Search Results (358)

Subsea pipelines, operating in high-pressure and high-salinity conditions, face ongoing risks of leakage. Pipeline leaks can pollute the marine environment and, in severe cases, cause safety incidents, endangering human lives and property. Regular integrity inspections of subsea pipelines are critical to prevent corrosion-related leaks. This study develops a magnetic flux leakage (MFL)-based pig for detecting corrosion in subsea pipelines. Using a three-dimensional finite element model, this study analyzes the effects of defect geometry, lift-off distance, and operating speed on MFL signals. It proposes a defect estimation method based on axial peak-to-valley values and radial peak spacing, with inversion accuracy validated against simulation results. This study establishes a theoretical and practical framework for subsea pipeline integrity management, providing an effective solution for corrosion monitoring. Full article

An underground utility tunnel (UUT) is essential for the efficient use of urban underground space. However, current maintenance systems rely on patrol personnel and professional equipment. This study explores industrial detection methods for identifying and monitoring natural gas leaks in UUTs. Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. Based on a convolutional recurrent neural network (CRNN), the image features at each moment were extracted, and the time series data were modeled to realize the risk-level classification mechanism based on the automatic classification of the leakage rate. The experimental results show that when the sliding window size was set to 10 frames, the classification accuracy of the CRNN was the highest, which could reach 0.98. This method improves early warning precision and response efficiency, offering practical technical support for UUT maintenance management. Full article

Carbon Capture and Storage (CCS) is a key technology for reducing anthropogenic greenhouse gas emissions, in which pipelines play a vital role in transporting CO₂ captured from industrial emitters to geological storage sites. To aid the efficient and safe operation of the CO₂ transport infrastructure, robust, accurate, and reliable solutions for monitoring pipelines transporting industrial CO₂ streams are urgently needed. This literature review study summarizes the monitoring objectives and identifies the problems and relevant mathematical algorithms developed for optimization of monitoring systems for pipeline transportation of water, oil, and natural gas, which can be relevant to the future CO₂ pipelines and pipeline networks for CCS. The impacts of the physical properties of CO₂ and complex designs and operation scenarios of CO₂ transport on the pipeline monitoring systems design are discussed. It is shown that the most relevant to liquid- and dense-phase CO₂ transport are the sensor placement optimization methods developed in the context of detecting leaks and flow anomalies for water distribution systems and pipelines transporting oil and petroleum liquids. The monitoring solutions relevant to flow assurance and monitoring impurities in CO₂ pipelines are also identified. Optimizing the CO₂ pipeline monitoring systems against several objectives, including the accuracy of measurements, the number and type of sensors, and the safety and environmental risks, is discussed. Full article

Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article

Background: In recent decades, aortic valve surgery has transitioned from conventional median sternotomy (MS) to minimally invasive techniques, including partial upper mini-sternotomy (PUMS) and right anterolateral mini-thoracotomy (RAMT). This study retrospectively compares the outcomes of aortic valve replacement (AVR) using PUMS during the learning phase with those of standard MS. Methods: A retrospective analysis was conducted on patients (n = 211) who underwent AVR for aortic stenosis. They were divided into MS (n = 119) and PUMS (n = 92) groups. Various preoperative, surgical and postoperative parameters, including survival, were examined. Results: Preoperatively, the main difference was age, with PUMS patients being older (67.5 ± 7 vs. 66.5 ± 9.6; p = 0.010). PUMS patients also had longer cardiopulmonary bypass (CPB) and cross-clamping times (99 ± 25 vs. 80 ± 16 min; p < 0.002; 79 ± 18 vs. 65 ± 13 min; p < 0.024). There were no significant differences in body mass index, prosthesis size, indexed effective orifice area, hospitalisation duration or any other monitored parameter. Echocardiographic follow-up found no differences in prosthetic pressure gradients, flow velocity or paravalvular leak between the PUMS and MS groups. Survival rates were similar over 1000 days. Conclusions: The data suggest that PUMS offers comparable surgical outcomes to MS for AVR with additional cosmetic benefits, undeterred by a learning curve. Full article

