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The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities.

This paper develops an intelligent real-time learning framework for the last-mile delivery of mobility as a service in city planning, based upon safe infrastructure use. Through a hybrid approach integrating statistics and supervised machine learning techniques, knowledge-driven solutions based on the specific user rather than generalized safe mobility practices are suggested. One of the most important aspects influencing transport mode and route selection, and safe infrastructure usage, i.e., the age of the user, is simulated. This is because this variable has been described in the literature as a significant variable. Nonetheless, few works deal with such modelling or the learning system. The learning system was applied in the Northumbria region of England’s northeast as a case study. It comprised four building toolkits: (a) Input toolkit, (b) Safety Predictive toolkit, (c) Variable causation toolkit, and (d) Route choice toolkit. An accurate dynamic road safety model and understanding of the critical parameters influencing bicycle rider safety is created. The developed deep learning model’s average distinguishing power to reliably predict the riskiest age group was 95%, with a standard deviation of 0.02, suggesting a good prediction accuracy across all age groups. According to the study’s findings, different infrastructural networks represent varying risks to bicycle riders of different ages. The rider’s age impacts how other road users engage with them. The regional diversity in trip intent and traffic flow conditions were significant elements influencing the safe use of infrastructure for a specific age group. The study’s findings have the potential to considerably influence infrastructure route selection, modelling, and planning. The constructed model, which integrates the rider’s fragility, sensitivity to externalities, and the varied safety impact dependent on its features, may even be used for the infrastructure still in the planning/design phase. It is envisaged that this research would aid in adopting sustainable (green) transportation options and the last-mile delivery of mobility as a service. Future work should aim to uncover the sensitivities of a rider from different countries and make a baseline comparison scenario.

To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using a combination of deep neural networks. The learning system is used as a case study in the Northumbria region in England’s northeast. It is made up of three components: (a) input data unit, (b) knowledge processing unit, and (c) output unit. It is demonstrated that various infrastructure characteristics influence bikers’ safe interactions, which is used to estimate the riskiest age and gender rider groups. Two accurate prediction models are built, with a male accuracy of 88 per cent and a female accuracy of 95 per cent. The findings concluded that different infrastructures pose varying levels of risk to users of different ages and genders. Certain aspects of the infrastructure are hazardous to all bikers. However, the cyclist’s characteristics determine the level of risk that any infrastructure feature presents. Following validation, the built learning system is interoperable under various scenarios, including current heterogeneous and future semi-autonomous and autonomous transportation systems. The results contribute towards understanding the risk variation of various infrastructure types. The study’s findings will help to improve safety and lead to the construction of a sustainable integrated cycling transportation system.

In the early 20th century, people shifted from horse drawn vehicles to motorized vehicles. In the mid 21st century, we will be changing to Autonomous Vehicles (AV). There are certain perceptions about the safety, fuel efficiency, and the sustainability of autonomous vehicles. To justify huge investment that is required for the same and the required behavior driver change, sustainability and fuel efficiency are to be evaluated. This article looks at these challenges that Autonomous vehicles are facing and it is essential to evaluate them properly. It is also essential to evaluate the benefits of AVs in terms of these challenges. In this present world of Information and Communication Technology (ICT), any development in the developed countries has direct impact on the developing countries, even the opinions of the people. AVs present a lot of challenges and opportunities for developing countries such as India.

This paper is concerned with modelling cyclist road safety by considering various factors including infrastructure, spatial, personal and environmental variables affecting cycling safety. Age is one of the personal attributes, reported to be a significant critical variable affecting safety. However, very few works in the literature deal with such a problem or undertaking modelling of this variable. In this work, we propose a hybrid approach by combining statistical and supervised deep learning with neural network classifier, and gradient descent backpropagation error function for road safety investigation. The study area of Tyne and Wear County in the north-east of England is used as a case study. An accurate dynamic road safety model is constructed, and an understanding of the key parameters affecting the cyclist safety is developed. It is hoped that this research will help in reducing the cyclist crash and contribute towards sustainable integrated cycling transportation system, by making use of cut above methodologies such as deep learning neural network.

A cyclist is a vulnerable road user whose safety is affected by several externalities. The global aim of the research is to investigate the effect of critically identified variables of rider attributes of age, gender, varied environmental condition of lighting, meteorology, and micro-infrastructure variables on the safe usage of the infrastructure for a cyclist. Presently, very few works have attempted to undertake such modelling. A novel methodological framework is developed, consisting of descriptive, statistical, artificial intelligence and mathematical approaches. Accurate prediction models are developed, and in-depth knowledge of how different variables affect cyclist safety are identified, modelled, and quantified. It is found that the variables of age, gender, varied environmental conditions, and micro-infrastructure variable are critical variables affecting the safe usage of infrastructure. These variables, both individually and in combination, impact cyclist safety. Cycling safety is a dynamic variable that varies temporally and spatially. The spatial and environmental variables have a significantly varied effect on safety depending upon the rider personal attribute. As the number of safety variables that the cyclist must conform to grows, so does the risk. The riskiest environmental conditions are exacerbated by the prevailing traffic flow regime, posing a significant safety risk to cyclists. The modelling requirement of a cyclist is significantly different from motorists. A hybrid intelligent modelling paradigm is required, as demonstrated in this research. The study results can significantly impact the route choice, modelling, and planning of infrastructure. A shift in the road safety analysis towards nanoscopic modelling can help achieve zero-vision road traffic fatality. The research reinforces a need for planning and design of infrastructure to move towards a more holistic approach while considering the limitations of this vulnerable road user.

