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Transient bending analysis of the graphene nanoplatelets reinforced sandwich concrete building structure validated by machine learning algorithm

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2025年第3期32卷 260-281页

作者： Zhou, Xia Chen, Yuyuan Abbas, Mohamed Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou Sichuan Peoples R China Luzhou Fuyu Engn Planning Co Ltd Luzhou Sichuan Peoples R China King Khalid Univ Coll Engn Elect Engn Dept Abha Saudi Arabia

This paper demonstrates a thorough examination of the bending behavior of sandwich concrete building structures that are reinforced with graphene nanoplatelets (GPLs). The analysis is confirmed using a machine learning technique. Sandwich structures have notable benefits in terms of strength, longevity, and thermal insulation, making them well-suited for many building applications. Integrating GPLs into the concrete matrix improves the mechanical characteristics and performance of these structures, especially in terms of bending behavior. This study utilizes a machine learning technique to verify the characterization of the temporary bending behavior of a concrete building structure reinforced with graphene nanoplatelets. The approach utilizes a dataset consisting of simulated bending data to create a prediction model that can reliably estimate the temporary bending response of the reinforced structure under different loading situations. The machine learning algorithm's effectiveness and dependability in optimizing the design and performance of graphene nanoplatelets reinforced sandwich concrete building structures are demonstrated through validation against simulated results. This provides engineers and designers with a powerful tool. This study enhances the comprehension and use of machine learning approaches in analyzing and designing sophisticated structural materials and systems.

关键词： Transient bending GPLs concrete building structure machine learning algorithm quasi-3D higher-order plate theory

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A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

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SEPARATION SCIENCE AND TECHNOLOGY 2025年第1期60卷 75-93页

作者： Vijayalakshmi, Muniyandi Kunjiappan, Selvaraj Ravindranath, Akkala Kannan, Suthendran Sundar, Krishnan Pandian, Sureshbabu Ram Kumar Kalasalingam Acad Res & Educ Dept Biotechnol Krishnankoil 626126 Tamil Nadu India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil India Kalasalingam Acad Res & Educ Dept Informat Technol Krishnankoil India

Ultrasound-assisted solvent extraction (UASE) was appslied to extract phytoconstituents from Semecarpus anacardium L. The effective extraction parameters were optimized using the Response Surface Methodology (RSM) with Central Composite Face Centered design (CCFC), and the optimization parameters were validated through Adaptive Neuro-Fuzzy Inference System (ANFIS) and machine learning (ML) algorithm models. The four independent parameters, i.e. ethanol concentration (X-1: 62-72% v/v), temperature (X-2: 38-48 degrees C), ultrasonic-exposure time (X-3: 12-24 min), and particle size (X-4: 2-5 mm) were optimized. The maximum yield of phytocompounds was achieved at X-1 = 67%, X-2 = 43 degrees C, X-3 = 18 min, and X-4 = 3.5 mm. The optimized yield of total phenolic, flavonoid, and antioxidant activity was considered when selecting process parameters. In this scenario, the optimized yields of total polyphenolic content (y(1) = 619.25 mg gallic acid equivalent (GAE)/g), total flavonoid content (y(2) = 151.24 mg Quercetin equivalents (QE)/g), free radical scavenging abilities (%DPPH*sc (y(3) = 66.41%), and %ABTS*sc (y(4) = 56.94%). The LC-MS peaks identify the presence of semecarpetin, butein, and amentoflavone, whereas GS-MS peaks represent seventeen gaseous components. Furthermore, the bioactive-rich optimized extract was nontoxic and supported the growth of macrophage- and epithelial cells in vitro. Conclusively, optimizing the UASE parameters enhanced the yield of bioactive compounds of S. anacardium L. [GRAPHICS]

关键词： Semecarpus anacardium Linn. optimization response surface methodology ANFIS machine learning algorithm ultrasound-assisted solvent extraction

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Construction and application of intelligent human resource management system based on machine learning algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Yang, Ping Li, Jie Sichuan Univ Sci & Engn Sch Econ Zigong 643000 Sichuan Peoples R China Sichuan Univ Sci & Engn Sch Foreign Languages Zigong Peoples R China

Traditional human resource management systems face the problems of low efficiency and insufficient accuracy in data processing and decision support, while intelligent human resource management systems based on machine learning algorithms can significantly improve management efficiency and decision-making accuracy through advanced data analysis and automated decision-making. By analyzing the training duration and response efficiency of various algorithms, it was observed that the machine learning algorithm's training time is approximately 10.3 times longer than that of the non-optimized machine learning algorithm and 3.6 times longer than that of the intelligent management system, indicating strong time performance. Additionally, the response time of the classification model after applying feature selection is slightly improved compared to the original machine learning algorithm, measuring 225% of the non-optimized version and 98% of the response time of the intelligent HR management system. The key technologies involved in system development include neural network models and classification analysis techniques, and the implementation of these algorithms in the intelligent HR management system provides notable benefits.

