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A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第5期7卷 4889-4910页

作者： Uddin, Md Nasir Ye, Junhong Haque, M. Aminul Yu, Kequan Li, Lingzhi Tongji Univ Coll Civil Engn Dept Disaster Mitigat Struct Shanghai Peoples R China Texas State Univ Ingram Sch Engn San Marcos TX 78666 USA Southwest Jiaotong Univ Inst Smart City & Intelligent Transportat Chengdu Peoples R China

3D concrete printing (3DCP) is crucial in the construction because of the low labor cost, eco-friendly behavior;however, getting a proper mixture is always a challenge. This study focuses on predicting the compressive strength (CS) of fiber-reinforced concrete produced with 3DCP using eight machine learning (ML) algorithms to get optimized mixture. The ML models were trained and tested using a comprehensive database on CS collected from literature considering the various fiber-reinforced cementitious composites, comprising over 299 mixtures with 11 features. The results show that the trained ML models could predict CS with R2 ranging from 0.927 to 0.990 and 0.914 to 0.988 for the training and testing dataset, respectively. Furthermore, supplementary experiments were conducted to create a new dataset to validate the predictive model's accuracy, with the extreme gradient boosting (XGB) and gene expression programming (GEP). Based on the GEP, a novel empirical equation was proposed and rigorously validated using experiments. The equation exhibits a high accuracy with the GEP algorithm (R2 = 0.89), providing real-world field applications that might be improve decision-making and mixture optimization, which contributes to advancements, efficiency, and innovative solutions in 3D printing practical domains.

关键词： Fiber-reinforced concrete 3D concrete printing machine learning algorithm Hyperparameter optimization Gene expression programming

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machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology

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EUROPEAN JOURNAL OF MEDICAL RESEARCH 2024年第1期29卷 1页

作者： Chen, Ronghui Wu, Jun Che, Yinwei Jiao, Yuzhuo Sun, Huashan Zhao, Yinuo Chen, Pingping Meng, Lingxin Zhao, Tao Shandong Second Med Univ Sch Clin Med Weifang 261053 Peoples R China Peoples Hosp Rizhao Dept Oncol Rizhao 276826 Peoples R China Peoples Hosp Rizhao Dept Cent Lab Shandong Prov Key Med & Hlth Lab Rizhao Key Lab Basic Res Anesthesia & Resp Intens Rizhao 276826 Shandong Peoples R China Peoples Hosp Rizhao Dept Pathol Rizhao 276826 Peoples R China

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs-ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT-that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.

关键词： Clear cell renal cell carcinoma Prognostic risk model machine learning algorithm Cuproptosis Disulfidptosis Long non-coding RNA Targeted drugs Immune inhibitors

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Tree species identification in ex situ conservation areas using WorldView-2 Satellite Data and machine learning Methods: a case study in the Bogor Botanic Garden

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TROPICAL ECOLOGY 2024年第1期65卷 81-91页

作者： Yudaputra, Angga Yuswandi, Ade Yusuf Witono, Joko Ridho Cropper, Wendell P. Usmadi, Didi Natl Res & Innovat Agcy Bot Gardens & Forestry Res Ctr Plant Conservat KST Soekarno Jalan Raya Jakarta-Bogor Km 46 Cibinong 16911 West Java Indonesia Natl Res & Innovat Agcy Directorate Sci Collect Management KST Soekarno Jalan Raya Jakarta-Bogor Km 46 Cibinong 16911 West Java Indonesia Natl Res & Innovat Agcy Res Ctr Biosystemat & Evolut KST Soekarno Jalan Raya Jakarta-Bogor Km 46 Cibinong 16911 West Java Indonesia Univ Florida Sch Forest Fisheries & Geomat Sci Gainesville FL 32611 USA Natl Res & Innovat Agcy Res Ctr Ecol & Ethnobiol KST Soekarno Jalan Raya Jakarta-Bogor Km 46 Cibinong 16911 West Java Indonesia

