Search Results (283)

Introducing deep learning methods into the quality inspection of production lines can reduce labor and improve efficiency, with great potential for the development of manufacturing systems. However, in specific closed production-line environments, robust and high-quality 3D fixed-area quality inspection is a common and challenging problem due to improper assembly, high data resolution, pose perturbation, and other reasons. In this article, we propose a robust 3D fixed-area quality inspection framework for production lines consisting of two steps: recursive segmentation and one-class classification. First, a Focal Segmentation Module (FSM) is proposed to gradually focus on the areas to be inspected by recursively segmenting the downsampled low-resolution point cloud, thereby ensuring efficient high-resolution segmentation. Moreover, Local Reference Frame (LRF)-based rotation-invariant local feature extraction is introduced to improve the robustness of the proposed method to pose variations. Second, a uniquely designed Semi-Nested Point Cloud Autoencoder (SN-PAE) is proposed to improve data imbalance and hard-to-classify samples. Particularly, we first introduce rotation-invariant feature extraction to a point cloud autoencoder to learn descriptive latent variables, then measure the latent variables using a semi-nested Latent Autoencoding Module (LAM). This avoids unreliable chamfer distance measurement and makes SN-PAE a more robust measurement method. In addition, we implement a set of experiments using solder joints as an example. Compared with PointNet++, the memory usage of recursive segmentation is reduced by 92%, and the time cost is reduced by 97.5%. The recall of SN-PAE on unaligned samples exceeds that of competitors by nearly 30% in the classification stage. The results demonstrate the feasibility and effectiveness of the proposed framework. Full article

The malicious contamination of food has been recognized by the World Health Organization (WHO) as a real and current threat that must be integrated into food safety systems to ensure preparedness for deliberate attacks. Traditional approaches, such as HACCP, effectively address unintentional hazards but remain insufficient against intentional contamination and sabotage. Food defense frameworks such as HACCP (Hazard Analysis and Critical Control Points), VACCP (Vulnerability Assessment and Critical Control Points), and TACCP (Threat Assessment and Critical Control Points) represent complementary methodologies, addressing unintentional, economically motivated, and deliberate threats, respectively. This review critically examines food defense frameworks across the European Union, the United States, and the United Kingdom, as well as standards benchmarked by the Global Food Safety Initiative (GFSI), drawing on peer-reviewed and grey literature sources. In the United States, the Food Safety Modernization Act (FSMA) mandates the development and periodic reassessment of food defense plans, while the European Union primarily relies on general food law and voluntary certification schemes. The United Kingdom’s PAS 96:2017 standard provides TACCP-based guidance that also acknowledges cybercrime as a deliberate threat. Building on these regulatory and operational gaps, this paper proposes the Cyber-FSMS model, an integrated framework that combines traditional food defense pillars with cyber risk management to address cyber–physical vulnerabilities in increasingly digitalized supply chains. The model introduces six interconnected components (governance, vulnerability assessment, mitigation, monitoring, verification, and recovery) designed to embed cyber-resilience into Food Safety Management Systems (FSMS). Priority actions include regulatory harmonization, practical support for small and medium-sized enterprises (SMEs), and the alignment of cyber-resilience principles with upcoming GFSI benchmarking developments, thereby strengthening the integrity, robustness, and adaptability of global food supply chains. Full article

Floods are among the most destructive natural disasters, and accurate flood susceptibility mapping (FSM) is crucial for disaster prevention and mitigation amid climate change. The Poyang Lake basin, characterized by complex flood formation mechanisms and high spatial heterogeneity, poses challenges for the application of FSM models. Currently, the use of machine learning models in this field faces several bottlenecks, including unclear model applicability, limited sample quality, and insufficient machine interpretation. To address these issues, we take the 2020 Poyang Lake flood as a case study and establish a high-precision flood inundation sample database. After feature screening, the performance of three hybrid models optimized by Particle Swarm Optimization (PSO)—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) is compared. Furthermore, the Shapley Additive exPlanations (SHAP) framework is employed to interpret the contributions and interaction effects of the driving factors. The results demonstrate that the ensemble learning models exhibit superior performance, indicating their greater applicability for flood susceptibility mapping in complex basins such as Poyang Lake. The RF model has the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) value of 0.9536. Elevation is the most important global driving factor, while SHAP local interpretation reveals that the driving mechanism has significant spatial heterogeneity, and the susceptibility of local depressions is mainly controlled by the terrain moisture index. A nonlinear phenomenon is observed where the SHAP value was negative under extremely high late rainfall, which is preliminarily attributed to the “spatial transfer that is prone to occurrence” mechanism triggered by the backwater effect, highlighting the complex nonlinear interactions among factors. The proposed “high-precision sampling, model comparison, SHAP explanation” framework effectively improves the accuracy and interpretability of FSM. These research findings can provide a scientific basis for smart flood control and precise flood risk management in basins. Full article

