HAO LV¹, BO ZHU^1,2, DEGAO CHEN^1,2,*

BIOCELL, Vol.48, No.3, pp. 363-378, 2024, DOI:10.32604/biocell.2023.046367

Abstract Tumor-associated macrophages (TAMs) are emerging as targets for tumor therapy because of their primary role in promoting tumor progression. Several studies have been conducted to target TAMs by reducing their infiltration, depleting their numbers, and reversing their phenotypes to suppress tumor progression, leading to the development of drugs in preclinical and clinical trials. However, the heterogeneous characteristics of TAMs, including their ontogenetic and functional heterogeneity, limit their targeting. Therefore, in-depth exploration of the heterogeneity of TAMs, combined with immune checkpoint therapy or other therapeutic modalities could improve the efficiency of tumor treatment. This review focuses on the heterogeneous ontogeny and… More >

Yaping Zhou^1,2,5,#, He Zhong^3,#, Xiaojun Li^4,*, Yanhui Xiang^1,2,5,*

International Journal of Mental Health Promotion, Vol.26, No.2, pp. 139-148, 2024, DOI:10.32604/ijmhp.2023.046598

Abstract Fathers play an important role in adolescents’ development, which is significant for their development and influences their mental health, including feeling of loneliness. However, the effects and mechanisms of father-love absence on individual loneliness are not clear. Based on the social functionalist theory and the social needs theory, this study examines the influence of individual father-love absence on loneliness and its underlying mechanisms. A questionnaire survey was administered to 319 junior high school students and 1,476 high school students. The results showed that adolescents with father-love absence had higher levels of loneliness, and that father-love absence affected loneliness levels through… More >

Ruirui Zhang¹, Xuguang Zhang², Minxin Yang³, Haoran Zhang^4,5,*

International Journal of Mental Health Promotion, Vol.26, No.2, pp. 117-126, 2024, DOI:10.32604/ijmhp.2023.045819

Abstract Contingent self-esteem captures the fragile nature of self-esteem and is often regarded as suboptimal to psychological functioning. Self-compassion is another important self-related concept assumed to promote mental health and well-being. However, research on the relation of self-compassion to contingent self-esteem is lacking. Two studies were conducted to explore the role of self-compassion, either as a personal characteristic or an induced mindset, in influencing the effects of contingent self-esteem on well-being. Study 1 recruited 256 Chinese college students (30.4% male, mean age = 21.72 years) who filled out measures of contingent self-esteem, self-compassion, and well-being. The results found that self-compassion moderated… More >

Zhenhua Yu, Liangxue Bai, Ou Ye^*, Xuya Cong

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1773-1795, 2024, DOI:10.32604/cmc.2023.047130

Abstract Social robot accounts controlled by artificial intelligence or humans are active in social networks, bringing negative impacts to network security and social life. Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships, which makes it difficult to accurately describe the difference between the topological relations of nodes, resulting in low detection accuracy of social robots. This paper proposes a social robot detection method with the use of an improved neural network. First, social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social… More >

Adnan Ali¹, Jinlong Li¹, Huanhuan Chen¹, Uzair Aslam Bhatti², Asad Khan^3,*

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 241-258, 2023, DOI:10.32604/iasc.2023.041645

Abstract Spammer detection is to identify and block malicious activities performing users. Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces. Previous research aimed to find spammers based on hybrid approaches of graph mining, posted content, and metadata, using small and manually labeled datasets. However, such hybrid approaches are unscalable, not robust, particular dataset dependent, and require numerous parameters, complex graphs, and natural language processing (NLP) resources to make decisions, which makes spammer detection impractical for real-time detection. For example, graph mining requires neighbors’… More >

Abdullah B. Alansare^*

International Journal of Mental Health Promotion, Vol.26, No.1, pp. 11-20, 2024, DOI:10.32604/ijmhp.2023.044656

Abstract Emerging evidence suggests the existence of ‘paradoxical’ relationships between domain-specific sedentary behavior (SB) and health outcomes. This study assessed the associations of total and domain-specific SB, by pattern, with symptoms of mental disorders in Saudi adults. Participants (n = 554) completed a web-based survey between January 18th, 2023 and February 5th, 2023. Total SB was measured by using the Sedentary Behavior Questionnaire. Total SB was then partitioned into leisure, occupational, and commuting SB during weekdays and on weekend days. Symptoms of mental disorders including symptoms of depression, anxiety, and stress were evaluated by using the DASS-21 questionnaire. Adjusted linear regressions… More >

Siyuan Bi¹, Junfeng Yuan^1,2, Lin Luo^1,2,3,*

International Journal of Mental Health Promotion, Vol.26, No.1, pp. 51-60, 2024, DOI:10.32604/ijmhp.2023.041614

Abstract Purpose: The core component theory of addiction behavior provides a multidimensional theoretical model for measuring social networking addiction. Based on this theoretical model, the Social Networking Addiction Scale (SNAS) was developed. The aim of this study was to test the psychometric properties of the Chinese version of the SNAS (SNAS-C). Methods: This study used a sample of 3383 Chinese university students to conduct confirmatory factor analysis (CFA) to explore the structural validity of the SNAS-C. This study examined the Pearson correlations between the six subscales of the SNAS-C (i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse) and “social… More >

Fang Xu^1,2,3, Songhao Jiang^1,2, Yi Ma^1,2,3,*, Manzoor Ahmed^1,3,*, Zenggang Xiong^1,2,3, Yuanlin Lyu^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1095-1113, 2024, DOI:10.32604/cmc.2023.046577

Abstract Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing the strength of social connections… More >

Amira M. Idrees^1,*, Abdul Lateef Marzouq Al-Solami²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1115-1133, 2024, DOI:10.32604/cmc.2023.046457

Abstract The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular design ensures flexibility, scalability, and… More >

Samina Amin¹, Muhammad Ali Zeb¹, Hani Alshahrani^2,*, Mohammed Hamdi², Mohammad Alsulami², Asadullah Shaikh³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921

Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the… More >