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Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

The purpose of this study is to identify the factors that influence the willingness of Saudi Arabian students from four universities in Saudi Arabia, to adopt learning management systems (LMSs). This will be accomplished by using two popular technology acceptance models unified theory of acceptance and use of technology (UTAUT) and theory of planned behavior (TPB).

In total, 445 undergraduates from four Saudi educational institutions participate in filling out the study questionnaire. To investigate the correlations between the variables, the study used structural equation modeling for data analysis.

The results of the study show that effort expectancy (EE), subjective norm (SN), attitude toward behavior (ATB) and perceived behavioral control (PBC) are found to be substantially connected with their intentions to use (ITU) LMSs. The findings also show that there is a strong relationship between students’ intentions and their actual use of LMSs.

Like many studies, this research has some limitations. The primary limitation is that the findings of the study cannot be extrapolated to other settings since the report’s analysis and investigation were limited to four Saudi universities. Therefore, to generalize the study’s findings, similar research needs to be conducted in other Gulf and similar cultural universities.

The integrated model identifies key factors that influence the intent of Saudi Arabian students to use LMS, including EEs, social influence, ATB and PBC. This model can help develop solutions for the obstacles that prevent students from using LMS. The findings can be used to provide assistance to increase the likelihood of LMS acceptance as part of the educational experience. The model may also inspire further research on this topic in the Gulf nations, particularly in Saudi Arabia.

As none of the relevant studies conducted previously in Saudi Arabia has integrated the two models to study the students’ ITU LMSs, this study combines two major theories, TPB and UTAUT, in the context of Saudi Arabia, contributing to the field of technology use in education by expanding empirical research and providing a thorough understanding of the challenges associated with the use of LMS in Saudi universities. This study should be viewed as filling a crucial gap in the field. Moreover, this integrated model, using more than one theoretical perspective, brings a thorough comprehension of the barriers that hinder students’ adoption of LMSs in the academic context in Saudi Arabia and thus assists in making effective decisions and reaching viable solutions.

This conceptual article presents a schematic of rat maternal behavior and niche stress epigenetic effects as a case study that is then aligned with current evolutionary concepts, which raises new questions regarding immigrant assimilation and niche dynamics.

The necessary background material for rat maternal and niche(s) stress factors are incorporated into a recursive, test-operate-test (rTOT), information-only-transfer, schematic (Patterson, 2023), which is an extension and refinement of the test-operate-test-exit (TOTE) schematic of Miller et al. (1960).

The generated epigenetic rTOT demonstrates the fundamental evolutionary unit of the flexible organism within its niche(s). The rTOT also confirms that epigenetic processes, epigenetic inheritance and phenotype plasticity are significant conceptual tools for understanding evolution. The teleology of rat adaptations for niche fitness via maternal behavior has been demonstrated. Sterling's (2011) allostasis, or predictive homeostasis, is extended to include species-niche(s) interaction(s) that are governed by recursive information feedback loops that function via self-organized criticality (SOC) for species and niche(s). Use of a rat model for biosocial issues in humans is strengthened.

Epigenetic rTOT only covers the species side of the evolutionary unit. Niche(s) require(s) a separate rTOT schematic. The information modeled does not include the entire system producing epigenetic effects but models a substantial portion of it.

Epigenetic rTOT demonstrates the utility of phenotypic plasticity, epigenetics and epigenetic inheritance as explanations for inheritable behavior patterns. rTOT is a useful computational model for evolutionary issues. The issues involved in niche modeling using an rTOT schematic are briefly reviewed.

When the demonstrated epigenetic model of rat genetics and inherited behavior are applied to the issues of immigrant enclaves, epigenetic complications for the difficulties of assimilation into the culture within which the enclaves are embedded become apparent. However, the questions raised must be addressed with extreme care to avoid cultural imperialism. Such cultural issues must be modeled with an rTOT application that covers the materials involved. The limitations of human Learning III restrictions when attempting to model Learning IV issues are addressed. Research into the means by which abuse and trauma are maintained by epigenetic means is urgently needed.

