Chenyang Shi^1,2,3, Fankun Meng^1,2,3,*, Hongyou Zhang⁴, HuiJiang Chang⁴, Xun Zhong^1,2,3, Jie Gong^1,2,3, Fengling Li⁵

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1441-1468, 2022, DOI:10.32604/fdmp.2022.019788

Abstract As current calculation models for inter-well connectivity in oilfields can only account for vertical wells, an updated model is elaborated here that can predict the future production performance and evaluate the connectivity of horizontal wells (or horizontal and vertical wells). In this model, the injection-production system of the considered reservoir is simplified and represented with many connected units. Moreover, the horizontal well is modeled with multiple connected wells without considering the pressure loss in the horizontal direction. With this approach, the production performance for both injection and production wells can be obtained by calculating the bottom-hole flowing pressure and oil/water… More >

Yin Liang^1,*, Gaoxu Xu¹, Sadaqat ur Rehman²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4645-4661, 2022, DOI:10.32604/cmc.2022.026999

Abstract Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC patterns for the ASD diagnosis.… More >

Yuhao Chen¹, Jiajun Liu¹, Yaxi Peng¹, Ziyi Liu², Zhipeng Yang^1,*

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1501-1516, 2022, DOI:10.32604/iasc.2022.026346

Abstract As a neural fingerprint, functional connectivity networks (FCNs) have been used to identify subjects from group. However, a number of studies have only paid attention to cerebral cortex when constructing the brain FCN. Other areas of the brain also play important roles in brain activities. It is widely accepted that the human brain is composed of many highly complex functional networks of cortex. Moreover, recent studies have confirmed correlations between signals of cortex and white matter (WM) bundles. Therefore, it is difficult to reflect the functional characteristics of the brain through a single-layer FCN. In this paper, a multilayer FCN… More >

Changsung Lee^1,3, Junsung Kim^2,3, Jaewook Jung³, Jungsuk Baik³, Jong-Moon Chung^3,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 727-738, 2022, DOI:10.32604/cmc.2022.024824

Abstract Packet duplication (PD) with dual connectivity (DC) was newly introduced in the 5G New Radio (NR) specifications to meet the stringent ultra reliable low latency communication (URLLC) requirements. PD technology uses duplicated packets in the packet data convergence protocol (PDCP) layer that are transmitted via two different access nodes (ANs) to the user equipment (UE) in order to enhance the reliability performance. However, PD can result in unnecessary retransmissions in the lower layers since the hybrid automatic retransmission request (HARQ) operation is unaware of the transmission success achieved through the alternate DC link to the UE. To overcome this issue,… More >

Shabir Ahmad, Faheem Khan, Taeg Keun Whangbo^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2653-2666, 2022, DOI:10.32604/cmc.2022.022821

Abstract Serious games have recently enticed many researchers due to their wide range of capabilities. A serious game is a mean of gaming for a serious job such as healthcare, education, and entertainment purposes. With the advancement in the Internet of Things, new research directions are paving the way in serious games. However, the internet connectivity of players in Internet-of-things-enabled serious games is a matter of concern and has been worth investigating. Different studies on topologies, frameworks, and architecture of communication technologies are conducted to integrate them with serious games on machine and network levels. However, the Internet of things, whose… More >

Khattab Alheeti¹, Abdulkareem Alaloosy¹, Haitham Khalaf², Abdulkareem Alzahrani^3,*, Duaa Al_Dosary⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3311-3319, 2022, DOI:10.32604/cmc.2022.019773

Abstract Self-driving and semi-self-driving cars play an important role in our daily lives. The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information. However, external infrastructures also play significant roles in the transmission and reception of control data, cooperative awareness messages, and caution notifications. In this case, roadside units are considered one of the most important communication peripherals. Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost, bandwidth, connectivity, and radio coverage area. In this paper, a new distributed roadside unit is proposed… More >

Kalaipriyan Thirugnansambandam¹, Debnath Bhattacharyya², Jaroslav Frnda³, Dinesh Kumar Anguraj², Jan Nedoma^4,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3629-3644, 2021, DOI:10.32604/cmc.2021.018939

Abstract In Wireless Sensor Network (WSN), coverage and connectivity are the vital challenges in the target-based region. The linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement position. In this paper, a multiobjective problem on target-based WSN (t-WSN) is derived, which minimizes the number of deployed nodes, and maximizes the cost of coverage and sensing range. An Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is incorporated to tackle this multiobjective problem efficiently. Multiobjective problems are… More >

Mahmood ul Hassan^1,*, Amin Al-Awady¹, Khalid Mahmood², Shahzad Ali³, Ibrahim Algamdi¹, Muhammad Kashif Saeed⁴, Safdar Zaman⁵

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3413-3427, 2021, DOI:10.32604/cmc.2021.018544

Abstract Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving the challenges associated with connectivity… More >

Mahmood ul Hassan^1,*, Shahzad Ali², Khalid Mahmood³, Muhammad Kashif Saeed⁴, Amin Al-Awady¹, Kamran Javed⁵, Ansar Munir Shah⁶

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1003-1019, 2021, DOI:10.32604/cmc.2021.018264

Abstract Node failure in Wireless Sensor Networks (WSNs) is a fundamental problem because WSNs operate in hostile environments. The failure of nodes leads to network partitioning that may compromise the basic operation of the sensor network. To deal with such situations, a rapid recovery mechanism is required for restoring inter-node connectivity. Due to the immense importance and need for a recovery mechanism, several different approaches are proposed in the literature. However, the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration. Moreover, most of these approaches rely on the excessive mobility… More >

Lan Yang¹, Shun Qi^2,3,#, Chen Qiao^1,*, Yanmei Kang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 215-237, 2020, DOI:10.32604/cmes.2020.010796

Abstract Schizophrenia (SZ) is one of the most common mental diseases. Its main characteristics are abnormal social behavior and inability to correctly understand real things. In recent years, the magnetic resonance imaging (MRI) technique has been popularly utilized to study SZ. However, it is still a great challenge to reveal the essential information contained in the MRI data. In this paper, we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data, in which biomarkers represent both abnormal brain functional connectivity and… More >