Search Results (3,088)

This paper presents a novel approach using machine learning methods for the automated segmentation of acceleration signals measured during ball burnishing (BB) operations performed on a computer numerical controlled (CNC) milling machine. The study addresses the challenge of accurately finding actual feedrates in that finishing operation, which often deviate from programmed values due to various dynamic reasons. The method involves a two-stage process: first, an automatic signal segmentation algorithm employing Gaussian Mixture Modeling (GMM) and K-means clustering is applied to the ball burnishing (BB) process and acceleration data. Second, a Taguchi L9 experimental design is used to assess the influence of some regime parameters on the actual feedrate and the BB’s cycle duration. Results show successful segmentation of the toolpaths based on X-axis accelerations and deforming force data, with the Calinski–Harabasz Index confirming good cluster separability. Programmed feedrate and the number of toolpath points were identified as the most significant factors affecting the percentage delay between programmed and obtained feedrates. The main contribution is the development and testing of a new method for segmenting different toolpath states in ball burnishing operations, based on measured accelerations and momentary deforming force magnitudes. The present work offers valuable insights into autonomous monitoring and control in BB operations. Full article

Background/Objectives: Diabetic neuropathy (DN) is one of the most common complications of type 2 diabetes mellitus (T2DM), involving complex metabolic, vascular, and epigenetic mechanisms. MicroRNA-210-3p (miR-210-3p), a hypoxia-responsive molecule, has been implicated in various diabetic complications, but its role in DN is not well defined. This study aimed to investigate the relationship between miR-210-3p expression, measured as delta Ct (ΔCt), and the presence and type of diabetic neuropathy, as well as correlations with corneal nerve parameters assessed by corneal confocal microscopy (CCM). Methods: Eighty patients with T2DM were stratified into four groups: no neuropathy, autonomic neuropathy, peripheral neuropathy, and combined neuropathy. Expression of miR-210-3p was quantified using RT-qPCR, and CCM was used to measure corneal nerve fiber density (CNFD), length (CNFL), and branch density (CNBD). Results: ΔCt values were significantly lower in patients with combined neuropathy compared to those without neuropathy, indicating higher miR-210-3p expression. Intermediate values were observed in autonomic and peripheral neuropathy groups. CCM parameters were significantly reduced in patients with DN. ΔCt was inversely correlated with neuropathy severity but positively associated with diabetes duration. Conclusions: These findings suggest that miR-210-3p may serve as a biomarker of nerve damage and cellular stress in diabetes, and that combining gene expression profiling with CCM could improve DN diagnosis and monitoring. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

Music is considered a non-pharmacological adjunct in human anaesthesia, contributing to anaesthetic- and analgesic-sparing effects, modulating autonomic responses, and enhancing recovery. However, its effects in veterinary surgical settings remain largely unexplored. This study aimed to explore the potential influence of intraoperative music on anaesthetic and analgesic requirements, autonomic parameters, intraoperative adverse effects, and recovery quality in dogs undergoing elective ovariohysterectomy under general anaesthesia. In this prospective, randomized exploratory study, client-owned female dogs (n = 28) were randomly assigned to either a music group (exposed to instrumental classical music intraoperatively) or a control group (no music). All dogs received a standardized anaesthetic protocol. Mean end-tidal isoflurane concentrations, intraoperative analgesic requirements, heart rate, respiratory rate, blood pressure, adverse effects, and recovery quality were recorded and compared between groups using unpaired t-test, Mann–Whitney U test, or Fisher’s exact test, as appropriate (p < 0.05). No statistically significant differences were observed. Therefore, intraoperative music did not produce measurable effects on the assessed parameters. While no apparent benefit was observed in this study, future studies with larger sample sizes should investigate music-based interventions in more challenging or variable clinical scenarios. Additionally, further research is needed to clarify the extent to which anaesthetics suppress auditory processing. This exploratory investigation contributes to the limited body of evidence on auditory stimulation in veterinary anaesthesia. Full article