This paper presents the design and evaluation of a lightweight, minimally actuated lower limb exoskeleton that emphasizes hip–knee coordination for natural and efficient gait assistance. The system adopts a hip-only motorized actuation strategy in combination with an electromagnetically controlled knee locking mechanism, ensuring rigid stability during stance while providing compliant assistance during swing. To support sit-to-stand transitions, a gas spring–ratchet mechanism is integrated, which remains disengaged in the seated position, delivers assistive torque during rising, and provides cushioning during the descent to enhance safety and comfort. The control framework fuses foot pressure and thigh-mounted IMU signals for finite state machine (FSM)-based gait phase detection and employs a fuzzy PID controller to achieve adaptive hip torque regulation with coordinated hip–knee control. Preliminary human-subject experiments demonstrate that the proposed design enhances lower-limb coordination, reduces muscle activation, and improves gait smoothness. By integrating a minimal-actuation architecture, a practical sit-to-stand assist module, and an intelligent control strategy, this exoskeleton strikes an effective balance between mechanical simplicity, functional support, and gait naturalness, offering a promising solution for everyday mobility assistance in elderly or mobility-impaired users. Full article

As onshore space resources become exhausted, the migration of wind turbines to offshore areas is an inevitable trend. The blades of offshore wind turbines are typically over 100 m long, and this increased nonlinearity in the blades escalates the risk of flutter. Addressing the flutter phenomenon in these ultra-long flexible blades, this research establishes a full-scale model (FSM) considering geometric and material nonlinearities to accurately characterize the nonlinear dynamic response. Compared to the equivalent beam model, the proposed FSM better lays a foundation for flutter suppression research. On this basis, a bidirectional TMD was innovatively applied to the wind turbine blade and compared against a unidirectional TMD. The results demonstrate that bidirectional TMD can enhance the flutter control rate of 15 MW blades to over 90%, significantly improving flutter characteristics. Compared to the original blade, the steady-state amplitude is reduced by up to 45.73%, markedly suppressing flutter levels. These findings provide theoretical and data support for subsequent studies on aeroelastic instability and flutter suppression in ultra-long flexible blades, offering significant engineering application value and potential for broader implementation. Full article

Background/Objectives: Blood flows through the body and reaches all tissues, contributing to homeostasis and physiological functions. Providing information and understanding on how the transcriptome of whole blood behaves in response to physiological or pathological stimuli is critical. Methods: We collected blood from four healthy individuals and performed long-read RNA sequencing (lrRNA-seq) for the precise identification and expression quantification of RNA variants. Moreover, we compared two genome references: the Genome Reference Consortium Human Build 38 (GRCh38) and the Telomere-to-Telomere (T2T) assembly of the CHM13 cell line (T2T-CHM13). Results: With GRCh38, we could identify an average of about 46,000 genes, 1.3-fold more genes than T2T-CHM13. Similarly, we identified about 185,000 isoforms with GRCh38 and 140,000 with T2T-CHM13, finding similar differences for full splice match (FSM) and incomplete splice match (ISM) transcript isoforms. There were about 90,000 novel isoforms for GRCh38 and 70,000 for T2T-CHM13, 47% and 50% of the total number of identified isoforms, respectively. Differences in isoform numbers between GRCh38 and T2T-CHM13 were identified for the subcategories “Genic Genomic”, “Intergenic”, and “Genic Intron”. Using GRCh38, we generally identified a higher number of non-coding isoforms, as well as a higher number of isoforms aligning within intron and intergenic regions. Nonetheless, GRCh38 might incur false positive results, and T2T-CHM13 is likely more accurate for genome sequences in the repetitive regions. Conclusions: LrRNA-seq is a valid method for the identification of novel isoforms in blood, and this study is a first step toward the creation of a comprehensive database of the structure and expression of transcript isoforms for optimized predictive medicine. Full article

