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Augmented reality (AR) is revolutionizing the tourism and hospitality industry by offering immersive experiences as well as creating more engaging, informative and accessible travel experiences that attract tourists from around the globe. From virtual tours and immersive historical site recreations to navigation assistance and cultural education, AR technology is transforming the way we explore and interact with the destinations. This study aims to identify benefits, risks, tools and techniques of AR in the tourism and hospitality literature.

The authors conducted a systematic literature review to answer six research questions. The authors also identified 33 primary studies, dated from January 2010 to February 2024 and coded them via a thematic analysis. Related studies were obtained through searching in Web of Science and Scopus.

The results identified nine themes for benefits, eight themes for risks/disadvantages and four tools and applications-related themes. Through the thematic analysis, the major benefits of AR in the tourism and hospitality were found to be differentiated travel experiences, improved performance of tourism value chain, more effective marketing efforts of tourism businesses, enhanced tourists’ engagement, enhanced performance of tourism destinations, stimulated behavioral intentions, tourist empowerment and providing more value, interactivity and integrity. Furthermore, eight risks were identified: physical, privacy and security, social, service failure, technical, psychological, managerial, information and knowledge gaps. The authors also recognized four tools and applications-related themes, namely, AR-enabled tools, AR applications, AR-enabled apps and AR-based techniques.

To the best of the authors’ knowledge, this review provides the first systematic exploration of the existing literature on usage of AR in the context of tourism and hospitality value chain.

The current air traffic management (ATM) operational approach is changing; “time” is now integrated as an additional fourth dimension on trajectories. This notion will impose on aircraft the compliance of accurate arrival times over designated checkpoints (CPs), called time windows (TWs). This paper aims to clarify the basic requirements and foundations for the practical implementation of this functional framework.

This paper reviews the operational deployment of 4D trajectories, by defining its relationship with other concepts and systems of the future ATM and communications, navigation and surveillance (CNS) context. This allows to establish the main tools that should be considered to ease the application of the 4D-trajectories approach. This paper appraises how 4D trajectories must be managed and planned (negotiation, synchronization, modification and verification processes). Then, based on the evolution of a simulated 4D trajectory, the necessary corrective measures by evaluating the degradation tolerances and conditions are described and introduced.

The proposed TWs model can control the time tolerance within less than 100 s along the passing CPs of a generic trajectory, which is in line with the expected future ATM time-performance requirements.

The main contribution of this work is the provision of a holistic vision of the systems and concepts that will be necessary to implement the new 4D-trajectory concept efficiently, thus enhancing performance. It also proposes tolerance windows for trajectory degradation, to understand both when an update is necessary and what are the conditions required for pilots and air traffic controllers to provide this update.

The use of the 4D trajectory operational concept in the future air traffic management (ATM) system will require the aircraft to meet very accurately an arrival time over a designated checkpoint. To do this, time intervals known as time windows (TW) are defined. The purpose of this paper is to develop a methodology to characterise these TWs and to manage the uncertainty associated with the evolution of 4D trajectories.

4D trajectories are modelled using a point mass model and EUROCONTROL’s BADA methodology. The authors stochastically evaluate the variability of the parameters that influence 4D trajectories using Monte Carlo simulation. This enables the authors to delimit TWs for several checkpoints. Finally, the authors set out a causal model, based on a Bayesian network approach, to evaluate the impact of variations in fundamental parameters at the chosen checkpoints.

The initial results show that the proposed TW model limits the deviation in time to less than 27 s at the checkpoints of an en-route segment (300 NM).

The objective of new trajectory-based operations is to efficiently and strategically manage the expected increase in air traffic volumes and to apply tactical interventions as a last resort only. We need new tools to support 4D trajectory management functions such as strategic and collaborative planning. The authors propose a novel approach for to ensure aircraft punctuality.

The main contribution of the paper is the development of a model to deal with uncertainty and to increase predictability in 4D trajectories, which are key elements of the future airspace operational environment.

This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models.

An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between crypto-price and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision.

The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models.

The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies’ prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.

In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.

The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.

This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.

This study aim to use the finite volume method to solve differential equations related to three-dimensional simulation of a solar collector. Modeling is done using ANSYS-fluent software program. The investigation is done for a photovoltaic (PV) solar cell, with the dimension of 394 × 84 mm², which is the aluminum type and receives the constant heat flux of 800 W.m⁻². Water is also used as the working fluid, and the Reynolds number is 500.

