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Establishing and developing digital science and education evaluation platforms (DSEEPs) have several practical implications for the development of China's science, technology and education. Identifying and analyzing the key factors influencing DSEEP user experience (UX) can improve the users' willingness to use the platform and effectively promote its sustainable development.

First, a literature survey, a five-element model of UX and semi-structured interviews were used in this study to develop a DSEEP UX-influencing factor model, which included five dimensions and 22 influencing factors. Second, the model validity was verified using questionnaire data. Finally, the key influencing factors were identified and analyzed using a fuzzy decision-making trial and evaluation laboratory (fuzzy-DEMATEL) method.

Fourteen influencing factors, including diverse information forms and comprehensive information content, are crucial for the DSEEP UX. Its optimization path is “‘Function Services’ → ‘Information Resources’ → ‘Interaction Design’ → ‘Interface Design’ and ‘Visual Design’.” In this regard, platform managers can take the following measures to optimize UX: strengthening functional services, improving information resources, enhancing the interactive experience and considering interface effects.

This study uses a combination of qualitative and quantitative research methods to determine the key influencing factors and optimization path of DSEEP UX. Optimization suggestions for UX are proposed from the perspective of platform managers, who provide an effective theoretical reference for innovating and developing a DSEEP.

This paper aims to present a case study of virtual-reality-based product demonstrations featuring items of furniture. The results will be of use in further design and development of virtual-reality-based product demonstration systems and could also support effective student learning.

A new method was introduced to guide the experiment by confirming orthogonal arrays. User interactions were then planned, and a furniture demonstration system was implemented. The experiment comprised two stages. In the evaluation stage, participants were invited to experience the virtual-reality (VR)-based furniture demonstration system and complete a user experience (UX) survey. Taguchi-style robust design methods were used to design orthogonal table experiments and planning and design operation methods were used to implement an experimental display system in order to obtain optimized combinations of control factors and levels. The second stage involved a confirmatory test for the optimized combinations. A pilot questionnaire was first applied to survey demonstration scenarios that are important to customers.

The author found in terms of furniture products, product interactive display through VR can achieve good user satisfaction through quality design planning. VR can better grasp the characteristics of products than paper catalogs and website catalogs. And VR can better grasp the characteristics of products than online videos. For “interactive inspection”, “function simulation”, “style customization” and “set-out customization” were the most valuable demonstration scenarios for customers. The results of the experiment confirmed that the “overall rating”, “hedonic appeal” and “practical quality” were the three most important optimized operating methods, constituting a benchmark of user satisfaction.

The author found that it is possible to design and build a VR-based furniture demonstration system with a good level of usability when a suitable quality design method is applied. The optimized user interaction indicators and implementation experience for the VR-based product demonstration presented in this study will be of use in further design and development of similar systems.

While prior studies have explored the relationship between visual appeal and purchasing decisions, the role of bookmarking has largely been underemphasized. This research aims to address this gap by focusing on the impact of bookmarking on consumer behavior, guided by the cognitive load theory and dual-system theory.

The authors executed a controlled experiment and analyzed the results using a two-stage regression method that linked visual appeal, bookmarking and purchase intent. Further empirical analysis was conducted to authenticate the authors' proposed model, utilizing real-world mobile commerce data from a clothing company.

This study's findings suggest that visual appeal influences purchase intent primarily through the full mediation of bookmarking, rather than exerting a direct influence. Furthermore, an increase in colorfulness corresponds positively with visual appeal, while visual complexity exhibits an inverted U-shaped relationship with it.

This study provides novel insights into the choice-set formation process through the theoretical lens of dual-system theory. Additionally, the authors employed an image processing technique to quantify a product's visual appeal as depicted in a photograph. This study also incorporates a comprehensive econometric analysis to connect the objective aspects of visual appeal with subjective responses.

Social media has emerged as a prominent platform for marketers and brands to disseminate brand-related information. This study aims to investigate the impact of color congruence between themes and background on marketing effectiveness, focusing specifically on Instagram.

A laboratory experiment was conducted to investigate how color congruence between themes and background in brand posts influences flow and aesthetic experience, subsequently affecting marketing communication parameters such as brand attitude, visit intention, and eWOM on Instagram. Moreover, Adidas Originals was selected as the focal brand, and blue and white color was chosen as the primary color palette for the experimental material.

