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Search Results (402)

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Keywords = leak distribution

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8 pages, 855 KiB  
Case Report
Severe Malaria Due to Plasmodium falciparum in an Immunocompetent Young Adult: Rapid Progression to Multiorgan Failure
by Valeria Sanclemente-Cardoza, Harold Andrés Payán-Salcedo and Jose Luis Estela-Zape
Life 2025, 15(8), 1201; https://doi.org/10.3390/life15081201 - 28 Jul 2025
Viewed by 200
Abstract
Plasmodium falciparum malaria remains a major cause of morbidity and mortality, particularly in endemic regions. We report the case of a 21-year-old male with recent travel to an endemic area (Guapi, Colombia), who presented with febrile symptoms, severe respiratory distress, and oxygen saturation [...] Read more.
Plasmodium falciparum malaria remains a major cause of morbidity and mortality, particularly in endemic regions. We report the case of a 21-year-old male with recent travel to an endemic area (Guapi, Colombia), who presented with febrile symptoms, severe respiratory distress, and oxygen saturation below 75%, necessitating orotracheal intubation. During the procedure, he developed pulseless electrical activity cardiac arrest, achieving return of spontaneous circulation after advanced resuscitation. Diagnosis was confirmed by thick blood smear, demonstrating P. falciparum infection. The patient progressed to multiorgan failure, including acute respiratory distress syndrome with capillary leak pulmonary edema, refractory distributive shock, acute kidney injury with severe hyperkalemia, and consumptive thrombocytopenia. Management included invasive mechanical ventilation, vasopressor support, sedation-analgesia, neuromuscular blockade, methylene blue, unsuccessful hemodialysis due to hemorrhagic complications, and platelet transfusions. Despite these interventions, the patient experienced a second cardiac arrest and died. This case highlights the severity and rapid progression of severe malaria with multisystem involvement, underscoring the critical importance of early diagnosis and intensive multidisciplinary management. It also emphasizes the need for preventive strategies for travelers to endemic areas and the development of clinical protocols to improve outcomes in complicated malaria. Full article
(This article belongs to the Section Medical Research)
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16 pages, 803 KiB  
Article
Temporal Decline in Intravascular Albumin Mass and Its Association with Fluid Balance and Mortality in Sepsis: A Prospective Observational Study
by Christian J. Wiedermann, Arian Zaboli, Fabrizio Lucente, Lucia Filippi, Michael Maggi, Paolo Ferretto, Alessandro Cipriano, Antonio Voza, Lorenzo Ghiadoni and Gianni Turcato
J. Clin. Med. 2025, 14(15), 5255; https://doi.org/10.3390/jcm14155255 - 24 Jul 2025
Viewed by 357
Abstract
Background: Intravascular albumin mass represents the total quantity of albumin circulating within the bloodstream and may serve as a physiologically relevant marker of vascular integrity and fluid distribution in sepsis. While low serum albumin levels are acknowledged as prognostic indicators, dynamic assessments [...] Read more.
Background: Intravascular albumin mass represents the total quantity of albumin circulating within the bloodstream and may serve as a physiologically relevant marker of vascular integrity and fluid distribution in sepsis. While low serum albumin levels are acknowledged as prognostic indicators, dynamic assessments based on albumin mass remain insufficiently explored in patients outside the intensive care unit. Objectives: To describe the temporal changes in intravascular albumin mass in patients with community-acquired sepsis and to examine its relationship with fluid balance and thirty-day mortality. Methods: This prospective observational study encompassed 247 adults diagnosed with community-acquired sepsis who were admitted to a high-dependency hospital ward specializing in acute medical care. The intravascular albumin mass was calculated daily for a duration of up to five days, utilizing plasma albumin concentration and estimated plasma volume derived from anthropometric and hematologic data. Net albumin leakage was defined as the variation in intravascular albumin mass between consecutive days. Fluid administration and urine output were documented to ascertain cumulative fluid balance. Repeated-measures statistical models were employed to evaluate the associations between intravascular albumin mass, fluid balance, and mortality, with adjustments made for age, comorbidity, and clinical severity scores. Results: The intravascular albumin mass exhibited a significant decrease during the initial five days of hospitalization and demonstrated an inverse correlation with the cumulative fluid balance. A greater net leakage of albumin was associated with a positive fluid balance and elevated mortality rates. Furthermore, a reduced intravascular albumin mass independently predicted an increased risk of mortality at thirty days. Conclusions: A reduction in intravascular albumin mass may suggest ineffective fluid retention and the onset of capillary leak syndrome. This parameter holds promise as a clinically valuable, non-invasive indicator for guiding fluid resuscitation in cases of sepsis. Full article
(This article belongs to the Section Intensive Care)
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20 pages, 3386 KiB  
Article
Evaluating Acoustic vs. AI-Based Satellite Leak Detection in Aging US Water Infrastructure: A Cost and Energy Savings Analysis
by Prashant Nagapurkar, Naushita Sharma, Susana Garcia and Sachin Nimbalkar
Smart Cities 2025, 8(4), 122; https://doi.org/10.3390/smartcities8040122 - 22 Jul 2025
Viewed by 401
Abstract
The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system [...] Read more.
