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  1. 1741

    Mathematical crystal chemistry II: Random search for ionic crystals and analysis on oxide crystals registered in ICSD by Ryotaro Koshoji

    Published 2025-07-01
    “…Additionally, discrete variables and constraint functions, which give a choice of creatable types of geometrical constraints depending on the spatial order of atoms, are implemented to formalize the feasible atomic environment, such as the composition of coordination polyhedra, resulting in acceleration of structure search and decrease of the number of local minima. …”
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  2. 1742

    Associations of aortic and carotid artery health with cerebrovascular markers and cognition in older adults from the Whitehall II imaging study by Georgina Hobden, Graham Reid, Scott T. Chiesa, Congxiyu Wang, Lucy Jobbins, Clare E. Mackay, Alun D. Hughes, Klaus P. Ebmeier, Jemma Pitt, Mika Kivimäki, Archana Singh-Manoux, Sana Suri

    Published 2025-06-01
    “…Wave-2 additionally included vascular sonography of the aorta and carotid artery, and 3T functional MRI scans to measure CVR (mean % change BOLD signal change during a CO2 challenge). …”
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  3. 1743

    A transient homotypic interaction model for the influenza A virus NS1 protein effector domain. by Philip S Kerry, Juan Ayllon, Margaret A Taylor, Claudia Hass, Andrew Lewis, Adolfo García-Sastre, Richard E Randall, Benjamin G Hale, Rupert J Russell

    Published 2011-03-01
    “…While RBD dimerization seems functionally conserved, two possible apo ED dimers have been proposed (helix-helix and strand-strand). …”
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  4. 1744

    Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools by Shailendra Pawanr, Kapil Gupta

    Published 2025-06-01
    “…One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. …”
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  5. 1745

    Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

    Published 2025-06-01
    “…Specifically, the proposed model comprises three key components: (i) a mutual information-based feature selection method to identify the most significant input variables influencing energy consumption; (ii) a variational mode decomposition (VMD) approach to decompose the original energy consumption signal into intrinsic mode functions (IMFs), capturing relevant trends and eliminating noise; and (iii) a long short-term memory (LSTM) neural network to perform time-series forecasting of the target energy consumption values. …”
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  6. 1746

    The Relationship between the Reorganization of Higher Education Institutions' Operations in Poland During the COVID-19 Pandemic and Student Loyalty by Sojkin Bogdan, Bartkowiak Paweł, Michalak Szymon

    Published 2024-12-01
    “…Using exploratory factor analysis (EFA), the main components were identified for various variables pertaining to the functioning, organization, and delivery of online classes, as well as for aspects associated with university operations during the COVID-19 pandemic. …”
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  7. 1747

    RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

    Published 2025-02-01
    “…A novel application of RL for dynamic feature refinement and adjustment based on reward functions was employed to optimize the detection capabilities of the model. …”
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  8. 1748

    Development of a High-Cell-Density Production Process for a Biotherapeutic Yeast, <i>Saccharomyces cerevisiae</i> var. <i>boulardii</i>, for Use as a Human Probiotic by Ghaneshree Moonsamy, Sarisha Singh, Yrielle Roets-Dlamini, Koketso Kenneth Baikgaki, Santosh Omrajah Ramchuran

    Published 2025-04-01
    “…Once the optimised batch was validated in the replicate batches, the statistical analysis indicated a high level of reproducibility, with low variability across key performance indicators such as biomass concentration (unit), CFU production (CFU.mL<sup>−1</sup>), and substrate utilization efficiency (g.g<sup>−1</sup>). …”
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  9. 1749

    Optimization of the encapsulation of arazá pulp by spray drying: Physicochemical, morphological and in vitro digestion studies by Greilis Quintero-Gamero, Francis S. Sánchez-Garzón, Aureliano Rodríguez-Cortina, María Hernández-Carrión, Luz Stella Nerio

    Published 2025-06-01
    “…Yield (Y), moisture (M), water activity (aw), water solubility index (WSI), hygroscopicity, ascorbic acid and phenolics encapsulation efficiency (EEAA, EEP), and the retention efficiency of DPPH and radical scavenging capacity (DPPH, ABTS) were studied as response variables; being Y, M, aw, WSI and EAA the ones considered for optimization purposes. …”
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  10. 1750

    Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849 by L. Zhu, L. Zhu, L. Zhu, L. Zhu, P. Ciais, Y. Yao, D. Goll, S. Luyssaert, I. Martínez Cano, A. Fendrich, A. Fendrich, L. Li, H. Yang, S. Saatchi, W. Li, W. Li

    Published 2025-08-01
    “…This study presents an efficient and accurate approach to enhancing the simulation of Amazonian carbon pools and fluxes in DGVMs by assimilating existing observational data, offering valuable insights for future model development and parameterization.…”
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  11. 1751

    “Why would I bother?” Understanding prosumer motivations and engagement in renewable energy communities: a qualitative study of polish photovoltaic installation owners by Maksymilian Bielecki, Ewa Neska, Anna Kowalska-Pyzalska

