Parameter Optimization of Milling Process for Surface Roughness Constraints by GUO Bin, YUE Caixu, ZHANG Anshan, JIANG Zhipeng, YUE Daxun, QIN Yiyuan

“… In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal, the surface roughness regression model is established based on extreme gradient boosting (XGBOOST) with the spindle speed, feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed, feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048μm, while the minimum material removal rate increases by 2458.048mm3/min.While achieving surface roughness, the processing efficiency is improved, and the manufacturing costs are reduced, resulting in good optimization effects, which has a certain guiding role in the actual processing.…”
Get full text

Using single-sample networks and genetic algorithms to identify radiation-responsive genes in rice affected by heavy ions of the galactic cosmic radiation with different LET values by Yan Zhang, Wei Wang, Meng Zhang, Binquan Zhang, Shuai Gao, Meng Hao, Dazhuang Zhou, Lei Zhao, Guenther Reitz, Guenther Reitz, Yeqing Sun

“…The LET regression models were constructed from both gene expression and interaction pattern perspectives respectively, and the radiation response genes that played significant roles in the models were identified. We designed a gene selection algorithm based on GA to enhance the performance of LET regression models.ResultsThe experimental results demonstrate that all our models exhibit excellent regression performance (R2 values close to 1), which indicates that both gene expressions and interaction patterns can reflect the molecular changes caused by heavy ions with different LET values. …”
Get full text

Identifying pesticide mixtures at country-wide scale by Cairo, Milena, Monnet, Anne-Christine, Robin, Stéphane, Porcher, Emmanuelle, Fontaine, Colin

Get full text

ADVCSO: Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization for Combinatorial Optimization Problems by Kunwei Wu, Liangshun Wang, Mingming Liu

“…This study proposes an Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization (ADVCSO) algorithm. First, to address the uneven initial solution distribution in the original algorithm, we design an elite perturbation initialization strategy based on good point sets, combining low-discrepancy sequences with Gaussian perturbations to significantly improve the search space coverage. …”
Get full text

Optimization and Verification of One-dimensional Three-field Model for Transient Analysis of Pressurized Water Reactor by YUAN Leqi1, LI Zanwang1, 2, FAN Jie1, 3, GOU Junli1, SHAN Jianqiang1

Get full text

Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…This study focuses on developing machine learning algorithms that accurately and rapidly predict affinity energy between novel chemical structures of membrane materials and human serum proteins, based on a molecular docking dataset. …”
Get full text

TMEM132A: a novel susceptibility gene for lung adenocarcinoma combined with venous thromboembolism identified through comprehensive bioinformatic analysis by Pei Xie, Yingli Liu, Pingping Bai, Yue Ming, Qi Zheng, Li Zhu, Yong Qi

“…Molecular crosstalk analysis identified candidate genes through differential expression algorithms and disease-association metrics. Functional annotation employed GO and KEGG analyses to elucidate the biological significance of identified CGs. …”
Get full text

Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data by Ali Asghar Zolfaghari, Maryam Raeesi, Giuseppe Longo-Minnolo, Simona Consoli, Miles Dyck

Get full text

Automation Mangrove Identification with Case Based Reasoning Process by Arie Vatresia, Asahar Johar, Rendra Regen, John Kennedy

“…The method used is case based reasoning method using the KNN algorithm which is used to calculate the similarity value between cases that will be applied to the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkulu. …”
Get full text

Energy-Efficient Hybrid Adaptive Clustering for Dynamic MANETs by Kudret Yilmaz, Resul Kara, Ferzan Katircioglu

“…In the second phase, the clustering is executed by identifying the member nodes and their roles of the selected CHs using the Enhanced Density Based Spatial Clustering of Applications with Noise (Enhanced-DBSCAN) algorithm, which is one of the unsupervised learning methods. …”
Get full text

Hierarchical Service Composition via Blockchain-enabled Federated Learning by Li Huang, Lu Zhao, Yansong Liu, Yao Zhao

“…Cloud Service Composition (CSC) has become pivotal in this context, playing a crucial role in enhancing efficiency, Quality of Service (QoS), and customer satisfaction through the aggregation of diverse Cloud Services (CSs) to create composite services. …”
Get full text

Role of diffuse endocrine system and cellular homeostasis of epithelial cells of the stomach mucosa in development and progression of peptic ulcer of the duodenum

Get full text

Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

“…All models employed the random forest algorithm and were trained on daily-scale ET observations, in-situ meteorological data and satellite remote sensing. …”
Get full text

The potential role of next-generation sequencing in identifying MET amplification and disclosing resistance mechanisms in NSCLC patients with osimertinib resistance by Xiao Xiao, Xiao Xiao, Ren Xu, Ren Xu, Jun Lu, Beibei Xin, Chenyang Wang, Kexin Zhu, Hao Zhang, Xinyu Chen

Get full text

Improving Unplugged Computational Thinking Skills Through Integrated Problem-Based and Differentiated Learning in Indonesia by Dewi Oktaviani, Susilo Satanti

“…Differentiated learning (content and process) plays a role in improving students’ computational thinking through ability-based scaffolding and content based on their interests and learning modalities. …”
Get full text

The role of epigenetic regulation in pancreatic ductal adenocarcinoma progression and drug response: an integrative genomic and pharmacological prognostic prediction model by Kang Fu, Junzhe Su, Yiming Zhou, Xiaotong Chen, Xiao Hu

“…Weighted gene co-expression network analysis (WGCNA) identified key epigenetic modules. A machine learning-based prognostic model was constructed using multiple algorithms, including Lasso and Random Survival Forest. …”
Get full text

OpenPheno: an open-access, user-friendly, and smartphone-based software platform for instant plant phenotyping by Tianqi Hu, Peng Shen, Yongshuai Zhang, Jiafei Zhang, Xin Li, Chuanzhen Xia, Ping Liu, Hao Lu, Tingting Wu, Zhiguo Han

“…In particular, OpenPheno allows developers to contribute new algorithmic tools, further expanding its capabilities to continuously facilitate the plant phenotyping community. …”
Get full text

Integrative Role of RNA N7-methylguanosine in epilepsy: Regulation of neuronal oxidative phosphorylation, programmed death and immune microenvironment. by Jiangli Zhao, Qingyuan Sun, Xuchen Liu, Jiwei Wang, Ning Yang, Chao Li, Xinyu Wang

“…Our findings also suggested that active m7G levels could promote oxidative phosphorylation in the neurons of epilepsy patients and decrease neuronal necroptosis activity. Machine learning algorithms were used to identify key m7G regulators (EIF4E3, NUDT3, SNUPN, LSM1, and METTL1), and a nomogram model was constructed based on these findings. …”
Get full text

Identification of stemness subtypes and prognostic modeling in thyroid cancer: the critical role of DPYSL3 in tumor progression and immune microenvironment by Jialong Yu, Wei Luo, Guangwei Xu, Mei Tao, Yuqi Wang, Qiman Dong, Linfei Hu, Xiukun Hou, Jingzhu Zhao, Dapeng Li, Songfeng Wei, Xianhui Ruan, Xiangqian Zheng

Get full text

Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis by Jinyang Wang, Haonan Shi, Xiaowei Wang, Enhong Dong, Jian Yao, Yonghan Li, Ye Yang, Tingting Wang

“…Furthermore, advanced machine learning algorithms have provided fresh insights into the interactions among various risk factors. …”
Get full text