Multiple regression analysis of risk factors related to radiation pneumonitis

BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of life.AIM To carry out multiple regression analysis on the influencing factors of radiation pneumonitis.METHODS Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or not.Among them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control group.General characteristics,and radiation and imaging examination data of the two groups were collected and compared.Due to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other factors.RESULTS The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation pneumonitis.CONCLUSION We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy parameters as risk factors for radiation pneumonitis.Comprehensive evaluation and examination should be carried out before radiotherapy to effectively prevent radiation pneumonitis.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

Change Point Analysis to Detect the Effect of Pruning Severity on Tree Growth

The effect of pruning severity on tree growth was analyzed by change point detection using segmented regression. The present study applied this analysis to a well-known published data set including diameter growth response, tree age, pruning severity and pretreatment crown size. First, multiple regression analysis was performed to assess the effect of tree age, pruning severity and pretreatment crown size on diameter growth response. Next, segmented regression analysis was performed to assess the effect of pruning severity on diameter growth response. The results of the multiple regression showed that diameter growth response was significantly influenced by pruning severity and pretreatment crown size. The results of the segmented regression showed that in the whole data set, an abrupt change toward a decrease in diameter growth response was detected at 25% of the live crown removed. However, in the group of fully crowned and open-grown, diameter growth response continuously decreased with increasing pruning severity with no significant abrupt change, whereas in the group of 70% - 90% live crown, diameter growth response did not significantly decrease up to the break point (53% crown removed) and then abruptly decreased. This may be the first study to show the numerical evaluation of the effect of pruning severity on tree growth by change point analysis.

Logistics Regression Analysis between TCM Constitution Types of Patients with Hypertension,Insomnia and ABPM,PSQI

[Objectives]The research aimed to explore the distribution characteristics of TCM constitution types of patients with hypertension and insomnia,and study the clinical characteristics of patients with different constitutions,in order to provide new ideas for the treatment of patients with hypertension and insomnia.[Methods]Cross sectional observation method was used,and 420 patients with hypertension and insomnia were selected.Required information was collected,and the constitution type of traditional Chinese medicine was determined,and relevant data were recorded.SPSS and Logistic regression analysis method were used to explore the correlation between the distribution of TCM constitution types and gender,age,24 h-SBP,24 h-DBP,24 h-BPV,PSQI score,etc.[Results]Among 420 patients,the proportion of gentleness constitution was the most,and others in turn were Qi deficiency constitution>Yang deficiency constitution>phlegm dampness constitution>Qi stagnation constitution>Yin deficiency constitution>blood stasis constitution>damp heat constitution>special constitution.Among male patients,the proportion of gentleness constitution was the most.Among female patients,the proportion of Qi deficiency constitution was the most.In each constitution,the proportion of men and women was different,and the difference in gentleness constitution,Qi deficiency constitution and Yin deficiency constitution had statistical significance(P<0.05).The proportion of gentleness constitution for young and middle-aged patients was the most,while elderly patients with Qi deficiency constitution was the most.There was difference in the distribution of TCM constitution in different age groups,and the difference had statistical significance(P<0.05).Compared with the patients with gentleness constitution,the patients with Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,damp heat constitution,blood stasis constitution and Qi stagnation constitution had different differences in terms of age,24 h-SBP,24 h-DBP,24 h-BPV and PSQI score,and there was statistical significance(P<0.05).Except damp heat constitution,blood stasis constitution and special constitution,other constitutions had certain correlation with age,24 h-SBP,24 h-DBP,24 h-BPV and PSQI score.[Conclusions]TCM constitution types of patients with hypertension and insomnia were dominant by gentleness constitution,Qi deficiency constitution and Yang deficiency constitution.The distribution of TCM constitution in different gender and age groups was different,and different TCM constitution was related to ABPM and PSQI.

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

Factor Decomposition and Regression Analysis of the Energy Related Carbon Emissions in Shandong, China: A Perspective of Industrial Structure

An in-depth study of the energy related carbon emissions has important practical significance for carbon emissions reduction and structural adjustment in Shandong Province and throughout China.Based on the perspective of industrial structure,the expanded KAYA equation to measure the energy related carbon emissions of the primary industries(Resources and Agriculture)and secondary industries(Manufacturing and Construction)and tertiary industries(Retail and Service)was utilized in Shandong Province from 2011 to 2017.The carbon emissions among industries in Shandong Province were empirically analyzed using the Logarithmic Mean Divisia Index decomposition approach.The results were follows:(1)Under the three industrial dimensions,the energy structure effect and the energy intensity effect have a restraining influence on the carbon emissions of the three industries.(2)The development level effect and the employment scale effect play a pulling role in carbon emissions.(3)From the perspective of the employment structure effect of the primary industry,there is a restraining effect on carbon emissions,while the employment structure effects of the secondary and tertiary industries play a pulling role in carbon emissions,and the employment structure effect of the tertiary industry has a greater pulling effect on carbon emissions than the secondary industry.

