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Evaluating strength behavior and failure mode of ice materials based on large-scale database and machine learning algorithms

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OCEAN ENGINEERING 2024年第Part1期313卷

作者： Ai, Xihui Li, Jiao Zhou, Zhiwei Ma, Wei Chinese Acad Sci Northwest Inst Ecoenvironm & Resources State Key Lab Frozen Soil Engn Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The strength characteristic and failure mode of ice materials are widely used to analyze the interaction between ice and structure to ensure the construction stability in ocean engineering and ice engineering. This study establishes a large-scale database (comprising 5542 testing samples) for the ice mechanics to study the influences of six effective features (grain size, density, porosity, salinity, temperature, and strain rate) on mechanical behaviors. The correlation degrees between effective features and strength behavior (failure mode) are investigated through correlation analysis. The Regression algorithm and Classification algorithm of four machine learning models are respectively used to evaluate and predict the strength and deformation behaviors of ice materials. Four evaluation parameters (R-2, RMSE, MAE, and MBE) are adopted to further investigate the predictive ability of those machine learning models. Based on the partial dependence interpretation, the contributions of effective features to strength prediction are quantitatively described. The results indicate that temperature is the most important factor for strength behavior. In the classification prediction for failure mode, the prediction accuracy for ductile behavior is enhanced in the Random Forest algorithm to improve the overall classification accuracy, and the Random Forest algorithm exhibits well performance compared to the other three algorithms.

关键词： Ice material Strength prediction Failure mode classification Correlation analysis machine learning algorithm

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Design and application of optical communication technology based on machine learning algorithms in physical education teaching information management system

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OPTICAL AND QUANTUM ELECTRONICS 2024年第1期56卷 1-19页

作者： Zhai, Qingwen Chen, Xiao Guangdong Ocean Univ Sch Sport & Recreat Zhanjiang 524088 Guangdong Peoples R China

With the rapid development of information technology, physical education information management system has become an important part of modern education. However, the traditional physical education information management system has some problems in optical communication technology, such as bandwidth limitation and slow transmission speed, which affect the performance and effect of the system. This paper aims to improve the application of optical communication technology in physical education teaching information management system by using machine learning algorithm, and improve the performance and effect of the system. This paper collects the relevant data of PE teaching information management system, and carries on the pre-processing and feature extraction. The appropriate machine learning algorithm was then selected to train and optimize the model and applied to optical communication technology. The new system has higher bandwidth and transmission speed, can process and transmit data faster, and improve the teaching effect.

关键词： machine learning algorithm Optical communication technology Physical education teaching Information management system

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An integrated study fusing systems biology and machine learning algorithms for genome-based discrimination of IPF and NSIP diseases: a new approach to the diagnostic challenge

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SOFT COMPUTING 2024年第6期28卷 5721-5749页

作者： Amjad, Elham Asnaashari, Solmaz Dastmalchi, Siavoush Sokouti, Babak Tabriz Univ Med Sci Biotechnol Res Ctr Tabriz Iran Tabriz Univ Med Sci Sch Pharm Tabriz Iran

Idiopathic pulmonary fibrosis (IPF) and nonspecific interstitial pneumonia (NSIP) are the two types of idiopathic interstitial pneumonia that are most prevalent. IPF and NSIP, often known as chronic interstitial pneumonia, must be differentiated from other forms of idiopathic interstitial pneumonia. However, distinguishing IPF from NSIP on radiographic imaging is challenging. Our goal in this work is to propose a novel approach to this clinical diagnostic challenge by distinguishing IPF from NSIP and healthy individuals via a complete systems biology analysis of existing microarray datasets. The Gene Expression Omnibus (GEO) database was searched, and two microarray datasets were identified. These datasets included normal, IPF, and NSIP samples. A second dataset was retrieved to validate further the built prediction models trained on the first dataset. Following the completion of the stages for data preparation and normalization, the profiles of gene expression were analyzed to determine the differentially expressed genes (DEGs). After that, we constructed module analysis and identified possible biomarkers by leveraging the prioritized and statistically significant DEGs to construct protein-protein interaction networks. The DEGs with the most important priority were also utilized to determine the implicated Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene ontology (GO) enrichment analyses. Using the Kaplan-Meier approach, we performed three separate assessments of the gene biomarkers' effect on patients' chances of survival. In addition, the found genes were validated not just through several different categorization models, but also by analyzing the published experimental work on the target genes. A total of 32 distinct genes were found when comparing IPF to normal, NSIP to normal, and IPF to NSIP. This was accomplished by identifying seven (14 genes), six (7 genes), and eight (13 genes) modules, as well as three genes (i.e., C6, C5, STAT1

