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  • Open Access


    On the Engineering Properties of TPV derived from Hypalon, PP and a Compatibilizer (PMES-MA) prepared by Dynamic Vulcanization


    Journal of Polymer Materials, Vol.38, No.1-2, pp. 21-34, 2021, DOI:10.32381/JPM.2021.38.1-2.3

    Abstract Elastomeric chlorosulfonated polyethylene (Hypalon) and thermoplastic polypropylene (PP) based thermoplastic Vulcanizates (TPVs) were prepared in presence of different doses of partial methyl ester of styrene-maleic anhydride copolymer (PMES-MA) as compatibilizer employing dynamic vulcanization technique. The mechanical analysis of the prepared TPVs exhibited significant improvements in stress at 25% modulus, ultimate tensile strength (UTS), and hardness values. FTIR studies revealed that a chemical interaction had taken place between hypalon and compatibilizer during the process of dynamic vulcanization which led to an enhancement of interfacial adhesion between them. The two-phase morphologies were clearly observed by scanning electron microscopic studies. The Tg values… More >

  • Open Access


    Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

    Kaicheng Liu1,3,*, Chen Liang2, Xiaoyang Dong2, Liping Liu1

    Energy Engineering, Vol.121, No.4, pp. 933-949, 2024, DOI:10.32604/ee.2023.043658

    Abstract Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of PV generation is conducted, and… More >

  • Open Access



    Mohamed S. El-Genka,b,c,∗, Amir F. Alia,c

    Frontiers in Heat and Mass Transfer, Vol.3, No.4, pp. 1-14, 2012, DOI:10.5098/hmt.v3.4.3001

    Abstract Advanced spreaders for cooling a 10 x 10 mm underlying computer chip with a central hot spot (CHS) could remove > 85 W of dissipated thermal power at junctions’ temperature < 100o C. The spreaders comprise a 1.6 - 3.2 mm thick Cu substrate and an 80-μm thick micro-porous copper (MPC) surface cooled by saturation nucleate boiling of PF-5060 dielectric liquid. Investigated are the effects of varying the heat flux at the chip’s 1 and 4 mm2 CHS and the impedance of thermal interface material (TIM) between the Cu substrate and underlying chip. Results confirmed the effectiveness of the MPC… More >

  • Open Access


    Research on Regulation Method of Energy Storage System Based on Multi-Stage Robust Optimization

    Zaihe Yang1,*, Shuling Wang1, Runhang Zhu1, Jiao Cui2, Ji Su2, Liling Chen3

    Energy Engineering, Vol.121, No.3, pp. 807-820, 2024, DOI:10.32604/ee.2023.028167

    Abstract To address the scheduling problem involving energy storage systems and uncertain energy, we propose a method based on multi-stage robust optimization. This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method, which helps overcome the limitations of traditional methods in terms of time scale. The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day. To achieve this, a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power. The generalized… More >

  • Open Access


    An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

    Zhihua Liu, Shengquan Liu*, Jian Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

    Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for… More >

  • Open Access


    Implementation of a High-Risk Outpatient Clinic for Children with Complex Congenital Heart Disease in a Reference Service in Brazil

    Gustavo Foronda1,2, Vanessa Ferreira Amorim de Melo2,3,*, Claudia Regina Pinheiro de Castro Grau4, Ingrid Magatti Piva1, Glaucia Maria Penha Tavares4, Ana Cristina Sayuri Tanaka1, Nana Miura1

    Congenital Heart Disease, Vol.18, No.6, pp. 649-656, 2023, DOI:10.32604/chd.2023.027987

    Abstract Background: Children with congenital heart disease (CHD), even after surgical approaches, and especially those who undergo staged procedures in the first months of life, remain vulnerable to readmissions and complications, requiring very close monitoring and differentiated intervention strategies. Methods: Descriptive and exploratory study, of the experience report type, which presents the process of building the high-risk outpatient clinic for complex congenital heart diseases (AAR) at the Instituto do Coração (InCor). Results: Report of the path taken to structure the AAR, demonstrating the organization, interface with the multidisciplinary team, admission and discharge criteria, training, and patient profile. In these five years… More >

  • Open Access


    The Relationship between Mental Disorders and Personality of Outpatients in a Psychiatric Clinic in Nanjing, China

    Yiteng Zang1, Biyun Xu2, Sizhen Chen1, Grace Mutale1, Qiuyun Cao3,*, Bingwei Chen1,*

    International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1287-1302, 2023, DOI:10.32604/ijmhp.2023.042584

    Abstract Psychosis has increasingly become a social problem, emphasizing the need to understand the relationship between mental disorders and personality. This study aimed to investigate the relationship between mental disorders and personality among psychiatric outpatients based on real-world data. Symptom Checklist 90 (SCL-90) and Eysenck Personality Questionnaire (EPQ) were used to evaluate the personality and psychopathological symptoms of patients (n = 8409) in the Psychiatric Outpatient Department at Nanjing Drum Tower Hospital. t-test was used to compare scores between patients and national norms. Pearson’s correlation coefficient and path analysis were used to explore the relationship between mental health status and personality.… More >

  • Open Access


    Prediction and Output Estimation of Pattern Moving in Non-Newtonian Mechanical Systems Based on Probability Density Evolution

    Cheng Han1,*, Zhengguang Xu1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 515-536, 2024, DOI:10.32604/cmes.2023.043464

    Abstract A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems, assuming that the system satisfies the generalized Lipschitz condition. As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics, the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables, which poses difficulties in predicting and estimating the system’s output. In this article, the temporal variation of the system is described by constructing pattern category variables, which are non-deterministic variables. Since pattern category variables have… More >

  • Open Access


    Flag-Based Vehicular Clustering Scheme for Vehicular Ad-Hoc Networks

    Fady Samann1,*, Shavan Askar2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2715-2734, 2023, DOI:10.32604/cmc.2023.043580

    Abstract Clustering schemes in vehicular networks organize vehicles into logical groups. They are vital for improving network performance, accessing the medium, and enabling efficient data dissemination. Most schemes rely on periodically broadcast hello messages to provide up-to-date information about the vehicles. However, the periodic exchange of messages overwhelms the system and reduces efficiency. This paper proposes the Flag-based Vehicular Clustering (FVC) scheme. The scheme leverages a combination of Fitness Score (FS), Link Expiration Time (LET), and clustering status flags to enable efficient cluster formation in a hybrid manner. The FVC relies on the periodic broadcast of the basic safety message in… More >

  • Open Access


    Inversion of Water Quality TN-TP Values Based on Hyperspectral Features and Model Validation

    Yaping Luo1, Na Guo1,*, Dong Liu2, Shuming Peng3, Xinchen Wang4, Jie Wu3

    Revue Internationale de Géomatique, Vol.32, pp. 39-52, 2023, DOI:10.32604/RIG.2023.046014

    Abstract Using hyperspectral data collected in January and June 2022 from the Sha River, the concentrations of total nitrogen (TN) and total phosphorus (TP) were estimated using the differential method. The results indicate that the optimal bands for estimation vary monthly due to temperature fluctuations. In the TN model, the power function model at 586 nm in January exhibited the strongest fit, yielding a fit coefficient (R2) of 0.95 and F-value of 164.57 at a significance level (p) of less than 0.01. Conversely, the exponential model at 477 nm in June provided the best fit, with R2 = 0.93 and F… More > Graphic Abstract

    Inversion of Water Quality TN-TP Values Based on Hyperspectral Features and Model Validation

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