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This paper aims to discuss the scanning methodology depending on the close-range photogrammetry technique, which is appropriate for the precise three-dimensional (3D) modelling of objects in millimetres, such as the dimensions and structures in sub-millimetre scale.

The camera was adjusted to be tilted around the horizontal axis, while coded dot targets were used to calibrate the digital camera. The experiment was repeated with different rotation angles (5°, 10°, 15°, 20°, 25°, 30°, 50° and 60°). The images were processed with the PhotoModeler software to create the 3D model of the sample and estimate its dimensions. The features of the sample were measured using high-resolution transmission electron microscopy, which has been considered as a reference and the comparative dimensions.

The results from the current study concluded that changing the rotation angle does not significantly affect the results, unless the angle of imagery is large which prevent achieving about 20: 30% overlap between the images but, the more angle decreases, the more number of images increase as well as the processing duration in the programme.

Develop an automatic appropriate for the precise 3D modelling of objects in millimetres, such as the dimensions and structures in sub-millimetre scale using photogrammetry.

This study argues that online user comments on social media platforms provide feedback and evaluation functions. These functions can provide services for the relevant departments of organizations or institutions to formulate corresponding public opinion response strategies.

This study considers Chinese universities’ public opinion events on the Weibo platform as the research object. It collects online comments on Chinese universities’ network public opinion governance strategy texts on Weibo, constructs the sentiment index based on sentiment analysis and evaluates the effectiveness of the network public opinion governance strategy adopted by university officials.

This study found the following: First, a complete information release process can effectively improve the effect of public opinion governance strategies. Second, the effect of network public opinion governance strategies was significantly influenced by the type of public opinion event. Finally, the effect of public opinion governance strategies is closely related to the severity of punishment for the subjects involved.

The theoretical contribution of this study lies in the application of image repair theory and strategies in the field of network public opinion governance, which further broadens the scope of the application of image repair theory and strategies.

This study expands online user comment research to network public opinion governance and provides a quantitative method for evaluating the effect of governance strategies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0269

The young population of the globe is defined by individuals aged 15 to 24 years. Based on statistics from the Instituto Brasileiro de Geografia e Estatística (IBGE), the second largest women population among 15 years as well as 19 years was in 2017 only behind 35 and 39 years. At this time, the Brazilian male population was higher. The difficulties of the young generation affected the preceding generation and promoted social dynamism. The worldwide data shows that the generation of young and the digital world have been constantly sought, but in reality, approximately one-third of the population in 2017 had no access to the internet.

The worldwide movement around topics such as strategy on its threefold basis and Industry 4.0 enable a link to company duty towards society to be established. This present study was produced from 1 March 2020 to 2 September 2020 via resources of human and literature evaluation relating to the idea of strategic, Industry 4.0, the responsibility of society and the creation of youth. Its motive is the global creation of youth. Two recommendations should be made after studying the literature and information gathering that enabled “analyzing social responsibility of the company and industry 4.0 with a pivot on young creation: a strategic framework for resources of human management”.

The adoption of defensible practices and technology bring forth by the revolution in industrial is emphasized worldwide.

The focus on the usage of these ideas is essential, so that young people can absorb the workforce in the labour market. To achieve this, the CSR idea combines this theoretical triple-created recent study.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

The purpose of this paper is to highlight the key technical challenges that derive from the recently proposed European Artificial Intelligence Act and specifically, to investigate the applicability of the requirements that the AI Act mandates to high-risk AI systems from the perspective of AI security.

This paper presents the main points of the proposed AI Act, with emphasis on the compliance requirements of high-risk systems. It matches known AI security threats with the relevant technical requirements, it demonstrates the impact that these security threats can have to the AI Act technical requirements and evaluates the applicability of these requirements based on the effectiveness of the existing security protection measures. Finally, the paper highlights the necessity for an integrated framework for AI system evaluation.

The findings of the EU AI Act technical assessment highlight the gap between the proposed requirements and the available AI security countermeasures as well as the necessity for an AI security evaluation framework.

AI Act, high-risk AI systems, security threats, security countermeasures.

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

Through a theoretical lens of psychological ownership, this study aims to investigate how technology mindfulness may stimulate metaverse tourism users’ feelings of individual psychological ownership, aesthetic value and conversational value, which in turn fosters intention to engage in prosocial behaviors.

The study used a scenario-based survey that allowed U.S.-based participants to create their own avatars and imagine using their avatars to explore heritage sites in the metaverse. Structural equality modeling was applied for data analysis.

