Configuration Detection of Grounding Grid: Static Electric Field Based Nondestructive Technique

Grounding grid configuration which, is key to its fault diagnosis, changes continuously with the extension in a substation. Furthermore, older substations grounding grid configurations are unknown. Existing literature regarding configuration detection mainly accounts for the magnetic field that required a gradient to locate the grounding conductor. The gradient of raw measurement in the substation vicinity enhances electromagnetic noise and distorts the results. Therefore, in this paper, we have developed a new algorithm, Configuration Detection of Grounding Grid (CDGG) based on the static electric field and the concept of ordered pairs to draw the configuration of the unknown grounding grid. Unlike, the practiced magnetic field, the electric field does not require a gradient. The maximum electric field value indicates the location of a grounding conductor. The connection between nodes is verified by measuring the electric field on the circle. Furthermore, the proposed algorithm also locates any diagonal conductor in the configuration. Mathematical reasoning and simulation results illustrate that our proposed algorithm is feasible to draw the configuration of the unknown grounding grid

Utilizing Computational Complexity to Protect Cryptocurrency against Quantum Threats: A Review

Digital currency is primarily designed on problems that are computationally hard to solve using traditional computing techniques. However, these problems are now vulnerable due to the computational power of quantum computing. For the postquantum computing era, there is an immense need to reinvent the existing digital security measures. Problems that are computationally hard for any quantum computation will be a possible solution to that. This research summarizes the current security measures and how the new way of solving hard problems will trigger the future protection of the existing digital currency from the future quantum threat

Environmental footprint assessment of a cleanup at hypothetical contaminated site

Contaminated site management is currently a critical problem area all over the world, which opens a wide discussion in the areas of policy, research and practice at national and international levels. Conventional site management and remediation techniques are often aimed at reducing the contaminant levels to an acceptable level in a short period of time at low cost. Owing to the fact that the conventional approach may not be sustainable as it overlooks many ancillary environmental effects, there is an immense need of “sustainable” or “green” approaches. Green approaches address environmental, social and economic impacts throughout the remediation process and are capable of conserving the natural resources and protecting air, water and soil quality through reduced emissions and other waste burdens. This paper presents a methodology to quantify the environmental footprint of a cleanup for a hypothetical contaminated site by using the US Environmental Protection Agency’s (EPA) Spreadsheet for Environmental Footprint Assessment (SEFA). The hypothetical contaminated site is selected from a metropolitan city of Pakistan and the environmental footprint of the cleanup is analyzed under three different scenarios: cleanup without any renewable energy sources at all, cleanup with a small share of renewable energy sources, and cleanup with a large share of renewable energy sources. It is concluded that integration of renewable energy sources into the remedial system design is a promising idea which can reduce CO2, NOx, SOx, PM and HAP emissions up to 68%

A review on impact of mining and mineral processing industries through life cycle assessment

This paper analysed and summarised the significant research outputs published on the environmental impact assessment of mining and mineral processing industries through life cycle assessment. The paper presents valuable insights in identifying the gaps, where should the focus be in the mining and mineral processing industries for a sustainable future. Mining and mineral processing industries have been the key focus of research in many countries due to its increasing sustainability concerns that affect global warming and climate change. Use of heavy equipment that consumes electrical energy, mechanical energy, and an enormous amount of process heat is a key contributor to the overall impacts in the industry. Due to the use of heavy equipment and associated energy consumption, these industrial sectors contribute notably to global warming, human health, ecosystems, and resources. Among the various environmental impact assessment tools which are widely used to identify sustainability indicators, life cycle assessment (LCA) is a well-justified approach among the practitioners and researchers. Though state of the art technological tools and resources are being used now a days, there is still a research gap in identifying the key mining processes which need to be the focus of attention. Renewable energy integration in the mineral processing sector and process heating from green energy sources is becoming the emergent field of research. The review results reveal, the assessment indicators in human health and ecosystems are key factors that are mostly missing in the previous studies which are crucial for people or community living nearby mining area. This review paper identifies the research gaps to the existing literature that can form the base for future research direction in the field of LCA and sustainable energy integration in mining and mineral processing industries

DistB-Condo: Distributed Blockchain-based IoT-SDN Model for Smart Condominium

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model

Solar Process Heat in Industrial Systems- A Global Review

In developing countries, industries and manufacturing sectors consume a major portion of the total consumption of energy, where most of the energy is used for low, medium or high temperature heat generation to be used for process applications known as process heat. The necessity to commercialize clean, cheap and efficient renewable sources of energy in industrial applications emerges from increasing concerns about greenhouse gas emissions and global warming and decreasing fossil fuel use in commercial sectors. As an abundant source of energy, solar energy technologies have proven potential. Recent research shows currently only a few industries are employing solar energy in industrial processes to generate process heat while replacing fossil fuels. Solar thermal power generation is already very well-known and getting popular in recent years while other potential applications of the concentrated heat from solar radiation are little explored. This review paper presents a detailed overview of the current potential and future aspects of involving solar industrial process heating systems in industrial applications. In order to keep pace with this emerging and fast growing sector for renewable energy applications, it is necessary to get in depth knowledge about the overall potential of industrial processes in individual industrial sector where solar process heat is currently in use and identifying industrial processes are most compatible for solar system integration depending on temperature level and the type of solar collector in use. Furthermore, the promising sectors needs to be identified for the use of solar heat using industrial processes for the integration of solar heat, so that countries with immense solar energy potential can use those technologies in future to reduce fossil fuel consumption and develop sustainable industrial systems. This paper presents a comprehensive review of the potential industrial processes that can adopt solar process heating systems and thus driving towards sustainable production in industries

Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

Towards smart healthcare: UAV-based optimized path planning for delivering COVID-19 self-testing kits using cutting edge technologies

Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic since late 2019 and has affected all forms of human life and economic developments. Various techniques are used to collect the infected patients’ sample, which carries risks of transferring the infection to others. The current study proposes an AI-powered UAV-based sample collection procedure through self-collection kits delivery to the potential patients and bringing the samples back for testing. Using a hypothetical case study of Islamabad, Pakistan, various test cases are run where the UAVs paths are optimized using four key algorithms, greedy, intra-route, inter-route, and tabu, to save time and reduce carbon emissions associated with alternate transportation methods. Four cases with 30, 50, 100, and 500 patients are investigated for delivering the self-testing kits to the patients. The results show that the Tabu algorithm provides the best-optimized paths covering 31.85, 51.35, 85, and 349.15 km distance for different numbers of patients. In addition, the algorithms optimize the number of UAVs to be used in each case and address the studied cases patients with 5, 8, 14, and 71 UAVs, respectively. The current study provides the first step towards the practical handling of COVID-19 and other pandemics in developing countries, where the risks of spreading the infections can be minimized by reducing person-to-person contact. Furthermore, the reduced carbon footprints of these UAVs are an added advantage for developing countries that struggle to control such emissions. The proposed system is equally applicable to both developed and developing countries and can help reduce the spread of COVID-19 through minimizing the person-to-person contact, thus helping the transformation of healthcare to smart healthcare

Using multivariate regression and ANN models to predict properties of concrete cured under hot weather: a case of Rawalpindi Pakistan

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete

Monitoring of renewable energy systems by IoT‐aided SCADA system

With the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectivenes