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Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

The purpose of this paper is to explore the prospect of using neurophenomenology to understand, design and test phygital consumer experiences. It aims to clarify interpretivist approaches to consumer neuroscience, wherein theoretical models of individual phenomenology can be combined with modern neuroimaging techniques to detect and interpret the first-person accounts of phygital experiences.

The argument is conceptual in nature, building its position through synthesizing insights from phenomenology, phygital marketing, theoretical neuroscience and other related fields.

Ultimately, the paper presents the argument that interpretivist neuroscience in general, and neurophenomenology specifically, provides a valuable new perspective on phygital marketing experiences. In particular, we argue that the approach to studying first-personal experiences within the phygital domain can be significantly refined by adopting this perspective.

One of the primary goals of this paper is to stimulate a novel approach to interpretivist phygital research, and in doing so, provide a foundation by which the impact of phygital interventions can be empirically tested through neuroscience, and through which future research into this topic can be developed. As such, the success of such an approach is yet untested.

Phygital marketing is distinguished by its focus on the quality of subjective first-personal consumer experiences, but few papers to date have explored how neuroscience can be used as a tool for exploring these inner landscapes. This paper addresses this lacuna by providing a novel perspective on “interpretivist neuroscience” and proposes ways that current neuroscientific models can be used as a practical methodology for addressing these questions.

Additive Manufacturing (AM) conventionally necessitates an intermediary slicing procedure using the standard tessellation language (STL) data, which can be computationally burdensome, especially for intricate microcellular architectures. This study aims to propose a direct slicing method tailored for digital light processing-type AM processes for the efficient generation of slicing data for microcellular structures.

The authors proposed a direct slicing method designed for microcellular structures, encompassing micro-lattice and triply periodic minimal surface (TPMS) structures. The sliced data of these structures were represented mathematically and then convert into 2D monochromatic images, bypassing the time-consuming slicing procedures required by 3D STL data. The efficiency of the proposed method was validated through data preparations for lattice-based nasopharyngeal swabs and TPMS-based ellipsoid components. Furthermore, its adaptability was highlighted by incorporating 2D images of additional features, eliminating the requirement for complex 3D Boolean operations.

The direct slicing method offered significant benefits upon implementation for microcellular structures. For lattice-based nasopharyngeal swabs, it reduced data size by a factor of 1/300 and data preparation time by a factor of 1/8. Similarly, for TPMS-based ellipsoid components, it reduced data size by a factor of 1/60 and preparation time by a factor of 1/16.

The direct slicing method allows for bypasses the computational burdens associated with traditional indirect slicing from 3D STL data, by directly translating complex cellular structures into 2D sliced images. This method not only reduces data volume and processing time significantly but also demonstrates the versatility of sliced data preparation by integrating supplementary features using 2D operations.

Tourists’ irresponsible behaviours (e.g. damaging flowers, writing and painting on the walls and throwing waste material in the water and around the sea site) damage the coastal environment. The irresponsible behaviour of tourists has raised concerns about the sustainability of the coastal tourism environment. The purpose of this study is to identify and explain the behavioural patterns of tourists that can influence the environmentally responsible behaviours (ERBs) of tourists, particularly in the context of coastal tourism. The study aims to provide a theoretical and practical explanation of destination image and perceived destination value (PDV) in shaping ERB with the mediating role of destination social responsibility (DSR) among tourists at the coastal touring destination.

The study used a quantitative research design and data were gathered from the five beaches in Karachi. Structured equation model was used to analyse the direct and mediating effect while stepwise regression was used to analyse the moderating effect of DSR. The results of the direct effect showed that cognitive image has a significant effect on the affective image, while the insignificant effect on conative image and ERB. While the affective image has a significant effect on conative and ERB, and finally, results showed a significant effect of conative image on ERB. Results of the study revealed that PDV significantly mediated the relationship between cognitive, affective and conative destination image and ERB. Finally, the study’s results revealed that DSR has significantly moderated the relationship between affective, conative destination image, PDV and ERB.

The results are divided into three categories, direct effect, mediating effect and moderating effect. The results of the direct effect showed that cognitive image has a significant effect on the affective image, while the insignificant effect on conative image and ERB. While affective image has a significant effect on conative and ERB, and finally, results showed a significant effect of conative image on ERB. It is found in the results that PDV significantly mediated the relationship between cognitive, affective and conative destination image and ERB. Finally, the study’s results revealed that DSR has significantly moderated the relationship between affective, conative destination image, PDV and ERB.