Ambient monitoring in chemical laboratories and industrial sites that use toxic, hazardous, or flammable materials is essential to protect the lives of workers, material resources, and infrastructure at these sites. In this research paper, we present an innovative approach for developing a low-cost and portable sensor node that detects and warns of hazardous chemical gas and vapor leaks. The system also enables leak location tracking using an indoor tracking and positioning system operating in ultra-wideband (UWB) technology. An array of sensors is used to detect gases, vapors, and airborne particles, while the leak location is identified through a UWB unit integrated with an Internet of Things (IoT) processor. This processor transmits real-time location data and sensor readings via wireless fidelity (Wi-Fi). The real-time indoor positioning system (IPS) can automatically select a tracking area based on the distances measured from the three nearest anchors of the movable sensor node. The environmental sensor data and distances between the node and the anchors are transmitted to the cloud in JSON format via the user datagram protocol (UDP), which allows the fastest possible data rate. A monitoring server was developed in Python to track the movement of the portable sensor node and display live measurements of the environment. The system was tested by selecting different paths between several adjacent areas with a chemical leakage of different volatile organic compounds (VOCs) in the test path. The experimental tests demonstrated good accuracy in both hazardous gas detection and location tracking. The system successfully issued a leak warning for all tested material samples with volumes up to 500 microliters and achieved a positional accuracy of approximately 50 cm under conditions without major obstacles obstructing the UWB signal between the active system units. Full article

Leak management in water distribution networks (WDNs) is essential for minimising water loss, improving operational efficiency, and supporting sustainable water management. However, effectively identifying, preventing, and locating leaks remains a major challenge owing to the ageing infrastructure, pressure variations, and limited monitoring capabilities. Leakage management generally involves three approaches: leakage assessment, detection, and prevention. Traditional methods offer useful tools but often face limitations in scalability, cost, false alarm rates, and real-time application. Recently, artificial intelligence (AI) and machine learning (ML) have shown growing potential to address these challenges. Deep Reinforcement Learning (DRL) has emerged as a promising technique that combines the ability of Deep Learning (DL) to process complex data with reinforcement learning (RL) decision-making capabilities. DRL has been applied in WDNs for tasks such as pump scheduling, pressure control, and valve optimisation. However, their roles in leakage management are still evolving. To the best of our knowledge, no review to date has specifically focused on DRL for leakage management in WDNs. Therefore, this review aims to fill this gap and examines current leakage management methods, highlights the current role of DRL and potential contributions in the water sector, specifically water distribution networks, identifies existing research gaps, and outlines future directions for developing DRL-based models that specifically target leak detection and prevention. Full article

Anastomotic leaks after colorectal resection are serious surgical complications. We have compared the integrity of two common colorectal anastomosis techniques, end-to-side (ES) and end-to-end (EE), to control specimens using a novel experimental setup that mimics anastomotic air leak tests, which are typically performed during surgeries. Freshly harvested porcine colonic sections from 23 F1 cross-species pigs were used. Pressure measurements and video imaging were used to monitor the ex vivo experiments on EE, ES, and Control specimens. Using EE (n = 16), ES (n = 12), and Control (n = 22) specimens, leak pressure was 282.6 ± 3.0 mm Hg for EE, 282.8 ± 2.6 mm Hg for ES, and 294.4 ± 12.1 for the Control. Time to leakage was 106.3 ± 28.1 s for EE, 263.9 ± 2127.0 s for ES, and 194.5 ± 90.2 s for the Control. We found that, while EE and ES have nearly identical leak pressures, ES was superior in terms of time to leakage and tissue expansion, which may explain why ES anastomoses have a lower clinical anastomotic leak rate. Two dependent variables representing stress and strain of colonic tissues were introduced. These variables showed ES was comparable to the Control. The experiments were simulated successfully using the finite element method (FEM). This research provides a reproducible ex vivo system with a corresponding FEM system to study the differences between anastomosis techniques and may help design anastomoses with lower leak rates and improve patient outcomes in colorectal surgeries. Full article

