Meeting the Hospital Oxygen Demand with a Decentralized Autonomous PV System: Effect of PV Tracking Systems

When it comes to supplying oxygen, current standard hospitals in Iran have proven inadequate in the face of the COVID-19 pandemic, particularly during infection peaks. Power disruptions drastically reduce the oxygen pressure in hospitals, putting patients' health at risk. The present study is the first to attempt to power an oxygen concentrator with a solar-energy-based system. The HOMER 2.81 package was used for technical–economic–environmental–energy analysis. The most notable aspects of this work include evaluating different available solar trackers, using up-to-date equipment price data and up-to-date inflation rate, considering the temperature effects on solar cell performance, sensitivity analysis for the best scenario, considering pollution penalties, and using a three-time tariff system with price incentives for renewable power. The study has been carried out at Hajar Hospital, Shahrekord, Chaharmahal and Bakhtiari Province, Iran. The study showed that, by supplying 60% of the power demand, the dual-axis solar tracking system offered the highest annual power output (47,478 kWh). Furthermore, generating power at—$0.008/kWh due to selling power to the grid, the vertical-axis tracker was found to be the most economical design. Comparing the configuration with a vertical-axis tracker with the conventional scenario (relying on the power distribution grid), the investment is estimated to be recovered in three years with $234,300 in savings by the end of the 25th year. In the best economic scenario, 6137 kg CO2 is produced, and the analysis revealed the negative impact of a temperature rise on the performance and solar power output.

Work in Progress: Benefits of Learning Engineering Sciences to Transition from Face-to-Face to Virtual Learning of Engineering, Technology, and Computing due to COVID-19

For equatorial African countries such as Rwanda the power grid in some regions is either absent or highly unreliable even though these locations are blessed with reliable solar radiation most of the time. Designing and implementing solar power systems capable of supporting micro-computer systems such as Raspberry Pi devices that can be used in educational environments is a way to overcome grid challenges while at the same time imparting valuable lessons covering Engineering, Technology, and Computing. Using Learning Engineering Sciences best practices effectively mitigates how COVID-19 that has required standard face-to-face project and learning strategies to transition to virtual or hybrid strategies that utilize Open Educational Resources (OER). These strategies include video conferencing, file sharing platforms, and messaging applications to generate learning activities, create courses to construct the learning program for training teachers in the use of OER and Raspberry Pi desktop devices. © 2023 IEEE.

Crisis management of authoritarian regimes in the digital era: the case of epidemic prevention and control in China

Purpose: The purpose of this paper is to figure out how authoritarian regimes conduct crisis management through application of technology, institutions and people. Design/methodology/approach: By means of a literature review, this paper briefly reviews the digital governance of authoritarianism and its approach in crisis management. Then, a case study with empirical analysis is conducted to explain how an authoritarian regime would perceive and manage crises in the digital era. Findings: China's response towards COVID-19 was mainly based on digitalised grid management. Government's perception of the crisis directly influences directions of institutions, while technology is developed, applied and iterated with the needs of institutions, rather than the public interests. And for the general public, the level of trust in the government directly affects the acceptance of technology. Originality/value: Previous studies on crisis management of authoritarian governments in the digital era have mostly been conducted from a techno-ethical perspective. However, this paper verifies that the use of technology in crisis management requires involvement of institutions and public. © 2023, Emerald Publishing Limited.

Data-Driven Analysis and Optimization for Urban Energy Systems Equitable Resilience

Electric vehicles (EVs) can be leveraged as power resources to support the grid operation in challenging scenarios, e.g., natural disasters or health crises such as the COVID-19 pandemic. This paper aims to enhance equity of power resilience in urban energy systems by means of strategic allocation of EV charging infrastructure. We first use data-driven approaches to infer the relationships between communities' power resilience equity and available EV charging infrastructure as well as other prominent social-demographic factors. This inference leads to the development of a machine learning model for power resilience inequity prediction. We further develop an optimization frame-work that jointly considers equitable resiliency and resource utilization to guide the optimized EV charging infrastructure allocation across the city. Case studies demonstrate the capability of the devised approach in enhancing power resilience equity in marginalized communities. © 2023 IEEE.

