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multivariate regression analysis of Skin Temperature Rises for Millimeter-Wave Dosimetry

IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2022年第4期64卷 941-950页

作者： Li, Kun Kagawa Univ Fac Engn & Design Takamatsu Kagawa 7610396 Japan

This study presents a fast and simple estimation method for dosimetry assessment of a clothed human body exposed to electromagnetic fields in millimeter-wave bands. A multivariate regression algorithm was given instead of conventional numerical methods for the first time, which utilized the exact value of skin surface temperature rise that was computed by the Monte-Carlo method by changing the oblique incidence wave angle, the cross-polarization power ratio, and the air gap spacing from the cloth to the body. The detailed formula for bioheat transfer analysis considering the abovementioned key parameters is first introduced. Then, a polynomial function is derived by multivariate regression analysis using the simulation result to estimate the skin temperature rises under various exposure conditions and scenarios. In comparison with those results obtained by the traditional methods, the proposed regression algorithm significantly reduced the expense of computational resources. The results estimated using the developed function show a maximum error of less than 0.013 degrees C for the skin tissues of different body parts and different cloth materials, demonstrating that the proposed method is easy and efficient for the estimation of a high degree of skin temperature rise in a relatively complicated radio-wave exposure environment in millimeter-wave bands.

关键词： Skin Air gaps Temperature measurement Density measurement Analytical models multivariate regression Monte Carlo methods Dosimetry electromagnetic fields (EMFs) millimeter wave (mmWave) Monte-Carlo simulation multivariate regression analysis skin temperature rise

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Digital Image Processing Applied in the Deformation analysis of Hip Prosthesis: multivariate regression analysis

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IEEE ACCESS 2024年 12卷 176938-176948页

作者： Ramos-Frutos, Jorge Miguel-Andres, Israel Oliva, Diego Perez-Cisneros, Marco Alonso-Rasgado, Teresa Bailey, Colin G. Francisco del Valle-Mojica, Jose Ctr Innovac Aplicada Tecnol Competit Dept Posgrad Leon 37545 Mexico CIATEC Lab Nacl CONAHCYT Biomecan Cuerpo Humano Leon 37545 Guanajuato Mexico Univ Guadalajara CUCEI Dept Ingn Electrofoton Guadalajara 44430 Mexico Queen Mary Univ Sch Engn & Mat Sci London E1 4NS England

Total Hip Arthroplasty (THA) is one of the most performed surgical procedures to treat osteoarthritis of the hip joint or other skeletal disorders, such as Developmental Dysplasia of the Hip (DDH) or malformations. The purpose of the THA is to replace damaged bone/cartilage and recover the biomechanics of the joint in the best possible way. Placing hip prostheses is common practice in the treatment of THA, but the effect of this treatment must be evaluated before surgery. This work proposes a method to assess hip deformation with an implanted acetabular cup, using digital image processing and multivariate linear regression. Images were acquired when a hemipelvis was subjected to mechanical load ranging from 0 to 1800 Newtons. Eighteen geometric characteristics were analyzed, and only seven were significant in the multivariate linear regression model. To decide on the variables that are considered important, three criteria were used on the multivariate linear regression model established in the different characteristics. The first criterion considers the significance of the model coefficients;the second criterion considers a coefficient of determination greater than 75%, and the third criterion considers that the proposed model is significant. If all three criteria are met, the response variable is considered significant. This analysis gives specialists a tool to make decisions before performing THA treatment. Moreover, this methodology could be applied to assess the deformation of the hip with different pathologies such as DDH, malformations, and overweight conditions. Finally, a relationship is generated between the extracted geometric characteristics and the experimental results obtained in the laboratory.

关键词： Hip Surgery Digital images Image segmentation Feature extraction Prosthetics Implants Image color analysis Linear regression multivariate regression Image processing multivariate regression analysis total hip arthroplasty

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Role of the vessel morphology on the lenticulostriate arteries hemodynamics during atrial fibrillation: A CFD-based multivariate regression analysis

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 254卷 108303-108303页

作者： Saglietto, Andrea Tripoli, Francesco Zwanenburg, Jaco Biessels, Geert Jan De Ferrari, Gaetano Maria Anselmino, Matteo Ridolfi, Luca Scarsoglio, Stefania Citta Salute & Sci Hosp Cardiovasc & Thorac Dept Div Cardiol Turin Italy Univ Turin Dept Med Sci Turin Italy Politecn Torino Dept Mech & Aerosp Engn Turin Italy Univ Med Ctr Utrecht Ctr Image Sci Utrecht Netherlands Univ Med Ctr Utrecht UMC Brain Ctr Utrecth Netherlands Politecn Torino Dept Environm Land & Infrastruct Engn Turin Italy

