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• Fluorobenzoic acid can serve as a preferential flow tracer in the landfill. • The dual-porosity model coupled with indirect streamline. • Diffusion is important in the tracer transport. • The main leakage area can be determined based on the test result and the model. A field tracer test was carried out in an uncontrolled valley-type landfill. Fluorobenzoic acid (FBA) was firstly used in the landfill to study the preferential flow. A two-dimensional advection–diffusion dual-porosity model coupled with indirect streamline and terrain conditions was developed to analyze the breakthrough curves. The leakage location method was proposed based on the volume proportion of fracture domain in the total domain w f distribution. The results show that FBA is an excellent tracer due to its lower dosage, high peak concentration and long residence time at monitoring wells. The tracer transport depth and length can reach up to 15 m and 86.3 m, respectively. Diffusion drive the tracer flow to upstream with high velocity. The anisotropy value is mainly influenced by the effect of compression rather than the waste age. The horizontal preferential flows dominate in the landfill. The preferential flow is observed to be more obvious with the increasement of the depth due to the increasement of the content of 2D particles. The leakage probability of different part in the landfill is determined by the proposed dual-porosity model and leakage location method. The proposed leakage detection method can be used for active landfills, especially those with thick layers of wastes. It can also provide scientific guidance for the design of subsequent vertical barrier for the landfills. [ABSTRACT FROM AUTHOR]

Due to the significant advantages of methane sensitivity and area-type leakage detection, tunable diode laser absorption spectroscopy (TDLAS) gas detection has been promoted as an effective method for microleakage monitoring in oil and gas stations. However, the output of the TDLAS detector is the integral concentration (IC). Based on the relevant research, the alarm threshold and risk are assessed via the gas concentration rather than the IC. How to evaluate the concentration of leaked gas clouds based on IC from TDLAS detectors is still a challenge. To address this problem, the characteristics of IC and the influence of the laser path, wind speed and leakage rate were studied via computational fluid dynamics (CFD). A neural network classification model (NNCM) was proposed to obtain the probability distribution of the maximal concentration along the laser path (Cmax). The results indicated that the IC is strongly correlated with the Cmax. Considering the accuracy and operability, the NNCM with input features of IC, wind speed and angle of laser path was selected. Field tests showed that the developed model achieved the concentration evaluation of leaked gas clouds. Additionally, the NNCM can also quantify the uncertainty of the results, which avoids misjudgments caused by deviations. [ABSTRACT FROM AUTHOR]

In the transition towards a hydrogen-based energy system, the strategic use of pipelines is crucial for efficient hydrogen distribution. Leveraging existing natural gas pipelines to carry a mix of hydrogen and natural gas offers a cost-effective alternative to building new infrastructure. This study explores the development of a leak detection system compatible with existing pipelines, specifically tailored for the blended hydrogen and natural gas mix. Given the scarcity of leak data for blended hydrogen-natural gas pipelines, the study introduces a Real-Time Transient Model (RTTM) for blended gases, simulating leak dynamics to generate necessary data. Additionally, a leak detection system (LDS) is developed using a fusion of Convolutional Neural Network (CNN) and Explainable Artificial Intelligence (XAI) through Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This LDS framework overcomes the "black box" issue common in AI-driven systems, enabling reliable detection. The integration of Explainable and traditional AI techniques holds promising implications for blended hydrogen pipelines by enhancing the safety and efficiency of hydrogen transportation, thereby mitigating economic and environmental impacts, and addressing public concerns. • Development of a Real-Time Transient Model (RTTM) for simulating blended hydrogen and natural gas leak dynamics. • AI-based leak detection system (LDS) that addresses the typical "black box" issue in AI-driven systems. • The LDS combines outputs from Convolutional Neural Networks (CNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). • In simulated tests, the framework demonstrates high predictive accuracy using data from common field instrumentation. [ABSTRACT FROM AUTHOR]

Underground hydrogen storage (UHS) addresses the volume issue in hydrogen storage but the storage sites are prone to leakage. Currently, modeling-based approaches that utilize the reservoir's history for leakage detection are critically lacking for UHS. These methods offer a cheap and reliable approach to complement monitoring efforts of a UHS process. In this research, a semi-analytical modeling approach using pressure transient analysis is presented to detect leakage, estimate the leakage rate, and identify the most influential factors affecting the magnitude of the leakage rate in a multi-layered reservoir, in which hydrogen migrates from a target to a non-target layer. Multi-parameter sensitivity analysis using factorial design reveals hydraulic conductivity and layer flow capacity as key factors affecting leakage rate. A double evaluation algorithm with the capability of estimating the hydraulic conductivity is introduced that offers its highest accuracy (90%) when the first layer's flow capacity is lower than the second layer's. • Pressure transient approach for leakage detection in underground hydrogen storage. • Hydraulic conductivity and layer flow capacity parameters affect leakage the most. • Algorithm for hydraulic conductivity estimation using target layer's pressure data. [ABSTRACT FROM AUTHOR]

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety. [ABSTRACT FROM AUTHOR]