关键词： machine learning algorithm intelligent management system human resource management response speed

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Building a machine learning algorithm-Based Model to Suggest Tourist Attractions in Response to Travelers' "Slow Life" Requirements

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JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2025年第1期27卷

作者： Li, Xiuxia Xinxiang Univ Urumqi Peoples R China

In order to promote the development of personalized recommendation system in the tourism industry, this study adopts a machine learning collaborative filtering algorithm model, and uses data provided by users and data mining technology to reduce the overload of Internet messages and provide customized content recommendations. The construction of tourist attractions recommendation model based on machine learning is 18% better than the traditional algorithm under the demand of tourists' "slow life". This research can effectively solve the problems of sparse data and cold start. It provides users with more personalized and people-centered travel route recommendations, which significantly enhancing the travel experience.

关键词： Slow Living machine learning algorithm Tourist Attraction Recommendation Model

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Nonlinear deflection and thermal post-buckling analysis of sector annular poroelastic composite nanodisks using mathematical simulation and machine learning algorithm

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THIN-WALLED STRUCTURES 2025年 207卷

作者： Xu, Zhijun Han, Yang El-Meligy, Mohammed El Hindi, Khalil Safarpour, Hamed Henan Univ Technol Sch Civil Engn Zhengzhou 450001 Peoples R China Jadara Univ Jadara Univ Res Ctr PO Box 733 Irbid Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11543 Saudi Arabia

Poroelastic nanodisks offer mechanical engineers enhanced control over material properties, enabling precise tuning of mechanical responses for advanced applications in sensors, actuators, and nano-mechanical systems. This study presents a comprehensive analysis of thermally-affected multi-directional functionally graded sector annular nanodisks, focusing on their thermal-post buckling and nonlinear deflection behaviors. Utilizing a refined quasi-3D logarithmic theory (RQLT), the study incorporates the effects of Von-Karman nonlinearity to accurately capture the large deflection responses of these advanced nanostructures under thermal loading. The material properties of the nanodisks are graded in multiple directions, enhancing their ability to withstand thermal stresses and maintain structural integrity. To solve the complex governing equations derived from the RQLT, a nonlinear discrete-singular convolution (DSC) solution procedure is employed. This novel numerical technique allows for precise computation of the nonlinear deformation and stability characteristics of the nanodisks, providing insights into their behavior under various thermal conditions. The nonlinear DSC method's ability to handle singularities and discontinuities makes it particularly suitable for this type of advanced analysis. After obtaining the mathematics simulation data, a machine learning algorithm is used to test, train, and validate the results for future analysis of the mentioned problem with low computational cost. The results demonstrate the critical influence of thermal gradients and material gradation on the post-buckling and nonlinear deflection responses of sector annular nanodisks. The interplay between thermal effects and material properties highlights the necessity for incorporating multi-directional functionally graded materials in the design of high-performance nanostructures. This study's findings are pivotal for the development of next-generation nanodisks used in thermal en

关键词： Poroelastic thermal post-buckling TD-MD-FGM RQLT Sector nanodisk machine learning algorithm

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On the measurement of the nonlinear dynamics of sandwich sector plate surrounded by the auxetic concrete foundation: Introducing a machine learning algorithm for nonlinear problems

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MEASUREMENT 2025年 241卷

作者： Li, Zhonghong Yan, Gongxing Fouly, Ahmed Albahkali, Thamer Chongqing Chem Ind Vocat Coll Sch Architectural Engn Chongqing 401228 Peoples R China Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou 646000 Sichuan Peoples R China Luzhou Key Lab Intelligent Construct & Low Carbon Luzhou 646000 Sichuan Peoples R China King Saud Univ Coll Engn Dept Mech Engn POB 800 Riyadh 11421 Saudi Arabia

The measurement of the nonlinear dynamics of sandwich rotary sector plates is crucial for measurement engineers as it enables the precise analysis and understanding of the behavior of advanced composite structures under dynamic conditions. These measurements help in identifying and mitigating potential issues related to vibration, stability, and fatigue, which are critical in industries like aerospace, automotive, and civil engineering. Accurate data on nonlinear dynamics aids in the optimization of design, enhancing performance, durability, and safety. Furthermore, it supports the development of predictive maintenance strategies, reducing downtime and operational costs, and contributing to the advancement of engineering materials and techniques. So, in the current work, for the first time, nonlinear dynamics of sandwich rotary sector plate via a mathematical modeling and machine learning algorithm is presented. First, due to a lack of information on the nonlinear dynamics of sandwich rotary sector plates, a dataset for training, testing, and validating the machine learning algorithm is presented. For this purpose, using Hamilton's principle, Von-Karman nonlinearity, and first-order shear deformation theory (FSDT), the nonlinear governing equations are obtained. Also, due to increasing stiffness and stability, an auxetic concrete foundation covers the sandwich structure. After that using the two-dimensional generalized differential quadrature method (GDQM) and Newmark's time integration method (NTIM), the nonlinear equations are solved. This research provides valuable insights for engineers in designing advanced composite structures with improved dynamic properties. The results also contribute to the broader understanding of nonlinear dynamic interactions in complex material systems, paving the way for innovative applications in various engineering fields.

