Fuzzy control system of multioperational machine status by Andrey K. Tugengold, Andrey I. Izyumov, Roman N. Voloshin, Mikhail Y. Solomykin

“…The work objective is to develop a system for monitoring the condition, detecting dimensional wear, and determining the period of tool life on the basis of the fuzzy logic methods. …”
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Trends and advances in Leptospira, a bibliometric analysis by Wei Wang, Wei Wang, Wei Wang, Yamin Gao, Yamin Gao, Yamin Gao, Jianyu Ji, Jianyu Ji, Jianyu Ji, Zhai Huang, Zhai Huang, Bin Xiong, Bin Xiong, Shulin Xiang, Shulin Xiang

“…This study applies bibliometric analysis to generate a visual knowledge map, identifying research hotspots and forecasting future trends in leptospirosis investigations.MethodsData were extracted from the Web of Science Core Collection (WOSCC), encompassing all publications up to May 1, 2024. …”
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Hyperion Image Analysis Depicts a Preliminary Landscape of Tumor Immune Microenvironment in OSCC with Lymph Node Metastasis by Shang Xie, Xin-Yuan Zhang, Xiao-Feng Shan, Vicky Yau, Jian-Yun Zhang, Wei Wang, Yong-Pan Yan, Zhi-Gang Cai

“…This study is aimed at utilizing the Hyperion imaging system to depict a preliminary landscape of the tumor immune microenvironment in OSCC with lymph node metastasis. Methods. We collected neoplasm samples from OSCC patients. …”
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The impact of land use/land cover change on ecosystem services in Golestan province by Hamidreza Kamyab, Nasim Shabani

“…We also used sensitivity analysis to explore the robustness of results by 50% adjustment of value coefficients. Material and methods: Golestan Province is one of the 31 provinces of Iran, located in the south of the Caspian Sea. …”
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Application of deep learning in cloud cover prediction using geostationary satellite images by Yeonjin Lee, Seyun Min, Jihyun Yoon, Jongsung Ha, Seungtaek Jeong, Seonghyun Ryu, Myoung-Hwan Ahn

“…Traditional methods for cloud cover prediction encounter significant limitations in capturing complete spatial and temporal cloud dynamics. …”
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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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Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review by S. M. Atoar Rahman, Md Ibrahim Khalil, Hui Zhou, Yu Guo, Ziyun Ding, Xin Gao, Dingguo Zhang

“…Graph neural networks (GNNs), as a new kind of deep learning method, can use EEG signals as graph vertices, capturing the hidden topological connections between signals. …”
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Prediction and diagnosis of cardiovascular disease using cloud and machine learning design by K. Babu, A. Gokula Chandar, S. Kannadhasan

“…This is done to improve the process of forecasting patients’ health information. Detecting and categorising cardiac illness can be accomplished through the application of two distinct kinds of machine learning techniques. …”
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Content of macro- and microelements in the plants of Artemisia annua L., A. ludoviciana Nutt. and A. austriaca L. by О. А. Корабльова, Д. Б. Рахметов, M. І. Шанайда, O. M. Вергун, Л. В. Свиденко, В. І. Войцехівський

“…., A. ludoviciana Nutt. and A. austriaca L. Methods. Determination of the elemental composition of plant material was carried out by the X-ray fluorescence method. …”
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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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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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Advancing pancreatic cancer management: the role of artificial intelligence in diagnosis and therapy by Dilber Uzun Ozsahin, Natacha Usanase, Ilker Ozsahin

“…Main body Artificial intelligence (AI) technology enables rapid detection of high-risk groups for pancreatic cancer using various techniques such as medical imaging, pathological examination, biomarkers, and other methods, facilitating early detection of pancreatic cancer. …”
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A two-year wastewater-based surveillance of SARS-CoV-2 trends within the Tshwane region, South Africa by Miss Gugulethu Dhlamini, Doctor David-Noel Nogbou, Doctor John Y Bolukaoto, Miss Prudence F Ndzinisa, Proffesor Maphoshane Nchabeleng, Doctor Andrew M Musyoki

“…Introduction: Wastewater-Based epidemiology uses biological and chemical indicators for detection of infectious pathogens. This surveillance strategy forecast information on the presence, distribution, or the resurgence of diseases in countries with centralize sewage infrastructure. …”
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An AI-Based Approach for the Assessment of the Lost Efficiencies of a PVT Plant by Stefano Aneli, Giuseppe M. Tina, Bekkay Hajji, Safae Morgoum, Antonio Gagliano

“…Artificial Intelligence (AI)-based methods have become prominent due to their capability of producing accurate predictions against the uncertainty and nonlinearity phenomena. …”
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Scenario-based validation and prediction of land use changes in Birjand watershed in 1404 by Elham Yusefiroobiat, Fatemeh Jahanishakib

“…Introduction: Nowadays, detection and modeling land use changes using satellite imagery is a useful tool for understanding future environmental changes associated with human activities. …”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…A key innovation of this study is the development of an algorithm that processes low-amplitude high-frequency signals, which are often difficult to detect with conventional methods. Additionally, a neural network-based forecasting algorithm utilizing the Daubechies wavelet transform is introduced to improve the predictive accuracy of subsurface characteristics. …”
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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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New aspects of pathogenesis of respiratory dysfunction in case of chronic rheumatic heart disease by G. S. Taktashov

“…Data received enable to define the criteria of early detection and forecast of course of the disturbance of the next lungs’ function: moisture separating, diffusion, conditioning and surfactant generating.…”
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Hydrogen Safety in Solid Oxide Fuel Cells: an LSTM-Based Model for Predicting Temperature Anomalies and Change Points by Tomaso Vairo, Davide Clematis, Maria Paola Carpanese, Bruno Fabiano

“…The results indicate that the LSTM-based model significantly outperforms traditional methods in both prediction accuracy and early anomaly detection. …”
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Automated methane emission monitoring systems based on satellite data: Radiation transfer model analysis by K. Aghayeva, G. Krauklit

“…The seasonal analysis demonstrated an increase in concentrations in winter (3 ppm) and summer (2.7 ppm) due to a decrease in the dispersion rate and intensification of mining. Machine learning methods (XGBoost, Random Forest) improved the forecasting accuracy (R2=0.91 for XGBoost) by identifying key factors: wind speed, temperature, mining intensity and humidity. …”
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