The hydrogen (H2) energy industry has continued to expand in recent years due to the decarbonization of the global energy system and the drive towards sustainable development. Due to hydrogen's high flammability and significant safety risks, the efficient detection of hydrogen has become an increasingly hot issue today. In this work, a new type of relatively fast and responsive conducting polymer sensor has been demonstrated for tracing H2 gas in a nitrogen environment. Inspiration of unique properties of carbon nanotube (CNT) and graphene, polyaniline (PANI) hollow nanotubes, PANI thin films are fabricated to study for structural-properties investigation. The PANI hollow nanotube sensor ensures the 1 ppm hydrogen gas detection at room temperature, and exhibits high sensitivity (29%) and fast response and recovery times of 15 and 17 s, follows by PANI thin film sensor (20%), response and recovery times of 65s and 45s. This conducting polymer-based hydrogen sensor holds promise for the early detection of H2 leaks in a wide range of industries. [ABSTRACT FROM AUTHOR]

Hydrogen (H2) is increasingly employed in industrial applications, such as developing hydrogen fuel cells for vehicles and power plants. However, H2 explodes at a concentration limit of 4%, necessitating the development of ultrasensitive hydrogen sensors capable of early‐stage detection of hydrogen leaks. In this study, nano‐patterned polycrystalline cupric oxide (CuO) nanowire array nanogap gas sensors with voids are fabricated using electron‐beam lithography and ex situ oxidization by annealing, which could detect 5 ppb H2 and show response and recovery times of less than 10 s without a baseline shift. Combining a pre‐H2 annealing process in Ar/H2 for Cu nanowires and a low‐rate oxidation process enhances the crystallinity of CuO nanowires, facilitating the preparation of polycrystalline CuO nanowires with voids, which is a significantly practical approach for the improvements of gas sensing properties. Response and recovery times of <10 s can be obtained for a gap separation of ≈30 nm. These improvements are discussed based on a high electric field of ≈1.3 MV cm−1. The relationship between the normalized response and H2 concentration is discussed based on the power law. This paper presents highly reliable and fast H2 sensors without a baseline shift to meet the demands of the hydrogen industry. [ABSTRACT FROM AUTHOR]

Cracks represent a significant challenge to the structural integrity of piping systems. Therefore, this study is devoted to the detection of macro-micro cracks in pipes with silicate-enamel coatings, which are commonly used to enhance corrosion resistance. While the process of detecting macro cracks on the surface of the coating is currently underway, the identification of macro-micro cracks remains a significant challenge. The article highlights the significance of their timely detection to prevent potential leakage and design failures, underscoring the implications for economic advancement and security. An algorithm for detecting microcracks on the surface of the pipes with silicate-enamel coatings was developed. The test voltage of the enhanced holiday detector was increased to 40 kV, which allowed the microcracks as small as 1.5 mm long to be detected. A methodology for the remote detection of oil leaks from oil trunk pipelines was presented. Following the testing process, the coordinates and geometric parameters of the cracks were determined. [ABSTRACT FROM AUTHOR]

Aiming at the problem of detecting and locating the leakage position of urban pipelines, an underwater navigation and positioning method combining the jet link inertial navigation system and the simultaneous composition positioning algorithm is proposed. The sonar sensor is used to collect the characteristic position information of urban pipelines, and the pipeline map is constructed under the action of the simultaneous composition positioning algorithm to obtain high-precision positioning information. The positioning information obtained above is then combined with the Jet link inertial navigation system using a particle filtering algorithm to compensate for its position error accumulation. The simulation experiment results show that the positioning accuracy of the described combination method is high, reaching 0.1% of the total range. [ABSTRACT FROM AUTHOR]

The vast expanses of remote onshore areas in oil-producing countries are home to a network of flow and collection pipelines that are susceptible to leaks. Most of these areas lack the infrastructure to enable the use of remote monitoring systems equipped with sensors and real-time data analysis to provide early detection of anomalies. This paper proposes a proof of concept for a monitoring system based on the Internet of Things (IoT) for real-time detection of pipeline leaks in onshore oil production fields. The proposed system, based on a thermal imaging leak detection method, informs the operator of the system's operating status via a web page. The leak detection system communicates via a Zigbee network between the IoT devices and a 4G mobile network. The results of the tests carried out show that a visual and automatic IoT-based leak detection system is possible and plausible. The proposed leak detection system enables supervisors at remote stations and field workers to monitor the operating status of pipelines via computers, tablets, or smartphones, regardless of where they are. [ABSTRACT FROM AUTHOR]

This study focused on developing machine learning models to detect leak size and location in transient state conditions. The model was designed for an onshore methane–hydrogen blending gas pipeline in Canada. Base case simulations revealed significant effects on mass flow and pressure due to leaks, with the system taking approximately 6 h to reach a steady state from transient conditions. This made it essential to analyze the flow characteristics during the transient state. Trend data from the pipeline's inlet and outlet were examined, considering the leak size and location. To better represent the data over time, a method was used to create two-dimensional images, which were then fed into a CNN (convolutional neural network) for training. The model's accuracy was assessed using classification accuracy and a confusion matrix. By refining the data acquisition process and implementing targeted screening procedures, the model's classification accuracy increased to over 80%. In conclusion, this study demonstrates that machine learning can enable rapid and accurate leak detection in transient state conditions. The findings are expected to complement existing leak detection methods and support operators in making faster, more informed decisions. [ABSTRACT FROM AUTHOR]

