Analysis and Design of an In-Pipe System for Water Leak Detection

In most cases the deleterious effects associated with the occurrence of leaks may present serious problems and therefore, leaks must be quickly detected, located and repaired. The problem of leakage becomes even more serious when it is concerned with the vital supply of fresh water to the community. In addition to waste of resources, contaminants may infiltrate into the water supply. The possibility of environmental health disasters due to delay in detection of water pipeline leaks has spurred research into the development of methods for pipeline leak and contamination detection. Leaking in water networks has been a very significant problem worldwide, especially in developing countries, where water is sparse. Many different techniques have been developed to detect leaks, either from the inside or from the outside of the pipe; each one of them with their advantages, complexities but also limitations. To overcome those limitations we focus our work on the development of an in-pipe-floating sensor. The present paper discusses the design considerations of a novel autonomous system for in-pipe water leak detection. The system is carefully designed to be minimally invasive to the flow within the pipe and thus not to affect the delicate leak signal. One of its characteristics is the controllable motion inside the pipe. The system is capable of pinpointing leaks in pipes while operating in real network conditions, i.e. pressurized pipes and high water flow rates, which are major challenges.Center for Clean Water for Clean Energy at MIT & KFUP

Design and Evaluation of an In-Pipe Leak Detection Sensing Technique Based on Force Transduction

Leakage is the major factor for unaccounted fluid losses in almost every pipe network. In most cases the deleterious effects associated with the occurrence of leaks may present serious economical and health problems and therefore, leaks must be quickly detected, located and repaired. The problem of leakage becomes even more serious when it is concerned with the vital supply of fresh water to the community. Leaking water pipelines can develop large health threats to people mostly because of the infiltration of contaminants into the water network. Such possibilities of environmental health disasters have spurred research into the development of methods for pipeline leakage detection. Most state of the art leak detection techniques have limited applicability, while some of them are not reliable enough and sometimes depend on user experience. Our goal in this work is to design and develop a reliable leak detection sensing system. The proposed technology utilizes the highly localized pressure gradient in the vicinity of a small opening due to leakage in a pressurized pipeline. In this paper we study this local phenomenon in detail and try to understand it with the help of numerical simulations in leaking pipelines (CFD studies). Finally a new system for leak detection is presented. The proposed system is designed in order to reduce the number of sensing elements required for detection. The main concept and detailed design are laid out. A prototype is fabricated and presented as a proof of concept. The prototype is tested in a simple experimental setup with artificial leakages for experimental evaluation. The sensing technique discussed in this work can be deployed in water, oil and gas pipelines without significant changes in the design, since the concepts remain the same in all cases.King Fahd University of Petroleum and Minerals (Project Number R7-DMN-08

Characterization of In-Pipe Acoustic Wave for Water Leak Detection

This paper presents experimental observations on the characteristics of the acoustic signal propagation and attenuation inside water-filled pipes. An acoustic source (exciter) is mounted on the internal pipe wall, at a fixed location, and produces a tonal sound to simulate a leak noise with controlled frequency and amplitude, under different flow conditions. A hydrophone is aligned with the pipe centerline and can be re-positioned to capture the acoustic signal at different locations. Results showed that the wave attenuation depends on the source frequency and the line pressure. High frequency signals get attenuated more with increasing distance from the source. The optimum location to place the hydrophone for capturing the acoustic signal is not at the vicinity of source location. The optimum location also depends on the frequency and line pressure. It was also observed that the attenuation of the acoustic waves is higher in more flexible pipes like PVC ones.Center for Clean Water and Clean Energy at MIT and KFUP

Quantifying Acoustic and Pressure Sensing for In-Pipe Leak Detection

Experiments were carried out to study the effectiveness of using inside-pipe measurements for leak detection in plastic pipes. Acoustic and pressure signals due to simulated leaks, opened to air, are measured and studied for designing a detection system to be deployed inside water networks of 100 mm (4 inch) pipe size. Results showed that leaks as small as 2 l/min can be detected using both hydrophone and dynamic pressure transducer under low pipe flow rates. The ratio between pipe flow rate and leak flow rate seems to be more important than the absolute value of leak flow. Increasing this ratio resulted in diminishing and low frequency leak signals. Sensor location and directionality, with respect to the leak, are important in acquiring clean signal.King Fahd University of Petroleum and Mineral

Investigation of adsorption kinetics and isothermal thermodynamics for optimizing methylene blue adsorption onto a modified clay with cellulose using the response surface approach

