A Dynamic Prediction Method for Rolling Bearings Residual Life via Multi-Stage Exponential Model by Xueyan Ye, Suhua Qiao, Chen Sun, Yinjun Wang

“…This method entails the computation of multiple signal characteristics from sample data to depict the bearing degradation process comprehensively. …”
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Giant malignant fibrous histiocytoma of the face: case report of microsurgical repair using a transverse rectus abdominis myocutaneous flap by Rui Antonio Aquino De Azevedo, Breno Ricelly Ávila Pinheiro, Raina Caterina Coelho Arrais, Bruno Fabrício Ávila Pinheiro

“…Case Report: A male patient sought medical care for a giant tumoral lesion in the right hemiface. Computed tomography of the skull revealed a voluminous expansive process of vegetating aspect with poorly defined borders. …”
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Uncovering critical transitions and molecule mechanisms in disease progressions using Gaussian graphical optimal transport by Wenbo Hua, Ruixia Cui, Heran Yang, Jingyao Zhang, Chang Liu, Jian Sun

“…However, the high dimensionality of data and the lack of aligned samples across disease stages have posed challenges in addressing these tasks. We present a computational framework, Gaussian Graphical Optimal Transport (GGOT), for analyzing disease progressions. …”
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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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Research progress in sintering methods and grain inhibition of ultrafine/nano WC-Co cemented carbide by LIU Jiaying, ZHAO Zhiwei, GAO Zimei, JIANG Hao

“…The rapid sintering method can be combined with computer simulation to promote its wide application， and the types and adding methods of composite grain inhibitors require furthur exploration.…”
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Quadratic descriptors and reduction methods in a two-layered model for compound inference by Jianshen Zhu, Naveed Ahmed Azam, Shengjuan Cao, Ryota Ido, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

“…Furthermore, we introduce different methods to reduce descriptors, aiming to avoid computational complexity and overfitting issues during the learning process caused by the large number of quadratic descriptors. …”
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An Analytical Approach to Power Optimization of Concentrating Solar Power Plants with Thermal Storage by Andrii Cheilytko, Spiros Alexopoulos, Andriy Pozhuyev, Oliver Kaufhold

“…Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. …”
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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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Bridging the gap: From petri dish to patient - Advancements in translational drug discovery by Mohamed El-Tanani, Syed Arman Rabbani, Yahia El-Tanani, Ismail I. Matalka, Ikramy A. Khalil

“…These advancements include, but are not limited to, sophisticated in vitro models such as organ-on-a-chip and computational models that utilize artificial intelligence to predict drug efficacy and safety. …”
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Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients by Yuan Liu, Zhuolan Gui, De Yan, Zhuang Wang, Ruisi Gao, Ningxin Han, Junying Chen, Jialing Wu, Dong Ming

“…Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. …”
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Implementation of Chaotic Synchronization and Artificial Neural Networks in Modified OTP Scheme for Image Encryption by Hristina Stoycheva, Georgi Mihalev, Stanimir Sadinov, Krasen Angelov

“…This is proven by a study of resistance to various crypto–graphic attacks such as brute force attack, differential attack, noise and data cut attacks, key sensitivity, and computational complexity.…”
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A DFT-D4 investigation of the complexation phenomenon between pentachlorophenol and β-cyclodextrin by Zoubir Kabouche, Youghourta Belhocine, Tahar Benlecheb, Ibtissem Meriem Assaba, Abdelkarim Litim, Rabab Lalalou, Asma Mechhoud

“…Complexation energies were computed at the molecular level to provide insight into the inclusion of PCP inside the β-CD cavity. …”
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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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A case of primary ovarian lymphoma with autoimmune hemolytic anemia achieving complete response with Rituximab-based combination chemotherapy by N S Ghadyalpatil, R Chandrasekar, D Snehalatha, B M Reddy

“…Ovarian involvement as primary or secondary lymphomatous process is extremely uncommon. In most cases, the diagnosis is usually not suspected initially and is confirmed only after detailed histopathological evaluation. …”
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