First, data has been collected from a single geographic area of Pakistan. Therefore, cross-country data are required to compare the ERB of tourists. Second, only local respondents are considered in the study; future studies may include foreign tourists as well. Finally, data has been collected during one month in summer, which may have measured the experience of only summer. The respondent may have different perceived values and destination images during winter. The future study may split data collection into summer and winter to cover diverse perceptions of tourists.

It is almost impossible for coastal destinations to achieve a competitive advantage without attaining sustainable coastal environments. Clean and green beaches and responsible behaviour towards marine mammals can only be achieved through tourists’ ERB. This study has major contributions towards society by reserving the natural environment of coastal areas.

This research will significantly contribute to the existing literature by extending the ERB knowledge through the theoretical lens of cognitive-affective-conative models and social expectancy theory. Moreover, PDV as a mediator and DSR as a moderator will enhance the understanding of ERB and extend the existing literature. Further research has provided a strong understanding of how cognitive, effective and conative image helps in influencing the ERB of tourists. Moreover, research will benefit destination managers and policymakers to enhance the image and perceived value of touring destinations. Finally, this study is a unique attempt to present a comprehensive model which could be applicable to diverse situations and areas.

In recent years, using aberration-corrected transmission electron microscopy, the authors have achieved precisely detecting the structural evolution of passive film as well as its interface zone at atomic scale. The purpose of this paper aims to make a brief review to show the authors’ new understanding and perspective on the issue of critical factors determining stability of passive film of Fe-Cr alloy.

The introduction of single crystal enabled the authors to obtain a distinct metal/passive film interface and better characterize the structure of the interface region. The authors use aberration-corrected TEM to conduct cross-sectional observation and directly capture the details across the entire film at a high spatial and energy resolution.

Apart from the passive film itself, the interface zone, including metal/film (Me/F) interface and the adjacent metal side, is also the site which is attacked. Accordingly, the nature of the interface zone, such as microstructure, composition and atomic configuration, is one of the critical factors determining the stability of passive film.

Deciphering the critical factors determining the stability of passive film is of great significance and has been a fundamental issue in corrosion science. Great attention has been paid to the nature of the passive film itself. In contrast, the possible role of the interface between the passive film and the metal is rarely taken into account. Based on the advanced analytical tool with high spatial resolution, the authors have specified the significant role of interface structures on the macro-scale stability of passive film.

Due to the intense rivalry in the smartphone market, manufacturers of mobile phones are becoming increasingly interested in knowing the factors that influence consumers' purchase intention. This paper aims to examine the effect of country-of-origin image, brand image and attitude towards the brand on the purchase intention of smartphone users.

An empirical study was performed based on the information gathered from smartphone users. The structural equation modeling (SEM) technique was applied to examine the hypotheses.

The authors found that brand image and attitude towards the brand significantly influence consumer purchase intention. Additionally, there is an indirect effect even when the nation of origin image does not directly influence the consumer's purchase intention. Indeed, brand image and attitude towards the brand act as a mediator between the country-of-origin image and purchase intention.

This study presents a conceptual model on the impact of country-of-origin image on the propensity of consumers to buy smartphones in a field where little research has been done. The investigation offers a consumer-focused analysis regarding the country-of-origin image. This suggests a significant shift from the current strategy, which is frequently centered on the viewpoint of the companies.

This paper aims to assess the sense-of-community role in affecting young Muslim loyalty towards Muslim-majority tourism destinations. Specifically, this research assesses the sense of community dimension in the halal tourism context and evaluates its effects on destination satisfaction, image and loyalty.

This research used a quantitative approach by using data from 376 young Indonesian Muslim tourists with past travel experiences to destinations where Muslims are the majority. The dimension of the sense of community was evaluated using exploratory factor analysis. The association between variables was tested using partial least square-structural equation modelling.

The finding exhibits three notable sense of community dimensions: membership, influence and need fulfilment and emotional connection. Emotional connection shapes, directly and indirectly, destination loyalty, while influence and need fulfilment affect destination loyalty by satisfaction and destination image mediating role. Lastly, membership has no impact on developing destination loyalty.

This study offers tourism destinations in Muslim-majority countries an opportunity to draw and create loyalty among young Muslim tourists. Besides offering superior halal services and products, Muslim-majority tourism destinations need to develop young Muslim tourists' emotional connection to the destinations.

To the best of the authors’ knowledge, this is the first empirical examination of the sense of community's role in influencing tourist loyalty, specifically in halal tourism.

This study aims to examine the mediating and moderating processes that link airline brand image to passenger loyalty through perceived value.

The research participants were Taiwanese passengers with experience travelling abroad by air. Structural equation modelling and multigroup analysis were used to investigate the effect of airline brand image and perceived value on the loyalty of passengers using full-service and low-cost carriers.

