How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities

This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy. The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone. In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation;2) object detection;and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework's usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.

Cotrimoxazole in hospitalised patients with severe COVID-19 - interim results from the CoTroxCov study

Background: Severe COVID-19 has been attributed to a hyperimmune response mediated by cytokines. The mainstay of therapy remains largely supportive along with steroids. Co-trimoxazole in addition to having antimicrobial properties has immunomodulatory and anti-inflammatory properties and could potentially improve outcomes in patients with severe COVID-19 . Hypothesis: We hypothesised that Co-trimoxazole given to patients with severe COVID-19 could prevent progression to critical illness, mortality and reduce time to recovery. Method(s): We conducted an interim analysis in our single center open-label randomised control trial, in which hospitalised patients with severe COVID-19 requiring supplemental oxygen via non-rebreathe mask between 10 -15 Litres per minute and maintaining saturations between 92-96% were assigned in a 1:1 ratio to receive either oral Cotrimoxazole in addition to standard therapy or standard therapy alone. Result(s): 111 patients were recruited into the study, of which 56 patients received Co-trimoxazole and 55 received standard therapy alone. The mean age was 50 years in the Co-trimoxazole group versus 53 years in the standard therapy group (p=0.083). In-hospital mortality was 11% in the Co-trimoxazole group vs 29% in the standard therapy group (p=0.020). Mechanical ventilation was offered to 9% of the patients in the Co-trimoxazole group versus 13% of the patients in the standard therapy group. Time to recovery was 6 days in the Co-trimoxazole group versus 7 days in the standard therapy group (p=0.466). Conclusion(s): In this interim analysis oral Co-trimoxazole reduces mortality in patients with severe Covid-19. Further recruitment is underway.

Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be “more expensive, difficult, and time-consuming”. Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes “comprehension reading work” is treated as fundamental in the proposal of disclosures. In case of any, “the programme appeared to positively affect contributing students”. Techniques of classification like “decision tree and Mixed Model Database Miner (MMDBM)” are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Optimum e-Healthcare Resource Provisioning using Network Slicing Approach for Future 5G Networks

The modernization of advanced healthcare infrastructure in the early 21st century is still failing to cope up as the whole world is struggling to get rid of the deadly disease named COVID-19. The scarcity of clinical resources is one of the most fundamental as well as critical reasons behind this calamity. The entire healthcare system faces severe challenges to re-stabilize the system. Digitization of technology which is primarily driven by the next-generation communication networks has given an exclusive paradigm shift to resolving the issues. 5th generation of mobile communication-(5G) introduces classical techniques which play crucial roles in e-healthcare transformations. Software-Defined Networking(SDN), Network Function Virtualization (NFV), Network Slicing (NS), and the concept of programmable networks introduce URLLC communication and time-critical service delivery in a resource-restricted environment. Leveraging the concept of programmable slicing approach, in this work, we have framed a flexible e-healthcare slicing model for dedicated and optimized resource provisioning. We have introduced a vSDN server that can significantly balance the healthcare slice and classify the complex medical databases into simplified segments for quick data processing, management, and orchestration. Considering the reformation of global healthcare customization, our proposed approach will play a vital role in the field of the e-healthcare domain.

