Improved Low Complexity Predictor for Block-Based Lossless Image Compression by Huang-Chun Hsu, Jian-Jiun Ding, De-Yan Lu

“…As the predictor exploits no inter-block relation, it enables parallel processing and random access if encoded by fix-length coding (FLC).…”
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Small Language Models for Speech Emotion Recognition in Text and Audio Modalities by José L. Gómez-Sirvent, Francisco López de la Rosa, Daniel Sánchez-Reolid, Roberto Sánchez-Reolid, Antonio Fernández-Caballero

“…Speech emotion recognition has become increasingly important in a wide range of applications, driven by the development of large transformer-based natural language processing models. However, the large size of these architectures limits their usability, which has led to a growing interest in smaller models. …”
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Internet traffic data recovery via a low-rank spatio-temporal regularized optimization approach without d-th order T-SVD by Yuxuan Duan, Chen Ling, Jinjie Liu, Xinmin Yang

“…Accurate recovery of Internet traffic data can mitigate the adverse impact of incomplete data on network task processes. In this study, we propose a low-rank recovery model for incomplete Internet traffic data with a fourth-order tensor structure, incorporating spatio-temporal regularization while avoiding the use of d-th order T-SVD. …”
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CFD Study of Concave Turbine by A. Sinkakarimi, A. Ghadi

“…Computational fluid dynamics (CFD) is a powerful numerical tool that is becoming widely used to simulate many processes in the industry. …”
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Enhancing E-Government Services through State-of-the-Art, Modular, and Reproducible Architecture over Large Language Models by George Papageorgiou, Vangelis Sarlis, Manolis Maragoudakis, Christos Tjortjis

“…Integrating Large Language Models (LLMs) into e-government applications has the potential to improve public service delivery through advanced data processing and automation. This paper explores critical aspects of a modular and reproducible architecture based on Retrieval-Augmented Generation (RAG) for deploying LLM-based assistants within e-government systems. …”
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Evaluating Graph Attention Networks as an Alternative to Transformers for ABSA Task in Low-Resource Languages by Gabriel Gomes, Alexandre T. Bender, Arthur Cerveira, Larissa A. Freitas, Ulisses B. Corrˆea

“…Opinions toward subjects and products hold immense relevance in business to guide decision-making processes. However, due to the increase in user-generated content, manual analysis is unrealistic. …”
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Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging by Randy Guzmán Gómez, Guadalupe Lopez Lopez, Victor M. Alvarado, Froylan Lopez Lopez, Eréndira Esqueda Cisneros, Hazel López Moreno

“…The rapid growth of artificial intelligence, particularly in the field of deep learning, has opened up new advances in analyzing and processing large and complex datasets. Prospects and emerging trends in this area engage the development of methods, techniques, and algorithms to build autonomous systems that perform tasks with minimal human action. …”
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SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising by Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen, Jiameng Han

“…In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly in multimedia applications. …”
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Buffer or Enabler? The Effect of Financial Slack on R&D Investment in Different Environments by Hye Kyung Yu, Minji Kim, Tohyun Kim

“…Using data from Compustat and the Center for Research in Security Prices (CRSP) database on 578 computer-processing firms in innovation-intensive industries in the United States, our results reaffirm that financial slack is a strategic asset that enhances R&D investment. …”
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Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity by Toshitake Asabuki, Claudia Clopath

“…The brain learns an internal model of the environment through sensory experiences, which is essential for high-level cognitive processes. Recent studies show that spontaneous activity reflects such a learned internal model. …”
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Molten Pool Image Segmentation Based on Adaptive Multi-Scale Attention Mechanism by Yuefeng Chen, Weihua Liu, Qing Yang, Huabin Chen, Qi Jiang

“…The model performs well in computational efficiency, data processing and complexity balance, making it suitable for industrial field deployment.…”
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Unveiling the Power of Large Language Models: A Comparative Study of Retrieval-Augmented Generation, Fine-Tuning, and Their Synergistic Fusion for Enhanced Performance by Gulsum Budakoglu, Hakan Emekci

“…Large-language model optimization for a particular application is crucial and challenging in natural language processing. This study compares two salient techniques for retrieve-augmented generation (RAG) and fine-tuning along with a new hybrid method that combines both. …”
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Artificial intelligence and machine learning-powered GIS for proactive disaster resilience in a changing climate by Justin Diehr, Ayorinde Ogunyiola, Oluwabunmi Dada

“…The findings highlight the need for improved data governance, transparent modelling approaches, and enhanced computational frameworks to overcome these barriers. …”
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Time Discriminator for Satellite Radar Altimeter Tracker System by D. S. Borovitsky, A. E. Zhesterev, V. P Ipatov, R. M. Mamchur

“…In modern space-based altimeters, final data processing is imposed on the ground-based center receiving information from the spacecraft through a telemetry line, while the main task of an onboard delaylocked loop is to reliably retain the received echo-signal within the tracking window. …”
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Schur Complement Optimized Iterative EKF for Visual–Inertial Odometry in Autonomous Vehicles by Guo Ma, Cong Li, Hui Jing, Bing Kuang, Ming Li, Xiang Wang, Guangyu Jia

“…Accuracy and nonlinear processing capabilities are critical to the positioning and navigation of autonomous vehicles in visual–inertial odometry (VIO). …”
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Experimental Research and Development of Nonlinearity Models for FR1 Range Mixers for Electromagnetic Compatibility Analysis by E. V. Sinkevich, V. I. Mordachev, Wan Fayu, D. A. Tsyanenka, I. V. Shakinka

“…The obtained models provide high adequacy and computational efficiency of quantitative analysis of nonlinear processes and radio interference occurring in receivers of 4G/5G/6G radio networks in a complex electromagnetic environment.…”
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The neuroscientific basis of flow: Learning progress guides task engagement and cognitive control by Hairong Lu, Dimitri Van der Linden, Arnold B. Bakker

“…The intrinsic factors driving such engagement and facilitating subjective feelings of flow remain unclear. Building on computational theories of intrinsic motivation, this study examines how learning progress predicts engagement and directs cognitive control. …”
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Learning physics and temporal dependencies: real-time modeling of water distribution systems via Kolmogorov–Arnold attention networks by Zekun Zou, Zhihong Long, Gang Xu, Raziyeh Farmani, Tingchao Yu, Shipeng Chu

“…In comparison with traditional mechanistic simulators, data-driven models offer faster computation and reduced calibration demands, making them more suitable for real-time applications. …”
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A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network by Zheng Yang, Hua Zhu, Zhao Li, Gang Wang, Meng Su

“…To this end, this paper proposes a malware detection method based on genetic algorithm optimization of the CNN-SENet network, which firstly introduces the SENet attention mechanism into the convolutional neural network to enhance the spatial feature extraction capability of the model; then, the application programming interface (API) sequences corresponding to different software behaviors are processed by segmentation and de-duplication, which in turn leads to the sequence feature extraction through the CNN-SENet model; finally, genetic algorithm is used to optimize the hyperparameters of CNN-SENet network to reduce the computational overhead of CNN and to achieve the recognition and classification of different malware at the output layer. …”
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Composite layered materials from soft hardwood species imitating oak structure by Lukash Aleksandr, Sivakova Ekaterina, Chernyshev Oleg, Akhtyamova Leisan, Kiiamova Leisan, Garkin Igor, Fazylzyanova Guzaliya

“…A regression equation was derived to adequately describe the dependence of decorative strip density on bonding conditions, including pressing duration, veneer package thickness, and press plate temperature. Statistical processing and graphical dependencies were developed using computer programs such as PlanExp B-D13, Excel, and SigmaPlot. …”
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