Creating Balance on Bandwidth Consumption using Network Coding in Wireless Sensor Networks by Ehsan Kharati

“…For this purpose, we need to calculate the derivative of the Lagrangian function relative to its variables, in order to determine the condition as a multi-excited multi-equation device. …”
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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. …”
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Relationship between Communication Skills and General Health in Nursing Students of Hamadan University of Medical Sciences by Roya Amini, Fariba Soleimani, Nahid Mohammadi, Tapak Leili

“…Communication skills had a significant relationship with most aspects of general health, except social function (p<0.001). There was no correlation between demographic variables and communication skills (p<0.05). …”
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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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Cognitive performance classification of older patients using machine learning and electronic medical records by Monika Richter-Laskowska, Ewelina Sobotnicka, Adam Bednorz

“…Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales. …”
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Sinusoidal regression model for assessment of mudflows dynamics by B. A. Ashabokov, A. A. Tashilova, L. A. Kesheva, N. V. Teunova

“…The article shows that the use of the sinusoidal regression function makes it possible to identify the cyclicity of time series, which will help in assessing the dynamics of mudflows taking into account long-term changes in climate variables. …”
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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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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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Hydrodynamic and sensitivity analysis of a polymeric calendering process for non-Newtonian fluids with temperature-dependent viscosity by Ali Fateh, Zahid Muhammad, Farmer M., Al Taisan Nada, Faqihi Abdullah A., Souayeh Basma, Raju S. Suresh Kumar, Alam Mir Waqas

“…Additionally, using the response surface method, Nusselt number (Nu)(\text{Nu}), sheet thickness HH0\left(\phantom{\rule[-0.75em]{}{0ex}},\frac{H}{{H}_{0}}\right), and shear stress (Sxy)({S}_{xy}), simulations were carried out to investigate the influence of variable viscoelastic parameters on the response functions (Nu\text{Nu}, HH0\frac{H}{{H}_{0}}, and Sxy{S}_{xy}). …”
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Preoperative Anteroposterior and Lateral Assessment of Sagittal Spinopelvic Parameters Show High Positional Correlation and Measurement Reliability Preceding Both Hip Preservation... by Benjamin D. Kuhns, M.D., M.S., Tyler R. McCarroll, M.D., Roger Quesada-Jimenez, M.D., Ady H. Kahana-Rojkind, M.D., Drashti Sikligar, M.Eng., Meredith F. Cohen, B.A., Benjamin G. Domb, M.D.

“…Purpose: To determine the reliability of sagittal radiographic variables across multiple positions and radiographic views and to correlate measures obtained on anteroposterior (AP) radiographs to lateral pelvis imaging. …”
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Estimation of Above-Ground Biomass for <italic>Dendrocalamus Giganteus</italic> Utilizing Spaceborne LiDAR GEDI Data by Huanfen Yang, Zhen Qin, Qingtai Shu, Li Xu, Jinge Yu, Shaolong Luo, Zaikun Wu, Cuifen Xia, Zhengdao Yang

“…The outcomes reveal that 1) the results showed that the power function emerged as the most efficacious model, with coefficient of determination (<italic>R</italic>²) &#x003D; 0.87 and root mean square error (RMSE) &#x003D; 0.00051 Mg, in estimating the AGB of <italic>Dendrocalamus giganteus</italic>. 2) Based on the feature importance ranking of Random Forest, five variables were selected from the 40 extracted from GEDI, achieving RMSE &#x003D; 8.21 Mg&#x002F;ha and mean absolute error (MAE) &#x003D; 6.12 Mg&#x002F;ha. …”
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A simple model of the rheological curve of HPAM solutions at different temperatures by Eduar Pérez, Dario Alviso, Mauricio Carmona, Eduardo Manrique, Guillermo Artana

