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Improving the efficiency and stability of perovskite solar cell: Application of innovative machine learning algorithm

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第27期31卷 9270-9296页

作者： Li, Haiyang Diao, Xinliu Ragab, Adham E. Henan Polytech Inst Dept Basic Courses Nanyang 473000 Henan Peoples R China King Saud Univ Coll Engn Ind Engn Dept Riyadh Saudi Arabia

Solar cells are modern inventions that use the photovoltaic effect to directly convert light energy into electricity, generating electrical charges that are free to move through semiconductors. The semiconductor is typically utilized as the raw material for solar cells. In order to convert energy, electron-hole pairs that are responsible for producing light (photon) energy must be absorbed in a semiconductor, followed by charge carrier separation. Enhancing solar cells' stability is crucial in engineering because they are used in a variety of environments. Moreover, graphene nanoplatelets (GPLs) have a great deal of potential to enhance ceramic-GNP composites' mechanical, tribological, electrical, thermal, and biological characteristics, all at once. machine learning algorithms (MLA) are often used to forecast how various systems would behave. In an MLA network, hyperparameters like the number of hidden layers and learning rate are often selected manually as required. MLA is used in this work to examine spinning cylindrical constructions' stability at the microscale. In this context, the particle swarm optimization (PSO) is used to optimize the weights and biases of the network. The number of perceptions in the two hidden layers is optimized in a second parallel process using a genetic algorithm. The modified torque-stress theory (MCST) equation's numerical solution for the dynamic behavior of GPL-reinforced perovskite solar cells was used to train the MLA. It is proposed to guide the spatial discretization of governing equations using the variational differential quadrature (VDQ) method as a direct discretization of the energy functional in the space domain. Lastly, the findings demonstrate that the stability of the current cantilevered solar cell reinforced by GPLs is significantly influenced by curvature, length scale, the shape of the solar cell, and mode number factors.

关键词： Perovskite solar cells photovoltaics efficiency enhancement stability improvement machine learning algorithm

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Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using machine learning algorithm

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REMOTE SENSING 2021年第16期13卷 3273-3273页

作者： Lao, Ping Liu, Qi Ding, Yuhao Wang, Yu Li, Yuan Li, Meng Univ Sci & Technol China Sch Earth & Space Sci Hefei 230026 Peoples R China Univ Sci & Technol China Sch Environm Sci & Optoelect Technol Hefei 230026 Peoples R China Chinese Acad Sci Anhui Inst Opt & Fine Mech Key Lab Atmospher Opt HFIPS Hefei 230031 Peoples R China

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.

关键词： rainrate estimation mesoscale convective system machine learning algorithm cloud top temperature FY-4A meteorological satellite

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Analysis of Main Control Factors of Horizontal Well Production Capacity based on machine learning algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2024年第3期10卷 228-237页

作者： Haibiao Wang Yancai Gao Zheng Yuan Kaixing Li Mingyue Pang

Accurately identifying the main controlling factors of the fracturing effect of horizontal wells in tight gas reservoirs, and then effectively guiding the optimisation of the fracturing scheme, is the key to enhance the fracturing capacity of horizontal wells in tight gas reservoirs. Relying on geological and engineering data of Block A of a tight gas reservoir, four algorithms, namely, Spearman coefficient, maximum mutual information coefficient, Copula entropy and grey correlation, were used to identify the main controlling factors affecting the fracturing effect of horizontal wells under different completion methods, and the weights of the four algorithms were weighted and combined in order to reduce the contingency of the evaluation results of a single weight, and the geological and engineering big data of the 126 barehole horizontal wells and 21 cased horizontal wells of this block were summed up. The study summarises the relationship between the geological and fracturing parameter characteristics of 126 openhole horizontal wells and 21 cased horizontal wells in the block and the test production of a single well, and puts forward suggestions for efficient development of horizontal wells at a later stage. The study shows that the formation pressure coefficient is the main controlling factor for determining the single-well productivity of horizontal wells in this zone, and the productivity of barehole completed horizontal wells is controlled by the fracturing fluid return rate, effective reservoir encounter rate and mud content, while the productivity of cased horizontal wells is controlled by the number of fractured sections, effective reservoir encounter rate and average section length.

