Search Results (555)

Background and Objectives: Physician burnout, commonly described as emotional exhaustion (EE), depersonalization (DP), and reduced personal accomplishment (PA), remains common. We assessed whether structured, individual-focused programs improve Maslach Burnout Inventory (MBI) subscale scores among physicians. Materials and Methods: Registration, Open Science Framework, doi: 10.17605/OSF.IO/UAZ6B (unfunded). PubMed (MEDLINE) was searched from 1 January 2009 to 9 December 2023 (last searched 9 December 2023) to conduct a meta-analysis. Eligible English language studies evaluated a physician-focused intervention intended to reduce burnout and reported MBI outcomes; eligible designs were randomized trials, crossover trials, prospective cohort studies, or single-group pre–post studies. Risk of bias was rated using the original Cochrane Risk of Bias by two reviewers with consensus resolution. For quantitative synthesis, we pooled mean differences (MD) using fixed-effect inverse-variance meta-analysis with 95% confidence intervals (CI); heterogeneity was summarized with I², and funnel plots were inspected qualitatively. Results: Of 2769 records, 17 studies met criteria for qualitative analysis, and 6 studies (n = 585 physicians; 273 intervention, 312 control) were pooled. Interventions included mindfulness curricula, professional coaching, or structured peer discussion groups. Compared with controls, interventions were associated with lower EE (MD −5.56; 95% CI, −6.68 to −4.44; I² = 42%), lower DP (MD −2.11; 95% CI, −2.64 to −1.58), and higher PA (MD 2.01; 95% CI, 1.41 to 2.60). Funnel plots suggested asymmetry for EE. Evidence was limited by few trials, frequent high or unclear risk of bias in at least one domain, and variable intervention formats, and one pooled study used a single-group pre–post design. Conclusions: Structured individual-focused programs were associated with small but statistically significant changes in MBI subscale scores in physicians, but confidence in magnitude and generalizability are limited by study quality and a small evidence base. These programs may be useful adjuncts to organizational approaches to burnout. Full article

Ensuring reliable inspection and quality control in complex industrial settings remains a significant challenge, particularly when traditional manual methods are applied to dynamic, multi-object environments. This paper presents and validates a new hybrid framework that integrates Object-Centric Process Mining (OCPM) with Support Vector Machines (SVMs) to improve industrial safety and quality assurance. The aims are: (1) to uncover and model the complex, multi-object processes characteristic of modern manufacturing using OCPM; (2) to assess these models in terms of conformance, performance, and the detection of bottlenecks; and (3) to design and embed a predictive layer based on Support Vector Regression (SVR) to anticipate process outcomes and support proactive control.The proposed methodology comprises a comprehensive pipeline: data fusion and OCEL structuring, OCPM for process discovery and conformance analysis, feature engineering, SVR for predictive modeling, and a multi-objective optimization layer. By applying this framework to a timber sawmill dataset, the study successfully modeled complex lumber drying operations, identified key object interactions, achieving a process conformance fitness score of 0.6905, and testing the integration of a predictive SVR layer. The SVR model’s predictive accuracy for production yield was found to be limited ($R^2=0.0255$) with the current feature set, highlighting the challenges of predictive modeling in this complex, multi-object domain. Despite this predictive limitation, the multi-objective optimization effectively balanced defect rates, energy consumption, and process delays, yielding a mean objective function value of 0.0768. These findings demonstrate the framework’s capability to provide deep, object-centric process insights and support data-driven decision-making for operational improvements in Industry 4.0. Future research will focus on improving predictive model performance through advanced feature engineering and exploring diverse machine learning techniques. Full article

Surface defect detection is vital in industrial quality control. While deep learning has largely automated inspection, accurately locating defects with large-scale variations or those difficult to distinguish from similar backgrounds remains challenging. Furthermore, achieving high-precision and real-time performance under limited computational resources in deployment environments complicates effective solutions. In this work, we propose SD-GASNet, a network based on a self-distillation model compression strategy. To identify subtle defects, we design an Alignment, Enhancement, and Synchronization Feature Pyramid Network (AES-FPN) fusion network incorporating the Frequency Domain Information Gathering-and-Allocation (FIGA) mechanism and the Channel Synchronization (CS) module for industrial images from different sensors. Specifically, FIGA refines features via the Multi-scale Feature Alignment (MFA) module, then the Frequency-Guided Perception Enhancement Module (FGPEM) extracts high- and low-frequency information to enhance spatial representation. The CS module compensates for information loss during feature fusion. Addressing computational constraints, we adopt self-distillation with an Enhanced KL divergence loss function to boost lightweight model performance. Extensive experiments on three public datasets (NEU-DET, PCB, and TILDA) demonstrate that SD-GASNet achieves state-of-the-art performance with excellent generalization, delivering superior accuracy and a competitive inference speed of 180 FPS, offering a robust and generalizable solution for sensor-based industrial imaging applications. 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