Compressed air ranks among the most expensive forms of energy. In recent decades, increased efforts have been made to enhance the overall energy efficiency of pneumatic actuator systems and develop reliable fault detection methods for preventing energy losses. However, most of the methods developed so far require additional sensors, resulting in extra costs, and/or are not applicable during machine operation, which leads to their limited use in the industry. This article introduces a cycle time-based method for detecting faults in pneumatic actuators through the use of proximity switches, enabling cost-effective monitoring in real time without the necessity of further sensors. A systematic analysis is conducted, expanding the current state of knowledge by detailing the influence of all potential leakage points on the movement times of a pneumatic drive and taking into account the different velocity control strategies (meter-out and meter-in) and operating points expressed via the pneumatic frequency ratio. Previously unassessed specifics of internal leakage, including the impact of pressure profiles and differences between differential cylinders and cylinder with equal piston areas, are also presented. The applicability of the proposed method and its detection limits in an industrial environment are examined using pneumatic assembly machines. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of -0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period. [ABSTRACT FROM AUTHOR]

With the advancement of Fuel Cell Vehicles (FCVs), detecting hydrogen leaks is critically important in facilities such as hydrogen refilling stations. Despite its significance, the dynamic response performance of hydrogen sensors in confined spaces, particularly under ceilings, has not been comprehensively assessed. This study utilizes a catalytic combustion hydrogen sensor to monitor hydrogen leaks in a confined area. It examines the effects of leak size and placement height on the distribution of hydrogen concentrations beneath the ceiling. Results indicate that hydrogen concentration rapidly decreases within a 0.5–1.0 m range below the ceiling and declines more gradually from 1.0 to 2.0 m. The study further explores the attenuation pattern of hydrogen concentration radially from the hydrogen jet under the ceiling. By normalizing the radius and concentration, it was determined that the distribution conforms to a Gaussian model, akin to that observed in open space jet flows. Utilizing this Gaussian assumption, the model is refined by incorporating an impact reflux term, thereby enhancing the accuracy of the predictive formula. [ABSTRACT FROM AUTHOR]

Hydrogen is currently considered to be the best clean energy to replace fossil fuels, so hydrogen leak detection is crucial. The current research field of hydrogen sensors focuses on the discovery and optimization of sensitive materials. ZnFe 2 O 4 powder is prepared by the sol-gel method and sintered at different temperatures. The effects of sintering temperature on electrode morphology and hydrogen sensing performance are investigated. The sensitivity reaches a maximum value of −101.32 mV/decade at the sintering temperature of 1100 °C. The sensor has different sensing performance at different operating temperatures. It has the best response value and sensitivity at 450 °C, and the best response/recovery rate and response curve stability at 550 °C. The sensor responds and recovers in seconds. The material has good H 2 selectivity and is suitable for potentiometric hydrogen sensors. CO, NH 3 , and CH 4 have less effect on the response signal and exhibit good long-term stability. • Spinel ferrite is first used as an H 2 detection electrode for potentiometric sensors. • ZnFe 2 O 4 has high response value and sensitivity to hydrogen gas. • ZnFe 2 O 4 has a low detection limit and fast response/recovery rate for hydrogen gas. [ABSTRACT FROM AUTHOR]

Recent years have seen increased interest in object detection‐based applications for fire detection in digital images and videos from edge devices. The environment's complexity and variability often lead to interference from factors such as fire and smoke characteristics, background noise, and camera settings like angle, sharpness, and exposure, which hampers the effectiveness of fire detection applications. Limited picture data for fire and smoke scenes further challenges model accuracy and robustness, resulting in high false detection and leakage rates. To address the need for efficient detection and adaptability to various environments, this paper focuses on (1) proposing a cloud‐edge collaborative architecture for real‐time fire and smoke detection, incorporating an iterative transfer learning strategy based on user feedback to enhance adaptability; (2) improving the detection capabilities of the base model YOLOv8 by enhancing the data augmentation method and introducing the coordinate attention mechanism to improve global feature extraction. The improved algorithm shows a 2‐point accuracy increase. After three iterations of transfer learning in the production environment, accuracy improves from 93.3% to 96.4%, and mAP0.5:0.95 increases by nearly 5 points. This program effectively addresses false detection issues in fire and smoke detection systems, demonstrating practical applicability. [ABSTRACT FROM AUTHOR]

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments. [ABSTRACT FROM AUTHOR]

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the methods and techniques developed for detecting leaks in water distribution systems, with a focus on highlighting their strengths, weaknesses, and areas for future research. Given the substantial economic, social, and environmental impacts of undetected leaks, timely detection and precise location of leaks are critical concerns for water authorities. This review categorizes existing methods into traditional approaches, such as manual sounding, and modern techniques involving smart water management and sensor technologies. A multidimensional bibliometric analysis was employed to systematically identify, select, and evaluate 600 scholarly articles on water leak detection, sourced from the Scopus database over a 23-year period (2000–2023). The paper evaluates each method based on leak sensitivity, burst detection, continuous monitoring, alarm accuracy, and implementation costs. Novel insights include an analysis of emerging smart water technologies and their integration into real-world water distribution networks, offering improved efficiency in leak detection. The paper also identifies key gaps in current research and suggests future directions for advancing the accuracy and cost-effectiveness of these technologies. [ABSTRACT FROM AUTHOR]