关键词： Nonlinear dynamics Sandwich rotary sector plate Von-Karman nonlinearity Auxetic concrete foundation machine learning algorithm

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Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on machine learning algorithm

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JOURNAL OF CLINICAL HYPERTENSION 2025年第3期27卷 e70017页

作者： Chen, Minghui Xiong, Jing Li, Moran Hu, Tao Zhang, Yi Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai Peoples R China Tongji Univ Shanghai Peoples Hosp 10 Sch Med Dept Cardiol Shanghai Peoples R China Fourth Mil Med Univ Xijing Hosp Dept Cardiol Xian Shanxi Peoples R China

Carotid-femoral pulse wave velocity (cf-PWV) is an important but difficult to obtain measure of arterial stiffness and an independent predictor of cardiovascular events and all-cause mortality. The objective of this study was to develop a predictive model for cf-PWV based on brachial-ankle pulse wave velocity (baPWV) and other the accessible clinical *** model aims to allow patients to estimate their cf-PWV in advance without the need for direct measurement. We selected participants of the Northern Shanghai community from 2013 to 2022 as the study object. The Pearson correlation coefficient was employed for correlation analysis in feature selection. The linear regression models demonstrated low root mean square error (RMSE), error term (epsilon), and R2 values, indicating good predictive performance. A Cox proportional hazards model revealed a significant association between machine learning-predicted cf-PWV and mortality risk, supporting the validity of prediction model. Using a threshold of cf-PWV greater than 10 m/s as the criterion, a classification prediction model was developed. Shapley Additive Explanations (SHAP) analysis was then applied to the Gradient Boosting model to elucidate the predictive mechanism of the optimal model. Without precise instruments, doctors often cannot determine a patient's cf-PWV. When the cf-PWV value predicted by the machine learning algorithm is high, patients can be recommended for more precise measurements to confirm the prediction and emphasize the importance of follow-up health management and psychological support. It is feasible to use a machine learning algorithm based on baPWV and other readily available clinical parameters to predict cf-PWV.

关键词： cardiovascular patients cervical and femoral pulse velocity machine learning algorithm

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machine learning algorithm for activity-aware demand response considering energy savings and comfort requirements

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IET SMART GRID 2020年第5期3卷 730-737页

作者： Zhang, Yue Srivastava, Anurag. K. Cook, Diane Washington State Univ Sch Elect Engn & & Comp Sci Pullman WA 99164 USA

Due to the high cost of peak hour power generation and a push towards sustainability, the need for demand response (DR) is increasing. Compared to commercial-level DR, residential-level DR is more challenging. Residents are reluctant to participate, and DR controllers lack sufficient real-time activity information to balance energy savings with residents' need for comfort and convenience. To address the above challenges, we propose a sensor data-driven activity-based controller for heating, ventilation, and air conditioning devices. Using our proposed novel strategy, resident activities are recognized in real-time through a random forest machine learning approach. Integrating activity information and forecasted electricity pricing, the proposed controller can simultaneously reduce energy consumption for sustainability and maintain resident constraints for comfort based on recognized activities. Results demonstrate the superiority of the proposed approach.

关键词： building management systems power engineering computing energy conservation demand side management learning (artificial intelligence) power consumption HVAC power generation control machine learning algorithm activity-aware demand response energy savings comfort requirements peak hour power generation commercial-level DR residential-level DR DR controllers real-time activity information sensor data-driven activity-based controller air conditioning devices random forest machine energy consumption resident constraints heating-ventilation-air conditioning devices

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machine learning algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population

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JPAD-JOURNAL OF PREVENTION OF ALZHEIMERS DISEASE 2019年第3期6卷 185-191页

作者： Uspenskaya-Cadoz, O. Alamuri, C. Wang, L. Yang, M. Khinda, S. Nigmatullina, Y. Cao, T. Kayal, N. O'Keefe, M. Rubel, C. IQVIA Cent Nervous Syst Ctr Excellence Med Strate Therapeut Sci & Strategy Unit St Ouen France IQVIA Analyt Ctr Excellence Durham NC USA IQVIA Project Leadership 500 Brook DrGreen Pk Reading RG2 6UU Berks England