Covid-19 cases prediction using SARIMAX Model by tuning hyperparameter through grid search cross-validation approach.

SARS-Coronavirus was first detected in December 2019, later named COVID-19, and declared a pandemic by the World Health Organization (WHO). As prediction models assist policymakers in making decisions based on expected outcomes. Existing models were only used to anticipate a smaller range of data resulting in irrelevant predictions. Our research focuses on predicting COVID-19 confirmed, recovered, and deceased Indian cases for 20 days ahead. Tuning of hyperparameters is performed with a grid search cross-validation approach. The dataset is collected from the Kaggle. Our forecast indicates that the count of confirmed and deceased cases is higher whereas, recovered cases prediction shows a decreasing trend. The R 2 Score achieved is 0.5112 and root-mean-square error (RMSE) is 1251 using optimized SARIMAX. Finally, Monte Carlo simulation has also been performed to justify the prediction accuracy as compared to other models such as linear, polynomial, prophet, and SARIMAX without grid search cross validation.

Tuning of controller parameters for suppressing low frequency oscillations in electric railway traction networks using meta-heuristic algorithms

Due to the interaction of electric multiple units (EMUs), and the electric traction networks, low frequency oscillations (LFOs) appear leading to traction blockade and overall stability related issues. For suppressing LFOs, coronavirus herd immunity optimiser (CHIO), a recently developed meta-heuristic, has been applied for tuning controller parameters. Controller parameters are tuned to minimise the integral time absolute error (ITAE) that regulates DC-link capacitor voltage. Results obtained using CHIO are compared with those found using other well-established algorithms like symbiotic organisms search (SOS) and particle swarm optimisation (PSO). The supremacy of CHIO over other mentioned algorithms for mitigating LFOs was demonstrated for a diverse range of operating conditions. Results demonstrates that overshoot for the proposed algorithm-based traction unit is 1.0061% whereas those for SOS and PSO based algorithm are obtained as 6.4542 % and 20.6166%, respectively which are quite high. CHIO is more stable than SOS and PSO and requires settling time of 0.1934 s only to reach steady-state condition, which is 50.21% faster than SOS and 65.03% faster than PSO. Also, the total harmonic distortion (THD) for line currents of the secondary side of traction transformer (TT) are obtained as 0.88%, 2.17%, and 12.48% for CHIO, SOS, and PSO, respectively.

The theoretical grid. An antifragile strategy for Rome post-COVID mobility

Mobility represents a central issue for sustainable urban planning and regeneration processes in large cities, concerning the impact on environmental quality, equity, and social inclusion. However, the pandemic has strongly affected mobility trends, influenced by international and national social distancing measures and new "safe” lifestyles. Thus, many cities have been adopting mobility emergency strategies for urban resilience. In this context, as a result of a research developed in the framework of a collaboration between Roma Tre University and the Sapienza University of Rome, this essay proposes an "antifragile” strategy for Rome's "post-COVID” mobility, adaptable to other European metropolitan contexts, based on an integrated approach to urban planning and mobility. The research methodology is articulated in three phases: the analysis of the main scientific references related to urban resilience and antifragility concerning the relations between urban form, ways of living and mobility models in pre- and postpandemic scenarios, the study of the main ongoing practices in European cities and the proposal for an antifragile strategy for the city of Rome based on the theoretical grid. The theoretical grid is an urban grammar that proposes a model of reorganization for the city based on elementary urban units and defines an integrated strategy for the reorganization of mobility, the reconfiguration of local flows, and the regeneration of public space. This grammar is declined in specific ways according to the different urban fabrics, within an articulation in four "cities,” the historical city, the consolidated city, the modernist city, the peripheral urban fringes. © 2023 Elsevier Inc. All rights reserved.

