Search Results (23,942)

Addressing the current situation where in situ observations in the Arctic primarily target physical and a few biogeochemical parameters, leaving a gap in systematic direct observation of biological populations beneath sea ice, this study developed a polar ice-based ecological observation buoy system. Building upon conventional meteorological and oceanographic hydrographic sensors, this system innovatively integrates an underwater imaging module and key technologies such as machine learning-based automatic fish target recognition and reliable dual-channel satellite data transmission in polar environments. Its successful deployment during the 2025 15th Chinese National Arctic Research Expedition verified the system’s stability. During the initial one-month operation period (designed for a monitoring cycle of not less than one year), the data return rates for conventional and image data reached 100% and 96.8%, respectively, achieving quasi-real-time continuous observation of physical and ecological parameters at the air–sea interface in the Arctic Ocean, and it is capable of acquiring not only physical parameters but also visual observations of under-ice fauna. The system successfully acquired and transmitted images containing suspected biological targets and reference objects, providing the first in situ, image-based biological observation dataset for the central Arctic Ocean. This work establishes a new methodological capability for direct ecological monitoring, offering essential equipment support for quantifying biological presence, studying population dynamics, and informing evidence-based polar ecosystem governance. Full article

Accurate multi-leaf collimator (MLC) motion prediction is a prerequisite for precise dose delivery in advanced techniques such as IMRT and VMAT. Traditional patient-specific quality assurance (QA) methods remain resource-intensive and prone to physical measurement uncertainties. This study aimed to develop machine learning (ML) models to predict delivered MLC positions using kinematic parameters extracted from DICOM-RT plans for the Elekta Versa HD system. A dataset comprising 200 patient plans was constructed by pairing planned MLC positions, velocities, and accelerations with corresponding delivered values parsed from unstructured trajectory logs. Four regression models, including linear regression (LR), were trained to evaluate the deterministic nature of the Elekta servo-mechanism. LR demonstrated superior prediction accuracy, achieving the lowest mean absolute error (MAE) of 0.145 mm, empirically confirming the fundamentally linear relationship between planned and delivered trajectories. Subsequent dosimetric validation using ArcCHECK measurements on 17 clinical plans revealed that LR-corrected plans achieved statistically significant improvements in gamma passing rates, with a mean increase of 2.24% under the stringent 1%/1 mm criterion (p < 0.001). These results indicate that the LR model successfully captures systematic mechanical signatures, such as inertial effects. This study demonstrates that a computationally efficient LR model can accurately predict Elekta MLC performance, providing a robust foundation for implementing ML-based virtual QA. This approach is particularly valuable for time-sensitive workflows like adaptive radiotherapy (ART), as it significantly reduces reliance on physical QA resources. Full article

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles. Full article

Background: Advancements in low-cost additive manufacturing and artificial intelligence have enabled new avenues for developing accessible myoelectric prostheses. However, achieving reliable real-time control and ensuring mechanical durability remain significant challenges, particularly for affordable systems designed for resource-constrained settings. Objective: This study aimed to design and validate a low-cost, 3D-printed prosthetic arm that integrates single-channel electromyography (EMG) sensing with machine learning for real-time gesture classification. The device incorporates an anatomically inspired structure with 14 passive mechanical degrees of freedom (DOF) and 5 actively actuated tendon-driven DOF. The objective was to evaluate the system’s ability to recognize open, close, and power-grip gestures and to assess its functional grasping performance. Method: A Fast Fourier Transform (FFT)-based feature extraction pipeline was implemented on single-channel EMG data collected from able-bodied participants. A Support Vector Machine (SVM) classifier was trained on 5000 EMG samples to distinguish three gesture classes and benchmarked against alternative models. Mechanical performance was assessed through power-grip evaluation, while material feasibility was examined using PLA-based 3D-printed components. No amputee trials or long-term durability tests were conducted in this phase. Results: The SVM classifier achieved 92.7% accuracy, outperforming K-Nearest Neighbors and Artificial Neural Networks. The prosthetic hand demonstrated a 96.4% power-grip success rate, confirming stable grasping performance despite its simplified tendon-driven actuation. Limitations include the reliance on single-channel EMG, testing restricted to able-bodied subjects, and the absence of dynamic loading or long-term mechanical reliability assessments, which collectively limit clinical generalizability. Overall, the findings confirm the technical feasibility of integrating low-cost EMG sensing, machine learning, and 3D printing for real-time prosthetic control while emphasizing the need for expanded biomechanical testing and amputee-specific validation prior to clinical application. Full article