Spatially-explicit data on the species composition of forest plants can be an important tool for forest management and conservation. One specific application of these data is for identifying tropical tree species through machine learning techniques to classify satellite remote sensing images. This study aims to examine the ability to use Worldview-2 high-resolution data with various machine learning methods to identify tree species in the Bogor Botanic Garden. Eighteen species from 11 families were selected as samples representing an ecologically and taxonomically diverse data set. Using aggregated image variables, each tree species was found to have different reflectance, texture, and spectral vegetation index variable values. Cluster analysis showed that the 18 tree species could be separated into three clusters that partly reflected taxonomic relationships. Four machine learning algorithms (Support Vector machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), and Bayesian) were used to predict the species identity of pixels in the image data. A multicollinearity test using a Variance Inflation Factor method reduced the predictor variables from 54 to 9. The highest accuracy (0.96) was observed using SVM, followed by RF (0.91), KNN (0.86), and Bayesian (0.74). The implementation of high-resolution satellite imagery and machine learning for species identification in tropical ex situ plant conservation areas, such as botanic gardens is reported here for the first time.

关键词： Classification Conservation machine learning algorithm Modeling ecology Remote sensing Tropical tree Urban forest

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Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

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PEERJ COMPUTER SCIENCE 2022年 8卷 e898页

作者： Islam, Saima Sharleen Haque, Md Samiul Miah, M. Saef Ullah Bin Sarwar, Talha Nugraha, Ramdhan Amer Int Univ Bangladesh AIUB Fac Sci & Technol Dept Comp Sci Dhaka Bangladesh Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pahang Malaysia Telkom Univ Fac Elect Engn Bandung Indonesia

Thyroid disease is the general concept for a medical problem that prevents one's thyroid from producing enough hormones. Thyroid disease can affect everyone-men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret due to the enormous amount of data necessary to forecast results. For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. This study utilizes the Sick-euthyroid dataset, acquired from the University of California, Irvine's machine learning repository, for this purpose. Since the target variable classes in this dataset are mostly one, the accuracy score does not accurately indicate the prediction outcome. Thus, the evaluation metric contains accuracy and recall ratings. Additionally, the F1-score produces a single value that balances the precision and recall when an uneven distribution class exists. Finally, the F1-score is utilized to evaluate the performance of the employed machine learning algorithms as it is one of the most effective output measurements for unbalanced classification problems. The experiment shows that theANNClassifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy.

关键词： Thyroid Disease Thyroid Risk Prediction machine learning algorithm machine learning Classifier Sick-euthyroid

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Predictive Role of Serum C-Peptide in New-Onset Renal Dysfunction in Type 2 Diabetes:A Longitudinal Observational Study Using machine learning algorithms

Predictive Role of Serum C-Peptide in New-Onset Renal Dysfun...

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浙江省免疫学会第十三次学术大会

作者： Huabin Wang Dongmei Sun Yifei Hu Yongjun Ma Department of Clinical Laboratory Affiliated Jinhua HospitalZhejiang University School of Medicine

Background:Our previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes(T2 D) in ***,the role of serum C-peptide in in predicting the risk of new-onset renal dysfunction remains unclear and needs to be further ***:This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction,then construct and select the optimal predictive model based on serum C-peptide and other clinical parameters using machine learning *** and methods:The patients with T2 D and normal renal function at baseline were recruited in this *** LASSO algorithm was performed to filter potential predictors from the baseline *** classical machine learning algorithms were used to construct predictive models for new-onset renal dysfunction *** performances of these five models were compared to select a optimal predictive *** analysis was performed to assess the statistical power of the ***:During a 2-year follow-up period,21.08%(35/166) of subjects with T2 D and normal renal function at baseline progressed to renal *** predictors were determined using LASSO regression,including baseline albumin-to-creatinine ratio,glycated hemoglobin,hypertension,retinol-binding protein-tocreatinine ratio,quartiles of fasting C-peptide,and quartiles of fasting C-peptide to 2 h postprandial C-peptide *** 6 predictors were incorporated to develop models for renal dysfunction risk prediction using 5 machine learning ***,logistic regression(LR) model had the highest efficiency,with an AUC of 0.83,an accuracy of 75.80%,a sensitivity of88.60%,and a specificity of 70.80%.According to the power analysis,the statistical power of the LR model was found to be 0.81,which was at a relatively high ***,A nomogram was develo