Multiple Regression Analysis of Influencing Factors on Yield of New Sugarcane Variety Yuetang 03-373

[Objectives]The purpose of this study was to provide reference for cultivation and promotion of a new sugarcane variety Yuetang 03-373,on the basis of analyzing and summarizing the characters of the variety.[Methods]Correlation,multiple regression and path analyses were performed for the yield and yield components of Yuetang 03-373.[Results]Correlation analysis shows that cane yield was significantly correlated with millable stalk number,stalk length and stalk diameter,and among them,the correlation with millable stalk number was the strongest.Multiple regression and path analyses show that millable stalk number contributed the most to cane yield,followed by stalk length,and stalk diameter contributed the least.The regression equation of cane yield against the three yield components was y=-2.8713+1.5497x1+5.8990x2-395.4294x3(R=0.9672**).[Conclusions]Millable stalk number and stalk length were the important and major factors for high yield of Yuetang 03-373,indicating that Yuetang 03-373 is a sugarcane variety of millable stalk type.In cultivation,full play should be given to the advantage of Yuetang 03-373 in millable stalk number,as well as stalk length(plant height),in order to achieve the purpose of increasing yield.

Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).

A Predictive Modeling Based on Regression and Artificial Neural Network Analysis of Laser Transformation Hardening for Cylindrical Steel Workpieces

Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To exploit the benefits presented by the laser hardening process, it is necessary to develop an integrated strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive modelling approach for predicting the hardened surface physical and geometrical attributes. The laser surface transformation hardening of cylindrical AISI 4340 steel workpieces is modeled using the conventional regression equation method as well as artificial neural network method. The process parameters included in the study are laser power, beam scanning speed, and the workpiece rotational speed. The upper and the lower limits for each parameter are chosen considering the start of the transformation hardening and the maximum hardened zone without surface melting. The resulting models are able to predict the depths representing the maximum hardness zone, the hardness drop zone, and the overheated zone without martensite transformation. Because of its ability to model highly nonlinear problems, the ANN based model presents the best modelling results and can predict the hardness profile with good accuracy.

Logistic Regression Analysis of Influencing Factors on Serum ALT and HCV RNA Changes in Patients with Chronic Hepatitis C

Objective This study was undertaken to investigate the influencing factors on serum ALT level and hepatitis C virus(HCV)RNA titer in chronic hepatitis C(CHC)patients.Methods All patients enrolled into this study were anti-HCV positive.Retrospective tracing method was applied to detect serum ALT level and HCV RNA titer and to collect general information of the patients such as genders,age groups,interferon medication history,infection pathways,height and weight.Then the multi-factor analysis was adopted with the application of binominal logistic regression mode.Results The abnormal rate of ALT level was positively correlated to HCV RNA and gender while negatively correlated to interferon medication history and age group,with Wald value of the 4 factors as 39.604,11.823,18.991 and 7.389,respectively.The positive rate of HCV RNA was negatively correlated to interferon medication history and gender while positively correlated to ALT level,with corresponding Wald value of the 3 factors as81.394,7.618 and 27.562,respectively.Conclusions The normal ALT level in HCV infected patients was associated with viral load,age,gender and interferon medication history,while the normal rate of HCV RNA titer was closely associated with gender,interferon medication history and ALT level.

Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT

Nowadays,Wireless Sensor Network(WSN)is a modern technology with a wide range of applications and greatly attractive benefits,for example,self-governing,low expenditure on execution and data communication,long-term function,and unsupervised access to the network.The Internet of Things(IoT)is an attractive,exciting paradigm.By applying communication technologies in sensors and supervising features,WSNs have initiated communication between the IoT devices.Though IoT offers access to the highest amount of information collected through WSNs,it leads to privacy management problems.Hence,this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique(LRECC)to establish a secure IoT structure for preventing,detecting,and mitigating threats.This approach uses the Elliptical Curve Cryptography(ECC)algorithm to generate and distribute security keys.ECC algorithm is a light weight key;thus,it minimizes the routing overhead.Furthermore,the Logistic Regression machine learning technique selects the transmitter based on intelligent results.The main application of this approach is smart cities.This approach provides continuing reliable routing paths with small overheads.In addition,route nodes cooperate with IoT,and it handles the resources proficiently and minimizes the 29.95%delay.