In the present study, the effect of fluid flow path on the thermal, electrical and fluid flow characteristics of a PV thermal (PVT) collector is investigated. Three alternatives for flow paths, namely, direct, curved and spiral for coolant flow, are considered, and a numerical model to simulate the system performance is developed.

The results show that the highest efficiency is achieved by the solar cell with a curved fluid flow path. Additionally, it is found that the curved path’s efficiency is 0.8% and 0.5% higher than that of direct and spiral paths, respectively. Moreover, the highest pressure drop occurs in the curved microchannel route, with around 260 kPa, which is 2% and 5% more than the pressure drop of spiral and direct.

To the best of the authors’ knowledge, there has been no study that investigates numerically heat transfer, fluid flow and electrical performance of a PV solar thermal cell, simultaneously. Moreover, the effect of the microchannel routes which are considered for water flow has not been considered by researchers so far. Taking all the mentioned points into account, in this study, numerical analysis on the effect of different microchannel paths on the performance of a PVT solar collector is carried. The investigation is conducted for the Reynolds number of 500.

The direction of causality between job satisfaction and job performance (known as the holy grail of industrial psychologists) is undetermined and related research findings in different organizational contexts are mixed. Based on the ample literature, mainly from Western countries, on the relationship between job satisfaction and job performance, a non-recursive bow pattern model was utilized to investigate the direct relationship between these two variables in an Asia–Pacific higher education system.

This study is quantitative in approach and survey in design. Additionally, to meet the statistical requirements of non-recursive bow pattern analysis, the authors added welfare as a theory-driven instrumental variable to introduce exogenous variability. Using the efficient partial least squares (PLSe2) estimator, the authors fitted the model to the data collected from 2008 academics affiliated with Malaysian public universities and polytechnics.

The results showed that while job satisfaction is considerably influenced by welfare, it is not a significant predictor of job performance directly. In addition, a meaningful positive correlation between the disturbance terms of job satisfaction and job performance was observed, suggesting the existence of other factors that could increase both job satisfaction and job performance. The findings' theoretical and practical implications are discussed, and a list of theory-driven evidenced-based policies in this regard is provided.

This is the first study to test a non-recursive bow pattern model and examine the holy grail of industrial psychology based on the PLSe2 methodology, as a parametric approach to partial least squares (PLS), in a higher education context. This study also provides higher education researchers with the advantages of the PLSe2 method, especially in causal-predictive modeling, in the context of applied higher education research.

Considering the direct impact of retailers' performance on the economy, this paper aimed to propose a comprehensive framework to evaluate the performance of different branches of a retailer.

Through a case study, the weights of indicators were calculated by the best-worst method (BWM) and the branches' performance was appraised using data envelopment analysis (DEA).

The branches were ranked in terms of performance, and sensitivity analysis and statistical tests were conducted to realize the weaknesses and strengths of the branches. Then, some strategies were proposed using strengths, weaknesses, opportunities and threats (SWOT) analysis to improve the performance of the weak branches.

This paper contributes to previous studies on the evaluation of retailers' performance by proposing a triple framework based on resilience, sustainability and sales-marketing indicators. This paper focused on branches' operations and branches' optimization by improving performance in terms of these three indicators. This paper also offers a qualitative and quantitative analysis of retailers' performance, which has received less attention in previous studies.

Universities play a central role in scientific development and progress in societies, so, the need for supporting new businesses in universities becomes very important that science and technology park-based incubators can meet it. The purpose of this paper is to prioritize the factors influencing strategic management of university business incubators (UBIs).

Present research prioritizes the factors influencing strategic management of incubators using analytic network process (ANP). The authors gathered data from UBIs affiliated with science and technology park of Guilan, located in city of Rasht (Iran) using the ANP questionnaire during year 2017.

Factors influencing strategic management of incubators comprised of four main criteria and 14 sub-criteria. The criteria and sub-criteria were ranked based on their importance. Results show that “talented managers” has the highest importance for strategic management of UBIs.

This study offers a novel analysis and contribution to the knowledge of ranking UBIs with a multi-criteria decision-making technique. Results of this research show the relative importance of criteria and sub-criteria, which contributes to further improvement of incubator management. University managers and incubator directors can utilize the findings for better resource allocation and aligning the strategies of incubators with macro strategies of the country.