Background and objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia, inducing accelerated and irregular beating. Beside well-known disabling symptoms - such as palpitations, reduced exercise tolerance, and chest discomfort - there is growing evidence that an alteration of deep cerebral hemodynamics due to AF increases the risk of vascular dementia and cognitive impairment, even in the absence of clinical strokes. The alteration of deep cerebral circulation in AF represents one of the least investigated among the possible mechanisms. Lenticulostriate arteries (LSAs) are small perforating arteries mainly departing from the middle cerebral artery (MCA) and susceptible to small vessel disease, which is one of the mechanisms of subcortical vascular dementia development. The purpose of this study is to investigate the impact of different LSAs morphologies on the cerebral hemodynamics during AF. Methods: By combining a computational fluid dynamics (CFD) analysis of LSAs with 7T high-resolution magnetic resonance imaging (MRI), we performed different CFD-based multivariate regression analyses to detect which geometrical and morphological vessel features mostly affect AF hemodynamics in terms of wall shear stress. We exploited 17 cerebral 7T-MRI derived LSA vascular geometries extracted from 10 subjects and internal carotid artery data from validated 0D cardiovascular-cerebral modeling as inflow conditions. Results: Our results revealed that few geometrical variables - namely the size of the MCA and the bifurcation angles between MCA and LSA - are able to satisfactorily predict the AF impact. In particular, the present study indicates that LSA morphologies exhibiting markedly obtuse LSA-MCA inlet angles and small MCA size downstream of the LSA-MCA bifurcation may be more prone to vascular damage induced by AF. Conclusions: The present MRI-based computational study has been able for the first time to: (i) investigate the net impact of LSAs vascular morpho

关键词： Computational fluid dynamics Atrial fibrillation Magnetic resonance imaging Lenticulostriate arteries Cerebral circulation Cognitive decline multivariate regression analysis

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Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications: A simulation model using multivariate regression analysis

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DIABETES & METABOLIC SYNDROME-CLINICAL RESEARCH & REVIEWS 2020年第4期14卷 319-323页

作者： Ghosal, Samit Sinha, Binayak Majumder, Milan Misra, Anoop Nightingale Hosp Kolkata India AMRI Hosp Kolkata India Fortis CDOC Hosp Diabet & Allied Sci New Delhi India Natl Diabet Obes & Cholesterol Fdn NDOC Diabet Fdn India New Delhi India

Introduction: and aims: To prevent the spread of coronavirus disease (COVIDI9) total lockdown is in place in India from March 24, 2020 for 21 days. In this study, we aim to assess the impact of the duration of the lockdown on glycaemic control and diabetes-related complications. Materials and methods: A systematic search was conducted using Cochrane library. A simulation model was created using glycemic data from previous disasters (taken as similar in impact to current lockdown) taking baseline HBAlc and diabetes-related complications data from India-specific database. A multivariate regression analysis was conducted to analyse the relationship between the duration of lockdown and glycaemic targets & diabetes-related complications. Results: The predictive model was extremely robust (R2 = 0.99) and predicted outcomes for period of lockdown up to 90 days. The predicted increment in HBAlc from baseline at the end of 30 days and 45 days lockdown was projected as 2.26% & 3.68% respectively. Similarly, the annual predicted percentage increase in complication rates at the end of 30-day lockdown was 2.8% for non-proliferative diabetic retinopathy, 2.9% for proliferative diabetic retinopathy, 1.5% for retinal photocoagulation, 9.3% for microalbuminuria, 14.2% for proteinuria, 2.9% for peripheral neuropathy, 10.5% for lower extremity amputation, 0.9% for myocardial infarction, 0.5% for stroke and 0.5% for infections. Conclusion: The duration of lockdown is directly proportional to the worsening of glycaemic control and diabetes-related complications. Such increase in diabetes-related complications will put additional load on overburdened healthcare system, and also increase COVIDI9 infections in patients with such uncontrolled glycemia. (C) 2020 Published by Elsevier Ltd on behalf of Diabetes India.

