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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.

Although many scholars have investigated different aspects of the notion of innovation ambidexterity, the conceptualization of examining this concept in a technological setting remained unclear, as no serious attempts have been made to figure out the core concept of innovation ambidexterity in a technological context, which is a critical concept for high-tech firms.

This paper aims to develop an optimization model to enhance pipeline assembly performance. It focuses on predicting the pipeline’s assembly pose while ensuring compliance with clamp constraints.

The assembly pose of the pipeline is quantitatively assessed by a proposed indicator based on joint defects. The assembly interference between the pipeline and assembly boundary is characterized quantitatively. Subsequently, an analytical mapping relationship is established between the assembly pose and assembly interference. A digital fitting model, along with a novel indicator, is established to discern the fit between the pipeline and clamp. Using the proposed indicators as the optimization objective and penalty term, an optimization model is established to predict the assembly pose based on the reinforced particle swarm optimization, incorporating a proposed adaptive inertia weight.

The optimization model demonstrates robust search capability and rapid convergence, effectively minimizing joint defects while adhering to clamp constraints. This leads to enhanced pipeline assembly efficiency and the achievement of a one-time assembly process.

The offset of the assembly boundary and imperfections in pipeline manufacturing may lead to joint defects during pipeline assembly, as well as failure in the fit between the pipeline and clamp. The assembly pose predicted by the proposed optimization model can effectively reduce the joint defects and satisfy clamp constraints. The efficiency of pipeline modification and assembly has been significantly enhanced.

This study evaluates the reliability of a multi-state system (MSS) with n components, each having two s-dependent components via copulas.

The study employs copula functions to model dependencies between components in an MSS. Specifically, it analyzes a (1,1)-out-of-n three-state system using Frank and Clayton copulas for reliability evaluation. A simulation-based case study of a micro-inverter solar panel system is also conducted using the Farlie–Gumbel–Morgenstern (FGM) copula.

The study finds that incorporating component dependencies significantly impacts the reliability of multi-state systems. Using Frank and Clayton copulas, the analysis shows how dependency structures alter system performance compared to independent models. The case study on a micro-inverter solar panel system, using the FGM copula, demonstrates that real-world systems with dependent components exhibit different performances. Also some effects of dependence parameters on the performance characteristics of the system such as mean residual lifetime and mean past lifetime are also examined.

This study is original in its use of copula functions to evaluate the performance of multi-state systems, particularly focusing on a (1,1)-out-of-n three-state system with dependent components. By applying Frank and Clayton copulas, the research advances reliability analysis by considering component dependencies, often overlooked in traditional models. Additionally, a case study on a micro-inverter solar panel system using the FGM copula highlights the practical application of these methods.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

The waste-to-energy (WtE) project plays a significant role in the sustainable development of urban environments. However, the inherent “Not in my backyard” (NIMBY) effect presents substantial challenges to site selection decisions. While effective public participation is recognized as a potential solution, research on incorporating it into site selection decision-making frameworks remains limited. This paper aims to establish a multi-attribute group decision-making framework for WtE project site selection that considers public participation to enhance public satisfaction and ensure project success.

Firstly, based on consideration of public demand, a WtE project site selection decision indicator system was constructed from five dimensions: natural, economic, social, environmental and other supporting conditions. Next, the Combination Ordered Weighted Averaging (C-OWA) operator and game theory were applied to integrate the indicator weight preferences of experts and the public. Additionally, an interactive, dynamic decision-making mechanism was established to address the heterogeneity among decision-making groups and determine decision-maker weights. Finally, in an intuitive fuzzy environment, an “acronym in Portuguese of interactive and multi-criteria decision-making” (TODIM) method was used to aggregate decision information and evaluate the pros and cons of different options.

This study develops a four-stage multi-attribute group decision-making framework that incorporates public participation and has been successfully applied in a case study. The results demonstrate that the framework effectively handles complex decision-making scenarios involving public participation and ranks potential WtE project sites. It can promote the integration of expert and public decision-making preferences in the site selection of WtE projects to improve the effectiveness of decision-making. In addition, sensitivity and comparative analyses confirm the framework’s feasibility and scientificity.

