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  • Open Access

    ARTICLE

    NUMERICAL INVESTIGATIONS ON FLOW STRUCTURE AND HEAT TRANSFER IN A SQUARE DUCT EQUIPPED WITH DOUBLE VORIFICE

    Amnart Boonloia, Withada Jedsadaratanachaib,*

    Frontiers in Heat and Mass Transfer, Vol.14, No.1, pp. 1-11, 2020, DOI:10.5098/hmt.14.27

    Abstract Numerical predictions on heat transfer characteristic, flow topology and thermal performance assessment in a square duct are presented. The passive technique, insertion of the vortex generator, is opted to develop the heat transfer rate in the square duct heat exchanger. The vortex generator of the present research is Double V-Orifice (DVO). The square duct equipped with DVO is tested with various parameters. The influences of DVO height, b, to the duct height, H, or b/H, gap spacing between the outer edge of the orifice and the duct wall, s, to the duct height or s/H and flow directions (tip-pointing-Downstream and… More >

  • Open Access

    ARTICLE

    PREDICTION OF MASS TRANSFER COEFFICIENT OF THE CONTINUOUS PHASE IN A STRUCTURED PACKED EXTRACTION COLUMN IN THE PRESENCE OF SIO2 NANOPARTICLES

    Fereshteh Salimi Nanadegani, Bengt Sunden*

    Frontiers in Heat and Mass Transfer, Vol.14, No.1, pp. 1-11, 2020, DOI:10.5098/hmt.14.21

    Abstract In this experimental study, mass transfer and hydrodynamic parameters of water/kerosene/acetic acid system in a packed column were investigated, in which the mass transfer direction was set from the continuous phase (saturated water of kerosene and acetic acid) to the dispersed phase (saturated kerosene of water) in all the experiments. To assess the impact of nanoparticles on mass transfer, the experiments were performed in the presence of SiO2 nanoparticles and absence of the nanoparticles. The results showed that the addition of the nanoparticles to the base fluid (saturated kerosene of water) increased the mass transfer efficiency to the critical concentration,… More >

  • Open Access

    ARTICLE

    Clinical implication of naive and memory T cells in locally advanced cervical cancer: A proxy for tumor biology and short-term response prediction

    YUTING WANG1,2,3, PEIWEN FAN1,2,3, YANING FENG1,2,3, XUAN YAO4, YANCHUN PENG4, RUOZHENG WANG1,2,3,*

    BIOCELL, Vol.47, No.6, pp. 1365-1375, 2023, DOI:10.32604/biocell.2023.027201

    Abstract Background: This study was designed to investigate the feasibility of tumor-infiltrating immune cells with different phenotypic characteristics for predicting short-term clinical responses in patients with locally advanced cervical cancer (LACC). Methods: Thirty-four patients who received concurrent chemoradiotherapy and twenty-one patients who merely underwent radiotherapy were enrolled in this study. We retrospectively analyzed the T cell markers (i.e., CD3, CD4, CD8), memory markers (i.e., CD45, CCR7), and differentiation markers (i.e., CD27) in the peripheral blood and tumor tissues of patients with LACC before treatment based on flow cytometry. We also analyzed the relationship of T cell subsets between peripheral blood and… More >

  • Open Access

    ARTICLE

    Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy: Combined machine learning and experimental validation in mice

    WEI LIANG1,2,*, ZONGWEI ZHANG1,2, KEJU YANG1,2,3, HONGTU HU1,2, QIANG LUO1,2, ANKANG YANG1,2, LI CHANG4, YUANYUAN ZENG4

    BIOCELL, Vol.47, No.6, pp. 1253-1263, 2023, DOI:10.32604/biocell.2023.027373

    Abstract Background: Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN; however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to enhance diagnostic performance. Methods: We applied high-throughput RNA sequencing to obtain the genes related to DN tubular… More >