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

Water distribution is no arguably the most important factor in modern times, and water leak breaks are typically a consequence of failures in water distribution networks. But pipeline leakage breaks have become one of the most frequent consequences affecting the operation of water distribution networks (WDNs) and monitoring their health is often complicated. This paper proposes a leakage break diagnosis method based on an LSTM-FCN neural network model from high-frequency pressure data. Data preprocessing is used to avoid the influence of noise and information redundancy, and the LSTM module and the FCN module are used to extract and concatenate different leakage break features. The leakage break feature is sent to a dense classifier to obtain the predicted result. Two sample sets, steady state and water consumption, were obtained to verify the performance of the proposed leakage break diagnosis method. Three other models, LSTM, FCN, and ANN, were compared using the sample sets. The proposed LSTM-FCN model achieved an overall accuracy of 85% for leakage break detection, illustrating that the model could effectively learn the leakage break features in high-frequency time-series data and had a high accuracy for leakage break detection and leakage break degree prediction of new samples in WDNs. Meanwhile, the proposed method also had good adaptability to the variations in water consumption in actual WDNs. Full article

Water, an essential asset for life and growth, is under growing pressure due to climate change, overpopulation, pollution, and industrialization. At the same time, water distribution within cities relies on piping networks that are over 30 years old and thereby prone to leaks, cracking, and losses. Taking this into account, non-revenue water (i.e., water that is distributed to homes and facilities but not returning revenues) is estimated at almost 50%. To this end, intelligent water management via computational advanced tools is required in order to optimize water usage, to mitigate losses, and, more importantly, to ensure sustainability. To address this issue, a case study was developed in this paper, following a step-by-step methodology for the city of Heraklion, Greece, in order to introduce an intelligent water management system that integrates advanced technologies into the aging water distribution infrastructure. The first step involved the digitalization of the network’s spatial data using geographic information systems (GIS), aiming at enhancing the accuracy and accessibility of water asset mapping. This methodology allowed for the creation of a framework that formed a “digital twin”, facilitating real-time analysis and effective water management. Digital twins were developed upon real-time data, validated models, or a combination of the above in order to accurately capture, simulate, and predict the operation of the real system/process, such as water distribution networks. The next step involved the incorporation of a hydraulic simulation and modeling tool that was able to analyze and calculate accurate water flow parameters (e.g., velocity, flowrate), pressure distributions, and potential inefficiencies within the network (e.g., loss of mass balance in/out of the district metered areas). This combination provided a comprehensive overview of the water system’s functionality, fostering decision-making and operational adjustments. Lastly, automatic meter reading (AMR) devices could then provide real-time data on water consumption and pressure throughout the network. These smart water meters enabled continuous monitoring and recording of anomaly detections and allowed for enhanced control over water distribution. All of the above were implemented and depicted in a web-based environment that allows users to detect water meters, check water consumption within specific time-periods, and perform real-time simulations of the implemented water network. Full article

The aging of dry meat enhances its flavor and tenderness; however, continuous internal quality monitoring throughout the aging process is challenging. We developed and validated a novel electromagnetic response measurement system for meat aging that enables continuous bioimpedance monitoring under stable, optimal temperature/humidity conditions. The system comprises a temperature-controlled dry aging fridge and a newly designed puncture-type semi-rigid coaxial probe, allowing for minimally invasive internal measurements over a broad frequency range. The probe achieved stable measurements across 10 kHz to 10 MHz, and its small diameter (1.25 mm) enabled almost non-destructive internal sensing. Beef and pork samples were monitored over 14 days via multi-channel bioimpedance measurements. After an initial stabilization period, bioimpedance steadily decreased throughout aging. This decline reflected progressive increases in tissue conductivity as cell membranes broke down and intracellular fluids leaked out. High-frequency measurements (e.g., around 10 MHz) were more sensitive to environmental disturbances. Periodic defrost cycles in the chamber caused temporary impedance dips at these frequencies, highlighting the influence of short-term temperature/humidity fluctuations. The system enables long-term continuous measurement without removing samples from the fridge, thus maintaining aging conditions during monitoring. Overall, the system enables the stable, long-term, and multi-channel electromagnetic monitoring of meat quality under optimal aging conditions—a capability not achieved in previous studies. This new method offers a minimally invasive, frequency-resolved approach for assessing meat quality evolution during aging. This advance demonstrates a new approach for tracking meat quality changes during dry aging. Full article

