Identifying the best common factor model via exploratory eactor analysis

Currently,there is no solid criterion for judging the quality of the estimators in factor analysis.This paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor extraction.The proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor model.Examples are presented to demonstrate how the method is implemented and to verify its effectiveness.

Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Chemokine platelet factor 4 accelerates peripheral nerve regeneration by regulating Schwann cell activation and axon elongation

Schwann cells in peripheral nerves react to traumatic nerve injury by attempting to grow and regenerate.Howeve r,it is unclear what factors play a role in this process.In this study,we searched a GEO database and found that expression of platelet factor 4 was markedly up-regulated after sciatic nerve injury.Platelet factor is an important molecule in cell apoptosis,diffe rentiation,survival,and proliferation.Further,polymerase chain reaction and immunohistochemical staining confirmed the change in platelet factor 4 in the sciatic nerve at different time points after injury.Enzyme-linked immunosorbent assay confirmed that platelet factor 4 was secreted by Schwann cells.We also found that silencing platelet factor 4 decreased the proliferation and migration of primary cultured Schwann cells,while exogenously applied platelet factor 4 stimulated Schwann cell prolife ration and migration and neuronal axon growth.Furthermore,knocking out platelet factor 4 inhibited the prolife ration of Schwann cells in injured rat sciatic nerve.These findings suggest that Schwann cell-secreted platelet factor 4 may facilitate peripheral nerve repair and regeneration by regulating Schwann cell activation and axon growth.Thus,platelet factor 4 may be a potential therapeutic target for traumatic peripheral nerve injury.

A growth-regulating factor 7(GRF7)-mediated gene regulatory network promotes leaf growth and expansion in sugarcane

Knowledge of the function of growth-regulating factors(GRFs)in sugarcane(Saccharum officinarum and S.spontaneum)growth and development could assist breeders in selecting desirable plant architectures.However,limited information about GRFs is available in Saccharum due to their polyploidy.In this study,22 GRFs were identified in the two species and their conserved domains,gene structures,chromosome location,and synteny were characterized.GRF7 expression varied among tissues and responded to diurnal rhythm.SsGRF7-YFP was localized preferentially in the nucleus and appears to act as a transcriptional cofactor.SsGRF7 positively regulated the size and length of rice leaves,possibly by regulating cell size and plant hormones.Of seven potential transcription factors binding to the SsGRF7 promoter in S.spontaneum,four showed positive expression patterns,and two showed negative expression patterns relative to SsGRF7.

Sugarcane transcription factor ScWRKY4 negatively regulates resistance to pathogen infection through the JA signaling pathway

WRKY transcription factors,transcriptional regulators unique to plants,play an important role in defense response to pathogen infection.However,the resistance mechanisms of WRKY genes in sugarcane remain unclear.In the present study,gene ontology(GO)enrichment analysis revealed that WRKY gene family in sugarcane was extensively involved in the response to biotic stress and in defense response.We identified gene ScWRKY4,a classⅡc member of the WRKY gene family,in sugarcane cultivar ROC22.This gene was induced by salicylic acid(SA)and methyl jasmonate(MeJA)stress.Interestingly,expression of ScWRKY4 was down-regulated in smut-resistant sugarcane cultivars but up-regulated in smutsusceptible sugarcane cultivars infected with Sporisorium scitamineum.Moreover,stable overexpression of the ScWRKY4 gene in Nicotiana benthamiana enhanced susceptibility to Fusarium solani var.coeruleum and caused down-regulated expression of immune marker-related genes.Transcriptome analysis indicated suppressed expression of most JAZ genes in the signal transduction pathway.ScWRKY4 interacted with ScJAZ13 to repress its expression.We thus hypothesized that the ScWRKY4 gene was involved in the regulatory network of plant disease resistance,most likely through the JA signaling pathway.The present study depicting the molecular involvement of ScWRKY4 in sugarcane disease resistance lays a foundation for future investigation.

NFA:A neural factorization autoencoder based online telephony fraud detection

The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.

