Application of Mixed-Integer Linear Programming Models for the Sustainable Management of Vine Pruning Residual Biomass: An Integrated Theoretical Approach by Leonel J. R. Nunes

“…<i>Background</i>: This study explores the use of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a crucial resource for sustainable energy and material production. …”
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Methodological provisions for determining the main parameters of the organisation of the transportation process during the introduction of cars with increased axial and linear load by M. I. Mekhedov, E. A. Sotnikov, P. S. Kholodnyak, A. A. Kapyrin, N. V. Kornienko

“…One of the ways to increase the traffic-carrying capacity of railway lines is increasing the amount of cargo carried in cars, while taking into account the inevitable increase in the axial and linear load of the car. The decision to use cars with increased axial load should be based on an assessment of their technical and economic efficiency in comparison with the innovative and widely used open-box wagons with a load of 25 tonnes per axle. …”
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Economic management of microgrid using flexible non-linear load models based on price-based demand response strategies by Bishwajit Dey, Gulshan Sharma, P.N. Bokoro

“…To evaluate the impacts of demand response efforts, it is necessary to utilise a mix of nonlinear and linear models for creating load-responsive models in a manner that is realistic. …”
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Comparison between Linear Regression and Robust Regression Models by Using Error Criteria and Information Criteria Applied to Human Sample by Ismat Mousa Ibrahim

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Joint Relay Ordering and Linear Finite Field Network Coding for Multiple-Source Multiple-Relay Wireless Sensor Networks by Yulun Cheng, Longxiang Yang

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Retail Demand Forecasting: A Comparative Analysis of Deep Neural Networks and the Proposal of LSTMixer, a Linear Model Extension by Georgios Theodoridis, Athanasios Tsadiras

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Optimal Operation of a Tablet Pressing Machine Using Deep-Neural-Network-Embedded Mixed-Integer Linear Programming by Jialong Li, Lan Wu, Yuang Qin, Haojun Zhi

“…This paper presents a deep neural network (DNN)-embedded mixed-integer linear programming (MILP) model for fault prediction and production optimization in tablet pressing machines. …”
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A Broadband Millimeter-Wave Circularly Polarized Folded Reflectarray Antenna Based on Transmissive Linear-to-Circular Polarization Converter by Yue Cao, Zhuwei Wang, Qing Wang, Mingzhu Du, Miaojuan Zhang

“…In this paper, a wideband circularly polarized folded reflectarray antenna (CPFRA) based on a transmissive linear-to-circular polarization converter is proposed. …”
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Investigation of Heat Flux Propagation in Heat-Conducting Oxide Substrates with Different Heat Conductivity by the Linear Heat Source Method by I. A. Vrublevsky, K. V. Chernyakova, E. N. Muratova, N. V. Lushpa

“…For controlled thermal management of power electronics devices, an important task is to increase the efficiency of heat removal from active components. Aim. …”
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GENETIC PARAMETERS OF LINEAR TRAITS AND THE EFFECT OF COW'S FINAL TYPE ASSESSMENT ON THE LONGEVITY OF UKRAINIAN BLACK-AND-WHITE DAIRY BREED by Leontii KHMELNYCHYI, Victoria VECHORKA, Serhii KHMELNYCHYI, Igor RUBTSOV, Evgeniya SAMOKHINA, Cennadiy SMOLYAROV

“…The purpose of this study was to determine the heritability level of cow’s linear traits of Ukrainian Black-and-White dairy breed in herds of Ukraine, to define phenotypic relationships between linear traits and dairy productivity, and to study the impact of final assessment on traits of cow's longevity. …”
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Estimation and diagnostic for single-index partially functional linear regression model with $ p $-order autoregressive skew-normal errors by Lijie Zhou, Liucang Wu, Bin Yang

