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This study aims to use deep neural networks (DNNs) to learn the conduction heat transfer physics and estimate temperature distribution images in a physical domain without using any physical model or mathematical governing equation.

Two novel DNNs capable of learning the conduction heat transfer physics were defined. The first DNN (U-Net autoencoder residual network [UARN]) was designed to extract local and global features simultaneously. In the second DNN, a conditional generative adversarial network (CGAN) was used to enhance the accuracy of UARN, which is referred to as CGUARN. Then, novel loss functions, introduced based on outlier errors, were used to train the DNNs.

A UARN neural network could learn the physics of heat transfer. Within a few epochs, it reached mean and outlier errors that other DNNs could never reach after many epochs. The composite outlier-mean error as a loss function showed excellent performance in training DNNs for physical images. A UARN could excellently capture local and global features of conduction heat transfer, whereas the composite error could accurately guide DNN to extract high-level information by estimating temperature distribution images.

This study offers a unique approach to estimating physical information, moving from traditional mathematical and physical models to machine learning approaches. Developing novel DNNs and loss functions has shown promising results, opening up new avenues in heat transfer physics and potentially other fields.

The purpose of this study is to use structural equation modeling to examine the interaction between the variables of corporate image, credibility, trust and satisfaction in the context of a virtual sporting event. The aim is to determine whether these variables have significant relationships with each other and which of them has the greatest influence on the prediction of participants' satisfaction.

A structured questionnaire was used, based on previously validated scales. The survey was administered using the LimeSurvey platform. The sample consisted of a total of 588 participants of the Medellín virtual marathon.

The results of the study reveal significant findings regarding the relationships between the variables of corporate image, credibility, trust, and satisfaction in virtual sporting events. In particular, it is highlighted that trust emerges as the most influential factor in participants' satisfaction, which offers an insightful understanding of the importance of this variable in the user experience in virtual sporting events.

This study emphasizes the importance of brand analysis in the sports environment, stressing that the actions undertaken by managers should highlight both the corporate image and the connections with users, given their fundamental role in customer satisfaction. Likewise, the study of these variables within the sports context provides new knowledge and fills existing gaps within the academy. Limitations include the sample and the lack of consideration of all brand variables.

The need to cultivate a strong and well-managed image to build trust with participants is emphasized for organizers of virtual sporting events. It is crucial to work on establishing long-term credibility, especially in the relatively new context of virtual racing. Maintaining, and building the virtual career offering is essential to strengthening relationships, demonstrating a robust corporate image. In addition, since trust and credibility have a significant impact on participant satisfaction in this type of event, managers must communicate the assurance that virtual careers offer an experience free of uncertainty and risk, which is particularly attractive to a new customer base interested in this format.

This article presents an original contribution by investigating the relationships between corporate image, credibility, trust, and satisfaction in the context of virtual sporting events. It employs a structural equation model to assess the significance and predictive capacity of these variables. Notably, the study identifies trust as the most influential factor in predicting participant satisfaction. These findings offer valuable insights into the relative importance of brand variables in shaping user satisfaction within the virtual sporting event domain. By shedding light on these dynamics, the research aids event managers in making informed resource allocation decisions, contributing to a nuanced understanding of brand impact in this context.

This study investigated the impacts of the technology acceptance model (TAM) determinants and image congruence on attitude and, in turn, behavioral intentions. In addition, the differences between the US and Korean consumers in terms of the predictors of attitude were assessed.

The data were collected via an online survey from 342 South Korean and 353 American consumers who patronized a restaurant within a three-month timeframe.

The findings revealed that two dimensions of TAM and three sub-factors of self-image congruence positively affected customer attitude toward face recognition (FR) payment. Customer attitude also significantly influenced behavioral intentions toward FR payment. Lastly, the cultural differences between the Korean and American consumers played significant moderating roles in the relationships between perceived usefulness and attitude as well as between actual self-image congruence and attitude.

No prior empirical research has incorporated cultural differences into the FR payment acceptance model. Unlike previous research, the current study included cultural differences as a moderator of the relationships between the five predictors (i.e. two predictors from TAM and three dimensions of self-image congruence) and attitude toward FR payment in the research model.

This paper aims to evaluate children’s literature that focuses on body size issues for elementary readers.

The paper used an evaluative tool based on three categories: content, audience and other considerations.

