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The internet of vehicles (IoV) communication has recently become a popular research topic in the automotive industry. The growth in the automotive sector has resulted in significant standards and guidelines that have engaged various researchers and companies. In IoV, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in IoV and inconsistent network coverage in different areas make routing challenging. This paper aims to provide a lane-based advanced forwarding protocol for internet of vehicles (LAFP-IoV) for efficient data distribution in IoV. The proposed protocol’s main feature is that it can identify the destination zone by using position coordinates and broadcasting the packets toward the direction of destination. The novel suppression technique is used in the broadcast method to reduce the network routing overhead.

The proposed protocol considers the interferences between different road segments, and a novel lane-based forwarding model is presented. The greedy forwarding notion, the broadcasting mechanism, and the suppression approach are used in this protocol to reduce the overhead generated by standard beacon forwarding procedures. The SUMO tool and NS-2 simulator are used for the vehicle's movement pattern and to simulate LAFP-IoV.

The simulation results show that the proposed LAFP-IoV protocol performs better than its peer protocols. It uses a greedy method for forwarding data packets and a carry-and-forward strategy to recover from the local maximum stage. This protocol's low latency and good PDR make it ideal for congested networks.

The proposed paper provides a unique lane-based forwarding for IoV. The proposed work achieves a higher delivery ratio than its peer protocols. The proposed protocol considers the lanes while forwarding the data packets applicable to the highly dense scenarios.

The purpose of this paper is to perform a comparative analysis of the delay associated in running two real-time machine learning-based applications, namely, a video quality assessment (VQA) and a phishing detection application by using the edge, fog and cloud computing paradigms.

The VQA algorithm was developed using Android Studio and run on a mobile phone for the edge paradigm. For the fog paradigm, it was hosted on a Java server and for the cloud paradigm on the IBM and Firebase clouds. The phishing detection algorithm was embedded into a browser extension for the edge paradigm. For the fog paradigm, it was hosted on a Node.js server and for the cloud paradigm on Firebase.

For the VQA algorithm, the edge paradigm had the highest response time while the cloud paradigm had the lowest, as the algorithm was computationally intensive. For the phishing detection algorithm, the edge paradigm had the lowest response time, and the cloud paradigm had the highest, as the algorithm had a low computational complexity. Since the determining factor for the response time was the latency, the edge paradigm provided the smallest delay as all processing were local.

The main limitation of this work is that the experiments were performed on a small scale due to time and budget constraints.

A detailed analysis with real applications has been provided to show how the complexity of an application can determine the best computing paradigm on which it can be deployed.

In this paper, the brain potential changes caused by touching fabrics for handle evaluation were recorded by event related potential (ERP) method, compared with subjective evaluation scores and physical index of KES, explore the cognitive mechanism of the transformation of tactile sensation into neural impulses triggered by subtle mechanical stimuli such as material, texture, density and morphology in fabrics. By combining subjective evaluation of fabric tactile sensation, objective physical properties of fabrics and objective neurobiological signals, explore the neurophysiological mechanism of tactile cognition and the signal characteristics and time process of tactile information processing.

The ERP technology was first proposed by a British psychologist named Grey Walter. It is an imaging technique of noninvasive brain cognition, whose potential changes are related to the human physical and mental activities. ERP is different from electroencephalography (EEG) and evoked potentials (EP) on the fact that it cannot only record stimulated physical information which is transmitted to brain, but also response to the psychological activities which related to attention, identification, comparison, memory, judgment and cognition as well as to human’s neural physiological changes which are caused by cognitive process of the feeling by stimulation.

According to potential changes in the cerebral cortex evoked by touching four types of silk fabrics, human brain received the physical stimulation in the early stage (50 ms) of fabrics handle evaluation, and the P50 component amplitude showed negative correlation with fabric smoothness sensations. Around 200 ms after tactile stimulus onset, the amplitude of P200 component show positive correlation with the softness sensation of silk fabrics. The relationship between the amplitude of P300 and the sense of smoothness and softness need further evidence to proof.

In this paper, the brain potential changes caused by touching fabrics for handle evaluation were recorded by event related potential (ERP) method, compared with subjective evaluation scores and physical index of KES, the results shown that the maximum amplitude of P50 component evoked by fabric touching is related to the fabrics’ smoothness and roughness emotion, which means in the early stage processing of tactile sensation, the rougher fabrics could arouse more attention. In addition, the amplitude of P200 component shows positive correlation with the softness sensation of silk fabrics.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

The purpose of this study was to explore the potential benefits of Coriandrum sativum (C. sativum) on anxiety, depression, sleep quality and memory among students.

This randomized controlled trial involved university students aged 18–25 years, who had no known allergies to C. sativum or were using psychotropic medication. After giving informed consent, participants were randomly assigned using a computer-generated random sequence to either 500 mg C. sativum seeds or a placebo. Primary outcomes measured changes in memory (prospective and retrospective memory questionnaire [PRMQ]), anxiety and depression (Hospital Anxiety and Depression Scale), while secondary outcomes assessed sleep quality (Pittsburgh sleep quality inventory [PSQI]).