The results from 357 valid responses indicate that technology mindfulness arouses tourists’ individual psychological ownership, aesthetic value, conversational value and prosocial behavioral intentions. The moderating role of biospheric value orientation on willingness to donate and intention to volunteer is investigated.

The research sheds light on the significance of technology mindfulness, conversational value and psychological ownership perspectives in the metaverse, which have been previously overlooked. The authors used a scenario-based survey for mental stimulation due to current metaverse technology limitations.

The study is one of the first to explore the possibility of encouraging prosocial behaviors using metaverse-facilitated technology. The research offers guidelines to engage hospitality and tourism customers in the metaverse that can blend their virtual experiences into the real world.

This study represents one of the pioneering efforts to gain an in-depth understanding of the application of metaverse in triggering prosocial behavior toward heritage sites, explained via a technology mindfulness-driven model with a psychological ownership perspective.

This study aims to investigate the factors influencing the adoption intention of artificial intelligence (AI) by micro, small and medium enterprises (MSMEs) in Jordan.

The study adopts the technology–organization–environment (TOE) model. It examines the moderating effects of innovation culture, employee digital skill level and market competition on the relationships between the independent and dependent variables. A survey was utilized to collect data from 537 MSME owners or managers in Jordan and employed partial least squares structural equation modeling to test the hypotheses.

The results of the study support seven out of eight hypotheses. Business innovativeness, management support, perceived benefits and technological infrastructure have positive and significant effects on AI adoption intention, while perceived costs have no significant effect. However, the innovation culture, employee digital skill level and market competition were found to moderate the relationships between some of the independent variables and dependent variables.

The study provides valuable insights and recommendations for MSME owners, managers, employees, policymakers, educators and researchers interested in promoting and facilitating AI adoption by MSMEs in Jordan.

The current attempt extends the TOE framework by adding significant constructs representing the three contexts. Moreover, it is one of the few studies that analyzed the factors influencing the adoption intention of AI by MSMEs in Jordan, which are significant to the Jordanian economy and represent 99.5% of enterprises.

Many economic, political and socio-cultural events in the 2020s have been strong headwinds for architecture, engineering and construction (AEC). Nevertheless, technological advancements (e.g. artificial intelligence (AI), big data and robotics) provide promising avenues for the development of AEC. This study aims to map the state of the literature on automation in AEC and thereby be of value not only to those researching automation and its composition of a variety of distinct technological and system classes within AEC, but also to practitioners and policymakers in shaping the future of AEC.

This review adopts scientometric methods, which have been effective in the research of large intra and interdisciplinary domains in the past decades. The full dataset consists of 1,871 articles on automation in AEC.

This overarching scientometric review offers three interdisciplinary streams of research: technological frontiers, project monitoring and applied research in AEC. To support the scientometric analysis, the authors offer a critical integrative review of the literature to proffer a multilevel, multistage framework of automation in AEC, which demonstrates an abundance of technological paradigm discussions and the inherent need for a holistic managerial approach to automation in AEC.

The authors underline employee well-being, business sustainability and social growth outcomes of automation and provide several managerial implications, such as the strategic management approach, ethical management view and human resource management perspective. In doing so, the authors seek to respond to the Sustainable Development Goals proposed by the United Nations as this becomes more prevalent for the industry and all levels of society in general.

The purpose of this study is to prepare thermal transfer ribbons with good alcohol resistance.

A variety of alcohol-resistant thermal transfer inks were prepared using different polyester resins. The printing temperature, printing effect, adhesion and alcohol resistance of the inks on the label were studied to determine the feasibility of using the ink for manufacturing thermal transfer ribbons. The ink formulations were prepared by a simple and stable grinding technology, and then use mature coating technology to make the ink into a thermal transfer ribbon.

The results show that the thermal transfer ink has good scratch resistance, good alcohol resistance and low printing temperature when the three resins coexist. Notably, the performance of the ribbon produced by 500 mesh anilox roller was better than that of other meshes. Specifically, the ink on the matte silver polyethylene terephthalate (PET) label surface was wiped with a cotton cloth soaked in isopropyl alcohol under 500 g of pressure. After 50 wiping cycles, the ink remained intact.

The proposed method not only ensures good alcohol resistance but also has lower printing temperature and wider label applicability. Therefore, it can effectively reduce the loss of printhead and reduce production costs, because of the low printing temperature.