In response to the challenges posed by the complex evolution of risks and the static nature of traditional assessment methods during the construction of long-span prestressed concrete (PC) continuous rigid-frame bridges, this study proposes a risk assessment framework that integrates visual perception with dynamic probabilistic reasoning. By combining an improved YOLOv8 model with the Decision-making Trial and Evaluation Laboratory–InterpretiveStructure Modeling (DEMATEL–ISM) algorithm, the framework achieves intelligent identification of risk elements and causal structure modelling. On this basis, a dynamic Bayesian network (DBN) is constructed, incorporating a sliding window and forgetting factor mechanism to enable adaptive updating of conditional probability tables. Using the Tongshun River Bridge as a case study, at the identification layer, we refine onsite targets into 14 risk elements (F1–F14). For visualization, these are aggregated into four categories—“Bridge, Person, Machine, Environment”—to enhance readability. In the methodology layer, leveraging causal a priori information provided by DEMATEL–ISM, risk elements are mapped to scenario probabilities, enabling scenario-level risk assessment and grading. This establishes a traceable closed-loop process from “elements” to “scenarios.” The results demonstrate that the proposed approach effectively identifies key risk chains within the “human–machine–environment–bridge” system, revealing phase-specific peaks in human-related risks and cumulative increases in structural and environmental risks. The particle filter and Monte Carlo prediction outputs generate short-term risk evolution curves with confidence intervals, facilitating the quantitative classification of risk levels. Overall, this vision-guided dynamic risk assessment method significantly enhances the real-time responsiveness, interpretability, and foresight of bridge construction safety management and provides a promising pathway for proactive risk control in complex engineering environments. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

The development of autonomous train systems relies heavily on machine learning (ML) models, which in turn depend on large, high-quality annotated datasets for training and evaluation. The railway domain lacks adequate public datasets and efficient annotation tools. To address this gap, we present Labels4Rails, a tool designed specifically for the annotation of railway scenes. It captures track topology, switch states including switch directions, and informational tags regarding the images’ content and leverages consistent camera perspectives and the fixed track geometries inherent to railways for annotation efficiency. We used Labels4Rails to create the L4R_NLB reference dataset from Norwegian railway footage. The dataset contains 10,253 annotated images across four seasons, including 1415 switch annotations. Both the tool and dataset are publicly available. Full article

Although intensified flotation cells have been introduced as fast-kinetic and plug-flow-type flotation machines, there is limited empirical verification and information about their fluid flow patterns and dispersion regimes. The present communication paper investigates this for an Imhoflot^TM G-06 cell operated in an open-circuit mode using an impulse method to measure and model the residence time of a liquid–gas system. For experimental measurements, a concentrated KCl solution was employed, and water conductivity was monitored for 20 min. By fitting several relevant models, such as large and small tanks in series (LSTS), Weller, N-Mixer, and perfect mixer, to the experimental data, it was revealed that the N-Mixer represented the dispersion pattern the best (N = 1.3–1.6). Further, the obtained practical mean retention time (MRT) of 4.11 ± 0.16 min was somewhat aligned with the theoretical value, i.e., 5.0 min per pass, indicating a back-calculated gas hold-up magnitude of 18%–22% in the separator. These results provide an in-depth perception of scale-up procedures and requirements for cell modification. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide, representing a major public health challenge. Despite advances in screening strategies, surgical techniques, and systemic therapies, patient prognosis is often compromised by late diagnosis, tumor heterogeneity, and therapeutic resistance. In recent years, the integration of advanced imaging analytics and artificial intelligence (AI) has opened new avenues for precision oncology. Radiomics, defined as the high-throughput extraction of quantitative features from medical images, has emerged as a promising tool to capture intratumoral heterogeneity and predict clinical outcomes in a non-invasive manner. When combined with AI, particularly machine learning and deep learning approaches, radiomics enables the development of predictive and prognostic models that may support treatment personalization. This narrative review provides a comprehensive overview of CRC epidemiology and risk factors, summarizes current diagnostic and clinical management strategies, and focuses extensively on radiomics and AI applications in CRC, including workflow standardization, feature extraction, clinical applications, and challenges for implementation in daily practice. Full article

The Internet of Things (IoT) has established an exceptional ecosystem of interconnected devices where a vast multitude of heterogeneous devices can communicate, collect, and share data for enhanced decision-making processes. To effectively analyze this immense volume of data, researchers have deployed advanced machine learning algorithms and deep neural networks. However, these approaches typically rely on centralized data storage for training, which raises significant privacy concerns. Federated Learning (FL) addresses this issue by allowing devices to train local models on their own data and share only model updates. Despite this advantage, FL remains vulnerable to several security threats, including model poisoning, data manipulation, and Byzantine attacks. Therefore, robust and scalable authentication mechanisms are essential to ensure secure participation in FL environments. This study provides a comprehensive survey of authentication in FL. We examine the authentication process, discuss the associated key challenges, and analyze architectural considerations relevant to securing FL deployments. Existing authentication schemes are reviewed and evaluated in terms of their effectiveness, limitations, and practicality. To provide deeper insight, we classify these schemes along two dimensions as follows: their underlying enabling technologies, such as blockchain, cryptography, and AI-based methods, and the system contexts in which FL operates. Furthermore, we analyze the datasets and experimental environments used in current research, identify open research challenges, and highlight future research directions. To the best of our knowledge, this study presents the first structured and comprehensive analysis of authentication mechanisms in FL, offering a foundational reference for advancing secure and trustworthy federated learning systems. Full article

