Search Results (1,619)

Effective leak detection and leak size identification are essential for maintaining the operational safety, integrity, and longevity of industrial pipelines. Traditional methods often suffer from high noise sensitivity, limited adaptability to non-stationary signals, and excessive computational costs, which limits their feasibility for real-time monitoring applications. This study presents a novel acoustic emission (AE)-based pipeline monitoring approach, integrating Empirical Wavelet Transform (EWT) for adaptive frequency decomposition with customized one-dimensional DenseNet architecture to achieve precise leak detection and size classification. The methodology begins with EWT-based signal segmentation, which isolates meaningful frequency bands to enhance leak-related feature extraction. To further improve signal quality, adaptive thresholding and denoising techniques are applied, filtering out low-amplitude noise while preserving critical diagnostic information. The denoised signals are processed using a DenseNet-based deep learning model, which combines convolutional layers and densely connected feature propagation to extract fine-grained temporal dependencies, ensuring the accurate classification of leak presence and severity. Experimental validation was conducted on real-world AE data collected under controlled leak and non-leak conditions at varying pressure levels. The proposed model achieved an exceptional leak detection accuracy of 99.76%, demonstrating its ability to reliably differentiate between normal operation and multiple leak severities. This method effectively reduces computational costs while maintaining robust performance across diverse operating environments. Full article

Unidirectional ultrasonic guided waves (UGWs) play a crucial role in the nondestructive testing and evaluation (NDT&E) domains, offering unique advantages in detecting material defects, evaluating structural integrity, and improving the accuracy of thickness measurements. This review paper thoroughly studies the state of the art of unidirectional UGWs before presenting a comprehensive review of the foundational mathematical principles of unidirectional UGWs, focusing on the recent advancements in their methodologies and applications. This review introduces ultrasonic guided waves and their modes before looking at mode excitability and selectivity, signal excitation, and mechanisms used to generate and receive guided waves unidirectionally. This paper outlines the applications of unidirectional UGWs to reflect their effectiveness, for instance, in measuring thickness and in identifying defects such as cracks and corrosion in pipelines, etc. The paper also studies the challenges associated with unidirectional UGW generation and utilisation, such as multi-mode and side lobes. It includes a review of the literature to mitigate these challenges. Finally, this paper highlights promising future perspectives and develops directions for the technique. This review aims to create a useful resource for researchers and practitioners to comprehend unidirectional ultrasonic guided waves’ capabilities, challenges, and prospects in NDT&E applications. Full article

Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of object detection models (YOLOv8n and YOLOv8s) on both resource-constrained edge devices and cloud environments. The objective is to carry out a comparative performance analysis using a representative real-time UAV image processing pipeline. Specifically, the NVIDIA Jetson Orin Nano, Orin NX, and Raspberry Pi 5 (RPI5) devices have been tested to measure their detection accuracy, inference speed, and energy consumption, and the effects of post-training quantization (PTQ). The results show that YOLOv8n surpasses YOLOv8s in its inference speed, achieving 52 FPS on the Jetson Orin NX and 65 fps with INT8 quantization. Conversely, the RPI5 failed to satisfy the real-time processing needs in spite of its suitability for low-energy consumption applications. An analysis of both the cloud-based and edge-based end-to-end processing times showed that increased communication latencies hindered real-time applications, revealing trade-offs between edge (low latency) and cloud processing (quick processing). Overall, these findings contribute to providing recommendations and optimization strategies for the deployment of AI models on UAVs. Full article

This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. Data clumps refer to clusters of code that are often repeated and should ideally be refactored to improve code quality. The pipeline leverages ChatGPT’s capabilities to understand context and generate structured outputs, making it suitable for addressing complex software refactoring tasks. Through systematic experimentation, our study not only addresses the research questions outlined but also demonstrates that the pipeline can accurately identify data clumps, particularly excelling in cases that require semantic understanding—where localized clumps are embedded within larger codebases. While the solution significantly enhances the refactoring workflow, facilitating the management of distributed clumps across multiple files, it also presents challenges such as occasional compiler errors and high computational costs. Feedback from developers underscores the usefulness of LLMs in software development but also highlights the essential role of human oversight to correct inaccuracies. These findings demonstrate the pipeline’s potential to enhance software maintainability, offering a scalable and efficient solution for addressing code smells in real-world projects, and contributing to the broader goal of enhancing software maintainability in large-scale projects. Full article

