Decentralized queue control with delay shifting in edge-IoT using reinforcement learning by Viacheslav Kovtun

“…Analytical expressions for key QoS indicators (delay, variability, loss, energy consumption) as functions of the shift parameter are derived, and a multi-factor reward function is constructed. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

“…Results: The Generalized Linear Model Boosting combined with Random Forest (glmBoost + RF) algorithm using 14 variables achieved comparable performance (Area Under the Curve [AUC] = 0.981) to the more complex Least Absolute Shrinkage and Selection Operator combined with Random Forest (Lasso + RF) algorithm using 25 variables (AUC = 0.985). …”
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Modeling Overall Survival in Patients With Pancreatic Cancer From a Pooled Analysis of Phase II Trials by Eva Rahman Kabir, Faruque Azam, Tanisha Tabassum Sayka Khan, Hasina Yasmin, Namara Mariam Chowdhury, Syeda Maliha Ahmed, Baejid Hossain Sagar, Nasrin Ahmed Tahrim

“…The relationship between predictors and OS was explored by a gamma generalized linear model (GLM) with a log‐link function and compared with linear models. Results The Spearman rank correlation coefficient between PFS/TTP and OS was 0.88 (95% confidence interval [CI] 0.85–0.89; p < 0.0001; n = 610) and between ORR and OS was 0.58 (0.52–0.64; p < 0.0001; n = 514). …”
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A Quasi-Convex RKPM for 3D Steady-State Thermomechanical Coupling Problems by Lin Zhang, D. M. Li, Cen-Ying Liao, Li-Rui Tian

“…A meshfree, second-order, quasi-convex reproducing kernel scheme is employed to approximate field variables for solving the linear Poisson equation and the elastic thermal stress equation in sequence. …”
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Establishment of an evaluation system for conversion to laparotomy in laparoscopic cholecystectomy and exploration of surgical grading management by ZHANG Nannan, GUO Jinxing, WU Gang, YI Hui, ZHOU Yuanhang, LIAO Zhiwei, HUANG Qi, DONG Jian

“…Then, the risk factors were analyzed by multiple Logistic regression, and the pre-coefficient of each variable of the risk factors was assigned according to the established conversion to laparotomy possibility function. …”
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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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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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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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Research on Side Lobe Suppression of Time-Modulated Sparse Linear Array Based on Particle Swarm Optimization by Lei Liang, Jie Sun, Hailin Li, Jialing Liu, Yachao Jiang, Jianjiang Zhou

“…An efficient pattern synthesis approach is proposed for the synthesis of a time-modulated sparse linear array (TMSLA) in this paper. …”
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Global dynamics and time-optimal control studies for additional food provided holling type-III mutually interfering prey-predator systems with applications to pest management by D. Bhanu Prakash, D. K. K. Vamsi

“…Abstract In this study, we derive and analyze an additional food provided prey-predator model with Holling type-III functional response, incorporating mutual interference among predators. …”
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Exploring Purpose-Driven Methods and a Multifaceted Approach in Dam Health Monitoring Data Utilization by Zhanchao Li, Ebrahim Yahya Khailah, Xingyang Liu, Jiaming Liang

“…Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. …”
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Comparison of Numerical Results With Experiment Using Digital Image Correlation System of Multiobjective Structural Optimised Wind Turbine Blade by Ramazan Özkan, Mustafa Serdar Genç

“…The optimisation algorithm defined several variables, including structural constraints, the type of composite material and the number of composite layers to form a mathematical model. …”
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Barriers in Proliferating Digital Technologies at Russian Companies: Causes and Effects by A. S. Melnikov, E. G. Kalabina

“…As a result a systematized picture of enterprise functioning was developed in the context of overcoming barriers in introducing digital technologies and identification of situational and contextual variables influencing the effect of their introduction. …”
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Parallel Primal-Dual Method with Linearization for Structured Convex Optimization by Xiayang Zhang, Weiye Tang, Jiayue Wang, Shiyu Zhang, Kangqun Zhang

“…This paper presents the Parallel Primal-Dual (PPD3) algorithm, an innovative approach to solving optimization problems characterized by the minimization of the sum of three convex functions, including a Lipschitz continuous term. The proposed algorithm operates in a parallel framework, simultaneously updating primal and dual variables, and offers potential computational advantages. …”
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VARX Granger analysis: Models for neuroscience, physiology, sociology and econometrics. by Lucas C Parra, Aimar Silvan, Maximilian Nentwich, Jens Madsen, Vera E Parra, Behtash Babadi

“…We also provide methods for enhancing model efficiency, such as L2 regularization for limited data and basis functions to cope with extended delays. …”
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Elastostatic analysis of tapered FGM beams with spatially varying material properties by Justín Murín, Stephan Kugler, Juraj Paulech, Juraj Hrabovský, Vladimír Kutiš, Herbert Mang, Mehdi Aminbaghai

“…In this article an effective method for elastostatic analysis of tapered beams made of functionally-graded material (FGM) is presented. The spatially variable stiffness of the beam is the consequence of the continuous longitudinal variability of the cross-sectional dimension, accompanied by the variability of the material properties in three orthogonal directions. …”
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MAGNETOHYDRODYNAMICS NANOFERRO FLUID FLOWS PASSING THROUGH A MAGNETIC POROUS SPHERE UNDER THERMAL RADIATION EFFECT by Basuki Widodo, Eirene Juwita Ningtyas Pamela, Dieky Adzkiya, Chairul Imron, Tri Rahayuningsih

“…The dimensional mathematical model is further transformed into non-dimensional mathematical model by using non-dimensional variables. The non-dimensional mathematical model is simplified using the similarity equation by utilizing the stream function. …”
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Understanding the Role of Organic Hole Transport Layers on Pinhole Blocking and Performance Improvement in Sb2Se3 Solar Cells by Thomas P. Shalvey, Christopher H. Don, Leon Bowen, Tim D. Veal, Jonathan D. Major

“…After successive record‐efficiencies using a range of device structures, spiro‐OMeTAD has emerged as the default hole transport material (HTM), however, the function of HTM layers remains poorly understood. …”
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Mi-DETR: For Mitosis Detection From Breast Histopathology Images an Improved DETR by Fatma Betul Kara Ardac, Pakize Erdogmus

“…In the decoder layer, unnecessary model parameters have been filtered out using a layer reduction strategy to improve model efficiency and reduce computational costs. Additionally, a more stable model has been obtained by using the CIoU loss function instead of the L1+GIoU loss function used in the DETR model. …”
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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

“…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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