“…The solvent viscosity and relaxation time-employed as references to define these parameters-are functions of temperature. On the master surface, while the power coefficient of Carreau-Yasuda (n) exhibits only a slight dependency on temperature, the relative viscosity depends monotonically on this variable. …”
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Agreement of glomerular filtration rate estimation equations for chemotherapy dosing in cancer patients at a tertiary referral hospital in Sub-Saharan Africa. by Wubshet Jote Tolossa, Tigist Workneh Leulseged, Abdu Adem, Feyissa Challa, Tirumebet Mezgebu, Ruth S Aytehgeza, Nebiat Adane Mera, Kalsidagn Girma Asfaw, Momina M Ahmed, Kebede H Begna

“…The Cockcroft-Gault formula remains the most widely used equation in cancer patients, despite the availability of more precise kidney function estimation equations. Therefore, the aim of the study was to assess the agreement between Cr and cystatin-C (CysC) based GFR estimation equations and GFR estimated by Cockcroft-Gault for appropriate chemotherapy dosing in cancer patients undergoing assessment for first-Line chemotherapy at an oncology unit of St. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…The independent treatment variables were two climatic factors and five human activity factors affecting vegetation changes in East Africa. …”
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Revisiting the Group Classification of the General Nonlinear Heat Equation <i>u_t</i> = (<i>K</i>(<i>u</i>)<i>u_x</i>)<i>_x</i> by Winter Sinkala

“…In this paper, we revisit the group classification of the general nonlinear heat (or diffusion) equation <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>u</mi><mi>t</mi></msub><mo>=</mo><msub><mfenced separators="" open="(" close=")"><mi>K</mi><mrow><mo>(</mo><mi>u</mi><mo>)</mo></mrow><mspace width="0.166667em"></mspace><msub><mi>u</mi><mi>x</mi></msub></mfenced><mi>x</mi></msub><mo>,</mo></mrow></semantics></math></inline-formula> where <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>K</mi><mo>(</mo><mi>u</mi><mo>)</mo></mrow></semantics></math></inline-formula> is a non-constant function of the dependent variable. We present the group classification framework, derive the determining equations for the coefficients of the infinitesimal generators of the admitted symmetry groups, and systematically solve for admissible forms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>K</mi><mo>(</mo><mi>u</mi><mo>)</mo></mrow></semantics></math></inline-formula>. …”
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Mathematical solutions for coupled nonlinear equations based on bioconvection in MHD Casson nanofluid flow by Khalil Ur Rehman, Nosheen Fatima, Wasfi Shatanawi, Nabeela Kousar

“…We established an artificial neural network (ANN) model, incorporating Tan-Sig and Purelin transfer functions, to enhance the accuracy of predicting skin friction coefficient (SFC) values along the surface. …”
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Leveraging Cognitive and Speech Ecological Momentary Assessment in Individuals With Phenylketonuria: Development and Usability Study of Cognitive Fluctuations in a Rare Disease Pop... by Shifali Singh, Lisa Kluen, Katelin Curtis, Raquel Norel, Carla Agurto, Elizabeth Grinspoon, Zoe Hawks, Shawn Christ, Susan Waisbren, Guillermo Cecchi, Laura Germine

“…Similarly, to assess variability in task performance over the course of all EMAs, the coefficient of variability was computed; 28% for the task measuring sustained attention, 37% for semantic fluency, 15.8% for the task measuring executive functioning, and 17.6% for processing speed. …”
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Cross-sectional associations between physical activity and sedentary time with cardiovascular health in children from the ALSPAC study using compositional data analysis by K. M. Sansum, B. Bond, R. M. Pulsford, A. McManus, A. O. Agbaje, A. M. Skinner, A. R. Barker

“…Abstract This study adopted a compositional framework to cross-sectionally examine the associations between physical activity (PA) and sedentary time (ST) with vascular structure and function and clustered cardiovascular disease (CVD) risk factors in 4277 children (2,226 girls), aged 10.6±0.2 years. …”
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Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 s... by Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński

“…The best prediction model was found to be the ANN model when training the conjugate gradient with the Polak-Ribiere updates (Traincgp) training function with the hyperbolic tangent sigmoid transfer function (Tansig) with the mean squared error (MSE) and correlation coefficient (R2) values recorded as 0.01886 and 0.94973, respectively. …”
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