For both airline types, airline brand image had a significant and positive effect on passenger perceived value. Perceived value had a significant and positive effect on passenger loyalty, perceived value was a crucial mediator and airline type was not a key moderator in the model.

In this study, focusing on the perspective of Taiwanese passengers, a conceptual model of the factors that lead to passenger loyalty, with a focus on brand image, was developed. This paper contributes to the literature and application field by examining the mediating effect of perceived value and the moderating role of airline type in the aviation industry; on the basis of the results, potential recovery strategies for airlines in the post-COVID-19 era are provided.

本研究檢驗透過知覺價值將航空公司品牌形象與乘客忠誠度連結起來的中介和調節過程。

受訪者是曾經有搭乘飛機出國旅行經驗的台灣乘客。本研究採用結構方程模式和多群組分析進行驗證, 分別從搭乘全服務型航空與低成本航空的乘客探討航空公司品牌形象和知覺價值對乘客忠誠行為的影響。

不論是就全服務型航空或低成本航空而言, 航空公司品牌形象對乘客知覺價值都具有顯著的正向影響, 知覺價值對乘客忠誠行為同樣具有顯著的正向影響; 知覺價值是一個重要的中介變數, 而航空公司類型不是關鍵的調節變數。

在這項以台灣乘客的視角為重點的研究中, 開發了一個以品牌形象為重點的導致乘客忠誠度因素的概念模型。本研究通過檢驗知覺價值的中介作用和航空公司類型在航空業中的調節作用, 為文獻和應用領域做出了貢獻; 最後根據研究結果, 提供後疫情時代航空公司的潛在恢復策略。

este estudio examinó los procesos de mediación y moderación que vinculan la imagen de marca de la aerolínea con la lealtad de los pasajeros a través del valor percibido.

los participantes de la investigación eran pasajeros taiwaneses con experiencia en viajes al extranjero por vía aérea. Se emplearon modelos de ecuaciones estructurales y análisis multigrupo para investigar el efecto de la imagen de marca de la aerolínea y el valor percibido en la lealtad de los pasajeros que utilizan líneas aéreas de servicio completo y de bajo costo.

para ambos tipos de aerolíneas, la imagen de marca de la aerolínea tuvo un efecto significativo y positivo en el valor percibido por los pasajeros. El valor percibido tuvo un efecto significativo y positivo en la lealtad de los pasajeros, el valor percibido fue un mediador crucial y el tipo de aerolínea no fue un moderador clave en el modelo.

En este estudio centrado en la perspectiva de los pasajeros taiwaneses, se desarrolló un modelo conceptual de los factores que conducen a la lealtad de los pasajeros, con un enfoque en la imagen de marca. Este documento contribuye al campo de la literatura y la aplicación al examinar el efecto mediador del valor percibido y el papel moderador del tipo de aerolínea en la industria de la aviación; Sobre la base de los resultados, se proporcionan posibles estrategias de recuperación para las aerolíneas en la era posterior a la COVID-19.

In the growing information systems (IS) literature on metaverse, augmented reality (AR) technology is regarded as a cornerstone of the metaverse which enables interaction services. Interaction has been identified as a core technology characteristic of metaverse shopping environments. Based on previous human–technology interaction research, the authors further explicate interaction to be multimodal sensory. The purpose of this study is thus to better understand the unique nature of interaction in AR technology and highlight the technology's benefits for shopping in metaverse spaces.

An experiment has been conducted to empirically examine the authors' research model. The authors use the structural equation modeling (SEM) approach to analyze the collected data.

This study conceptualizes image, motion and touchscreen interactions as the three dimensions of multimodal sensory interaction, which can reflect visual-, kinesthetic- and haptic-based sensation stimulation. The authors' findings show that multimodal sensory interaction of AR activates consumers' intention to purchase via a psychological process. To delineate this psychological process, the authors use feelings-as-information theory to posit that experiential factors can influence cognitive factors. More specifically, multimodal sensory interaction is shown to increase multisensory experience and spatial presence, which can effectively reduce product uncertainty and information overload. The two outcomes have been considered to be key issues in online shopping environments.

This study is one of the first ones that shed light on the multimodal sensory peculiarity of AR interactions in the extant IS literature. The authors further highlight the benefits of AR in addressing major online shopping concerns about product uncertainty and information overload, which are largely overlooked by prior research. This study uses feelings-as-information theory to explain the impacts of AR interactions, which reveal the essential role of the experiential process in sensory-enabling technologies. This study enriches the existing theoretical frameworks that mostly focus on the cognitive process. The authors' findings about AR interactions provide noteworthy guidelines for the design of metaverse environments and extend the authors' understanding of how the metaverse may bring benefits beyond traditional online shopping settings.