To enhance the adaptability and human-machine coordination of intelligent prosthetic knees, this study proposes a motion intention mapping direct myoelectric control method based on an LSTM network and a human-machine coupling model. Multichannel surface electromyography (sEMG) and knee joint angle data were collected during level-ground walking. Time-domain features were extracted to construct an LSTM prediction model, enabling temporal mapping between muscle activity and joint kinematics. Experimental results show that the LSTM model outperforms traditional neural networks in terms of prediction accuracy and temporal consistency. Furthermore, by integrating the human-machine coupling dynamics model with a hydraulic actuation system, a direct myoelectric control framework for a variable-damping prosthetic knee was established, achieving continuous damping adjustment and smooth gait transition. The results verify the feasibility and effectiveness of the proposed method in human-machine coordinated control. Full article

To meet the requirements of high resolution, compact size, and ultra-lightweight for micro–nano satellite optoelectronic payloads while ensuring high structural stability during launch and in-orbit operation, mirrors were designed with high surface accuracy. The opto-thermo-mechanical system of the space camera was designed and analyzed accordingly. First, an optical system was designed to achieve high resolution and a compact form factor. A coaxial triple-reflector configuration with multiple refractive paths was adopted, which significantly shortened the optical path and laid the foundation for a lightweight, compact structure. This design also defined the accuracy and tolerance requirements for the primary and secondary mirrors. Subsequently, mathematical models for topology optimization and dimensional optimization were established to optimize the design of the main support structure, primary mirror, and secondary mirror. Two design schemes for the main support structure and primary mirror were compared. Steady-state thermal analysis and thermal control design were carried out for both mirrors. Simulations were then performed on the main system (including the primary/secondary mirror assemblies and the main support structure). Under the combined effects of gravity, a 4 °C temperature increase, and an assembly flatness deviation of 0.01 mm, the surface accuracy of both mirrors, the displacement of the secondary mirror relative to the primary mirror reference, and the tilt angle all met the overall specification requirements. The system’s first-order natural frequency was 156.731 Hz. After precision machining, fabrication, and assembly, wavefront aberration testing was conducted on the main system with the optical axis horizontal. Under gravity, the root mean square (RMS) wavefront error at the center of the field of view was 0.073λ, satisfying the specification of ≤1/14λ. The fundamental frequency measured during vibration testing was 153.09 Hz, which aligned closely with the simulated value and well exceeded the requirement of 100 Hz. Additionally, in-orbit imaging verification was conducted. All results satisfied the technical specifications of the satellite’s overall requirements. Full article

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

In recent years, financial statement fraud schemes have evolved to become markedly more sophisticated and concealed, thereby posing severe threats to both social stability and economic health. Traditional detection methods, which rely primarily on fragmented corporate data, exhibit significant limitations in capturing the dynamic evolution and spatial diffusion characteristics of fraudulent behaviors over time and space. To address this issue, in this study, we undertake a thorough analysis of the intrinsic nature of fraud risk from a sociotechnical systems perspective and construct a multi-level indicator system to comprehensively quantify risk elements. Furthermore, recognizing the dynamic evolution nature and propagating characteristics of fraud risk, we propose a novel financial statement fraud detection framework to capture behavior patterns in temporal and spatial dimensions. Experiments on A-share-listed companies of high-risk industries in China demonstrate that the proposed framework significantly outperforms other mainstream machine learning and deep learning techniques. In addition, we open the “black box” of the detection framework and empirically validate fraud risk patterns with respect to social–technical elements by leveraging explainable AI techniques. Practically, the proposed framework and interpretable analysis are capable of providing precise early warnings and supervision. Full article

High-valent metal species (iron, manganese, cobalt, copper, and ruthenium) based advanced oxidation processes (AOPs) have emerged as sustainable technologies for water remediation. These processes offer high selectivity, electron transfer efficiency, and compatibility with circular chemistry principles compared to conventional systems. This comprehensive review discusses recent advances in the synthesis, stabilization, and catalytic applications of high-valent metals in aqueous environments. This study highlights their dual functionality, not only as conventional oxidants but also as mechanistic mediators within redox cycles that underpin next-generation AOPs. In this review, the formation mechanisms of these species in various oxidant systems are critically evaluated, highlighting the significance of ligand design, supramolecular confinement, and single-atom engineering in enhancing their stability. The integration of high-valent metal-based AOPs into photocatalysis, sonocatalysis, and electrochemical regeneration is explored through a newly proposed classification framework, highlighting their potential in the development of energy efficient hybrid systems. In addition, this work addresses the critical yet underexplored area of environmental fate, elucidating the post-oxidation transformation pathways of high-valent species, with particular attention to their implications for metal recovery and nutrient valorization. This review highlights the potential of high-valent metal-based AOPs as a promising approach for zero wastewater treatment within circular economies. Future frontiers, including bioinspired catalyst design, machine learning-guided optimization, and closed loop reactor engineering, will bridge the gap between laboratory research and real-world applications. Full article