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Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1044-1044页

作者： Pawus, Dawid Porazko, Tomasz Paszkiel, Szczepan Opole Univ Technol Fac Elect Engn Automat Control & Informat Proszkowska 76 St PL-45758 Opole Poland Opole Univ Hosp Dept IT Witosa 26 St PL-46020 Opole Poland Univ Opole Inst Med Sci Dept Internal Med & Nephrol Oleska 48 St PL-45052 Opole Poland Opole Univ Hosp Dept Nephrol Dialysis Unit Witosa 26 St PL-46020 Opole Poland

Focal segmental glomerulosclerosis (FSGS) presents significant challenges in diagnosis, treatment, and management due to its complex etiology and clinical variability. Traditional approaches often rely on clinician judgment and are prone to inconsistencies. This study introduces an advanced expert system integrating Artificial Intelligence (AI) with Machine Learning (ML) to support nephrologists in assessing, treating, and managing FSGS. The proposed system features a modular design comprising diagnostic workflows, risk stratification, treatment guidance, and outcome monitoring modules. By leveraging ML algorithms and clinical data, the system offers personalized, data-driven recommendations, enhancing decision-making and patient care. The evaluation demonstrates the system's efficacy in reducing diagnostic errors and optimizing treatment pathways. These findings underscore the potential of AI-driven tools in transforming nephrology practice and improving clinical outcomes for FSGS patients.

关键词： expert system FSGS automation system practical approach kidney insufficiency numerical algorithms machine learning classification nephrology artificial intelligence in medicine

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Improved Generalized Eigenvalue Method for Compressor Stall Prediction

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AIAA JOURNAL 2025年第4期63卷 1647-1652页

作者： Li, Zhenyu Hu, Jiahao Sun, Dakun Dong, Xu Xu, Dengke Sun, Xiaofeng Beihang Univ Shen Yuan Honors Coll Sch Energy & Power Engn Beijing 100191 Peoples R China Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Beihang Univ Res Inst Aeroengine Beijing 100191 Peoples R China

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Strong global convergence properties of algorithms for nonlinear symmetric cone programming

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2025年 1-25页

作者： Andreani, R. Haeser, G. Ramos, A. Santos, D. O. Secchin, L. D. Serranoni, A. Univ Estadual Campinas Dept Appl Math Campinas SP Brazil Univ Sao Paulo Dept Appl Math Sao Paulo SP Brazil Univ Tarapaca Dept Matemat Arica Chile Univ Fed Sao Paulo Paulista Sch Polit Econ & Business Osasco SP Brazil Univ Fed Espirito Santo Dept Appl Math Sao Mateus ES Brazil Univ Sao Paulo Dept Comp Sci Sao Paulo SP Brazil

Sequential optimality conditions have played a major role in establishing strong global convergence properties of numerical algorithms for many classes of optimization problems. In particular, the way complementarity is handled defines different optimality conditions and is fundamental to achieving a strong condition. Typically, one uses the inner product structure to measure complementarity, which provides a general approach to conic optimization problems, even in the infinite-dimensional case. In this paper we exploit the Jordan algebraic structure of symmetric cones to measure complementarity, resulting in a stronger sequential optimality condition related to the well-known complementary approximate Karush-Kuhn-Tucker conditions in standard nonlinear programming. Our results improve some known results in the setting of semidefinite programming and second-order cone programming in a unified framework. In particular, we obtain global convergence that are stronger than those known for augmented Lagrangian and interior point methods for general symmetric cones.

关键词： Nonlinear symmetric cone optimization Sequential optimality conditions numerical algorithms Global convergence

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numerical investigation on performance of moisture separator by Lagrangian-Eulerian strategy: Physical mechanisms, theoretical models, and advanced algorithms

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ANNALS OF NUCLEAR ENERGY 2020年 137卷 107081-107081页

作者： Zhang, Huang Liu, Qianfeng Washington Univ Dept Energy Environm & Chem Engn St Louis MO 63130 USA Tsinghua Univ Dept Energy & Power Engn Beijing 100084 Peoples R China Tsinghua Univ Key Lab Adv Reactor Engn & Safety Inst Nucl & New Energy Technol Beijing 100084 Peoples R China

Moisture separator (MS) plays a key role in safe and economic operation of most nuclear power plant of light water reactor. numerical simulation of two-phase flows is widely used to understand the mechanism of MS, and optimize its design. Lagrangian-Eulerian (LE) simulation strategy is a promising modeling approach that can provide the detailed interaction between droplets and steam, and the size distribution of droplets at a certain time, which is able to depict a complete picture of droplet-laden flows in MS. However, there lacks research on systematically reviewing the state-of-the-art progress on developing practical models used in LE simulation for MS. In this work, the physical understanding of different droplet behaviors, which includes droplet generation, droplet motion, droplet collision, droplet-wall impaction and droplet break-up, are first discussed. Second, the practical models of these droplet behaviors are presented, in particular, the models of droplet generation and droplet-wall impaction are examined in detail. Further, four numerical techniques, which covers from numerical discretization schemes, steam properties interpolation, locating a droplet to collision droplet pair search, are introduced for accelerating the implementation of modeling droplet trajectories in MS. At last, needs for future concerned issues are discussed. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Lagrangian-Eulerian simulation Droplet dynamics Moisture separators numerical algorithms

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Enhancing Building Thermographic Analysis: Novel Pre and Post-processing algorithms for Non-georeferenced Images 9th

Enhancing Building Thermographic Analysis: Novel Pre and Pos...

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9th International Building Physics Conference

作者： Moga, Ligia Moga, Ioan Radulescu, Mihai Radulescu, Adrian Moldovan, Ioana Soimosan, Teodora Ionut, Iancu Tech Univ Cluj Napoca 25 Baritiu St Cluj Napoca 400020 Romania

ISBN: (纸本)9789819783113;9789819783090;9789819783083

Energy retrofit of existing buildings demands an accurate assessment of the thermal performance of the building envelope. In response, the THERMOG research project is developing a comprehensive tool to meet this requirement by delivering precise and practical insight into building envelope thermal efficiency. The paper presents the development and application of innovative algorithms for the pre- and post-processing of thermal images, specifically tailored to address challenges in analyzing 20 buildings subjected to comprehensive photogrammetric and thermographic investigations. A significant obstacle in this study was the lack of georeferencing in the thermal images, an issue that poses considerable challenges in aligning and correlating these images with their photogrammetric counterparts. To address these challenges, a novel methodology that primarily focuses on the processing of thermal images was developed, combining image processing techniques with algorithms tailored to reconstruct building facades and other areas of interest. The pre-processing phase centers on refining thermal image quality through noise reduction, image sharpening, and contrast enhancement, followed by detailed facades and reconstruction of critical building elements. Although the complete testing of this algorithm is planned for the 20 buildings, preliminary assessments have shown promising results in improving the fidelity and utility of thermal data. Also, it provides a framework for more nuanced and detailed analysis of building envelopes, which is crucial for energy efficiency diagnostics and architectural conservation.

关键词： Thermal performance thermography numerical algorithms building investigation reconstruction

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Automated biomedical measurements analysis: Innovative models based on machine learning for predicting laboratory results in nephrology

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 270卷

Current laboratory prediction systems in nephrology face challenges such as handling non-stationary datasets, limited accuracy, and insufficient personalization. To address these issues, this study introduces three machine learning-based models: the Adaptive Predictive Model for Laboratory Results with Patient-specific Adaptation (APMLR), the Adaptive Input-Output Model for eGFR Prediction based on Other Results (AIOM), and the Intelligent Assessment Model for Renal Function (IAMRF). These models leverage advanced algorithms to improve the accuracy and reliability of predictions for critical parameters such as eGFR, creatinine, and urea levels. The APMLR system achieved superior performance with Linear SVR, reaching a prediction accuracy of up to 96.97%, while Gradient Boosting emerged as the most effective method for both AIOM and IAMRF systems (approx. 95%). These findings highlight the potential of machine learning to enhance nephrology patient care by automating diagnoses, improving operational workflows, and setting anew standard for renal function assessment in clinical practice.

