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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.

In this chapter, we explore the dark side of the metaverse and the need for user protection. While the metaverse offers multiple opportunities it also poses significant risks for users, such as privacy concerns, addiction, harassment, and cyberbullying. First, we discuss the various threats that users may encounter such as online harassment, assaults, cyberbullying, hate speech, identity theft, and virtual property theft. As per the Center for Countering Digital Hate, an incident of violation occurs every seven minutes within VRChat, a popular virtual reality game. The level of misconduct in the metaverse can surpass the extent of internet harassment. Virtual reality gaming has been associated with various health issues like sleep deprivation, and insomnia as well as mental health concerns such as depression, anger, and anxiety. We examine how these issues may impact user’s physical and mental health. The sensors and devices used in the metaverse collect a vast amount of user biometric data and spatial data. Interactions between users and metaverse could be leaked. We examine different methods that improve user protection, including everyone from enhanced security protocols via the application of privacy-enhanced technology to several avatars, two-factor authentication, and user educational and awareness programs. Moreover, we explore how the newest technologies, like blockchain and artificial intelligence, play a role in making user safety more important. We finished the course with the study of the case of Second Life, the virtual reality gaming platform, and pointing out some of the problems that exist within it.

The research explores how artificial intelligence-driven natural language processing can be integrated into digital game-based learning to enhance science education worldwide. It conducted a thorough literature review focusing on electronic bibliographic databases, identifying nine key themes. The chapter discusses the potential of digital games to improve science education, including theoretical frameworks, integration techniques and problem-solving methods. It also examines the impact of gaming on critical thinking and natural language processing. The findings suggest that integrating natural language processing into digital game-based learning shows promise for improving inquiry-based teaching and student performance in science education. Digital platforms can offer personalized feedback, support natural language interactions and customize content for each student. However, addressing pedagogical concerns and technological limitations is crucial for fully realizing these benefits. Practically, the research offers guidance for educators, curriculum developers and policymakers. It emphasizes the importance of educating educators on natural language processing, developing inquiry-driven digital learning platforms and establishing supportive policies. This signifies a move toward student-centered learning and the integration of innovative technologies into education. In terms of originality and value, the study contributes to the existing knowledge by discussing challenges and potential strategies for integrating digital game-based learning with natural language processing globally. It suggests that future research and practical efforts in this area can benefit from a comprehensive understanding of the opportunities and implications of this educational approach, informed by extensive research across various themes.

In today’s turbulent and complex era, initiative behavior is becoming more drawn to construction projects but challenging to arouse as it is free of the established regulations in project practice. Given the prevalence of social media (SM) in modern workplaces, this study is thereby motivated to investigate whether and how SM use can act to drive initiative behavior of construction project members (PMs) in this context.

This study sharply examines two distinct types of SM use – work-related and social-related – to explore their roles in driving the initiative behavior of construction PMs. Additionally, self-determination theory is employed to explore their underlying translation mechanisms and associated boundary conditions. A survey dataset collected from 229 construction PMs is used to empirically test the proposed theoretical model.

Empirical results show that role-breadth self-efficacy, psychological safety and project identification, by satisfying basic psychological needs respectively, act as crucial bridging roles in translating SM use into initiative behavior of PMs. Such mediation effects are applied to both work-related and social-related SM use with varied mechanisms. Besides, prevention focus is found to be a contingent moderator on these relationships, with a strengthening role toward role-breadth self-efficacy and a weakening role toward project identification.

This study digs into the nuanced mechanisms of how SM use benefits construction projects, especially in terms of PMs’ initiative. The findings of this research afford new insights into effectively invigorating the initiative behavior of construction PMs under the current digital momentum.

The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as show the importance of this disruptive tool for tourism marketing.

This paper used the Web of Science and Google Scholar databases to provide updated studies and expert authors to explore GenAI in the tourism industry. Analysing hyper-segmentation and hyper-personalization modalities through GenAI and their new challenges for tourists, tourism cities and companies.

Findings reveal that GenAI technology exponentially improves consumers’ segmentation and personalization of products and services, allowing tourism cities and organizations to create tailored content in real-time. That is why the concept of hyper-segmentation is substantially focused on the customer (understood as a segment of one) and his or her preferences, needs, personal motivations and purchase antecedents, and it encourages companies to design tailored products and services with a high level of individual scalability and performance called hyper-personalization, never before seen in the tourism industry. Indeed, contextualizing the experience through GenAI is an important way to enhance personalization.

This paper also contributes to enhancing and bootstrapping the literature on GenAI in the tourism industry because it is a new field of study, and its functional operability is in an incubation stage. Moreover, this viewpoint can facilitate researchers and companies to successfully integrate GenAI into different tourism and travel activities without expecting utopian results. Recently, there have been no studies that tackle hyper-segmentation and hyper-personalization methodologies through GenAI in the tourism industry.

