Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. …”
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Improved Artificial Bee Colony Optimization Underwater Localization Algorithm by Logistic Chaos Mapping and Differential Evolution by Jiaxing CHEN, Yang LIU, Xiaoqian LIU, Zhihua LIU

“…The chaotic sequence generated by the mapping function was employed as a replacement for the random number generator, with the objective of achieving a more uniform distribution of the population during the initial distribution phase. …”
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A Novel Color Image Encryption Algorithm Based on Hybrid Two-Dimensional Hyperchaos and Genetic Recombination by Yaoqun Xu, Jiaoyang Liu, Zelong You, Tianqi Zhang

“…This study integrates the Ackley function and the Styblinski–Tang function into a novel two-dimensional hyperchaotic map for optimization testing. A randomness test is run on the chaotic sequence created by the system to check that the new chaotic system can better sustain the chaotic state. …”
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A Three-Stage Optimization Algorithm for the Stochastic Parallel Machine Scheduling Problem with Adjustable Production Rates by Rui Zhang

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Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms by Chunxia Huang, Qiong Ma, Xiao Zeng, Jiawei He, Fengming You, Xi Fu, Yifeng Ren

“…Although the predominant genera were consistent between the groups, significant disparities were observed in their relative abundances. By leveraging the random forest algorithm, ten characteristic microbial variables were identified and incorporated into six models, which effectively facilitated PN diagnosis. …”
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An Image Encryption Method Based on the Feistel Network and Dynamic DNA Encoding by Xuncai Zhang, Zheng Zhou, Ying Niu

“…Based on the Feistel network and dynamic deoxyribonucleic acid (DNA) encoding technology, an image encryption method is proposed using the “permutation–diffusion–scrambling” structure. First, the SHA-3 algorithm is used to calculate the hash value of the plaintext image as the initial value of the hyperchaotic system, and the chaos-generated sequence is used to generate the Hill cipher matrix to replace the image pixel. …”
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Capturing resilience from phenotypic deviations: a case study using feed consumption and whole genome data in pigs by Enrico Mancin, Christian Maltecca, Jicaj Jiang, Yi Jian Huang, Francesco Tiezzi

“…We subsequently integrated these indicators with Whole Genome Sequencing using SLEMM algorithm, data from 1,250 animals to assess their efficacy in capturing resilience and their independence from the mean of daily feed consumption. …”
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Evolution of compressed sensing theory and dynamic electric energy measurement method by WU Wenqian, WANG Xuewei

“…Aiming at the problems of high complexity and low accuracy of compressed measurement algorithm in the current application of compressed sensing theory, an accurate compressed measurement method of dynamic electric energy value of pseudo-random signal is proposed. …”
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A sector fast encryption algorithm for color images based on one-dimensional composite sinusoidal chaos map. by Ye Tao, Wenhua Cui, Shanshan Wang, Yayun Wang

“…When scrambling, each pixel changes position with the three pixels before it according to the chaotic sequence. Finally, through many experiments, it is proved that the image encryption algorithm not only greatly improves the encryption and decryption speed, but also improves various indexes. …”
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Lightweight Image Encryption Algorithm Using 4D-NDS: Compound Dynamic Diffusion and Single-Round Efficiency by Yunlong Liao, Yiting Lin, Qiutong Li, Zheng Xing, Xiaochen Yuan

“…By leveraging the high dynamic properties of non-degenerate chaos, the scheme effectively addresses the insufficient sensitivity of pseudo-random sequences to initial values and enhances the sensitivity of the key to initial conditions. …”
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Research on Motion Transfer Method from Human Arm to Bionic Robot Arm Based on PSO-RF Algorithm by Yuanyuan Zheng, Hanqi Zhang, Gang Zheng, Yuanjian Hong, Zhonghua Wei, Peng Sun

“…To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. …”
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Research on 3D Path Optimization for an Inspection Micro-Robot in Oil-Immersed Transformers Based on a Hybrid Algorithm by Junji Feng, Xinghua Liu, Hongxin Ji, Chun He, Liqing Liu

