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    Internal Benchmarking of Thermal Power Plants оf Electric Power Systems by E. M. Farhadzadeh, A. Z. Muradaliyev, Y. Z. Farzaliyev, U. K. Ashurova

    Published 2020-12-01
    “…A necessary condition for the feasibility of using these or those integral indicators is their functional and statistical independence. The results of the studies performed using the simulation method made it possible to establish that the smallest correlation occurs between the integral indicator calculated as the arithmetic mean of random variables and the integral indicator calculated as the coefficient of variation of the same random variables. …”
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  3. 523

    Integrated deep eutectic solvent (DES) extraction and microwave-assisted process for algal protein extraction: Process improvement, characterizations, functional properties, and an... by May Thu Zin, Thida Kaewkod, Jeeraporn Pekkoh, Wasu Pathom-aree, Supakit Chaipoot, Gochakorn Kanthakat, Phisit Seesuriyachan, Yan-Yu Chen, Kuan Shiong Khoo, Benjamas Cheirsilp, Sirasit Srinuanpan

    Published 2025-03-01
    “…These findings highlight the potential of Spirulina and Chlorella proteins as sustainable, algal-based ingredients for functional foods and nutraceutical applications while addressing challenges in optimization, scalability, and biomass variability.…”
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    Logical minimization for combinatorial structure in FPGA by P. N. Bibilo, Yu. Yu. Lankevich, V. I. Romanov

    Published 2021-03-01
    “…The paper describes the research results of application efficiency of minimization programs of functional descriptions of combinatorial logic blocks, which are included in digital devices projects that are implemented in FPGA. …”
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  6. 526

    Natural assets, changes, and variations of the socioeconomic-environmental systems along the Asian drylands belt by Jiquan Chen, Ranjeet John, Venkatesh Kolluru, Elizabeth A Mack, Peilei Fan, Jing Yuan, Zutao Ouyang, Jingyan Chen, Pavel Groisman, Changliang Shao, Amarjargal Amartuvshin, Garik Gutman

    Published 2024-01-01
    “…We found increased variability as well as spikes in extreme values in each of these three measures of SES function among the 23 PEs over the study period. …”
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    Exploration design for Q-learning-based adaptive linear quadratic optimal regulators under stochastic disturbances by Vina Putri Virgiani, Shiro Masuda

    Published 2025-12-01
    “…Q-learning optimizes the state-action policy by estimating the Q-function iteratively. This study proposes exploration signal design for the bias-free Q-learning algorithm that modifies the recursively defined Q-function by adding a disturbance-influenced term and utilizes an Instrumental Variable (IV) technique to resolve the bias-error issue. …”
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    Jump around: Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food web model quality and ecosystem representation by Gemma Gerber, Ursula M. Scharler

    Published 2024-12-01
    “…However, little information exists on how each LIM-MCMC algorithm parameter affects the degree of empirical data variability introduced into the ensemble. Further, post hoc algorithm quality diagnostics with commonly used trace plots and the coefficient of variation (CoV) rarely address critical aspects of algorithm quality, such as (1) if the returned ensemble successfully targeted the solution space distribution (stationarity), (2) correlation between ensemble solutions (mixing), and (3) if the ensemble contains enough solutions to adequately capture input data variability (sampling efficiency). …”
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    An Improved Adaptive Car-Following Model Based on the Unscented Kalman Filter for Vehicle Platoons’ Speed Control by Caixia Huang, Wu Tang, Jiande Wang, Zhiyong Zhang

    Published 2025-07-01
    “…Traditional models generally assume a fixed maximum speed within the optimal velocity function, which constrains effective platoon speed regulation across road segments with varying speed limits and lacks adaptability to dynamic scenarios such as changes in the platoon leader’s speed or substitution of the lead vehicle. …”
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  14. 534

    Research on Small-Target Detection of Flax Pests and Diseases in Natural Environment by Integrating Similarity-Aware Activation Module and Bidirectional Feature Pyramid Network Mod... by Manxi Zhong, Yue Li, Yuhong Gao

    Published 2025-01-01
    “…Additionally, this paper adopts the EIOU loss function to further optimize the model’s bounding box regression, reducing the distortion of bounding boxes caused by high sample variability. …”
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    Mathematical modeling describing the performance of open loop multivariable vapor compression chiller model: implications for sustainable HVAC in social housing development by Basim Touqan, Basim Touqan, Muna Salameh

    Published 2025-07-01
    “…IntroductionThis study introduces a simplified multi-variable state-space model of a vapour compression chiller system, focusing on key components such as the condenser and evaporator.MethodsThe model is based on a previously validated state-space framework and has been further developed to generate a transfer function matrix with two inputs (refrigerant and cold carrier flow rates) and two outputs (cooling capacity and outlet temperature). …”
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    Forecasting Industrial Electricity Consumption in Iran: A Novel Hybrid Approach for Sustainable Energy Management by Mohsen Rezaei

    Published 2024-10-01
    “…Accurately predicting industrial electricity consumption is essential for optimizing energy efficiency, and reducing costs in industrial operations. …”
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  19. 539

    CLINICAL SIGNIFICANCE OF ASPIRIN RESISTANCE IN PATIENTS WITH CARDIO-VASCULAR DISEASES by P. J. Dovgalevsky, N. V. Furman, N. F. Puchinian

    Published 2016-01-01
    “…Possible reasons for this are discussed: low compliance with treatment, peculiarities of functional condition of platelets, genetically given variability of platelet receptors etc. …”
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  20. 540

    Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores by Jose W. Naal-Pech, Leonardo Palemón-Arcos, Youness El Hamzaoui

    Published 2025-05-01
    “…This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis function (RBF), Bayesian regularized (BR), scaled conjugate gradient (SCG), and Levenberg–Marquardt (LM)—to predict UCS from three readily measured variables: water content, interconnected porosity, and real density. …”
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