关键词： Diabetes Lockdown Disaster multivariate regression analysis Complications

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Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller

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BUILDING AND ENVIRONMENT 2020年 175卷 106810-000页

作者： Zhang, Tianhu Liu, Yuanjun Rao, Yandi Li, Xiaopeng Zhao, Qingxin Yanshan Univ State Key Lab Metastable Mat Sci & Technol Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Heibei Prov Low Carbon & Clean Bldg Heating Techn Qinhuangdao 066006 Hebei Peoples R China Yanshan Univ Key Lab Green Construct & Intelligent Maintenance Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Appl Chem Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

Computational cost poses a major obstacle to the design of indoor environments with the current optimal method and computational fluid dynamics (CFD). A novel optimization method integrating a genetic algorithm (GA), an artificial neural network (ANN), multivariate regression analysis (MRA), and a fuzzy logic controller (FLC) was proposed in this paper to optimize the indoor environment and energy consumption based on simulation results. Thermal comfort (predicted mean vote) was set as the restrictive design objective. Indoor air quality (air age) and energy consumption were set as the optimal design objectives. Air supply parameters, such as ventilation rate, inlet temperature, and angle, were used as the design variables. The GA process was used to search for the optimal solution (individual), while the ANN and CFD tool were used to obtain the values of the objectives for each individual. MRA was used to reduce the variable space, and FLC was used to control the execution routine of the CFD process to reduce the computational cost. The results indicated that the ventilation rate has a lower impact on the design result compared with the other two design variables. When the MRA and FLC were included in the design process, the variable space and computational cost were reduced by 50% and 35.7%, respectively. The design efficiency was improved while the best found solution was maintained.

关键词： Artificial neural network Genetic algorithm multivariate regression analysis Fuzzy logic controller Thermal comfort Building energy consumption

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Application of multivariate regression analysis in Teaching Management

Application of Multivariate Regression Analysis in Teaching ...

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第二届文化、教育与现代社会经济发展国际会议

作者： Xiuli Li Pengcheng Zhao Yuxue Yang School of Mathematics and Physics Qingdao University of Science and Technology

ISBN: (纸本)9781510861909

This thesis made multivariate statistical analysis of academic scores of freshmen,sophomores and juniors and constructed a multivariate regression model,with the purpose of revealing the main factors influencing teaching quality management of colleges and universities as well as putting forward solutions to major influencing factors.

关键词： multivariate regression analysis regression equation significance testing

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multivariate regression analysis on the Factors Affecting China's Offshore Service Outsourcing

Multivariate Regression Analysis on the Factors Affecting Ch...

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2017 7th ICEA International Conference on Information Science, Library Science and Social Sciences (ILS 2017)

作者： Fang Sun Xiaofeng Ling Nanjing University of Science & Technology

With the deepening globalization, the service outsourcing industry also goes internationalized. Offshore service outsourcing, a product of globalization of service outsourcing, has been expanding their presence around the world. China's offshore service outsourcing has also developed rapidly in recent years, and has become the second largest country to undertake global offshore service outsourcing. This paper uses the data from 2000 to 2014 and uses the multivariate regression model to analyze the factors that affect China's offshore service outsourcing. The results indicate that macroeconomic environment, human resource quality, scientific and technological development level have positive relations with offshore service outsourcing, and labor cost has a negative relation with offshore service outsourcing. And the paper also discusses ways on how to expand China's offshore service outsourcing.

关键词： Offshore service outsourcing Factors multivariate regression analysis

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Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis

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FOOD CONTROL 2016年 68卷 303-309页

作者： Elzey, Brianda Pollard, David Fakayode, Sayo O. N Carolina Agr & Tech State Univ Dept Chem 1601 East Market St Greensboro NC 27411 USA Winston Salem State Univ Dept Chem Winston Salem NC 27014 USA

Natural oils are increasingly being used in the food, cosmetic, and agrochemical industries in recent years. However, natural oils with high market values are often counterfeited and adulterated with cheap, poor quality oils, with serious economic implications for the food and cosmetic industries, and potential health implications to consumers. This study reports the first combined use of Fourier transform infrared spectroscopy (FTIR) and partial-least-square (PLS) multivariate regression analysis for rapid, accurate, and low cost determination of the % compositions of two natural oils (neem oil (NO) and flaxseed oil (FO)) adulterated either with edible vegetable oil (VO) or extra virgin olive oil (EVOO). The FTIR of the calibration sample sets containing adulterated NO and FO with VO and EVOO at a concentration range of 1-99% w/w were measured and subjected to PLS multivariate regression analyses. The obtained FTIR spectra profile of the adulterated samples are highly dependent on the type of natural oil as well as the type of adulterant oil. The developed PLS models were subsequently used to determine the % compositions of independently prepared validation samples of NO and FO adulterated with VO and EVOO. The figures-of-merit of the PLS regression models were excellent and with good linearity (R-2 > 0.998814). The score plots of the PLS regressions revealed interesting and useful information for pattern recognition of adulterated NO and FO samples. The PLS regression models correctly determined % compositions of adulterated NO and FO with VO with low root-mean-square-relative-percent errors (RMS%RE) of determination of 3.02%, and 4.24%, with an overall RMS%RE of 3.63%. The models also correctly determined % compositions of adulterated NO and FO with EVOO with RMS%RE of 7.13% and 2.00%, with an overall RMS%RE of 4.56%. The simplicity and high accuracy of this low-cost study presents an attractive model with potential real-world applications in quality control and