    Published 2025-08-01
    “…The development and efficient functioning of RECs depend not only on technical or economic factors but also on numerous socio-psychological variables deeply rooted in local historical, political, economic, and cultural contexts. …”
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  12. 1752

    Impact of Wall Material Composition (Maltodextrin vs. Inulin vs. Nutriose) and Emulsion Preparation System (Nano- vs. Microemulsion) on Properties of Spray-Dried Linseed Oil by Dorota Ogrodowska, Iwona Zofia Konopka, Grzegorz Dąbrowski, Beata Piłat, Józef Warechowski, Fabian Dajnowiec, Małgorzata Tańska

    Published 2025-01-01
    “…For these independent variables, the properties of the prepared emulsions (flow curves and viscosity) and the resulting powders (encapsulation efficiency, particle size distribution, water activity, bulk and tapped density, Carr’s index, color parameters, and thermal stability) were determined. …”
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  13. 1753

    Imidacloprid decreases energy production in the hemolymph and fat body of western honeybees even though, in sublethal doses, it increased the values of six of the nine compounds in... by Jerzy Paleolog, Jerzy Wilde, Marek Gancarz, Aneta Strachecka

    Published 2025-01-01
    “…The concentrations of the strongly correlated NADH2, ATP and acetyl-CoA decreased both in IM-200 and IM-5, whereas the levels of the other compounds decreased in IM-200 but increased in IM-5.…”
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  14. 1754

    Removal of <i>para</i>-Phenylenediamine (PPD) Dye from Its Aqueous Solution by Adsorption Using the Activated Carbon Nanoparticles by Shabaa Fayyad Bdewi, Hanaa Hassan Hussein, Shireen Abdulmohsin Azeez

    Published 2024-12-01
    “…This study focused on the development of an efficient preparation method of activated carbon for the removal of para-phenylenediamine (PPD) dye in an aqueous solution. …”
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  15. 1755

    Pulmonary vein isolation does not alter cardiovascular afferent autonomic reflexes in atrial fibrillation by Varun Malik, Adrian D. Elliott, Gijo Thomas, Bradley Pitman, John L. Fitzgerald, Glenn D. Young, Leonard F. Arnolda, Dennis H. Lau, Prashanthan Sanders

    Published 2025-06-01
    “…Mean age was 64 ± 3 years (78% male); BMI 28 ± 1 kg/m2; LA size 37 ± 2 mL/m2; and left ventricular function 65 ± 3%. Despite alterations in heart rate variability (HRV), there was no difference in IHR, Valsalva, or LBNP responses pre‐ versus post‐PVI. …”
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  16. 1756

    Life cycle greenhouse gas emissions of diesel oil and zero-emission trucks: Systematic review of status and perspectives by Gaia Gentilucci, Antonella Accardo, Ezio Spessa

    Published 2025-07-01
    “…Quantitatively, the analysis highlights a significant variability of the carbon footprint results, ranging between 431–1780 tons CO2eq for diesel ICEVs, 175–1626 tons CO2eq for battery electric vehicles, 366–2944 tons CO2eq for fuel cell electric vehicles. …”
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  17. 1757

    How Life Experience Shapes Cognitive Control Strategies: The Case of Air Traffic Control Training. by Sandra Arbula, Mariagrazia Capizzi, Nicoletta Lombardo, Antonino Vallesi

    Published 2016-01-01
    “…This experience-based enhancement of reactive control strategy denotes how cognitive control and executive functions in general can be shaped by real-life training and underlines the importance of experience in explaining inter-individual variability in cognitive functioning.…”
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  18. 1758

    On a Solution of a Third Kind Mixed Integro-Differential Equation with Singular Kernel Using Orthogonal Polynomial Method by Ahmad Alalyani, M. A. Abdou, M. Basseem

    Published 2023-01-01
    “…The singular kernel is modified to take a logarithmic form, while the kernels of time are continuous and positive functions. Using the separation of variables technique, we have a system of Fredholm integral equations (FIEs) that can be transformed into an algebraic system after using orthogonal polynomials. …”
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  19. 1759
  20. 1760

    ELM‐Wet: Inclusion of a Wet‐Landunit With Sub‐Grid Representation of Eco‐Hydrological Patches and Hydrological Forcing Improves Methane Emission Estimations in the E3SM Land Model... by Theresia Yazbeck, Gil Bohrer, Madeline E. Scyphers, Justine E. C. Missik, Oleksandr Shchehlov, Eric J. Ward, Sergio L. Merino, Robert Bordelon, Diana Taj, Jorge A. Villa, Kelly Wrighton, Qing Zhu, William J. Riley

    Published 2025-02-01
    “…Our results show that ELM‐Wet reduces the model's CH4 emission root mean squared error by up to 33% and is able to represent inter‐daily CO2 and CH4 flux variability across the wetland's eco‐hydrological patches, including during periods of extreme dry or wet conditions.…”
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