Jie Yang^1,#, Wenhui Ma^2,#, Yan Tang^3,*, Bin Gao^4,*

Journal of Psychology in Africa, Vol.36, No.4, pp. 507-516, 2026, DOI:10.32604/jpa.2026.078648 - 31 August 2026

Abstract Previous research has indicated heterogeneity in Internet Gaming Disorder (IGD) profiles in college students. However, longitudinal evidence regarding the stability and transition patterns of IGD subgroups among college students remains limited. This two-wave longitudinal study, with a one-year interval, included 343 Chinese undergraduate students (57.7% female; mean age = 18.28 years, SD = 1.14) who participated in both surveys. Latent profile analysis identified three distinct IGD subgroups—High, Moderate, and Low IGD—which were consistent across both time points. Regression analyses indicated that male gender, greater gaming time, loneliness, and depression were associated with higher IGD severity. More >

Maryam Omar Abdullah Sawad¹, Said Jadid Abdulkadir^1,2,*, Hitham Seddig Alhussian^1,2, Majdy Mohamed Eltayeb Eltahir³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085472 - 28 August 2026

Abstract The increasing complexity and heterogeneity of cyberattacks targeting Internet of Things (IoT) environments, driven by the diversity of interconnected nodes and communication channels, necessitate the development of more advanced and intelligent cyber defence techniques. However, the most effective methods are Machine Learning (ML)-based and Deep Learning (DL)-based intrusion detection systems (IDS), which perform well but still face significant limitations and challenges. To address these issues, Deep Reinforcement Learning (DRL) has been proposed in recent years to automatically resolve the issues by detecting attacks in IoT environments. Therefore, this Systematic Literature Review (SLR) presents an up-to-date… More >

Shounak Mandal¹, Subhadip Pati^1,#, Nirmallyadeb Ray^1,#, Bipasha Guha Roy^2,#, Priyanka Saha³, Deepsubhra Guha Roy^2,*

Digital Engineering and Digital Twin, Vol.4, pp. 27-47, 2026, DOI:10.32604/dedt.2026.081859 - 14 August 2026

Abstract HealthyBrain is a scalable, interoperable, and intelligent Remote Patient Monitoring (RPM) platform built on Internet of Things (IoT) technologies and a modular microservices architecture. The system integrates wearable IoT devices, MQTT (Message Queuing Telemetry Transport)-based lightweight messaging, and high-throughput real-time data streaming via Apache Kafka. Edge-side preprocessing enables low-latency analytics, while machine learning-based anomaly detection models facilitate early identification of critical health events. To ensure clinical interoperability, the platform adheres to the HL7 FHIR (Fast Healthcare Interoperability Resources) standard for electronic health record exchange. The system’s novel contribution lies in the unified integration of edge… More >

Bareera Anam, Muhammad Asim, Muhammad Nadeem Ali, Byung-Seo Kim^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084949 - 13 August 2026

Abstract The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport… More >

Zulfiqar Ali, Azhar Mahmood^*, Shaheen Khatoon, Seyed Ali Ghorashi

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084386 - 13 August 2026

Abstract Smart city systems increasingly depend on data analytics and visualization to support informed and timely decision making in complex urban environments. However, existing middleware solutions predominantly focus on data acquisition and communication, while analytical processing and visualization are typically delegated to external applications, resulting in increased development complexity, reduced reusability, and fragmented system architectures. This study presents analytics and visualization-centric middleware named “Service-Oriented Middleware for Smart City Applications” (SOMSCA), in which these capabilities are embedded directly within the middleware layer. SOMSCA adopts a service-oriented approach, exposing analytics and visualization functionalities as reusable platform services, and… More >

Supriya Wadekar¹, Shailendra Mittal¹, Ganesh Wakte^2,*, Mrunali Kite², Aditya Ghonmode², Riya Devkate²