With the aim of addressing the problem of spot drift caused by laser transmission in atmospheric turbulence, the effects of different weather conditions such as sunny, cloudy, rainy and sandstorm conditions on spot drift were measured at 0.42 km, 2 km and 10 km transmission distances, and the correction performance of a fast-steering mirror (FSM) was evaluated. The results show that under weak-turbulence conditions such as sunny, cloudy and short-distance conditions, the mean and variance of spot drift are relatively small, and the disturbance is dominated by low frequency. The FSM achieves more effective correction, significantly reduces the drift amplitude and improves the system stability. Under strong-turbulence conditions such as rainy days, dust storms and long distances, the mean and variance of drift increase significantly, and the spot disturbance frequency is higher. The response ability of the FSM to high-frequency disturbance is limited, and the correction effect decreases. In general, the FSM is more suitable for low-intensity disturbance scenarios, and its correction performance has certain limitations under strong-disturbance and long-distance conditions. Full article

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application has not yet reached full maturity. We focus on applying machine learning models to create flood susceptibility maps (FSMs) for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. This study utilizes 13 explanatory variables derived from topographical, hydrological, hydraulic, environmental and infrastructure data to train the models, using Storm Daniel—one of the most severe recent events in the region—as the primary reference for model training. The most significant of these variables were obtained from satellite data of the affected areas. Four machine learning algorithms were employed in the analysis, i.e., Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Accuracy evaluation revealed that tree-based models (RF, XGBoost) outperformed other classifiers. Specifically, the RF model achieved Area Under the Curve (AUC) values of 96.9%, followed by XGBoost, SVM and LR, with 96.8%, 94.0% and 90.7%, respectively. A flood susceptibility map corresponding to a 1000-year return period rainfall scenario at 24 h scale was developed, aiming to support long-term flood risk assessment and planning. The analysis revealed that approximately 20% of the basin is highly prone to flooding. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision making for disaster preparedness in the region. Full article

This research introduces a novel de-anonymization technique targeting the Tor network, addressing limitations in prior attack models, particularly concerning router positioning following the introduction of bridge relays. Our method exploits two specific, inherent protocol-level vulnerabilities: the absence of a continuity check for circuit-level cells and anomalous residual values in RELAY_EARLY cell counters, working by manipulating cell headers to embed a covert signal. This signal is composed of reserved fields, start and end delimiters, and a payload that encodes target identifiers. Using this signal, malicious routers can effectively mark data flows for later identification. These routers employ a finite state machine (FSM) to adaptively switch between signal injection and detection. Experimental evaluations, conducted within a controlled environment using attacker-controlled onion routers, demonstrated that the embedded signals are undetectable by standard Tor routers, cause no noticeable performance degradation, and allow reliable correlation of Tor users with public services and deanonymization of hidden service IP addresses. This work reveals a fundamental design trade-off in Tor: the decision to conceal circuit length inadvertently exposes cell transmission characteristics. This creates a bidirectional vector for stealthy, protocol-level de-anonymization attacks, even though Tor payloads remain encrypted. Full article

Background: Kigali, Rwanda’s rapidly growing capital, faces major challenges in household-level Fecal Sludge Management (FSM), with over 89% of households using pit latrines and only 48% accessing unshared sanitation. FSM services are limited, costly, and poorly executed, leading to frequent illegal dumping. Objective: This review analyzes the literature on sanitation in Kigali to identify key gaps, synergies, and recommendations for improvement. Methods: Following PRISMA 2020 guidelines, 73 relevant publications were selected from various scientific and governmental sources. Publications were included only if they were published from 2013 to 2024 and had information on sanitation in Kigali. NOS and JBI tools were utilized to assess the quality of included publications. Results: Data were categorized into four themes, (1) access to sanitation, (2) FSM services, (3) public health, and (4) sanitation governance, and analyzed using thematic, narrative, and descriptive methods. Findings reveal a dysfunctional FSM service chain, weak policy enforcement due to overlapping responsibilities, underfunding, and limited private sector participation. These issues contribute to poor sanitation, inadequate hygiene, and prevalence of diarrheal diseases and Tropical Neglected Diseases, especially among young children. Conclusions: The review recommends strengthening governance and clarifying roles, enforcing adaptable regulations, promoting public–private partnerships, and managing the full FSM service chain more effectively. Future research should focus on developing context-specific technologies and financing strategies to support sustainable FSM solutions in Kigali. Full article

