Super-Resolution of Landsat-8 Land Surface Temperature Using Kolmogorov–Arnold Networks with PlanetScope Imagery and UAV Thermal Data by Mahdiyeh Fathi, Hossein Arefi, Reza Shah-Hosseini, Armin Moghimi

“…Super-Resolution Land Surface Temperature (LST_SR) maps are essential for urban heat island (UHI) analysis and temperature monitoring. While much of the literature focuses on improving the resolution of low-resolution LST (e.g., MODIS-derived LST) using high-resolution space-borne data (e.g., Landsat-derived LST), Unmanned Aerial Vehicles (UAVs)/drone thermal imagery are rarely used for this purpose. …”
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PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation by Shengyuan Liu, Ruofan Zhang, Mengjie Fang, Hailin Li, Tianwang Xun, Zipei Wang, Wenting Shang, Jie Tian, Di Dong

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Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study by Jin-Hyun Park, Inyong Jeong, Gang-Jee Ko, Seogsong Jeong, Hwamin Lee

“…The model was trained using dual-energy x-ray absorptiometry data from KNHANES (2008-2011) and validated internally with bioelectrical impedance analysis data from KNHANES 2022. …”
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A Machine Learning Model for Predicting Intensive Care Unit Admission in Inpatients with COVID-19 Using Clinical Data and Laboratory Biomarkers by Alfonso Heriberto Hernández-Monsalves, Pablo Letelier, Camilo Morales, Eduardo Rojas, Mauricio Alejandro Saez, Nicolás Coña, Javiera Díaz, Andrés San Martín, Paola Garcés, Jesús Espinal-Enriquez, Neftalí Guzmán

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An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…Regression scores equal to 1 indicate a good match between the actual data and the predictions. Conclusively, the proposed investigation provides insightful AI, ML and CFD-proposed analysis of blood-based nano-particles which can improve imaging techniques, provide tailored drug delivery, reduce hyperthermia, improve blood flow, and show potential for application in medicine.Highlights Artificial intelligence and machine learning-based CFD simulation of the blood-mediated tri-hybrid nano-fluid flow is presented.An improved finite difference scheme (the Keller-Box method), is utilized to numerically evaluate the problem.The LMA-ANN forecasts with an absolute error range of [Formula: see text] to [Formula: see text] relative to the actual data.Regression scores equal to 1 indicate a strong correlation between forecasts and actual data.…”
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Enhancing Reliability Engineering Through Weibull Distribution in R by Sameera Othman

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Toward Greener Matrix Operations by Lossless Compressed Formats by Francesco Tosoni, Philip Bille, Valerio Brunacci, Alessio de de Angelis, Paolo Ferragina, Giovanni Manzini

“…Notably, selecting the appropriate compressed format can reduce energy consumption by an order of magnitude on both servers and single-board computers. Moreover, our experiments reveal that while data parallelism can improve execution speed and energy efficiency, optimizing both simultaneously poses interesting challenges. …”
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A two stage grading approach for feature selection and classification of microarray data using Pareto based feature ranking techniques: A case study by Rasmita Dash

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Numerical Simulation of Low-Level Wind Shear Using CFD and LSTM Technology Based on the WRF Model by Zexin DONG, Shuoyan WU, Fang YE, Lijing CHEN, Yi LI, Chenbo SUN, Feng XU, Lei LIU

