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This study aims to understand how midwifery students view ethical rights concerning the usage of reproductive technologies.

A series of focus groups were conducted with midwifery students who were asked to discuss four novel scenarios highlighting some of the most controversial issues on the ethics of assisted reproductive technologies. The sample consisted of third-year students studying at the Department of Midwifery of a university in the fall semester of the 2020–2021 academic year. In total, 60 students aged between 20 and 23 participated in the study (mean age 21.5 years).

Four scenarios that may be encountered in the use of assisted reproductive techniques were presented to the students. These scenarios were about IVF treatment, using the preimplantation gender identification technique, surrogate mother and using frozen zygotes. Four themes were identified from the analysis of the focus group data related to all four scenarios. These themes were: female reproduction, sex selection, surrogacy and reproductive cloning.

This study reveals what midwifery students consider essential components of assisted reproduction techniques, focusing on the professional attributes of shared decision-making. The results suggest that student midwives are sensitive to the range of ethical dilemmas associated with the increased use of technology in human reproduction, and construct distinct bound arise in relation to what is considered of benefit or good to the mother, parents, the child and to society. They also expressed their opposition to the excessive use of technological intervention, preferring instead to maintain a more naturalistic approach to reproduction.

Challenges of property data in developing markets have been reported by several authors. However, a deep understanding of the actual nature of this phenomenon in developing markets is largely lacking as in-depth studies into the actual nature of data challenge in such markets are scarce in literature. Specifically, the available literature lacks clarity about the actual nature of data challenges that developing markets pose to valuers and how this affects valuation practice. This study provides this understanding with focus on the Lagos property market.

This study utilises a qualitative research approach. A total of 24 valuers were selected using snowballing sampling technique, and in-depth semi-structured interviews were conducted. Data collected were analysed using thematic analysis with the aid of NVivo 12 software.

The study finds that the main data-related challenge in the Lagos property market is the lack of database of market property transactions and not the lack or absence of transaction data as it has been emphasised in previous studies. Other data-related challenges identified include weak property rights institution with attendant transaction costs, underhand dealings among professionals, undocumented charges, undisclosed information, scarcity of data relating to specialised assets and limited access to the subject property and required documents during valuation. Also, the study unbundles the factors responsible for these challenges and how they affect valuation practice.

The study has implication for practice in the sense that the deeper knowledge of data challenges could provide insight into strategy to tackle the challenges.

This study contributes to the body of knowledge by offering a fresh and in-depth perspective to the issue of data challenges in developing markets and how the peculiar nature of the real estate market affects the nature of data challenges. The qualitative approach adopted in this study allowed for a deep enquiry into the phenomenon and resulted into an extended insight into the peculiar nature of data challenges in a typical developing property market.

This paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.

This work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.

In this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.

In this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

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 is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and their limitations. The security of SCs has received increasing attention from researchers, due to the emerging risks associated with their distributed nature. The increase in risk in SCs comes from threats that are inherently similar regardless of the type of SC, thus, requiring similar defence mechanisms. Being able to identify the types of threats will help developers to build effective defences.

In this work, we provide an analysis of the threats, possible attacks and traceability solutions for SCs, and highlight outstanding problems. Through a comprehensive literature review (2015–2021), we analysed various SC security solutions, focussing on tracking solutions. In particular, we focus on three types of SCs: digital, food and pharmaceutical that are considered prime targets for cyberattacks. We introduce a systematic categorization of threats and discuss emerging solutions for prevention and mitigation.

Our study shows that the current traceability solutions for SC systems do not offer a broadened security analysis and fail to provide extensive protection against cyberattacks. Furthermore, global SCs face common challenges, as there are still unresolved issues, especially those related to the increasing SC complexity and interconnectivity, where cyberattacks are spread across suppliers.

This is the first time that a systematic categorization of general threats for SC is made based on an existing threat model for hardware SC.

How do radical technological fields become naturalized and taken for granted? This is a fundamental question given both the positive and negative hype surrounding the emergence of many new technologies. In this chapter, we study the emergence of the US nanotechnology field, focusing on uncovering the mechanisms by which leaders of the National Nanotechnology Initiative managed hype and its concomitant legitimacy challenges which threatened the commercial viability of nanotechnology. Drawing on the cultural entrepreneurship literature at the interface of strategy and organization theory, we argue that the construction of a naturalizing frame – a frame that focuses attention and practice on mundane, “rationalized” activity – is key to legitimating a novel and uncertain technological field. Leveraging the insights from our case study, we further develop a staged process model of how a naturalizing frame may be constructed, thereby paving the way for a decrease in hype and the institutionalization of new technologies.

Our prior work has identified a trade-off that new entrants face in obtaining favorable market reception, whereby initial entrants suffer from a deficit of legitimacy whereas later entrants suffer from a deficit of authenticity. This research has also proposed that a single mechanism is responsible for this trade-off: the tendency for customers and other stakeholders to assess the entrant's claim to originality based on the visible work that it has done to legitimate the new product or organizational form. This chapter extends and deepens our understanding of such “legitimation work” by showing how it can illuminate cases that seem in the first instance to defy this trade-off. In particular, we focus on two “off-diagonal” cases: (a) when, as in the case of “patent trolls” and fraudulent innovators, early entrants are viewed as inauthentic despite having a credible claim to originality; (b) when late entrants, as in the case of Dell Computers, mechanical watches and baseball ballparks, are viewed as authentic despite obviously not being the originators. We clarify how each off-diagonal case represents an ‘exception that proves the rule’ whereby audiences attribute authenticity on the basis of legitimation work rather than on the order of entry per se. The last case also leads to an opportunity to clarify why “cultural appropriation” can sometimes project authenticity and sometimes inauthenticity, why audiences bother to make inferences about a producer's authenticity on the basis of visible legitimation work, and why legitimacy is a universal goal of early movers whereas authenticity varies in its importance.