关键词： AI in medicine eGFR prediction numerical algorithms Automation system Machine Learning algorithms in nephrology

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Applying the FastIAS algorithm to the Redlich-Peterson isotherm

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Adsorption 2025年第5期31卷 1-8页

作者： Brandani, Stefano Friedrich, Daniel Mangano, Enzo School of Engineering The University Edinburgh Edinburgh UK

The Redlich-Peterson isotherm is widely used in liquid phase adsorption studies but the combination with the Ideal Adsorbed Solution Theory is hampered by the fact that an analytical expression for the reduced grand potential does not exist in the range of low pressures or concentrations. In this contribution we demonstrate an efficient approach to approximate the reduced grand potential using a Padé approximant allowing to perform the calculations with the Fast-IAS algorithm leading to execution times that are slightly slower but comparable to a dual site Langmuir/Fast-IAS combination. While the non-autonomous initial value approach remains a simpler method for this isotherm, the proposed method is recommended when execution times have to be minimized.

关键词： Adsorption equilibria Ideal adsorbed solution theory numerical algorithms Redlich-Peterson isotherm

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A Hybrid Artificial Neural Network Approach for Modeling the Behavior of Polyethylene Terephthalate (PET) Under Conditions Applicable to Stretch Blow Molding

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POLYMERS 2025年第8期17卷 1067-1067页

作者： Teng, Fei Menary, Gary Yan, Shiyong Nixon, James Stevens, John Boyet Sinopec Res Inst Petr Proc Co Ltd Beijing 100083 Peoples R China Queens Univ Belfast Sch Mech & Aerosp Engn Belfast BT7 1NN North Ireland Procter & Gamble Co Corp R&D Cincinnati OH 45202 USA

Stretch blow molding (SBM) is widely utilized in industrial applications, yet the deformation characteristics of materials during this process are intricate and challenging to precisely articulate. To accurately forecast the stress-strain response of polyethylene terephthalate (PET) in SBM, a hybrid Artificial Neural Network (ANN)-based constitutive model has been developed. The model has been created by combining a physical-based function for capturing the small-strain behavior in parallel with an ANN-based model for capturing the temperature-dependent large-strain nonlinear viscoelastic behavior. The architecture of the ANN has been designed to ensure stability in a load-controlled scenario, thus making it suitable for use in FEA simulations of stretch blow molding. Data for training the model have been generated by a new semi-automatic experimental rig which is able to produce 850 stress-strain curves over a wide range of process conditions (temperature range 95-115 degrees C and strain rates ranging from 1/s to 100/s) directly from blowing preforms using a combination of high-speed video, digital image correlation and sensors for pressure and force. The model has already been implemented in the commercial FEA package Abaqus via a VUMAT subroutine, with its performance validated by comparing the prediction of the evolution of preform shape during blowing vs. high-speed images. In conclusion, the developed hybrid ANN model, when integrated into Abaqus, offers a more accurate simulation of SBM processes, contributing to improved design efficiency and product quality.

关键词： numerical algorithms multiaxial stress-strain behavior artificial neural network (ANN) rate-dependent material stretch blow molding

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Conic optimization for control, energy systems, and machine learning: Applications and algorithms

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ANNUAL REVIEWS IN CONTROL 2019年 47卷 323-340页

作者： Zhang, Richard Y. Josz, Cedric Sojoudi, Somayeh Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Optimization is at the core of control theory and appears in several areas of this field, such as optimal control, distributed control, system identification, robust control, state estimation, model predictive control and dynamic programming. The recent advances in various topics of modern optimization have also been revamping the area of machine learning. Motivated by the crucial role of optimization theory in the design, analysis, control and operation of real-world systems, this tutorial paper offers a detailed overview of some major advances in this area, namely conic optimization and its emerging applications. First, we discuss the importance of conic optimization in different areas. Then, we explain seminal results on the design of hierarchies of convex relaxations for a wide range of nonconvex problems. Finally, we study different numerical algorithms for large-scale conic optimization problems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Conic optimization numerical algorithms Control theory Energy Machine learning

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numerical Solution of a General Quadratic Algebraic Matrix Riccati Equation via Probability-1 Homotopy algorithms 1

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IFAC Proceedings Volumes 2000年第24期33卷 75-80页

作者： Vassilios A. Tsachouridis Department of Engineering University of Leicester Leicester LE1 7RH UK

The design of an algorithm for the numerical solution of a generalized quadratic algebraic matrix Riccati equation is presented. The approach is based on probability-1 homotopy methods. The algorithm is illustrated wi... 详细信息

关键词： numerical algorithms numerical methods Riccati equations

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