Stress Related to COVID-19 and Loneliness Among Middle-Aged and Older Adults in the United States

Intensified levels of stress and loneliness have been attributed to the COVID-19 pandemic (Havnen et al., 2020; Luchetti et al., 2020). Moreover, loneliness has been reported to exacerbate psychological and physical health issues (Holt-Lunstad et al., 2015). The current research aims to investigate the impact of stress-related to COVID-19 on loneliness. The roles of age, sex, living arrangements, health, and resilience were also investigated. Data was collected on 267 middle-aged and older adults (ages 45 through 88) living in the U.S during COVID-19. A path analysis was employed. For both the direct and indirect effects, 95% confidence intervals were estimated using bootstrapping (a bootstrap sample of 1,000 was specified). Model fit was acceptable. X2 (5) = 7.913, p > 0.05, CFI=0.972, RMSEA =0.047. Regarding direct effects, the results indicate that COVID-19 related stress (hereafter stress) was negatively associated with perceived good health (hereafter health) (β = -.213, p<0.001). It was also found that health was positively associated with resilience (β = .324, p<0.001). Being male was positively associated with resilience (β= .144, p<0.05), and resilience was negatively associated with loneliness (β= .230, p<0.001). Meanwhile, stress had negative indirect effects on resilience, whereas stress had positive indirect effects on loneliness. Finally, health and being male had negative indirect effects on loneliness. The findings indicate that health practitioners and service providers should develop programs to improve and maintain good health, resilience, and social support among middle-aged and older adults during the COVID-19 pandemic. Moreover, gender-based services are also needed.

A nomogram prediction model for lymph node metastasis in endometrial cancer patients

Background This study aimed to explore the risk factors for lymph node metastasis (LNM) in patients with endometrial cancer (EC) and develop a clinically useful nomogram based on clinicopathological parameters to predict it. Methods Clinical information of patients who underwent staging surgery for EC was abstracted from Qilu Hospital of Shandong University from January 1st, 2005 to June 31st, 2019. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. A nomogram based on the multivariate results was constructed and underwent internal and external validation to predict the probability of LNM. Results The overall data from the 1517 patients who met the inclusion criteria were analyzed. 105(6.29%) patients had LNM. According the univariate analysis and multivariate logistic regression analysis, LVSI is the most predictive factor for LNM, patients with positive LVSI had 13.156-fold increased risk for LNM (95%CI:6.834–25.324; P < 0.001). The nomogram was constructed and incorporated valuable parameters including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels from training set. The nomogram was cross-validated internally by the 1000 bootstrap sample and showed good discrimination accuracy. The c-index for internal and external validation of the nomogram are 0.916(95%CI:0.849–0.982) and 0.873(95%CI:0.776–0.970), respectively. Conclusions We developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.

Impulsivity traits and the rational cognitive process of entrepreneurship: empirical evidence from Vietnam

PurposeThis study aims to integrate predictions from clinical psychology and UPPS impulsivity with the theory of planned behaviors (TPB) to draw a conceptual framework and test the prediction that attention deficit hyperactivity disorder (ADHD) symptoms, impulsivity would contribute to the prediction of the reasoned cognitive process of entrepreneurship over and above key predictors from an extended TPB model.Design/methodology/approachThis study utilized a sample of 2,482 students from 14 universities/institutes in Vietnam; confirmatory factor analysis was employed to test the validity and reliability. Then, regression analysis with PROCESS macro approach (5,000 bootstrap sample and 95% confidence interval) was employed to estimate the association paths and multiple mediators.FindingsThe study reveals that ADHD symptoms and impulsivity substantially contribute to the exploration of an entrepreneurial intention throughout TPB predictors, with those higher in ADHD symptoms and impulsivity having higher intentions to engage in business venturing. Moreover, UPPS impulsiveness might valuably be incorporated with TPB predictors while predicting behaviors that are often examined as the process of rational cognitive strategies business venturing.Practical implicationsThis study showed that a start-up business can be seen as a career choice for students who exhibit extensive ADHD symptoms to use their talents effectively, thus contributing to creating value for society and improving personal well-being.Originality/valueThis article stood to make contributions to entrepreneurship literature by investigating the effects of ADHD symptoms, four impulsivity traits on an entrepreneurial intention via three precursors in TPB, including attitude toward entrepreneurship, subjective norms and perceived behavioral control.

Hybrid ensemble learning approaches to customer churn prediction

PurposeCustomer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.Design/methodology/approachIn this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.FindingsTo examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.Originality/valueIn this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.

