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The proliferation of smartwatches in the digital age has radically transformed health and fitness management, offering users a multitude of functionalities that extend beyond mere physical activity tracking. While these modern wearables have empowered users with real-time data and personalized health insights, their environmental implications remain relatively unexplored despite a growing emphasis on sustainability. To bridge this gap, this study extends the UTAUT2 model with smartwatch features (mobility and availability) and perceived security to understand the drivers of smartwatch usage and its consequent impact on environmental sustainability.

The proposed theoretical model is evaluated based on data collected from 303 smartwatch users using a hybrid structural equation modeling–artificial neural network (SEM-ANN) approach.

The PLS-SEM results supported smartwatch features’ effect on performance and effort expectancy. The results also supported the role of performance expectancy, social influence, price value, habit and perceived security in smartwatch usage. The use of smartwatches was found to influence environmental sustainability significantly. However, the results did not support the association between effort expectancy, facilitating conditions and hedonic motivation with smartwatch use. The ANN results further complement these outcomes by showing that habit with a normalized importance of 100% is the most significant factor influencing smartwatch use.

Theoretically, this research broadens the UTAUT2 by introducing smartwatch features as external variables and environmental sustainability as a new outcome of technology use. On a practical level, the study offers insights for various stakeholders interested in smartwatch use and their environmental implications.

The purpose of this paper is to investigate how previous literature has analyzed the role of augmented reality and virtual reality in the field of tourism, distinguishing between studies focused on one technology or the other as both have characteristics that profoundly differentiate them.

This study carries out a critical review to assess and synthesize the literature on augmented reality and virtual reality in tourism. Literature searches are conducted using various keywords, resulting in a selection of 84 articles (19 on augmented reality and 65 on virtual reality) from 39 indexed journals.

The research findings demonstrate an increasing scholarly focus on exploring the application of virtual reality and augmented reality within the realm of tourism. These results highlight a noticeable progression in recent years with respect to different matters, such as methodologies, used theories or considered variables, among others. Based on these results, it is proposed a future research agenda that seeks to establish a cohesive framework and drive the development of both augmented reality and virtual reality research in tourism.

By conducting an individual and comparative review of the literature on the application of augmented reality and virtual reality in tourism, this research helps elucidate the specific lines of research for each technology while providing a better understanding of how each technology can be used to generate effective tourist experiences.

El objetivo de este artículo es investigar cómo la literatura previa ha analizado el papel de la realidad aumentada y la realidad virtual en el ámbito del turismo, distinguiendo entre estudios centrados en una u otra tecnología ya que ambas tienen características que las diferencian profundamente.

Este estudio realiza una revisión crítica para evaluar y sintetizar la literatura sobre realidad aumentada y realidad virtual en turismo. Se realizan búsquedas de literatura utilizando diversas palabras clave, que dan como resultado la selección de 84 artículos (19 sobre realidad aumentada y 65 sobre realidad virtual) procedentes de 39 revistas indexadas.

Los resultados de la investigación demuestran que existe un interés creciente en explorar la aplicación de la realidad virtual y la realidad aumentada en el ámbito del turismo. Asimismo, se pone de manifiesto una notable progresión en los últimos años con respecto a diferentes cuestiones, como las metodologías aplicadas, las teorías empleadas o las variables consideradas, entre otras. A partir de estos resultados, se propone una agenda de investigación futura que pretende establecer un marco cohesionado e impulsar el desarrollo de la investigación en el ámbito tanto de la realidad aumentada como de la realidad virtual en turismo.

Al realizar una revisión individual y comparativa de la literatura sobre la aplicación de la realidad aumentada y la realidad virtual en el turismo, esta investigación ayuda a esclarecer las líneas de investigación específicas de cada tecnología a la vez que proporciona una mejor comprensión de cómo cada tecnología puede ser utilizada para generar experiencias turísticas efectivas.

本文旨在研究以往的文献是如何分析增强现实和虚拟现实在旅游领域的作用的, 并对侧重于其中一种技术或另一种技术的研究加以区分, 因为这两种技术都有深刻区别于其他技术的特点。

本研究通过批判性综述来评估和归纳有关旅游业中增强现实和虚拟现实技术的文献。通过使用各种关键词进行文献检索, 从 39 种索引期刊中筛选出 84 篇文章（19 篇关于增强现实技术, 65 篇关于虚拟现实技术）。

研究结果表明, 学术界越来越重视探索虚拟现实和增强现实在旅游业中的应用。这些结果突显了近年来在方法论、采用的理论或考虑的变量等不同方面的明显进步。在这些成果的基础上, 提出了未来的研究议程, 旨在建立一个具有凝聚力的框架, 推动旅游业中增强现实和虚拟现实研究的发展。

本研究通过对增强现实和虚拟现实技术在旅游业中应用的文献进行单独和比较审查, 有助于阐明每种技术的具体研究方向, 同时让人们更好地了解如何使用每种技术来产生有效的旅游体验。

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

Tactile communication that relies on the human sense of touch replicated using vibration motors is increasingly being explored for seamless communication on construction jobsite. However, the technological efficacy cannot secure the users’ acceptability of the tactile communication devices. This study aims to assess the factors affecting the wearability of such a portable tactile device based on the responses from practicing professionals.