Various charging Methods in Electric Vehicle Application: A Review

During the covid pandemic, air quality has improved due to prolonged lockdown conditions. Hence according to the international energy agency, about 22% of environmental pollution is contributed by the transportation sector. Electric vehicles help in reducing the contribution towards carbon emission and help in mitigating the fossil fuel crisis and also promotes sustainable transportation. To enhance the growth of electric vehicle, charging infrastructure and range anxiety issues in the long drive has to be resolved. This paper reviews the various charging methods available for an electric vehicle. Some charging methods are wired and wireless charging, solar-powered, battery-swapping, vehicle-to-grid and vehicle-to-vehicle charging. A comparative study of these methods is tabulated. Based on the limitation of each method the optimum charging method for a vehicle is adapted for a particular application. © 2023 IEEE.

Investment Optimization and Auxiliary Decision Support System of Power Grid Construction Project Under Double Carbon Target

Due to China's novel coronavirus pneumonia and the deepening of the reform of the power grid market, the implementation and implementation of China's dual carbon policy and the current international related quality prices, the State Grid Limited by Share Ltd has proposed that "one industry is the leading force to drive four sides, all elements to develop together”, aiming at promoting the fine governance. Continuously improve quality and efficiency. Therefore, it is urgent to promote the optimization of power grid construction projects and the improvement of auxiliary decision-making system under the dual carbon goal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Applicability of edge computing paradigm for Covid-19 mitigation

With lockdowns and overburdened medical facilities during the Covid-19 pandemic, technology and computing paradigms play a vital role in providing remote healthcare solutions. We assess as how the existing computing paradigms could be deployed to prevent the spread of the disease, expedite the diagnosis, and facilitate remote monitoring of patients to reduce the burden on the overstretched medical facilities. The chapter will include a literature survey based on the articles published in but not limited to Science Direct, Google Scholar, Research Gate, and PubMed. This study weighs the pros and cons of using different paradigms in diverse scenarios and provides recommendations for efficient healthcare solutions. The chapter also focuses on the issues related to edge computing, such as resource provisioning, energy preservation, etc. In this era of technology, edge computing can be used to enhance the efficacy of healthcare solutions without burdening healthcare professionals and facilities. In this chapter, experimentation will focus on deploying intelligent techniques in the edge computing paradigm. © 2023 Elsevier Inc. All rights reserved.

Teaching Adults: Learning Languages in the Pandemic Span

All over the world the COVID-19 pandemic has led to the urgent transfer to distance education or blended learning in various educational establishments. As a result of such consequences of the governments' requirements to prevent the virus spreading those of isolation, social distancing and quarantine, both students and academicians unexpectedly and instantly were forced to switch to distance education within days. Based on the changes, the present study is to analyze the challenges adult learners face and to offer ways to meet them, as well as to devise and develop measures providing language promotion sustainability in the pandemic. These measures are referred to in the present study as a cushion grid could serve as an imperative of the pandemic span style (a teaching style which is seen by the authors as an umbrella term for methods and approaches used for distant teaching during the COVID-19 pandemic) in teaching adult learners. The study is based on analysing the variables producing e-learning satisfaction among the adult learners of the UNICO Language Centre of Siberian Federal University. The Centre was established on the TEMPUS (Trans-European Mobility Programme for University Studies) programme which encourages higher education institutions in the EU Member States and partner countries to engage in structured cooperation to implement Joint European Projects (JEPs) with a clear set of objectives. On completion of the project activities the UNICO (University-Career-Opportunities) Centre was transformed into a sustainable educational establishment where the adult learners could take a non-formal course of studies. The study group included 92 learners of the UNICO Language Centre of Siberian Federal University. The results demonstrated two learning imperatives: those of the social and the linguistic one. © 2022, Springer Nature Switzerland AG.

Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd

Hyperparameter Search for CT-Scan Classification Using Hyperparameter Tuning in Pre-Trained Model CNN With MLP

The common approach to find best hyperparameter in CNN training is grid search, by observing one set to another of hyperparameter for obtaining the best result. However, this approach is considered inefficient, time-consuming, and ineffectively computational. In this study, we are observing 2 hyperparameter tuning algorithms (bayesian optimization and random search) in search of the best hyperparameter for CT-Scan classification case. The used dataset is COVID-19 and non-COVID-19 lung CT-Scans. Several CNN architectures are also used such as: InceptionV4, MobileNetV3, and EfficientnetV2 with additional multi-layer perceptron on top layers. Based on the experiments, model EfficientnetV2-L architecture using hyperparameter from bayesian-optimization can outperform other models, with batch size of 32, learning rate of 0.01, dropout 0.5, Adam optimizer and SoftMax activation, resulting in the accuracy rate of 0.94% and a model training time of 50 minutes 40 seconds. © 2022 IEEE.

Topologies of power in China's grid-style social management during the COVID-19 pandemic

This article analyses the organization of Chinese grassroots social management during the COVID-19 pandemic. Drawing on a range of local cases researched through policy documents, media coverage and interviews, we scrutinize the appropriation of emergency measures and the utilization of grid-style social management since the outbreak of COVID-19. Grid-style social management – a new grassroots administrative division aiming to mobilize neighbourhood control and services – is a core element in China's pursuit of economic growth without sacrificing political stability. Conceptualizing grids as confined spaces of power, we show how the Chinese party-state is able to flexibly redeploy diverse forms of power depending on the particular purpose of social management. During non-crisis times, grid-style social management primarily uses security power, casting a net over the population that remains open for population elements to contribute their share to the national economy. Once a crisis has been called, sovereign power swiftly closes the net to prevent further circulation while disciplinary power works towards a speedy return to a pre-crisis routine. [ FROM AUTHOR] Copyright of Security Dialogue is the property of Sage Publications, Ltd. 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 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 . (Copyright applies to all s.)

Grid management system and students' life satisfaction during the period of the pandemic in Chinese universities

Although the grid management system has been in practice for more than a decade, less is known about the system and satisfaction among the people to whom the system applies. This study answers three questions;(i) what is a grid management system? (ii) How did higher education institutions use it during the pandemic? And (iii) How was the students' satisfaction with their level of engagement and perceived college support (P_C_S)? A total of 306 international students at Zhejiang Normal University completed an online survey. SPSS 26 and PROCESS macro was used to analyze the results. The results showed a strong positive correlation of P_C_S with students' engagement (r = 0.635, p < 0.05) and life satisfaction (r = 0.694, p < 0.05), while P_C_S significantly affected students' engagement (β = 0.540, SE = 0.082, p < 001) and life satisfaction (β = 0.524, SE = 0.082). The system was an imperative means of controlling the spread of the pandemic, with P_C_S playing a critical role in ensuring students' engagement. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Telemedical Early Medical Abortion: Notes for a Debate in Italy

The activism of non-profit organizations such as Women on Web has shown that telehealth abortion services represent an important way to access a safe abortion where antiabortion laws are in place. At the same time, this use of telehealth provides an innovative model to rethink abortion services also within a formal healthcare system, as became particularly clear with the start of the COVID-19 pandemic. Drawing on the experience of telehealth services offered by groups such as Women on Web and the recent policy on telemedicine in England, this timely and innovative contribution illustrates how the implementation of telemedicine can improve abortion access in Italy. © Annali dell'Istituto Storico Itali-Germanico in Trento. All rights reserved.

Exploring the Acceptance of Smart Grid Technology in India: Role of Government Policies and Regulatory Framework

In the last few decades, technological advancements in the power sector have accelerated the evolution of the smart grid to make the grid more efficient, reliable, and secure. Being a consumer-centric technology, a lack of knowledge and awareness in consumers may lead to consumer opposition, which could imperil the grid modification process. This research aims to identify and prioritize the factors that can be considered barriers to technology acceptance for smart grid development in India. This study follows an integrated approach of literature review, AHP, and FERA. In the present work, 17 barriers have been identified and ranked on the basis of the social, technical, and economic paradigm. This study finds the impact of government policies and stakeholders' involvement in consumers' acceptance of smart grid technology and its importance towards improving the quality of life of Indians. The government should play as the main proponent. The present work will contribute to developing and upgrading the basic framework for the smart grid in a developing country like India. Copyright © 2022, IGI Global.