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks. [ABSTRACT FROM AUTHOR]

This study presents a meticulous investigation facilitated by a bespoke high-pressure test rig. The rig stands as a cornerstone of the research endeavour, providing the capability to simulate diverse leakage scenarios under controlled conditions. With its capacity to replicate conditions akin to real-world hydraulic systems, including various levels of severity such as healthy operation, moderate leakage and severe leakage. The study delves into the intricate analysis of time-series discharge flow rate signals within this experimental framework. The study utilizes sophisticated signal processing techniques, particularly the Continuous Wavelet Transform (CWT), to observe frequency components and temporal occurrences. Despite prevalent challenges distinguishing between leakage severity levels, the CWT-based analysis provides crucial insights into the dominant low frequencies (2.3–3.3 hz) characterising all leakage conditions. An advanced computer vision-based methodology is devised to address the complexities inherent in leakage differentiation, integrating cutting-edge models such as You Only Look Once (YOLO-V7) and You Only Look Once-Neural Architecture Search (YOLO-NAS). These models are meticulously trained using annotated CWT images of flow rates corresponding to different leakage conditions. Notably, the superiority of YOLO-NAS in terms of both speed and accuracy underscores its efficacy in automated leakage detection. In summary, this comprehensive approach, underpinned by the innovative hydraulic test rig and advanced signal processing coupled with state-of-the-art computer vision techniques, presents a significant advancement in the realm of internal leakage detection in hydraulic systems. [ABSTRACT FROM AUTHOR]

This research paper underscores the critical importance of advancing submarine technology by proposing a fully hydrogen‐powered submarine integrated with an air‐independent propulsion (AIP) system and artificial intelligence (AI)‐driven safety measures. Such an innovative approach not only addresses the limitations of traditional diesel‐electric submarines but also promises to revolutionize naval operations. The adoption of hydrogen as the primary power source significantly extends the submarine's operational range, reducing its dependence on fossil fuels and minimizing environmental impact. The incorporation of AIP technology enables sustained underwater endurance, reducing vulnerability and enhancing mission effectiveness. Concurrently, AI‐driven safety measures provide robust leak detection and safe hydrogen handling, ensuring the well‐being of the crew while optimizing operational efficiency. This integrated solution represents a transformative step toward achieving a more sustainable, capable, and stealthy submarine fleet for modern warfare scenarios. [ABSTRACT FROM AUTHOR]

The efficient detection of leakages in water distribution networks (WDNs) is crucial to ensuring municipal water supply safety and improving urban operations. Traditionally, machine learning methods such as Convolutional Neural Networks (CNNs) and Autoencoders (AEs) have been used for leakage detection. However, these methods heavily rely on local pressure information and often fail to capture long-term dependencies in pressure series. In this paper, we propose a transformer-based model for detecting leakages in WDNs. The transformer incorporates an attention mechanism to learn data distributions and account for correlations between historical pressure data and data from the same time on different days, thereby emphasizing long-term dependencies in pressure series. Additionally, we apply pressure data normalization across each leakage scenario and concatenate position embeddings with pressure data in the transformer model to avoid feature misleading. The performance of the proposed method is evaluated by using detection accuracy and F1-score. The experimental studies conducted on simulated pressure datasets from three different WDNs demonstrate that the transformer-based model significantly outperforms traditional CNN methods. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid (CSF) leaks can lead to serious complications if left untreated, making rapid and accurate diagnosis essential. Biomarkers such as β2-transferrin (B2TRF) and β-trace protein are used to detect CSF leaks, but their limitations warrant the exploration of alternative markers. This study investigates the potential of phosphorylated tau at threonine 181 (p-tau181) as a biomarker for CSF leaks. Methods: Samples from 56 subjects were analyzed for B2TRF and p-tau181 using immunoaffinity blotting and chemiluminescent enzyme immunoassay, respectively. Data analysis included Mann–Whitney test to assess the overall difference in median p-tau181 concentrations between B2TRF positive and negative patients and a receiver operating characteristic (ROC) curve analysis to determine optimal p-tau181 cutoff values for predicting B2TRF positivity. Results: p-tau181 levels were significantly higher in B2TRF positive samples compared to negative samples (p < 0.001). ROC analysis showed high diagnostic performance for p-tau181, with an optimal cutoff of 13.22 pg/mL providing 92.0% sensitivity and 93.1% specificity. Excluding hemolyzed samples improved further the diagnostic performances, maintaining high sensitivity (90.9%) and achieving perfect specificity (100.0%). Conclusions: This study highlights the potential of p-tau181 as a valuable biomarker for the detection of CSF leaks due to its high diagnostic accuracy and practical advantages over the current biomarkers. The characteristics of p-tau181 assay being both quantitative and rapid, with high diagnostic accuracy, suggest that it could be a valuable tool for the detection of CSF leaks. Further research are now needed to validate these findings. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Boiler tube leaks significantly reduce the operational availability of power units, yet their early detection and prediction have not been fully realised in the industry. This paper introduces a novel approach employing deep feedforward neural networks for early detection of boiler tube leaks in pulverised coal-fired boilers. Early detection enhances repair planning, minimising downtime and production losses. It also improves monitoring and control of boiler tube failures, thereby optimising power plant operations and revenue. Diverse deep neural network models were developed and rigorously tested by leveraging 9 years of operational data (2012-2020). Exhaustive hyper-parameter optimisation, involving seven parameters, substantially improved predictive accuracy. By achieving training and testing accuracies of 82.8% to 99.3%, the study assessed their ability to detect boiler tube leaks over the same 9-year span, providing insights into leak detection capabilities. The models notably predicted all 12 identified tube leak events, averaging a 14-day lead time before boiler shutdown. In addition to leak prediction, a leak detection matrix was devised to analyse residual behaviour and reduce the likelihood of false alarms. However, the models' predictive performance was observed to be limited to the following year, with satisfactory results for 2021 only. Incorporating the 2021 data into retraining significantly improved the predictions for 2022. The study concludes that while the models demonstrate robust short-term prediction capabilities, they require continuous retraining to maintain accuracy and relevance. This ongoing refinement is essential for keeping the models up-to-date and reliable in predicting future boiler tube leaks. [ABSTRACT FROM AUTHOR]