In this study, the clay was interwoven with cellulose to change its structure. The product clay/cellulose was used to assess the efficacy of the methylene blue (MB) dye removal from aqueous solutions (CC25). The response surface methodology and Box-Behnken design were used to optimize the influence of crucial parameters (cellulose load, adsorbent dosage, solution pH, temperature, and contact duration) (RSM-BBD). The greatest removal effectiveness was 98.76% for a cellulose loading of 25.0% and the following working conditions, i.e., adsorbent dosage of 0.06 g/L, pH 7, temperature of 45 °C, and contact length of 20 min. At the time, the maximum adsorption capacity was 254.8 mg/g. The pseudo-second-order adsorption model, according to the adsorption kinetics research, was used to describe the process. The MB adsorption process was endothermic and spontaneous, according to computed thermodynamic functions. The developed composite material, according to our results, has a very high capacity for the color absorption and removal.Universidade de Vigo/CISU

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Deep learning techniques for power allocation problems in cognitive relay-aided networks.

Les futures générations de réseaux sans fil sont confrontées à de grands défis en termes de capacité du réseau, de débit du système, de densité d'utilisateurs, le tout avec un budget énergétique serré. Afin d'atteindre ces objectifs ambitieux, plusieurs technologies émergentes, telles que la radio cognitive, les communications coopératives, le full duplex, l'intelligence artificielle, etc. ont été proposées, chacune d'entre elles se concentrant sur une amélioration spécifique.L'objectif de cette thèse de doctorat est d'exploiter conjointement ces technologies émergentes afin de maximiser le débit de Shannon sous contraintes et non convexe dans un réseau de radio cognitive assisté par des nœuds relais. Ce réseau se compose d'une paire utilisateur-destination primaire et secondaire et d'un relais secondaire full-duplex effectuant la Compresser-et-Transférer (CF) ou Décoder-et-Transférer (DF). La communication primaire est protégée par une contrainte de qualité de service (QoS) exprimée en termes de dégradation tolérée du débit de Shannon.Plus précisément, nous abordons les problèmes d'allocation de puissance non convexes dans le cas d'information sur l'état du canal (CSI) parfaite et imparfaite et pour CF et DF. Dans le cas d'une information parfaite sur l'état du canal, nous obtenons une solution analytique pour CF. Cependant, pour DF, en raison de débits atteignables plus complexes et des contraintes non convexes, aucune solution analytique ne semble possible. Pour relever ce défi, nous proposons une politique d'allocation de puissance non supervisée basée sur l'apprentissage profond qui exploite une architecture entièrement connectée conjointement avec une fonction de coût adaptée à la radio cognitive que le réseau de neurones profond (DNN) apprend à minimiser. Cette fonction de coût adaptée repose sur la relaxation de la contrainte de QoS dans la fonction objectif en introduisant un hyperparamètre permettant le compromis entre une optimisation axée sur le débit et une optimisation axée sur la QoS. Ainsi, seuls les gains du canal sont fournis en entrée de notre DNN.Lorsque seul une CSI imparfaite est disponible à l'émetteur, nous proposons d'exploiter notre DNN en le rendant robuste aux erreurs d'estimation des gains du canal. Étant donné que notre solution analytique pour CF repose sur une CSI parfaite, nous proposons également d'utiliser notre DNN pour optimiser la politique d'allocation de puissance pour CF en présence de CSI imparfaite. Pour faire face à la CSI imparfaite, nous adoptons une approche auto- supervisée où, dans la phase d'apprentissage, des estimations de canaux sans erreur sont fournies à la fonction de coût, et seuls les gains de canaux altérés par des erreurs d'estimation sont fournis à l'entrée du DNN. La robustesse de la solution proposée a été validée par des simulations numériques.Une fois notre solution robuste basée sur le DNN validée, nous recherchons des politiques plus générales exploitant des DNN, à savoir le choix du meilleur schéma de relayage parmi CF et DF, ainsi que la généralisation sur les paramètres du système de réseau, tels que les budgets de puissance individuels et le niveau de dégradation primaire toléré. En ce qui concerne le problème de sélection du schéma de relayage, nous exploitons à nouveau un DNN entièrement connecté avec la fonction de coût d'entropie croisée, particulièrement bien adaptée aux problèmes de classification. Ce dernier exploite la puissance prédite par le DNN que nous avons proposé précédemment, à la fois pour CF et DF. En ce qui concerne la généralisation sur les paramètres du système, nous généralisons d'abord séparément sur chaque paramètre, puis nous proposons un DNN capable de généraliser conjointement sur le budget de puissance et le niveau de dégradation du débit primaire toléré.Future generations of wireless networks face great expectations in terms of network capacity, system throughput, user density, all on a tight energy budget. In order to reach such ambitious objectives, several emerging technologies, such as: cognitive radio, cooperative communications, full-duplexing, and AI, etc. have been proposed, each of them focusing on a specific improvement.The objective of this PhD thesis is to jointly exploit these emerging technologies, in order to investigate a constrained and non-convex Shannon rate maximization problem in a relay- aided cognitive radio network. This network consists of a primary and a secondary user- destination pair and a secondary full-duplex relay performing compress-and-forward (CF) or decode-and-forward (DF). The primary communication is protected by a quality of service (QoS) constraint expressed in terms of tolerated Shannon rate degradation.More precisely, we tackle the non-convex power allocation problems under both perfect and imperfect channel state information (CSI) and for CF and DF relaying. In the perfect CSI case, we derive a closed-form solution for CF relaying. However, for DF relaying, no closed-form solution seems feasible due to more complex achievable rate expressions and non-convex constraints. To address this challenge, we propose an unsupervised deep learning-based power allocation policy exploiting a fully connected architecture jointly with a custom cognitive radio-tailored loss function that the deep neural network (DNN) learns to minimize. This custom loss function relies on the relaxation of the QoS within the objective function by introducing an hyperparameter to trade-off between a rate-driven and a QoS-driven optimization problem. As such, only the channel gains are provided as the input of our proposed DNN.When only an imperfect CSI is available at the transmitter side, we propose to build on our proposed DNN by rendering it robust to channel gains estimation errors. Since our closed- form solution under CF relies on the perfect CSI assumption, we propose to use our DNN approach to optimize the power allocation policy under CF and imperfect CSI as well. To cope with imperfect CSI, we turn to a self-supervised approach, where in the training phase, error- free channel estimations are provided to the loss function, and only channel gains impaired by estimation errors are provided at the input of the DNN. The robustness of the proposed solution was validated through numerical simulations.Once our robust DNN-based solution validated, we seek for more general DNN-based policies, namely choosing among the best relaying scheme among CF and DF, as well as generalizing over the network system parameters, such as the individual power budgets and the level of tolerated primary degradation. Regarding the relaying scheme selection problem, we again exploit a fully connected DNN with the cross-entropy loss function, especially well-suited for classification problems. The later exploits the power predicted by our previous proposed DNN under both CF and DF. Regarding the generalization over the system parameters, we first generalize separately over each parameter and then we propose a DNN able to generalize jointly over both the power budget and level of tolerated primary rate degradation