  • Open Access

    ARTICLE

    Performance Prediction of an Optimized Centrifugal Pump with High Efficiency

    Yuqin Wang1,2,3,*, Luxiang Zhou3, Mengle Han1, Lixiang Shen1

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2215-2228, 2023, DOI:10.32604/fdmp.2023.027188

    Abstract The main structural parameters of the IR100-80-100A type chemical centrifugal pump have been optimized by means of an orthogonal test approach. The centrifugal pump has been modeled using the CFturbo software, and 16 sets of orthogonal-test schemes have been defined on the basis of 4 parameters, namely, the blade number, blade outlet angle, impeller outlet diameter, and impeller outlet width. Such analysis has been used to determine the influence of each index parameter on the pump working efficiency and identify a set of optimal combinations of such parameters. The internal flow field in the centrifugal pump has been simulated by… More > Graphic Abstract

    Performance Prediction of an Optimized Centrifugal Pump with High Efficiency

  • Open Access

    ARTICLE

    Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

    Wenhua Xu1,3, Yuming Zhu2, Yingrong Wei2, Ya Su2, Yan Xu1,3, Hui Ji1, Dehua Liu1,3,*

    Energy Engineering, Vol.120, No.7, pp. 1701-1715, 2023, DOI:10.32604/ee.2023.027703

    Abstract Drilling costs of ultra-deep well is the significant part of development investment, and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost. In order to improve the prediction accuracy of ultra-deep well drilling costs, the item and the dominant factors of drilling costs in Tarim oilfield are analyzed. Then, those factors of drilling costs are separated into categorical variables and numerous variables. Finally, a BP neural network model with drilling costs as the output is established, and hyper-parameters (initial weights and bias) of the BP neural network is optimized by genetic… More >

  • Open Access

    ARTICLE

    Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

    Wenchao Ma*

    Energy Engineering, Vol.120, No.7, pp. 1685-1699, 2023, DOI:10.32604/ee.2023.025404

    Abstract The power output state of photovoltaic power generation is affected by the earth's rotation and solar radiation intensity. On the one hand, its output sequence has daily periodicity; on the other hand, it has discrete randomness. With the development of new energy economy, the proportion of photovoltaic energy increased accordingly. In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation, this paper proposes the short-term prediction of photovoltaic power generation based on the improved multi-scale permutation entropy, local mean decomposition and singular spectrum analysis algorithm.… More >

  • Open Access

    ARTICLE

    INTEGRATED MICRO X-RAY TOMOGRAPHY AND PORE-SCALE SIMULATIONS FOR ACCURATE PERMEABILITY PREDICTIONS OF POROUS MEDIA

    Fangzhou Wanga,* , Gennifer A. Rileyb, Munonyedi Egboc, Melanie M. Derbyb, Gisuk Hwangc, Xianglin Lia,†

    Frontiers in Heat and Mass Transfer, Vol.15, No.1, pp. 1-8, 2020, DOI:10.5098/hmt.15.1

    Abstract This study conducts pore-scale simulations and experiments to estimate the permeability of two different types of porous materials: metal foams and sintered copper particles with porosities of approximately 0.9 and 0.4, respectively. The integration of micro X-ray computed tomography with pore-scale computational fluid dynamics simulations develops a unique tool to capture the pore-scale geometry of porous media and accurately predict non-isotropic permeability of porous media. The pore-scale simulation not only results in improved prediction accuracy but also has the capability to capture non-isotropic properties of heterogeneous materials, which is a huge challenge for empirical correlations, volume averaged simulations, and simulations… More >

  • Open Access

    ARTICLE

    Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

    Salah Alzghoul1,*, Othman Smadi1, Ali Al Bataineh2, Mamon Hatmal3, Ahmad Alamm4

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 455-470, 2023, DOI:10.32604/iasc.2023.038338

    Abstract Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or… More >

  • Open Access

    ARTICLE

    Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

    S. Balaji*, S. Karthik

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 727-743, 2023, DOI:10.32604/iasc.2023.037409

    Abstract The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to be an effective tool for… More >

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