On Fixed Point Property under Lipschitz and Uniform Embeddings by Jichao Zhang, Lingxin Bao, Lili Su

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An investigation of optimality and controllability for quasilinear impulsive evolution systems in abstract spaces by B. Radhakrishnan, P. Anukokila, M. Tamilarasi, P. Shanmugasundram

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Riesz lacunary uniform integrability and statistical convergence via power series method by Jun-Jie Quan, Devia Narrania, Kuldip Raj, Qing-Bo Cai

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q , ω $q,\omega $ -Tricomi expansions and exponential integral associated with Hahn difference operator by Karima M. Oraby

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Minimal locating-paired-dominating sets in triangular and king grids by Mariam Kinawi, Zaid Hussain, Ludovit Niepel

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Hankel Matrix Correlation Function-Based Subspace Identification Method for UAV Servo System by Minghong She, Pengju Zhao

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Black holes of quartic quasitopological gravity, conformal scalar field and extended quasitopological electromagnetism by Askar Ali

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The Silent Invaders: Microplastic Accumulation, Impacts, and Monitoring Approaches by Grigorios L. Kyriakopoulos, Antonis A. Zorpas, Vassilis J. Inglezakis, Rocío Rodríguez-Barroso

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Superselection rules, quantum error correction, and quantum chromodynamics by Ning Bao, ChunJun Cao, Aidan Chatwin-Davies, Gong Cheng, Guanyu Zhu

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Applications of Graph Kannan Mappings to the Damped Spring-Mass System and Deformation of an Elastic Beam by Mudasir Younis, Deepak Singh, Adrian Petruşel

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CompactTree: a lightweight header-only C++ library and Python wrapper for ultra-large phylogenetics by Niema Moshiri

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Quasinormal modes and the switchback effect in Schwarzschild-de Sitter by Mir Mehedi Faruk, Facundo Rost, Jan Pieter van der Schaar

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py.Aroma: An Intuitive Graphical User Interface for Diverse Aromaticity Analyses by Zhe Wang

“…In this article, I introduce py.Aroma 4, a freely available and open-source Python package designed specifically for analyzing (anti-)aromaticity. …”
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The EFT bootstrap at finite M PL by Carl Beadle, Giulia Isabella, Davide Perrone, Sara Ricossa, Francesco Riva, Francesco Serra

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Domain walls in super Yang-Mills: worldvolume TQFTs and deconfinement from semiclassics on ℝ3 × 𝕊1 by Andrew A. Cox

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Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data by Austin A. Barr, Joshua Quan, Eddie Guo, Emre Sezgin, Emre Sezgin

“…Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…With the continuous development of artificial intelligence technology and the gradual advancement of the smart mining concept, traditional open-pit mining operations are being replaced by automated methods. …”
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Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study by Océane Dorémus, Dylan Russon, Benjamin Contrand, Ariel Guerra-Adames, Marta Avalos-Fernandez, Cédric Gil-Jardiné, Emmanuel Lagarde

“…ObjectiveThe objective of our study is to design, implement, and evaluate deidentification algorithms using fine-tuned moderate-sized open-source language models, ensuring their suitability for production inference tasks on personal computers. …”
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Exact surface energies and boundary excitations of the Izergin-Korepin model with generic boundary fields by Pengcheng Lu, Junpeng Cao, Wen-Li Yang, Ian Marquette, Yao-Zhong Zhang

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COMPARISON OF POROSITY PREDICTION FROM SEISMIC DATA IN THE F3 BLOCK, NETHERLANDS USING MACHINE LEARNING by Urip Nurwijayanto Prabowo, Sudarmaji Sudarmaji, Jarot Setyowiyoto, Sismanto Sismanto

“…This demonstrates that cycle-GAN can achieve more accurate porosity estimation in locations with insufficient labelled or well-logging data compared to open-loop and closed-loop schemes. However, cycle-GAN requires more computational time than the other methods due to the need to train multiple networks. …”
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