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  • Open Access


    The Effect of Sleep and Cognition Enhancement Multimodal Intervention for Mild Cognitive Impairment with Sleep Disturbance in the Community-Dwelling Elderly

    Eun Kyoung Han, Hae Kyoung Son*

    International Journal of Mental Health Promotion, Vol.25, No.11, pp. 1197-1208, 2023, DOI:10.32604/ijmhp.2023.041560

    Abstract Dementia prevalence has soared due to population aging. In Mild Cognitive Impairment (MCI) as a pre-dementia stage, sleep disturbances have raised much interest as a factor in a bidirectional relationship with cognitive decline. Thus, this study developed the Sleep and Cognition Enhancement Multimodal Intervention (SCEMI) based on Lazarus’ multimodal approach and conducted a randomized controlled experiment to investigate the effects of the novel program on sleep and cognition in MCI elderly. The participants were 55 MCI elderly with sleep disturbances at two dementia care centers located in S-city, Gyeonggi-do, South Korea (n = 25 in the experimental group and n… More >

  • Open Access


    Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

    Jieren Cheng1,2, Xiaolong Chen1,*, Wenghang Xu3, Shuai Hua3, Zhu Tang1, Victor S. Sheng4

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1779-1793, 2023, DOI:10.32604/cmc.2023.042980

    Abstract In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model’s capacity… More >

  • Open Access



    Withada Jedsadaratanachaia, Amnart Boonloib,*

    Frontiers in Heat and Mass Transfer, Vol.6, pp. 1-19, 2015, DOI:10.5098/hmt.6.13

    Abstract Numerical investigations on flow topology, heat transfer behavior and thermal performance evaluation in a circular tube heat exchanger with the punched delta winglet vortex generators (PDWVG) inserted in the middle of the test section are presented. The effects of the flow attack angles that converging to the center of the tube; α = 0°, 5°, 10°, 15°, 20°, 25°, and flow directions; winglet tips pointing downstream and upstream, are investigated for the Reynolds numbers; Re = 100 – 2000. The finite volume method and SIMPLE algorithm are used for the current study. The results are presented in terms of flow… More >

  • Open Access



    Makoto Shibahara*

    Frontiers in Heat and Mass Transfer, Vol.6, pp. 1-4, 2015, DOI:10.5098/hmt.6.9

    Abstract Overall heat transfer coefficients of the heat exchanger inserted twisted plates for CO2 were measured. The overall heat transfer coefficients increased with mass flow rates of water at the same Reynolds number in the experiment. It is considered that the helically twisting fluid motions in the twisted heat exchanger were contributed to the heat transfer enhancements. More >

  • Open Access



    Divyprakash C. Pal, Abhik Majumder*

    Frontiers in Heat and Mass Transfer, Vol.7, pp. 1-9, 2016, DOI:10.5098/hmt.7.1

    Abstract In this present work heat transfer characteristics, thermal performance and pressure drop are studied numerically for a finned-tube bank heat exchanger with Delta Winglet Vortex Generator (DWVG) on the fin at different angle of attack (α). The thermo-fluid is studied by varying α at 15o , 25o , 30o and 35o . The base length (lDW) of the winglet also varied from 20mm to 35mm. The heat transfer coefficient and pressure drop increased in all the cases with increase in Reynolds number. However, significant increase in heat transfer of about 23.92% was observed for α at 35o when compared with… More >

  • Open Access


    Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted Computational Offloading

    Baofeng Ji1,2,3,*, Ying Wang1,2,3, Weixing Wang1, Shahid Mumtaz4, Charalampos Tsimenidis4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1885-1905, 2024, DOI:10.32604/cmes.2023.030872

    Abstract The utilization of mobile edge computing (MEC) for unmanned aerial vehicle (UAV) communication presents a viable solution for achieving high reliability and low latency communication. This study explores the potential of employing intelligent reflective surfaces (IRS) and UAVs as relay nodes to efficiently offload user computing tasks to the MEC server system model. Specifically, the user node accesses the primary user spectrum, while adhering to the constraint of satisfying the primary user peak interference power. Furthermore, the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes, namely time switching (TS) and power splitting… More >

  • Open Access


    An Adaptive Edge Detection Algorithm for Weed Image Analysis

    Yousef Alhwaiti1,*, Muhammad Hameed Siddiqi1, Irshad Ahmad2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110

    Abstract Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a… More >

  • Open Access


    RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

    Tian Ma, Chenhui Fu*, Jiayi Yang, Jiehui Zhang, Chuyang Shang

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1103-1122, 2023, DOI:10.32604/cmc.2023.042416

    Abstract Low-light image enhancement methods have limitations in addressing issues such as color distortion, lack of vibrancy, and uneven light distribution and often require paired training data. To address these issues, we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network (RF-Net), which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms. This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images. In the first stage, we design a… More >

  • Open Access


    Internet of Things (IoT) Security Enhancement Using XGboost Machine Learning Techniques

    Dana F. Doghramachi1,*, Siddeeq Y. Ameen2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 717-732, 2023, DOI:10.32604/cmc.2023.041186

    Abstract The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a… More >

  • Open Access


    DFE-GCN: Dual Feature Enhanced Graph Convolutional Network for Controversy Detection

    Chengfei Hua1,2,3, Wenzhong Yang2,3,*, Liejun Wang2,3, Fuyuan Wei2,3, KeZiErBieKe HaiLaTi2,3, Yuanyuan Liao2,3

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 893-909, 2023, DOI:10.32604/cmc.2023.040862

    Abstract With the development of social media and the prevalence of mobile devices, an increasing number of people tend to use social media platforms to express their opinions and attitudes, leading to many online controversies. These online controversies can severely threaten social stability, making automatic detection of controversies particularly necessary. Most controversy detection methods currently focus on mining features from text semantics and propagation structures. However, these methods have two drawbacks: 1) limited ability to capture structural features and failure to learn deeper structural features, and 2) neglecting the influence of topic information and ineffective utilization of topic features. In light… More >

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