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This study aims to critically evaluate the impact of government tourism promotion methods on city image and visiting intentions and to explore the moderating effect of tourism experience on two of the conceptualized relationships, using Macao as a case study.

The research analyzed responses from 407 Macao visitors during the pandemic to determine the effects of tourism promotion methods on the city’s image and visiting intention using structural equation modeling (SEM) to test the hypotheses.

This study revealed that tourism promotion methods positively impact the city’s image and visiting intentions. City image also mediates the relationship between tourism promotion methods and visiting intentions. Furthermore, tourism experience moderates the relationships between promotion methods and city image and between promotion methods and visiting intentions.

The study’s findings on the effects of promotion methods utilized by local governments support and expand upon existing theoretical frameworks within the realm of nonprofit organizations’ management and marketing for destination marketing organizations (DMOs). However, the cross-sectional design limits causality, and findings may not be generalizable beyond Macao without further comparative research.

This study provides valuable insights for policymakers and marketers to design promotional campaigns that deliver authentic and memorable experiences, aligning with their promotional promises, especially in postpandemic contexts.

This research contributes to the theoretical and methodological advancement of management and marketing for DMOs from a public sector perspective, highlighting the critical role of government involvement in urban tourism promotion.

The research offers valuable insights into how promotional activities influence visiting intentions, advancing tourism management and marketing for DMOs from a nonprofit perspective. The study validates theories and adds unique insights by focusing on Macao’s postpandemic recovery, offering practical implications for similar urban settings.

The purpose of this paper is to analyse whether publicly shared images on Instagram are representative of tourist behaviour in a destination. This aspect is crucial for destination image management, as it can influence the way tourists perceive the destination.

The research compares three different factors: the route followed by a group of tourists, the whole set of photographs taken by them and the images that they made publicly available on Instagram. It relies on a field work done by a group of 122 tourists in Turin (Italy). At a qualitative level, the answers given by tourists to the motivations that led them to share some of the image are analysed.

The results showed how the spatial distribution of the images shared publicly on Instagram only partially coincides with the whole set of images taken by tourists.

It is important to avoid basing marketing and management policies on just the places featured in publicly shared images. If not, there is a risk of taking decisions based on the behaviour of some, rather than all, tourists.

Many papers claim to be based on the Instagram image as elements to study tourism. However, most of these papers only analyse public images. This fact can affect the results as there may be, for example, areas visited by tourists where photos are not taken. This paper therefore contributes to a better understanding of Instagram as a tool for the study of urban tourism.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

By involving themselves in this emerging technology, libraries can directly impact their patrons for good and demonstrate their relevance to a world where text and images are increasingly interchangeable.

This column will provide an overview of how Midjourney, DALL·E, Stable Diffusion and Google fund their text-to-image generation and the incentives those models create.

The ability to easily generate complex images from text creates marketing and information literacy opportunities for libraries, as well as raising issues of stewardship and disinformation.

By understanding how Midjourney, DALL-E, Stable Diffusion and Google fund their text-to-image generation and the incentives created by their operation, libraries will be better positioned to imagine their role in meeting the challenges created by this technology.

This paper aims to propose a multilevel framework of fashion consumer ethics that unpacks how ethical consumers publicly express their identity through sustainable fashion (SF). The author explores SF’s cognitive, relational and contextual dynamics, highlighting how attitude–behavior (A-B) gaps might impede consumers’ ethical identity and social image alignment.

The framework theoretically reconstructs fashion consumer ethics by integrating social intuitionism, social representation theory and the public sphere. This theorizing process sheds light on fragmented attempts found in previous research to understand how ethical consumers express their self-identity and socially represent their image through SF, avoiding A-B gaps.

The theoretical propositions suggest how ethical consumers’ self-concept is expressed at the cognitive level, leading to ethical commitment toward self-associated fashion brands; social image is manifested at the relational level, giving rise to consumers’ ethical engagement in SF; and ethical consumers’ self-verify their identity-image alignment in the public sphere, thus addressing A-B issues.

The sociopsychological approach suggests a novel understanding of ethical consumers’ individual and social representation through SF consumption. The framework interprets SF as an “aesthetic of existence,” co-constructed collectively and symbolically expressed publicly. As a result, the proposed model combines different theories to introduce new causal mechanisms and constructs of ethical consumers’ cognition, sociological relations and public spheres.

The distinct characteristics of slow tourism compared to mass tourism determine the differences between slow travelers and mass travelers. This transformation is essential for potential tourists to transition into slow travelers when promoting slow tourism to them. Can travel vlogs, a “fast” way of promoting, achieve such a “slow” transformation? To address this question, the purpose of this paper is to introduce the resonance theory and investigate the impact of resonance with slow tourism vlogs on potential travelers’ destination image and travel intention.

Through the distribution of online questionnaires on site, this study collected a total of 349 valid questionnaires. Subsequently, the researcher used partial least squares structural equation modeling to analyze the data and test the hypotheses.

The research findings indicate that cognitive resonance only positively affected cognitive image, while emotional resonance was positively associated with both cognitive image and affective image. In addition, it was observed that cognitive image and affective image played a positive mediating role between resonance with slow tourism vlogs and travel intention.

This research is pioneering in quantitatively validating the structure linking resonance, destination image and travel intention in the context of slow tourism. By validating the second-order structure of cognitive and emotional resonance, this study underscores the crucial role of resonance in shaping tourists’ transition toward a slow mindset in slow tourism.

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.

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 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.