关键词： machine learning algorithm Systems biology Biomarkers Idiopathic pulmonary fibrosis Nonspecific interstitial pneumonia Diagnosis

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A Study on Global Oceanic Chlorophyll-a Concentration Inversion Model for MODIS Using machine learning algorithms

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IEEE ACCESS 2024年 12卷 128843-128859页

作者： Chen, Kehai Zhang, Jinlan Zheng, Yan Xie, Xuetong Guangdong Polytech Ind & Commerce Sch Mapping & Remote Sensing Guangzhou 510510 Peoples R China Guangzhou Univ Sch Geog & Remote Sensing Guangzhou 510006 Peoples R China

machine learning (ML) algorithms can accurately extract quantitative patterns from datasets without requiring prior knowledge, playing an increasingly crucial role in tasks such as information extraction from remotely sensed data. This study employs several ML algorithms, including Back Propagation Network (BPN), Support Vector machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop global oceanic chlorophyll-a (Chl-a) concentration inversion models for MODIS/Aqua (MODISA). The models utilize the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) dataset, which contains in situ Chl-a concentration measurements paired with MODISA remote sensing reflectance ( $R_{\mathrm {rs}}$ ) for various water color bands from 2002 to 2017. This study includes the following processing steps. First, the SeaBASS dataset is randomly divided into training dataset and testing dataset. Second the Chl-a concentration inversion models are developed using the training dataset. Third, the testing dataset is utilized to evaluate the developed models. Finally, the inversion experiment is conducted using MODISA data. Experimental results indicate that all ML algorithms achieve higher inversion accuracy compared to the traditional ocean color index (OCI) algorithm. Particularly, the SVR model demonstrates the best performance. Additionally, to address the challenge of multidimensional parameter optimization in ML algorithms, this study introduces the Differential Evolution (DE) algorithm and develops a DE-based SVM model (DE-SVM). Compared to the standard SVM model, the DE-SVM model significantly reduces the mean deviation, mean absolute deviation, and root mean square error of the inverted chlorophyll-a concentration, while increasing the coefficient of determination (R2) from 0.87 to 0.926. These results indicate that the DE-SVM model further improves the inversion accuracy of Chl-a concentration, and holds substantial potential in satellite remote sensing

关键词： MODIS imagery machine learning algorithm machine learning algorithm chlorophyll-a inversion chlorophyll-a inversion ocean color remote sensing ocean color remote sensing ocean color remote sensing

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Prediction of battery critical parameters using machine learning algorithms for electric vehicles

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INTERNATIONAL JOURNAL OF ELECTRIC AND HYBRID VEHICLES 2024年第3期16卷 247-260页

作者： Hegde, Vasudha Sohal, Jaskaran Singh Balaraman, Gopi Karn, Aayush Pandey, Kumar Bhaskar Nitte Meenakshi Inst Technol Dept E&EE Bengaluru 560064 India

To enhance the adaptability of electric vehicles (EVs) and mitigate the intermittent nature of renewable energy sources, energy storage via batteries is imperative. Accurate forecasting of battery performance parameters is vital for optimal utilisation. This study introduces a machine learning algorithm for electric vehicle battery management systems (BMS), focusing on predicting state of charge (SoC) efficiently and precisely. Utilising linear regression and long short-term memory (LSTM) models, the algorithm constructs and deploys predictions. Training data, obtained from Li-ion battery packs during charge-discharge cycles via smart BMS, enables precise modelling. Predicted values are validated against empirical results, and the resultant error guides algorithm refinement for enhanced accuracy. The algorithm, integrated into a web application using Streamlit, achieved a remarkable 99% R2_score, indicating its robust performance. This framework advances EV battery management, facilitating informed decision-making and optimising energy utilisation in conjunction with renewable sources.