关键词： type 2 diabetes renal dysfunction serum C-peptide machine learning algorithm new-onset

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Predicting the prevalence of cardiovascular diseases using machine learning algorithms

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Intelligence-Based Medicine 2025年 11卷 100199-100199页

作者： Sianga, Bernada E. Mbago, Maurice C. Msengwa, Amina S. Department of Official Statistics Eastern Africa Statistical Training Centre P.O. Box 35103 Tanzania Department of Statistics University of Dar es Salaam P.O. Box 35047 Dar es Salaam Tanzania

Cardiovascular Diseases (CVDs) are the major cause of morbidity, disability, and mortality worldwide and are the most life-threatening diseases. Early detection and appropriate action can significantly reduce the effects and complications of CVD. Prediction of the likelihood that an individual can develop CVD adverse outcomes is essential. machine learning methods are used to predict the risk of CVD incidences. Optimal model parameters were obtained using the grid search and randomized search methods. A hyperparameter tuning method with the highest accuracy was used to find the optimal parameters for the six algorithms used in this study. Two experiments were deployed: the first was training and testing the CVD dataset using hyperparameterized ML algorithms excluding geographical features, and the second included geographical features. The geographical features are air humidity, temperature and education status of a location. The performances of the two experiments were compared using classification metrics. The findings revealed that the performance of the second experiment outperformed the first experiment. XGBoost achieved the highest accuracy of 95.24 %, followed by the decision tree 93.87 % and support vector machine 92.87 % when geographical features were included (second experiment). Including geographical risk factors in predicting CVD is crucial as they contribute to the probability of developing CVD incidences. © 2025 The Authors

关键词： And XGBoost Cardiovascular disease machine learning algorithm

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Optimal machine learning for detecting lathe machining parameters

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第1-2期128卷 779-788页

作者： Rall, Keven Loker, David Nikhare, Chetan P. Penn State Univ Elect & Comp Engn Technol Dept Erie PA USA Penn State Univ Mech Engn Dept Erie PA 16801 USA

Remote manufacturing process monitoring is becoming more popular in industry for many reasons including increased production, reduced production costs, and better product quality. However, a downside to remote monitoring is that there is minimal human interaction with the machine to ensure optimal performance. Many types of sensors have been used to monitor the manufacturing process parameters of a machine, but these types of sensors are typically invasive to the machine, costly to install, and can interfere with the machining operation. A solution to this problem is to use a non-invasive sensor such as a microphone array to remotely detect manufacturing process parameters. Acoustic signals produced by a lathe are used to monitor spindle speed, depth of cut, and feed rate to ensure optimal performance. Based on these acoustic signals, machine learning algorithms are used to predict manufacturing process parameters. Two common learner algorithms were discussed and compared, such as k-nearest neighbor (kNN) and Gaussian support vector machine (Gaussian SVM). Each algorithm was classified as either "fine" or "medium." Various pre-processing algorithms were also discussed and compared, such as autocorrelation and/or cross-correlation in both time domain and frequency domain. This study found that autocorrelation in frequency domain yielded the best overall results for all three machining parameters, with learner algorithms of fine kNN or fine Gaussian SVM for depth of cut, fine Gaussian SVM for feed rate, and either fine kNN or fine Gaussian SVM for spindle speed. For prediction of machining parameters, an accuracy range of 90-100% was obtained.

关键词： machine monitoring machine learning algorithm Microphone Acoustic signal

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Image reconstruction method of gamma emission tomography based on prior-aware information and machine learning for partial-defect detection of PWR-type spent nuclear fuel

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NUCLEAR ENGINEERING AND TECHNOLOGY 2024年第11期56卷 4770-4781页

作者： Choi, Hyung-Joo Jeong, Jaewon Lee, Hakjae Kim, Seong Gon Ahn, Jae Joon Lee, Hyun Cheol Min, Chul Hee Yonsei Univ Dept Radiat Convergence Engn Seoul South Korea ARALE Lab Co Ltd Seoul South Korea Samsung Elect Co Ltd Suwon South Korea Yonsei Univ Div Data Sci Wonju South Korea Korea Inst Nucl Nonproliferat & Control KINAC Nucl Mat Anal Team Daejon South Korea

