The IoT Technologies Acceptance in Education by the Students From the Economic Studies in Romania by Liliana Ionescu-Feleaga, Bogdan-Ștefan Ionescu, Mariana Bunea

“…The study included 1,179 students from four university centers from Romania and the included factors from the acceptance of IoT technologies model (Internet of Things) were analyzed using the quantitative statistical methods in SPSS, applying a series of tests processed to highlight the research results, respectively the reliability test, the validity test, the chi-square test and the Person's correlation coefficient. The results of the study show the existence of a positive correlation between the research variables and indicate that the students from the economic studies, are ready to accept the new technological advances in IoT and to implement them in their future jobs…”
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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…This study investigates the operational efficiency of the lab-scale oxidation ditch (OD) functioning in simultaneous nitrification and denitrification modes, focusing on forecasting biochemical oxygen demand (BOD₅) concentrations over a five-day horizon. …”
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Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI) by Dawid Maciejewski, Krzysztof Mudryk, Maciej Sporysz

“…Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. …”
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Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…Remote sensing (RS) data effectively estimate and map AGB and Corg in wetlands using various techniques, but there is still room to improve the efficiency of machine learning (ML)-based approaches. …”
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Modeling of project activities for UAV modernization to conduct swarm drone attack missions by Oleg Fedorovych, Leonid Malieiev, Oleksii Hubka, Andrei Popov

“…The models are intended to optimize new functions related to control, coordination, and navigation, enabling UAVs to perform as part of drone swarms in military missions. …”
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Climate Change Risk, Performance, and Value Added in Agricultural Sector by Ramin Amani, Zanko Ghorbani, Zana Mozaffari

“…Results and Discussion The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. …”
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Optimal selection of satellite XCO₂ images for urban CO₂ emission monitoring by A. Danjou, G. Broquet, A. Schuh, F.-M. Bréon, T. Lauvaux, T. Lauvaux

“…This study analyzes the performances of an automated method for estimating urban emissions as a function of the targeted cities and of the atmospheric conditions. …”
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Human saliva exerts strong type-dependent effects on adenovirus infectivity by Erwan Sallard, Wenli Zhang, Nikita Chilakamarri, Setareh Farzanehkari, Setareh Farzanehkari, Inga Marte Charlott Seuthe, Anja Ehrhardt, Malik Aydin, Malik Aydin

“…All human Ads used were replication-competent and expressed a GLN cassette containing a green-fluorescent protein, nano-luciferase, and neomycin resistance. Loss-of-function experiments were conducted by immunoprecipitation or enzymatic digestion of specific saliva components to decipher related mechanisms.ResultsTemporal and inter-individual variability in saliva samples were observed, validating the use of a saliva pool to represent the population. …”
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The Role of the Gut Microbiome in Exercise-Induced Inflammation and Recovery: A Narrative Review by Stanisław Dudek, Weronika Koziak, Aleksandra Bętkowska, Agata Kornacka, Kamila Szostak, Rafał Tomaka, Michalina Makieła, Wojciech Dudek, Anna Byra

“…While moderate exercise enhances immune function and microbial diversity, excessive training can lead to gut dysbiosis, increased intestinal permeability, and chronic inflammation. …”
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Statistical mechanics of dynamical system identification by Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz, Krithika Manohar

“…To establish this analogy, we define the hyperparameter optimization procedure as a two-level Bayesian inference problem that separates variable selection from coefficient inference and enables the computation of the posterior parameter distribution in closed form. …”
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How to personalise ventilation of infants with congenital diaphragmatic hernia? A simulation study by Barbara Stankiewicz, Magdalena Mierzewska-Schmidt, Krzysztof J. Pałko, Marek Darowski, Maciej Kozarski

“…The T1/T2 and CW/CL correlated with defect size (gamma coefficient: 1; P < 0.05), duration of mechanical ventilation and hospitalization (Spearmen’s coefficient: 0.99; P < 0.01). …”
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Which Metrics Can I Monitor? Test-Retest Reliability of Countermovement Jump and Countermovement Rebound Jump Force-Time Metrics in Youth Soccer Players In-Season by Andrew Badby, Paul Comfort, Nicholas Ripley, Matthew Cuthbert, Francisco Robles-Palazón, Peter Mundy, John McMahon

