Yantao Xu¹, Yufei Han¹, Jianzhang Li¹, Jing Luo², Sheldon Q. Shi³, Jingchao Li¹, Qiang Gao^1,*, An Mao^4,*

Journal of Renewable Materials, Vol.10, No.10, pp. 2519-2541, 2022, DOI:10.32604/jrm.2022.020750

Abstract Traditional formaldehyde-based adhesives rely excessively on petrochemical resources, release toxic gases, and pollute the environment. Plant-derived soybean protein adhesives are eco-friendly materials that have the potential to replace the formaldehyde-based adhesives used to fabricate wood-based panels. However, the poor water resistance, high brittleness, and poor mildew resistance of soybean protein adhesives limit their industrial applications. This article reviews recent research progress in the modification of soybean protein adhesives for improving the bonding performance of adhesives used for wood-based panel fabrication. Modification methods were summarized in terms of water resistance, solid content, and mildew resistance. The modification mechanisms and remaining problems… More >

Shervin Shameli Derakhshan¹, Lina Zhou^1,*, Chun Ni²

Journal of Renewable Materials, Vol.10, No.10, pp. 2655-2667, 2022, DOI:10.32604/jrm.2022.020671

Abstract Nail joints are one of the key components that control the lateral performance of light wood frame shear walls. In previous experimental studies, researchers have used different loading rates, which failed specimens from less than a minute to more than an hour, to study the characteristics of nail joints. Moreover, there have been different loading protocols used for testing of timber nail joints or shear walls. Although some efforts have been made to address this subject, it is still unclear how the loading protocol and loading rate may influence the performance of nail joints. In this study, a total of… More >

Qiong Wang¹, Yuewen Luo¹, Hongliang Guo¹, Peng Guo², Jinghao Wei¹, Tie Lin^1,*

Journal of New Media, Vol.3, No.4, pp. 153-153, 2021, DOI:10.32604/jnm.2021.019519

Abstract This article has no abstract. More >

Tao Li¹, Zhiwei Jiang^1,*, Rui Han², Jinyue Xia³, Yongjun Ren⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 941-956, 2023, DOI:10.32604/iasc.2023.029644

Abstract A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network’s generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer vision field. Sub-pixel convolution is used instead of transposed convolution or interpolation… More >

Tahir Alyas¹, Nadia Tabassum², Muhammad Waseem Iqbal^3,*, Abdullah S. Alshahrani⁴, Ahmed Alghamdi⁵, Syed Khuram Shahzad⁶

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1165-1179, 2023, DOI:10.32604/iasc.2023.028815

Abstract Kubernetes, a container orchestrator for cloud-deployed applications, allows the application provider to scale automatically to match the fluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures. Cloud-based platforms usually entail users define application resource supplies for eco container virtualization. There is a constant problem of over-service in data centers for cloud service providers. Higher operating costs and incompetent resource utilization can occur in a waste of resources. Kubernetes revolutionized the orchestration of the container in the cloud-native age. It can adaptively manage… More >

Mohamed Ali Rakrouki^1,2,*, Abeer Aljohani¹, Nawaf Alharbe¹, Abdelaziz Berrais², Talel Ladhari²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1119-1134, 2023, DOI:10.32604/iasc.2023.028604

Abstract In this paper, we consider the problem of minimizing the total tardiness in a deterministic two-machine permutation flowshop scheduling problem subject to release dates of jobs and known unavailability periods of machines. The theoretical and practical importance of minimizing tardiness in flowshop scheduling environment has motivated us to investigate and solve this interested two-machine scheduling problem. Methods that solve this important optimality criterion in flowshop environment are mainly heuristics. In fact, despite the -hardness in the strong sense of the studied problem, to the best of our knowledge there are no approximate algorithms (constructive heuristics or metaheuristics) or an algorithm… More >

B. Murugeshwari^1,*, D. Selvaraj², K. Sudharson³, S. Radhika⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 839-851, 2023, DOI:10.32604/iasc.2023.028548

Abstract Protecting the privacy of data in the multi-cloud is a crucial task. Data mining is a technique that protects the privacy of individual data while mining those data. The most significant task entails obtaining data from numerous remote databases. Mining algorithms can obtain sensitive information once the data is in the data warehouse. Many traditional algorithms/techniques promise to provide safe data transfer, storing, and retrieving over the cloud platform. These strategies are primarily concerned with protecting the privacy of user data. This study aims to present data mining with privacy protection (DMPP) using precise elliptic curve cryptography (PECC), which builds… More >

G. Arutperumjothi^1,*, K. Suganya Devi², C. Rani³, P. Srinivasan⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1069-1086, 2023, DOI:10.32604/iasc.2023.028423

Abstract In recent years, Peripheral blood smear is a generic analysis to assess the person’s health status. Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error. This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques. Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images. In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with the classification of eight groups… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1245-1259, 2023, DOI:10.32604/iasc.2023.028290

Abstract Smoking is a major cause of cancer, heart disease and other afflictions that lead to early mortality. An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives. Smoking activities often accompany other activities such as drinking or eating. Consequently, smoking activity recognition can be a challenging topic in human activity recognition (HAR). A deep learning framework for smoking activity recognition (SAR) employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules (ResNetSE) to increase the effectiveness of the SAR framework. The proposed model was tested against… More >

A. Sivaranjani^1,*, B. Vinod²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1135-1150, 2023, DOI:10.32604/iasc.2023.028126

Abstract Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment. Reinforcement Learning methods enable a state action function in mobile robots suited to their environment. During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own. The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles. But it requires a large number of training iterations. This research mainly focuses on a mobility robot’s best path prediction… More >