The introduction of remote sensing technologies, AI and big data analytics in the utility sector is warranted by the need to provide critical services with the least disruption to customers, but also to enable preventive maintenance, extend the life cycle of infrastructure components and reduce grid loss—or overall, to exhibit 'durability' and 'resilience' when faced with the certainty of breakage and decay. In this paper, we first explore the concept of 'resilience' and the nature of practice from a performativist perspective in order to set the scene for discussing the impact of 'datafication' on maintenance practices and infrastructure durability. We then describe an instance of introducing remote sensing technologies in district heating network surveillance and leak detection: drone-operated thermographic cameras and underground wire sensors. Based on insights from this case study, we discuss the specificity of data-driven infrastructure maintenance practices, and what it means to exhibit practical resilience in relation to how such practices unfold, interrelate and evolve over time. We reflect on how the use of remote sensing technologies and data analytics (1) potentially changes district heating workers' epistemic worlds (i.e., how knowledge emerges, is negotiated and ordered in practice) and (2) provides opportunities for 'messy' pipe repair work to tacitly adopt proactive and preventive logics to meet continuously evolving organizational and societal needs. [ABSTRACT FROM AUTHOR]

As the main support part of the working platform of a high-rise building machine, the bearing pin support (BPS) plays a crucial role in the safety and stability of the platform, the conventional method has the problems of low detection efficiency, low accuracy, and high cost. To improve the accuracy and robustness of the detection algorithm under weak light, this paper proposes an intelligent detection algorithm for the BPS-piece states of the BS-YOLOV8, to improve the feature map utilization and reduce the model leakage detection error detection rate, Swin transformer is used to improve the YOLOV8 backbone network. In addition, the BiFormer attention mechanism is used to weigh the feature map to solve the problem of feature information loss in different feature layers and weak lighting conditions, and then the Scylla-IOU loss function is used instead of the original localization loss function to guide the model to learn to generate a predicted bounding box closer to the real target bounding box. Finally, the BS-YOLOV8 algorithm is used to compare with its classical algorithm on the self-constructed dataset of this study, The results show that the mAP0.5, mAP0.5:0.95, and FPS values of the BS-YOLOV8 algorithm reach 97.9%, 96.3% and 40 under normal lighting. The mAP0.5 value reaches 87.6% under low light conditions, which effectively solves the problems of low detection efficiency and poor detection under low light conditions, and is superior compared to other algorithms. [ABSTRACT FROM AUTHOR]

The saline aquifer CCS is a crucial site for carbon storage. Safety monitoring is a key technology for saline aquifer CCS. Current CO2 leakage detection methods include microseismic, electromagnetic, and well-logging techniques. However, these methods face challenges, such as difficulties in determining CO2 migration fronts and predicting potential leakage events; as a result, the formulation of test timing and methods for these safety monitoring techniques are somewhat arbitrary. This study establishes a gas–water two-phase seepage model and solves it using a semi-analytical method to obtain the injection pressure and the derivative curve characteristics of the injection well. The pressure derivative curve can reflect the physical properties of the reservoir through which CO2 flows underground, and it can also be used to determine whether CO2 leakage has occurred, as well as the timing and amount of leakage, based on boundary responses. This study conducted sensitivity analyses on eight parameters to determine the impact of each parameter on the bottom-hole pressure and its derivatives, thereby obtaining the influence of its parameters on different flow stages. The research indicates that, when a steady-state flow characteristic appears at the outer boundary, CO2 leakage will occur. Additionally, the leakage location can be determined by calculating the distance from the injection well. This can guide the placement and measurement of safety monitoring methods for saline aquifer CCS. The method proposed in this paper can effectively monitor the timing, location, and amount of leakage, providing a technical safeguard for promoting CCS technology. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technologies with distributed wireless sensors have been increasingly adopted in water utilities to build smart water networks (SWNs) for monitoring purposes. Based on the daily data collected from an accelerometer-based SWN, this paper proposes a new data analytic approach to detect developing cracks in water networks. The daily signals over a continuous period (e.g., 100 days) have been converted to a time-frequency power spectral density (PSD) heatmap. An analytic approach to detect developing cracks on the PSD heatmap has been formulated using a Spearman's rank correlation coefficient. With flexible temporal window lengths and frequency-associated weights adopted, the method involves an optimal search concept in the time-frequency domain for evidence of developing cracks. The implementation of the new method to field data collected from an SWN illustrates that the method can robustly detect developing cracks at their incipient stage, and thus allow adequate time for proactive repair before evolving into pipe breaks. The method is tolerant of noise, which is commonly present in the data collected by the sensors deployed in city areas. [ABSTRACT FROM AUTHOR]

