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Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.

The study explored how information institutions can support the resilience of parents of adolescents affected by the Russia–Ukraine war. Ukrainian parents are facing major challenges of supporting their teenagers through a difficult developmental phase while also “buffering” their war-related hardships. By supporting parents, information institutions can also support children.

Fifteen parents were interviewed about mental health challenges and resources that are helpful and/or missing from their support systems. Recordings of participant narratives were analyzed using the qualitative thematic analysis technique.

The findings indicate that both teens and parents rely on internal resilience skills, family, friends and community resources to support themselves. However, a number of additional resources could be offered by information institutions, including content for (1) teens on developing skills in communication, interpersonal relationships, problem solving and academic pursuits; (2) parents on child development and opportunities in their host countries; both groups on (3) both groups on mental health first aid and safe spaces to meet peers. The study recommendations will be of interest to information professionals working with families, especially families affected by disasters.

The study relied on a small convenience sample of participants.

Study recommendations would be of interest to information professionals who develop and provide services to families affected by natural and manmade disasters.

Study recommendations improve understanding of the (potential) role of information institutions and libraries in strengthening family and community resilience.

The study offers a rare insight into experiences of war-affect families and provides evidence-driven recommendations for information institutions to support family and community resilience.

This paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.

A two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.

The evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.

Investigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

This study aims to propose resident-based brand equity models on green, creative and smart development themes through a multi-sample telephone survey on Hong Kong residents (n = 751).

This research adopted a quantitative approach with a round of questionnaire-based survey carried out anonymously on adult citizens who have stayed in Hong Kong for more than one year. Telephone survey was performed by a professional survey research centre with trained interviewers between May and July 2022.

The study identifies the magnitude of these city brand equity attributes and reconfigured their composition under separate samples of Hong Kong residents. The results reveal the relatively stronger brand equity for developing Hong Kong as a smart city brand compared with green and creative branding.

The research findings might carry a major limitation of varied interpretations and stereotypes of each city theme (green, creative and smart) by local residents. To minimize the expected bias, two core questions were added to provide respondents with information on each theme before the main survey questions. The questions’ wording was also simplified to ensure the constraint and inconsistency of layman effect.

The common attributes across the themes, including distinctiveness, uniqueness, confidence, positive image, liveability, long-term residence, feature familiarity and top-of-mind, indicate the most prominent aspects of brand equity formation and enhancement. Since urban sustainability does not follow a single path of strategies and infrastructure development, city brand process should also follow a selective approach, which clearly identifies a multiplicity of local interests that could create the best outcomes and the strongest brand equity for the city.

The factor allocation and regression analysis elucidate different configurations of the determining factors with a three-factor model for green city brand equity and two-factor models for the other ones. The findings encore some previous studies supporting the differentiation between common attributes and distinctive attributes, and the overlapping approach to unleash the strongest integration of attributes of brand equity.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

Ulam is a traditional salad that contains high levels of antioxidants and is commonly consumed in raw form. However, the average ulam consumption among the low-income Malaysian population was only ½ serving daily. Thus, this study aimed to explore the motivators and barriers to ulam consumption among residents of low-cost housing areas (household income RM4849 or $1015.50) in Kuala Lumpur, Malaysia.

Six focus group discussions were conducted with 27 Malay residents aged 18–59 years in Kuala Lumpur, Malaysia. The researchers asked the participants a series of semi-structured questions. All the interviews were audio-recorded and transcribed verbatim. Two researchers coded the transcripts independently, and several themes were identified. The data were analysed using Nvivo version 12 software.

Three main factors for ulam consumption were identified in this study: personal, environmental and behavioural. The motivators and barriers were identified based on nine main themes and 16 sub-themes.

The results of this study identified potential areas for an effective intervention to increase ulam intake among residents in low-cost housing areas.

This work has the potential to identify the factors that have an impact on consumers' ulam preferences to help fulfil daily vegetable recommendations.

China's Belt and Road Initiative (BRI) is the most ambitious investment strategy for infrastructural development belonging to the significant potential for stimulating regional economic growth in Asia, Europe and Africa. This study aims to investigate the impact of infrastructure on spurring inward foreign direct investment (FDI) within the purview of human capital, GDP per capita, foreign aid, trade, domestic investment, population and institutional quality in BRI countries.