“…In an effort to elevate the precision of low-level wind shear forecasting， this paper amalgamates European Centre for Medium-Range Weather Forecasts （ECMWF） fifth-generation reanalysis data （ERA5） and National Centers for Environmental Prediction Final Operational Global Analysis （FNL） reanalysis data， high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model （ASTER GDEM） terrain data， and real-time observational data from Lanzhou Zhongchuan Airport.It employs the Weather Research and Forecasting Model （WRF）， WRF integrated with Computational Fluid Dynamics （CFD）， and Long Short-Term Memory （LSTM） neural network methods to simulate and analyze two wind shear events at Lanzhou Zhongchuan Airport on April 15-16， 2021.The findings reveal that： （1） within grids smaller than 1 kilometer utilizing Large Eddy Simulation （LES）， the WRF model demonstrates superior performance in wind speed simulation for individual stations， yet it falls short when compared to the WRF model combined with Computational Fluid Dynamics （CFD） models in simulating near-surface horizontal wind field wind speeds； （2） concerning the simulation of two low-level wind shears encountered during aircraft landing， both Weather Research and Forecasting Model - Large Eddy Simulation （WRF-LES） and Weather Research and Forecasting Model - Computational Fluid Dynamics （WRF-CFD） models are capable of simulating the first wind shear， however， the second appears to be influenced by the potentially lower wind speed data input into the models， with neither model achieving the threshold for wind speed difference， necessitating further validation in future work； （3） under low wind speed conditions （6 meters per second）， the LSTM-based single-variable wind speed prediction model maintains an average absolute error of approximately 0.59 meters per second， effectively capturing the nonlinear relationship of wind speed changes under various terrain and circulation background conditions.Despite being constrained by WRF errors and incomplete observational elements， multi-variable wind speed prediction can achieve wind speed forecasting with higher computational efficiency and generalization capabilities while ensuring that the average absolute percentage error is less than 6.60%.This paper not only verifies the differences between WRF-CFD and WRF-LES coupling schemes in wind field and low-level wind shear forecasting but also explores the feasibility and accuracy of LSTM-based wind speed prediction， aspiring to offer new perspectives and methods for enhancing wind field simulation accuracy and reducing the time required for detailed wind field simulation.…”
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Automated recognition and sorting of recycled textiles for sustainable fashion by Zlatin Zlatev, Julieta Ilieva

“…This accuracy depends on the spectral characteristics used, the method for reducing the volume of data, and the type of classifier. The obtained results can be used in the development of recognition systems for sorting textile fabrics depending on the composition of their fibers. …”
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Discovering patterns of play in netball with network motifs and association rules by Browne P., Morgan S., Bahnisch J., Robertson S.

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COMPLEX CLINICAL AND INSTRUMENTAL EVALUATION OF LUNG INJURY IN PATIENTS WITH RHEUMATOID ARTHRITIS by I. I. Nesterovich, K. V. Nochevnaya, Yu. D. Rabik, A. A. Speranskaya, V. P. Zolоtnitskaya, N. A. Amosova, Yu. E. Kim, V. I. Amosov, T. D. Vlasov, V. I. Trofimova

“…The data of physical examination  were nonspecific and unconvincing.  …”
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A Numerical Study for the Self-Propulsion Performance of a Propulsion System Using the Coanda Effect by Jun-Hee Lee, Kwang-Jun Paik, Do-Han Oh

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Fast TILs—A pipeline for efficient TILs estimation in non-small cell lung cancer by Nikita Shvetsov, Anders Sildnes, Masoud Tafavvoghi, Lill-Tove Rasmussen Busund, Stig Manfred Dalen, Kajsa Møllersen, Lars Ailo Bongo, Thomas Karsten Kilvær

“…We evaluate the effectiveness of the patch sampling procedure, the pipeline's ability to identify informative patches and computational efficiency, and the clinical value of produced scores using patient survival data.Our pipeline demonstrates the ability to selectively process informative patches, achieving a balance between computational efficiency and prognostic integrity. …”
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Rapid discovery of Transglutaminase 2 inhibitors for celiac disease with boosting ensemble machine learning by Ibrahim Wichka, Pin-Kuang Lai

“…The advancement of computational drug design, particularly using bio-cheminformatics-oriented machine learning, offers promising avenues for developing such therapies. …”
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Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for rheumatoid arthritis by integrated bioinformatics analysis and single-cell RNA sequenc... by Xianbin Li, Xianbin Li, Xianbin Li, Xueli Zhang, Tao Liu, Guodao Zhang, Dan Chen, Suxian Lin

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Detection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Market by Kurban Kotan, Bayram Kotan, Serdar Kirisoglu

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Potentials and Limitations of Using Sentinel Data for Power System Operation and Control: Case Study of Protection Against Forest Fires and Aerosol Contamination by Amalija Božiček, Božidar Filipović-Grčić, Bojan Franc, Davor Škrlec, Frano Tomašević

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Modification of the laser correlation spectrometer for analysis of biological fluids by E.N. Velichko, E.K. Nepomnyashchaya, E.T. Aksenov

“…Thus, the possibility for analyzing the composition of the blood serum of various donors and for obtaining diagnostic data has been proved.…”
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Sustainable strengthening of concrete deep beams with openings using ECC and Bamboo: An equation and data-driven approach through abaqus modeling and GEP by Fayiz Amin, Ijaz Ali, Ali Husnain, Muhammad Faisal Javed, Hisham Alabduljabbar, Asher Junaid

“…Initially, the model was validated with experimental data, followed by an analysis of various ECC and bamboo configurations to select the most economical strengthening approach for each material. …”
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