The rTOT schematic visualizes rat maternal behavior and stress epigenetic effects that produce inheritable behavior patterns, which answers Jablonka's (2017) request for new computational modeling representations. The concept of allostasis, or predictive homeostasis, (Sterling, 2011) is extended to the niche(s) of the organism under study so that allostasis becomes a fully cybernetic concept governed by SOC for both the organism and its niche(s). This new case study confirmed evolutionary effects of epigenetics, epigenetic inheritance and phenotypic plasticity. Niche control of organism evolution is presented. Epigenetic applications for immigrant assimilation issues have been suggested and niche dynamic questions have been raised.

The below-average innovative capacity of the tourism sector raises the question on the potentials of digital business ecosystems (DBEs) to overcome these shortages at a destination level – especially within a smart city environment. Using the example of the German Capital Berlin, this article aims to discuss both the possibilities and inhibitors of innovative knowledge-creation by building scenarios on one specific design option: the integration of digital deep learning (DL) functionalities and traditional organizational learning (OL) processes.

Using the qualitative GABEK-method, major characteristics of a DBE as resource-, platform- and innovation systems are analyzed toward their interactions with the construction of basic action models (as the basic building blocks of knowledge).

Against the background of the research findings, two scenarios are discussed for future evolution of the Berlin DBE, one building on cultural emulation as a trigger for optimized DL functionalities and one following the idea of cultural engineering supported by DL functionalities. Both scenarios focus specifically on the identified systemic inhibitors of innovative capabilities.

While this study highlights the potential of the GABEK method to analyze mental models, separation of explicit and latent models still remains challenging – so does the reconstruction of higher order mental models which require a combined take on interview techniques in the future.

The resulting scenarios innovatively combine concepts from OL theory with the concept of DBE, thus indicating possible pathways into a tourism future where the limitations of human learning capacities could be compensated through the targeted support of general artificial intelligence (AI).

This study aims to improve the Unified Theory of Acceptance and Use of Technology (UTAUT) model by examining technological anxiety and other influential factors on international students' adoption of mobile learning (m-learning) during COVID-19 emergency remote teaching (ERT).

This study utilized the modified UTAUT framework to test hypotheses through a cross-sectional survey method. Participants were university students studying Chinese as a foreign language who were selected using a convenience sampling approach. An online questionnaire was then administered. The data collected from the surveys were analyzed using the partial least squares method with SmartPLS 4 software.

The study examined 16 hypotheses and found support for six of them. The results confirmed that performance expectancy (PE) is a significant predictor of behavioral intention (BI), and anxiety influences both PE and effort expectancy. The negative effect of social influence on anxiety was found to be significant, while facilitating conditions had a negative impact on learners' self-efficacy. The model fit indices indicated a good overall fit for the model.

This study presents a valuable contribution to the literature on m-learning in emergency education by incorporating technological anxiety into the enhanced UTAUT model. Examining the relationships between the key factors of the model provides a better understanding of learners' intentions and can inspire researchers to establish new theoretical foundations to evaluate the roles of these factors in diverse educational settings.

The study found that performance expectations are linked to learners' intentions, and anxiety indirectly affects BIs to use mobile learning platforms. Thus, these platforms should be designed to meet learners' expectations with minimum effort and eliminate anxiety triggers to facilitate ease of use. Language curriculum developers and policymakers should incorporate mobile learning applications to support diverse language skills, address students' needs and encourage their use through professional development opportunities for instructors.

Social factors have been found to significantly influence anxiety levels among learners. Therefore, it is crucial for teachers and family members to play an essential role in mitigating anxiety's adverse effects. Discussing related issues can enhance the quality of mobile learning and stimulate social initiative by providers, ultimately improving the learning experience for all learners, regardless of their location or circumstances. This can also contribute to the growth and development of society.

This study contributes to the originality of m-learning development by proposing an enhanced UTAUT model that considers anxiety and emphasizes the critical role of foreign learners' BIs. It provides fundamental guidelines for designing and evaluating m-learning in ERT contexts.

The purpose of this conceptual paper is to demonstrate how memes perpetuate trauma with a schematic. This conceptual paper uses the “begin with the end in mind” meme to format the presentation of the necessary components for the schematic of how trauma persists across generations. It is the third paper in a series of applications of the recursive, test-operate-test schematic to the systemic effects of the information processes involved in trauma. The schematic presented permits evaluations of solutions to interrupt the generational trauma cycle.