关键词： Tight Gas Reservoir machine learning algorithm Horizontal Wells Production Capacity

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Obtaining the molar heat capacities and entropies of HCl and HBr by combining density functional theory and machine learning algorithm

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INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY 2023年第18期123卷 e27175-e27175页

作者： Yang, Zhangzhang He, Guiling Wei, Qinqin Fu, Jia Fan, Qunchao Xie, Feng Zhang, Yi Ma, Jie Xihua Univ Sch Sci Key Lab High Performance Sci Computat Chengdu Peoples R China Tsinghua Univ Inst Nucl & New Energy Technol Collaborat Innovat Ctr Adv Nucl Energy Technol Minist EducKey Lab Adv Reactor Engn & Safety Beijing Peoples R China Natl Univ Def Technol Coll Adv Interdisciplinary Studies Changsha Peoples R China Shanxi Univ Coll Phys & Elect Engn Laser Spect Lab State Key Lab Quantum Opt & Quantum Opt Devices Taiyuan Peoples R China Xihua Univ Sch Sci Key Lab High Performance Sci Computat Chengdu 610039 Peoples R China

An integrated approach combing density functional theory (DFT) and machine learning algorithm (MLA) is proposed here to obtain the molar heat capacities and entropies of diatomic macroscopic gasses with high quality. The DFT approach takes care of the main physical effects, while machine learning takes care of the intricate details it leaves out. After machine learning algorithm correction, a complete set of accurate prediction of vibrational energy spectrum is obtained, which is better than the results of DFT methods in accuracy. And then it is used to replace the vibrational part in the ro-vibrational energy calculated by DFT to obtain the rectified ro-vibrational energy. Furthermore, through the quantum ensemble theory, the thermodynamic properties of the macroscopic gas are calculated by the predicted ro-vibrational energy spectrum, and are modified again by the machine learning algorithm. The study of the HCl$$ HCl $$ and HBr$$ HBr $$ system show that, compared with CCSD(T)/cc-pV5Z and the improved variational algebraical method, the macroscopic thermodynamic properties calculated by this work in the temperature range of 300-6000 K are the closest to the experimental values. The relative error is less than 1% at each temperature.

关键词： density functional theory diatomic macroscopic gasses machine learning algorithm macroscopic thermodynamic properties

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Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 227卷 107221-107221页

作者： Qu, Xing Zhang, Chao Houser, Shannon H. Zhang, Jian Zou, Jing Zhang, Wei Zhang, Qiong Sichuan Univ Inst Hosp Management West China Hosp Chengdu 610041 Peoples R China Sichuan Univ West China Hosp West China Biomed Big Data Ctr Chengdu 610041 Peoples R China Sichuan Univ Med X Ctr Informat Chengdu 610041 Peoples R China Univ Alabama Birmingham Dept Hlth Serv Adm Birmingham AL 35294 USA Sichuan Univ Dept Pediat Dent West China Hosp Stomatol Chengdu 610041 Sichuan Peoples R China Sichuan Univ Natl Clin Res Chengdu 610041 Sichuan Peoples R China

Background: An easily accessible caries risk prediction model (CRPM) based on nonbiological predictors is lacking. Developing a CRPM for community screening is essential for children's dental health promotion by a public health approach. Objective: This study aimed to develop and validate a caries risk prediction model (CRPM) for children using a machine learning algorithm based on dental care behavioral factors and other nonbiological factors using a 3-month multicenter cohort. Methods: Children aged 12 months to 60 months were recruited at three primary care settings and three kindergartens in Chengdu, China. Dental examination was conducted for all enrolled children by calibrated pediatric dentists at baseline and three months later. All parents of the enrolled children were asked to complete a questionnaire with dental-related information. machine learning algorithms, including random forest, logistic regression, and adaptive boosting, were used to develop a prediction model. Sensitivity, specificity, accuracy, precision, negative predictive value and F-score were reported to estimate the internal validation of the models. Results: A total of 481 out of 745 children without a history of caries experience at baseline remained for analysis. In the total sample population, 236 (49.1%) children were female, and the mean age was 31.2 months. During the follow-up exams, 66 (13.6%) children had new-onset caries. The child's age, height, weight, family caries status, brush teeth two minutes per time, fluoride toothpaste usage, brushing twice per day, parental monitoring brushing teeth, mother delivery method, brushing child's teeth every day, child number counts, and night feeding frequency in the last month were measured and included in a prediction model. Of the prediction models, the highest area under the curve of RF was 0.91 (95% CI: 0.87- 0.94), followed by 0.86 (95% CI: 0.81-0.91) of LR and 0.81 (95% CI: 0.76-0.86) of AdaBoost. Conclusion: In this CRPM, new onset

关键词： Early childhood caries Caries risk prediction model machine learning algorithm Dental-related behaviors

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Evaluating the integrated performance and effectiveness of RUSLE through machine learning algorithm on soil erosion susceptibility in tropical plateau basin, India

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JOURNAL OF SEDIMENTARY ENVIRONMENTS 2024年第3期9卷 665-693页

作者： Kundu, Moumita Ghosh, Arnab Abu Zafor, Md Maiti, Ramkrishna Vidyasagar Univ Dept Geog Midnapore West Bengal India Integrated Inst Adv Res & Informat IIARI Res Incubat Ctr Kolkata West Bengal India Bangladesh Army Int Univ Sci & Technol Dept Civil Engn Cumilla Bangladesh