Pipeline networks have a wide range of applications, from the transportation of energy sources such as oil and natural gas to the conveyance and distribution of water resources. However, leaks and ruptures in pipelines can cause significant harm to the environment. Therefore, it is crucial to accurately detect pipeline faults in order to avoid economic losses and protect the environment. In this study, pipeline networks carrying water fluid are represented using graph structures. The graph convolutional network (GCN) algorithm is employed for the detection of leaks and blockages in pipeline networks. Experimental methods are employed to collect the necessary data (pressure data) for the GCN algorithm, creating two datasets by considering five different scenarios. The fault detection performance of the GCN algorithm is compared with other graph machine learning algorithms, namely, RGCN, HinSAGE, and GraphSAGE. The results of this study indicate that the performance of the GCN model surpasses that of the other algorithms. Reviewing the literature, accuracy rates for fault diagnosis in pipeline networks using machine learning algorithms range from 78.51% to 99%. In this study, it is found that the GCN, GraphSAGE, HinSAGE, and RGCN algorithms achieve fault detection accuracies of 91%, 90%, 87%, and 89%, respectively, in pipeline networks. Classical machine learning SVM model was used to compare the performance of graph-based algorithms. It is seen that the performances of the algorithms face the literature and the results are above the literature average. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JOCSTEC) is the property of PT. Padang Tekno Corp and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The recent transition to H2-based energy storage demands reliable H2 sensors that allow for easy, fast, and reliable detection of leaks. Conventional H2 detectors are based on the changes of physical properties of H2 probes induced by subsurface H-atoms to a material such as electrical conductivity. Herein, we report on highly reactive gasochromic H2 detectors based on the adsorption of H2 on the material surface. We prepared supraparticles (SPs) containing different types of noble metal nanoparticles (NPs), silica NPs, and the dye resazurin by spray-drying and tested their performance for H2 detection. The material undergoes a distinct color change due to the hydrogenation of the purple resazurin to pink resorufin and, finally, colorless hydroresorufin. The stepwise transition is fast and visible to the naked eye. To further improve the performance of the sensor, we tested the reactivity of SPs with different catalytically active NPs by means of in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). We show that the choice of the NP catalyst has a pronounced effect on the response of the H2 indicator. In addition, we demonstrate that the performance depends on the size of the NPs. These effects are attributed to the availability of reactive H-atoms on the NP surface. Among the materials studied, Pt-containing SPs gave the best results for H2 detection. [ABSTRACT FROM AUTHOR]

WO 3 -based optical hydrogen sensors, while advantageous due to their ignition-free operation and resistance to electromagnetic interference, face limitations due to slow response kinetics in hydrogen gasochromic processes. This study presents a novel post-treatment method using aqueous sulfuric acid, which significantly enhances the response speed of mesoporous Pt/WO 3 films. After treatment, the films exhibit an ultrashort response time of 2.4 s towards 4% H 2 /Ar, a marked improvement from 7.1 s prior to the treatment. The treated films also show excellent reversibility of 120 gasochromic cycles. The adsorbed acid layer is proved to be the cause of accelerated hydrogen gasochromic response. This exceptional performance is attributed to increased proton conductivity and the high porosity of the mesoporous structure. A modified model is proposed for the acceleration effect by acid post-treatment. Our post-treatment method represents a significant advancement in the reliable detection of hydrogen leaks at room temperature. • Post-treatment in acid significantly accelerate gasochromic response of Pt/WO 3 film. • A coloration response time of 2.4 s was obtained by treatment in 1 M sulfuric acid. • The adsorbed acid layer is the cause of accelerated hydrogen gasochromic response. • A modified model is proposed for the acceleration effect by acid post-treatment. [ABSTRACT FROM AUTHOR]

In order to reduce the probability of leakage and improve detection accuracy, multiple radar equipment is required to perform multiple detections on the same airspace. Due to the overlap of airspace, there are multiple local tracks reported by various radar equipment that belong to the same target. The local tracks reported by each radar require track fusion to form a system track, and track association is a prerequisite for track fusion. This paper proposes a multi-target and multi-sensor track association analysis method, which can distinguish whether local tracks come from the same target and associate local tracks with the same target, providing conditions for track fusion to form system tracks. This method divides track association into two steps: Coarse association and fine association, which greatly improves the accuracy of track association. [ABSTRACT FROM AUTHOR]