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.082685 - 06 August 2026

Abstract The rapid expansion of electric vehicles (EVs) and the emergence of the Internet of Electric Vehicles (IoEV) have created considerable operational challenges for modern power systems. Large-scale EV charging can cause peak demand surges, voltage instability, and inefficient utilization of renewable energy resources when charging activities are not effectively coordinated. This study proposes an uncertainty-aware distributed optimization framework for smart EV charging in cyber-physical smart grids, in which charging schedules are coordinated while simultaneously considering grid capacity constraints, stochastic EV arrival patterns, renewable energy variability, and battery degradation effects. A multi-objective optimization model is formulated… More >

Ziyan Zhou¹, Haiyun Peng², Menghao Ren^1,3, Sufei Xin^4,*, Daoqun Ding^1,5,*

International Journal of Mental Health Promotion, Vol.28, No.7, 2026, DOI:10.32604/ijmhp.2026.079929 - 30 July 2026

Abstract Background: Internet addiction and depression are important mental health concerns among adolescents. Although prior research has examined their bidirectional relationship, the underlying mechanisms remain unclear. Drawing on conservation of resources theory, this study examined their bidirectional relationship and tested the mediating role of self-control, with attention to gender differences. Methods: A two-wave longitudinal survey (T1: November 2021; T2: May 2022) was conducted in China among 1908 adolescents (1026 females, 882 males; mean age = 13.546, SD = 1.463). At both waves, participants completed self-report measures of internet addiction, self-control, and depression (using the Internet Addiction Scale,… More >

Md. Fahmid-Ul-Alam Juboraj¹, Fahmid Al Farid^2,3, Mahe Zabin⁴, Jia Uddin⁵, Muhammad Iqbal Hossain^1,*, Sarina Mansor^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081254 - 27 July 2026

Abstract The integration of Internet of Things (IoT) technologies in agriculture enables precision farming but introduces significant cybersecurity vulnerabilities. This paper presents FICNet (Feature Integrated Convolutional Network), a lightweight deep learning architecture for intrusion detection in agricultural IoT environments. Evaluated on the Farm-Flow AG-IoT security dataset, FICNet achieves 100% binary classification accuracy and 81.25% multiclass accuracy (macro F1: 80.43%, precision: 91.26%, ROC-AUC: 96.78%) across 8 traffic categories. A multi-dimensional component analysis confirms the contribution of each architectural component: multi-scale convolutions provide 5.3% noise robustness advantage, squeeze-and-excitation attention controls per-class detection trade-offs, and the full architecture achieves More >

Joong-Hyun Park¹, Jiho Choi², Libor Mesicek³, Hoon Ko^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084659 - 23 July 2026

Abstract With the rapid expansion of the Internet of Medical Things (IoMT), the importance of digital identity–based security has significantly increased. However, conventional static authentication mechanisms are insufficient to effectively address various identity misuse and abuse attacks. In this study, we model digital identity as a dynamic security entity and propose an AI-based framework that integrates a risk scoring model—combining unsupervised anomaly detection with context-aware analysis—and a multi-level risk-adaptive access control mechanism (Permit, Step-Up, Restrict). Experimental results using an extended version of the CERT Insider Threat Dataset tailored for IoMT environments provide proof-of-concept evidence that the More >

Shih-Lin Lin^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084571 - 23 July 2026

Abstract Connected automated driving increasingly relies on cooperative perception from onboard sensors, roadside units (RSUs), smart traffic lights, and vehicle-to-everything (V2X) links, but communication uncertainty can concentrate residual risk on vulnerable road users (VRUs). This study proposes an Ethical-Improved risk-allocation objective for edge-assisted Internet of Vehicles (IoV) cooperative autonomous driving. The objective internalizes responsibility as a bounded risk weight, normalizes equality and maximin terms, and adds explicit VRU tail-risk and VRU/Ego ratio penalties. The evaluation is organized into two strictly separated tracks. In the planner-objective track, Ethical-Improved reduces physical collision rate, aggregate harm, inequality, and VRU More >