Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (96)
  • Open Access


    The role of AFAP1-AS1 in mitotic catastrophe and metastasis of triple-negative breast cancer cells by activating the PLK1 signaling pathway


    Oncology Research, Vol.31, No.3, pp. 375-388, 2023, DOI:10.32604/or.2023.028256

    Abstract Triple-negative breast cancer (TNBC) is characterized by fast growth, high metastasis, high invasion, and a lack of therapeutic targets. Mitosis and metastasis of TNBC cells are two important biological behaviors in TNBC malignant progression. It is well known that the long noncoding RNA AFAP1-AS1 plays a crucial role in various tumors, but whether AFAP1-AS1 is involved in the mitosis of TNBC cells remains unknown. In this study, we investigated the functional mechanism of AFAP1-AS1 in targeting Polo-like Kinase 1 (PLK1) activation and participating in mitosis of TNBC cells. We detected the expression of AFAP1-AS1 in the TNBC patient cohort and… More > Graphic Abstract

    The role of AFAP1-AS1 in mitotic catastrophe and metastasis of triple-negative breast cancer cells by activating the PLK1 signaling pathway

  • Open Access


    Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

    Umashankar Ganesan1,*, A. Vimala Juliet2, R. Amala Jenith Joshi3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2849-2863, 2023, DOI:10.32604/iasc.2023.033759

    Abstract Brain signal analysis plays a significant role in attaining data related to motor activities. The parietal region of the brain plays a vital role in muscular movements. This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements; perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm. This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease (PD). To play out this handling… More >

  • Open Access


    Numerical Simulation of Low Cycle Fatigue Behavior of Ti2AlNb Alloy Subcomponents

    Yanju Wang1, Zhenyu Zhu2, Aixue Sha1, Wenfeng Hao3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2655-2676, 2023, DOI:10.32604/cmes.2023.025749

    Abstract Many titanium alloy subcomponents are subjected to fatigue loading in aerospace engineering, resulting in fatigue failure. The fatigue behavior of Ti2AlNb alloy subcomponents was investigated based on the Seeger fatigue life theory and the improved Lemaitre damage evolution theory. Firstly, the finite element models of the standard openhole specimen and Y-section subcomponents have been established by ABAQUS. The damage model parameters were determined by fatigue tests, and the reliability of fatigue life simulation results of the Ti2AlNb alloy standard open-hole specimen was verified. Meanwhile, the fatigue life of Ti2AlNb alloy Y-section subcomponents was predicted. Under the same initial conditions, the… More >

  • Open Access


    LSM12 facilitates the progression of colorectal cancer by activating the WNT/CTNNB1 signaling pathway


    Oncology Research, Vol.30, No.6, pp. 289-300, 2022, DOI:10.32604/or.2022.028225

    Abstract Aberrant activation of the WNT signaling pathway is a joint event in colorectal cancer (CRC), but the molecular mechanism is still unclear. Recently, RNA-splicing factor LSM12 (like-Sm protein 12) is highly expressed in CRC tissues. This study aimed to verify whether LSM12 is involved in regulating CRC progression via regulating the WNT signaling pathway. Here, we found that LSM12 is highly expressed in CRC patient-derived tissues and cells. LSM12 is involved in the proliferation, invasion, and apoptosis of CRC cells, similar to the function of WNT signaling in CRC. Furthermore, protein interaction simulation and biochemical experiments proved that LSM12 directly… More >

  • Open Access


    A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

    Saqib Ali1,2, Shazia Riaz2,3, Safoora2, Xiangyong Liu1, Guojun Wang1,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1783-1800, 2023, DOI:10.32604/cmc.2023.035736

    Abstract Short-term load forecasting (STLF) is part and parcel of the efficient working of power grid stations. Accurate forecasts help to detect the fault and enhance grid reliability for organizing sufficient energy transactions. STLF ranges from an hour ahead prediction to a day ahead prediction. Various electric load forecasting methods have been used in literature for electricity generation planning to meet future load demand. A perfect balance regarding generation and utilization is still lacking to avoid extra generation and misusage of electric load. Therefore, this paper utilizes Levenberg–Marquardt (LM) based Artificial Neural Network (ANN) technique to forecast the short-term electricity load… More >

  • Open Access


    Modifications of the Optimal Auxiliary Function Method to Fractional Order Fornberg-Whitham Equations

    Hakeem Ullah1, Mehreen Fiza1,*, Ilyas Khan2,*, Abd Allah A. Mosa3, Saeed Islam1, Abdullah Mohammed4

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 277-291, 2023, DOI:10.32604/cmes.2023.022289

    Abstract In this paper, we present a new modification of the newly developed semi-analytical method named the Optimal Auxilary Function Method (OAFM) for fractional-order equations using the Caputo operator, which is named FOAFM. The mathematical theory of FOAFM is presented and the effectiveness of this method is proven by using it with well-known Fornberg-Whitham Equations (FWE). The FOAFM results are compared with other method results along with their exact solutions with the help of tables and plots to prove the validity of FOAFM. A rapidly convergent series solution is obtained from FOAFM and is validated by comparison with other results. The… More >

  • Open Access


    A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III

    Naret Ruttanaprommarin1, Zulqurnain Sabir2,3, Rafaél Artidoro Sandoval Núñez4, Emad Az-Zo’bi5, Wajaree Weera6, Thongchai Botmart6,*, Chantapish Zamart6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5915-5930, 2023, DOI:10.32604/cmc.2023.034362

    Abstract The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs). The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past. Three different cases based on the delay differential system with the Holling 3rd type of the functional response have been used to solve through the proposed LVMBPNNs solver. The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification. Thirteen numbers of neurons have been used based… More >

  • Open Access


    An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

    Wajaree Weera1, Thongchai Botmart1,*, Charuwat Chantawat1, Zulqurnain Sabir2,3, Waleed Adel4,5, Muhammad Asif Zahoor Raja6, Muhammad Kristiawan7

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2711-2724, 2023, DOI:10.32604/cmc.2023.033233

    Abstract The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1. The data… More >

  • Open Access


    Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

    Narongsak Yotha1, Qusain Hiader2, Zulqurnain Sabir3, Muhammad Asif Zahoor Raja4, Salem Ben Said5, Qasem Al-Mdallal5, Thongchai Botmart6, Wajaree Weera6,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2415-2430, 2023, DOI:10.32604/cmc.2023.031352

    Abstract This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD.… More >

  • Open Access


    GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

    Walaa N. Ismail1,2,*, Hessah A. Alsalamah3,4, Ebtesam Mohamed2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3945-3976, 2023, DOI:10.32604/cmc.2023.031194

    Abstract As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent… More >

Displaying 1-10 on page 1 of 96. Per Page  

Share Link