Oriented object detection has become a hot topic in SAR image interpretation. Due to the unique imaging mechanism, SAR objects are represented as clusters of scattering points surrounded by coherent speckle noise, leading to blurred outlines and increased false alarms in complex scenes. To address these challenges, we propose a novel noise-to-convex detection paradigm with a hierarchical framework based on the scattering-keypoint-guided diffusion detection transformer (SKG-DDT), which consists of three levels. At the bottom level, the strong-scattering-region generation (SSRG) module constructs the spatial distribution of strong scattering regions via a diffusion model, enabling the direct identification of approximate object regions. At the middle level, the scattering-keypoint feature fusion (SKFF) module dynamically locates scattering keypoints across multiple scales, capturing their spatial and structural relationships with the attention mechanism. Finally, the convex contour prediction (CCP) module at the top level refines the object outline by predicting fine-grained convex contours. Furthermore, we unify the three-level framework into an end-to-end pipeline via a detection transformer. The proposed method was comprehensively evaluated on three public SAR datasets, including HRSID, RSDD-SAR, and SAR-Aircraft-v1.0. The experimental results demonstrate that the proposed method attains an ${\mathrm{AP}}_{50}$ of 86.5%, 92.7%, and 89.2% on these three datasets, respectively, which is an increase of 0.7%, 0.6%, and 1.0% compared to the existing state-of-the-art method. These results indicate that our approach outperforms existing algorithms across multiple object categories and diverse scenes. Full article

Caliciviruses including noro- and sapoviruses of family Caliciviridae are important enteric human and swine pathogens, while others, like valoviruses, are less known. In this study, we developed a detection and typing pipeline for the most prevalent swine enteric caliciviruses—sapovirus GIII (Sw-SaV), norovirus GII (Sw-NoV), and valovirus GI (Sw-VaV). The pipeline integrates triplex RT-qPCR, 3′RACE semi-nested PCR, and next-generation sequencing (NovaSeq, Illumina) techniques. A small-scale epidemiological investigation was conducted on archived enteric and, for the first time, on oral fluid/saliva samples of diarrheic and asymptomatic swine of varying ages from Hungary and Slovakia. In enteric samples, Sw-SaV was the most prevalent, detected in 26.26% of samples, primarily in diarrheic pigs with low Cq values, followed by Sw-NoV (2.53%) in nursery pigs. In oral fluid samples, Sw-NoV predominated (7.46%), followed by Sw-SaV (4.39%). Sw-VaVs were sporadically found in both sample types. A natural, asymptomatic Sw-SaV outbreak was retrospectively detected where the transient shedding of the virus was <2 weeks. Complete capsid sequences (n = 59; 43 Sw-SaV, 13 Sw-NoV, and 3 Sw-VaV) including multiple (up to five) co-infecting variants were identified. Sw-SaV sequences belong to seven genotypes, while Sw-NoV and Sw-VaV strains clustered into distinct sub-clades, highlighting the complex diversity of these enteric caliciviruses in swine. Full article

In order to improve the accuracy of leakage detection in water pipelines, this paper proposes a novel method based on Transformer and transfer learning. A laboratory test platform was established to obtain datasets with rich leakage characteristics. An enhanced feature extraction technique using a shift window input method mapped the NPW sequences into embedding vectors, effectively capturing the fine-grained features while reducing the sequence length, thereby enhancing the Transformer’s retention of sequence details. An improved Transformer encoder was pre-trained on the Experimental pipeline dataset and refined with limited leakage data from real pipelines for accurate detection. Additionally, a novel signal difference-based method was introduced for precise leak localization. The pressure signal was denoised, and the inflection points were identified by subtracting two signals. The points between the inflection and lowest signal points were traversed, with slope calculations optimizing the time delay computations. A leakage simulation test was conducted on a section of a raw water pipeline in Shanghai, and the test results confirmed the effectiveness of these methods. A 100% detection rate, zero false alarms, and a relative positioning error of less than 3.14% were achieved on a test set of 45 instances. Full article