Prognostic factors for lacrimal gland adenoid cystic carcinoma:a retrospective study in Chinese patients

AIM:To explore the prognostic factors for lacrimal gland adenoid cystic carcinoma(LGACC)in Chinese patients.METHODS:Clinical and histopathological data were reviewed in patients with pathologically confirmed LGACC.Local recurrence,metastasis,and disease-specific death were the main outcome measures.Univariate and multivariate analyses were performed by the Kaplan-Meier method and a Cox proportional hazard model.RESULTS:This retrospective cohort study included 45 patients with pathologically confirmed LGACC between January 2008 and June 2022.Tumor(T)classification(P=0.005),nodal metastasis(N)classification(P=0.018)and positive margin(P=0.008)were independent risk factors of recurrence;T(P=0.013)and N(P=0.003)classification and the basaloid tumor type(P=0.032)were independent risk factors for metastasis;T classification(P<0.001)was an independent factor of death of disease.In the further analysis,the durations from first surgery to radiotherapy is correlated with metastatic risk in LGACC patients with basaloid component(P=0.022).CONCLUSION:Histological subtype should be emphasized when evaluating prognosis and guiding treatment.Timely radiotherapy may reduce the risk of metastasis in patients with basaloid component.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Variation Characteristics of Root Traits of Different Alfalfa Cultivars under Saline-Alkaline Stress and their Relationship with Soil Environmental Factors

Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield.It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments.In this study,the root biomass,root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline soil as the saline-alkaline treatment group.At the same time,the correlation analysis of soil salinity indexes,soil nutrient indexes and the activities of key enzymes involved in soil carbon,nitrogen and phosphorus cycles was carried out.The results showed that compared with the control group,the pH,EC,and urease(URE)of the soil surrounding the roots of two alfalfa cultivars were significantly increased,while soil total nitrogen(TN),total phosphorus(TP),organic carbon(SOC),andα-glucosidase activity(AGC)were significantly decreased under saline-alkaline stress.There was no significant difference in root biomass and root morphological parameters of saline-alkaline tolerant cultivar GN under saline-alkaline stress.The number of root tips(RT),root surface area(RS)and root volume(RV)of AG were reduced by 61.16%,44.54%,and 45.31%,respectively,compared with control group.The ratios of K^(+)/Na^(+),Ca^(2+)/Na^(+)and Mg^(2+)/Na^(+)of GN were significantly higher than those of AG(p<0.05).The root fresh weight(RFW)and dry weight(RDW),root length(RL),RV and RT of alfalfa were positively regulated by soil SOC and TN,but negatively regulated by soil pH,EC,and URE(p<0.01).Root Ca^(2+)/Na+ratio was significantly positively correlated with soil TN,TP and SOC(p<0.01).The absorption of Mg and Ca ions in roots is significantly negatively regulated by soilβ-glucosidase activity(BGC)and acid phosphatase activity(APC)(p<0.05).This study improved knowledge of the relationship between root traits and soil environmental factors and offered a theoretical framework for elucidating how plant roots adapt to saline-alkaline stressed soil environments.

Association of education with cholelithiasis and mediating effects of cardiometabolic factors:A Mendelian randomization study

BACKGROUND Education,cognition,and intelligence are associated with cholelithiasis occurrence,yet which one has a prominent effect on cholelithiasis and which cardiometabolic risk factors mediate the causal relationship remain unelucidated.AIM To explore the causal associations between education,cognition,and intelligence and cholelithiasis,and the cardiometabolic risk factors that mediate the associations.METHODS Applying genome-wide association study summary statistics of primarily European individuals,we utilized two-sample multivariable Mendelian randomization to estimate the independent effects of education,intelligence,and cognition on cholelithiasis and cholecystitis(FinnGen study,37041 and 11632 patients,respectively;n=486484 participants)and performed two-step Mendelian randomization to evaluate 21 potential mediators and their mediating effects on the relationships between each exposure and cholelithiasis.RESULTS Inverse variance weighted Mendelian randomization results from the FinnGen consortium showed that genetically higher education,cognition,or intelligence were not independently associated with cholelithiasis and cholecystitis;when adjusted for cholelithiasis,higher education still presented an inverse effect on cholecystitis[odds ratio:0.292(95%CI:0.171-0.501)],which could not be induced by cognition or intelligence.Five out of 21 cardiometabolic risk factors were perceived as mediators of the association between education and cholelithiasis,including body mass index(20.84%),body fat percentage(40.3%),waist circumference(44.4%),waist-to-hip ratio(32.9%),and time spent watching television(41.6%),while time spent watching television was also a mediator from cognition(20.4%)and intelligence to cholelithiasis(28.4%).All results were robust to sensitivity analyses.CONCLUSION Education,cognition,and intelligence all play crucial roles in the development of cholelithiasis,and several cardiometabolic mediators have been identified for prevention of cholelithiasis due to defects in each exposure.

