Translation of single channel electro encephalic signals into limb motion by A.B.R. Lara, Oscar E. Ruiz, L.O. Araujo Junior, F.P. Bhering

“…The advantages of this method are: (a) lower hardware expense and (b) lower computing load. …”
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A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines by Mathew Habyarimana, Abayomi A. Adebiyi

“…The operational efficiency of many industrial processes is greatly affected by condition monitoring, which has become more and more important in the detection and forecast of electrical machine failures. …”
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A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA by Kapu V. Sri Ram Prasad, K. Dhananjay Rao, Guruvulu Naidu Ponnada, Umit Cali, Taha Selim Ustun

“…Simulation analysis shows the detection and isolation method with great sensitivity indicating the incipient winding failures.…”
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Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia by Pakiza Amin, Saima Gulzar Ahmad, Hikmat Ullah Khan, Ehsan Ullah Munir, Naeem Ramzan

“…The performance of our proposed models in the public datasets was an AUC-ROC of more than 95% and in the clinical dataset an even higher 96%. These ensemble methods accurately show that they have effective results in improving the precision and reliability of pre-eclampsia forecasts. …”
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The emerging role of circulating tumor DNA in brain tumor research by Amir Modarresi Chahardehi, Niki Faraji, Nikoo Emtiazi, Reza Nasiri, Maryam Daghagheleh, Helia Mohammadaein, Fatemeh Masoudi, Kimia Ghazi Vakili, Aylin Sefidmouy Azar, Hossein Fatemian, Hossein Motedayyen, Reza Arefnezhad, Fatemeh Rezaei-Tazangi, Zahra Niknam, Marziye Ranjbar Tavakoli

“…Research indicates that ctDNA can detect actionable mutations, forecast little residual illness, and enable real-time monitoring of disease development and resistance. …”
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Trackwise Prediction of GNSS-R Delay–Doppler Maps With DDM-PredRNN Network by Weichen Sun, Xiaochen Wang, Bing Han, Dongkai Yang

“…Global navigation satellite system reflectometry (GNSS-R) is an emerging Earth observation method that utilizes reflection signals from navigation satellites for remote sensing of physical parameters, particularly in detecting ocean wind speed. …”
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Traffic Status Evolution Trend Prediction Based on Congestion Propagation Effects under Rainy Weather by Yongjie Xue, Rui Feng, Shaohua Cui, Bin Yu

“…In rainy weather, the accurate prediction of traffic status not only helps road traffic managers to formulate traffic management methods but also helps travelers design travel routes and even adjust travel time. …”
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Intelligent Optimization of OSPF Path Selection Using Machine Learning Models for Adaptive Network Routing by Rebeen Rebwar Hama Amin

“…Four important ML functions namely traffic forecast, anomaly detection, failure prediction, and dynamic cost optimization—have been used to improve OSPF performance. …”
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Socio-demographic disparities in global trends of lip and oral cavity neoplasms from 1990 to 2021 by Amr Sayed Ghanem, Ágnes Tóth, Attila Csaba Nagy

“…We analyzed annual incidence, mortality, and DALYs across 204 countries, using age-standardized rates and the Socio-demographic Index (SDI) to assess development-related impacts. Statistical methods included Kruskal–Wallis tests, linear regression, joinpoint regression for trends, and Exponential Smoothing for forecasts (2022–2030), with analyses conducted in STATA and Python, and p < 0.05 as significant. …”
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Extraction of Significant Wave Height from Spreading First-Order Bragg Peaks of Shipborne High-Frequency Surface Wave Radar with a Single Antenna by Xinbo Zhang, Junhao Xie, Guowei Yao, Chenghui Cao

“…Simulations and field experiments validate the feasibility and accuracy of the method across various scenarios, with a detection range of up to 120 km without auxiliary measurements. …”
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AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis by João Paulo Costa, José Torres Farinha, Mateus Mendes, Jorge O. Estima

“…The results obtained from the testing phase reveal a high level of accuracy in predicting belt failures, with the developed models consistently outperforming traditional methods. The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. …”
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Exploring the Limitations of Federated Learning: A Novel Wasserstein Metric-Based Poisoning Attack on Traffic Sign Classification by Suzan Almutairi, Ahmed Barnawi

