Lifeng Liu¹, Menghe Shi², Jianhui Wang³, Wendong Wang^2,*, Yuliang Su², Xinyu Zhuang²

FDMP-Fluid Dynamics & Materials Processing, Vol., , DOI:10.32604/fdmp.2023.041962

Abstract Ultra-low permeability reservoirs are characterized by small pore throats and poor physical properties, which are at the root of well-known problems related to injection and production. In this study, a gas injection flooding approach is analyzed in the framework of numerical simulations. In particular, the sequence and timing of fracture channeling and the related impact on production are considered for horizontal wells with different fracture morphologies. Useful data and information are provided about the regulation of gas channeling and possible strategies to delay gas channeling and optimize the gas injection volume and fracture parameters. It is shown that in order… More >

Rui Qiao, Shi Dong^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.043476

Abstract Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains. Therefore, this paper proposes a new consortium chain generation model, deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes, and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security. Furthermore, the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is derived. When either of the… More >

Muhammad Tahir^1,2,*, Mingchu Li^1,2, Irfan Khan^1,2, Salman A. Al Qahtani³, Rubia Fatima⁴, Javed Ali Khan⁵, Muhammad Shahid Anwar⁶

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042403

Abstract Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with resource allocation tactics.… More >

Medhat A. Tawfeek^1,2, Nacim Yanes^3,4, Leila Jamel^5,*, Ghadah Aldehim⁵, Mahmood A. Mahmood^1,6

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042179

Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations and then uses this data… More >

Junqiang Jiang^1,2, Zhifang Sun¹, Xiong Jiang¹, Shengjie Jin¹, Yinli Jiang³, Bo Fan^1,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.041973

Abstract The grey wolf optimizer (GWO) is a swarm-based intelligence optimization algorithm by simulating the steps of searching, encircling, and attacking prey in the process of wolf hunting. Along with its advantages of simple principle and few parameters setting, GWO bears drawbacks such as low solution accuracy and slow convergence speed. A few recent advanced GWOs are proposed to try to overcome these disadvantages. However, they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence. To solve the abovementioned issues, a high-accuracy variable grey wolf optimizer (VGWO) with low time complexity… More >

Mamona Arshad¹, Ahmad Karim¹, Salman Naseer², Shafiq Ahmad³, Mejdal Alqahtani³, Akber Abid Gardezi⁴, Muhammad Shafiq^5,*, Jin-Ghoo Choi⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2022.028680

Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area. A… More >

Gul Nawaz¹, Muhammad Junaid¹, Adnan Akhunzada², Abdullah Gani^2,*, Shamyla Nawazish³, Asim Yaqub³, Adeel Ahmed¹, Huma Ajab⁴

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.026952

Abstract Distributed denial of service (DDoS) attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user. We proposed a deep neural network (DNN) model for the detection of DDoS attacks in the Software-Defined Networking (SDN) paradigm. SDN centralizes the control plane and separates it from the data plane. It simplifies a network and eliminates vendor specification of a device. Because of this open nature and centralized control, SDN can easily become a victim of DDoS attacks. We proposed a supervised Developed Deep Neural Network (DDNN) model that can classify the DDoS attack traffic… More >

CONGCHEN BAI¹, QIHANG KONG², HAO TANG², SHUWEN ZHANG², JUNTENG ZHOU^3,*, XIAOJING LIU^2,4,*

BIOCELL, Vol., , DOI:10.32604/biocell.2023.043864

Abstract Background: Various physiological mechanisms are linked to dilated cardiomyopathy (DCM) development, including oxidative stress, immune irregularities, inflammation, fibrosis, and genetic changes. However, precise molecular drivers of DCM, especially regarding abnormal immune responses, remain unclear. This study investigates immune-related long non-coding RNAs (lncRNAs) in DCM’s diagnostic and therapeutic potential. Methods: GSE141910, GSE135055, and GSE165303 datasets were acquired from the GEO database. LASSO, SVM-RFE, and random forest algorithms identified DCM-associated immune-related lncRNAs. Diagnostic capabilities were assessed by Nomogram and receiver operating characteristic (ROC) curves. Multivariate linear regression explored lncRNA correlations with ejection fraction. Single-sample gene set enrichment analysis (ssGSEA) gauged immune cell… More >

Anuj Singh^*, Raman Kumar, Arvind Kumar Tiwari

Computer Systems Science and Engineering, Vol., , DOI:10.32604/csse.2023.047533

Abstract This article has no abstract. More >

Abdus Saboor^1,4, Arif Hussain², Bless Lord Y. Agbley³, Amin ul Haq^3,*, Jian Ping Li³, Rajesh Kumar^1,*

Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2023.047463

Abstract This article has no abstract. More >