Status quo and factors of depression and anxiety in patients with non-muscle invasive bladder cancer after plasma electrocision

BACKGROUND Bladder cancer is a type of cancer with a high incidence in men.Plasma electrosurgery(PES)is often used in the treatment of bladder cancer.Postoperative complications often cause depression and anxiety in patients after surgery.AIM To investigate the current state of depression and anxiety after PES in patients with non-muscle-invasive bladder cancer and analyze the factors affecting them.METHODS A retrospective study was conducted to compare the baseline data of patients by collecting their medical history and grouping them according to their mental status into negative and normal groups.Logistic regression analysis was used to explore the risk factors affecting the occurrence of anxiety and depression after surgery in patients with bladder cancer.RESULTS Comparative analyses of baseline differences showed that the patients in the negative and normal groups differed in terms of their first surgery,economic status,educational level,and marital status.A logistic regression analysis showed that it affected the occurrence of anxiety in patients with bladder cancer,and the results showed that whether the risk factors were whether or not it was the first surgery,monthly income between 3000 and 3000-6000,secondary or junior high school education level,single,divorced,and widowed statuses.CONCLUSION The risk factors affecting the onset of anxiety and depression in bladder cancer patients after PES are the number of surgeries,economic status,level of education,and marital status.This study provides a reference for the clinical treatment and prognosis of bladder cancer patients in the future.

Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.

Examining the Hypothesis of Common Factors Shared by Different Addictive Behaviors and Gender Effects on Propensity to Addiction Type

Background: From the two facts reported by previous research: 1) frequent co-occurrence of more than one addictive behavior, 2) childhood adversities identified as origins of different types of addictive behaviors, it is assumed that all types of addictive behaviors, regardless of substance, behavioral, or relationship, share common factors which have not yet been proven by epidemiological research. The Shorter PROMIS Questionnaire (SPQ) was previously developed to assess 16 types of addictive behaviors. Its factor structure, however, has not been fully investigated. Confirming the factor structure will enable us to hypothesize the common factor(s) shared by all, or if not all, most types of addictive behaviors. Aims: This study aimed at 1) examining the factor structure of the SPQ, 2) confirming the reliability of the questionnaire, and 3) examining the impacts of gender and age on each addictive behavior. Methods: Data obtained from 232 Japanese adults who completed all items of the SPQ were used for the analyses. After confirming the one-factor structure model for each of the 16 subscales, the validity of the one-factor structure of the SPQ was evaluated using Confirmatory Factor Analysis (CFA), by adapting 16 subscale scores as observed variables. If its validity was not confirmed, another model which showed better compatibility to the data was explored. The reliability of the SPQ as well as that of all 16 subscales was evaluated. Also, the impacts of gender and age on each subscale score were examined. Results: The one-factor structure for each of the 16 subscales was confirmed. The compatibility of the SPQ one-factor model was not acceptable. The best fit model was a bi-factor model in which one main factor was shared by all 16 subscales, and three factors were shared by some specific addictive behaviors. Male respondents were more likely than female respondents to show high scores in Alcohol, Tobacco, Gambling, Sex, and Recreational Drugs, and low scores only in Shopping. Respondents’ age did not impact any of the 16 subscale scores. Conclusion: It was demonstrated that there are common factors shared by all different types, as well as selected types of addictive behaviors, by conducting CFAs of the SPQ. Reliability was proven for the SPQ and for all 16 subscales. Male respondents were more likely to show physically hedonic addictive behaviors.

Analysis of clinicopathological features and prognostic factors of breast cancer brain metastasis