Weighted Bagging in Decision Trees: Data Mining

The main focus of this paper is on the use of resampling techniques to construct predictive models from data and the goal is to identify the best possible model which can produce better predications. Bagging or Bootstrap aggregating is a general method for improving the performance of given learning algorithm by using a majority vote to combine multiple classifier outputs derived from a single classifier on a bootstrap resample version of a training set. A bootstrap sample is generated by a random sample with replacement from the original training set. Inspired by the idea of bagging, we present an improved method based on a distance function in decision trees, called modified bagging (or weighted Bagging) in this study. The experimental results show that modified bagging is superior to the usual majority vote. These results are confirmed by both real data and artificial data sets with random noise. The Modified bagged classifier performs significantly better than usual bagging on various tree levels for all sample sizes. An interesting observation is that the weighted bagging performs somewhat better than usual bagging with sumps.

Yumurta Verimi Üzerine Bazı Özelliklerin Etkisinin Regresyon Analiz Yöntemlerinden Bagging Mars ile Belirlenmesi ve R Uygulaması

In the study, it has been demonstrated its use for a data set obtained from layer hens in a hybrid approach obtained by combining BAGGING and MARS. In the study, the data of 2018 of the egg production enterprise in a private livestock enterprise in the Çukurova Region of Adana province were used. In the research, a data set obtained from Lohman breed chickens, who are at an average age of 60 weeks, was used. Earth (enhanced adaptive regression through hinges) and caret (classification and regression training), mda (Mixture Discriminant Analysis) packages were used in R STUDIO program to provide a stronger solution of regression problems in the created MARS and Bagging MARS algorithm. The estimation performance of the bagging MARS technique was evaluated with the goodness of fit criteria by taking the B value of the bootstrap sample number 3. In the study, the effect of temperature and humidity on egg yield, broken / cracked eggs, number of dead animals and feed consumption was investigated using MARS and bagging MARS analysis. While the effect of evening temperature(t3) on egg yield was found to be significant, it was not included in the estimation equation since morning (t1) and noon(t2) temperatures did not have a significant effect. Since the number of broken / cracked eggs and dead animals is less than 5 weeks, these variables are not included in the estimation equation in MARS and Bagging MARS models. It has been observed that feed consumption has a positive contribution in both models.

Determination of Optimum Sampling Fraction for Bootstrap Resampling

Lately, there has been much discussion on the bootstrap resampling method, both as a way of estimating standard error and as a way of improving estimations with access to only one sample. However, little is found in literature discussing the size the bootstrap sample should take. This study aims to determine the existence of an optimum sampling fraction for resampling, analysing different estimators and number of resamples. An optimum fraction exists if, and only if, for every estimator and every amount of resamples, a fraction (or region) performs better in every population. Ten random populations were created by adding together different normal, Poisson and exponential distributions such that their means and variances are diverse. A Monte Carlo simulation with ten thousand iterations was done, taking random systematic samples from the populations and from these, bootstrap samples to estimate the mean, variance and respective standard errors. Results show the inexistence of a single optimum fraction. However, it does point to an optimum region for standard error estimation above 37.5%.

A nomogram prediction model for lymph node metastasis in endometrial cancer patients

Background: The determination of lymph node(LN) status is critical for evaluating prognosis and identifying the necessity of adjuvant therapy of endometrial cancer(EC) patients. However, the significance of systematic lymphadenectomy remains controversial. This study aimed to explore the risk factors for lymph node metastasis(LNM) in patients with EC and develop a clinically useful nomogram based on clinicopathological parameters to predict it.Methods: A total of 1517 consecutive patients who underwent staging surgery for EC were abstracted from Qilu Hospital of Shandong University. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. Based on the multivariate results, a nomogram was constructed and underwent further validation to predict the probability of LNM.Results: The nomogram was constructed and incorporated valuable parameters from the final multivariate model including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels. The nomogram was cross-validated internally by the 200 bootstrap sample and showed good discrimination accuracy with an AUC of 0.899.Conclusions: We developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.

Design of Bootstrap Sample and Hold Circuit

This paper describes the design and implementation of open loop sample and hold circuit using bootstrap technique, which can be used as front end sampling circuit for high speed analog-to-digital converters. Different design criteria viz. speed, power, resolution, linearity, noise and harmonic analysis have been dealt with. Both theoretical analysis and simulation results are carried out. The bootstrap circuit is designed and then compared in a 0.18μm and 0.35μm CMOS process. It is observed that signal to noise and distortion ratio (SNDR) and effective number of bits (ENOB) are higher for 0.35µm technology. But these advantages are at the cost of higher power dissipation. Hence there exists a trade-off between these performance metrics.