关键词： Food-analysis Natural oils Adulteration FTIR multivariate regression analysis Chemometrics Quality assurance

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Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship

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TALANTA 2014年 126卷 151-156页

作者： Fakayode, Sayo O. Mitchell, Breanna S. Pollard, David A. North Carolina A&T State Univ Greensboro NC 27411 USA Winston Salem State Univ Dept Chem Winston Salem NC 27110 USA

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (03710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in 03710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Gas chromatography Petrochemicals Boiling point determination multivariate regression analysis Structural activity relationship Pattern recognition

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Cancer risk factors in systemic lupus erythematosus: multivariate regression analysis in 16,409 patients

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ARTHRITIS RESEARCH & THERAPY 2014年第SUPPL 1期16卷 A35-A35页

作者： Bernatsky, Sasha Ramsey-Goldman, Rosalind Boivin, Jean-Francois Joseph, Lawrence Petri, Michelle A. Zoma, Asad Manzi, Susan Urowitz, Murray B. Gladman, Dafna D. Fortin, Paul R. Ginzler, Ellen M. Yelin, Edward Bae, Sang-Cheol Wallace, Daniel J. Edworthy, Steven M. Jacobsen, Soren Gordon, Caroline Dooley, Mary A. Peschken, Christine A. Hanly, John G. Alarcon, Graciela S. Nived, Ola Ruiz-Irastorza, Guillermo Isenberg, David Rahman, Anisur Witte, Torsten Aranow, Cynthia Kamen, Diane L. Steinsson, Kristjan Askanase, Anca Barr, Susan G. Criswell, Lindsey A. Sturfelt, Gunnar Patel, Neha M. Senecal, Jean-Luc Zummer, Michel Pope, Janet E. Ensworth, Stephanie El-Gabalawy, Hani McCarthy, Timothy St Pierre, Yvan Clarke, Anne E. McGill Univ Ctr Hlth Montreal PQ Canada Northwestern Univ Feinberg Sch Med Chicago IL 60611 USA McGill Univ Montreal PQ Canada Johns Hopkins Univ Sch Med Baltimore MD USA Hairmyres Hosp E Kilbride Lanark Scotland Temple Univ West Penn Allegheny Hlth Syst Pittsburgh PA USA Toronto Western Hosp Toronto ON M5T 2S8 Canada Univ Laval Quebec City PQ G1K 7P4 Canada Suny Downstate Med Ctr Brooklyn NY 11203 USA Univ Calif San Francisco San Francisco CA 94143 USA Hanyang Univ Hosp Rheumat Dis Seoul 133791 South Korea Univ Calif Los Angeles David Geffen Sch Med Cedars Sinai Med Ctr Los Angeles CA 90095 USA Univ Calgary Calgary AB T2N 1N4 Canada Copenhagen Univ Hosp Rigshosp Copenhagen Denmark Univ Birmingham Coll Med & Dent Sci Birmingham W Midlands England Univ N Carolina Chapel Hill NC USA Univ Manitoba Winnipeg MB Canada Dalhousie Univ & Capital Hlth Halifax NS Canada Univ Alabama Birmingham Birmingham AL USA Univ Lund Hosp S-22185 Lund Sweden Rheumat Dis Res Unit Bizkaia Spain UCL London England Hannover Med Sch Hannover Germany Feinstein Inst Med Res Manhasset NY USA Med Univ S Carolina Charleston SC 29425 USA Landspitali Univ Hosp Reykjavik Iceland NYU Sch Med New York NY USA Univ Montreal Montreal PQ H3C 3J7 Canada Univ Western Ontario London ON Canada Univ British Columbia Vancouver BC V5Z 1M9 Canada

A35

关键词： Systemic Lupus Erythematosus Cancer Risk Systemic Lupus Erythematosus Patient multivariate regression analysis Standardize Incidence Ratio

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