“…Once the optimal node sequence is determined, detailed path planning between adjacent points is executed through a synergistic combination of the A algorithm*, Rapidly exploring Random Tree (RRT), and Particle Swarm Optimization (PSO). …”
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Optimizing Stack-Yard Positioning in Full Shoreline Loading Operations by Xueqiang Du, Bencheng Luo, Jing Wang, Jieting Zhao, Dahai Li, Qian Sun, Haobin Li

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Integrative analyses of 16S rDNA sequencing and serum metabolomics demonstrate significant roles for the oral microbiota and serum metabolites in post-kidney transplant diabetes me... by Chao Liu, Sheng Chao, Lei Jia, Qizhen Yang, Qian Chen, Yulin Niu

“…PTDM group showed upregulation of 36 metabolites and downregulation of 19 metabolites. Based on the random forest machine learning algorithm, genera such as UCG−005 (AUC = 0.9355), Succinivibrio (AUC = 0.8108); Akkermansia (AUC = 0.7742), Anaerovibrio (AUC = 0.2667), and Schwartzia (AUC = 0.2667), and serum metabolites such as LPI 18:0 (AUC: 0.8086), methylglyoxal (AUC: 0.7946), Vulgarin (AUC: 0.7828), 2-mercaptobenzothiazole (AUC: 0.7591), and PI(18:0/20:3(5Z,8Z,11Z)) (AUC: 0.7419) showed high diagnostic potential and may serve as clinical biomarkers. …”
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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

“…Fifteen combinations of linguistic features were trained using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Model performance was evaluated by prediction accuracy and the mean absolute error between predicted and actual readability. …”
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Effects of Increasing Temperature on Bacterial Community Diversity in Mixed Stands of <i>Artemisia argyi</i> and <i>Solidago canadensis</i> in Eastern China by Haochen Yu, Guangqian Ren, Zhiyun Huang, Shanshan Qi, Biying Zhao, Xue Fan, Zhaoqi Zhu, Zhicong Dai, Daolin Du

“…We observed significant shifts in invasion community soil bacteria in response to warming, with Acidobacteria, Actinobacteria, and others showing distinct responses between baseline and warmed conditions, while groups like Chlorobi and Cyanobacteria only differed significantly at higher temperature extremes. The random forests algorithm identified 14 taxa as biomarkers and a model was established to correlate <i>S. canadensis</i> invasion community soil microbiota with progressive warming. …”
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A Comparative Analysis of Different Algorithms for Estimating Evapotranspiration with Limited Observation Variables: A Case Study in Beijing, China by Di Sun, Hang Zhang, Yanbing Qi, Yanmin Ren, Zhengxian Zhang, Xuemin Li, Yuping Lv, Minghan Cheng

“…The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. …”
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Preliminary study on objective evaluation algorithm of human infrared thermogram seriality and its clinical application in population with metabolic syndrome by Yu Chen, Jia-Yang Guo, Yan-Hong An, Xian-Hui Zhang, Jia-Min Niu, Xiao-Ran Li, Hui-Zhong Xue, Yi-Meng Yang, Lu-Qi Cai, Yu-Chen Xia, Quan-Yi Chen, Bing-Yang Cai, Wen-Zheng Zhang, Yong-Hua Xiao

“…According to the algorithm proposed in this article, the thermal sequence values of patients with MS were calculated and compared between the two groups. …”
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The Modified Sparrow Search Algorithm with Brown Motion and Levy Flight Strategy for the Class Integration Test Order Generation Problem by Chongyang Jiao, Qinglei Zhou, Wenning Zhang, Chunyan Zhang

“…Then, the discoverer learning strategy of Brownian motion is introduced and the Levy flight is utilized to renew the positions of the followers, which balances the global search and local search of the algorithm. Finally, the optimal solution is subjected to random wandering to increase the probability of the algorithm jumping out of the local optimum. …”
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Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms by Yi Liu, Bingxian Bian, Shiyu Chen, Bingqian Zhou, Peng Zhang, Lisong Shen, Hui Chen

“…Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes. …”
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