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The paper examines the role and significance of gastronomic manifestations in the context of destination branding, within the framework of image transfer mechanisms and the Associative Network Memory Model.

The research was conducted on a sample of 53 gastronomic events in the tourist destination of Vojvodina.

The results indicate that gastronomic manifestations image has a positive impact on the brand image and brand identity of the destination, as well as the destination's overall image. Furthermore, the study found that the food experience has a positive influence on the image of gastronomic events and the destination.

The study contributes to the advancement of research on tourist destination branding.

The purpose of this study is to show that the neo-documentary – or complimentary – approach in Library and Information Science by no means is conservative, but highly necessary also in today's digitized media landscape. An example from a digitized photo archive is chosen to demonstrate the importance of a complimentary analysis that considers both material aspects as well as social and mental ones.

By taking Jenna Hartel's description of the neo-documentary turn as point of departure, the paper focuses on one case, the portrait of Johannes Abrahamsen Motka taken by Sophus Tromholt in 1883 and discusses different versions of the photograph from glass plate negatives to digitized versions in different contexts and media.

Many of the same paratextual elements can be found in different versions, also the digitized ones, to help the viewer to establish a historical context, but the images exhibited today are nevertheless no longer the same ones taken by Tromholt at the end of the 19th century. Not only have the material properties changed, but also – and probably even more important in most cases – the social and mental aspects. More re-contextualization is needed for today's audiences to recognize and understand a historical photograph taken in a colonial context. Focusing on document's material elements is not novel within the LIS-field, but the so-called neo-documentary turn was also a reaction on political and technological developments during the 1980s and 1990s. The increased focus on understanding a document in a complimentary way has demonstrated its impact during the last decades and is, at the same time, still work in progress.

As a scholar in the humanities the author can only relate to and therefore analyze what the author can experience and observe on screen level.

In providing a case study, this article illustrates the necessity of employing a complimentary approach when analyzing documents. This also implicates the claim that the neo-documentary turn – or complimentary as it rather should be called – by no means is a conservative one, but a highly necessary one in today's digitized media landscape.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

This study aims to investigate the influence of cognitive and affective trust on the revisit intention of medical tourists to a developing country like India, focusing on the role of country image as a moderator.

This study used partial least square-based structural equation modelling to test the hypotheses using data from 297 medical tourists visiting India from abroad for treatment.

The findings of this study confirmed that all the dimensions of cognitive trust, namely, perceived expertise, performance and reputation of the health-care service providers, have a positive and significant impact on the revisit intention of medical tourists to India. In contrast, none of the two dimensions of affective trust have a significant effect on the revisit intention of medical tourists to India. This study also confirmed that country image significantly moderates the relationship between performance dimensions of cognitive trust and medical tourists’ revisit intention to India.

The study’s findings have significant theoretical and managerial implications as they explore the effect of cognitive and affective trust on medical tourists’ the revisit intention to visit an emerging economy, considering the country’s image as a moderator.

本研究旨在调查认知和情感信任对印度等发展中国家医疗游客重游意愿的影响, 重点关注国家形象的调节作用。

本研究采用基于偏最小二乘的结构方程模型, 使用 297 名从国外到印度接受治疗的医疗游客的数据来检验假设。

本研究的结果证实, 认知信任的所有维度, 即医疗服务提供者的专业知识、绩效和声誉, 对印度医疗游客的重游意愿产生积极且显着的影响。相比之下, 情感信任的两个维度都没有对印度医疗游客的重游意愿产生显着影响。这项研究还证实, 国家形象显着调节认知信任绩效维度与医疗游客重访印度意愿之间的关系。

该研究的结果具有重要的理论和管理意义, 因为他们探讨了认知和情感信任对医疗游客重访新兴经济体的意愿的影响, 并考虑到该国作为调节者的形象。

Este estudio tiene como objetivo investigar la influencia de la confianza cognitiva y afectiva en la intención de volver a visitar a un país en desarrollo como la India por parte de turistas médicos, centrándose en el papel de la imagen del país como moderador.

Este estudio empleó un modelo de ecuaciones estructurales parcial basado en mínimos cuadrados para probar las hipótesis utilizando datos de 297 turistas médicos que visitaron la India desde el extranjero para recibir tratamiento.

Los hallazgos de este estudio confirmaron que todas las dimensiones de la confianza cognitiva, es decir, la experiencia percibida, el desempeño y la reputación de los proveedores de servicios de atención médica, tienen un impacto positivo y significativo en la intención de volver a visitar a la India por parte de los turistas médicos. Por el contrario, ninguna de las dos dimensiones de la confianza afectiva tiene un efecto significativo en la intención de volver a visitar la India por parte de los turistas médicos. Este estudio también confirmó que la imagen del país modera significativamente la relación entre las dimensiones de desempeño de la confianza cognitiva y la intención de los turistas médicos de volver a visitar la India.