“…WMPA leverages historical information from the FL process to forecast the next round&#x2019;s global model as a reference. …”
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A literature review of economic evaluations for a neglected tropical disease: human African trypanosomiasis ("sleeping sickness"). by C Simone Sutherland, Joshua Yukich, Ron Goeree, Fabrizio Tediosi

“…Modelling was a common method to forecast long-term results, and publications focused on interventions by category, such as case detection, diagnostics, drug treatments, and vector control. …”
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Population immunity to SARS-CoV-2 virus in residents of the Irkutsk Region in the dynamics of the epidemic by S. V. Balakhonov, V. I. Dubrovina, M. V. Chesnokova, D. D. Bryukhova, N. O. Kiseleva, A. B. Pyatidesyatnikova, K. M. Korytov, V. V. Voitkova, A. N. Perezhogin, T. A. Gavrilova, A. A. Seledtsov

“…In the fight against this viral disease, an important role is assigned to the study of the development of population immunity to the SARSCoV-2 virus, which will make it possible to assess the dynamics of seroprevalence and the formation of post-infectious humoral immunity, forecasting the development of the epidemiological situation, elucidating the characteristics of the epidemic process, and will also contribute to planning activities for specific and non-specific prevention of the disease.The aim: to determine the dynamics of population immunity to SARS-CoV-2 among the population of the Irkutsk region during the COVID-19 pandemic.Materials and methods. …”
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How to trace the origins of short-lived atmospheric species: an Arctic example by A. Da Silva, L. Marelle, J.-C. Raut, Y. Gramlich, K. Siegel, K. Siegel, S. L. Haslett, S. L. Haslett, C. Mohr, C. Mohr, J. L. Thomas

“…However, the accuracy of these methods is not well quantified. This study provides an evaluation of these analysis protocols by combining backward trajectories from the FLEXible PARTicle dispersion model (FLEXPART) with simulations of tracers from the Weather Research and Forecast model including Chemistry (WRF-Chem). …”
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Research on Multiscale Atmospheric Chaos Based on Infrared Remote-Sensing and Reanalysis Data by Zhong Wang, Shengli Sun, Wenjun Xu, Rui Chen, Yijun Ma, Gaorui Liu

“…Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. …”
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An LJDRNN-based efficient energy intensity prediction in carbon fiber composite material manufacturing process by Rangaswamy Nikhil, Karthikeyan A G, Prabhu Loganathan, Tabrej Khan, Tamer A Sebaey

“…By enabling more precise energy intensity forecasting, the proposed method supports producers in optimizing their manufacturing processes, reducing energy costs, and aligning with sustainable production goals, ultimately driving greater operational efficiency and competitiveness in the CF industry.…”
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Analysis of lightning localization errors based on different station layouts by Yingting ZENG, Jiaying GU, Ruoyun ZHANG, Qinbo YANG, Jinquan ZENG

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Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

“…To address these gaps, this study proposes an online multi-fault prediction framework integrating three novel contributions: (1) a sensor fusion strategy leveraging existing thermal/electrochemical measurements (voltage, current, temperature, humidity, and pressure) without requiring embedded stack sensors; (2) a real-time sliding window mechanism enabling dynamic prediction updates every 1 s under variable load conditions; and (3) a modified CNN-based Bi-LSTM parallel model with attention mechanism (ConvBLSTM-PMwA) architecture featuring multi-input multi-output (MIMO) capability for simultaneous flooding/air-starvation detection. Through comparative analysis of different neural architectures using experimental datasets, the optimized ConvBLSTM-PMwA achieved 96.49% accuracy in predicting dual faults 64.63 s pre-occurrence, outperforming conventional LSTM models in both temporal resolution and long-term forecast reliability.…”
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The Application of Artificial Digital Models in X-Ray Computed Tomography (CT) of the Core in Solving the Problem of Binarization of the Void Space of Reservoir Rocks by O. A. Melkishev, Y. V. Savitsky, S. V. Galkin

“…These phantoms were then converted into tomograms, allowing us to determine statistical characteristics of the values for X-ray densities of the samples at the reconstruction stage.Based on the statistical analysis of the X-ray density distribution in the sample, we determined the boundary values that are most suitable for reliable void space detection. Using regression and correlation methods, we developed a model to estimate the optimal boundary value for X-ray density in void space allocation.We proposed an algorithm for determining and applying this value in the analysis of core X-ray CT data.This model was tested on real samples that were not used in the development of the forecast model. …”
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