Developing a novel layer network structure for a LSTM model to predict mean monthly river streamflow by Amin Gharehbaghi, Redvan Ghasemlounia, Shahaboddin Daneshvar, Farshad Ahmadi

“…For doing so, to select the most effective parameters on MRSF m , the Pearson’s correlation coefficient (PCC) and Cosine amplitude sensitivity (CAS) as features selection process are carried out for potential meteorological variables in the study area (i.e., average monthly temperature (T m ), evaporation (ET m ), and precipitation (P m )) and target (MRSF m ). …”
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VARIATION OF EMBRYO WEIGHT IN BREEDING ACCESSIONS OF CARROT by A. F. Bukharov, D. N. Baleev, M. I. Ivanova

“…The method of maximum likelihood was used to  reveal correspondences  of  experimental function  of distribution in embryo weight in seed population of accessions studied  and  normal  distribution.  …”
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Feasibility and psychometric quality of smartphone administered cognitive ecological momentary assessments in women with metastatic breast cancer by Ashley M Henneghan, Emily W Paolillo, Kathleen M Van Dyk, Oscar Y Franco-Rocha, Soyeong Bang, Rebecca Tasker, Tara Kaufmann, Darren Haywood, Nicolas H Hart, Raeanne C Moore

“…Test-retest reliability was examined using intraclass correlation coefficients (ICCs) for each EMA, and Pearson's correlation were used to evaluate convergent validity between cognitive EMAs and baseline clinical cognitive and psychosocial variables. …”
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Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys by Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy

“…This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. Among the ML methods explored, a backpropagation neural network (BPNN) model with a sigmoid activation function exhibited superior predictive accuracy compared to other algorithms. …”
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Ionospheric Correction Based on Ingestion of Global Ionospheric Maps into the NeQuick 2 Model by Xiao Yu, Chengli She, Weimin Zhen, Nava Bruno, Dun Liu, Xinan Yue, Ming Ou, Jisheng Xu

“…The accuracy of TEC prediction can be improved further through performing a four-coefficient function expression of Az versus MODIP. …”
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Torsional Vibration Suppression During Mode Transition Process in a Parallel Hybrid Electric Vehicle Based on Multiple Model Predictive Control by Cheng Huang, Changqing Du, Longjian Li, Xiangyu Gongye, Yifan Zhao

“…At the same time, the influence of the cost function, the weight coefficient, and the predictive time domain on the torsional vibration suppression effect during the mode transition process are explored in the mMPC strategy design, and the solution time is controlled within 10ms, which meet the real-time requirements. …”
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XPolypNet: A U-Net-Based Model for Semantic Segmentation of Gastrointestinal Polyps With Explainable AI by Arjun Kumar Bose Arnob, Muhammad Mostafa Monowar, Md. Abdul Hamid, M. F. Mridha

“…The presented model was evaluated on five benchmark datasets, achieving a Dice Coefficient of 0.8378 and a Mean Intersection over Union (mIoU) of 0.8427. …”
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Drug Release Analysis and Optimization for Drug-Eluting Stents by Hongxia Li, Yihao Zhang, Bao Zhu, Jinying Wu, Xicheng Wang

“…The diffusion coefficients and the coating thickness are selected as design variables. …”
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The Effect of Hydraulic Partitioning on Prediction the Rate of Bed Load Transport in Gravel-bed Rivers using Support Vector Machine by Kiyoumars Roushangar, Mohammad Hosseini, Saman Shahnazi

“…After optimization of parameters for kernel function, the bed load transport rate was predicted and obtained results from different models were investigated in terms of correlation coefficient (R), Root mean square error (RMSE) and Nash-Sutcliffe (NSE). in order to assess the capability of SVM in quantification of bed load under varied hydraulic conditions, Froude number (Fr) and bed slope of channel (S0) were selected as a parameters describing the hydraulic conditions and median diameter of the sediment particles (D50) and shear Reynolds number (Re*) were considered as a representative of sediment characteristic. …”
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Specific nature of the integrative (complex) effect of environmental factors on hazelnut cultivars in the Russian humid subtropics by T. D. Besedina, A. P. Boyko, Ts. V. Tutberidze, N. S. Kiseleva