Soil erosion is a huge problem in any plateau region as the population increases with rapid urbanization, which, in turn, increases the amount of sedimentation in the river through the increasing amount of soil erosion in the plateau region. If different methods identify these soil erosion zones, it is possible to take various measures to prevent soil erosion. This article discusses the nature, extent, and rate of soil erosion and its spatial pattern along the Rupnarayan basin with the help of RUSLE (Revised Universal Soil Loss Equation) and a machine learning algorithm. This area is highly affected by monsoonal rainfall and changes in land use patterns over time. By discussing the performance of RUSLE through machine learning models i.e. Frequency Ratio (FR), Artificial neural network (ANN), support vector machine (SVM), Extreme Gradient Boosting (XGB) and ensemble model using some permutation combination of aforesaid model with various GIS (Geographical Information System) and spatial basis parameters, it is seen that the undulating upper portion of the region is the highest soil erosion susceptible zone. Except for any other GIS platform, these models are developed through Google Earth Engine and interpreted by SHapley Additive Explanation (SHAP), based on game theory. This study developed a new interpretable model in the field of soil erosion that is helpful for global and local representation. Erosional locations obtained from field studies are focused on achieving this goal. This study used 389 soil erosion points as a dependent factor against 14 soil-affecting independent factors. The best-performing model is determined from the AUC (Area Under the Curve) curve and kappa indices and interpreted globally and locally by the SHAP method to evaluate the effectiveness of each factor in soil erosion for the entire study area and the specific region. The result shows that the AUC values of FR, SVM, ANN, XGB and ensemble XGB-FR-SVM are 0.842, 0.831, and 0.828. 0.920

关键词： Soil erosion RUSLE machine learning algorithm Ensemble model GEE (google earth engine) SHAP (shapley additive explanation)

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Big data processing with Apache Spark in university institutions: spark streaming and machine learning algorithm

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG learning 2019年第1-2期29卷 5-20页

作者： Boachie, Emmanuel Li, Chunlin Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Hubei Peoples R China Kumasi Tech Univ Dept Comp Sci Box 854 Kumasi Ghana

Data processing is an effective tool for educational sector, which can improve admission selection procedures and decisions. Most research papers focus on computational and theoretical aspect of education though little effort have been put on technological aspect of applying data mining techniques on students admission process. We therefore design a simple spark streaming framework together with machine learning algorithm to guide admission processing. We implement the spark streaming model and the proposed machine learning algorithm in a selected university using its admissions' data. The focus is on the number of students that can be admitted and those that should be rejected to reduce time and cost. The case study we evaluated show the practical usefulness of Spark streaming and machine learning algorithm for data processing in a real-time to reduce time and cost. The experiment results also confirm meaningful graphical interpretation of data using spark streaming and machine learning algorithm for students' selection for admissions.

关键词： spark streaming big data processing machine learning algorithm

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Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised machine learning algorithm Data Mining in a Secure Cloud Computing Environment through Restful API

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第5期13卷 1-4页

作者： Ige, Tosin Sikiru, Adewale Univ Texas El Paso Dept Comp Sci El Paso TX 79968 USA Virginia Technol Univ Dept Comp Sci Blacksburg VA USA

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.

关键词： Cloud computing data warehouse data mining window service Web API machine learning algorithm secure cloud computing

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Research and Analysis of machine learning algorithm in Artificial Intelligence

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Artificial Intelligence Advances 2020年第2期2卷 88-91页

作者： Yang Li Xueyuan Du Yiheng Xu University of Liverpool LiverpoolL693BXEngland Xi’an Jiaotong-Liverpool University SuzhouJiangsu215213China

This article firstly explains the concepts of artificial intelligence and algorithm separately,then determines the research status of artificial intelligence and machine learning in the background of the increasing popularity of artificial intelligence,and finally briefly describes the machine learning algorithm in the field of artificial intelligence,as well as puts forward appropriate development prospects,in order to provide theoretical reference for industry insider.

关键词： artificial intelligence machine learning algorithm application of science and technology

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The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm

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BMC INFECTIOUS DISEASES 2023年第1期23卷 881-881页

作者： Chen, Lijiao Yuan, Lingke Sun, Tingting Liu, Ruiqing Huang, Qing Deng, Shaoli Army Med Univ Daping Hosp Dept Lab Med Chongqing 400042 Peoples R China Carnegie Mellon Univ Sci Computat Finance Pittsburgh PA USA Chongqing Med & Pharmaceut Coll Coll Med Technol Chongqing Peoples R China

BackgroundTuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis *** total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study .46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model's predictive performance for dentifying active and latent tuberculosis *** verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing *** machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection.

关键词： Leukocyte VCS (volume, conductivity, light scatter) parameters machine learning algorithm Active tuberculosis infection Latent tuberculosis infection Distinguish

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