The ongoing COVID-19 pandemic is caused by SARs-CoV-2. The virus is transmitted from person to person through droplet infections i.e. when infected person is in close contact with another person. In January 2020, first report of detection of SARS-CoV-2 in faeces, has made it clear that human wastewater might contain this virus. This may illustrate the probability of environmentally facilitated transmission, mainly the sewage, however, environmental conditions that could facilitate faecal oral transmission is not yet clear. We used existing Pakistan polio environment surveillance network to investigate presence of SARs-CoV-2 using three commercially available kits and E-Gene detection published assay for surety and confirmatory of positivity. A Two-phase separation method is used for sample clarification and concentration. An additional high-speed centrifugation (14000Xg for 30 min) step was introduced, prior RNA extraction, to increase viral RNA yield resulting a decrease in Cq value. A total of 78 wastewater samples collected from 38 districts across Pakistan, 74 wastewater samples from existing polio environment surveillance sites, 3 from drains of COVID-19 infected areas and 1 from COVID 19 quarantine center drainage, were tested for presence of SARs-CoV-2. 21 wastewater samples (27%) from 13 districts turned to be positive on RT-qPCR. SARs-COV-2 RNA positive samples from areas with COVID 19 patients and quarantine center strengthen the findings and use of wastewater surveillance in future. Furthermore, sequence data of partial ORF 1a generated from COVID 19 patient quarantine center drainage sample also reinforce our findings that SARs-CoV-2 can be detected in wastewater. This study finding indicates that SARs-CoV-2 detection through wastewater surveillance has an epidemiologic potential that can be used as supplementary system to monitor viral tracking and circulation in cities with lower COVID-19 testing capacity or heavily populated areas where door-to-door tracing may not be possible. However, attention is needed on virus concentration and detection assay to increase the sensitivity. Development of highly sensitive assay will be an indicator for virus monitoring and to provide early warning signs.

COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.

Smart devices have accentuated the importance of geolocation information. Geolocation identification using smart devices has paved the path for incentive-based location-based services (LBS). However, a user’s full control over a smart device can allow tampering of the location proof. Witness-oriented location proof systems (LPS) have emerged to resist the generation of false proofs and mitigate collusion attacks. However, witness-oriented LPS are still susceptible to three-way collusion attacks (involving the user, location authority, and the witness). To overcome the threat of three-way collusion in existing schemes, we introduce a decentralized consensus protocol called MobChain in this paper. In this scheme the selection of a witness and location authority is achieved through a distributed consensus of nodes in an underlying P2P network that establishes a private blockchain. The persistent provenance data over the blockchain provides strong security guarantees; as a result, the forging and manipulation of location becomes impractical. MobChain provides secure location provenance architecture, relying on decentralized decision making for the selection of participants of the protocol thereby addressing the three-way collusion problem. Our prototype implementation and comparison with the state-of-the-art solutions show that MobChain is computationally efficient and highly available while improving the security of LPS.

Vaccination protects people from serious illness and associated complications. Conspiracy theories and misinformation on vaccines have been rampant during the COVID-19 pandemic and are considered significant drivers of vaccine hesitancy. Since vaccine hesitancy can undermine efforts to immunize the population against COVID-19 and interferes with the vaccination rate, this study aimed to ascertain the COVID-19-vaccine-related conspiracy beliefs, vaccine hesitancy, views regarding vaccine mandates, and willingness to pay for vaccines among the general population. A web-based, cross-sectional survey was conducted (April–August 2021) among the adult population in six countries (Pakistan, Saudi Arabia, India, Malaysia, Sudan, and Egypt). Participants were recruited using an exponential, non-discriminate snowball sampling method. A validated self-completed electronic questionnaire was used for the data collection. All the participants responded to questions on various domains of the study instrument, including conspiracy beliefs, vaccine hesitancy, and willingness to pay. The responses were scored according to predefined criteria and stratified into various groups. All data were entered and analyzed using SPSS version 22. A total of 2481 responses were included in the study (Pakistan 24.1%, Saudi Arabia 19.5%, India 11.6%, Malaysia 8.1%, Sudan 19.3%, and Egypt 17.3%). There was a preponderance of participants ≤40 years old (18–25 years: 55.8%, 26–40 years: 28.5%) and females (57.1%). The average score of the COVID-19 vaccine conspiracy belief scale (C19V-CBS) was 2.30 ± 2.12 (median 2; range 0–7). Our analysis showed that 30% of the respondents were found to achieve the ideal score of zero, indicating no conspiracy belief. The mean score of the COVID-19 vaccine hesitancy scale (C19V-HS) was 25.93 ± 8.11 (range: 10–50). The majority (45.7%) had C19V-HA scores of 21–30 and nearly 28% achieved a score greater than 30, indicating a higher degree of hesitancy. There was a significant positive correlation between conspiracy beliefs and vaccine hesitancy (Spearman’s rho = 0.547, p < 0.001). Half of the study population were against the vaccine mandate. Respondents in favor of governmental enforcement of COVID-19 vaccines had significantly (p < 0.001) lower scores on the C19V-CBS and C19V-HS scale. Nearly 52% reported that they would only take vaccine if it were free, and only 24% were willing to pay for COVID-19 vaccines. A high prevalence of conspiracy beliefs and vaccine hesitancy was observed in the targeted countries. Our findings highlight the dire need for aggressive measures to counter the conspiracy beliefs and factors underlying this vaccine hesitancy.