Assessing the suitability of the Langevin equation for analyzing measured data through downsampling by Pyei Phyo Lin, Matthias Wächter, Joachim Peinke, M Reza Rahimi Tabar

“…The measured time series from complex systems are renowned for their complex stochastic behavior, characterized by random fluctuations stemming from external influences and nonlinear interactions. …”
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Spin Logical and Memory Device Based on the Nonvolatile Ferroelectric Control of the Perpendicular Magnetic Anisotropy in PbZr0.2Ti0.8O3/Co/Pt Heterostructure by Zengyao Ren, Mengxi Wang, Pengfei Liu, Qi Liu, Kaiyou Wang, Gehard Jakob, Jikun Chen, Kangkang Meng, Xiaoguang Xu, Jun Miao, Yong Jiang

“…Furthermore, the multiferroic random access memory and logic device operations are demonstrated based on the ferroelectric‐modulated AHV, which can lower the operating current density. …”
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Biomass and carbon stock of conifer and broad-leaf forest stands in Talra Wildlife Sanctuary across Northwest Himalayas, India by Anil Kumar, Anil Kumar, Khilendra Singh Kanwal, Shiv Paul, Raj Kumar Verma

“…The data acquisition was performed through random sampling using 50 × 50 m plots along the different altitudinal gradients. …”
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Method and experimental verification of spatial attitude prediction for an advanced hydraulic support system under mining influence by Zhuang Yin, Kun Zhang, ZengBao Zhang, Hongyue Chen, Lingyu Meng, Zhen Wang, Mingchao Du, Xiangpeng Hu, Defu Zhao, Dan Tian

“…The selection of training parameters in the conventional Long Short-Term Memory (LSTM) network is often random and involves a significant amount of effort, which further limits prediction accuracy and real-time performance. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…This study presents a novel hybrid methodology that combines pre-trained CNN architectures, including VGG16, InceptionV3, and ResNet50, with advanced classification models such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and the deep learning-based Multi-Layer Perceptron (MLP). …”
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The Effect of Post-Deposition Heat Treatment on the Microstructure, Texture, and Mechanical Properties of Inconel 718 Produced by Hybrid Wire-Arc Additive Manufacturing with Inter-... by Dmitrii Panov, Gleb Permyakov, Stanislav Naumov, Vladimir Mirontsov, Egor Kudryavtsev, Liying Sun, Alexander Aksenov, Nikita Stepanov, Dmitriy Trushnikov, Gennady Salishchev

“…In the as-printed material, two characteristic zones were distinguished, as follows: (i) columnar grains with a preferable <100> orientation and (ii) fine grains with a random crystallographic orientation. The development of static recrystallization induced via inter-pass forging and further heating during the deposition of the next (upper) layer provoked the formation of the fine-grained zone. …”
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Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model by Jing Gao, Xu Jie, Yujun Yao, Jingdong Xue, Lei Chen, Ruiyao Chen, Jiayuan Chen, Weiwei Cheng

“…A total of 5 basic ML algorithms, including Ridge, SVM, Random Forest, extreme gradient boosting (XGBoost), and Multi-Layer Perceptron, were used to develop the prediction model, which was then averaged into an ensemble learning model. …”
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Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning by Mohamed Kamel Elshaarawy, Martina Zeleňáková, Asaad M. Armanuos

“…Abstract This study investigates the effectiveness of inclined double cutoff walls installed beneath hydraulic structures by employing five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). …”
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Artificial intelligence (AI) approaches to male infertility in IVF: a mapping review by Kowsar Qaderi, Foruzan Sharifipour, Mahsa Dabir, Roshanak Shams, Ali Behmanesh

“…AI also predicts IVF success (e.g., random forests with AUC 84.23% on 486 patients) and assesses sperm DNA fragmentation. …”
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Decay of Turbulent Upper-hybrid Waves in Weakly Magnetized Solar Wind Plasmas by F. J. Polanco-Rodríguez, C. Krafft, P. Savoini

“…Whereas the impact of magnetic field on decaying waves of large k  = ∣ k ∣ is weak, important differences with respect to the unmagnetized plasma case manifest at small k -scales, where a boundary layer delimiting a spectral domain free of ${ \mathcal L }{ \mathcal Z }$ energy is revealed. …”
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Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery by Hongyan Yang, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Jie Li, Qihong Da, Xuchun Li, Kejing Cheng

