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This study aims to research the usage of virtual reality as a tool to train employees in a changeover process in a company, and if it guarantees the same level of quality. Moreover, another goal is to understand how it can influence the machines’ availability, reducing the number of hours a machine is stopped to perform training sessions.

To achieve the goals proposed, the action research strategy was used. After defining the context and purpose, two full cycles were applied, composed by five activities: diagnosis, action planning, action taking, action evaluation and specify the learning.

The results are extremely satisfactory. On the one hand, it can be concluded that virtual reality is a reliable training tool, guaranteeing proper training to the employees. On the other hand, based on the previous point, the researchers were able to conclude that it is possible to increase machines’ availability, reducing by 50% the time needed to have the machines stopped to run a changeover training.

To the authors’ knowledge, this is the first study to use the action research strategy to develop a virtual reality simulation as a training tool for machines’ changeover in the automotive industry.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the corresponding address by transactions. The deployed smart contracts are immutable, even if there are bugs or vulnerabilities. Therefore, it is critical to verify smart contracts before deployment. This paper aims to help developers effectively and efficiently locate potential defects in smart contracts.

GethReplayer, a smart contract testing method based on transaction replay, is proposed. It constructs a parallel transaction execution environment with two virtual machines to compare the execution results. It uses the real existing transaction data on Ethereum and the source code of the tested smart contacts as inputs, conditionally substitutes the bytecode of the tested smart contract input into the testing EVM, and then monitors the environmental information to check the correctness of the contract.

Experiments verified that the proposed method is effective in smart contract testing. Virtual environmental information has a significant effect on the success of transaction replay, which is the basis for the performance of the method. The efficiency of error locating was approximately 14 times faster with the proposed method than without. In addition, the proposed method supports gas consumption analysis.

This paper addresses the difficulty that developers encounter in testing smart contracts before deployment and focuses on helping develop smart contracts with as few defects as possible. GethReplayer is expected to be an alternative solution for smart contract testing and provide inspiration for further research.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Augmented reality (AR) and virtual reality (VR) are increasingly recognized among technologies potentially driving the digital innovation of small and medium-sized enterprises (SMEs). Nevertheless, the implications of AR/VR adoption in innovation processes have yet to receive much attention to date, with the need to explore the issues facilitating a systematic implementation in the SME context. This study aims to investigate the possible innovation paths of SMEs as a result of AR/VR adoption.

The authors performed a multiple case study research involving six Italian SMEs in manufacturing and service sectors that are investing in AR and/or VR solutions and are digitally innovating thanks to these technologies.

AR/VR solutions lead to different types of innovation in SMEs, i.e. for innovating product or service offerings, business processes or even the business model, when AR/VR extends to the company business logic. SMEs demonstrate being able to leverage internal sources with the essential commitment of top management and low resistance of employees in all kinds of AR/VR-enabled innovations. Conversely, they involve different external innovation sources according to the type of innovation pursued. Organizational issues emerged as more relevant than technological issues.

Results contribute to the literature on digital transformation of SMEs and provide managerial guidance on innovation sources and organizational issues to be considered to effectively deploy AR/VR solutions into specific innovation paths.

This study explores the impact of emergent technologies in the innovation process along with multiple perspectives, degree of complexity, and strategic importance in the SME context.

Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.

To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.

Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.

The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

Lean production has been proved to be a cost-effective and efficient means of production that reduces non-valve added activities. Industry 4.0 (I4.0) is a technology-driven platform that allows machines to interact with other systems through artificial intelligence, machine learning, industrial Internet of Things (IoT), etc. that improve the production system with flexibility, quality and customization throughout the whole value chain. New approaches to digitization of lean production have recently been emerged and they are transforming the industry and increasing productivity throughout the value chain. Through this article, an effort has been made to review the research published in this field.

This paper reviews the literature published in various journals, the databases Web of science (WoS), ScienceDirect, Scopus, Emerald etc. were referred with a focus on lean concepts and tools and I4.0 technologies; it has been noticed that the integration of the lean tools with I4.0 technologies is a very effective tool for the industry.

It has been found in the literature published earlier in various journals that lean manufacturing (LM) is commonly acknowledged and considered a best practice to improve the productivity. It is concerned with the tight integration of people into the industrial process through continuous improvement which leads to value addition throughout the whole value chain by eliminating non vale added activities. The findings show that organizations can improve their productivity and flexibility with speed and accuracy by integrating I4.0 technologies with LM, which is foremost need of any industry across the world.

This article accentuates the connections between the principles and tools developed under the umbrella of I4.0 and those developed by the LM techniques, with a specific emphasis on how some of the principles and tools of I4.0 improve the implementation of lean principles dependent on the competence levels of the technology. Very few articles have been published in this area, and this paper is an original piece of research covering a review of extant research published in various journals.

Extended reality (XR) is an emerging technology, with its popularity rising in different industry sectors, where its application has been recently considered in construction safety. This study aims to investigate the applications of XR technologies in the safety of construction through projects lifecycle perspective.

Scientometric analysis was conducted to discover trends, keywords, contribution of countries and publication outlets in the literature. The content analysis was applied to categorize previous studies into three groups concerning the phase of lifecycle in which they used XR.

Results of the content analysis showed that the application of XR in the construction safety is mostly covered in two areas, namely, safety training and risk management. It was found that virtual reality was the most used XR tool with most of its application dedicated to safety training in the design phase. The amount of research on the application of augmented reality and mixed reality in safety training, and risk management in all phases of lifecycle is still insignificant. Finally, this study proposed three main areas for using the XR technologies regarding the safety issues in future research, namely, control of safety regulations and safety coordination in construction phase, and safety reports in the operation phase.

This paper inspected the utilization of all types of XR for safety in each phase of construction lifecycle and proposed future directions for research by addressing the safety challenges in each phase.

As the novel coronavirus 2019 (COVID-19) impacts the world, software practitioners are collaboratively working remotely from home. The pandemic has disrupted software practitioners’ productivity forcing changes to agile methodology adopted by software practitioners in software organizations. Therefore, this study aims to provide implication on the issues and recommendations for improving software practitioners’ productivity and also examine the impact of the COVID-19 pandemic on agile software development.

This paper adopts a narrative literature review to provide early assessment based on secondary data from the literature and available document reports from studies published from 2019 to 2022 to explore software practitioners’ productivity and agile software development during the working from home directive amidst the COVID-19 pandemic. A total of 60 sources which met the inclusion criteria were used to provide preliminary evidence grounded on secondary data from the literature. Descriptive analysis was used to provide qualitative findings from the literature.

Findings from this study present the significance of working from home directive on agile software development and software practitioners’ productivity. More importantly, findings from the secondary data shed light on software practitioners’ productivity adopting agile software development amidst the COVID-19 pandemic. Additionally, the findings present virtual collaborative platforms used by software practitioners, technical and social barriers of agile software development during the pandemic and recommendations for remote agile software development.

This study explores the significance of working from home directive on software practitioners’ productivity during COVID-19 pandemic and further investigates how are software practitioners’ productivity adopting agile software development practices amidst the COVID-19 pandemic. Besides, this study discusses the challenges software practitioners currently face and offers some strategies to bridge the gaps in agile software development to help software practitioners, system developers, software managers and software organizations adapt to the changes caused by the pandemic.

The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.

The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.

The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.

This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.