Remote Sensing-Based Multiscale Analysis of Total and Groundwater Storage Dynamics over Semi-Arid North African Basins by Abdelhakim Amazirh, Youness Ouassanouan, Houssne Bouimouass, Mohamed Wassim Baba, El Houssaine Bouras, Abdellatif Rafik, Myriam Benkirane, Youssef Hajhouji, Youness Ablila, Abdelghani Chehbouni

“…The root mean square errors (RMSE) were 0.99 cm and 0.88 cm, respectively. …”
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Deep Learning−Driven Exophthalmometry through Facial Photographs in Thyroid Eye Disease by Joonhyeon Park, PhD, Jin Sook Yoon, MD, PhD, Namju Kim, MD, PhD, Kyubo Shin, PhD, Hyun Young Park, MD, PhD, Jongchan Kim, MS, Jaemin Park, MS, Jae Hoon Moon, MD, PhD, JaeSang Ko, MD, PhD

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A Comprehensive Evaluation of a Coumarin Derivative and Its Corresponding Palladium Complex as Potential Therapeutic Agents in the Treatment of Gynecological Cancers: Synthesis, Ch... by Mirela Jevtić, Marijana Stanojević Pirković, Teodora Komazec, Marija Mojić, Sanja Mijatović, Danijela Maksimović-Ivanić, Dušan Dimić, Zoran Marković, Dušica Simijonović, Dejan Milenković, Edina Avdović

“…<b>Background:</b> The aim of this research is the synthesis and characterization of coumarin-palladium complex and the investigation of the cytotoxicity of both the ligand and the complex. …”
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The Impacts of Different PBL Schemes on the Simulation of PM2.5 during Severe Haze Episodes in the Jing-Jin-Ji Region and Its Surroundings in China by Tian Li, Hong Wang, Tianliang Zhao, Min Xue, Yaqiang Wang, Huizheng Che, Chao Jiang

“…The simulated concentrations of surface PM2.5 using the YSU, MYJ, and Boulac schemes all showed large negative errors during daytime in polluted days due to their inefficient descriptions of local atmospheric stability or diffusion processes in haze. …”
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Identifying preferential flow from soil moisture time series: Review of methodologies by John R. Nimmo, Inge Wiekenkamp, Ryoko Araki, Jannis Groh, Nitin K. Singh, Octavia Crompton, Briana M. Wyatt, Hoori Ajami, Daniel Giménez, Daniel R. Hirmas, Pamela L. Sullivan, Matthias Sprenger

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Advanced AI techniques for classifying Alzheimer’s disease and mild cognitive impairment by Sophie Tascedda, Sophie Tascedda, Sophie Tascedda, Pierfrancesco Sarti, Pierfrancesco Sarti, Veronica Rivi, Claudia Savia Guerrera, Giuseppe Alessio Platania, Mario Santagati, Filippo Caraci, Filippo Caraci, Johanna M. C. Blom, Johanna M. C. Blom

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NN-based visual servoing compensation control of a Gough–Stewart platform with uncertain load by Jun Qi, Shijie Song, Yuyang Zhao, Dawei Gong, Minglei Zhu

“…The uncertainty of this load leads to external disturbance to the parallel robot, which affects the dynamic coupling among the six degrees of freedom (DOF) and the tracking performance. Even though many researchers focus on improving the system robustness and tracking accuracy, there still exist two main problems: the system’s internal uncertainties, including the modeling, manufacturing, and assembly errors of the parallel robot affect the control accuracy; the uncertain external disturbance varies in an extensive range and reduces the stability and tracking accuracy of the system. …”
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Comparative Numerical Study of Four Biodiesel Surrogates for Application on Diesel 0D Phenomenological Modeling by Claude Valery Ngayihi Abbe, Raidandi Danwe, Robert Nzengwa

“…NOx emissions were well predicted for methyl butanoate but for the other three gave approximation errors over 50%.…”
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Thermal conductivity of granulated silicon doped with alkali metal atoms by Sokhibova Zarnigor, Sirojiddinova Iroda, Babayeva Gulnoza, Ulkanov Sardor, Sultonova Feruza, Atabayev Kamil

“…The method’s key advantage lies in its ability to minimize experimental errors by using a comparative approach. By passing identical heat flows through different samples simultaneously, the technique effectively isolates the thermal conductivity properties of the doped silicon granules. …”
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A Reconstruction Study of an Old Uyghur, Parthian, and Middle Persian Manuscript by Betül Özbay