The reliability of electric vehicle (EV) batteries requires detecting surface defects but also precisely locating them on the physical module for automated inspection, repair, and process optimisation. Conventional 2D computer vision methods, though accurate in image-space, do not provide traceable, real-world defect coordinates on complex or curved battery surfaces, limiting utility for digital twins, root cause analysis, and automated quality control. This work proposes a hybrid inspection pipeline that produces millimetre-level three-dimensional (3D) defect maps for EV battery modules. The approach integrates (i) calibrated dual-view multi-view geometry to project defect points onto the CAD geometry and triangulate them where dual-view coverage is available, (ii) single-image neural 3D shape inference calibrated to the module geometry to complement regions with limited multi-view coverage, and (iii) generative, physically informed augmentation of rare or complex defect types. Defects are first detected in 2D images using a convolutional neural network (CNN), then projected onto a dense 3D CAD model of each module, complemented by a single-image depth prediction in regions with limited dual-view coverage, yielding true as-built localisation on the battery’s surface. GenAI methods are employed to expand the dataset with synthetic defect variations. Synthetic, physically informed defect examples are incorporated during training to mitigate the scarcity of rare defect types. Evaluation on a pilot industrial dataset, with a physically measured reference subset, demonstrates that the hybrid 3D approach achieves millimetre-scale localisation accuracy and outperforms a per-view CNN baseline in both segmentation and 3D continuity. Full article

Printed Circuit Board (PCB) defect detection is critical for quality control in electronics manufacturing. Traditional manual inspection and classical Automated Optical Inspection (AOI) methods face challenges in speed, consistency, and flexibility. This paper proposes a CNN-based approach for automatic PCB defect detection using the YOLOv5 model. The method leverages a Convolutional Neural Network to identify various PCB defect types (e.g., open circuits, short circuits, and missing holes) from board images. In this study, a model was trained on a PCB image dataset with detailed annotations. Data augmentation techniques, such as sharpening and noise filtering, were applied to improve robustness. The experimental results showed that the proposed approach could locate and classify multiple defect types on PCBs, with overall detection precision and recall above 90% and 91%, respectively, enabling reliable automated inspection. A brief comparison with the latest YOLOv8 model is also presented, showing that the proposed CNN-based detector offers competitive performance. This study shows that deep learning-based defect detection can improve the PCB inspection efficiency and accuracy significantly, paving the way for intelligent manufacturing and quality assurance in PCB production. From a sensing perspective, we frame the system around an industrial RGB camera and controlled illumination, emphasizing how imaging-sensor choices and settings shape defect visibility and model robustness, and sketching future sensor-fusion directions. Full article

This study introduces a systematic methodology for modelling the radius of curvature of the arc-shaped section $\stackrel{⌢}{\mathrm{BC}}$ in a Michell–Banki cross-flow turbine blade. The method combines geometric modeling in polar coordinates with nonlinear regression, using both two- and three-parameter formulations estimated through the Ordinary Least Squares (OLS) method. Model performance is assessed through two complementary criteria: the coefficient of determination ($R^2$) and the computed arc length, ensuring that statistical accuracy aligns with geometric fidelity. The methodology was validated on digital measurements obtained from CATIA, using datasets with $N=187$ and a reduced subset of $N=48$ points. Results demonstrate that even with fewer data points, the regression model maintains high predictive accuracy and geometric consistency. The best-performing three-parameter model achieved $R^2=0.958$, with a five-point Gauss–Legendre quadrature yielding an arc length of approximately $145\mathrm{mm}$, representing 98.8% agreement with the reference value of $146.78\mathrm{mm}$. By representing the arc as a single smooth exponential function rather than a piecewise mapping, the approach simplifies analysis and enhances reproducibility. Coupling regression precision with arc-length verification provides a robust and reproducible basis for curvature modeling. This methodology supports turbine blade design, manufacturing, and quality control by ensuring that the blade geometry is validated with high statistical confidence and physical accuracy. Future research will focus on deriving analytical arc-length integrals and integrating the procedure into automated design and inspection workflows. Full article

