Identifying ferroptosis-related genes in lung adenocarcinoma using random walk with restart in the PPI network by Can Liu, Peng He, Ru Qiao, Xiaoyan Yang, Changsong Ding, Fuyuan He

“…In this study, we employed the random walk with restart (RWR) algorithm on the LUAD protein-protein interaction (PPI) network to predict ferroptosis-related target genes. …”
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In Situ Monitoring and Numerical Experiments on Vertical Deformation Profiles of Large-Scale Underground Caverns in Giant Hydropower Stations by Hao Wu, Jian Liu, Xiaogang Wang, Lipeng Liu, Zhenhua Tian

“…After fitting the complete VDP curve with a Levenberg-Marquardt algorithm, we verify its validity by comparing predicted data with in situ monitoring data. …”
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Power System Reliability Assessment with Quantification of Demand Response Uncertainty Based on Advanced Sigmoid Cloud Model by Bo Hu, Yue Sun, Wei Huang, Changzheng Shao, Tao Niu, Xin Cheng, Kaigui Xie

“…However, the price elasticity curve of the DR resources is influenced by consumers' behavioral uncertainty and therefore is difficult to predict. Consequently, additional risk may be introduced to composite power system reliability. …”
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Digital Health Technologies for Optimising Treatment and Rehabilitation Following Surgery: Device-Based Measurement of Sling Posture and Adherence by Joss Langford, Ahmed Barakat, Engy Daghash, Harvinder Singh, Alex V. Rowlands

“…Results: We found that upper arm angle and posture type during sling wear can be predicted from a sling sensor alone (R² = 0.79, <i>p</i> < 0.001 and Cohen’s kappa = 0.886, respectively). …”
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LSAF-LSTM-Based Self-Adaptive Multi-Sensor Fusion for Robust UAV State Estimation in Challenging Environments by Mahammad Irfan, Sagar Dalai, Petar Trslic, James Riordan, Gerard Dooly

“…Validated on an in-house integrated UAV platform and evaluated against high-precision RTK ground truth, the algorithm incorporates deep learning-predicted fusion weights into an optimization-based odometry pipeline. …”
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Determining IFI44 as a key lupus nephritis’s biomarker through bioinformatics and immunohistochemistry by Yue Tan, Xueyao Wang, Deyou Zhang, Jiahui Wang, Shuxian Wang, Jinyu Yu, Hao Wu

“…The levels of IFI44 positively correlate with serum creatinine and the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and inversely with serum complement C3 and initial estimated glomerular filtration rate (eGFR).Conclusion IFI44 is identified as a promising biomarker for LN, offering potential to refine the assessment of disease progression and predict clinical outcomes. This facilitates the development of more personalized treatment strategies for LN patients.…”
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EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain–Computer Interface Performance by Hamidreza Darvishi, Ahmadreza Mohammadi, Mohammad Hossein Maghami, Meysam Sadeghi, Mohamad Sawan

“…After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. …”
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Identification of nitric oxide-mediated necroptosis as the predominant death route in Parkinson’s disease by Ting Zhang, Wenjing Rui, Yue Sun, Yunyun Tian, Qiaoyan Li, Qian Zhang, Yanchun Zhao, Zongzhi Liu, Tiepeng Wang

“…Using the Scaden deep learning algorithm, we predicted neurocyte subtypes and modelled dynamic interactions for five classic cell death pathways to identify the predominant routes of neuronal death during PD progression. …”
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Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms by Ruitong Han, Chang-Bo Liu, Wanting Sun, Shuai Yu, Haoran Zheng, Lin Deng

“…To ensure that the model predictions are sufficiently reliable, a multi-level gradcam algorithm is also introduced for checking the regions of interest of the Hierarchical model at two levels and the comparisons are made with human experts. …”
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ATA ENCRYPTION USING THE CHAOTIC DYNAMICS IN WIRELESS SENSOR NETWORKS by A. V. Sidorenko, K. S. Mulyarchik

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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…Chemical components were analyzed for statistical significance across leaf positions, and their influence on model predictions was interpreted using SHapley Additive exPlanations (SHAP). …”
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ELIMINATION OF ERRORS IN CODEWORDS ACCORDING TO THE RESULTS OF PARITY CHECKING by A. S. Poljakov, V. E. Samsonov

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Synthesis of the optimum two-channel direction finder with linear scanning of the antenna pattern by M. M. Bulash, S. V. Shaliapin

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Transfer Kernel Extreme Learning Machine Based on Bidirectional Cross Domain Approximation by Yuanxiao Zeng, Huimin Li, Yanbing Song

“…Finally, by combining the predictions from both transfer KELMs, our model significantly boosts its robustness. …”
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Consumer Behaviour: Analysing Marketing Campaigns through Recommender Systems and Statistical Techniques

“…This approach addresses the formidable challenges of accurately predicting consumer behaviour. We provide a detailed introduction to recommendation systems, emphasizing their vital role in the modern marketing landscape. …”
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Hybrid CNN-based Recommendation System by Muhammad Alrashidi, Roliana Ibrahim, Ali Selamat

“…In order to enhance the accuracy of predictions and address the challenges posed by sparsity, the proposed model incorporates both the extracted attributes and explicit interactions between items and users. …”
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Noise correlations and neuronal diversity may limit the utility of winner-take-all readout in a pop out visual search task. by Ori Hendler, Ronen Segev, Maoz Shamir

“…The analysis identifies specific response statistics that require further empirical characterization to accurately predict WTA performance in biologically plausible models of visual pop out detection.…”
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Modeling Lane Changes at Freeway On-Ramps With a Novel Car-Following Model Based on Desired Time Headways by Moritz Berghaus, Markus Oeser

“…The model also includes components to predict the lane change start time based on surrogate safety measures and to describe the lateral behavior during the lane change. …”
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3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning by Anastasia Skonta, Myrto G. Bellou, Haralambos Stamatis

“…The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. …”
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Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning by Jos P. Kanning, Junfeng Wang, Shahab Abtahi, Mirjam I. Geerlings, Ynte M. Ruigrok

“…Using the UK Biobank, we identified aSAH cases via hospital-based ICD codes and analysed 618 baseline variables covering demographics, lifestyle, medical history, and physical measurements. The CatBoost ML algorithm and Shapley Additive Explanations (SHAP) identified the top 25 variables most influential in predicting aSAH. …”
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