Smart contracts, as a critical application of blockchain technology, significantly enhance its programmability and scalability, offering broad application prospects. However, frequent security incidents have resulted in substantial economic losses and diminished user trust, making security issues a key challenge for further development. Since smart contracts cannot be modified after deployment, flaws in their design or implementation may lead to severe consequences. Therefore, rigorous pre-deployment verification of their correctness is particularly crucial. This paper explores the symmetry in control flows and state transitions of Solidity smart contracts and leverages this inherent structural symmetry to develop a normalized state transition model based on a finite state machine. The FSM model is subsequently formalized into a Promela model with the Spin model checker. By integrating manually defined Linear Temporal Logic formulas with those generated by Smart Pulse, the Promela model is formally verified in Spin to ensure the correctness and security of smart contracts. This approach establishes a systematic verification framework, providing effective support to enhance the reliability and security of smart contracts. Full article

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

Although artificial intelligence technologies have significantly enhanced autonomous robots’ capabilities in perception, decision-making, and planning, their autonomy may still fail when faced with complex, dynamic, or unpredictable environments. Therefore, it is critical to enable users to take over robot control in real-time and efficiently through teleoperation. The lightweight, wearable myoelectric armband, due to its portability and environmental robustness, provides a natural human–robot gesture interaction interface. However, current myoelectric teleoperation gesture control faces two major challenges: (1) poor intuitiveness due to visual-motor misalignment; and (2) low efficiency from discrete, single-degree-of-freedom control modes. To address these challenges, this study proposes an integrated myoelectric teleoperation interface. The interface integrates the following: (1) a novel hybrid reference frame aimed at effectively mitigating visual-motor misalignment; and (2) a finite state machine (FSM)-based control logic designed to enhance control efficiency and smoothness. Four experimental tasks were designed using different end-effectors (gripper/dexterous hand) and camera viewpoints (front/side view). Compared to benchmark methods, the proposed interface demonstrates significant advantages in task completion time, movement path efficiency, and subjective workload. This work demonstrates the potential of the proposed interface to significantly advance the practical application of wearable myoelectric sensors in human–robot interaction. Full article

Using graphs as a data structure is a simple way to represent relationships between objects. Consequently, it has raised the need for algorithms to process, analyze, and extract meaningful information from graphs. Therefore, frequent subgraph mining (FSM) algorithms have been reported in the literature to discover interesting, unexpected, and useful patterns in graph databases. Frequent subgraph mining involves discovering subgraphs that appear no less than a user-specified threshold; this can be performed exactly or approximately. Although several algorithms for mining frequent approximate subgraphs exist, mining this type of subgraph in graph collections has scarcely been addressed. Thus, we propose AGCM-SLV, an algorithm for mining frequent approximate subgraphs within a graph collection that allows structural and label variations. Unlike other FSM approaches, our proposed algorithm tracks subgraph occurrences and their structural dissimilarities, allowing user-defined partial similarities between node and edge labels, and captures frequent approximate subgraphs (patterns) that would otherwise be overlooked. Experiments on real-world datasets demonstrate that our algorithm identifies more patterns than the most similar state-of-the-art algorithm with a shorter runtime. We also present experiments in which we add white noise to the graph collection at different levels, revealing that over 99% of the patterns extracted without noise are preserved under noisy conditions, making the proposed algorithm noise-tolerant. Full article

Flood susceptibility modeling (FSM) plays a key role in advancing proactive disaster risk reduction and spatial planning. This research developed FSM for the Buzău River catchment in Romania—a region historically vulnerable to recurrent flood events—using four state-of-the-art ensemble boosting algorithms: AdaBoost, CatBoost, LightGBM, and XGBoost. Initially, a comprehensive set of 13 flood conditioning factors was assessed, which was subsequently narrowed down to 9 essential factors through multi-tier feature selection strategies. Analysis of performance via receiver operating characteristic (ROC) andprecision–recall curves showed only marginal differences between the models; however, CatBoost excelled with an area under the ROC curve (AUC) of 0.972 and an average precision (AP) of 0.971, with XGBoost following closely behind. The SHAP (SHapley Additive exPlanations) analysis of the CatBoost model indicated that the Slope, Distance from Rivers, Topographic Wetness Index (TWI), and Land Use/Land Cover (LULC) are the key contributing factors. The novelty of this research is found in its comparative analysis of AdaBoost alongside three gradient boosting algorithms—CatBoost, LightGBM, and XGBoost—while utilizing explainable artificial intelligence (XAI) and a multi-tier feature selection strategy to create FSM that are precise and comprehensible. These strategies deliver robust tools for managing flood risks and reinforce the viability of data-driven modeling in the various catchments of Europe. Full article