BACKGROUND Breast cancer(BC)has become the most common malignancy in women.The incidence and detection rates of BC brain metastasis(BCBM)have increased with the progress of imaging,multidisciplinary treatment techniques and the extension of survival time of BC patients.BM seriously affects the quality of life and survival prognosis of BC patients.Therefore,clinical research on the clinicopathological features and prognostic factors of BCBM is valuable.By analyzing the clinicopathological parameters of BCBM patients,and assessing the risk factors and prognostic indicators,we can perform hierarchical diagnosis and treatment on the high-risk population of BCBM,and achieve clinical benefits of early diagnosis and treatment.AIM To explore the clinicopathological features and prognostic factors of BCBM,and provide references for diagnosis,treatment and management of BCBM.METHODS The clinicopathological data of 68 BCBM patients admitted to the Air Force Medical Center,Chinese People’s Liberation Army(formerly Air Force General Hospital)from 2000 to 2022 were collected.Another 136 BC patients without BM were matched at a ratio of 1:2 based on the age and site of onset for retrospective analysis.Categorical data were subjected to χ^(2) test or Fisher’s exact probability test,and the variables with P<0.05 in the univariate Cox proportional hazards model were incorporated into the multivariate model to identify high-risk factors and independent prognostic factors of BCBM,with a hazard ratio(HR)>1 suggesting poor prognostic factors.The survival time of patients was estimated by the Kaplan-Meier method,and overall survival was compared between groups by log-rank test.RESULTS Multivariate Cox regression analysis showed that patients with stage Ⅲ/Ⅳ tumor at initial diagnosis[HR:5.58,95% confidence interval(CI):1.99–15.68],lung metastasis(HR:24.18,95%CI:6.40-91.43),human epidermal growth factor receptor 2(HER2)-overexpressing BC and triple-negative BC were more prone to BM.As can be seen from the prognostic data,52 of the 68 BCBM patients had died by the end of follow-up,and the median time from diagnosis of BC to the occurrence of BM and from the occurrence of BM to death or last follow-up was 33.5 and 14 mo,respectively.It was confirmed by multivariate Cox regression analysis that patients with neurological symptoms(HR:1.923,95%CI:1.005-3.680),with bone metastasis(HR:2.011,95%CI:1.056-3.831),and BM of HER2-overexpressing and triple-negative BC had shorter survival time.CONCLUSION HER2-overexpressing,triple-negative BC,late tumor stage and lung metastasis are risk factors of BM.The presence of neurological symptoms,bone metastasis,and molecular type are influencing prognosis factors of BCBM.

A comprehensive approach to a variability analysis between earthquake activity and hydro-environmental factors on the Korean Peninsula

Following the Pohang and Gyeongju earthquakes and their aftershocks,there is no longer any zone that is safe from earthquake-related disasters in the Korean Peninsula.In order to monitor and predict earthquakes,correlation analysis of earthquakes and hydro-environmental factors are insufficient,and the development and application of hydro-environmental factor measurement equipment is still in the early stages.This study developes and verifies a more precise radon measurement device.Four specific earthquake cases(2019–2020)were selected,and the correlation of the analyses of the earthquakes and hydro-environmental factors(radon,electric conductivity(EC),water-level(WL),and water-temperature(WT))was conducted at the three specific groundwater stations.Accordingly,was confirmed that four factors are affected by earthquakes or seismic movement.Furthermore,the variability of the EC showed an identical tendency for a certain period before an earthquake occurred,and,in particular,the variability trends for radon,WL,and EC coincided at the time of the earthquake′s occurrence.

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.

Influencing factor analysis of interception probability and classification-regression neural network based estimation

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually coupled.Existing calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance law.This paper considers the influencing factors of both the interceptor and the target more comprehensively.Interceptor parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and vulnerability.In this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation results.Finally,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors.The proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.

Dealing with Multicollinearity in Factor Analysis: The Problem, Detections, and Solutions

Multicollinearity in factor analysis has negative effects, including unreliable factor structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties in interpreting factors. It also leads to reduced stability, hindered factor replication, misinterpretation of factor importance, increased parameter estimation instability, reduced power to detect the true factor structure, compromised model fit indices, and biased factor loadings. Multicollinearity introduces uncertainty, complexity, and limited generalizability, hampering factor analysis. To address multicollinearity, researchers can examine the correlation matrix to identify variables with high correlation coefficients. The Variance Inflation Factor (VIF) measures the inflation of regression coefficients due to multicollinearity. Tolerance, the reciprocal of VIF, indicates the proportion of variance in a predictor variable not shared with others. Eigenvalues help assess multicollinearity, with values greater than 1 suggesting the retention of factors. Principal Component Analysis (PCA) reduces dimensionality and identifies highly correlated variables. Other diagnostic measures include the condition number and Cook’s distance. Researchers can center or standardize data, perform variable filtering, use PCA instead of factor analysis, employ factor scores, merge correlated variables, or apply clustering techniques for the solution of the multicollinearity problem. Further research is needed to explore different types of multicollinearity, assess method effectiveness, and investigate the relationship with other factor analysis issues.