Trong-The Nguyen^1,2,3, Thi-Kien Dao^1,2,3,*, Trinh-Dong Nguyen^2,3

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3213-3234, 2023, DOI:10.32604/iasc.2023.041356

Abstract Wireless sensor networks (WSNs) are widely used for various practical applications due to their simplicity and versatility. The quality of service in WSNs is greatly influenced by the coverage, which directly affects the monitoring capacity of the target region. However, low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges. This study proposes an optimal node planning strategy for network coverage based on an adjusted single candidate optimizer (ASCO) to address these issues. The single candidate optimizer (SCO) is a metaheuristic algorithm with stable implementation procedures. However, it has limitations in avoiding local optimum traps in… More >

Mingming Shi^1,*, Jun Zhang², Xuefeng Ge¹, Juntao Fei¹, Jiajun Tan³

Energy Engineering, Vol.120, No.8, pp. 1885-1897, 2023, DOI:10.32604/ee.2023.025788

Abstract With the wide application of renewable energy power generation technology, the distribution network presents the characteristics of multi-source and complex structure. There are potential risks in the stability of power system, and the problem of power quality is becoming more and more serious. This paper studies and proposes a power optimization cooperative control strategy for flexible fast interconnection device with energy storage, which combines the flexible interconnection technology with the energy storage device. The primary technology is to regulate the active and reactive power of the converter. By comparing the actual power value of the converter with the reference value,… More >

Xiangli Guan¹, Yue Zhang², Yang Li¹, Yaqi Zhang^1,*, Jingjing Wang¹, Xuejiao Li¹, Mary C. Jobe³, Md Zahir Ahmed⁴, Oli Ahmed⁵

International Journal of Mental Health Promotion, Vol.25, No.6, pp. 755-766, 2023, DOI:10.32604/ijmhp.2023.027086

Abstract Because of factors such as energy and time one invests in an object, the stronger the connection, value, and reluctance to lose said object individual will have. Hoarding behavior arises when individuals incorporate a strong attachment with themselves to an object. The purpose of this study is to examine the effect of self-investment on hoarding tendency and the roles of possession-self link and liking level in this connection. A hypothetical model of the relationship between self-investment, possession-self link, liking level, and hoarding tendency was tested. A convenience sampling method was used to survey 450 college students in Yunnan Province on… More >

Anjani Kumar Singha¹, Swaleha Zubair¹, Areej Malibari², Nitish Pathak³, Shabana Urooj^4,*, Neelam Sharma⁵

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3491-3508, 2023, DOI:10.32604/csse.2023.029165

Abstract Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work… More >

Jin Wang^1,2, Yongsong Zou¹, Se-Jung Lim^3,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2743-2755, 2023, DOI:10.32604/iasc.2023.033869

Abstract Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the… More >

Xiaofeng Zhang¹, Lisheng Luo^2,*, Youfu Sun¹, Xinyue Cui², Yongqiang Zhang²

Journal of Renewable Materials, Vol.11, No.4, pp. 2033-2054, 2023, DOI:10.32604/jrm.2022.023651

Abstract Considering the glulam beam-column connection form and the number of bolts, monotonic loading test and finite element analysis was carried out on 9 connection specimens in 3 groups to study the rotational performance and failure mode of the connection. The test results revealed that compared with U-shaped connectors, T-shaped connectors can effectively improve the ductility of connections, and the increase in the number of bolts can reduce the initial stiffness and ductility of connections. By theoretical analysis, formulas for calculating the initial stiffness and ultimate moment of connections were deduced. Subsequently, the moment-rotation theoretical model of connections was established based… More > Graphic Abstract

Shidong Nie^1,2, Wei Fu^1,2, Hui Wang^1,2,*, Di Wu^1,2,3, Min Liu^1,2, Junlong Wang⁴

Journal of Renewable Materials, Vol.11, No.3, pp. 1401-1423, 2023, DOI:10.32604/jrm.2022.023446

Abstract Moso bamboos have attracted excessive attention as a renewable green building material to the concept of sustainable development. In this paper, the 20 bolted Moso bamboo connection specimens with embedded steel plates and grouting materials were designed according to connection configurations with different bolt diameters and end distance of bolt holes, and their bearing capacities and failure modes were analyzed by static tension tests. According to the test results of all connectors, the failure modes of the specimens are divided into four categories, and the effects of bolt diameter and bolt hole end distance on the connection bearing capacity and… More >

Hicham Lamtougui^1,*, Hicham El Moubtahij², Hassan Fouadi¹, Khalid Satori¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2871-2888, 2023, DOI:10.32604/cmc.2023.032550

Abstract In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a… More >

Muhammad Javaid¹, Ahmed Alamer², Aqsa Sattar^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1649-1667, 2023, DOI:10.32604/cmes.2022.022832

Abstract Topological indices (TIs) have been practiced for distinct wide-ranging physicochemical applications, especially used to characterize and model the chemical structures of various molecular compounds such as dendrimers, nanotubes and neural networks with respect to their certain properties such as solubility, chemical stability and low cytotoxicity. Dendrimers are prolonged artificially synthesized or amalgamated natural macromolecules with a sequential layer of branches enclosing a central core. A present-day trend in mathematical and computational chemistry is the characterization of molecular structure by applying topological approaches, including numerical graph invariants. Among topological descriptors, Zagreb connection indices (ZCIs) have much importance. This manuscript involves the… More >

Qi Guo, Shujun Zhang^*, Hui Li

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1653-1670, 2023, DOI:10.32604/cmes.2022.021784

Abstract Continuous sign language recognition (CSLR) is challenging due to the complexity of video background, hand gesture variability, and temporal modeling difficulties. This work proposes a CSLR method based on a spatial-temporal graph attention network to focus on essential features of video series. The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatial-temporal graph to reflect inter-frame relevance and physical connections between nodes. The graph-based multi-head attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration, and short-term motion correlation modeling is completed via a temporal… More > Graphic Abstract