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the application of Schlieren flow visualization for detecting leaks in pipelines carrying high-temperature fluids. Two experimental setups were constructed: one with a 25 mm PTFE tube featuring a 2 mm diameter perforation, and another with a 100 mm diameter pipe insulated with an aluminum jacket and featuring a 12 mm leak gap. A single-mirror-off-axis Schlieren system, employing a 150 mm diameter parabolic mirror, was used to visualize the leaks. The temperature of the leaking air varied between 20 and 100 °C, while the ambient temperature was maintained at 14 °C. To quantify the leaks, the coefficient of variation for pixel intensity within the leak region was calculated. Results showed that for the PTFE tube, leaks became detectable when the temperature difference exceeded 34 °C, with the coefficient of variation surpassing 0.1. However, in the insulated pipe, detecting clear leak patterns was challenging. This research demonstrates the potential of Schlieren visualization as a valuable tool in enhancing pipeline leak detection. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of hydrogen gas detection in pipelines, focusing on the highly flammable nature and low ignition energy of hydrogen. It highlights the limitations of traditional point-based detection methods and emphasizes the need for advanced safety technologies. An integrated approach combining onsite monitoring with Internet of Things (IoT) technology is proposed to enhance systematic safety management and quick leak detection in hydrogen infrastructures. The study reviews various hydrogen detection methods, emphasizing distributed temperature sensing (DTS) techniques to identify leaks through temperature variations. Over nine months of monitoring, the results indicate that DTS temperature variations are more influenced by sensor location than chamber configuration, suggesting that external DTS chamber installation is more effective for leak detection. Additionally, the study integrates Gaussian Process Regression with Machine Learning to predict temperature distribution in pipelines, providing valuable insights for future hydrogen leak detection research. • Developed new DTS technology with enhanced resolution for sensitive hydrogen leak detection. • Confirmed DTS system's reliability against IEC 61757-2-2 standards through rigorous testing. • Integrated IoT and 3D mapping in software for efficient hydrogen leak monitoring. • Found DTS temperature variations more influenced by installation location than chamber design. • Employed GPR and Machine Learning for predicting hydrogen pipeline temperature distribution. [ABSTRACT FROM AUTHOR]

Aiming at the leakage problem of the fluid loop system for the thermal control system of spacecraft caused by the impact of micro-meteoroid and orbital debris, we investigate the dependence of the leak rate, pressure drop and flow characteristics before and after leaking on leak position, inlet pressure, and leaking aperture, calculated in a straight pipeline with single leakage, using stationary and transient three-dimensional computational fluid dynamics simulations with commercial software (FLUENT). The working fluid adopts single-phase water without gravity. It is found that the pressure increases obviously near the leak point, which is caused by the local peak of pressure. As the length between the leak point and the inlet becomes larger, the local peak of pressure and the dimensionless leak rate are smaller. When the inlet pressure increases, the leaked mass flow rate increases; however, the ratio of the leaked flow rate to inlet flow rate decreases. Furthermore, it is observed that the dependence of dimensionless leaking rate on dimensionless leaking aperture has a scaling relation with the scaling exponent approximating to 2, which may be related to the proportion of the vortex formed by the backward flow at the leak hole, the amplitude, and the affected length along the pipe of the local peak of pressure. This study is instructive and meaningful to the leak detection and plugging of fluid loop in space. [ABSTRACT FROM AUTHOR]

Porous graphene membranes (PGMs) have nanopores with single atomic thickness, which enables the precise and stable supply of ultralow flow rate gas below 10−14 Pa·m3·s−1. Different from a conventional channel, the surface diffusion (SD) process in PGM has become increasingly important and unique. However, the physical process and mathematical model of gas molecule transport in nanopores with single atomic thickness remain unclear. These inadequacies constrained the application of PGM in ultrasensitive leak detection. In this paper, the SD process in PGM was investigated using molecular dynamics simulation. A test rig was constructed to verify the simulation results. The nanopores in PGM were quantitatively characterized using a transmission electron microscope. Results show that a transfer region encircling the nanopores was identified, which plays a crucial role in the SD process. Furthermore, the physical model of SD process is described with a two-step model. Finally, a mathematical model of the SD process is established and validated. This paper provides nanoscale insights for an in-depth understanding of the SD process in PGM and promotes ultrasensitive leak detection technology. [ABSTRACT FROM AUTHOR]

Leakage in submarine oil and gas pipelines can result in financial losses and marine environmental pollution.Timely detecting, analyzing and locating leaks can minimize losses and strengthen pipeline quality control. In this paper, theleak detection system of submarine pipeline in offshore oil jetty projects was studied. By comparing and analyzing existing leakdetection technologies, a practical application solution for the submarine pipeline leak detection system in offshore oil jettyprojects was proposed. The combination of negative pressure wave method and flow balance method can improve the accuracy ofleak detection and reduce false alarm rates, providing a reliable and efficient solution for the project. [ABSTRACT FROM AUTHOR]

