Yun-Jung Choi¹, Heewon Song^2,*

International Journal of Mental Health Promotion, Vol.26, No.6, pp. 475-480, 2024, DOI:10.32604/ijmhp.2024.050824

Abstract During the rapid spread of COVID-19, first responders are at risk of being exposed to COVID-19 due to their role in providing first aid and responding to an unspecified number of people. This uncertainty can have adverse mental health effects, such as increased anxiety and fear. This study aimed to investigate the degree of association between fear of COVID-19, obsession with COVID-19, and post-traumatic stress disorder (PTSD) in emergency rescue firefighters. The participants were 150 emergency rescue firefighters working in Region S, Korea. They filled out self-report questionnaires: The data obtained through the Fear of… More >

Jiajie Shen¹, Yan Wang^1,*, Dongxu Zhang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.047903

Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions.… More >

Mingming Zhao, Jialong Jiao^*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.5, pp. 1045-1061, 2024, DOI:10.32604/fdmp.2023.043744

Abstract Predicting the response of liquefied natural gas (LNG) contained in vessels subjected to external waves is extremely important to ensure the safety of the transportation process. In this study, the coupled behavior due to ship motion and liquid tank sloshing has been simulated by the Smoothed-Particle Hydrodynamics (SPH) method. Firstly, the sloshing flow in a rectangular tank was simulated and the related loads were analyzed to verify and validate the accuracy of the present SPH solver. Then, a three-dimensional simplified LNG carrier model, including two prismatic liquid tanks and a wave tank, was introduced. Different More >

Tao Bao^*, Xiyuan Ma, Zhuohuan Li, Duotong Yang, Pengyu Wang, Changcheng Zhou

Energy Engineering, Vol.121, No.6, pp. 1713-1737, 2024, DOI:10.32604/ee.2023.046150

Abstract The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able More >

Jianfeng He^1,2, Haowei Ye¹, Jie Ning¹, Hui Zhou^1,2,*, Bo She^3,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3355-3372, 2024, DOI:10.32604/cmc.2024.048706

Abstract In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our study introduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learning-based framework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques, which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency and overemphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective feature extraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Lie group domains to highlight fundamental motion patterns, coupled with employing competitive weighting for More >

Jun Li^1,2, Minqing Zhang^1,2,*, Ke Niu¹, Yingnan Zhang¹, Xiaoyuan Yang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2085-2103, 2024, DOI:10.32604/cmc.2024.048095

Abstract Among steganalysis techniques, detection against MV (motion vector) domain-based video steganography in the HEVC (High Efficiency Video Coding) standard remains a challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis method that can perfectly detect MV-based steganography in HEVC. Firstly, we define the local optimality of MVP (Motion Vector Prediction) based on the technology of AMVP (Advanced Motion Vector Prediction). Secondly, we analyze that in HEVC video, message embedding either using MVP index or MVD (Motion Vector Difference) may destroy the above optimality of MVP. And then, we define More >

J. Sheril Angel¹, A. Diana Andrushia^1,*, T. Mary Neebha¹, Oussama Accouche², Louai Saker², N. Anand³

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2427-2448, 2024, DOI:10.32604/cmc.2024.047326

Abstract Facial emotion recognition (FER) has become a focal point of research due to its widespread applications, ranging from human-computer interaction to affective computing. While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets, recent strides in artificial intelligence and deep learning (DL) have ushered in more sophisticated approaches. The research aims to develop a FER system using a Faster Region Convolutional Neural Network (FRCNN) and design a specialized FRCNN architecture tailored for facial emotion recognition, leveraging its ability to capture spatial hierarchies within localized regions of facial… More >

Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897

Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis.… More >

Yulei Chen¹, Zhaojun Chen^1,2, Shichao Wang¹, Yang Hang¹, Jianpeng Guo^1,*

International Journal of Mental Health Promotion, Vol.26, No.4, pp. 303-315, 2024, DOI:10.32604/ijmhp.2024.046863

Abstract Social-Emotional Competency (SEC), regarded as a critical psychological resource for individuals to adapt to social environments, is an effective protective factor for students’ mental health, impacting their future success and well-being. Analyzing the impact of SEC on university students’ mental health can offer valuable insights for nurturing talents with healthy psychological and physical development. Based on data from two large-scale surveys of Chinese university students, this study designed two comprehensive Multiple Mediation Models involving SEC, stress, coping strategies, and stress reaction to explore the pathway of emotion nurturing mentality. Study 1 utilized a parallel mediation model… More >

Lin Wang¹, Juan Zhao², Hu Song³, Xiaolong Xu^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1105-1135, 2024, DOI:10.32604/cmc.2024.048688

Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face… More >