The required schematic components are described. Trauma (actual or perceived threat to survival) will be briefly discussed. Evolutionary processes that create the psychophysiology necessary to support nominal social expectations (NSEs) memes and metaphors will be summarized. The development of NSEs will be discussed. Metaphors and memes necessary for the creation of the schematic and esoteric events at level Learning IV will be briefly described. Finally, Figure 3, which illustrates the maintenance of NSEs and attempts to prevent their violation, will be explained.

It is asserted that functional human social behavior requires NSEs. Trauma is found to persist through the presence of anti-nominal NSE memes that are transduced by the individual into anti-nominal metaphors, which then damage grid, place and dentate gyrus cell (GPDG) neurophysiology. The damaged neurocircuits allow the use of anti-nominal NSE metaphors within our individual neurophysiology. Furthermore, anti-NSE memes interfere with the self-organized criticalities (SOCs) of genetic-epigenetic processes necessary for the intergenerational transfer of functional social behavior. When anti-NSE nominal metaphors are transduced back into anti-NSEs, social niche memes, the trauma process is reiterated. Anti-NSE memes and metaphors are found to be inappropriate criteria central to the maintenance of persistent trauma. Therefore, anti-NSE memes have hijacked our epigenetics and our social niches. Solutions are available because during our evolution, the Homo clade developed esoteric capabilities and the ability to bring back what information we can from those encounters. This physiology operates around the 5HT2A neural receptors that process hallucinogens, such as psilocybin. Accessing this resource system, either through naturally occurring altered states of consciousness or through micro-dose pharmaceutical psilocybin and related neurotransmitters, produces a significant structural change in the GPDG system to reset the NSE system illustrated in the schematic to its nominal status so that we can maintain nominal NSE relationships within our meme niche(s).

The source of persistent trauma in our social niche(s), the means by which the trauma is maintained and the means to mitigate and perhaps eliminate persistent trauma are identified. Based on these three conclusions, it is difficult to make decisions regarding corrective actions because of ubiquitous anti-NSE memes and because of the limitations of our ordinary consciousness.

If we wish to survive as a species, we will need to discover the criteria necessary to maintain our niche(s) congruent SOCs and use them instead of tyrannical memes described by Dawkins (1989) to make decisions about ourselves and our niche(s).

Significant courage is required to identify the memes that maintain trauma because many of them are culturally sacred cows. Unfortunately, we have known since Bremner's (1995) MRI study of posttraumatic stress disorder that trauma causes brain damage. Fortunately, our NSE genes compel us to pursue restitution of the memes that maintain our trauma-inducing cultures.

Several original assertions are presented. While the Homo clade ancestors began the creation of the social niche(s) that led to Homo sapiens sapiens, it is asserted that the australopiths created the NSE memes which are the foundation behaviors that permit our social niche(s). Furthermore, NSEs were produced by enhanced intentionality skills and NSEs were created by both genetic and memetic processes. The evolution of intentionality-NSE neural networks is asserted as the source of intentional material manipulation. While anti-NSE memes are claimed as the source of persistent trauma, the practice of esoteric technologies is presented as a solution to persistent trauma.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

Competencies are significant predictors of employee outcome. Nowadays, new technologies are changing maintenance processes and workflow. The role of employees and their competencies will therefore undergo decisive changes in the future. Therefore, a well-designed competency model for maintenance departments is important. The purpose of this paper is to develop a maintenance 4.0 competency model applicable to all industrial sectors by adapting it to the specificities of each sector.

The research methods consist of a comprehensive literature review on the main characteristics of the competency model and the individual competencies needed for the maintenance 4.0 employees. Interviews were conducted in order to validate and prioritize the required competencies for maintenance 4.0 employees identified in the literature.

The maintenance 4.0 competency model combines the required competencies in maintenance 4.0 and crosses the three hierarchical levels: managers, engineers and technicians. These competencies are organized in terms of four categories: technical, personal, social and methodological. In addition, a degree of importance for each competency is assigned as very important, moderately important and slightly important. As a result, this study identified the essential competencies for maintenance 4.0 stakeholders, where 12 competencies are considered very important for maintenance 4.0 technicians, 19 for engineers and 18 for managers.