In doing so, the authors analyze panel data from 2000 to 2019 within the framework of the system generalized method of movement (GMM) approach for 66 BRI countries from Europe, Asia, Africa and the Middle East.

The investigated results demonstrate that aggregate and disaggregate infrastructure indices, e.g. transport, telecommunications, financial and energy infrastructures, are the driving forces in attracting foreign direct investment (FDI) in the BRI countries. In addition, control variables (i.e. institutional quality, human capital, trade, domestic investment, foreign aid and GDP per capita) play an essential role in spurring FDI inflows.

The authors’ study uniquely investigates both the pre- (2000–2012) and post- (2013–2019) BRI scenarios using the aggregate and disaggregate infrastructural components from the perspectives of full and clustered sample regions, such as Asia, Europe, Africa and the Middle East. The study provides several policy implications.

As a smart service, location-based advertising (LBA) integrates advanced technologies to deliver personalized messages based on a user’s real-time geographic location and needs. However, research has shown that privacy concerns threaten the diffusion of LBA. This research investigates how privacy-related factors (i.e. LBA type, privacy self-efficacy (PSE) and consumer generation) impact consumers’ value-in-use and their intention to use LBA.

This study developed and examined an LBA value-in-use framework that integrates the role of LBA type, consumers’ PSE and consumer generation into the technology acceptance model (TAM). Data were collected through two experiments in the field with a total of 374 consumers. The proposed relationships were tested using PROCESS modeling.

The results reveal that pull (vs push) LBA causes higher value-in-use in terms of perceived usefulness and perceived ease of use, leading to greater usage intention. Further, the differences in the mediated relationship between pull- and push-LBA are larger among consumers of low PSE (vs high PSE) and Generation Z (vs other generations). The findings suggest that the consumer value-in-use brought about by LBA diminishes when using push-LBA for low PSE and Generation Z consumers.

This research is the first to integrate the privacy-related interactions of LBA type and consumer characteristics into TAM to develop a TAM-based LBA value-in-use framework. This study contributes to the literature on service value-in-use, smart services and LBA by clarifying the boundary conditions that determine the effectiveness of LBA in enhancing consumers’ value-in-use.

The purpose of this paper is to study the corrosion behavior of Q235 steel in saturated acidic red and yellow soils.

The corrosion behavior of Q235 steel in saturated red and yellow soils was compared by weight-loss, SEM/EDS, 3D ultra-depth microscopy and electrochemical measurements.

R_p of the steel gradually increases and i_corr gradually decreases in both the red and yellow soils with time. The R_p of the steel in the red soil is lower, but its i_corr is higher than that in the yellow soil. The uniform corrosion rate, diameter and density of the corrosion pit on the steel surface in the red soil are greater than those in the yellow soil. Lower pH, higher contents of corrosive anions and high-valence Fe oxides in the red soil are responsible for its higher corrosion rates and local corrosion susceptibility.

This paper investigates the difference in corrosion behavior of carbon steel in saturated acidic red and yellow soils, which can help to understand the mechanism of soil corrosion.

The high-pressure accumulator has been widely used in the hydraulic system. Failure pressure prediction is crucial for the safe design and integrity assessment of the accumulators. The purpose of this study is to accurately predict the burst pressure and location for the accumulator shells due to internal pressure.

This study concentrates the non-linear finite element simulation procedure, which allows determination of the burst pressure and crack location using extensive plastic straining criterion. Meanwhile, the full-scale hydraulic burst test and the analytical solution are conducted for comparative analysis.

A good agreement between predicted and measured the burst pressure that was obtained, and the predicted failure point coincided very well with the fracture location of the actual shell very well. Meanwhile, the burst pressure of the shells increases with wall thickness, independent of the length. It can be said that the non-linear finite element method can be employed to predict the failure behavior of a cylindrical shell with sufficient accuracy.

This paper can provide a designer with additional insight into how the pressurized hollow cylinder might fail, and the failure pressure has been predicted accurately with a minimum error below 1%, comparing the numerical results with experimental data.