The operational efficiency and precision of boom sprayers, as critical equipment for protecting field crops, are vital to global food security and agricultural sustainability. In precision agriculture systems, achieving uniform pesticide application fundamentally depends on maintaining stable boom posture during operation. However, severe boom vibration not only directly causes issues like missed spraying, double spraying, and pesticide drift but also represents a critical bottleneck constraining its functional realization in cutting-edge applications. Despite its importance, achieving absolute boom stability is a complex task. Its suspension system design faces a fundamental technical contradiction: effectively isolating high-frequency vehicle vibrations caused by ground surfaces while precisely following large-scale, low-frequency slope variations in the field. This paper systematically traces the evolutionary path of self-balancing boom technology in addressing this core contradiction. First, the paper conducts a dynamic analysis of the root causes of boom instability and the mechanism of its detrimental physical effects on spray quality. This serves as a foundation for the subsequent discussion on technical approaches for boom support and balancing systems. The paper also delves into the evolution of sensing technology, from “single-point height measurement” to “point cloud morphology perception,” and provides a detailed analysis of control strategies from classical PID to modern robust control and artificial intelligence methods. Furthermore, this paper explores the deep integration of this technology with precision agriculture applications, such as variable rate application and autonomous navigation. In conclusion, the paper summarizes the main challenges facing current technology and outlines future development trends, aiming to provide a comprehensive reference for research and development in this field. Full article

Autonomous Unmanned Aerial Vehicles (UAVs) are widely used in smart agriculture, logistics, and warehouse management, where precise trajectory prediction is important for safety and efficiency. Traditional approaches require complex physical modeling including mass properties, moment of inertia measurements, and aerodynamic coefficient calculations, which creates significant barriers for custom-built UAVs. Existing trajectory prediction methods are primarily designed for motion forecasting from dense historical observations, which are unsuitable for scenarios lacking historical data (e.g., takeoff phases) or requiring trajectory generation from sparse waypoint specifications (4–6 constraint points). This distinction necessitates architectural designs optimized for spatial interpolation rather than temporal extrapolation. To address these limitations, we present a segmented LSTM framework for complete autonomous flight trajectory prediction. Given target waypoints, our architecture decomposes flight operations, predicts different maneuver types, and outputs the complete trajectory, demonstrating new possibilities for mission-level trajectory planning in autonomous UAV systems. The system consists of a global duration predictor (0.124 MB) and five segment-specific trajectory generators (∼1.17 MB each), with a total size of 5.98 MB and can be deployed in various edge devices. Validated on real Crazyflie 2.1 data, our framework demonstrates high accuracy and provides reliable arrival time predictions, with an Average Displacement Error ranging from 0.0252 m to 0.1136 m. This data-driven approach avoids complex parameter configuration requirements, supports lightweight deployment in edge computing environments, and provides an effective solution for multi-UAV coordination and mission planning applications. Full article

Light Detection and Ranging (LiDAR) is fundamental to autonomous driving and robotics, as it provides reliable 3D geometric information. However, snowfall introduces numerous spurious reflections that corrupt range measurements and severely degrade downstream perception. Existing desnowing techniques either rely on handcrafted filtering rules that fail under varying snow densities, or require paired snowy–clean scans, which are nearly impossible to collect in real-world scenarios. Self-supervised LiDAR desnowing approaches address these challenges by projecting raw 3D point clouds into 2D range images and jointly training a point reconstruction network (PR-Net) and a reconstruction difficulty network (RD-Net). Nevertheless, these methods remain limited by their reliance on the outdated Noise2Void training paradigm, which restricts reconstruction quality. In this paper, we redesign PR-Net with a blind-spot architecture to overcome the limitation. Specifically, we introduce a 3D-KNN encoder that aggregates neighborhood features directly in Euclidean 3D space, ensuring geometrically consistent representations. Additionally, we integrate residual state-space blocks (RSSB) to capture long-range contextual dependencies with linear computational complexity. Extensive experiments on both synthetic and real-world datasets, including SnowyKITTI and WADS, demonstrate that our method outperforms state-of-the-art self-supervised desnowing approaches by up to 0.06 IoU while maintaining high computational efficiency. Full article