关键词： battery management system BMS long short-term memory LSTM linear regression machine learning algorithm state of charge SoC

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machine learning algorithms to study the relative roles of jet convection and natural convection in the presence of surface roughness elements on enhancement of jet impingement heat transfer

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2024年 198卷

作者： Nagesha, K.

Based on data -driven approaches, eight machine -learning algorithms have been proposed to characterize the heat transfer performance of turbulent jet impingement cooling. These algorithms are designed to cool high -heatflux surfaces modified with single protrusions, multi -protrusions, or V -grooves. To train these algorithms, an experimental dataset was used, which included 184 experiments with Reynolds numbers ranging from 10,000 to 33,000, nozzle exit -to -plate distances ranging from 2 to 10 jet diameters, and two different heat inputs of 60 W and 90 W. Multiple linear regression algorithms in Python programming were used to model jet impingement cooling, showing that the proposed machine learning models provided more accurate predictions with a 20.4 % to 66.5 % relative residual improvement over the existing conventional regression model based on performance metrics like R2 and relative residual. This improvement was leveraged to quantify natural convection's impact on the effectiveness of heat transfer enhancement due to different types and sizes of roughness elements in hightemperature applications. The research found that as the protrusion surface area increased from 0.02 % to 0.188 % compared to the flat plate, the effectiveness of heat transfer enhancement decreased from 2.69 % to 8.37 % at similar operating conditions. These results revealed that natural convection, which opposes forced jet impingement, was strengthened with increased heat input values, roughness element surface area, and the local turbulence generated. The higher natural convection weakened the enhancement afforded by jet impingement on the modified plates relative to the flat plate. Additionally, the capabilities of machine learning algorithms were explored to unlock the full potential of the jet impingement technique and to overcome the associated complexity in jet impingement applications for next -generation electronics cooling. Finally, the Decision Tree Classifier algorithm was ap

关键词： Jet impingement Surface roughness elements Natural convection Heat transfer enhancement machine learning algorithm Python

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Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第38期29卷 57345-57356页

作者： Abu El-Magd, Sherif Ahmed Maged, Ali Farhat, Hassan, I Suez Univ Fac Sci Geol Dept POB 43518 El Salam City Suez Governorat Egypt

Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naive Bayes (NiB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NiB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications.

关键词： Flash floods Naive Bayes machine learning algorithm Vulnerability map Hydrologic indices

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Association between stress hyperglycemia ratio and diabetes mellitus mortality in American adults: a retrospective cohort study and predictive model establishment based on machine learning algorithms (NHANES 2009-2018)

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DIABETOLOGY & METABOLIC SYNDROME 2024年第1期16卷 1页

作者： Yan, Fengjuan Zhao, Limin Quan, Xiaoqing Zhu, Jialiang Shenzhen Longhua Dist Cent Hosp Dept Geriatr Shenzhen Guangdong Peoples R China Jinan Univ Affiliated Hosp 1 Guangzhou Guangdong Peoples R China

Background Stress hyperglycemia is a physiological response of the body under stress to make adaptive adjustments in response to changes in the internal environment. The stress hyperglycemia ratio (SHR) is a new indicator after adjusting the basal blood glucose level of the population. Previous studies have shown that SHR is associated with poor prognosis in many diseases, such as cardiovascular and cerebrovascular diseases and delirium in elderly patients. However, there are currently no studies on the correlation between SHR and the general U.S. population. The purpose of this study was to examine the association between SHR and adverse outcomes among adults in the United States in general. Methods Data on 13,315 follow-up cohorts were extracted from NHANES. The study population was divided into four groups according to quartiles of SHR. The primary outcomes were all-cause mortality and diabetes mellitus mortality. The relationship between SHR and outcomes was explored using restricted cubic splines, COX proportional hazards regression, Kaplan-Meier curves, and mediation effects. SHR is incorporated into eight machine learning algorithms to establish a prediction model and verify the prediction performance. Results A total of 13,315 individual data were included in this study. Restricted cubic splines demonstrated a "U-shaped" association between SHR and all-cause mortality and diabetes mellitus mortality, indicating that increasing SHR is associated with an increased risk of adverse events. Compared with lower SHR, higher SHR was significantly associated with an increased risk of all cause mortality and diabetes mellitus mortality (HR > 1, P < 0.05). The mediating effect results showed that the positively mediated variables were segmented neutrophils and aspartate aminotransferase, and the negatively mediated variables were hemoglobin, red blood cell count, albumin, and alanine aminotransferase. The ROC of the eight machine learning algorithm models are XGBoost (

