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With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Compensatory consumption focuses on the psychological value of products. Special agricultural products have symbolic and social functions that effectively meet psychological needs and stimulate compensatory consumption behavior. The social commerce context not only enriches consumer experience but also influences consumer purchase decisions. This study constructs a model based on the elaboration likelihood model (ELM) and the stimulus-organism-response (SOR) theory to explore the mechanism of compensatory consumption behavior of special agricultural products in a social commerce context.

This study uses a two-stage method of partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze 523 valid samples collected through random sampling. PLS-SEM was used to examine the relationships and effects between the variables; fsQCA was used to conduct a cohort analysis between the variables to further reveal the complexity and diversity of compensatory consumption behaviors.

PLS-SEM indicates that product attributes and social affordances influence consumers’ triggering of compensatory consumption behavior for control and belongingness needs. fsQCA shows that there are three different modes, and the satisfaction of belongingness or control needs is a necessary condition for triggering compensatory consumption behavior.

There is limited research on compensatory consumption behavior specifically focused on special agricultural products. This study explores the influencing factors and mechanisms of compensatory consumption behavior related to special agricultural products. The occurrence of compensatory consumption behavior is not only influenced by product attributes but also by the social commerce environment. In marketing strategies, it is important to not only consider product characteristics but also pay attention to consumers’ social and psychological needs.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.

This study aims to investigate the process of developing loyalty in the Chinese mobile health community from the information seeking perspective.

A covariance-based structural equation model was developed to explore the mobile health community loyalty development process from information seeking perspective and tested with LISREL 9.30 for the 191 mobile health platform user samples.

The empirical results demonstrate that the information seeking perspective offers an interesting explanation for the mobile health community loyalty development process. All hypotheses in the proposed research model are supported except the relationship between privacy and trust. The two types of mobile health community loyalty—attitudal loyalty and behavioral loyalty are explained with 58 and 37% variance.

This paper has brought out the information seeking perspective in the loyalty formation process in mobile health community and identified several important constructs for this perspective for the loyalty formation process including information quality, communication with doctors and communication with patients.

In recent years, terrorist attacks have gradually become one of the important factors endangering social security. In this context, this research aims to propose methods and principles which can be utilized to make effective evacuation plans to reduce casualties in terrorist attacks.

By analyzing the statistical data of terrorist attack videos, this paper proposes an extended cellular automaton (CA) model and simulates the panic evacuation of the pedestrians in the terrorist attack.

The main findings are as follows. (1) The panic movement of pedestrians leads to the dispersal of the crowd and the increase in evacuation time. (2) Most deaths occur in the early stage of crowd evacuation while pedestrians gather without perceiving the risk. (3) There is a trade-off between escaping from the room and avoidance of attackers for pedestrians. Appropriate panic contagion enables pedestrians to respond more quickly to risks. (4) Casualties are mainly concentrated in complex terrains, e.g. walls, corners, obstacles, exits, etc. (5) The initial position of the attackers has a significant effect on the crowd evacuation. The evacuation efficiency should be reduced if the attacker starts the attack from the exit or corners.

In this research, the concept of “focus region” is proposed to depict the different reactions of pedestrians to danger and the effects of the attacker’s motion (especially the attack strategies of attackers) are classified. Additionally, the influences on pedestrians by direct and indirect panic sources are studied.

The advancement of enterprise social networks (ESNs) facilitates information sharing but also presents the challenge of managing information boundaries. This study aims to explore the factors that influence the information-control behavior of ESN users when continuously sharing information.

This study specifies the information-control behaviors in the “wall posts” channel and applies communication privacy management (CPM) theory to analyze the effects of the individual-specific factor (disposition to value information), context-specific factors (work-relatedness and information richness) and risk-benefit ratio (public benefit and public risk). Data on actual information-control behaviors extracted from ESN logs are examined using multilevel mixed-effects logistic regression analysis.

The study's findings show the direct effects of the individual-specific factor, context-specific factors and risk-benefit ratio, highlighting interactions between the individual motivation factor and ESN context factors.

This study reshapes the relationship of CPM theory boundary rules in the ESN context, extending information-control research and providing insights into ESNs' information-control practices.