“…Many researchers have studied the considerably diverse contents and an assortment of artifacts from the region of Turfan since they had been introduced to the world with the discoveries at the beginning of the 20th century. …”
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Analysis of Cognitive Biases in Construction Health and Safety in New Zealand by Mahesh Babu Purushothaman, Pricilia Jessica, Funmilayo Ebun Rotimi

“…The construction industry’s complexity and high-risk nature present significant decision-making challenges, often resulting in errors that jeopardise health and safety performance. …”
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H-DSAE: a hybrid technique to recognize heart disease by K. Uma Maheswari, A. Valarmathi

“…Conventional methods for diagnosing diseases often lead to inaccurate diagnoses and take longer to complete due to human errors. In order to increase the diagnostic accuracy, an ensemble method is used. …”
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A New Model for Image Inpainting for Different Color Spaces by zainab al-bataat, Ahmed K. Al-Jaberi

“…In order to evaluate the quality of image inpainting, statistical quality metrics such as MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), and SSIM (Structural Similarity Index) are utilized. …”
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Digital twin manifesto for the pathology laboratory by Albino Eccher, Fabio Pagni, Massimo Dominici, Luca Reggiani Bonetti, Stefano Marletta, Enrico Munari, Giorgio Cazzaniga, Anil V Parwani, Vincenzo L’Imperio, Angelo Paolo Dei Tos

“…The framework highlights measurable gains such as up to 90% reduction in labeling errors, 20–30% improvements in slide quality, and 30–50% reductions in diagnostic turnaround time. …”
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Time Series Prediction Method of Clean Coal Ash Content in Dense Medium Separation Based on the Improved EMD-LSTM Model by Kai Cheng, Xiaokang Zhang, Keping Zhou, Chenao Zhou, Jielin Li, Chun Yang, Yurong Guo, Ranfeng Wang

“…SSA optimizes LSTM parameters (learning rate, hidden layers, epochs) to minimize prediction errors. Results demonstrate the EMD-IMF1-LSTM-SSA model achieves superior accuracy (RMSE: 0.0099, MAE: 0.0052, MAPE: 0.047%) and trend consistency (NSD: 12), outperforming baseline models. …”
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A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems by Vakkar Ali, Praveen Pachauri, Azhar Equbal, Osama Khan, Mohd Parvez, Haidar Howari, Taufique Ahamad, Ashok Kumar Yadav, Brahmdeo Yadav

“…Also, the percentage errors came out to be minimum for machine learning model of around 5.3 %, 5 % and 4.7 % for energy efficiency, equipment durability and process throughput respectively. …”
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A quantum-resilient lattice-based security framework for internet of medical things in healthcare systems by Zeyad Ghaleb Al-Mekhlaf, Murtaja Ali Saare, Jalal Mohammed Hachim Altmemi, Mahmood A. Al-Shareeda, Badiea Abdulkarem Mohammed, Gharbi Alshammari, Reem alrashdi, Yasser A. Alkhabra, Ibrahim Alreshidi

“…This paper proposes a quantum-resistant healthcare security framework based on lattice-based cryptographic primitives such as Learning With Errors (LWE), Ring-LWE (RLWE), and Short Integer Solution (SIS). …”
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Neutron-capture Element Abundances of 491 Stars in Milky Way Dwarf Satellite Galaxies from Medium-resolution Spectra by Lauren E. Henderson, Evan N. Kirby, Mithi A. C. de los Reyes, Roman Gerasimov, Viraj Manwadkar

“…We present abundance measurements of the neutron-capture elements Sr, Y, Ba, and Eu with errors <0.4 dex—as well as new measurements of Mg—in 491 stars in Sculptor, Fornax, Draco, Sextans, and Ursa Minor. …”
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Multi-Scale Validation of Suspended Sediment Retrievals in Dynamic Estuaries: Integrating Geostationary and Low-Earth-Orbiting Optical Imagery for Hangzhou Bay by Yi Dai, Jiangfei Wang, Bin Zhou, Wangbing Liu, Ben Wang, C. K. Shum, Xiaohong Yuan, Zhifeng Yu

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Rainfall-runoff generation patterns and key influencing factors in the plain of the Taihu Lake Basin, China by Pingnan Zhang, Gang Chen, Chuanhai Wang, Pengxuan Zhao, Lanlan Li, Jingyi Cao, Youlin Li

“…Additionally, existing Taihu Basin models were used to simulate the rainfall-runoff process, with a focus on model errors and the significant role of rainfall-runoff patterns and micro-topography in the hydrological cycle. …”
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