Modeling the Impact of Fake Data Dissemination During Covid-19

The Covid-19 pandemic has increased the global dependency on the internet. Millions of individuals use social networking sites to not only share information, but also their personal opinions. These facts and opinions are frequently unconfirmed, which result in the spread of incorrect information, generally alluded to as "Fake Content”. The most challenging aspect of social media is in determining the source of information. It's difficult to figure out who generated fake news once it's gone viral. Most available computational models have a key flaw in that they rely on the presence of inaccurate information to generate meaningful features, making disinformation mitigation measures difficult to predict. This paper presents a parallel approach to false information mitigation drawn from the field of Epidemiology using SIR(Susceptible, Infected, Recovered) to model the impact of fake data dissemination during Covid-19. SIR simulation is done using NetLogo in which the population is made up of two agents: Fake news believers and non-believers. To confirm our work, the concept of trust is also discussed which is a fundamental component of any fake news interaction. The level of trust can be expressed by assigning each node a pair of trust scores. We ran our experiments based on three common evaluation metrics: Accuracy, Precision, and Recall. The hybrid model shows an increase in accuracy by 81.4%, 77.1%, and 91.8% for the respective networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Securing Remote User Authentication in Industrial Internet of Things

The Industrial Internet of Things (IIoT) enhances the benefit of the Internet of Things (IoT) to a higher level, especially in industries where human error can lead to catastrophic effects. However, security is a major concern in IIoT as hackers can gain access to connected systems, thus potentially subjecting operations to a shutdown. Besides, the outbreak of the COVID-19 pandemic changed the operations style of organizations into a remote work model. Consequently, there has been a significant increase in cyber-attacks leveraging vulnerabilities of IoT devices connected to the Internet. Considering the above factors, we propose a method of remote user authentication combining Photo Response Non-Uniformity (PRNU) with fingerprint bio-metric, which can prevent attacks. PRNU uniquely identifies the scanner, thereby authenticates the device of the user. To prove the effectiveness of PRNU, we collect fingerprint images from various scanners prototyped using Raspberry Pi and evaluate the performance. Our performance evaluation with a set of 10 fingerprint scanners shows promising results. Moreover, our analysis shows that the proposed scheme achieves a classification accuracy of 99%. © 2022 IEEE.

Energy justice issues in renewable energy megaprojects: implications for a socioeconomic evaluation of megaprojects

Purpose The purpose of this paper is to investigate and discuss stakeholder issues faced by renewable energy megaprojects and in particular solar and wind power projects and their relevance to socioeconomic evaluation of megaprojects. Design/methodology/approach The paper uses secondary data collected from the recent literature published on stakeholder issues face by mega solar and wind power energy generation projects around the world. The issues are then analysed across specific challenges in five continents where these projects are being developed. The paper then focuses on the literature on energy justice to elaborate the type of issues being faced by renewable energy megaprojects contributing to the achievement of UN Sustainable Goal 7 and their impact on vulnerable communities where these projects are situated. Findings Renewable energy megaprojects are rarely discussed in the project management literature on megaprojects despite their size and importance in delivering sustainable development goals. While these projects provide social benefits they also create issues of justice due to their impact of vulnerable populations living is locations where these projects are situated. The justice issues faced include procedural justice, distributive justice, recognition inequalities. The type of justice issues was found to vary intensity in the developed, emerging and developing economies. It was found that nonprofit organisations are embarking on strategies to alleviate energy justice issues in innovative ways. It was also found that, in some instances, smaller local projects developed with community participation could actually contribute more equitable to the UN sustainable development goals avoiding the justice issues posed by mega renewable energy projects. Research limitations/implications The research uses secondary data due to which it is difficult to present a more comprehensive picture of stakeholder issues involving renewable energy megaprojects. The justice issues revealed through thesis paper with renewable energy megaprojects are also present in conventional megaprojects which have not been discussed in the project management literature. Post-COVID-19 these justice issues are likely to become mor prevalent due to the pandemic's impact on vulnerable population exacerbating the issues and increasing their severity on these populations. Therefore it is becoming even more critical to take these into account while developing renewable energy megaprojects. Practical implications Proper identification and response to energy justice issues can help in alleviating stakeholder issues in renewable energy megaprojects. Social implications Contributes to the equitable achievement of the United Nations Sustainable Development Goal 7. Originality/value This paper addresses a gap in the project management literature on the exploration of stakeholder issues on renewable energy megaprojects. It also brings out the importance of justice issues which can assist in expanding stakeholders issues faced by megaprojects as these issues have not received sufficient attention in the past in the project management literature.

Experimental and theoretical insights into the formation of weak hydrogen bonds and H center dot center dot center dot H bonding interactions in the solid-state structure of two eucalyptol derivatives

Two eucalyptol derivatives, namely 1,3,3-trimethyl-2-oxabicycle[2.2.2]oct-5-yl acetate (4) and 1,3,3trimethyl-2-oxabicycle[2.2.2]oct-5,8-yl acetate (6) have been synthesized and characterized. Their crystal structures have been solved by single-crystal X-ray diffraction methods indicating that the molecular conformation of both compounds is stabilized by intramolecular C-H center dot center dot center dot O bonds between the H-atoms of the methyl group from the eucalyptol moiety and the O-atom of the acetoxy group. In addition, we have performed a detailed investigation of the intermolecular interactions that stabilize the crystal packing of both structures by using Hirshfeld surface analysis and their associated two-dimensional fingerprint plots. The analysis reveals that the solid-state structure of compounds 4 and 6 is mainly stabilized by C-H center dot center dot center dot O hydrogen bonds and H center dot center dot center dot H bonding interactions. These interactions have also been described and studied energetically using DFT calculations. The nature and strength of these intermolecular contacts have been rationalized by using several computational tools including molecular electrostatic potential (MEP) surfaces, natural bond orbital analysis (NBO), Bader's theory of atoms in molecules (QTAIM) and non-covalent interaction plot (NCI plot) index methods. Furthermore, the intermolecular contacts observed in the crystal lattice of both compounds were experimentally studied through vibrational (IR and Raman) and H-1 and C-13 NMR spectra. The computational molecular docking analysis of the compounds has been carried out against five potential leishmanial drug targets and the main protease of SARS-CoV-2.