This work has some limitations. First, although the articles related to competencies and their classification were selected very carefully, it is difficult to eliminate the probability of overlooking publications. Second, the limitation of the study is based on the difficulty of implementing the model in a case study, given that a minority of industrial companies have implemented maintenance 4.0 technologies in Morocco.

This work has practical implications for both individuals and institutions (companies and academies) to cope with new competency requirements in maintenance 4.0. Organizations can use the model in the recruitment process and for the identification of training needs. The results of the research will also contribute to identifying the scope of competencies of the maintenance 4.0 actors (engineer, manager and technician), which, in practice, contributes to the creation of requirements for the candidates applying for a job in the maintenance department. Additionally, educational institutions should make the necessary changes to their curricula to suitably prepare students for the required maintenance 4.0 competencies.

The social implications of the article result from the contribution to the development of maintenance competencies. Individuals can use this model for their own personal development. Furthermore, companies can use this model to define job profiles for vacancies in M4.0. Therefore, using the model for training program implementation has a positive effect on employee job satisfaction and employees ’morale.

This research develops a novel maintenance 4.0 competency model by categorizing the maintenance workforce into three hierarchical levels: managers, engineers and technicians. In addition, the competency requirement is prioritized to three degrees: very important, moderately important and slightly important. According to the previous studies conducted on maintenance 4.0 and employees' competencies, this study revealed that no research has developed a competency model for maintenance 4.0. Hence, this model is unique, generic and integrative since it presents the most relevant competencies for the three hierarchical levels. Moreover, this work combines the results of the literature review and the experts' returns. This model can be useful in the recruitment of new maintenance employees, the evaluation of their performance and the identification of training needs to cope with new changes in maintenance competencies.

This conceptual study investigates the adoption of education technology (EdTech) products among college students, focusing on identifying the key factors influencing the adoption process within educational institutions. Technology integration in education has rapidly gained prominence, with EdTech offering innovative solutions to enhance teaching and learning experiences. However, understanding the determinants that affect EdTech adoption remains critical for its successful implementation and impact. This paper aims (1) to identify the factors influencing the adoption of EdTech by college students (2) to create a conceptual model that shows the connections between the elements that lead to college students adopting EdTech.

The research employed a mixed-methods approach, combining qualitative data analysis and conceptual modeling to achieve the objectives. The underlying knowledge required to create a qualitative data gathering tool was obtained through a thorough literature analysis on innovation dissemination, educational psychology and technology adoption. College students, teachers and administrators participated in semi-structured interviews, focus groups and surveys to provide detailed perspectives on their attitudes about and experiences with EdTech. The Scopus and Web of Science databases are searched for relevant information in an organized manner in order to determine the factors influencing the adoption of EdTech. Second, an extended version of the technology adoption model is adopted to develop a qualitative data-based conceptual framework to analyze EdTech adoption in the Indian context.

Overall, by highlighting the critical components that emotionally influence college students' adoption of EdTech products in educational institutions, this course adds to the body of information already in existence. The conceptual framework model serves as a roadmap for educational stakeholders seeking to leverage EdTech effectively to enrich the learning environment and improve educational outcomes. By recognizing the significance of the identified factors, academic institutions can make informed decisions to foster a climate conducive to successful EdTech integration.

A comprehensive conceptual framework model was developed based on qualitative data analysis to illustrate the interrelationships between the identified factors influencing EdTech adoption. This model presents a valuable tool for educational institutions, policymakers and EdTech developers to comprehend the complex dynamics of implementing these technological solutions.

The findings of this study demonstrated a number of important variables that affect the uptake of EdTech products in educational settings. These factors encompassed technological infrastructure, ease of use, perceived usefulness, compatibility with existing academic practices, institutional support, financial constraints and individual attitudes towards technology. Additionally, the research explored the significance of institutional preparation for embracing technological advancements as well as the influence of socio-cultural elements.

This article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.

The model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.

The findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.

The proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.