Background/Objectives: Short tandem repeat expansions are the most common cause of inherited neurological diseases. These disorders are clinically and genetically heterogeneous, such as in myotonic dystrophy and spinocerebellar ataxia, and they are caused by different repeat motifs in different genomic locations. Major advances in bioinformatic tools used to detect repeat expansions from short read sequencing data in the last few years have led to the implementation of these workflows into next generation sequencing pipelines in healthcare. Here, we aimed to evaluate the clinical utility of analysing repeat expansions through exome sequencing in a large cohort of genetically undiagnosed patients with neurological disorders. Methods: We here analyse 27 disease-causing DNA repeats found in the coding, intronic and untranslated regions in 12,496 exomes in patients with a range of neurogenetic conditions. Results: We identified—and validated by polymerase chain reaction—29 repeat expansions across a range of loci, 48% (n = 14) of which were diagnostic. We then analysed the genotyping performance across all repeat loci and found that, despite high coverage in most repeats in coding regions, some loci had low genotyping rates, such as those that cause spinocerebellar ataxia 2 (ATXN2, 0.1–8.4%) and Huntington disease (HTT, 0.2–58.2%), depending on the capture kit. Conversely, while most intronic repeats were not genotyped, we found a high genotyping rate in the intronic locus that causes spinocerebellar ataxia 36 (NOP56, 30.1–98.3%) and in the one that causes myotonic dystrophy type 1 (DMPK, myotonic dystrophy type 1). Conclusions: We show that the key factors that influence the genotyping rate of repeat expansion loci analysis are the sequencing read length and exome capture kit. These results provide important information about the performance of exome sequencing as a genetic test for repeat expansion disorders. Full article

Water transport pipelines in the mining industry face significant corrosion challenges due to extreme environmental conditions, such as arid climates, temperature fluctuations, and abrasive soils. This study evaluates the effectiveness of three advanced inspection technologies—Guided Wave Ultrasonic Testing (GWUT), Metal Magnetic Memory (MMM), and In-Line Inspection (ILI)—in maintaining pipeline integrity under such conditions. A structured methodology combining diagnostic assessment, technology research, and comparative evaluation was applied, using key performance indicators like detection capability, operational impact, and feasibility. The results show that GWUT effectively identifies surface anomalies and wall thinning over long pipeline sections but faces depth and diameter limitations. MMM excels at detecting early-stage stress and corrosion in inaccessible locations, benefiting from minimal preparation and strong market availability. ILI provides comprehensive internal and external assessments but requires piggable pipelines and operational adjustments, limiting its use in certain systems. A case study of critical aqueducts of mining site water supply illustrates real-world technology selection challenges. The findings underscore the importance of an integrated inspection approach, leveraging the complementary strengths of these technologies to ensure reliable pipeline integrity management. Future research should focus on quantitative performance metrics and cost-effectiveness analyses to optimize inspection strategies for mining infrastructure. Full article

Gas pipeline networks are vital urban infrastructure, susceptible to leaks caused by natural disasters and adverse weather, posing significant safety risks. Detecting and localizing these leaks is crucial for mitigating hazards. However, existing methods often fail to effectively model the time-varying structural data of pipelines, limiting their detection capabilities. This study introduces a novel approach for leak detection using a spatial–temporal attention network (STAN) tailored for small-hole leakage conditions. A graph attention network (GAT) is first used to model the spatial dependencies between sensors, capturing the dynamic patterns of adjacent nodes. An LSTM model is then employed for encoding and decoding time series data, incorporating a temporal attention mechanism to capture evolving changes over time, thus improving detection accuracy. The proposed model is evaluated using Pipeline Studio software and compared with state-of-the-art models on a gas pipeline simulation dataset. Results demonstrate competitive precision (91.7%), recall (96.5%), and F1-score (0.94). Furthermore, the method effectively identifies sensor statuses and temporal dynamics, reducing leakage risks and enhancing model performance. This study highlights the potential of deep learning techniques in addressing the challenges of leak detection and emphasizes the effectiveness of spatial–temporal modeling for improved detection accuracy. Full article