“…This paper introduced a novel single-index partially functional linear regression model with $ p $-order autoregressive skew-normal errors, addressing the dual challenges of autocorrelation and skewness in high-dimensional functional data. …”
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C6EnPLS: A High-Performance Computing Job Dataset for the Analysis of Linear Solvers’ Power Consumption by Marcello Artioli, Andrea Borghesi, Marta Chinnici, Anna Ciampolini, Michele Colonna, Davide De Chiara, Daniela Loreti

“…In recent decades, driven by global efforts towards sustainability, the priorities of HPC facilities have changed to include maximising energy efficiency besides computing performance. In this regard, a crucial open question is how to accurately predict the contribution of each parallel job to the system’s energy consumption. …”
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Magnetic Field Distribution and Energy Losses in a Permanent Magnet Linear Synchronous Motor Under Stick-Slip Friction by Paweł Olejnik, Yared D. Desta, Marcin Mydłowski

“…This study investigates the modeling and dynamic analysis of three coupled electromechanical systems, emphasizing interactions between a magnetic linear drive and frictional contact with flat springs. …”
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Interactive Learning Tool for System of Linear Equations: Bridging the Gap Between Novice and Expert Through MATLAB-Based Solutions by Reem A. Alnanih, Lamiaa A. Elrefaei

“…A total of 20 participants&#x2014;10 experts with prior knowledge of linear equations and 10 novices without such knowledge&#x2014;were involved in the evaluation. …”
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Research on Coordinated Oscillation Control Strategy of AC/DC Hybrid Distribution Network Based on Mixed-Integer Linear Programming by Shumin Sun, Peng Yu, Jiawei Xing, Yuejiao Wang, Song Yang

“…In order to improve the self-healing control ability of AC-DC hybrid distribution network and ensure the stability of distribution network operation, a coordinated oscillation control strategy for AC-DC hybrid distribution network based on mixed-integer linear programming (MILP) is proposed. By constructing a multiscale reactive power optimization model and considering the comprehensive cost minimization goal including network loss cost, regulation cost, and operation cost, the operation efficiency and reliability of distribution network are improved. …”
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Predicting the Traffic Crashes of Taxi Drivers by Applying the Non-Linear Learning of ANFIS-PSO with M5 Model Tree by E. Abbasi, M. Hadji Hosseinlou

“…Moreover, the linear relationships generated by M5 tree show the sensitivity of ensembled model accuracy on the single ANFIS models, which have a partial tendency in underestimation of the traffic crashes.…”
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Application of Three-Phase Linearized Power Flow and Line Loss Analysis of Distribution Network Driven by Data and Physics Fusion by Huaitian MU, Hongliang LIAN, Juan LIU, Yanqiong LI

“…The proposed model compensated for linearized errors, greatly improving the convergence and calculation efficiency of the power flow model while ensuring linearized accuracy, and it could adapt to different load levels to achieve precise error compensation. …”
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Multi-environment trials data analysis: linear mixed model-based approaches using spatial and factor analytic models by Tarekegn Argaw, Berhanu Amsalu Fenta, Habtemariam Zegeye, Girum Azmach, Assefa Funga

“…The analysis of multi-environment trials (MET) data in plant breeding and agricultural research is inherently challenging, with conventional ANOVA-based methods exhibiting limitations as the complexity of MET experiments grows. This study presents linear mixed model-based approaches for MET data analysis. …”
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Rendering algorithm for 3D model of goods in power warehouse based on linear interpolation and 2D texture mapping by Gui Luo, Xin Chen, Quan Liu, Anlin Liu, Xuan Zhang, Li Tian, Yueshi Gao

“…To address texture distortion and detail loss in 3D visualization of such equipment, this study establishes a fused rendering algorithm integrating Dynamic Weighted Linear Interpolation (DWLI) and Adaptive Texture Mapping (ATM). …”
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Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping by André William Boroh, Alpha Baster Kenfack Fokem, Martin Luther Mfenjou, Firmin Dimitry Hamat, Fritz Mbounja Besseme

“…The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy and efficiency of geostatistical analysis, particularly in mineral exploration. …”
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