Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory

This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaks.

Operation of the Egyptian Power Grid with Maximum Penetration Level of Renewable Energies Using Corona Virus Optimization Algorithm

Countries around the world are looking forward to fully sustainable energy by the middle of the century to meet Paris climate agreement goals. This paper presents a novel algorithm to optimally operate the Egyptian grid with maximum renewable power generation, minimum voltage deviation and minimum power losses. The optimal operation is performed using Corona Virus Algorithm (CVO). The proposed CVO is compared to the Teaching and Learning-Based Optimization (TLBO) algorithm in terms of voltage deviation, power losses and share of renewable energies. The real demand, solar irradiance and wind speed in typical winter and summer days are considered. The 2020 Egyptian grid model is developed, simulated, and optimized using DIgSILENT software application. The results have proved the effectiveness of the proposed CVO, compared to the TLBO, to operate the grid with the highest share possible of renewables. The paper is a step forward to achieve Egyptian government targets to reach 20% and 42% penetration level of renewable energies by 2022 and 2035, respectively.

Processing of Waste from Enrichment with the Production of Cement Clinker and the Extraction of Zinc

This paper presents studies on the processing of enrichment tailings as a component of a raw mixture in order to obtain cement clinker, with simultaneous distillation of zinc. Thermodynamic studies were carried out in the temperature range of 600–1600 °C using the software application “HSC Chemistry 6” developed by the metallurgical company Outokumpu (Finland). As a result of the conducted studies, we found that zinc contributes to the intensification of mineral formation of cement clinker. In particular, it was found that the formation of belite is possible in the temperature range from 990.7 to 1500 °C with Gibbs energy values of −0.01 and −323.8 kJ (which is better than the standard process by −11.4 kJ), respectively; the formation of alite is possible in the temperature range from 982.9 to 1500 °C with Gibbs energy values of −0.05 and −402.1 kJ (better than the standard process by −11.4 kJ), respectively; the formation of tricalcium aluminate is thermodynamically possible in the temperature range from 600 °C at ΔGTo = −893.8 kJ to 1500 °C at ΔGTo = −1899.3 kJ (better than the standard process by −1570.1 kJ), respectively; and the formation of four calcium aluminoferrite is possible in the temperature range from 600 °C at ΔGTo = −898.9 kJ to 1500 °C at ΔGTo = −1959.3 kJ (better than the standard process by −1570.2 kJ), respectively, with simultaneous distillation of zinc into a gaseous state for its further capture.

The Influence of Nationalism's Attitude and Tax Morals on Taxpayer Compliance through Tax Awareness

The purpose of this study was to examine the direct effect of nationalism and tax morale on tax awareness. As well as testing the effect of the two independent variables on taxpayer compliance through tax awareness. The research method used is quantitative with data collection techniques through questionnaire, method in taking samples using the hair method, namely the number respondent 5 – 10 times the number of indicators. The results of the sample obtained are 180 respondents with the status of Taxpayers. The sample was tested for validity and reliability testing and tested the hypothesis through the Smarts PLS software application. The results of hypothesis testing prove that there is a significant direct effect on nationalism and tax morale on tax awareness, a significant direct effect on tax awareness on taxpayer compliance, and a significant indirect effect on nationalism and tax morale attitudes on taxpayer compliance. Tax through tax awareness. From this research it can be concluded that when the Government will. To increase tax awareness, the first strategy that must be improved is tax morale, then the attitude of nationalism. And to increase taxpayer compliance through tax awareness, the first strategy that must be improved is tax morale, then nationalism.

Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks

Whole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion. Hearts from male Sprague Dawley rats (n = 10) were decellularized using 1% sodium dodecyl sulfate (SDS) in a modified Langendorff device in the presence of an alternating rectangular electric field. Spectrophotometric measurements of deoxyribonucleic acid (DNA) and total proteins concentration from the decellularization solution were taken every 30 min. A monitoring system supervised the sessions, collecting a large number of photos saved in corresponding folders. This system aimed to prove a strong correlation between the data gathered by spectrophotometry and the state of the heart that could be visualized with an OpenCV-based spectrometer. A decellularization completion metric was built using a DCNN based classifier model trained using an image set comprising thousands of photos. Optimizing the decellularization process using a machine learning approach launches exponential progress in tissue bioengineering research.

Practical, Low-Cost Fault Injection Attacks on Personal Smart Devices

Fault attacks are traditionally considered under a threat model that assumes the device under test is in the possession of the attacker. We propose a variation on this model. In our model, the attacker integrates a fault injection circuit into a malicious field-replaceable unit, or FRU, which is later placed by the victim in close proximity to their own device. Examples of devices which incorporate FRUs include interface cards in routers, touch screens and sensor assemblies in mobile phones, ink cartridges in printers, batteries in health sensors, and so on. FRUs are often installed by after-market repair technicians without properly verifying their authenticity, and previous works have shown they can be used as vectors for various attacks on the privacy and integrity of smart devices. We design and implement a low-cost fault injection circuit suitable for placement inside a malicious FRU, and show how it can be used to practically extract secrets from a privileged system process through a combined hardware-software approach, even if the attacker software application only has user-level permissions. Our prototype produces highly effective and repeatable attacks, despite its cost being several orders of magnitude less than that of commonly used fault injection analysis lab setups. This threat model allows fault attacks to be carried out remotely, even if the device under test is in the hands of the victim. Considered together with recent advances in software-only fault attacks, we argue that resistance to fault attacks should be built into additional classes of devices.

The role of accounting information systems in firms’ performance during the COVID-19 pandemic

This paper investigates the influence of accounting information systems (AIS) on firms’ performance during the COVID-19 pandemic and how they help enhance employees’ performance and the external auditing process. This paper is qualitative in nature using the inductive approach. In-depth primary data were gathered through semi-structured interviews conducted in the year 2020. Due to the pandemic, the interviews with ten auditors were done online through the Zoom software application. The empirical findings of this paper show a positive impact of AIS on firms’ performance and a more significant influence on employees’ performance and the auditing process. AIS reduces costs and human errors, eases operations, speeds up work tasks, and increases employees’ productivity during the COVID-19 pandemic. The findings also show that there is no direct impact on firms’ overall cash flow/revenues. This paper increases our understanding of how AIS can influence and improve firms’ performance and the significance of implementation factors such as training. It provides practical guidelines for regulators and managers to utilize accounting information systems to perform better.

A State of the art Survey on Face Recognition Methods

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.