Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions by Shumaila Javaid, Ruhul Amin Khalil, Nasir Saeed, Bin He, Mohamed-Slim Alouini

“…We outline the current architecture of ISATNs and highlight the significant role LLMs can play in optimizing data flow, signal processing, and network management to advance 5G/6G communication technologies through advanced predictive algorithms and real-time decision-making. …”
Get full text

Differentiable Deep Learning Surrogate Models Applied to the Optimization of the IFMIF-DONES Facility by Galo Gallardo Romero, Guillermo Rodríguez-Llorente, Lucas Magariños Rodríguez, Rodrigo Morant Navascués, Nikita Khvatkin Petrovsky, Rubén Lorenzo Ortega, Roberto Gómez-Espinosa Martín

“…Specifically, neural operators are employed to predict deuteron beam envelopes along the longitudinal axis of the accelerator and neutron irradiation effects at the end, after the beam collision. …”
Get full text

Two-Way Feature Extraction Using Sequential and Multimodal Approach for Hateful Meme Classification by Apeksha Aggarwal, Vibhav Sharma, Anshul Trivedi, Mayank Yadav, Chirag Agrawal, Dilbag Singh, Vipul Mishra, Hassène Gritli

“…These approaches utilize a combination of glove, encoder-decoder, and OCR with Adamax optimizer deep learning algorithms. Facebook Challenge Hateful Meme Dataset is utilized which contains approximately 8500 meme images. …”
Get full text

Computational Linguistics Applications in AI-Based Investment and Cost Structuring Models by Miralieva Dilafruz, Ramatov Jumaniyoz, Azimova Lola, Khusamiddinova Malika, Alimbaeva Shahlo, Omonova Laylo

“…Empirically, we draw on a cross-sectional research project, assessing the predictive power of linguistic data processing algorithms and the experiences of financial analysts in cost structuring contexts. …”
Get full text

Applying the Block-Based Programming Language ALICE for Developing Programming Competencies in University Students by Mariuxi Vinueza-Morales, Jesennia Cardenas-Cobo, Jessica Cabezas-Quinto, Cristian Vidal-Silva

“…The information society is part of modern life, and algorithmic thinking and programming are relevant to everybody, regardless of educational background. …”
Get full text

Switchable filter compensating installation in AC traction network by L. A. German, A. S. Serebryakov, V. L. Osokin, D. V. Yakunin

Get full text

Coordinated multi-level scheduling method considering uncertainty of renewable energy and load by Bingbing Song, Kangping Qin, Min Wen, Moyan Zhu, Kaiming Zou, Guangyu He

Get full text

Neuropsychological Principles of Rehabilitation Training in the Therapy of Patients with Facial Nerve Palsy by L. B. Zavaliy, G. R. Ramazanov, M. V. Kalantarova, A. A. Rakhmanina, A. B. Kholmogorova, S. S. Petrikov

“…Specialists of the Research Institute developed a six-step rehabilitation education program for patients with FNP, aimed at lifestyle correction, prevention of complications, and also directly at teaching methods of daily physical impact (rehabilitation) at home. …”
Get full text

Numerical investigation on damage characteristics of surrounding rocks in the deep underground tunnel subjected to full-face smooth blasting by Hongbo Du, Xiao Huang, Haoran Wang, Yu Wan, Shiyuan Huang, Wenjie Li

Get full text

Frequency-Based Cable Tension Identification Using a Nonlinear Model with Complex Boundary Constraints by Wen-ming Zhang, Zhi-wei Wang

Get full text

A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
Get full text

Uso de inteligencia artificial para predecir complicaciones en cirugías de columna toracolumbar degenerativa: revisión sistemática by G. Ricciardi, J.I. Cirillo Totera, R. Pons Belmonte, L. Romero Valverde, F. López Muñoz, A. Manríquez Díaz

“…Abstract: Objective: We aim to conduct a systematic review of the literature to evaluate the effectiveness of artificial intelligence prediction models in predicting complications in adult patients undergoing surgery for degenerative thoracolumbar pathology compared with other commonly used prediction techniques. …”
Get full text

基于数字图像比色法的食用油酸价检测研究Determination of acid value of edible oil by digital image colorimetry-based method by 李跃凡1，马改琴1，王媛媛1，张帆2，于修烛1LI Yuefan1, MA Gaiqin1, WANG Yuanyuan1, ZHANG Fan2, YU Xiuzhu1

“…The model established by the robust linear regression algorithm had the best prediction effect with a root mean square error of 0.734 3. …”
Get full text

Chaotic Mountain Gazelle Optimizer Improved by Multiple Oppositional-Based Learning Variants for Theoretical Thermal Design Optimization of Heat Exchangers Using Nanofluids by Oguz Emrah Turgut, Mustafa Asker, Hayrullah Bilgeran Yesiloz, Hadi Genceli, Mohammad AL-Rawi

“…The Mountain Gazelle Optimizer is a recently developed metaheuristic algorithm that simulates the foraging behaviors of Mountain Gazelles. …”
Get full text

Wave propagation-based tests for concrete piles – an overview by Reham Samaan, Abdelsalaam Mokhtar, Mohamed Saafan, Ahmed Ebid

“…This investigation establishes the practical and theoretical basis for implementing advanced machine learning predictive models that combine previous records with pattern recognition algorithms, potentially converting traditional PIT interpretation from an uncertain process to a reliable and precise evaluation system. …”
Get full text

Detecting anomalies in graph networks on digital markets. by Agata Skorupka

“…Methods are compared against each other, and state-of-the-art results from the literature and a new algorithm is proposed. The goal is to find an efficient method suitable for threat detection, both in terms of predictive performance and computational efficiency. …”
Get full text

Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. …”
Get full text

A Novel Long Short-Term Memory-Based Approach for Microgrid Fault Detection and Classification Using the Wavelet Scattering Transform by Naema M. Mansour, Abdelazeem A. Abdelsalam, Ibrahim A. Awaad

“…These limitations underscore the urgent need for intelligent, adaptive, and fast-responding fault detection and classification algorithms tailored specifically to the nature of microgrids. …”
Get full text

Fostemsavir resistance in clinical context: a narrative review by Jonathan M. Schapiro, Rolf Kaiser, Mark Krystal, Chris M. Parry, Allan R. Tenorio, Eugene Stewart, Bruce Gilliam, Margaret Gartland, Andrew Clark, Jose R. Castillo-Mancilla

“…Due to these factors and limited phenotypic clinical data, thus far, no relevant phenotypic cutoff or genotypic algorithms have been derived that reliably predict response to fostemsavir-based therapy in individuals who are HTE; therefore, pre-treatment temsavir resistance testing may be of limited benefit. …”
Get full text

Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmatio... by Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner

“…Improvements to early NGS methods and bioinformatics algorithms have dramatically improved variant calling accuracy, particularly for single nucleotide variants (SNVs), thus calling into question the necessity of confirmatory testing for all variant types. …”
Get full text