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Effect of vertical overturning circulation scale and moist static energy tendency on MJO phase speed

Atmospheric and Oceanic Science Letters 2022年第1期15卷 68-75页

作者： Feng Hu Tim Li School of Geographic Information and Tourism Chuzhou UniversityChuzhouChina Key Laboratory of Meteorological Disaster Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environmental Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina International Pacific Research Center and Department of Atmospheric Sciences School of Ocean and Earth Science and TechnologyUniversity of Hawaii at ManoaHonoluluHawaiiUSA

The combined effects of vertical overturning circulation(VOC) zonal length scale and moist static energy(MSE)tendency zonal asymmetry on MJO phase speed were investigated based on diagnosis of ERA-Interim data over a 40-year period(1979-2019).In a key region(80°-100°E),128 MJO events were *** was found that the larger the VOC zonal length scale,the faster the MJO eastward *** correlation coefficient between them was 0.52,exceeding the 99% confidence level.A significant positive correlation(0.59) was also identified between the phase speed and MSE tendency zonal asymmetry.A linear regression model based on the aforementioned two parameters was constructed,and the phase speed could be estimated based on the *** correlation coefficient between the reconstructed phase speed and its observed counterpart was 0.73,exceeding the 99% confidence level with an F-test.A composite analysis of the fast and slow groups indicated that the VOC zonal length scale was modulated by the background *** El Nino(La Ni?a)-like SST pattern and associated precipitation anomalies promoted a larger(smaller) VOC zonal length scale and thus a faster(slower)propagation speed.A sensitivity test with a reference point in a different longitudinal zone(120°-140°E) was ***,there were significant relationships between the MJO phase speed and the two *** correlation between the reconstructed and observed phase speed was 0.67,exceeding the 99% confidence level.

关键词： Vertical overturning circulation MJO phase speed Moist static energy tendency linear regression model

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A Superstore Vegetable Replenishment and Pricing Scheme Based on ARIMA Time Series Forecasting

A Superstore Vegetable Replenishment and Pricing Scheme Base...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wang, Yilong Chen, Rui Shenyang University of Technology Shenyang China

ISBN: (纸本)9798350309614

With the development of the sales industry, the superstore sales space problem is an important research topic in decision-making. In this paper, for the problems in automatic pricing and replenishment decision-making of superstore vegetable commodities, based on decision-making and optimization ideas, a time series model and optimization class model are established by determining the indicators such as sales volume, attrition rate, gross profit margin, etc., and aiming at the maximization of economic benefits. First of all, based on the daily sales volume to draw the box line diagram of categories and individual items, we understand the distribution of sales volume between different categories and individual items. Based on the spearman correlation coefficient, we analyze the correlation relationship of sales volume between different individual items and categories, and further cluster analysis for the higher correlation. Then, this paper analyzes the relationship between total sales volume and cost-plus pricing, constructs a cost-plus pricing model, predicts the total sales volume for each day of the coming week using ARIMA time series, and uses the model to formulate replenishment and pricing strategies to achieve the revenue maximization goal. Finally, the future total sales volume of each individual product is predicted based on the relationship between sales volume and wastage rate. And the individual products with high gross margin are selected for replenishment, and then the WASP algorithm is utilized to process the wastage rate to determine the unit price of sales. Ultimately, the dynamic planning model is utilized to obtain the optimal replenishment volume and pricing strategy during the process of formulating the replenishment plan for the individual products. The proposed model has excellent predictive ability and certain extensibility. © 2023 IEEE.

关键词： ARIMA time series cluster analysis cost-plus pricing model dynamic programming linear regression model

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Construction of linear models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

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ATMOSPHERE 2024年第8期15卷 923页

作者： Garcia, Ligia Veneros, Jaris Oliva-Cruz, Manuel Olivares, Neiro Chavez, Segundo G. Rojas-Briceno, Nilton B. Univ Nacl Toribio Rodriguez Mendoza Amazonas Inst Invest Desarrollo Sustentable Ceja de Selva I 342 Higos Urco Chachapoyas 01001 Peru Univ Nacl Toribio Rodriguez Mendoza Amazonas Fac Ingn Zootecnista Agronegocios & Biotecnol 342 Higos Urco Chachapoyas 01001 Peru Univ Nacl Moquegua Fac Ingn & Arquitectura Escuela Profes Ingn Ambiental Grp Invest Ciencia Informac Geoespacial Pacocha 18610 Peru

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas;(b) to investigate trends in climatic variables and their correlations with altitude and NDVI;and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices.