Semiconductor wafer manufacturing is one of the most complex and data-intensive processes in the industry, encompassing the front-end (FEOL), middle-end (MOL), and back-end (BEOL) stages, involving thousands of interdependent processes. Each stage can introduce potential variability, thereby reducing yield, making metrology and inspection crucial for process control. However, due to capacity, cost, and destructive testing constraints, exhaustive metrology for every wafer or die is impractical. Therefore, this study aims to introduce sampling strategies that have evolved to balance the accuracy, risk, and efficiency of measurement allocation. This review presents a literature review of static, adaptive, and dynamic sampling and discusses recent intelligent sampling techniques. The results show that traditional static sampling provides fixed, rule-based inspection schemes that ensure comparability and compliance but lack responsiveness to process variations. Adaptive sampling introduces flexibility, allowing measurement density to be adjusted based on detected drift, anomalies, or statistical control limits. Building on this, dynamic sampling represents a paradigm shift towards predictive, real-time decision-making driven by machine learning, risk analysis, and digital twin integration. The dynamic framework continuously assesses process uncertainties and prioritizes metrology to maximize information gain, thereby significantly reducing metrology workload without impacting yield or quality. Static, adaptive, and dynamic sampling together constitute a continuous evolution from deterministic control to self-optimizing intelligence. As semiconductor nodes move towards sub-3 nm, this intelligent sampling technology is crucial for maintaining yield, cost competitiveness, and process flexibility in autonomous, data-centric wafer fabs. Full article

This study proposes a novel artificial neural network-based methodology for classifying the incident wave direction during ship navigation using the heave–roll–pitch motion response spectra as input. The proposed model demonstrated a balanced performance with an overall accuracy of approximately 0.888, effectively classifying the wave direction into three major categories: head-sea, beam-sea, and following-sea. The methodology utilizes Response Amplitude Operators derived from linear potential flow theory to generate motion response spectra, which are then used to classify the incident wave direction. The model effectively learns the frequency-distribution characteristics of the response spectrum, enabling wave direction classification without the need for complex inverse analysis procedures. This approach is significant in that it allows wave direction recognition solely based on measurable ship motion responses, without the need for additional external sensors or mathematical modeling. This data-driven approach has strong potential for integration into autonomous ship situational awareness modules and real-time wave monitoring technologies. However, the study simplified the directional domain into three representative groups, and the model was validated primarily using a numerically generated dataset, indicating the need for future improvements. Future research will expand the dataset to include a broader range of sea states, improve directional resolution, and explore continuous wave direction prediction. Additionally, further validation using field-measured data will be conducted to assess the real-time applicability of the proposed model. Full article

Monitoring the curing process of concrete remains a challenging and critical aspect of modern construction, often hindered by labour-intensive, invasive, and inflexible methods. The primary aim of this study is to develop an integrated IoT-enabled platform for automated, real-time monitoring of concrete curing, using a combination of LoRa-based sensor networks and an LTE-M backhaul. The resulting ConMonity system employs embedded multi-sensor nodes—capable of measuring strain, temperature, and humidity–connected via an energy-efficient, TDMA-based LoRa wireless protocol to an LTE-M gateway with cloud-based management and analytics. By employing a robust architecture with battery-powered embedded nodes and adaptive firmware, ConMonity enables multi-modal, multi-site assessments and demonstrates stable, autonomous operation over multi-modal, multi-site assessment and demonstrates stable, autonomous operation over multi-month field deployments. Measured data are transmitted in a compact binary MQTT format, optimising cellular bandwidth and allowing secure, remote access via a dedicated mobile application. Operation in laboratory construction environments indicates that ConMonity outperforms conventional and earlier wireless monitoring systems in scalability and automation, delivering actionable real-time data and proactive alerts. The platform establishes a foundation for intelligent, scalable, and cost-effective monitoring of concrete curing, with future work focused on extending sensor modalities and enhancing resilience under diverse site conditions. Full article

Background: Coxiella burnetii is a common zoonotic pathogen that can lead not only to acute or chronic Q fever but also to post-infectious syndromes, where autonomic nervous system (ANS) dysfunction has been suggested as a contributing mechanism. This study aimed to assess autonomic function in patients presenting with polymorphic symptoms, dysautonomia, or ME/CFS who had serological evidence of acute infection with Coxiella burnetii. Methods: A total of 156 participants were evaluated, including 100 seropositive patients and 56 matched controls. All subjects underwent standardized cardiovascular reflex tests (CART), beat-to-beat analysis of heart rate and blood pressure with baroreflex indices, 24 h Holter ECG with HRV assessment, and, in the Coxiella group, head-up tilt testing (HUTT). Results: A significantly higher prevalence of autonomic dysfunction was observed in the Coxiella group, predominantly affecting parasympathetic regulation, with abnormal CART scores, reduced LF power and baroreflex effectiveness, and a high rate of positive HUTT findings characterized by extreme blood pressure variability. Although long-term HRV measures did not differ significantly between groups, short-term indices consistently indicated ANS impairment. Conclusions: These findings suggest that Coxiella burnetii infection may trigger persistent autonomic dysfunction, potentially contributing to the development of ME/CFS and syncope in affected individuals. Further longitudinal studies are needed to clarify pathophysiological mechanisms and clinical implications. Full article

