Yao Wang¹, Xiaojiao Li², Panpan Yang^1,*, Zengyan Yu^1,*

International Journal of Mental Health Promotion, Vol., , DOI:10.32604/ijmhp.2024.048483

Abstract Social anxiety is a common psychological problem among left-behind children (LBC) and has been a popular issue in recent years. Children with higher levels of social anxiety have more emotional and behavioral problems and are prone to negative life events. Although several studies have explored the differences in social anxiety between LBC and non-left-behind children (N-LBC), the findings have not been consistent. In this study, a systematic review and meta-analysis method was used, with 411 papers retrieved on October 01, 2023, from Pubmed, Embase, Web of Science, and Chinese databases (CNKI, VIP, and Wanfang) (PROSPERO registry number: CRD42023472463). Twenty-one studies… More >

Bo Yang^1,*, Yucun Qian¹, Jianzhong Xu², Yaxing Ren³, Yixuan Chen⁴

Energy Engineering, Vol., , DOI:10.32604/ee.2024.050083

Abstract This article has no abstract. More >

Shan Liu¹, Chengbin Chi¹, Fengze Han², Yanan Wu¹, Lin Zhu¹, Tuo Wang^2,*

Energy Engineering, Vol., , DOI:10.32604/ee.2024.049861

Abstract Voltage source converter based high voltage direct current (VSC-HVDC) can participate in voltage regulation by flexible control to help maintain the voltage stability of the power grid. In order to quantitatively evaluate its influence on the voltage interaction between VSC-HVDC and line commutated converter based high voltage direct current (LCC-HVDC), this paper proposes a hybrid multi-infeed interaction factor (HMIIF) calculation method considering the voltage regulation control characteristics of VSC-HVDC. Firstly, for a hybrid multi-infeed high voltage direct current system, an additional equivalent operating admittance matrix is constructed to characterize HVDC equipment characteristics under small disturbance. Secondly, based on the characteristic… More >

Maheesha Dhashantha Silva^*, Qi Liu

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.051289

Abstract In recent years, Non-Intrusive Load Monitoring (NILM) has become an emerging approach that provides affordable energy management solutions using aggregated load obtained from a single smart meter in the power grid. Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less of a burden for the energy monitoring process. However, conducted research works have limitations for real-time implementation due to the practical issues. This paper aims to identify the contribution of ML approaches to developing a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed, along with the research works… More >

Yu Song¹, Xusheng Qian², Nan Zhang³, Wei Wang², Ao Xiong^1,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.051217

Abstract To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront the challenge of managing the surging demand for data traffic. Within this realm, the network imposes stringent Quality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanisms in accommodating such extensive data flows. In response to the imperative of handling a substantial influx of data requests promptly and alleviating the constraints of existing technologies and network congestion, we present an architecture for QoS routing optimization with in Software Defined Network (SDN), leveraging deep reinforcement learning. This innovative approach entails the separation… More >

Xueya Wang¹, Yiming Zhang^2,3,*, Qi Liu⁴, Huanran Wang¹

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.050736

Abstract Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcing steel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concrete is closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve the resolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. To rapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adopted in this work, which is celebrated for its capabilities in image generation. The open-source dataset used herein features RGB images we generated… More >

Muchang Rao, Hang Qin^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.050380

Abstract More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment framework, followed by the formulation… More >

Zhenxiang He^*, Zhenyu Zhao, Ke Chen, Yanlin Liu

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.050281

Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for different vulnerabilities, which are used… More >

Muhammad Salman Qamar^1,*, Ihsan ul Haq¹, Amil Daraz², Atif M. Alamri³, Salman A. AlQahtani⁴, Muhammad Fahad Munir¹

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.050168

Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs subsequently transmit packets to neighboring… More >

Abdulwadood Alawadhi^1,*, Mohd. Hasbullah Omar¹, Abdullah Almogahed², Noradila Nordin³, Salman A. Alqahtani⁴, Atif M. Alamri⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.050154

Abstract The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use of Body Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, including contention during finite backoff periods, association delays, and traffic channel access through clear channel assessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions, and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet delivery ratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next… More >