关键词： NDVI coffee Climate Engine linear regression model Rodriguez de Mendoza

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Combustion of Raw Biogas/Hot Air in a Porous Radiant Burner and Performance Prediction Using a Machine Learning Algorithm

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JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS 2024年第6期16卷 064502-064502页

作者： Dash, Kishan Acharya, Saroj Kumar Samantaray, Sikata SOA Deemed Univ Dept Mech Engn ITER Bhubaneswar 751030 India

Combustion of raw biogas/hot air was performed in a porous radiant burner associated with a solar heater, and performance was predicted by a linear regression model using a machine learning algorithm. The test was conducted for the combustion of three different compositions of raw biogas mixtures having CO2 percentages of 25%, 30%, and 35% at the thermal load of 200-400 kW/m(2). The hot air was supplied at an average temperature of 50 degrees C from the solar heater air supply system for proper combustion in lean mixture conditions. The porous radiant burner associated with a solar heater has offered radiation efficiency of 15.34-47.93%, NOX of 1-3.1 ppm, and CO of 25-87 ppm for three different compositions of raw biogas mixtures at the thermal load of 200-400 kW/m(2) and equivalence ratio of 0.70-0.91. The increased radiation efficiency has indicated that the porous radiant burner can be an alternative for low-calorie fuel like raw biogas. Data analysis and processing have been performed using the machine learning algorithm, and the linear regression model has been developed using the python programming language. The error between predicted and experimentally calculated radiation efficiency is 1.67%.

关键词： porous radiant burner solar heater raw biogas combustion machine learning algorithm linear regression model

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Monitoring sequential structural changes in penalized high-dimensional linear models

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SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS 2021年第3期40卷 381-404页

作者： Ratnasingam, Suthakaran Ning, Wei Calif State Univ San Bernardino Dept Math San Bernardino CA 92407 USA Green State Univ Dept Math & Stat Bowling Bowling Green OH 43403 USA Beijing Inst Technol Sch Math & Stat Beijing Peoples R China

In this article, we propose a procedure to monitor the structural changes in the penalized regression model for high-dimensional data sequentially. Our approach utilizes a given historical data set to perform both variable selection and estimation simultaneously. The asymptotic properties of the test statistics are established under the null and alternative hypotheses. The finite sample behavior of the monitoring procedure is investigated with simulation studies. The proposed method is applied to a real data set to illustrate the detection procedure.

关键词： Sequential changes linear regression model High dimension SCAD

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To Measure the Influence of Collinearity Effectively: The Deficiency of Condition Number and an Improvement of Main Squared Error 2

To Measure the Influence of Collinearity Effectively: The De...

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2nd International Conference on Applied Mathematics, modelling, and Intelligent Computing (CAMMIC)

作者： Wang, Weili Wuhan Univ Sch Math & Stat 299 Bayi RdLuojiashan St Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9781510655201;9781510655195

We frequently use condition number (CN) to judge the collinearity of a linear regression model. If the CN is large, we tend to believe the result of the least square estimate (LSE) is unreliable. However, in this article, we will give an example to show that CN is not a perfect indicator to reflect the influence of collinearity. In some cases, a model with high collinearity is quite likely to give an ideal LSE. Therefore, we put forward relative mean squared error (RMSE) to judge whether the collinearity significantly influences the LSE. Though RMSE is not theoretically perfect now, it indeed does better than CN when tested by the data we generate.

关键词： linear regression model collinearity condition number relative mean squared error

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Estimation of deoxygenated and oxygenated hemoglobin by multispectral blind linear unmixing 25

Estimation of deoxygenated and oxygenated hemoglobin by mult...

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25th Euromicro Conference on Digital System Design (DSD)

作者： Granados-Castro, Liliana Gutierrez-Navarro, Omar Cruz-Guerrero, Ines A. Mendoza-Chavarria, Juan N. Zavala-Sanchez, Eric R. Campos-Delgado, Daniel U. Univ Autonoma San Luis Potosi Fac Ciencias San Luis Potosi San Luis Potosi Mexico Univ Autonoma Aguascalientes Dept Ingn Biomed Aguascalientes Aguascalientes Mexico Univ Autonoma San Luis Potosi IICO Fac Ciencias San Luis Potosi San Luis Potosi Mexico