Heart rate variability (HRV) is a non-invasive biomarker that reflects autonomic nervous system dynamics, providing valuable insights into physiological adaptation, stress, and recovery in athletes. Among the various HRV metrics, the root mean square of successive differences (RMSSD) has emerged as a robust and practical measure due to its strong association with parasympathetic activity, ease of calculation, and reliability in both short- and ultra-short-term recordings. This review examines the methodological considerations for using HRV to monitor training adaptations and recovery status in athletic populations. We highlight the superiority of routine, near-daily HRV measurements over isolated assessments, emphasizing the utility of weekly averages and the coefficient of variation (CV) to capture both chronic adaptations and acute homeostatic perturbations. Additionally, we discuss the selection of HRV devices, data recording procedures, and strategies to enhance athlete compliance. While RMSSD offers significant advantages for field-based monitoring, we also address its limitations, including its sole focus on parasympathetic activity and susceptibility to external confounders. Future directions include the integration of HRV data with other physiological markers and machine learning algorithms to optimize individualized training and recovery strategies. This review provides sport scientists and practitioners with evidence-based recommendations to enhance the application of HRV in both research and real-world athletic settings. Full article

The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a hierarchical control framework that divides the closed-loop system into a slow and a fast subsystem. The slow subsystem governs the gradual evolution of the USV pose and generates reference heading and surge commands from local scalar field information, providing a directional cue toward the field extremum. The fast subsystem applies actuator-level control inputs that ensure these references are tracked with sufficient accuracy through rapid corrective actions. A Lyapunov-based analysis is carried out to study the stability properties of the coupled slow–fast dynamics and to establish conditions under which convergence can be guaranteed in the presence of model nonlinearities and external disturbances. Numerical simulations are conducted to illustrate the resulting system behavior and to verify that the proposed framework maintains stable seeking performance under typical operating conditions. Full article

The exploration of marine minerals, essential for sustainable development, requires advanced techniques for accurate resource delineation. The self-potential (SP) method, sensitive to mineral polarization, has been increasingly deployed using autonomous underwater vehicles. This approach enables dense planar SP data acquisition, offering the potential to reduce inversion uncertainties through enhanced data volume. This study investigates the benefits of inverting planar SP datasets for improving the spatial delineation of subsurface deposits. An analytical solution was derived to describe SP responses of spherical polarization models under a planar measurement grid. An adaptive Markov chain Monte Carlo algorithm within the Bayesian framework was employed to quantitatively assess the constraints imposed by the enriched dataset. The proposed methodology was validated through two synthetic cases, along with a laboratory-scale experiment that monitored the redox process of a spherical iron–copper model. The results showed that, compared to single-line data, the planar data reduced the average error in parameter means from 10.9% and 6.4% to 4.1% and 1.7% for synthetic and experimental cases, respectively. In addition, the 95% credible intervals of model parameters narrowed by nearly 50% and 40%, respectively. Full article

This paper explores the capability of Large Language Models (LLMs) to perform zero-shot planning through multimodal reasoning, with a particular emphasis on applications to Unmanned Ground Vehicles (UGVs) and unmanned platforms in general. We present a modular system architecture that integrates a general-purpose LLM with visual and spatial inputs for adaptive planning to iteratively guide UGV behavior. Although the framework is demonstrated in a ground-based setting, it directly extends to other unmanned systems, where semantic reasoning and adaptive planning are increasingly critical for autonomous mission execution. To assess performance, we employ a continuous evaluation metric that jointly considers distance and orientation, offering a more informative and fine-grained alternative to binary success measures. We evaluate a foundational LLM (i.e., Gemini 2.0 Flash, Google DeepMind) on a suite of zero-shot navigation and exploration tasks in simulated environments. Unlike prior LLM-robot systems that rely on fine-tuning or learned waypoint policies, we evaluate a purely zero-shot, stepwise LLM planner that receives no task demonstrations and reasons only from the sensed data. Our findings show that LLMs exhibit encouraging signs of goal-directed spatial planning and partial task completion, even in a zero-shot setting. However, inconsistencies in plan generation across models highlight the need for task-specific adaptation or fine-tuning. These findings highlight the potential of LLM-based multimodal reasoning to enhance autonomy in UGV and drone navigation, bridging high-level semantic understanding with robust spatial planning. Full article