Quality of life of hospitalized patients after lung cancer operation and analysis of influencing factors

Objective:To explore the current status and influencing factors of quality of life in patients with lung cancer after surgery in a tertiary hospital in Hainan province.Methods:To investigate the influencing factors of quality of life of lung cancer patients after surgery in a tertiary hospital in Hainan province by cross‑sectional survey method.Results:The scores of insomnia,appetite loss,constipation and pain in 186 lung cancer patients after surgery in a tertiary hospital in Hainan Province were significantly higher than the reference value.Multiple linear regression analysis showed that older patients(>60 years)had lower scores in physical function domain(β=-0.193),and female patients had more appetite loss symptoms(β=0.245).Compared with other minority ethnic groups,Han ethnic group had lower scores in role function domain(β=0.179),more severe fatigue symptoms(β=-0.162),and higher general health level(β=0.166).Patients with employee medical insurance had lower scores of emotional function(β=0.194),cognitive function(β=0.281),the lowest score in social function(β=0.188),and severe pain in other parts(β=-0.227).Smokers had less cough symptoms(β=0.175)and more arm and shoulder pain symptoms(β=-0.21)than non‑smokers.Patients with secondhand smoke exposure had lower cognitive function scores(β=-0.158)and more obvious symptoms of oral ulcer(β=0.185).Patients who drank alcohol frequently(drinking frequency>1 time/day)had more severe cough symptoms(β=0.27).Patients with small number of children(0‑1)had milder cough symptoms(β=0.178).Patients who did not understand the disease had obvious symptoms of arm and shoulder pain(β=0.151).Patients with early pathological stage(stageⅠ‑Ⅱ)had more severe shortness of breath(β=-0.159)and pain(β=-0.181).The symptoms of appetite loss were more obvious in patients living in cities(β=0.192).The symptoms of peripheral neuropathy were more obvious(β=0.174).Patients who often consumed pickulated food had severe pain symptoms(β=-0.219),and pain in other parts was obvious(β=-0.149).Male patients had obvious alopecia symptoms(β=-0.306).Conclusion:Age,ethnicity,residence,type of medical insurance,number of children,pathological stage of lung cancer,smoking,second‑hand smoke exposure,alcohol consumption,and frequent consumption of pickled food were related to the quality of life of lung cancer patients in hospital after surgery.Medical staff and family members should pay attention to the emotional communication of patients during the treatment of lung cancer patients in hospital after surgery.Patients should avoid exposure to smoking,alcohol and second‑hand smoke,and reduce consumption of pickled food.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

Cycle Time Analysis of Open Pit Mining Dump Trucks

This study demonstrates a practical cycle time analysis of dump truck haulage system of “Ukhaa Khudag” open-pit coal mine located in Umnugobi Province, Mongolia. It examines the possibility of minimizing the cycle time of the haulage system as well as factors impacting the speed of the dump truck. The current study divides the open pit mine road for the dump trucks into five sections which are bench road, ramp, surface road, dump road uphill, and dump road. Meanwhile, it investigates the influence of the length, the grade, and the rolling resistance of the road section on the cycle time. The data is analyzed using mathematical regression methods via Microsoft Excel program. For each of the five road sections, we compare the statistical calculations of three regression models: linear, quadratic and exponential;thus, a total of thirty regression models are obtained in this research. Accordingly, the cycle time for each road section is predicted by the most accountable model. The loaded and empty direction of the movement is measured and calculated for each road section, and it appears that the difference between the calculated mean value and the actual cycle time of the models is 0.82 seconds with a relative error of 2.51 percent.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

Participatory Forest Management and Gender Inclusiveness within the Community Forest Management Groups of Bhutan

Community forest management groups (CFMGs) in Bhutan exhibit participatory forest management practices that recognize the importance of community’s collective participation in the management of natural forest resources. This approach involves the community in the stewardship of designated forest areas and resources to ensure sustainable livelihoods and realization of forest conservation objectives. The increase of CFMGs in the country has been successful. However, research on the extent of gender-inclusive participation in CFMGs is either insufficient or missing vis-à-vis the allocation of decision-making power. Therefore, this study analyzes the factors influencing gender participation in CFMGs and their integration into decision-making processes. Primary data were collected from 12 study sites spanning 4 regions, complemented by secondary data from the Forest Department. Regression models were used to identify factors significantly influencing CFMG member participation in decision-making. The empirical results of this study reveal that gender is a significant factor influencing participation in CFMG decision-making. The study concludes that there is insufficient participation of women members in decision-making processes. Therefore, consideration of gender should be included in the development phase of the CFMG policy in addition to promoting awareness of inequity between gender and the promotion of leadership roles for women in CFMGs.