A Study of Factors affecting Mutual Fund Investments in India

Consistent performance evaluation of mutual funds is crucial for investors and fund managers. In India mutual funds have not been as favourable investment options as in other developedcountries.The Indian mutual fund industry continuesextremely underpenetrated. India's AUM-to-GDP ratio is about 14% as compared to a worldwide average of 75-80% in March 2022. The equity AUM-to-GDP ratio is only 5%. Though, the situation is expected to change in the years to come.Retail investors are becoming more and more interested in mutual fund investments as per data shown by AMFI. The retail investors in the mutual funds' assets under management was 24%, which indicates an increase of 300 bps in September 2021 against the number in September 2020.In addition, AMFI's campaign 'Mutual Fund Sahi Hai' has attracted retail investors from smaller cities. To extend the investor base for mutual funds in India, it is essential to know the impact of income on monthly investment behaviour of investors towards mutual funds during COVID-19, and impact of investor awareness program on monthly investment. In these circumstances, the present research work is dedicated to achieving these objectives. The data were collected from individual investors from different states of India in 2022.The study aims to find out the impact of income andawareness programs on monthly investments towards mutual funds investment during COVID-19.An attempt has also been made to study the level of impact of 'Mutual Fund Sahi Hai' campaign on the retail investors. The data analysis has been done with the SPSS software and by using multivariate and bivariate techniques.

SoftHealth: Softwarized 5G-Driven Network Slicing for Real-time e-Healthcare Applications using ML

The recent years have whiteness the substandard situations of the modern healthcare system due to a fatal pandemic called COVID19. The rapid advancements of modern technology have disseminated the superficial benefits of medical infrastructure, but significant improvements are still extremely necessary over the massive e-healthcare system (mHS). Considering the fact of limited resources and unlimited demands, a highly stable end-to-end optimization model is required. Healthcare also struggles with real-time communication. The next-generation communication networks (e.g 5G and beyond) proficiently influence the network resource distribution for URLLC. In this work, we have envisioned a novel on-demand e-Healthcare dynamic network slice architecture that uses the ML algorithms at the edge server for real-time classification and access of the offloaded data from the central controller (vSDN-Control layer to Data plane layer). The comparative analysis over the datasets of patients consisting of special index parameters shows that our proposed model allows the end-user more efficient data accessibility over the conventional approaches. We have studied the model over the multi-classification ML models (kNN, DT and RF) and we have found an average improvement of 10% to 15% of average data offloading time efficiency from the local machines from the edge servers. This approach can be further extended as the QoS improvement of the healthcare data traffic over the dynamic network slice instances. We have kept the model simple but standard in nature. © 2022 IEEE.

Towards Framework for Edge Computing Assisted COVID-19 Detection using CT-scan Images

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an IoT and edge computing based framework is proposed to automatically diagnose COVID-19 from CT scans of the patients using Deep Learning techniques. The proposed method requires less computational power and uses ensemble learning to increase the models' overall predictive performance. In the simulation, it was found that each model performs better in some areas than the other. The proposed scheme uses ensemble learning to take advantage of such an occurrence and achieved an accuracy of 86.2% and an AUC score of 89.8% on the COVID-CT-Dataset. This accuracy is achieved keeping the hardware accessibility in mind by training the models using a labeled dataset of CT-scans of the patients. Unlike other works, we were able to train models on a single enterprise-level GPU. It can easily be provided on the edge of the network, which reduces communication overhead and latency. This work aims to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance combined with medical equipment and help ease the examination procedure.

Preventing COVID-19 Spread Using Information and Communication Technology

Without an effective vaccine, treatment, or therapy, the Coronavirus Disease 2019 (COVID-19) is spreading like fire and claiming lives. Countries began to adopt various strategies such as lockdown, mass testing, tracing, quarantine, sanitization, isolation, and treatment to contain COVID-19. However, it was soon realized that we need to take the help of powerful technologies to combat the spread of deadly COVID-19 until a vaccine or a drug is discovered. In this article, we discuss how the use of cutting edge technologies such as the Internet of Things (IoT), Big data, artificial intelligence (AI), unmanned aerial vehicles (UAVs)/drones, blockchain, robotics, autonomous ground vehicles, communication technologies in screening, testing, contact tracing, spread analysis, sanitization, and protocol enforcements can help prevent the COVID-19 spread.

Clinico-demographic profile of coronavirus infection among Bangladeshi children: a tertiary care hospital study. (Special Issue: Pandemic COVID19.)

Background: Variation and atypical presentation of COVID-19 in Bangladeshi children has been noticed.