Unmanned Aerial Vehicle (UAV)-based photogrammetric reconstruction is a key step in geometric digital twinning of bridges, but ensuring the quality of the reconstruction data through the planning of measurement configurations is not straightforward. This research investigates an approach for quantitatively evaluating the impact of different methodologies and configurations of UAV-based image collection on the quality of the collected images and 3D reconstruction data in the bridge inspection context. For an industry-grade UAV and a consumer-grade UAV, paths for image collection from different Ground Sampling Distance (GSD) and image overlap ratios are considered, followed by the 3D reconstruction with different algorithm configurations. Then, an approach for evaluating these data collection methodologies and configurations is discussed, focusing on trajectory accuracy, point-cloud reconstruction quality, and accuracy of geometric measurements relevant to inspection tasks. Through a case study on short-span road bridges, errors in different steps of the photogrammetric 3D reconstruction workflow are characterized. The results indicate that, for the global dimensional measurements, the consumer-grade UAV works comparably to the industry-grade UAV with different GSDs. In contrast, the local measurement accuracy changes significantly depending on the selected hardware and path-planning parameters. This research provides practical insights into controlling 3D reconstruction data quality in the context of bridge inspection and geometric digital twinning. Full article

This study develops an integrated quality inspection and production optimization framework for an imperfect production system, where system deterioration follows a zero-inflated non-homogeneous Poisson process (ZI-NHPP) characterized by a power-law intensity function. Parameters are estimated from historical data using the Expectation-Maximization (EM) algorithm, with a zero-inflation parameter π modeling scenario where the system remains defect-free. Operating in either an in-control or out-of-control state, the system produces products with Weibull hazard rates, exhibiting higher failure rates in the out-of-control state. The proposed model integrates system status, defect rates, employee efficiency, and market demand to jointly optimize the number of conforming items inspected and the production run length, thereby minimizing total costs—including production, inspection, correction, inventory, and warranty expenses. Numerical analyses, supported by sensitivity studies, validate the effectiveness of this integrated approach in achieving cost-efficient quality control. This framework enhances quality assurance and production management, offering practical insights for manufacturing across diverse industries. Full article

Manufacturing companies are increasingly confronted with critical challenges such as a shortage of skilled labor, rising production costs, and ever-stricter quality requirements. These challenges become particularly acute when defect types exhibit high visual variance, making consistent and accurate inspection difficult. Traditionally, visual inspection of high variance errors is performed manually by human operators—a process that is both costly and prone to errors. Consequently, there is a growing interest in replacing human inspection with AI-based visual quality control systems. However, the adoption of such systems is often hindered by limited access to training data, labor-intensive labeling processes, or the absence of real production data during early development stages. To address these challenges, this paper presents a methodology for training AI models using synthetically generated image data. The synthetic images are created using Physically Based Rendering, which enables precise control over rendering parameters and facilitates automated labeling. This approach allows for a systematic analysis of parameter importance and bypasses the need for large real training datasets. As a case study, the focus is on the inspection of laser welds in battery connectors for fully electric vehicles—a particularly demanding application due to the criticality of each weld. The results demonstrates the effectiveness of synthetic data in training robust AI models, thereby providing a scalable and efficient alternative to traditional data acquisition and labeling methods. The trained binary classifier reaches a precision of $0.94$ with a recall of $0.98$ solely trained on synthetic data and tested on real image data. Full article

High volume pencil manufacturing often generates substantial material waste due to a small proportion of products having missing or recessed graphite leads. Standard vision-based quality control processes discard entire wooden slats that carry any faulty pencils, causing excessive waste of usable wood and graphite resources. This study describes the design and implementation of a laser triangulation-based inspection system for lead defect detection after individual pencils are cut from the slat. The system combines a two-dimensional laser profile scanner with synchronized triggering sensors and a programmable logic controller (PLC)-controlled pneumatic rejection unit. Using the systematic design methodology for VDI 2221, a functional prototype was developed, which was then tested in a simulated production system with a throughput of up to 200 pencils per minute. The proposed system was able to detect missing and recessed leads highly accurately and correctly classified 98–100% of pencils without false rejections of acceptable products. The most common type of defect was missing or deeply recessed lead with an accuracy of 98.5%, and the less common partial-lead fractures had a lower percentage of detection of nearly 92% due to geometric sensitivity. The developed inline inspection system was successful in identifying and rejecting defective pencils without the waste of materials and provided a viable alternative of economical implementation with less than a one-year payback period. Through its increased resource efficiency and decreased raw material waste, the proposed system contributes to the United Nations Sustainable Development Goals, namely SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Full article

Coarse aggregate shape plays a critical role in determining surface performance and durability in pavement systems. Traditional manual shape inspection is laborious and subjective, especially for bulk aggregates in overlapped state. In this work, we propose an automated digital image-based evaluation method for stacked coarse aggregates, combining preprocessing (grayscale conversion, histogram equalization, Gaussian filtering), segmentation, and contour reconstruction via the Graham scan convex hull algorithm. Morphological parameters such as equivalent ellipse major/minor axes, area, and perimeter are then extracted to compute individual particle shape factors. To assess batch-level quality, shape factor standard deviations (σ) and mean shape factors were computed from 50 aggregate images. Comparison with manual measurement results shows mean relative errors below 15%. Our analysis reveals a strong correlation between σ and overall shape quality: lower σ indicates more uniform geometry, while higher σ suggests greater irregularity. Based on experimental data, we define three σ-based categories: excellent (σ ≤ 0.32), good (0.32 < σ ≤ 0.42), and poor (σ > 0.42). This σ-driven evaluation framework enables rapid, quantitative, and objective assessment of aggregate morphology in practical aggregate production and pavement quality control. Full article