In the context of the environmental degradation challenge in manufacturing firms, greening the supply chain (SC) is the most widely endorsed method to mitigate the adverse repercussions of climate change. Based on organizational learning and resource dependence theories, the aim of this research is to know how green supply chain external integration (GSCEI) and green supply chain internal integration (GSCII) influence ambidextrous green innovation (AGI). It also examines the mediating roles of green absorptive capacity (GAC) and green knowledge integration capability (GKIC), as well as the moderating role of green technology dynamism (GTD).

To assess the hypothesized model, data were obtained with 386 questionnaires from managers employed in manufacturing firms in Pakistan applying a cross-sectional approach. A partial least square structural equation modeling technique was implemented to evaluate the data.

The results revealed that GSCEI and GSCII substantially impact AGI. Moreover, GAC and GKIC serve as mediators between GSCEI and AGI. GAC and GKIC also intervene in the relationship between GSCII and AGI. GTD was significant as a moderator for the correlation between GSCEI and AGI. However, it does not moderate the relationship between GSCII and AGI.

This research offers significant comprehension and an innovative approach for manufacturing organizations to curb environmental corrosion by stimulating AGI through green SC integration. It suggests to practitioners that integrating internal knowledge with external partners expands communication and collaboration to ensure that resources connected with environmental preservation flow smoothly.

This research is a valuable addition to the field, as it explores for the first time the missing link among the studied constructs. It opened the black box of how knowledge-related capabilities facilitate knowledge resources to elicit AGI, an area that has not yet been explored.

This study aims to investigate the relationships between the perceived neighborhood walkable environment (PNWE), neighborhood interaction (NI) and residents’ mental health, with a focus on examining differences among residents of different age groups.

Using an electronic survey questionnaire, data on PNWE, NI and mental health were collected from 1,159 residents across 205 communities in Shanghai, China. Our study utilized a structural equation modeling (SEM), employing the maximum likelihood estimation method. The structural equation model was fitted using the MPLUS software.

The mental health of young and middle-aged adults is generally poor, and they are at higher risk of depression than children and older adults. The effects of PNWE and NI on the mental health of residents varied among different age groups. As residents get older, their mental health is more affected by the PNWE. In addition, the influence of the PNWE on children and older adults’ mental health is direct and not mediated by NI. For young and middle-aged adults, the influence of the PNWE on their mental health needs to be mediated by NI.

This study marks the first examination of the relationship between PNWE, NI and mental health among different age groups of residents in China. The findings of this research can assist policymakers in gaining a deeper understanding of the underlying mechanisms by which PNWE affects mental health. Furthermore, it can contribute to the development of more targeted walkable environment designs aimed at enhancing mental health among various age groups.

Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

Considering both the current opportunities of the Internet of things (IoT) and aviation, as well as the potential opportunities they may offer for the future, it is understood that they are among the important issues that need to be examined in the literature. This study aims to provide an idea by conducting bibliometric and visualization analyses of the current trends and development opportunities of IoT and aviation.

In this study, descriptive and bibliometric analyses within the framework of co-author, co-citation, bibliographic coupling, and keyword co-occurrence analysis were carried out for publications found to be published between 2007 and 2023 in the Web of Science (WoS) database related to IoT and aviation. VOSviewer (ver. 1.6.18) program and the Biblioshiny application were used to create bibliometric networks and provide visualization.

As a result of some descriptive and visualization analyses, the current trend of publications on IoT and aviation and future publication opportunities has been revealed. It has been understood that the subject of IoT and aviation is one of the subjects whose number of publications has increased in recent years and has not yet fully matured in terms of the number of publications and has the potential to make new publications.

In this study, bibliometric analysis of IoT and aviation, which could not be found examined before in the literature, and the creation of existing bibliometric networks by visualizing were carried out.

Workforce agility (WFA) is an emergent research topic in volatile times. However, there is a lack of research in understanding the leadership dimension that triggers such an attribute in organizations. Our study aims to understand the impact of workforce agility on empowering leadership behavior and employee performance dimensions (task performance, contextual performance and counterproductive work behavior).

We collected data from 236 employees using reliable, validated scales and conducted various statistical analyses.

Our results demonstrated that WFA (1) partially mediated the relationship between empowering leadership and contextual performance (CP), (2) has not mediated the relationship between empowering leadership and counterproductive behavior (CWB) and (3) mediated the relationship between empowering leadership and task performance (TP).

Our research has practical implications for management practitioners. It suggests hiring and developing an agile workforce through appropriate training and development programs can significantly impact organizational performance. Furthermore, it provides insights into building leadership capabilities that sustain workforce agility practices, empowering leaders to make informed decisions.

Our research fills a significant gap in the existing literature by exploring the effects of WFA on leadership and performance. This novel approach provides a fresh perspective on the dynamics of organizational behavior, making it a valuable addition to the field.