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Background The presence of symptom clusters is associated with poor treatment outcomes and decreased quality of life in patients with cancer, and holistic interventions for symptom clusters can improve the management efficiency. Numerous non-pharmacologic interventions are available for symptom clusters in patients with cancer, but there is disagreement as to which interventions is the most effective. Objective To explore and compare the effectiveness of different non-pharmacological interventions in reducing the severity of symptom clusters in cancer patients. Methods In September 2021, randomized controlled trials (RCTs) about non-pharmacological interventions in reducing the severity of symptom clusters in cancer patients were searched in SinoMed, CNKI, Wanfang Data, CQVIP, the Cochrane Library, PubMed, Web of Science and Wiley Online Library databases from inception to September 2021. Two researchers independently screened studies based on the inclusion and exclusion criteria, extracted data, and performed risk of bias assessment using the Cochrane handbook for systematic reviews of interventions (version 5.1.0) . RevMan 5.3 was adopted for heterogeneity test using a traditional method and publication bias analysis. Addis 16.6 was used to make a network meta-analysis. Results Twelve RCTs were finally included, involving 1 116 cases, and eight non-pharmacologic interventions: cognitive behavior intervention, mindfulness-based stress reduction, acupoint massage, multiple-modality exercise, exercise and health counseling, auricular point sticking therapy, narrative nursing, and attention control. Network meta-analysis results showed that, for reducing the severity of symptom clusters in cancer patients, acupoint massage was superior to interventions other than mindfulness-based stress reduction, mindfulness-based stress reduction was superior to usual care and narrative nursing, and cognitive behavioral intervention was superior to multiple-modality exercise, usual care and narrative nursing (Pmindfulness-based stress reduction>cognitive behavior intervention>auricular point sticking therapy/attention control>multiple-modality exercise>usual care>narrative nursing>exercise and health counseling. Conclusion Based on the results of network meta-analysis and probability ranking, acupoint massage was recommended as the clinical priority non-pharmacological intervention to alleviate the severity of symptom clusters in cancer patients, followed by mindfulness-based stress reduction. In the future, high-quality, large-sample, multicenter, double-blind RCTs are needed to further validate the effects of different non-pharmacological interventions on improving symptom clusters in cancer patients, and to provide more reliable evidence for supporting the development of interventions for symptom clusters in these patients.

Motivated by a certain type of infinite-patch metapopulation model, this current work proposes a new mixed INAR(p) model (SDMINAR(p)) based on binomial thinning and negative binomial thinning operators, where the innovations are assumed to be serially dependent in such a way that their mean is increased if the population at time { t − 1 , t − 2 , ... , t − p } are large. Strict stationarity ergodicity property is proved. Conditional maximum likelihood estimation are adopted to estimate the model parameters. Asymptotic normality property of the estimators are displayed. The performances of the estimators are investigated in simulation, which manifests that the maximum likelihood estimation performs better when sample size increases. Finally, the practical relevance of the model is demonstrated by the COVID-19 data of suspected cases and severe cases imported from outside China with a comparison with relevant models that exist so far in the literature. [ABSTRACT FROM AUTHOR]

AbstractLiquid nitrogen spray cooling is crucial in cryogenic wind tunnels, while the understanding of its basic phenomenon, liquid nitrogen droplet impact, remains elusive. In this study, a numerical model of a liquid nitrogen droplet impacting on a liquid nitrogen film is established, and the impact mechanisms and influencing factors are analyzed. The dynamic behavior of the liquid nitrogen droplets reveals that as the dimensionless liquid film thickness varies from 0.1 to 0.8, the droplet impact behavior changes from deposition, and crown formation to splash with the Weber number increasing from 0 to 1000. The variation of the dimensionless crown diameter with the dimensionless time solely negatively correlated with liquid film thickness. The maximum dimensionless crown height increases approximately linearly with the Weber number. The splash characteristics indicate that the lower surface tension and viscosity of liquid nitrogen make the droplet easier to exhibit splash as compared with water droplets. As the dimensionless liquid film thickness increases from 0.1 to 0.4, the critical Weber number enabling splash to occur increases from 170 to 375, indicating that a thick liquid film suppresses the splash. [ABSTRACT FROM AUTHOR]

Summary: Data related to the counting of elements of variable character are frequently encountered in time series studies. This paper brings forward a new class of k$$ k $$th‐order dependence‐driven random coefficient mixed thinning integer‐valued autoregressive time series model (DDRCMTINAR(k$$ k $$)) to deal with such data. Stationarity and ergodicity properties of the proposed model are derived in detail. The unknown parameters are estimated by conditional least squares, and modified quasi‐likelihood and asymptotic normality of the obtained parameter estimators is established. The performances of the adopted estimate methods are checked via simulations, which present that modified quasi‐likelihood estimators perform better than the conditional least squares considering the proportion of within‐Ω$$ \Omega $$ estimates in certain regions of the parameter space. The validity and practical utility of the model are investigated by epileptic seizure data and COVID‐19 data of suspected cases in China. [ABSTRACT FROM AUTHOR]

This article studies the two-step conditional least squares (CLS) estimation for the bivariate Z -valued INAR(1) model with bivariate Skellam innovations. For readers' convenience, we first give a brief review of the bivariate Skellam distribution, bivariate signed thinning operator and the definition of the bivariate Z -valued INAR(1) model with bivariate Skellam innovations (denoted as the BSK-BINARS(1) model). Then, we discuss the stationarity and ergodicity of the BSK-BINARS(1) model, give some stochastic properties. Second, we discuss the two-step CLS estimate of the parameters and establish their large-sample properties. Third, we conduct a simulation study to illustrate the finite sample performances of the two-step CLS estimators, which are compared with those obtained by the plug-in method. Last but not least, we apply the BSK-BINARS(1) model on the zonal annual means temperature (*100). [ABSTRACT FROM AUTHOR]