“…Rapid and accurate determination of TN in the tillage layer is essential for agricultural production. Although UAV-based multispectral remote sensing technology has shown potential in agricultural monitoring, research on its quantitative assessment of soil TN content remains limited. …”
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The fusion of machine olfactory data and UV–Vis-NIR-MIR spectra enabled accurate prediction of key soil nutrients by Shuyan Liu, Lili Fu, Xiaomeng Xia, Jiamu Wang, Yvhang Cao, Xinming Jiang, Honglei Jia, Zengming Feng, Dongyan Huang

“…Predicting multiple nutrient contents within the framework of the multi-layer perceptron combined with random forest (MLP-RF) fusion model showed superior performance, with the coefficient of determination (R2) ranging from 0.80 to 0.96. …”
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Age-Related Changes in Stand Structure, Spatial Patterns, and Soil Physicochemical Properties in <i>Michelia macclurei</i> Plantations of South China by Jiaman Yang, Jianbo Fang, Dehao Lu, Cheng Li, Xiaomai Shuai, Fenglin Zheng, Honyue Chen

“…The results revealed that (1) spatial distribution shifted from aggregated in young stands (5–10 a) to random in mature stands (42 a), with diameter and height class distributions becoming more diverse with age. …”
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Diagnostic model of microvasculature and neurologic alterations in the retina and optic disc for lupus nephritis by Yun Yu, Xia-fei Pan, Qi-hang Zhou, Xiao-yin Zhou, Qian-hua Li, Yu-qing Lan, Xin Wen

“…Background: Machine learning (ML) analysis of retinal nerve fiber layer (RNFL) thickness and vessel density (VD) alterations in the macular region and optic disc may provide a new diagnostic method for lupus nephritis (LN). …”
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Foreign object recognition for mine conveyor belt iron separators based on transfer learning with EfficientNet by Hailong YANG, Yiping YUAN, Panpan FAN, Lu XIAO, Feiyang ZHAO, Shaoke YUAN

“…To simulate real dust-fog conditions, a random fogging method was employed, improving the model's generalization ability. …”
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Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma by Kai Xie, Huan Jiang, Xinwei Chen, Youquan Ning, Qiang Yu, Fajin Lv, Rui Liu, Yuan Zhou, Lin Xu, Qiang Yue, Juan Peng

“…Radiomics features were extracted from the MRI images, and seven radiomics models based on single and combined sequences were developed via random forest (RF). A DL model was constructed via ResNet 18, where DL features were extracted from its final fully connected layer. …”
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Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images by Mohamed J. Saadh, Qusay Mohammed Hussain, Rafid Jihad Albadr, Hardik Doshi, M. M. Rekha, Mayank Kundlas, Amrita Pal, Jasur Rizaev, Waam Mohammed Taher, Mariem Alwan, Mahmod Jasem Jawad, Ali M. Ali Al-Nuaimi, Bagher Farhood

“…Classifiers such as XGBoost, CatBoost, Random Forest, and a Voting Classifier were used to evaluate diagnostic performance. …”
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The PLSR-ML fusion strategy for high-accuracy leaf potassium inversion in karst region of Southwest China by Zhihao Song, Zhihao Song, Wen He, Yuefeng Yao, Ling Yu, Jinjun Huang, Yong Xu, Haoyu Wang

“…Our results showed that hybrid models combining Partial Least Squares Regression (PLSR) with three machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP)—namely PLSR-RF, PLSR-XGBoost, and PLSR-MLP, demonstrated exceptional accuracy in estimating leaf potassium content. …”
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Strain Redistribution Effect Based Composite Structured Sensor for Decouplable Tactile‐Strain Double‐Mode Perception by Hanning Wang, Xiaofei Liu, Da Chen, Zhanbo Zhang, Xinyu Ma, Hongchen Yu, Quanlin Qu, Huifang Wang, Fujie Cao, Tong Zhang, Yijian Liu

“…The CAD‐assisted design enables the dual‐mode sensing structure to be configured as a three‐layer stacked composite. Utilizing differential Young's modulus distribution, the strain redistribution effect is achieved across the structured frame. …”
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Electric Field-Enhanced SERS Detection Using MoS₂-Coated Patterned Si Substrate with Micro-Pyramid Pits by Tsung-Shine Ko, Hsiang-Yu Hsieh, Chi Lee, Szu-Hung Chen, Wei-Chun Chen, Wei-Lin Wang, Yang-Wei Lin, Sean Wu

“…The experimental results confirm that this method effectively resolves the issue of random distribution of analyte molecules during droplet evaporation, thereby enhancing detection sensitivity and stability.…”
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