Indirect effect of Covid-19 on Vegetation Indices around the cement plant of Gabes region

To contain the Covid-19 pandemic, Tunisia imposed a national lockdown at the end of March 2020, a decision that resulted in a massive industrial complexes shutting down. The cement industry of Gabes was one of these complexes. However, to assess the impact of Covid-19 on the state of the vegetation around this industry three radiometric vegetation indices (RIs), NDVI, SAVI and EVI, were calculated from two Sentinel-2A imageries extracted at 22th December 2018 and 16th December 2020. Six plant species such as Oleo europaea, Ficus caria, Medicago sativa, Prunus persica vulgaris, Zygophyllumalbum and Helianthemum kahiricum were collected from 30 sites. Results suggest that, the period of pre-outbreak has the lowest averages of RIs. While, the after outbreak date has the higher levels of RIs presenting especially perennial species such as Oleo europaea. Then, EVI was the most higher index comparing to the rest of indices whatever the year studied. It was the most sensitive to cement dust and more susceptible to detect defoliation. Finally, through a remote application (RIs), the period of confinement allowed to improve the state of the vegetation surrounding the cement plant. It has helped the ecosystem to regenerate, especially perennial plant species. Cite as: Ben Atia Zrouga K, Ben Amor A, Dridi Almohandes B and Khebour Allouche F. Indirect effect of Covid-19 on Vegetation Indices around the cement plant of Gabes region. Alg. J. Eng. Tech. 2021, 4:59-65.  http://dx.doi.org/10.5281/zenodo.4592373 References Chen K, Wang M, Huang C, Kinney P L & Anastas P T. Air pollution reduction and mortality benefit during the COVID-19 outbreak in China. The Lancet Planetary Health. 2020, 4;6 : 210-212. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y & Cao B. 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