Rui Zhang^1,2, Tonghai Liu^1,2,*, Wenzheng Liu^1,2, Chaungchuang Yuan^1,2, Xiaoyue Seng^1,2, Tiantian Guo^1,2, Xue Wang^1,2

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1275-1296, 2024, DOI:10.32604/phyton.2024.052397

Abstract Rice diseases can adversely affect both the yield and quality of rice crops, leading to the increased use of pesticides and environmental pollution. Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance. Deep learning-based disease identification technologies have shown promise in automatically discerning disease types. However, effectively extracting early disease features in natural environments remains a challenging problem. To address this issue, this study proposes the YOLO-CRD method. This research selected images of common rice diseases, primarily bakanae disease, bacterial brown spot, leaf rice fever, and dry… More >

Abdulelah Alwabel^1,*, Chinmaya Kumar Swain²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4127-4148, 2024, DOI:10.32604/cmc.2024.048833

Abstract Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the… More >

KUN FENG^1,#, HAO PENG^2,#, QINGPENG LV¹, YEWEI ZHANG^1,*

Oncology Research, Vol.32, No.6, pp. 1063-1078, 2024, DOI:10.32604/or.2024.047078

Abstract Hepatocellular carcinoma (HCC) is a malignancy known for its unfavorable prognosis. The dysregulation of the tumor microenvironment (TME) can affect the sensitivity to immunotherapy or chemotherapy, leading to treatment failure. The elucidation of PHLDA2’s involvement in HCC is imperative, and the clinical value of PHLDA2 is also underestimated. Here, bioinformatics analysis was performed in multiple cohorts to explore the phenotype and mechanism through which PHLDA2 may affect the progression of HCC. Then, the expression and function of PHLDA2 were examined via the qRT-PCR, Western Blot, and MTT assays. Our findings indicate a substantial upregulation of… More >

Lassaad K. Smirani¹, Leila Jamel^2,*, Latifah Almuqren²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1315-1337, 2024, DOI:10.32604/cmes.2024.047551

Abstract This study presents a layered generalization ensemble model for next generation radio mobiles, focusing on supervised channel estimation approaches. Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout. The model, called Stacked Generalization for Channel Estimation (SGCE), aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput. The SGCE model incorporates six machine learning methods: random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LGBM), support vector regression (SVR), extremely randomized tree (ERT), and extreme gradient boosting (XGB). By generating meta-data from five… More >

Zineb Fergani¹, Zakaria Triki¹, Rabah Menasri¹, Hichem Tahraoui^1,2,*, Meriem Zamouche³, Mohammed Kebir⁴, Jie Zhang⁵, Abdeltif Amrane^6,*

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 455-473, 2024, DOI:10.32604/fhmt.2024.050332

Abstract Seawater desalination stands as an increasingly indispensable solution to address global water scarcity issues. This study conducts a thorough exergoenvironmental analysis of a multi-effect distillation with thermal vapor compression (MED-TVC) system, a highly promising desalination technology. The MED-TVC system presents an energy-efficient approach to desalination by harnessing waste heat sources and incorporating thermal vapor compression. The primary objective of this research is to assess the system’s thermodynamic efficiency and environmental impact, considering both energy and exergy aspects. The investigation delves into the intricacies of energy and exergy losses within the MED-TVC process, providing a holistic… More >

Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1957-1974, 2024, DOI:10.32604/cmc.2024.048956

Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources More >

KAIWEN TIAN^#, HANZHONG CHEN^#, QIANQIAN WANG, FENGLIAN JIANG, CHUNXIANG FENG, TENG LI, XIAOYONG PU, YANLIN TANG^*, JIUMIN LIU^*

BIOCELL, Vol.48, No.5, pp. 817-834, 2024, DOI:10.32604/biocell.2024.048737

Abstract Background: The incidence of clear cell renal cell carcinoma (ccRCC) is globally high; however, despite the introduction of innovative drug therapies, there remains a lack of effective biomarkers for evaluating treatment response. Recently, Caspase recruiting domain-containing protein 11 (CARD11) has garnered attention due to its significant association with tumor development and the immune system. Methods: The expression of CARD11 mRNA and protein in ccRCC were analyzed by public database and immunohistochemistry. The focus of this study is on the epigenomic modifications of CARD11, its expression of ccRCC immunophenotype, and its correlation with response to immunotherapy… More > Graphic Abstract

Sapiah Sakri¹, Shakila Basheer¹, Zuhaira Muhammad Zain¹, Nurul Halimatul Asmak Ismail^2,*, Dua’ Abdellatef Nassar¹, Manal Abdullah Alohali¹, Mais Ayman Alharaki¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1157-1185, 2024, DOI:10.32604/cmc.2024.048051

Abstract Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction… More >

Mohammed Alatiyyah^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 875-907, 2024, DOI:10.32604/cmc.2024.047790

Abstract In the contemporary era of technological advancement, smartphones have become an indispensable part of individuals’ daily lives, exerting a pervasive influence. This paper presents an innovative approach to passenger counting on buses through the analysis of Wi-Fi signals emanating from passengers’ mobile devices. The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels, thereby addressing a crucial aspect of public transportation. The proposed system comprises three crucial elements: Signal capture, data filtration, and the calculation and estimation of passenger numbers. The pivotal findings reveal that the system demonstrates commendable… More >

ZHEN LIU^1,#, LIANGWANG YANG^2,#, ZHENGXING XIE¹, HUI YU³, TIANYI GU³, DAOMING SHI⁴, NING CAI^1,*, SHENGHUA ZHUO^2,*

Oncology Research, Vol.32, No.5, pp. 965-981, 2024, DOI:10.32604/or.2024.045769

Abstract Clinical data indicates that glioma patients have poor treatment outcomes and clinical prognosis. The role of olfactory signaling pathway-related genes (OSPRGs) in glioma has not been fully elucidated. In this study, we aimed to investigate the role and relationship between OSPRGs and glioma. Univariate and multivariate Cox regression analyses were performed to assess the relationship between OSPRGs and the overall survival of glioma based on public cohorts, and the target gene (G Protein Subunit Alpha L, GNAL) was screened. The association of GNAL expression with clinicopathological characteristics, gene mutation landscape, tumor immune microenvironment (TIME), deoxyribonucleic acid… More >