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  • Open Access


    Preclinical evaluation of cyclophosphamide and fludarabine combined with CD19 CAR-T in the treatment of B-cell hematologic malignancies in vivo


    Oncology Research, Vol.32, No.6, pp. 1109-1118, 2024, DOI:10.32604/or.2024.049792

    Abstract Background: Chimeric antigen receptor T (CAR-T) cell therapy has achieved marked therapeutic success in ameliorating hematological malignancies. However, there is an extant void in the clinical guidelines concerning the most effective chemotherapy regimen prior to chimeric antigen receptor T (CAR-T) cell therapy, as well as the optimal timing for CAR-T cell infusion post-chemotherapy. Materials and Methods: We employed cell-derived tumor xenograft (CDX) murine models to delineate the optimal pre-conditioning chemotherapy regimen and timing for CAR-T cell treatment. Furthermore, transcriptome sequencing was implemented to identify the therapeutic targets and elucidate the underlying mechanisms governing the treatment regimen.… More >

  • Open Access


    Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

    Zhishuo Zhang, Xinhui Du*, Yaoke Shang, Jingshu Zhang, Wei Zhao, Jia Su

    Energy Engineering, Vol.121, No.6, pp. 1577-1605, 2024, DOI:10.32604/ee.2024.047706

    Abstract To address the issues of limited demand response data, low generalization of demand response potential evaluation, and poor demand response effect, the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis. Firstly, based on the demand response process and demand response behavior, obtain demand response characteristics that characterize the process and behavior. Secondly, establish a feature extraction and prediction model based on data mining, including similar day clustering,… More >

  • Open Access


    A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

    Sen-Hui Wang1,2,*, Xi Kang1, Cheng Wang1, Tian-Bing Ma1, Xiang He2, Ke Yang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281

    Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

  • Open Access


    A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

    Hao Qi1, Mohamed Sharaf2, Andres Annuk3, Adrian Ilinca4, Mohamed A. Mohamed5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1387-1404, 2024, DOI:10.32604/cmes.2024.048672

    Abstract Hot dry rock (HDR) is rich in reserve, widely distributed, green, low-carbon, and has broad development potential and prospects. In this paper, a distributionally robust optimization (DRO) scheduling model for a regionally integrated energy system (RIES) considering HDR co-generation is proposed. First, the HDR-enhanced geothermal system (HDR-EGS) is introduced into the RIES. HDR-EGS realizes the thermoelectric decoupling of combined heat and power (CHP) through coordinated operation with the regional power grid and the regional heat grid, which enhances the system wind power (WP) feed-in space. Secondly, peak-hour loads are shifted using price demand response guidance More >

  • Open Access


    A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

    Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini2, Sahil Verma3, Abdulatif Alabdulatif4, Ruba Abu Khurma5,7, Maribel Garcia-Arenas6, Pedro A. Castillo6,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 571-608, 2024, DOI:10.32604/csse.2024.042690

    Abstract As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The… More >

  • Open Access


    Arterial Duct Stenting Versus Modified Blalock-Taussig Shunt in Patient with Ductal-Dependent Pulmonary Circulation: Systematic Review & Meta-Analysis

    Ketut Putu Yasa1,2,*, Nyoman Satria Sadu Bhaskara2, Putu Febry Krisna Pertiwi2

    Congenital Heart Disease, Vol.19, No.2, pp. 139-156, 2024, DOI:10.32604/chd.2024.050348

    Abstract Objective: Patients with ductal-dependent pulmonary circulation require alternative blood flow to provide and maintain adequate oxygenation. Modified Blalock-Taussig Shunt (MBTS) has been the standard for providing such a result. Currently, less invasive methods such as Arterial Duct (AD) stenting have been performed as alternatives. This study aims to compare the outcome of AD stenting and MBTS. Method: Systematic research was performed in online databases using the PRISMA protocol. The outcomes measured were 30-day mortality, complication, unplanned intervention, oxygen saturation, duration of hospital, and ICU length of stay. Any comparative study provided with full text is… More >

  • Open Access


    Enhancing Relational Triple Extraction in Specific Domains: Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

    Jiakai Li, Jianpeng Hu*, Geng Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005

    Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first… More >

  • Open Access


    Federated Learning on Internet of Things: Extensive and Systematic Review

    Meenakshi Aggarwal1, Vikas Khullar1, Sunita Rani2, Thomas André Prola3,4,5, Shyama Barna Bhattacharjee6, Sarowar Morshed Shawon7, Nitin Goyal8,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1795-1834, 2024, DOI:10.32604/cmc.2024.049846

    Abstract The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles.… More >

  • Open Access


    FusionNN: A Semantic Feature Fusion Model Based on Multimodal for Web Anomaly Detection

    Li Wang1,2,3,*, Mingshan Xia1,2,*, Hao Hu1, Jianfang Li1,2, Fengyao Hou1,2, Gang Chen1,2,3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2991-3006, 2024, DOI:10.32604/cmc.2024.048637

    Abstract With the rapid development of the mobile communication and the Internet, the previous web anomaly detection and identification models were built relying on security experts’ empirical knowledge and attack features. Although this approach can achieve higher detection performance, it requires huge human labor and resources to maintain the feature library. In contrast, semantic feature engineering can dynamically discover new semantic features and optimize feature selection by automatically analyzing the semantic information contained in the data itself, thus reducing dependence on prior knowledge. However, current semantic features still have the problem of semantic expression singularity, as… More >

  • Open Access


    Enhancing Deep Learning Semantics: The Diffusion Sampling and Label-Driven Co-Attention Approach

    Chunhua Wang1,2, Wenqian Shang1,2,*, Tong Yi3,*, Haibin Zhu4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1939-1956, 2024, DOI:10.32604/cmc.2024.048135

    Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and More >

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