This study demonstrates that color congruence, regardless of brand layout or post, significantly influences flow and aesthetic experience, subsequently affecting marketing effectiveness.

This study contributes to the theoretical understanding of congruence theory and social media marketing, providing valuable insights for brands to enhance their communication through photographs and effectively manage their official Instagram accounts.

This research explores the impact of classical aesthetics (e.g. order and symmetry) and expressive aesthetics (e.g. creativity and distinctiveness) on consumer green consumption.

This research conducted three studies. Study 1 explored the main effect of appearance aesthetics (appearance: plain vs classical vs expressive) on green products purchase intention through a one-factor between-subjects design. Study 2 verified the mediating role of perceived naturalness through two types of appearance aesthetics (appearance: classical vs expressive) between-subjects design. Study 3 verified the moderating role of product identity-symbolic attributes through a 2 (product identity-symbolic attributes: non-identity-symbolic vs identity-symbolic attributes) × 2 (appearance: classical aesthetics vs expressive aesthetics) between-subjects design.

Consumers will be more likely to purchase a green product that has classical aesthetics appearance (vs expressive aesthetics). Perceived naturalness mediates the effect of aesthetic appearance on consumer green consumption. Product identity symbol attributes moderate this effect. Specifically, for non-identity-symbolic green products, classical aesthetics can effectively enhance consumer purchase intention. For identity-symbolic green products, expressive aesthetics can effectively enhance consumer purchase intention.

Existing research suggests that aesthetic appearance can increase consumers’ evaluation of electronic products, beauty products and food, but the difference between aesthetics has not yet been explored. This research compares two aesthetics, contributing to the literature on aesthetic appearance in green products and offering valuable insights for managers’ green products marketing.

This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

Residential buildings in Greece constitute an important portion of the existing building stock. Furthermore, most of these buildings were built prior to the first Thermal Insulation Code of 1981. The article focuses on existing, typical residences built after 1920, which are found mostly in suburban areas and settlements all around Greece. The purpose of the research is to evaluate the effect of simple bioclimatic interventions focused on the improvement of their diurnal, inter-seasonal and annual thermal performance.

The applied strategies include application of thermal insulation in the building shell and openings, passive solar systems for the heating period and shading and natural ventilation for the summer period. The effect of the strategies is analysed with the use of building energy analysis. The simulation method was selected because it provides the possibility of parametric analysis and comparisons for different proposals in different orientations.

The results show that the increased thermal mass of the construction is the most decisive parameter of the thermal behaviour throughout the year.

The typical residences under investigation are often found in urban and/or suburban surroundings. These mostly refer to free-standing buildings situated, which, in many cases, do not have the disadvantages and limitations that the geometrical characteristics of densely built urban locations impose on incident solar radiation (e.g. overshadowing during the winter) and air circulation (e.g. reduce natural ventilation during the summer). Nevertheless, even in these cases, the surrounding built environment may also have relevant negative effects, which were not taken under consideration and could be included in further, future research that will include the effect of various orientations, as well as of neighbouring buildings.

Existing residences built prior to the first Thermal Insulation Code (1981) form an important part of the building stock. Consequently their energy upgrade could contribute to significant conventional energy savings for heating and cooling, along with the inter-seasonal improvement of interior thermal comfort conditions.

The proposed interventions can improve thermal comfort conditions and lead to a reduction of energy consumption for heating and cooling, which is an important step against energy poverty and the on-going energy crisis.

The proposed interventions only involve the building envelope and are simple with relatively low cost.

This paper aims to identify and analyze the challenges for the Varanasi handloom industry after the COVID pandemic by considering their impact on different sections of the weavers and subsequently suggest the best possible solution for the same.

A combined approach of expert opinion and in-depth literature reviews are used to identify the challenges, and a multicriteria decision-making tool is used to rank the challenges for the type of weaver.

This research provides an elaborated view of the problems faced by the handloom industry after the COVID pandemic and suggests that the success of the handloom business is subjected to the eradication of a wide number of challenges according to the type of weaver.

The findings of this research will help the policymakers to make and align their policies and strategies for the upliftment of the Varanasi handloom industry efficiently and effectively.

To the best of the authors’ knowledge, this is the first kind of study that focuses on identifying and prioritizing the barriers affecting the success of the Varanasi handloom industry after the COVID pandemic. Furthermore, the uniqueness of this research lies in its ability to study all three independent sections of the handloom industry, having different capabilities and limitations.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.