Spent nuclear fuel (SNF) requires accurate and effective supervision to prevent radiation release to the public and the environment. Gamma emission tomography (GET) has been used to inspect partial-defects at the pin-by-pin level through the acquisition of tomographic images. However, due to the high density of nuclear fuel rods, GET shows relatively low accuracy for the central region. This study proposes an image reconstruction method for the internal fuels based on Monte Carlo simulation and machine learning algorithm. The gammas from the SNF were measured with the GET device and the sinograms were reconstructed with Maximum-Likelihood Expectation- Maximization (MLEM) with prior-aware information of SNF, and image quality was improved with the machine learning technique. Our results show that the MLEM with prior-aware information could dramatically improve the image quality and the denoising technique with the machine learning could clearly correct the over-and under-estimation. Based on the results of quantitative validation of the proposed reconstruction method, the pattern classification accuracy was calculated to be about 100 %. These techniques enabled the image acquisition of the central fuel rod and increased accuracy in partial defect detection. For further study, the performance of the proposed reconstruction method will be evaluated based on the experiment data.

关键词： Safeguards Spent nuclear fuel Partial defect Gamma emission tomography Monte Carlo simulation machine learning algorithm

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Simulation of sports injury prevention and rehabilitation monitoring based on fiber optic sensors and machine learning algorithms

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Optical and Quantum Electronics 2024年第4期56卷 616-616页

作者： Shiguang, Wang Department of Physical Education Yulin University Yulin 719000 China

Sports injury is a common problem in sports activities, which has a negative impact on the physical health and performance ability of athletes. Therefore, this study aims to explore a sports injury prevention and rehabilitation monitoring method based on fiber optic sensors and machine learning algorithms to improve the rehabilitation effect of athletes and reduce the risk of secondary injuries. The study used fiber-optic sensors to monitor athletes’ joint movement and muscle activity, and collected a large amount of data related to sports injuries. The data is then analyzed and modeled using machine learning algorithms to identify potential risk factors for sports injuries. The effectiveness and accuracy of the proposed method are verified by simulation experiments in the laboratory and in the field. Fiber-optic sensors are able to monitor athletes’ joint movements and muscle activity in real time and send alerts when they detect abnormal patterns. machine learning algorithms can accurately identify potential risk factors for sports injuries and provide guidance for the development of rehabilitation programs. © 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： machine learning algorithm Optical fiber sensor Rehabilitation monitoring simulation Sports injury prevention

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How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms

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PACIFIC-BASIN FINANCE JOURNAL 2024年 84卷

作者： Lu, Yao Zhao, Zhihui Tian, Yuan Zhan, Minghua Guangdong Univ Foreign Studies Sch Finance Guangzhou Peoples R China Guangdong Univ Finance Sch Finance & Investment Guangzhou Peoples R China Zhejiang Sci Tech Univ Sch Econ & Management Hangzhou Peoples R China Inst Fortune Management Res IFMR Guangzhou Peoples R China Guangdong Univ Foreign Studies Univ Mega Ctr Sch Finance Guangzhou 510006 Peoples R China

This study examines quarterly data from China spanning 1993 to 2016, employing machine learning algorithms to investigate the predictive ability of money and credit on output amidst significant structural changes, including the 2007 Global Financial Crisis and shifts in financial structure. The findings highlight that credit is a more effective predictor of output than money, capable of forecasting both short and long-term output trends. However, the predictive strength of both money and credit has diminished post-2007, with the advent of financial development further diminishing their forecasting effectiveness. The analysis demonstrates that machine learning offers more nuanced, long-term predictive insights compared to traditional Vector Autoregression (VAR) methods. For developing economies like China, the results indicate a significant reliance on the bank credit channel for monetary policy transmission. The study emphasizes the importance of market-oriented reforms to minimize financial market arbitrage and advocates for a refined classification of non-monetary assets into bank loans and other bonds to enhance the accuracy of general equilibrium analyses.

关键词： Structural break Money Credit machine learning algorithm

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