The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article
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18 pages, 2960 KiB  
Article
Early Leak and Burst Detection in Water Pipeline Networks Using Machine Learning Approaches
by Kiran Joseph, Jyoti Shetty, Rahul Patnaik, Noel S. Matthew, Rudi Van Staden, Wasantha P. Liyanage, Grant Powell, Nathan Bennett and Ashok K. Sharma
Water 2025, 17(14), 2164; https://doi.org/10.3390/w17142164 - 21 Jul 2025
Viewed by 434
Abstract
Leakages in water distribution networks pose a formidable challenge, often leading to substantial water wastage and escalating operational costs. Traditional methods for leak detection often fall short, particularly when dealing with complex or subtle data patterns. To address this, a comprehensive comparison of [...] Read more.
Leakages in water distribution networks pose a formidable challenge, often leading to substantial water wastage and escalating operational costs. Traditional methods for leak detection often fall short, particularly when dealing with complex or subtle data patterns. To address this, a comprehensive comparison of fourteen machine learning algorithms was conducted, with evaluation based on key performance metrics such as multi-class classification metrics, micro and macro averages, accuracy, precision, recall, and F1-score. The data, collected from an experimental site under leak, major leak, and no-leak scenarios, was used to perform multi-class classification. The results highlight the superiority of models such as Random Forest, K-Nearest Neighbours, and Decision Tree in detecting leaks with high accuracy and robustness. Multiple models effectively captured the nuances in the data and accurately predicted the presence of a leak, burst, or no leak, thus automating leak detection and contributing to water conservation efforts. This research demonstrates the practical benefits of applying machine learning models in water distribution systems, offering scalable solutions for real-time leak detection. Furthermore, it emphasises the role of machine learning in modernising infrastructure management, reducing water losses, and promoting the sustainability of water resources, while laying the groundwork for future advancements in predictive maintenance and resilience of water infrastructure. Full article
(This article belongs to the Special Issue Urban Water Resources: Sustainable Management and Policy Needs)
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39 pages, 784 KiB  
Review
A Review of Research on Secure Aggregation for Federated Learning
by Xing Zhang, Yuexiang Luo and Tianning Li
Future Internet 2025, 17(7), 308; https://doi.org/10.3390/fi17070308 - 17 Jul 2025
Viewed by 354
Abstract
Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods that can securely aggregate models have received widespread attention. [...] Read more.
Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods that can securely aggregate models have received widespread attention. Federated learning enables clients to train models locally and share their model updates with the server. While this approach allows collaborative model training without exposing raw data, it still risks leaking sensitive information. To enhance privacy protection in federated learning, secure aggregation is considered a key enabling technology that requires further in-depth investigation. This paper summarizes the definition, classification, and applications of federated learning; reviews secure aggregation protocols proposed to address privacy and security issues in federated learning; extensively analyzes the selected protocols; and concludes by highlighting the significant challenges and future research directions in applying secure aggregation in federated learning. The purpose of this paper is to review and analyze prior research, evaluate the advantages and disadvantages of various secure aggregation schemes, and propose potential future research directions. This work aims to serve as a valuable reference for researchers studying secure aggregation in federated learning. Full article
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33 pages, 6828 KiB  
Article
Acoustic Characterization of Leakage in Buried Natural Gas Pipelines
by Yongjun Cai, Xiaolong Gu, Xiahua Zhang, Ke Zhang, Huiye Zhang and Zhiyi Xiong
Processes 2025, 13(7), 2274; https://doi.org/10.3390/pr13072274 - 17 Jul 2025
Viewed by 309
Abstract
To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the [...] Read more.
To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article
(This article belongs to the Special Issue Design, Inspection and Repair of Oil and Gas Pipelines)
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19 pages, 3047 KiB  
Article
Identifying the Combined Impacts of Sensor Quantity and Location Distribution on Source Inversion Optimization
by Shushuai Mao, Jianlei Lang, Feng Hu, Xiaoqi Wang, Kai Wang, Guiqin Zhang, Feiyong Chen, Tian Chen and Shuiyuan Cheng
Atmosphere 2025, 16(7), 850; https://doi.org/10.3390/atmos16070850 - 12 Jul 2025
Viewed by 161
Abstract
Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts [...] Read more.
Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts. Full article
(This article belongs to the Section Air Pollution Control)
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19 pages, 667 KiB  
Review
A Review of Optimization Methods for Pipeline Monitoring Systems: Applications and Challenges for CO2 Transport
by Teke Xu, Sergey Martynov and Haroun Mahgerefteh
Energies 2025, 18(14), 3591; https://doi.org/10.3390/en18143591 - 8 Jul 2025
Viewed by 381
Abstract
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 [...] Read more.
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
(This article belongs to the Topic Oil and Gas Pipeline Network for Industrial Applications)
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27 pages, 8871 KiB  
Article
Towards a Realistic Data-Driven Leak Localization in Water Distribution Networks
by Arvin Ajoodani, Sara Nazif and Pouria Ramazi
Water 2025, 17(13), 1988; https://doi.org/10.3390/w17131988 - 2 Jul 2025
Viewed by 325
Abstract
Current data-driven methods for leak localization (LL) in water distribution networks (WDNs) rely on two unrealistic assumptions: they frame LL as a node-classification task, requiring leak examples for every node—which rarely exists in practice—and they validate models using random data splits, ignoring the [...] Read more.
Current data-driven methods for leak localization (LL) in water distribution networks (WDNs) rely on two unrealistic assumptions: they frame LL as a node-classification task, requiring leak examples for every node—which rarely exists in practice—and they validate models using random data splits, ignoring the temporal structure inherent in hydraulic time-series data. To address these limitations, we propose a temporal, regression-based alternative that directly predicts the leak coordinates, training exclusively on past observations and evaluating performance strictly on future data. By comparing five machine-learning techniques—k-nearest neighbors, linear regression, decision trees, support vector machines, and multilayer perceptrons—in both classification and regression modes, and using both random and temporal splits, we show that conventional evaluation methods can misleadingly inflate model accuracy by up to four-fold. Our results highlight the importance and suitability of a temporally consistent, regression-based approach for realistic and reliable leak localization in WDNs. Full article
(This article belongs to the Special Issue Sustainable Management of Water Distribution Systems)
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30 pages, 2697 KiB  
Review
Leak Management in Water Distribution Networks Through Deep Reinforcement Learning: A Review
by Awais Javed, Wenyan Wu, Quanbin Sun and Ziye Dai
Water 2025, 17(13), 1928; https://doi.org/10.3390/w17131928 - 27 Jun 2025
Viewed by 694
Abstract
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. [...] Read more.
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
(This article belongs to the Section Urban Water Management)
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17 pages, 1747 KiB  
Article
Persistence of Untreated Bed Nets in the Retail Market in Tanzania: A Cross-Sectional Survey
by Benjamin Kamala, Dana Loll, Ruth Msolla, David Dadi, Peter Gitanya, Charles Mwalimu, Frank Chacky, Stella Kajange, Mwinyi Khamis, Sarah-Blythe Ballard, Naomi Serbantez and Stephen Poyer
Trop. Med. Infect. Dis. 2025, 10(6), 175; https://doi.org/10.3390/tropicalmed10060175 - 19 Jun 2025
Viewed by 622
Abstract
The private sector in Tanzania has played an essential role in improving coverage and access to mosquito nets. This follow-up study assessed the overall market share for untreated and insecticide-treated nets (ITNs) and misleading or counterfeit ITN products in commercial markets. This study [...] Read more.
The private sector in Tanzania has played an essential role in improving coverage and access to mosquito nets. This follow-up study assessed the overall market share for untreated and insecticide-treated nets (ITNs) and misleading or counterfeit ITN products in commercial markets. This study was conducted from March to April 2024 in ten regions in Tanzania. The study used mixed methods: (1) a quantitative survey among sampled outlets supported by photographic documentation of all net products and (2) key informant interviews of retailers and wholesalers. We assessed the relationship between market share and population access using ANOVA and Pearson correlation. No counterfeit or misleading nets were found, consistent with results from 2017, 2021, and 2022 surveys. Untreated nets dominated all markets, comprising 99% of all products observed and 99% of estimated net sales 3 months before the survey. Legitimate ITNs were crowded out from the studied markets. Leaked nets from free distributions were present but extremely limited (1%) and at their lowest level of the survey rounds. Untreated nets were more expensive than leaked ITNs for both regular- and queen-size nets. Despite ongoing efforts, increasing the share of legitimate ITNs remains a significant challenge in a context of large-scale public sector distributions. Full article
(This article belongs to the Special Issue The Global Burden of Malaria and Control Strategies)
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20 pages, 2071 KiB  
Article
Leakage Break Diagnosis for Water Distribution Network Using LSTM-FCN Neural Network Based on High-Frequency Pressure Data
by Sen Peng, Hongyan Zeng, Xingqi Wu and Guolei Zheng
Water 2025, 17(12), 1823; https://doi.org/10.3390/w17121823 - 18 Jun 2025
Viewed by 324
Abstract
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 [...] Read more.
