companies. Full article