A grid management system for COVID-19 antigen detection based on image recognition

Objective: To develop a SARS-CoV-2 antigen detection management system for Chinese residents under community grid management, which is supported by "health information technology" and "neural network image recognition", so as to give full play to the advantages of "grid management". This system is applied to the normalized prevention and control of COVID-19 epidemic. Method(s): The model of image recognition algorithm was built based on deep learning and convolution neural network (CNN) artificial intelligence algorithm. The improved Canny edge detection algorithm was used to monitor and locate the image edge, and then the image segmentation and judgment value calculation were completed according to projection method. The system construction was completed combing with the grid number design. Result(s): The proposed method had been tested and showed the accuracy of the algorithm. With a certain robustness, the algorithm error was proved to be small. Based on the image recognition algorithm model, the development of SARS-CoV-2 antigen detection management system covering user login, paper-strip test image upload, paper-strip test management, grid management, grid warning and regional traffic management was completed. Conclusion(s): Antigen detection is an important supplementary means of COVID-19 epidemic prevention and control in the new stage. The SARS-CoV-2 antigen detection management system for Chinese residents under community grid managemen based on image recognition enables mobile communication devices to recognize the image of SARS-CoV-2 antigen detection results, which is helpful to form a grid management mode for the epidemic and improve the management framework of epidemic monitoring, detection, early warning and prevention and control.Copyright © 2023 The Authors

Smart short-term electric load forecasting considering the Covid-19 epidemic impact based on deep learning

Short-term load forecasting is essential for the power company's operation and grid operators because it is necessary to ensure adequate capacity and proper power generation arrangement;this will affect operating efficiency and short-term decisions. Meanwhile, the Covid-19 epidemic as a nonlinear factor will be effective in short-term load forecasting and based on previous solutions, electrical load forecasting may not be accurate. A nonlinear and complex relationship between the factors affecting the load forecasting problem explains the need to use intelligent methods such as machine learning. This paper analyses the effect of Covid-19 epidemic countermeasures on short-term electric load forecasting in Iran. To forecast the short term electrical load, a deep neural network with a hybrid architecture and peak power consumption data, average temperature, and Covid-19 epidemic countermeasure data over 15 months during the Covid-19 epidemic was used. The results indicate an increase in forecasting accuracy considering the countermeasure's data. Also, the proposed model validation with data related to the fourth wave of the Covid-19 epidemic and the data of countermeasures modeling in Iran show the effectiveness and reasonable accuracy of the proposed model during the Covid19 epidemic. © 2022 IEEE.

MARGINAL GRID EMISSIONS AND PRIMARY ENERGY TO ACTIVATE DEMAND SIDE MANAGEMENT: COVID19 CASE STUDY

The construction of Marginal Emissions Factor (MEF) and Marginal Primary Energy Factor (MPEF) time series of the electricity grid can be used as an effective method to activate demand-side strategies in buildings and thus reducing their carbon footprint and primary energy use. The robustness of a method to calculate MEF and MPEF in function of the load and the share of renewables of the power grid is tested in the present work. The construction of the MEF and MPEF signals is applied to historical and pandemic data sets to investigate potential differences. A specific analysis in the period of the COVID-19. Daily profiles of the marginal and average emissions and primary energy during pandemic are compared with the pre-pandemic period. Preliminary results show that the full pandemic caused a reduced electricity demand by 13% with a reduction of overall assocaited MEF and MPEF of 50% and 35% respectively. Robustness of the methodology is measured by an average year correlation being 85% for pre-pandemic period, whereas pandemic periods reach about 70%. Demand response strategies as activated by the marginal signals can be used to reduce the carbon footprint and primary energy use of the built environment. © 2022 by the authors. Licensee AEIPRO, Spain.