In response to the pressing need for long-range, non-contact detection in hazardous gas leakage monitoring within chemical industrial parks, this study proposes a gas detection algorithm based on an infrared radiation physical model that utilizes dual-band infrared radiation background reconstruction. The proposed method addresses the issues of the existing detection methods’ lack of physical model support. First, appropriate filter wavelength ranges are selected based on the absorption spectral characteristics of the target gas. Subsequently, a physical model incorporating atmospheric attenuation, background radiation, and gas absorption properties is established based on gas radiative transfer theory. The non-absorption band data are then employed to reconstruct the theoretical background radiation of the absorption band. Furthermore, leveraging the synergistic observation advantages of a dual-band infrared imaging system, gas morphology identification is achieved by inverting the difference between the theoretical background and the actual measured values in the absorption band. Experimental results demonstrate that this method enables gas morphology detection through background reconstruction without requiring pre-collected gas-free background images. By implementing dual-band infrared radiation background reconstruction, this study achieves effective gas detection, providing a reliable technical approach for real-time monitoring and early warning of industrial gas leaks. The proposed algorithm enhances detection capabilities, offering significant potential for applications in industrial safety and environmental monitoring. Full article

In order to improve the accuracy of pipe burst detection in water distribution networks (WDNs), a novel small sample diagnosis method (SSDM) based on the head loss ratio (HLR) method and deep transfer learning (DTL) method has been proposed. In this paper, the burst state was quickly detected through the limited data analysis of pressure monitoring points. The HLR method was introduced to enhance data features. DTL was introduced to improve the accuracy of small sample burst detection. The simulated data and real data were enhanced by HLR. Then, the model was trained and obtained through the DTL. The performance of the model was evaluated in both simulated and real scenarios. The results indicate that the leaked features can be improved by 350% by the HLR. The accuracy of SSDM reaches 99.56%. The SSDM has been successfully applied to the detection of real WDNs. The proposed method provides potential application value for detecting pipe bursts. Full article

In response to the limitations of experimental methods for detecting oil and gas well tubing leaks, this study developed a full-scale indoor simulation system for oil tubing leakage. The system consists of three components: a wellbore simulation device, a dynamic leakage simulation module, and a multi-parameter monitoring system. The wellbore simulator employs a jacketed structure to replicate real-world conditions, while the leakage module incorporates a precision flow control device to regulate leakage rates. The monitoring system integrates high-sensitivity acoustic sensors and pressure sensors. Through multi-condition experiments, the system simulated complex scenarios, including leakage apertures of 1–5 mm, different leakage positions relative to the annular liquid level, and multiple leakage point combinations. A comprehensive acoustic signal processing framework was established, incorporating time–domain features, frequency–domain characteristics, and time–frequency joint analysis. Experimental results indicate that when the leakage point is above the annular liquid level, the acoustic signals received at the wellhead exhibit high-frequency characteristics typical of gas turbulence. In contrast, leaks below the liquid level produce acoustic waves with distinct low-frequency fluid cavitation signatures, accompanied by noticeable medium-coupled attenuation during propagation. These differential features provide a foundation for accurately identifying leakage zones and confirm the feasibility of using acoustic detection technology to locate concealed leaks below the annular liquid level. The study offers experimental support for improving downhole leakage classification and early warning systems. Full article