Los hallazgos del estudio tienen importantes implicaciones teóricas y administrativas, ya que exploran el efecto de la confianza cognitiva y afectiva en la intención de los turistas médicos de visitar una economía emergente, considerando la imagen del país como moderador.

Selective laser sintering (SLS) is an essential technology in the field of additive manufacturing. However, SLS technology is limited by the traditional point-laser sintering method and has reached the bottleneck of efficiency improvement. This study aims to develop an image-shaped laser sintering (ISLS) system based on a digital micromirror device (DMD) to address this problem. The ISLS system uses an image-shaped laser light source with a size of 16 mm × 25.6 mm instead of the traditional SLS point-laser light source.

The ISLS system achieves large-area image-shaped sintering of polymer powder materials by moving the laser light source continuously in the x-direction and updating the sintering pattern synchronously, as well as by overlapping the splicing of adjacent sintering areas in the y-direction. A low-cost composite powder suitable for the ISLS system was prepared using polyether sulfone (PES), pinewood and carbon black (CB) powders as raw materials. Large-sized samples were fabricated using composite powder, and the microstructure, dimensional accuracy, geometric deviation, density, mechanical properties and feasible feature sizes were evaluated.

The experimental results demonstrate that the ISLS system is feasible and can print large-sized parts with good dimensional accuracy, acceptable geometric deviations, specific small-scale features and certain density and mechanical properties.

This study has achieved the transition from traditional point sintering mode to image-shaped surface sintering mode. It has provided a new approach to enhance the system performance of traditional SLS.

This paper aims to examine the role of social media communication, tourist satisfaction and destination brand equity components in enhancing destination brand equity based on the Stimulus – Organism – Response (S-O-R) theory.

The conceptual model and research hypotheses were assessed using covariance-based structural equation modeling (SEM). An online survey was used to collect data from 369 domestic tourists who had traveled to Danang and knew about content related to Danang generated by either DMOs or other users on social media.

Except for the effect of DMO-generated social media communication on tourist satisfaction and the impact of destination brand awareness on destination brand loyalty, the findings confirmed the sequential causal relationships between research concepts based on the S-O-R model.

Future research should explore the proposed model based on comparisons of different nationalities to better understand the impact of cultural factors.

DMOs should associate social media with their marketing strategies to enhance destination brand equity, using cutting-edge technologies to create content and update information in a significant way to make communications by DMOs more effective. The findings especially suggest that UGC plays a vital role in improving brand equity dimensions, so DMOs could exploit UGC to engage existing customers and build relationships with potential customers. This research provides guidance for DMOs to improve their brand equity based on social media.

This study has contributed to the destination marketing literature by applying the S-O-R theory to propose a pathway for effectively increasing destination brand equity and highlight the importance of social media communication as a driver to achieve a hierarchical relationship between destination brand equity components and tourist satisfaction from stimulus to organism (e.g. cognition to affect).

The halal cosmetics industry continues to grow significantly. Furthermore, using halal cosmetics is a must for Muslims. This study aims to analyze the factors influencing the switching intention to halal cosmetics in Indonesia.

This quantitative study uses a Partial Least Square-Structural Equation Modelling (PLS-SEM) on 214 respondents. The variables include halal certification, halal awareness, product image, perceived behavioral control, subjective norm, attitude, advertisement and switching intention.

The product image plays the most influential role in deriving the attitude toward switching intention to halal cosmetics, following perceived behavioral control, halal awareness and subjective norm, but not halal certification and advertisement. The result indicates that the image of halal cosmetics influences customers’ attitudes toward switching to using halal cosmetics. Indonesian customers know the obligation to use halal products because they are Muslim. However, the existence of halal certification does not derive the switching intention to halal cosmetics.

This study conducts research only in Indonesia. As a recommendation, further studies might conduct a comparative test using multicultural respondents in several countries. Other studies also suggested examining factors of switching intention through different generational, especially in countries with high individualism traits.

This study will encourage the halal industry, especially the halal cosmetics industry, to pay more attention to the product image. Meanwhile, the government can provide incentives or rewards to promote industry participation in halal cosmetics. The findings provide a more detailed understanding of how product image can influence someone to switch to halal cosmetics.

Research on switching intention to halal cosmetics is still limited. This study uses halal variables, while previous studies only used religiosity. This study also introduced the product images motivating customers’ switching intention to use halal cosmetics.

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.

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.