This study presents an effective method for detecting and classifying microscopic defects in optical thin films, aiming to enhance quality control in thin-film manufacturing. The proposed system utilizes thin-film surface defect images captured by an imaging microscope. It combines image preprocessing techniques, such as translation, scaling, and mirroring, to expand the dataset, thereby generating a rich and representative set of defect images. All images are manually labeled by experts to ensure high-quality annotations and to optimize training efficiency. The YOLOv7 object detection framework is employed for model training and optimization. Model performance is rigorously evaluated using metrics such as the confusion matrix and mean average precision (mAP). The trained model achieved an accuracy of 87.3% on the test dataset, demonstrating both high detection accuracy and practical applicability. This method offers significant potential for automating microscopic defect detection, thus improving the efficiency of film quality inspection and contributing to better production yield in optical thin-film processes. Full article

The operational range and reliability of most commercially available UAVs employed in surveillance, agriculture, and infrastructure inspection missions are limited due to the use of short-range radio frequency connections. To alleviate this issue, the present work investigates the possibility of real-time long-distance UAV control using a commercial 4G LTE network. The proposed system setup consists of a Raspberry Pi 4B as the onboard computer, connected to a Pixhawk-2.4 flight controller mounted on an F450 quadcopter platform. Flight tests were carried out in open-field conditions at altitudes up to 50 m above ground level (AGL). Communication between the UAV and the ground control station is established using TCP and UDP protocols. The flight tests demonstrated stable remote control operation, maintaining an average control delay of under 150 ms and a video quality resolution of 640×480, while the LTE bandwidth ranging from 3 Mbps to 55 Mbps. The farthest recorded test distance of around 4200 km from the UAV to the operator also indicates the capability of LTE systems for beyond-visual-line-of-sight operations. The results show that 4G LTE offers an effective method for extending UAV range at a reasonable cost, but there are limitations in terms of network performance, flight time and regulatory compliance. This study establishes essential groundwork for future UAV operations that will utilize 5G/6G and satellite communication systems. Full article

Pleurotus ostreatus is valued for its nutritional, medicinal, economic, and ecological benefits and is widely used in the food, pharmaceutical, and environmental protection industries. Pleurotus ostreatus, as a highly perishable edible fungus, faces significant challenges in supply chain quality control and food safety due to its short shelf life. As consumer demand for food freshness and full traceability increases, there is an urgent need to establish a reliable traceability system that enables real-time monitoring, spoilage prevention, and quality assurance. This study focuses on the Pleurotus ostreatus supply chain and designs and implements a multi-role flexible traceability system that integrates blockchain and the Internet of Things. The system collects key production and storage environment parameters in real time through sensor networks and enhances data accuracy and robustness using an improved adaptive weighted fusion algorithm, enabling precise monitoring of the growth environment and quality risks. The system adopts a “link-chain” mapping mechanism for multi-chain storage and dynamic reorganization of business processes. It incorporates attribute-based encryption strategies and smart contracts to support tiered data access and secure sharing among multiple parties. Key information is stored on the blockchain to prevent tampering, while auxiliary data is stored in off-chain databases and the Interplanetary File System to ensure efficient and verifiable data queries. Deployed at Shandong Qihe Ecological Agriculture Co., Ltd., No. 517, Xilou Village, Kunlun Town, Zichuan District, 255000, Zibo City, Shandong Province, China, the system covers 12 cultivation units and 60 sensor nodes, recording over 50,000 traceable data points. Experimental results demonstrate that the system outperforms baseline methods in query latency, data consistency, and environmental monitoring accuracy. The improved fusion algorithm reduced the total variance of environmental data by 20%. In practical application, the system reduced the spoilage rate of Pleurotus ostreatus by approximately 12.3% and increased the quality inspection pass rate by approximately 15.4%, significantly enhancing the supply chain’s quality control and food safety capabilities. The results show that the framework is feasible and scalable in terms of information credibility and operational efficiency and significantly improves food quality and safety monitoring throughout the production, storage, and distribution of Pleurotus ostreatus. This study provides a viable technological path for spoilage prevention, quality tracking, and digital food safety supervision, offering valuable insights for both food science research and practical applications. Full article