The investigation adapted a three-step phenomenological interviewing approach to seek feedback from construction personnel in Texas, the USA, regarding the viability of wearable tactile communication. The interviewees expressed various opinions about the on-body placement upon exposure to a portable tactile feedback prototype developed for this study, which was used to derive inferences regarding the factors affecting its on-field acceptability.

All the participants of the round-table study (11 out of 11) considered tactile feedback as a viable mode of communication on construction jobsite. Seven professionals supported the integration of a tactile device with the hard hat, whereas the rest preferred tactile eyeglasses. Weatherability, rechargeability, traceability, safety and social receptivity were identified as the major factors affecting the on-body placement of the wearable tactile communication device.

This paper presents a roadmap to gain construction industry opinion on the factors that can affect the on-body placement of a wearable tactile communication device. The five aforementioned factors impacting tactile communication acceptability were used to evaluate 10 potential on-body placements. The findings have implications for research and development of wearable tactile devices and the subsequent acceptability of such a device on the jobsite.

This paper aims to study passive control techniques for transonic flow over a backward-facing step (BFS) using square-lobed trailing edges. The study investigates the efficacy of upward and downward lobe patterns, different lobe widths and deflection angles on flow separation, aiming for a deeper understanding of the flow physics behind the passive flow control system.

Large Eddy Simulation and Reynolds-averaged Navier–Stokes were used to evaluate the results of the study. The research explores the impact of upward and downward patterns of lobes on flow separation through the effects of different lobe widths and deflection angles. Numerical methods are used to analyse the behaviour of transonic flow over BFS and compared it to existing experimental results.

The square-lobed trailing edges significantly enhance the reduction of mean reattachment length by up to 80%. At Ma = 0.8, the up-downward configuration demonstrates increased effectiveness in reducing the root mean square of pressure fluctuations at a proximity of 5-step height in the wake region, with a reduction of 50%, while the flat-downward configuration proves to be more efficient in reducing the root mean square of pressure fluctuations at a proximity of 1-step height in the near wake region, achieving a reduction of 71%. Furthermore, the study shows that the up-downward configuration triggers early spanwise velocity fluctuations, whereas the standalone flat-downward configuration displays less intense crosswise velocity fluctuations within the wake region.

The findings demonstrate the effectiveness of square-lobed trailing edges as passive control techniques, showing significant implications for improving efficiency, performance and safety of the design in aerospace and industrial systems.

This paper demonstrates that the square-lobed trailing edges are effective in reducing the mean reattachment length and pressure fluctuations in transonic conditions. The study evaluates the efficacy of different configurations, deflection angles and lobe widths on flow and provides insights into the flow physics of passive flow control systems.

The purpose of this paper is to examine the impact of bankruptcy reform in the year 2016 on the relation between leverage and firm performance for Indian firms, separately for business group and standalone firms.

Fixed effects panel regression is used to understand the role of bankruptcy reform on firm-level data to examine the relationship between leverage and firm performance after controlling for size, growth, age, liquidity and promoter shareholding. The authors also apply the generalized method of moments (GMM) to control for the endogeneity concerns.

The authors show that the introduction of the insolvency and bankruptcy code (IBC) positively moderates the relation between leverage and firm performance such that the extent of negative relation between leverage and firm performance is less in the post-IBC period. The positive impact of IBC on the relation between leverage and firm performance holds only for firms not affiliated to business groups and for firms with higher debt in their capital structure.

The study’s findings will help the regulators appreciate the effectiveness of bankruptcy reforms resulting from IBC implementation in terms of sound bankruptcy process and leading to safeguard the interests of minority shareholders.

The authors provide the only study to examine the role of bankruptcy law in moderating the relation between leverage and firm performance across a sample of business group and standalone firms.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

Artificial fruit ripening is hazardous to mankind. In the recent past, artificial fruit ripening is increasing gradually due to its commercial benefits. To discriminate the type of fruit ripening involved at the vendors’ side, there is a great demand for on-sight ethylene detection in a nondestructive manner. Therefore, this study aims to deal with a comparison of various laboratory and portable methods developed so far with high-performance metrics to identify the ethylene detection at fruit ripening site.

This paper focuses on various types of technologies proposed up to date in ethylene detection, fabrication methods and signal conditioning circuits for ethylene detection in parts per million and parts per billion levels. The authors have already developed an infrared (IR) sensor to detect ethylene and also developed a lab-based setup belonging to the electrochemical sensing methods to detect ethylene for the fruit ripening application.

The authors have developed an electrochemical sensor based on multi-walled carbon nanotubes whose performance is relatively higher than the sensors that were previously reported in terms of material, sensitivity and selectivity. For identifying the best sensing technology for optimization of ethylene detection for fruit ripening discrimination process, authors have developed an IR-based ethylene sensor and also semiconducting metal-oxide ethylene sensor which are all compared with literature-based comparable parameters. This review paper mainly focuses on the potential possibilities for developing portable ethylene sensing devices for investigation applications.

The authors have elaborately discussed the new chemical and physical methods of ethylene detection and quantification from their own developed methods and also the key findings of the methods proposed by fellow researchers working on this field. The authors would like to declare that the extensive analysis carried out in this technical survey could be used for developing a cost-effective and high-performance portable ethylene sensing device for fruit ripening and discrimination applications.

316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.

Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.

A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm³/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.

To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.