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces Full article

In many medical and pharmaceutical processes, continuous hygiene monitoring relies on manual detection of microorganisms in agar dishes by skilled personnel. While deep learning offers the potential for automating this task, it often faces limitations due to insufficient training data, a common issue in colony detection. To address this, we propose a simple yet efficient SAM-based pipeline for Copy-Paste data augmentation to enhance detection performance, even with limited data. This paper explores a method where annotated microbial colonies from real images were copied and pasted into empty agar dish images to create new synthetic samples. These new samples inherited the annotations of the colonies inserted into them so that no further labeling was required. The resulting synthetic datasets were used to train a YOLOv8 detection model, which was then fine-tuned on just 10 to 1000 real images. The best fine-tuned model, trained on only 1000 real images, achieved an mAP of $60.6$, while a base model trained on 5241 real images achieved 64.9. Although far fewer real images were used, the fine-tuned model performed comparably well, demonstrating the effectiveness of the SAM-based Copy-Paste augmentation. This approach matches or even exceeds the performance of the current state of the art in synthetic data generation in colony detection and can be expanded to include more microbial species and agar dishes. Full article

Retroviruses, like other RNA viruses, mutate at very high rates and exist as genetically heterogeneous populations. The error-prone activity of viral reverse transcriptase (RT) is largely responsible for the observed variability, most notably in HIV-1. In addition, RTs are widely used in biotechnology to detect RNAs and to clone expressed genes, among many other applications. The fidelity of retroviral RTs has been traditionally analyzed using enzymatic (gel-based) or reporter-based assays. However, these methods are laborious and have important limitations. The development of next-generation sequencing (NGS) technologies opened the possibility of obtaining reverse transcription error rates from a large number of sequences, although appropriate protocols had to be developed. In this review, we summarize the developments in this field that allowed the determination of RNA-dependent DNA synthesis error rates for different RTs (viral and non-viral), including methods such as PRIMER IDs, REP-SEQ, ARC-SEQ, CIR-SEQ, SMRT-SEQ and ROLL-SEQ. Their advantages and limitations are discussed. Complementary DNA (cDNA) synthesis error rates obtained in different studies, using RTs and RNAs of diverse origins, are presented and compared. Future improvements in methodological pipelines will be needed for the precise identification of mutations in the RNA template, including modified bases. Full article

With the growing prevalence of large-scale intelligent surveillance camera systems, the burden on real-time video analytics pipelines has significantly increased due to continuous video transmission from numerous cameras. To mitigate this strain, recent approaches focus on filtering irrelevant video frames early in the pipeline, at the camera or edge device level. In this paper, we propose Wi-Filter, an innovative filtering method that leverages Wi-Fi signals from wireless edge devices, such as Wi-Fi-enabled cameras, to optimize filtering decisions dynamically. Wi-Filter utilizes channel state information (CSI) readily available from these wireless cameras to detect human motion within the field of view, adjusting the filtering threshold accordingly. The motion-sensing models in Wi-Filter (Wi-Fi assisted Filter) are trained using a self-supervised approach, where CSI data are automatically annotated via synchronized camera feeds. We demonstrate the effectiveness of Wi-Filter through real-world experiments and prototype implementation. Wi-Filter achieves motion detection accuracy exceeding 97.2% and reduces false positive rates by up to 60% while maintaining a high detection rate, even in challenging environments, showing its potential to enhance the efficiency of video analytics pipelines. Full article

The adoption of automated machine learning (AutoML) in dentistry is transforming clinical practices by enabling clinicians to harness machine learning (ML) models without requiring extensive technical expertise. This narrative review aims to explore the impact of autoML in dental applications. A comprehensive search of PubMed, Scopus, and Google Scholar was conducted without time and language restrictions. Inclusion criteria focused on studies evaluating autoML applications and performance for dental tasks. Exclusion criteria included non-dental studies, single-case reports, and conference abstracts. This review highlights multiple promising applications of autoML in dentistry. Diagnostic tasks showed high accuracy, such as 95.4% precision in dental implant classification and 92% accuracy in paranasal sinus disease detection. Predictive tasks also demonstrated promise, including 84% accuracy for ICU admissions due to dental infections and 93.9% accuracy in orthodontic extraction predictions. AutoML frameworks like Google Vertex AI and H2O AutoML emerged as key tools for these applications. AutoML shows great promise in transforming dentistry by facilitating data-driven decision-making and improving patient care quality through accessible, automated solutions. Future advancements should focus on enhancing model interpretability, developing large and annotated datasets, and creating pipelines tailored to dental tasks. Educating clinicians on autoML and integrating domain-specific knowledge into automated platforms could further bridge the gap between complex ML technology and practical dental applications. Full article