The Impact of COVID-19 on the Majority Population, Ethno-Racial Minorities, and Immigrants A Systematic Literature Review on Threat Appraisals From an Inter-Group Perspective

The COVID-19 pandemic constitutes an unprecedented threat for individuals and societies, revealing stark inequalities in preparedness, exposure, and consequences. The present systematic literature review complements extant knowledge on disasters and pandemic diseases with programmatic research on the COVID-19 pandemic. Building upon an integrative definition of threat, we merge intra-personal threat regulation with group dynamics and inter-group relations. Via streamlined methods of knowledge synthesis, we first map out a broad taxonomy of threats, as appraised by the majority population and ethno-racial and immigrant minorities. Second, we delve into research linking threat appraisals with either conflict or prosociality within and across group boundaries. To conclude, we propose some guidelines for researchers to involve ethno-racial and immigrant minorities actively and for societies to cope cohesively with the impact of COVID-19.

Lightweight and Privacy-Preserving Remote User Authentication for Smart Homes

The rapid proliferation of embedded devices has led to the growth of the Internet of Things (IoT) with applications in numerous domains such as home automation, healthcare, education and agriculture. However, many of the connected devices particularly in smart homes are the target of attacks that try to exploit security vulnerabilities such as hard-coded passwords and insecure data transfer. Recent studies show that there is a considerable surge in the number of phishing attacks targeting smart homes during the COVID-19 pandemic. Moreover, many of the existing user authentication protocols in the literature incur additional computational overhead and need to be made more resilient to smart home targeted attacks. In this paper, we propose a novel lightweight and privacy-preserving remote user authentication protocol for securing smart home applications. Our approach is based on Photo Response Non-Uniformity (PRNU) to make our protocol resilient to smart home attacks such as smartphone capture attacks and phishing attacks. In addition, the lightweight nature of our solution is suitable for deployment on heterogeneous and resource constrained IoT devices. Besides, we leverage geometric secret sharing for establishing mutual authentication among the participating entities. We validate the security of the proposed protocol using the AVISPA formal verification tool and prototype it on a Raspberry Pi to analyze the power consumption. Finally, a comparison with existing schemes reveals that our scheme incurs a 20% reduction in communication overhead on smart devices. Furthermore, our proposed scheme is usable as it absolves users from memorizing passwords and carrying smart cards. Author

Predictive Modeling of the Spread of COVID-19: The Case of India

COVID-19 has been the most notorious pandemic affecting the entire world resulting in numerous deaths thus crippling the world economy. While vaccines are in the process of being developed for protection, countries are implementing measures such as social distancing to prevent the spread of the virus. Also, there exists a need for developing mathematical models to predict the rate of spread of COVID-19 and quantify its impact on countries such as India. Towards this goal, we developed a realistic COVID-19 dataset consisting of state-wide distribution of number of cases in India from March-July 2020. Further, we conduct exploratory data analysis on the dataset to understand the states and their corresponding growth rates. This enables us to cluster states with exponential and non-exponential growth rates as well as assess the effectiveness of lockdown imposed to curb the spread of virus. Finally, we develop predictive models using Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Networks (LSTM) on time-series data for top-10 affected states in India to predict the rate of spread and validate their accuracy. Finally, our models can be used to guide the development of mechanisms for optimal resource allocation of healthcare systems and response. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Approach to dyspnoea in pregnancy in the COVID-19 era.

IMPORTANCE: Dyspnoea and hypoxia in pregnant women during the COVID-19 pandemic may be due to causes other than SARS Co-V-2 infection which should not be ignored. Shared decision-making regarding early delivery is paramount. OBJECTIVE: To highlight and discuss the differential diagnoses of dyspnoea and hypoxia in pregnant women and to discuss the risks versus benefit of delivery for maternal compromise. DESIGN, SETTING AND PARTICIPANTS: Case series of two pregnant women who presented with dyspnoea and hypoxia during the COVID-19 pandemic. RESULTS: Two pregnant women presented with dyspnoea and hypoxia. The first case had COVID-19 infection in the 3rd trimester. The second case had an exacerbation of asthma without concurrent COVID-19. Only the first case required intubation and delivery. Both recovered and were discharged home. Conclusion and relevance: Our two cases highlight the importance of making the correct diagnosis and timely decision-making to consider if delivery for maternal compromise is warranted. Whilst COVID-19 is a current healthcare concern other differential diagnoses must still be considered when pregnant women present with dyspnoea and hypoxia.

SARS-CoV-2 Infection in Patients with Down Syndrome, Congenital Heart Disease, and Pulmonary Hypertension: Is Down Syndrome a Risk Factor?

With increasing information available about the epidemiology, pathophysiology, and management of patients affected with severe acute respiratory syndrome corona virus-2 infection, patients with Down syndrome, congenital heart disease, airway obstruction, and pulmonary hypertension present a unique challenge. This case series describes 3 patients with Down syndrome and respiratory failure secondary to coronavirus infection.