“…Objective measures provide the most effective means of monitoring the magnitude and time course of changes in neuromuscular function (NMF) resulting from physical activity if measurements are repeatable (reliability). …”
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A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study. by Patrizio Barca, Fabio Paolicchi, Giacomo Aringhieri, Federica Palmas, Daniela Marfisi, Maria Evelina Fantacci, Davide Caramella, Marco Giannelli

“…CT image quality for each scanner/acquisition protocol was assessed through noise level, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD) and non-uniformity index analyses. …”
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IRRIGATED YIELD ESTIMATION OF Populus Nigra STANDS IN NINEVEH PLANTATION by Muzahim .S. younis

“…Consequence of the use of Non-Linear function in this study, which is believed to be in harmonic with the characteristics of tree stand, twelve trees were selected which represents different location over an area planted with Populus nigra trees ,and after data analysis of variable values , three mathematical equations were obtains which link the yield per area ,a dependent variable and age ,mean height of stand quadratic mean diameter as the independent variable , in order to obtain yield table of revocable densities for Populus nigra , selecting the best model , different measures of precision , such as , coefficient of determination, standard error of estimate and residual analysis test, so following regressions selected : Y= 4.1356 G1.12355 R2= 0.9796 S.E=1.8 Y= 0.12788H2.27 R2= 0.913 S.E=3.54 Y= -0.64168+0.34744A2+0.063183Dq2.4 R2= 0.9674 S.E=2.29 Since there are good values of precision measures , so we will be able to use equations to prepare yield table at different densities of Populus nigra stands in Nineveh, depending on mean height or basal area per hectare or for age and quadratic mean. …”
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Crop classification with deep convolutional neural network based on crop feature by Mohamad Reza Gili, Davoud Ashourloo, Hosein Aghighi, Ali Akbar Matkan, Alireza SHakiba

“…Due to the spectral overlap of the crops in some time periods, network training was associated with a relatively high loss and therefore, for the test area, the overall classification accuracy was 69% (percent) and the kappa coefficient was 0.55. In the next step, the functions that were developed as phenological features for crops were applied on the time series of the bands, and for each crop, a feature channel was obtained as the special feature of that crop. …”
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Gas Transfer Across Air‐Water Interfaces in Inland Waters: From Micro‐Eddies to Super‐Statistics by Gabriel Katul, Andrew Bragg, Ivan Mammarella, Heping Liu, Qi Li, Elie Bou‐Zeid

“…As the coefficient of variation CVe in ϵ increases, αo must be adjusted by a multiplier 1+CVe2−3/32 that was derived from a log‐normal model for the probability density function of ϵ. …”
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Complementing Dynamical Downscaling With Super‐Resolution Convolutional Neural Networks by Deeksha Rastogi, Haoran Niu, Linsey Passarella, Salil Mahajan, Shih‐Chieh Kao, Pouya Vahmani, Andrew D. Jones

“…Modifications, such as incorporating elevation data and data pre‐processing enhances overall model performance, while using exponential and quantile loss functions improve the simulation of extremes. Our findings show SRCNN models efficiently and skillfully downscale precipitation from GCMs. …”
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Predicting the Permeability of Pervious Concrete Based on the Beetle Antennae Search Algorithm and Random Forest Model by Jiandong Huang, Tianhong Duan, Yi Zhang, Jiandong Liu, Jia Zhang, Yawei Lei

“…The results showed that the hyperparameters of RF can be tuned by the BAS efficiently; the BAS can combine the conventional RF algorithm to construct the evolved model to predict the permeability of pervious concrete; the cement/aggregate ratio was the most significant variable to determine the permeability, followed by the coarse aggregate proportions.…”
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Analytical methods for assessing the quality of sugarcane bagasse compost and improving the physicomechanical properties toward densification by Ehsan Sarlaki, Mohammad Hossein Kianmehr, Marzieh Ghorbani

“…For both responses of specific energy consumption and bulk density of SBCs, a two-factor interaction (2FI) model with the highest correlation coefficient and the lowest standard deviation was proposed. …”
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Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” by Wu Nile, Su Rina, Na Mula, Cha Ersi, Yulong Bao, Jiquan Zhang, Zhijun Tong, Xingpeng Liu, Chunli Zhao

“…Then, using grey relational analysis (GRA) and the maximum information coefficient (MIC) algorithm, we explored the complex relationship between the vegetation indices (VIs) and LCC, and further selected feature variables. …”
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