Infinite Loop?What Is Behind the Ongoing Turmoil in the Banking Sector?

The war in Ukraine is unfortunately not over,to add insult to injury,Silicon Valley Bank collapses and Credit Suisse acquired by UBS under the Swiss emergency legislation.The merger of Credit Suisse with UBS,Switzerland’s biggest bank,has also raised concerns about the proliferation of more institutions deemed“too big to fail”.Through the study of four financial crises in the past 100 years,this paper believes that behind this potential financial crisis is still the real estate bubble,but the significant problems in the United States are the most worrying.Post-financial crisis recessions are costlier and last longer than normal recessions.When credit booms are superimposed with asset price bubbles,financial crises are highly likely and economic recovery will be slower.In this paper,relative data and regression model are used to analyze the causes of the crisis;further this paper discusses the reasons behind the financial crisis and related conjectures and gives relevant development speculations.

Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China

Mobile information and communication technologies(MICTs) have fully penetrated everyday life in smart societies;this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel behaviour patterns. As a new mode of shared mobility, the sharing bicycle offers a variety of options for the daily travel of urban residents. Extant studies have mainly examined the travel characteristics and influencing factors of public bicycles with piles, while the travel patterns for sharing bicycles and their driving mechanisms have been largely ignored. Using one week’s travel data for Mobike, this study investigated the spatial and temporal distribution patterns of sharing bicycle travel behaviours in the central urban area of Guangzhou, China;furthermore, it identified the influences of built environment density factors on sharing bicycle travel behaviours based on the geographically weighted regression method. Obvious morning and evening peaks were observed in the sharing bicycle travel patterns for both weekdays and weekends. The old urban area, which had a high degree of mixed function, dense road networks, and cycling-friendly built environments, was the main travel area that attracted sharing bicycles on both weekdays and weekends. Furthermore, factors including the point of interest(POI) for the density of public transport stations, the functional mixing degree, and the density of residential POIs significantly affected residents’ travel behaviours. These findings could enrich discourse regarding shared mobility with a Chinese case characterised by rapidly developing MICTs and also provide references to local authorities for improving slow traffic environments.

Impacts of COVID-19 pandemic on urban park visitation:a global analysis

The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally,as of 1 October 2020.During the lockdown and restrictions placed on public activities and gatherings,green spaces have become one of the only sources of resilience amidst the coronavirus pandemic,in part because of their positive effects on psychological,physical and social cohesion and spiritual wellness.This study analyzes the impacts of COVID-19 and government response policies to the pandemic on park visitation at global,regional and national levels and assesses the importance of parks during this global pandemic.The data we collected primarily from Google’s Community Mobility Reports and the Oxford Coronavirus Government Response Tracker.The results for most countries included in the analysis show that park visitation has increased since February 16th,2020 compared to visitor numbers prior to the COVID-19 pandemic.Restrictions on social gathering,movement,and the closure of workplace and indoor recreational places,are correlated with more visits to parks.Stay-at-home restrictions and government stringency index are negatively associated with park visits at a global scale.Demand from residents for parks and outdoor green spaces has increased since the outbreak began,and highlights the important role and benefits provided by parks,especially urban and community parks,under the COVID-19 pandemic.We provide recommendations for park managers and other decision-makers in terms of park management and planning during health crises,as well as for park design and development.In particular,parks could be utilized during pandemics to increase the physical and mental health and social well-being of individuals.

Theoretical analysis of natural frequency of externally prestressed concrete beam based on rigidity correction

A dynamic test on externally prestressed simply supported concrete beams separately with three typical types of tendon distributions was conducted. The results show that the natural frequencies of the beams increase with the increase in the prestressing force at the tensioning stage, and the natural frequencies decrease after the cracks occur in the beams. Following the calculation formula of natural frequency of externally prestressed beam, which was reported in a literature, the natural frequencies of the experimental beams are calculated, and big errors are found between the test results and the calculated ones of natural frequency values. As a result, this paper has tried to adopt two methods to correct the rigidity parameter of the concrete beam in the formula for natural frequency calculation, and to use the corrected formula to calculate the frequencies of the experimental beams. The calculation results indicate a good consistency with the experimental ones, which verifies the feasibility of the corrected formula.