Parth Khandelwal¹, Harshit², Indranil Manna^1,3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1727-1755, 2024, DOI:10.32604/cmc.2024.042752

Abstract Metallic alloys for a given application are usually designed to achieve the desired properties by devising experiments based on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises. However, the influence of process parameters and material properties is often non-linear and non-colligative. In recent years, machine learning (ML) has emerged as a promising tool to deal with the complex interrelation between composition, properties, and process parameters to facilitate accelerated discovery and development of new alloys and functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles, to design novel copper alloys for achieving… More > Graphic Abstract

YAN ZHANG, HEYANG ZHANG, JING WANG, XIN WEI, YI QU, FENG XU, LIJUN ZHANG^*

Oncology Research, Vol.32, No.5, pp. 955-963, 2024, DOI:10.32604/or.2023.043922

Abstract Background: Bortezomib results in peripheral neuropathy (PN) in approximately 50% of patients, during multiple myeloma (MM) treatment, a complication known as Bortezomib-induced peripheral neuropathy (BIPN). The drug response varies among individuals. Genetic factor may play an important role in BIPN. Methods: A next-generation sequencing (NGS) panel containing 1659 targets from 233 genes was used to identify risk variants for developing BIPN in 204 MM patients who received bortezomib therapy. mRNA expression of MTHFR and ALDH1A1 in 62 peripheral blood samples was detected by real-time quantitative PCR (RT-qPCR). Serum homocysteine (Hcy) levels were detected in 40 samples by chemiluminescent microparticle immunoassay… More > Graphic Abstract

Rashmi Sharma¹, Ashok Pal¹, Nitin Mittal², Lalit Kumar², Sreypov Van³, Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3489-3510, 2024, DOI:10.32604/cmc.2024.046606

Abstract This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm (MOALO) which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm (ALO) and the Genetic Algorithm (GA). MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions. The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO. A first-time hybrid of these algorithms is employed to solve… More >

Dandan Wang, Xiaohong Li, Yanwen Zhang^*

Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 355-369, 2024, DOI:10.32604/phyton.2024.047183

Abstract Taxus cuspidata is a rare plant with important medicinal and ornamental value. Aiming at the obvious differences between wild and cultivated populations of T. cuspidata from Northeast China, a total of 61 samples, that is, 33 wild yews and 28 cultivated yews were used to analyze the differences and correlations of the kinship, genetic diversity, and genetic structure between them by specific length amplified fragment sequencing (SLAF-seq). Finally, 470725 polymorphic SLAF tags and 58622 valid SNP markers were obtained. Phylogenetic analysis showed that 61 samples were classified into 2 clusters: wild populations and cultivated populations, and some wild yews were… More >

Qinhui Liu, Laizheng Zhu, Zhijie Gao, Jilong Wang, Jiang Li^*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 811-843, 2024, DOI:10.32604/cmc.2023.046040

Abstract To improve the productivity, the resource utilization and reduce the production cost of flexible job shops, this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching. Firstly, a mathematical model is established to minimize the maximum completion time. Secondly, an improved two-layer optimization algorithm is designed: the outer layer algorithm uses an improved PSO (Particle Swarm Optimization) to solve the workpiece batching problem, and the inner layer algorithm uses an improved GA (Genetic Algorithm) to solve the dual-resource scheduling problem. Then, a rescheduling method is designed to solve the… More >

Liang Zeng^1,2, Junyang Shi¹, Yanyan Li¹, Shanshan Wang^1,2,*, Weigang Li³

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803

Abstract The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III algorithm based on differential evolution… More >

Hayder Makki Shakir, Jaber Karimpour^*, Jafar Razmara

Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545

Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource accessibility, and security. In this… More >

Jiamin Xiang¹, Ying Zhang¹, Xiaohua Cao^1,*, Zhigang Zhou²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3443-3466, 2023, DOI:10.32604/cmc.2023.045120

Abstract This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles (AGVs) under the composite operation mode. The multi-objective model aims to minimize the maximum completion time, the total distance covered by AGVs, and the distance traveled while empty-loaded. The improved hybrid algorithm combines the improved genetic algorithm (GA) and the simulated annealing algorithm (SA) to strengthen the local search ability of the algorithm and improve the stability of the calculation results. Based on the characteristics of the composite operation mode, the authors introduce the combined coding and parallel decoding… More >

Yajun Liang^1,2,#, Juyun Zheng^1,#, Junduo Wang^1,#, Zhaolong Gong¹, Zhiqiang Li³, Ling Min⁴, Zeliang Zhang², Zhiwei Sang², Yanying Qu², Xueyuan Li^1,*, Quanjia Chen^2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.12, pp. 3345-3357, 2023, DOI:10.32604/phyton.2023.028755

Abstract Upland cotton (Gossypium hirsutum) is the most important plant producing natural fibers for the textile industry. In this study, we first investigated the phenotypic variation of seven agronomic traits of 273 diverse cotton accessions in the years 2017 and 2018, which were from 18 geographical regions. We found large variations among the traits in different geographical regions and only half of the traits in either years 2017 or 2018 followed a normal distribution. We then genotyped the collection with 81,612 high quality SNPs. Phylogenetic tree and population structure revealed a diverse genetic structure of the core collection, and geographical diversification… More >

YAFEI WANG^1,2,#, JIELEI NI^1,#, YUHAN LIU², DINGYING LIAO³, QIANWEN ZHOU¹, XIAOYANG JI², GANG NIU², YANXIANG NI^1,*

BIOCELL, Vol.47, No.12, pp. 2567-2578, 2023, DOI:10.32604/biocell.2023.043781

Abstract Down syndrome (DS) is a genetic condition characterized by intellectual disability, delayed brain development, and early onset Alzheimer’s disease. The use of primary neural cells and tissues is important for understanding this disease, but there are ethical and practical issues, including availability from patients and experimental manipulability. Moreover, there are significant genetic and physiological differences between animal models and humans, which limits the translation of the findings in animal studies to humans. Advancements in induced pluripotent stem cells (iPSC) technology have revolutionized DS research by providing a valuable tool for studying the cellular and molecular pathologies associated with DS. Induced… More >