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An energy-absorbing device is an important tool that is capable of increasing the survivability of passengers in vehicles. Generally, empty metallic tubes are used, and it is found that the energy absorption capability is lower than the energy obtained using composite materials. Therefore, this paper numerically presents the crushing performances of hybrid tubes under axial and oblique compressions.

Three important parameters are used such as fiber thicknesses, fiber orientations and oblique compression angles. Epoxy-reinforced fibers are wrapped around the steel tubes and it is then modeled numerically using the ANSYS finite element program. Belytscho – Tsay shell element is used to model the composites, while a bilinear kinematic hardening model is used to model the steel tube. A proper contact algorithm is implemented to prevent interpenetration among elements and surfaces.

A proper contact algorithm is implemented to prevent interpenetration among elements and surfaces. Hybrid tubes are quasi-statically crushed and force–displacement curves are extracted and analyzed.

It is found that the introduction of oblique compressions has induced bending moments and therefore decreases the energy absorption capability. Varying fiber orientations also insignificantly changed the crushing performances. However, wrapping carbon/epoxy composite that is capable of strengthening the tubes, is also subjected to oblique compression compared with the glass/epoxy composites.

This study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.

This quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.

The results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.

The research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.

This study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

The purpose of this paper is to design and implement a fully integrated low‐phase noise and large tuning range dual‐band LC voltage‐controlled oscillator (VCO) in 0.13 μm complementary metal oxide semiconductor (CMOS) technology.

Two parallel‐connected single‐band VCOs are designed to implement the proposed VCO. Adopting a simple and straight‐forward architecture, the dual‐band VCO is configured to operate at two frequency bands, which are from 1.48 GHz to 1.78 GHz and from 2.08 GHz to 2.45 GHz. A band selection circuit is designed to perform band selection process based on the controlling input signal.

The proposed VCO features phase noise of −104.7 dBc/Hz and −108.8 dBc/Hz at 1 MHz offset frequency for both low corner and high corner end of the low‐band operation. For high‐band operation, phase‐noise performance of −101.1 dBc/Hz and −110.4 dBc/Hz at 1 MHz offset frequency are achieved. The measured output power of the dual‐band VCO ranges from −8.4 dBm to −5.8 dBm and from −9.6 dBm to −8.0 dBm for low‐band and high‐band operation, respectively. It was also observed that the power differences between the fundamental spectrum and the nearby spurious tone range from −67.5 dBc to −47.7 dBc.

The paper is useful to both the academic and industrial fields since it promotes the concept of multi‐band or multi‐standard system which is currently in demand in the telecommunication industry.

The main purpose of this review was to synthesize pre-service teachers' digital self-efficacy in the context of education 5.0 and to identify the elements that affect pre-service teacher's digital self-efficacy and preparedness for future technologies. A systematic review approach was employed to analyze the 22 articles about the pre-service teachers' digital self-efficacy of in the paradigm of education 5.0. The review was conducted from 2012 to 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In this study, PICO model was used in framing research questions. The findings of the review revealed that limited study has been carried out in the context of digital self-efficacy in the context of education 5.0. It was found that previous studies were only focusing general digital self-efficacy through traditional ways. Moreover, findings revealed there is lack of research on digital self-efficacy pre-service teachers in the realm of education 5.0 paradigm in the literature. More specifically, the results revealed that implementation of education 5.0 into teacher preparation programmes faces numerous challenges, including a lack of technological approach, poor digital infrastructure, the digital divide, a paucity of professional training opportunities for teachers and a lack of importance in policies. This synthesis of the literature review has practical implications for pre-service teachers along with policymakers. Pre-service teachers are required to increase their digital skills for the reason that they could teach the advanced generation. The policymakers can revisit and update the curriculum to incorporate education 5.0 principles in the teacher education programmes. This comprehensive review helps to better understand the pre-service teachers' digital self-efficacy towards education 5.0 and contributes to the body of existing knowledge on digital self-efficacy. Moreover, this study gives valuable insights on the incorporation of education 5.0.

This study aims to investigate the challenges associated with the integration and implementation of blockchain technology in the education sector. The primary objective is to identify and categorize these challenges using the Technology-Organization-Environment (TOE) framework, shedding light on the technological, organizational and environmental factors that influence the adoption of blockchain solutions in educational settings.

A comprehensive literature review was conducted across multiple databases including Science Direct, Web of Science, Springer, IEEE Xplore and MDPI. The selected articles were analyzed based on the TOE framework to categorize challenges from the technological, organizational and environmental perspectives. The methodology involves a systematic search, analysis and synthesis of relevant articles to provide an in-depth understanding of the challenges hindering blockchain adoption in education.

This review revealed a predominant focus on technological challenges, emphasizing scalability issues, integration complexities, security and privacy concerns and data immutability. However, there is a notable research gap in the exploration of organizational and environmental challenges. The scarcity of studies addressing these perspectives may impact acceptance and resistance to blockchain adoption in educational settings.

This study contributes to the literature by systematically categorizing and analyzing the challenges associated with blockchain implementation in education using the TOE framework. It identifies the need for further research on organizational and environmental aspects, addressing a significant gap in the current scholarship on blockchain adoption in educational institutions.

In this article, the authors try to determine why and under what conditions consumers intend to buy green and what the consequences are. Relying on theories of reasoned action and theory of planned behaviour (TPB), the authors offer that the green purchase intention (GPI) is impressed by environmental and personality components. Provide statements about the determinants and key implications of such market identification.

This research aimed to analyze and prioritize the factors affecting sustainable marketing (SM) and sustainable operation (SO) of manufacturing small and medium-sized enterprises SMEs through big data adoption (BDA).

The technology-organization-environment (TOE) framework was used as a theoretical base and data were gathered from manufacturing SMEs in Malaysia. The 159 questionnaire replies of chief executive officer (CEO)/managers were analyzed using a hybrid approach of structural equation modeling-artificial neural network (SEM-ANN).

The findings of this study showed that perceived benefits (PB), technological complexity (TC), organization's resources (OR), organization's management support (OMS) and government legislation (GL) are the factors that influence BDA and promote SM and SO. The findings of ANN showed that a perceived benefit is the most important factor, followed by OMS.

The findings of this study can assist SMEs managers in making strategic decisions and improving sustainable performance and thus contribute to overall economic development.

The manufacturing industry is under immense pressure to integrate sustainable practices for long-term success. BDA can assist industries in aligning industries' operational capabilities. The majority of the current research have mainly emphasized on BDA in corporations. However, the associations between BDA and sustainable performance of manufacturing SMEs have been less explored. To address this issue, this study developed a theoretical model and examined the influence of BDA on SM and SO of manufacturing SMEs. Meanwhile, the hybrid methodological approach can help to uncover both linear and non-linear relationships better.