关键词： Stress hyperglycemia ratio NHANES machine learning algorithm Retrospective cohort Mediating effect

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Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT

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ANNALS OF HEMATOLOGY 2024年第6期103卷 2089-2102页

作者： Shao, Wenwei Wang, Yixuan Liu, Li Ren, Yiran Wang, Jieru Cui, Yuqing Liu, Jia Zhang, Xiaoyu Zhang, Sudong Liu, Shuangjie Jiang, Erlie Feng, Sizhou Pei, Xiaolei Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Hematol Hematopoiet Stem Cell Transplantat Ctr Natl Clin Res Ctr Blood DisHaihe Lab Cell Ecosyst Tianjin 300020 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Blood Dis Hosp Tianjin 300020 Peoples R China

Infection post-hematopoietic stem cell transplantation (HSCT) is one of the main causes of patient mortality. Fever is the most crucial clinical symptom indicating infection. However, current microbial detection methods are limited. Therefore, timely diagnosis of infectious fever and administration of antimicrobial drugs can effectively reduce patient mortality. In this study, serum samples were collected from 181 patients with HSCT with or without infection, as well as the clinical information. And more than 80 infectious-related microRNAs in the serum were selected according to the bulk RNA-seq result and detected in the 345 time-pointed serum samples by Q-PCR. Unsupervised clustering result indicates a close association between these microRNAs expression and infection occurrence. Compared to the uninfected cohort, more than 10 serum microRNAs were identified as the combined diagnostic markers in one formula constructed by the Random Forest (RF) algorithms, with a diagnostic accuracy more than 0.90. Furthermore, correlations of serum microRNAs to immune cells, inflammatory factors, pathgens, infection tissue, and prognosis were analyzed in the infection cohort. Overall, this study demonstrates that the combination of serum microRNAs detection and machine learning algorithms holds promising potential in diagnosing infectious fever after HSCT.

关键词： Serum microRNA Infectious fever Hematopoietic stem cell transplantation machine learning algorithm

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Hot spot temperature prediction and operating parameter estimation of racks in data center using machine learning algorithms based on simulation data

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Building Simulation 2023年第11期16卷 2159-2176页

作者： Xianzhong Chen Rang Tu Ming Li Xu Yang Kun Jia School of Civil and Resources Engineering University of Science and Technology BeijingBeijing100083China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China China Electronics Engineering Design Institute CO. LTD.Beijing100142China

In this paper,models to predict hot spot temperature and to estimate cooling air’s working parameters of racks in data centers were established using machine learning algorithms based on simulation ***,simulation models of typical racks were established in computational fluid dynamics(CFD).The model was validated with field test results and results in literature,error of which was less than 3%.Then,the CFD model was used to simulate thermal environments of a typical rack considering different factors,such as servers’power,which is from 3.3 kW to 20.1 kW,cooling air’s inlet velocity,which is from 1.0 m/s to 3.0 m/s,and cooling air’s inlet temperature,which is from 16℃ to 26℃ The highest temperature in the rack,also called hot spot temperature,was selected for each ***,a prediction model of hot spot temperature was built using machine learning algorithms,with servers’power,cooling air’s inlet velocity and cooling air’s inlet temperature as inputs,and the hot spot temperatures as ***,based on the prediction model,an operating parameters estimation model was established to recommend cooling air’s inlet temperatures and velocities,which can not only keep the hot spot temperature at the safety value,but are also energy saving.

关键词： data center CFD simulation hot spot temperature machine learning algorithm prediction and estimation models

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