“…The expediency of the crop’s large-scale cultivation calls for measuring the parameters of agroclimatic indicators, ranging in variability, intensity, and duration of their impact. …”
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Slow and Quick Flow Models Explain the Temporal Dynamics of Daily Salinity in Streams by Thomas G. Westfall, Tim J. Peterson, Anna Lintern, Andrew W. Western

“…However, significant variability within high‐frequency data sets can result in poor explanation of temporal dynamics using the simple C‐Q equation. …”
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Automatic detection of optic canal fractures and recognition and segmentation of anatomical structures in the orbital apex based on artificial intelligence by Yu-Lin Li, Yu-Hao Li, Mu-Yang Wei, Guang-Yu Li

“…Timely and accurate diagnosis and treatment are crucial for preserving visual function. However, diagnosing OCF can be challenging for inexperienced clinicians due to atypical OCF changes in imaging studies and variability in optic canal anatomy. …”
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Examining the Factors Affecting the Use of Crypto Assets as Foreign Payment and Investment Instruments: A Quantitative Study by Güneş Yılmaz, Tayfur Süleyman Koç

“…Along with path coefficients, f² and q² effect sizes were also analyzed to examine the interaction between the variables. …”
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Measuring and estimation of suspended sediment variation using sediment rating curve approach in Mereg River by Sohila Aghabeigi Amin, Heidar Mirzaei

“…As a major component of hydrological, geomorphological, and ecological functioning of rivers, suspended sediment has been identified as the leading direct cause of river instabilities. …”
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THE TEMPERATURE DEPENDENCE OF UNSTEADY HEAT CONDUCTION IN SOLIDS by L. M. Ozherelkova, E. S. Savin

“…Both in the case of high and low temperatures, the temperature dependences of the heat capacity and the thermal conductivity coefficient are power-law, which allows us to bring the original heat conduction equation to a form that allows the use of the classical method of variable separation in solving the corresponding boundary value problems for the heat conduction equation. …”
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Dermatoglifia y Somatotipo en Jugadores de Voleibol del Departamento del Huila en los XX Juegos Deportivos Nacionales Colombia (Dermatoglyphia and Somatotype in Volleyball Players... by Oscar Alfredo Montenegro Arjona, Oscar Leonardo Cordoba Urriago, Angie Carolina Calderón Sterling, Cristian Javier Mendez Parra, Gian Carlos Tovar Osso

“…Bivariate correlations were performed with Kendall's tau coefficient (t) to determine the correlation between the variables. …”
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Modeling fin efficiency considering transverse temperature gradients in rocket engine cooling channels by Volodymyr Sliusariev, Valerii Bucharskyi

“…Its goal is to derive calculation relationships for finning coefficients that account for transverse temperature non-uniformity within the fin cross-section. …”
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Respiratory Muscle Strength as a Predictor of VO2max and Aerobic Endurance in Competitive Athletes by Gökhan Deliceoğlu, Banu Kabak, Veli O. Çakır, Halil İbrahim Ceylan, Muntean Raul-Ioan, Dan Iulian Alexe, Valentina Stefanica

“…A total of 41 athletes, drawn from the disciplines of biathlon, judo, and cross-country, participated in this study. Respiratory function tests (RFTs) were administered to assess various respiratory parameters, including changes in chest circumference. …”
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IrrigApp: An Online Tool for Determining Crop Water Use and Evaluating Irrigation Scheduling Options by José O. Payero

“…Two sample IrrigApp simulations were conducted to illustrate its functionality and outputs. The simulations were performed for cotton grown in Orangeburg County, South Carolina, for the 2020 growing season under dryland and irrigated conditions. …”
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Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0 by A. Lauer, L. Bock, B. Hassler, P. Jöckel, L. Ruhe, M. Schlund

“…While the examples shown here focus on atmospheric variables, the new functionality can be applied to any other ESM component such as land, ocean, sea ice, or land ice. …”
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