Anastomotic leak (AL) remains a severe complication following colorectal surgery, leading to increased morbidity and mortality, particularly in cases of delayed diagnosis. Existing diagnostic methods, including computed tomography (CT) scans, contrast enemas, endoscopic examinations, and reoperations can confirm AL but lack strong predictive value. Early detection is crucial for improving patient outcomes, yet a definitive and reliable predictive test, or "gold standard," is still lacking. A comprehensive PubMed review was focused on CT imaging, serum levels of C-reactive protein (CRP), and procalcitonin (PCT) to assess their predictive utility in detecting AL after colorectal resection. Three independent reviewers evaluated eligibility, extracted data, and assessed the methodological quality of the studies. Summarized in detailed tables, our analysis revealed the effectiveness of both CRP and PCT in the early detection of AL during the postoperative period. CT imaging, capable of identifying fluid collection, pneumoperitoneum, extraluminal contrast extravasation, abscess formation, and other early signs of leak, also proved valuable. Considering the variability in findings and statistics across these modalities, our study suggests a personalized, multimodal approach to predicting AL. Integrating CRP and PCT assessments with the diagnostic capabilities of CT imaging provides a nuanced, patient-specific strategy that significantly enhances early detection and management. By tailoring interventions based on individual clinical characteristics, surgeons can optimize patient outcomes, reduce morbidity, and mitigate the consequences associated with AL after colorectal surgery. This approach emphasizes the importance of personalized medicine in surgical care, paving the way for improved patient health outcomes. • Anastomotic leak (AL) after colorectal surgery demands urgent detection. • CRP and PCT show promise as biomarkers for early detection of AL. • Varied diagnostic methods at different postoperative stages enhance AL prediction. • Consider individual patient factors for personalized biomarker interpretation. [ABSTRACT FROM AUTHOR]

Leaks are a constant problem in water distribution systems, resulting in wasted resources, environmental impacts, and financial losses. Thus, it is crucial to develop effective and agile methods to detect network leaks. In this context, this study proposes a leak detection methodology using three different processes. The first consists of treating monitoring data through independent component analysis, and the other two detection processes use the interquartile range (IQR) and matrix profile (MP) techniques, respectively. The methodology is evaluated based on a set of benchmark data. The results indicate that the proposed approach is effective in detecting leaks, with some cases being detected in a few minutes after the beginning of the leak. It is worth mentioning that the IQR method presented better performance in detecting leaks with abrupt onset, whereas the MP method was more efficient in leaks with gradual increase in flow. In summary, the proposed methodology offers a robust and promising approach for fast and accurate leak detection in water distribution networks. [ABSTRACT FROM AUTHOR]

The problem of localizing two leaks using only flow rate and pressure measurements at the boundaries of a pipeline, and under steady-state flow conditions, is ill-posed due to the undetermined nature of the inverse problem, which involves two coupled equations with four unknowns associated with the presence of the leaks: two emitter coefficients and two locations. Therefore, attempting to solve this problem using any method without imposing additional constraints leads to an unbounded solution space, which contains solutions that may be physically meaningless. In this article, we propose a four-algorithm method that incorporates both spatial and hydraulic constraints to filter out physically infeasible solutions. The proposed method is based on Monte Carlo simulations, which use input data from hydraulic instruments installed at the boundaries of the pipeline, as well as random values with predefined probabilities that are bounded by the hydraulic-spatial constraints. The outputs of the method are probability distributions for the four unknowns. To demonstrate the feasibility of the method, results obtained through simulations and experimental testing on a test bed are presented. • A four-algorithm method based on Monte Carlo simulations to localize two leaks in a pipeline. • The method incorporates spatial and hydraulic constraints to eliminate physically infeasible localization estimations. • Tests results using both synthetic and experimental data from a test bed. [ABSTRACT FROM AUTHOR]

The volume of water loss because of leakage in the conveyance pipe has been alarming. Old and poorly constructed pipelines, inadequate corrosion protection, poorly maintained valves, and mechanical damage contribute to leakage. Water-carrying pipes were buried underground, so tracing leak points manually could be tasking, if not impossible. This work was to report on the effectiveness of a developed Automated Leak and Water Quality Detection (ALWQD) system. This device can detect leaks in the piped water system automatically and can also report any deterioration in the quality of water that flows through affected pipes. The ALWQD consisted of several drainpipe connections, pipe accessories, electronic components, and sensors to monitor water quality impairment. The control signal was the solenoid valves that interfaced with the ESP-32 microcontroller boards placed on the pipe manifold at intervals, along with water quality monitoring sensors of turbidity, Total Dissolved Solids (TDS), and pH. The fabrication and testing of the device followed standard procedures. Testing of ALWQD was done at 0, 5, and 10 minutes under load and no-load conditions, with average variation in reading recorded after three trials. The findings indicated that the efficiency of ALWQD was between 70% and 80%, which could be improved upon. The trend in the results of the monitored parameters was not different from that of similar previous work. Leaks caused pressure drops and disallowed the full flow of water found at pipe joints, which could be a pathway for the intrusion of contaminants into the water conveyance system. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)