ISBN: (纸本)9781665474047

Blood perfusion parameters can be used to evaluate the micro-circulatory health condition of a patient. Several non-invasive optical techniques have been used to estimate blood perfusion as near-infrared spectroscopy or pulse-oximetry. However, these techniques require contact with the patient, and the measurements are restricted to a single point evaluation. These disadvantages could be solved by multispectral imaging. Hence, this paper presents an approach based on multispectral imaging and blind linear unmixing, as an alternative to estimate blood perfusion parameters in the hand palm. This work evaluated changes in oxygenated and deoxygenated hemoglobin concentrations by employing an experimental occlusion protocol in healthy volunteers. We compared the results of several blind linear unmixing and linear regression models. The average cosine similarity values between the prediction model and the photoplethysmography estimations varied in the range 87% and 96%. The mean R-squared adjusted values for oxygenated and deoxygenated hemoglobin were greater or equal than 0.75 and 0.84, respectively. Our results demonstrated the feasibility of non-invasive estimation of hemoglobin in the hand palm, and opened the possibility for calculating other perfusion parameters that help to diagnose and monitor pathologies in large tissue regions.

关键词： blood perfusion hemoglobin linear regression model multispectral imaging unmixing

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Study on decomposition of fungi based on mathematical model 2

Study on decomposition of fungi based on mathematical model

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2nd International Conference on Applied Mathematics, modelling, and Intelligent Computing (CAMMIC)

作者： Lu, Baohui Hu, Tingting Wang, Qiyue Shandong Jianzhu Univ Jinan 250102 Shandong Peoples R China

ISBN: (纸本)9781510655201;9781510655195

Fungi, as decomposers in the ecosystem, play an important role in the decomposition of lignocellulose and promote the carbon cycle in the ecosystem. In this paper, the decomposition of fungi was simulated by establishing a mathematical model, and the effects of humidity and temperature on the decomposition rate of fungi were analyzed by linear regression model without considering the interaction between fungi. Combined with differential equations and metacellular automata model, the interaction between fungi was considered to describe how different populations of fungi interact in a given environment.

关键词： Fungi Decomposition rate Decomposition process linear regression model Cellular automata model Differential equation model

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linear shrinkage estimation of the variance of a distribution with unknown mean

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2021年第9期50卷 2039-2047页

作者： Ikeda, Yuki Nakada, Ryumei Kubokawa, Tatsuya Srivastava, Muni S. Univ Tokyo Grad Sch Econ Tokyo Japan Univ Tokyo Fac Econ Tokyo Japan Univ Toronto Dept Stat Toronto ON Canada

In estimation of the variance of a distribution with unknown mean, the paper suggests the linear shrinkage estimators motivated from a Bayesian perspective. The so-called Stein's truncated estimator of the variance can be derived as the linear shrinkage estimator when the distribution is normal. The method of the linear shrinkage estimation is extended to non normal distributions and to linear regression models. The linear shrinkage estimator with the optimal weight estimate, derived without assuming normality, is shown to have a good numerical performance for several distributions.

关键词： Kurtosis linear regression model linear shrinkage estimation quadratic loss function risk function variance

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Machine learning approaches for thermal updraft prediction in wind solar tower systems

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RENEWABLE ENERGY 2021年 177卷 1001-1013页

作者： Rushdi, Mostafa A. Yoshida, Shigeo Watanabe, Koichi Ohya, Yuji Kyushu Univ Res Inst Appl Mech RIAM Fukuoka 8168580 Japan Future Univ Egypt FUE Fac Engn & Technol New Cairo 11835 Egypt Kyushu Univ Platform Inter Transdisciplinary Energy Res Q PIT Kasuga Fukuoka 8168580 Japan

Wind solar towers constitute a fairly new scheme for harvesting renewable energy from solar and wind energy sources. In such a tower, solar radiation is collected and hot air is enforced to go fast through the tower, a process called thermal updraft, which fuels a wind turbine to generate power. Using vortex generators at the top of the tower creates a pressure difference, which increases the thermal updraft. In this work, we describe the setup of a wind solar tower system established at Kyushu University in Japan. Then, we demonstrate how data was collected from this system in order to train regression models for thermal updraft prediction. The feature selection process was guided by sensitivity analysis. After that, several machine learning models were investigated and the most suitable model was selected based on quality and time metrics. The linear regression model was particularly examined in detail, and was shown to have a satisfactory high accuracy of thermal updraft prediction graphically and numerically with a coefficient of determination of R-2 = 0.981. We also evaluated a reduced prediction model based on the six most essential features, which could be a reduced model description for the WST. This reduced model showed little performance degradation (R-2 = 0.974), with significant reduction in the needed effort and resources, as well as data collection requirements. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Wind solar tower Renewable energy Machine learning linear regression model

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