BACKGROUND: Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R&D pipeline includes around 112 agents. In the United States alone, 150 clinical trials are seeking 70,000 participants. Most people with early cognitive impairment consult primary care providers, who may lack time, diagnostic skills and awareness of local clinical trials. machine learning and predictive analytics offer promise to boost enrollment by predicting which patients have prodromal AD, and which will go on to develop AD. OBJECTIVES: The authors set out to develop a machine learning predictive model that identifies prodromal AD patients in the general population, to aid early AD detection by primary care physicians and timely referral to expert sites for biomarker confirmation of diagnosis and clinical trial enrollment. DESIGN: The authors use a classification machine learning algorithm to extract patterns within healthcare claims and prescription data three years prior to AD diagnosis/AD drug initiation. SETTING: The study focused on subjects included within proprietary IQVIA US data assets (claims and prescription databases). Patient information was extracted from January 2010 to July 2018, for cohorts aged between 50 and 85 years. PARTICIPANTS: A total of 88,298,289 subjects aged between 50 and 85 years were identified. For the positive cohort, 667,288 subjects were identified who had 24 months of medical history and at least one record with AD or AD treatment. For the negative cohort, 3,670,254 patients were selected who had a similar length of medical history and who were matched to positive cohort subjects based on the prevalence rate. The scoring cohort was selected based on availability of recent medical data of 2-5 years and included 72,670,283 subjects between the ages of 50 and 85 years. INTERVENTION (if any): None. MEASUREMENTS: A list of clinic

关键词： Alzheimer's disease prodromal AD machine learning algorithm AD clinical trial recruitment

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machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD

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JOURNAL OF HEPATO-BILIARY-PANCREATIC SCIENCES 2021年第7期28卷 593-603页

作者： Feng, Gong Zheng, Kenneth I. Li, Yang-Yang Rios, Rafael S. Zhu, Pei-Wu Pan, Xiao-Yan Li, Gang Ma, Hong-Lei Tang, Liang-Jie Byrne, Christopher D. Targher, Giovanni He, Na Mi, Man Chen, Yong-Ping Zheng, Ming-Hua Xian Med Univ Xian Peoples R China Wenzhou Med Univ Dept Hepatol NAFLD Res Ctr Affiliated Hosp 1 2 Fuxue Lane Wenzhou 325000 Peoples R China Wenzhou Med Univ Dept Pathol Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Dept Lab Med Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Dept Endocrinol Affiliated Hosp 1 Wenzhou Peoples R China Univ Hosp Southampton Southampton Natl Inst Hlth Res Biomed Res Ctr Southampton Gen Hosp Southampton Hants England Univ Verona Sect Endocrinol Diabet & Metab Dept Med Verona Italy Azienda Osped Univ Integrata Verona Verona Italy Xian Med Univ Dept Gastroenterol Affiliated Hosp 1 Xian Peoples R China Wenzhou Med Univ Inst Hepatol Wenzhou Peoples R China Key Lab Diag & Treatment Dev Chron Liver Dis Zhej Wenzhou Peoples R China

Background The presence of significant liver fibrosis is a key determinant of long-term prognosis in non-alcoholic fatty liver disease (NAFLD). We aimed to develop a novel machine learning algorithm (MLA) to predict fibrosis severity in NAFLD and compared it with the most widely used non-invasive fibrosis biomarkers. Methods We used a cohort of 553 adults with biopsy-proven NAFLD, who were randomly divided into a training cohort (n = 278) for the development of both logistic regression model (LRM) and MLA, and a validation cohort (n = 275). Significant fibrosis was defined as fibrosis stage F >= 2. MLA and LRM were derived from variables that were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. Results In the training cohort, the variables selected by LASSO algorithm were body mass index, pro-collagen type III, collagen type IV, aspartate aminotransferase and albumin-to-globulin ratio. The diagnostic accuracy of MLA showed the highest values of area under the receiver operator characteristic curve (AUROC: 0.902, 95% CI 0.869-0.904) for identifying fibrosis F >= 2. The LRM AUROC was 0.764, 95% CI 0.710-0.816 and significantly better than the AST-to-Platelet ratio (AUROC 0.684, 95% CI 0.605-0.762), FIB-4 score (AUROC 0.594, 95% CI 0.503-0.685) and NAFLD Fibrosis Score (AUROC 0.557, 95% CI 0.470-0.644). In the validation cohort, MLA also showed the highest AUROC (0.893, 95% CI 0.864-0.901). The diagnostic accuracy of MLA outperformed that of LRM in all subgroups considered. Conclusions Our newly developed MLA algorithm has excellent diagnostic performance for predicting fibrosis F >= 2 in patients with biopsy-confirmed NAFLD.

关键词： diagnosis fibrosis liver biopsy machine learning algorithm NAFLD

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