Rossi Silvia^1,2,*, Sandrine de Montgolfier^1,3, Joëlle Kivits⁴

Psycho-Oncologie, Vol.18, No.1, pp. 33-41, 2024, DOI:10.32604/po.2024.042709 - 25 March 2024

Abstract Objectif: La démocratie en santé nécessite de disposer d’outils et de méthodologies pour impliquer les acteurs non scientifiques dans l’élaboration et la mise en œuvre des politiques de santé. La narration pourrait être un des outils qui permettent de rendre la démocratie en santé effective. Notre objectif est de voir comment la narration est mobilisée en lien avec la démocratie en santé, de décrire les objectifs de son usage, la méthodologie adoptée et les résultats obtenus. Matériel et méthodes: Nous avons réalisé une revue de la littérature narrative. Notre équation de recherche a été composée par le… More >

Xuming Mo^*

Congenital Heart Disease, Vol.19, No.1, pp. 1-3, 2024, DOI:10.32604/chd.2024.051192 - 20 March 2024

Abstract This article has no abstract. More >

Qi Zhuang^1,*, Dong Liu², Zhuo Chen³

Energy Engineering, Vol.121, No.3, pp. 821-834, 2024, DOI:10.32604/ee.2023.044054 - 27 February 2024

Abstract Oil and gas pipelines are affected by many factors, such as pipe wall thinning and pipeline rupture. Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management. Aiming at the shortcomings of the BP Neural Network (BPNN) model, such as low learning efficiency, sensitivity to initial weights, and easy falling into a local optimal state, an Improved Sparrow Search Algorithm (ISSA) is adopted to optimize the initial weights and thresholds of BPNN, and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established. Taking 61 sets More >

Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607 - 30 January 2024

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long… More >

Syeda Wajiha Zahra¹, Muhammad Nadeem², Ali Arshad^3,*, Saman Riaz³, Muhammad Abu Bakr⁴, Ashit Kumar Dutta⁵, Zaid Alzaid⁶, Badr Almutairi⁷, Sultan Almotairi⁸

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 589-616, 2024, DOI:10.32604/cmc.2023.042386 - 30 January 2024

Abstract Various organizations store data online rather than on physical servers. As the number of user’s data stored in cloud servers increases, the attack rate to access data from cloud servers also increases. Different researchers worked on different algorithms to protect cloud data from replay attacks. None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack. This study presents the development of a TKN (Text, Key and Name) cryptographic algorithm aimed at protecting data from replay attacks. The program employs distinct ways to encrypt plain text [P], a user-defined Key… More >

Yuchen Zhou¹, Hongtao Huo¹, Zhiwen Hou¹, Lingbin Bu¹, Yifan Wang¹, Jingyi Mao¹, Xiaojun Lv², Fanliang Bu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 537-563, 2024, DOI:10.32604/cmes.2023.044895 - 30 December 2023

Abstract Graph Convolutional Neural Networks (GCNs) have been widely used in various fields due to their powerful capabilities in processing graph-structured data. However, GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions, resulting in substantial distortions. Moreover, most of the existing GCN models are shallow structures, which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures. To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information… More >

HONGYUAN LIANG^1,#, YANQIU LI^2,#, YONGGANG QU³, LINGYUN ZHANG^4,*

Oncology Research, Vol.32, No.2, pp. 393-407, 2024, DOI:10.32604/or.2023.031134 - 28 December 2023

Abstract Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as “edgeR” and “maftools” and visualized the characteristics of these genes using the “circlize” R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods.… More >

Ying Zhang¹, Wei Li^1,*, Guidong Yang¹, Snezana Kirin²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2177-2199, 2024, DOI:10.32604/cmes.2023.030728 - 15 December 2023

Abstract The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model with noise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian white noise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. As follows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPT is obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrence of the tumor from the extinction state to the tumor-present state is more concerned in this paper. A more… More >

Abdul Rehman, Dongsun Kim^*, Anand Paul

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435 - 29 November 2023

Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm.… More >

ZHIWANG LI, HAN YU, YUPING LIU, WEISHENG WU, HAIJING ZENG, EN LI^*

BIOCELL, Vol.47, No.11, pp. 2409-2422, 2023, DOI:10.32604/biocell.2023.031186 - 27 November 2023

Abstract Familial adenomatous polyposis (FAP) is an autosomal dominant disease with a high probability of becoming cancerous. Many RNAs potentially associated with FAP have not been identified. In this study, a circRNA (circular RNA) expression profile of FAP was established using a circRNA microarray, and differentially expressed circRNAs were verified by RT-qPCR. The effects of hsa_circ_0036740 on the malignant behavior of tumor cells (proliferation, apoptosis, and epithelial mesenchymal transition) and the levels of C3A complement protein expression were evaluated. Moreover, neutrophils were isolated and co-cultured with colorectal cancer cells (CRCs), followed by measurements of MPO-DNA, citrullinated… More > Graphic Abstract