This paper provides a new research perspective for the WtE project site selection decision-making, which is beneficial for public participation to play a positive role in decision-making. It also offers a valuable reference for managers seeking to effectively implement public participation mechanisms.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

Supply chain management (SCM) has evolved to fulfill the demands of the dynamic global business environment. The development of the Internet of Things (IoT), which offers unmatched connectivity and real-time data insights, has further transformed SCM. This chapter provides an overview of SCM development and its integration with IoTs. This integration led to improved inventory control, supply chain optimization (SCO), and visibility which further enhances the conventional SCM and provides benefits, such as more accurate real-time tracking and monitoring, improved data analytics, more efficient logistics and transportation management, and reduced costs and wastages. However, despite these benefits, there are various associated challenges and concerns, like privacy and data security, compatibility and interoperability, implementation costs, returns on investment, trained workforce, and training requirements, which are required to be addressed. Additionally, the outcomes of this study and managerial implications are provided along with the future research scope. Overall, this chapter provides valuable insight into the transformative potential of IoT in SCM and practical suggestions on how managers can successfully navigate difficulties and get benefits from the IoT-SCM integration. Organizations can enhance their supply chain operations, efficiency, and innovation by actively confronting challenges and taking advantage of the opportunities provided by IoT technologies. This will ultimately result in the delivery of greater value to both stakeholders and customers.

Brand information is ubiquitous online and offline; consumers exhibit brand avoidance tendencies towards brand stimuli when there is a discrepancy between a brand image/personality and one’s self-concept. Given the multifaceted culturally constituted self-domains and self-importance, this research investigates how cultural variation affects reactions to self-brand discrepancy, considering two types of narcissist orientations.

Using national culture as proxy for cultural orientation, sample data were collected through surveys administered to 410 participants (210 in China and 200 in the USA). A multi-group structural equation model was adopted to examine the conceptual model and proposed hypotheses. The follow-up qualitative study was conducted to allow further discussion of the quantitative results.

The results show that self-brand discrepancy can only be converted into brand avoidance tendency through the activation of cognitive dissonance for both Americans and Chinese. Specifically, for Chinese consumers only (ideal) social identity self-brand discrepancies can activate avoidance behaviour. In addition, grandiose and vulnerable narcissism orientations co-exist for both Chinese and Americans, these negatively moderate the relationship between social self-brand discrepancies and cognitive dissonance. For US consumers, idealised identity discrepancies mitigate dissonance; only those with a vulnerable narcissistic orientation would act on avoidance when experiencing dissonance.

By incorporating cultural variations in the investigations of self-brand discrepancy, this paper advances existing knowledge on dissonance and coping mechanisms. In addition, by bringing narcissistic orientations to the fore, it allows for a deeper understanding of how these cultural variations operate. In addition, our research provides important guidelines for brand practitioners to better leverage their marketing campaigns in offline and online contexts and to reduce brand avoidance tendencies across the international marketplace.

The purpose of the present study is to synthesize and organize the existing literature on sustainable supply chain management (SSCM) challenges and potential solutions to overcome these challenges. The four-step research method has been used to collect and analyze pertinent literature, define the unit of analysis, select the classification context, collect publications, and evaluate the material. The study found 10 prevalent SSCM challenges and 18 potential solutions to overcome these challenges. By implementing these solutions, organizations can implement SSCM practices and contribute to achieving various Sustainable Development Goals (SDGs). The study significantly contributes to stakeholder theory, triple bottom-line theory, and resource-based view theory. The current study provides insights to managers working in supply chain management on SSCM implementation. Furthermore, the study also has practical implications for academicians and policymakers. This study is the first of its kind to amalgamate the SSCM challenges and solutions to overcome these challenges in a single framework by reviewing the literature.