A sample of 86 students with a mean age of 20.05 ± 1.6 years was selected for the study. Initial assessments ensured no significant differences in demographic or study variables between the control and intervention groups. Statistical analysis revealed significant improvements in memory (PRMQ: p = 0.006), anxiety (Hospital Anxiety Scale: p = 0.04) and depression (Hospital Depression Scale: p = 0.002), as well as in sleep quality (PSQI: p = 0.03) in the group receiving C. sativum compared to the control group.

This research offers initial insights into the potential benefits of C. sativum intake, specifically its role in enhancing memory performance and mitigating anxiety among student populations. The results present a compelling case for further research in this domain to solidify these preliminary conclusions.

The purpose of this study synthesises the body of research revolving around blockchain technology (BCT) whilst drawing on the technology-organization-environment framework, resource-based theory and theory of constraints, to conceptualize capabilities (enablers) and constraints (barriers) of BCT in the hospitality and tourism (H&T) industry.

A systematic literature review of BCT in the hotel and tourism industry has been achieved through two databases, i.e. Scopus and Web of Science. From 544 articles selected between the years 2008 and 2023 (first quarter), a sample of 49 articles was used to structure existing research on this subject.

The findings of this systematic literature review of BCT in the H&T literature establish a solid groundwork for assessing the evolution of this research area over time. Findings are classified into two groups: capabilities (enablers) and constraints (barriers) of BCT based on publication year, different research methods, theoretical underpinnings and applicable contexts.

To the best of the authors’ knowledge, this is one of the first attempts to synthesize studies related to BCT in H&T research by combining three theoretical approaches. It serves as a foundation to evaluate the development of BCT studies in this field.

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.

The purpose of this study is to analyze the determinants influencing Japanese consumers’ behavior toward the purchase of 5G smartphones.

Empirical data was collected through an online survey of 320 Japanese respondents from ages 20 to 59. Data was analyzed by exploratory factor analysis, confirmatory factor analysis and multiple regression analysis.

The results indicate that hedonic value, user interface and price value (PV) positively influence attitude toward 5G smartphone purchase, while utilitarian value does not. In addition, the mediation effect of social influence (SI) is found to increase those positive effects. Male and female consumers displayed differences in the determinants, showcasing the effect of gender on attitude and behavior.

To the best of the authors’ knowledge, this study is the first research to analyze Japanese consumers’ behaviors’ toward 5G smartphone purchase. Past research for 3G/4G smartphones supported that usefulness positively influenced consumers’ behavior toward smartphones, so usefulness of 5G (e.g. high speed) was expected to be a factor to influence consumer behaviors’ toward 5G smartphones. However, this research reveals that a simple tagline emphasizing such usefulness may not be convincing enough to promote the 5G smartphones to Japanese consumers. Alternatively, the research indicates that introducing the concepts of joyfulness, improved HV and greater PV of the 5G smartphone could attract more consumers in Japan. In addition, SI could enhance the perception of the various benefits of 5G smartphones.

This study aims to investigate disclosure of asbestos-related liabilities in corporate accounts and counter-accounts to examine whether and how accounting contributes to corporate accountability for asbestos-contaminated products.

This study uses the Goffmanesque perspective on impression management to examine instances of concealed asbestos-related liabilities in corporate accounts vis-à-vis the revealing of such liabilities in counter-accounts.

The findings show counter-accounts provide significant information on liabilities originating from the exposure of employees and consumers to asbestos. By contrast, the malleability of accounting tools enables companies to eschew accounting disclosures. While the frontstage positive performance of companies served an impression management role, their backstage concealing actions enabled companies to cover up asbestos-related liabilities. These companies used three categories of mechanisms to avoid disclosure of asbestos-related liabilities: concealing via a “cloak of competence”, impression management via epistemic work and a silent strategy of concealment frontstage with strategic reorganisation backstage.

This study has policy relevance as regulators need to consider the limits of corporate disclosures as an accountability tool. The findings may also initiate academic and practitioner conversations about accounting standards for long-term liabilities.

This study highlights the strategies companies use both frontstage and backstage to avoid disclosing asbestos-related liabilities. Through analysis of accounts and counter-accounts, this study identifies the limits of accounting as an accountability tool regarding asbestos-induced diseases and deaths.

Blockchain for energy trading is a trending research area in the current context. However, a noticeable gap exists in the review articles focussing on solar energy trading with blockchain technology. Thus, this study aims to systematically examine and synthesise the existing research on implementing blockchain technology in sustainable solar energy trading.

The study pursued a systematic literature review to achieve its aim. The data extraction process focussed on the Scopus and Web of Science (WoS) databases, yielding an initial set of 129 articles. Subsequent screening and removal of duplicates led to 87 articles for bibliometric analysis, utilising VOSviewer software to discern evolutionary progress in the field. Following the establishment of inclusion and exclusion criteria, a manual content analysis was conducted on a subset of 19 articles.

The results indicated a rising interest in publications on solar energy trading with blockchain technology. Some studies are exploring the integration of new technologies like machine learning and artificial intelligence in this domain. However, challenges and limitations were identified, such as the absence of real-world solar energy trading projects.

This study offers a distinctive approach by integrating bibliometric and manual content analyses, a methodology seldom explored. It provides valuable recommendations for academia and industry, influencing future research and industry practices. Insights include integrating blockchain into solar energy trading and addressing knowledge gaps. These findings advance societal goals, such as transitioning to renewable energy sources (RES) and mitigating carbon emissions, fostering a sustainable future.