The evaluative tool was used to evaluate six children’s books identified as critical literature supporting body image. The books evaluated focused on body image but were also tied to other themes such as body positivity, body neutrality, self-love, acceptance, diversity and inclusivity. All books acknowledged and celebrated the uniqueness of varied body types.

Because of the number of books evaluated, the evaluative results may lack generalizability. Therefore, researchers are encouraged to evaluate other critical children’s literature focused on body image.

The paper offers recommendations for parents, teachers and schools.

This paper encourages the need for parents, teachers and schools to help children embrace body positivity and neutrality so that they would love their skin.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

The study aims to explore millennial tourist satisfaction in an Islamic tourism destination, which this research will clarify the relationship between Islamic attributes and destination image on visiting decisions and their influence on tourist satisfaction.

The quantitative research approach with exploratory by applying structural equation model techniques, which method of collection data by surveys. Purposive sampling was used to 400 Muslim millennials tourist who visit Islamic destination in Bandung, Indonesia. The questionnaire used in this study based on a review of previous studies.

The findings show that a destination’s Islamic attribute has substantial influence on destination image and millennials’ choice to visit the tourist site. Then Islamic attributes have positive influence on millennials’ willingness to visit Bandung as visitors. A strong identification with a location’s destination image has been demonstrated a positive impact on visiting destination. This research revealed that the visiting choice had a favorable and substantial influence on Islamic visitor satisfaction through visiting decision to Bandung.

This research focuses on millennials tourist whoever visit Bandung tourist destination, the research findings may be limited in their generalizability. As a result, researchers are encouraged to explore the offered hypotheses further.

Islamic tourism business players who are targeting the millennial generation as their target market are urged to pay close attention the Islamic attributes and destination image, because according to the research results. These variables have an impact on visiting decisions and tourist satisfaction. Then the online travel agent should maintain the destination image by promoting the destination in offline and online media.

The findings of this study are expected to enrich the literature on consumer behavior, especially in the tourism industry in the Tourism 4.0 era.

To the best authors’ knowledge, the research model is never been explore by other researchers in the field of Islamic tourism literatures. For further studies, the authors’ can modify this research by using different objects so that comparisons can be made and using other determinant factors that can affect the satisfaction of Muslim tourists.

The purpose of this paper is to present a novel image sensor technology for raised characters based on line structured‐light. It can convert raised character's three‐dimensional (3D) features into image's grayscale levels.

The measurement principle and mathematical model are described. An experimental device is established and system parameters are calibrated. A grayscale conversion algorithm is proposed to convert the distortion of laser stripe to the grayscale intensity of image. The article also introduces a four‐factor method to assess the image quality of characters.

Experimental results show that the method can get high‐contrast images of raised characters that are conventionally low‐contrast with the background. Besides, the method does not need complicated calibration and mass computation, which makes the system structure simple and increases the speed of image acquisition.

The paper presents a novel image acquisition method for raised characters.

Image‐based localisation has been widely investigated in mobile robotics. However, traditional image‐based localisation approaches do not work when the environment appearance changes. The purpose of this paper is to propose a new system for image‐based localisation, which enables the approach to work also in highly dynamic environments.

The proposed technique is based on the use of a distributed vision system (DVS) composed of a set of cameras installed in the environment and of a camera mounted on a mobile robot. The localisation of the robot is achieved by comparing the current image grabbed by the robot with the images grabbed, at the same time, by the DVS. Finding the DVS's image, most similar to the robot's image, gives a topological localisation of the robot.

Experiments reported in the paper proved the system to be effective, even exploiting a pre‐existent DVS not designed for this application.

Whilst, aware that DVSs, as the one used in this work, are not diffuse nowadays, this work is significant because a novel idea is proposed for dealing with dynamic environments in the image‐based localisation approach and the idea is validated with experiments. Camera Sensor networks currently are an emerging technology and they may be introduced in several daily environments in the future.

The promotion of pedagogic use of quality digital images through improved metadata is the aim of the JISC‐funded FILTER project. Having funded many content creation projects, the JISC seeks to boost the take‐up of digital images in teaching and learning, in order to enhance the learning experience and improve outcomes. Discovery of appropriate images will be facilitated through a metadata schema developed in the project, and the definition of a range of image types to aid selection.

Scanners make it possible to transfer images from paper into computer equipment. Drawings (line art) and photographic images (halftones) can be picked up by scanners and then enhanced with image editing software to make the images suitable for use in computer‐generated publications.