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
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14 pages, 9483 KiB  
Article
Optimizing an Urban Water Infrastructure Through a Smart Water Network Management System
by Evangelos Ntousakis, Konstantinos Loukakis, Evgenia Petrou, Dimitris Ipsakis and Spiros Papaefthimiou
Electronics 2025, 14(12), 2455; https://doi.org/10.3390/electronics14122455 - 17 Jun 2025
Viewed by 513
Abstract
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, [...] Read more.
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
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44 pages, 4528 KiB  
Article
Beyond the Leak: Analyzing the Real-World Exploitation of Stolen Credentials Using Honeypots
by Matej Rabzelj and Urban Sedlar
Sensors 2025, 25(12), 3676; https://doi.org/10.3390/s25123676 - 12 Jun 2025
Viewed by 923
Abstract
This study presents one of the most extensive analyses of the lifecycle of leaked authentication credentials to date, bridging the gap between database breaches and real-world cyberattacks. We analyze over 27 billion leaked credentials—nearly 4 billion unique—using a sophisticated data filtering and normalization [...] Read more.
This study presents one of the most extensive analyses of the lifecycle of leaked authentication credentials to date, bridging the gap between database breaches and real-world cyberattacks. We analyze over 27 billion leaked credentials—nearly 4 billion unique—using a sophisticated data filtering and normalization pipeline to handle breach inconsistencies. Following this analysis, we deploy a distributed sensor network of 39 honeypots running 14 unique services across 9 networks over a one-year-long experiment, capturing one of the most comprehensive authentication datasets in the literature. We analyze leaked credentials, SSH and Telnet session data, and HTTP authentication requests for their composition, characteristics, attack patterns, and occurrence. We comparatively assess whether credentials from leaks surface in real-world attacks. We observe a significant overlap of honeypot logins with common password wordlists (e.g., Nmap, John) and defaultlists (e.g., Piata, Mirai), and limited overlaps between leaked credentials, logins, and dictionaries. We examine generative algorithms (e.g., keywalk patterns, hashcat rules), finding they are widely used by users but not attackers—unless included in wordlists. Our analyses uncover unseen passwords and methods likely designed to detect honeypots, highlighting an adversarial arms race. Our findings offer critical insights into password reuse, mutation, and attacker strategies, with implications for authentication security, attack detection, and digital forensics. Full article
(This article belongs to the Special Issue Security, Privacy and Threat Detection in Sensor Networks)
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24 pages, 9569 KiB  
Article
Numerical Simulation of Annular Flow Field and Acoustic Field of Oil Casing Leakage
by Yun-Peng Yang, Bing-Cai Sun, Ying-Hua Jing, Jin-You Wang, Jian-Chun Fan, Yi-Fan Gan, Shuang Liang, Yu-Shan Zheng and Mo-Song Li
Processes 2025, 13(6), 1799; https://doi.org/10.3390/pr13061799 - 5 Jun 2025
Viewed by 503
Abstract
The generation and propagation mechanisms of acoustic waves from leakage below the annular liquid level in gas wells have attracted widespread attention. To study the characteristics of acoustic sources beneath the liquid level, a physical model of leakage in the casing–tubing annulus was [...] Read more.
The generation and propagation mechanisms of acoustic waves from leakage below the annular liquid level in gas wells have attracted widespread attention. To study the characteristics of acoustic sources beneath the liquid level, a physical model of leakage in the casing–tubing annulus was established by simulating the distribution patterns of the flow field and acoustic field within the annulus under tubing leakage conditions. Distinct from the traditional acoustic analysis of wellbore leakage in gas wells, this study focuses on acoustic waves generated by leaks located below the annular protection fluid level. It analyzes the flow regime and acoustic source characteristics beneath the liquid level under various operating conditions (including leakage aperture, velocity, and position). The research summarizes the evolution patterns of flow regimes when gas leaks into the annular protection fluid under different conditions and elucidates the generation mechanism of sub-liquid leakage noise and its propagation mechanism across the liquid surface. This work lays the theoretical foundation for detecting sub-liquid leakage at the wellhead using acoustic methods. Full article
(This article belongs to the Section Energy Systems)
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