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Shock-tube experiments on Richtmyer–Meshkov (RM) instability at a perturbed SF$_6$ layer surrounded by air, induced by a cylindrical divergent shock, are reported. To explore the effects of reverberating waves and interface coupling on instability growth, gas layers with various shapes are created: unperturbed inner interface and sinusoidal outer interface (case US); sinusoidal inner and outer interfaces that have identical phase (case IP); sinusoidal inner and outer interfaces that have opposite phase (case AP). For each case, three thicknesses are considered. Results show that reverberating waves inside the layer dominate the early-stage instability growth, while interface coupling dominates the late-stage growth. The influences of waves on divergent RM instability are more pronounced than the planar and convergent counterparts, which are estimated accurately based on gas dynamics theory. Both the wave influence and interface coupling depend heavily on the layer shape, leading to diverse growth rates: the quickest growth for case AP, medium growth for case US, the slowest growth for case IP. In particular, for the IP case, there exists a critical thickness below which the instability growth is suppressed by both the reverberating waves and interface coupling. This provides an efficient way to modulate the growth of divergent RM instability. It is found that divergent RM instability involves weak nonlinearity and strong interface coupling such that the linear theory of Mikaelian (Phys. Fluids, vol. 17, 2005, 094105) can well reproduce the instability growth at late stages for all cases. This constitutes the first experimental confirmation of the Mikaelian theory.
Adherence to healthy lifestyles can be beneficial for depression among adults, but the intergenerational impact of maternal healthy lifestyles on offspring depressive symptoms is unknown.
Methods
In total, 10 368 mothers in Nurses' Health Study II and 13 478 offspring in the Growing Up Today Study were paired. Maternal and offspring healthy lifestyles were defined as a composite score including a healthy diet, normal body mass index (BMI), never-smoking, light-to-moderate consumption of alcohol, and regular moderate-to-vigorous physical activity. Maternal lifestyles were assessed during their offspring's childhood. Offspring depressive symptoms were repeatedly assessed five times using the Center for Epidemiological Studies Depression Scale-10 (CESD-10); the offspring were between the ages of 14 and 30 when the first CESD-10 was assessed. Covariates included maternal variables (age at baseline, race/ethnicity, antidepressant use, pregnancy complications, etc.) and offspring age and sex.
Results
Children of mothers with the healthiest lifestyle had significantly fewer depressive symptoms (a 0.30 lower CESD-10 score, 95% confidence interval (CI) 0.09–0.50) in comparison with children of mothers with the least healthy lifestyle. The association was only found significant in female offspring but not in males. For individual maternal lifestyle factors, a normal BMI, never-smoking, and adherence to regular physical activity were independently associated with fewer depressive symptoms among the offspring. The association between maternal healthy lifestyles and offspring depressive symptoms was mediated by offspring's healthy lifestyles (mediation effect: 53.2%, 95% CI 15.8–87.3).
Conclusions
Our finding indicates the potential mechanism of intergenerational transmission of healthy lifestyles to reduce the risk of depressive symptoms in offspring.
Energy drinks are consumed for a variety of reasons, including to boost mental alertness and energy. We assessed associations between demographic factors and various high-risky behaviours with energy drink consumption as they may be linked to adverse health events.
Design:
We conducted cross-sectional analysis including basic descriptive and multivariable-adjusted logistic regression analyses to characterise demographic and behavioural factors (including diet quality, binge drinking and illicit drug use, among others obtained via questionnaires) in relation to energy drink consumption.
Setting:
We used data from two large US-based cohorts.
Participants:
46 390 participants from Nurses’ Health Study 3 (NHS3, n 37 302; ages 16–31) and Growing Up Today Study (GUTS, n 9088, ages 20–55).
Results:
Of the 46 390 participants, 13·2 % reported consuming ≥ 1 energy drink every month. Several risky behaviours were associated with energy drink use, including illegal drug use (pooled OR, pOR: 1·45, 95 % CI: 1·16, 1·81), marijuana use (pOR: 1·49, 95 % CI: 1·28, 1·73), smoking (pOR: 1·88. 95 % CI: 1·55, 2·29), tanning bed use (pOR: 2·31, 95 % CI: 1·96, 2·72) and binge drinking (pOR: 2·53, 95 % CI: 2·09, 3·07). Other factors, such as high BMI, e-cigarette use and poor diet quality were found to be significantly associated with higher energy drink consumption (P values < 0·001).
Conclusions:
Our findings show that energy drink consumption and high-risk behaviours may be related, which could potentially serve as not only as a talking point for providers to address in outreach and communications with patients, but also a warning sign for medical and other health practitioners.
Chapter 10, in contrast to all the previous chapters that focused on the performance of the downlink, analyzes the performance of the uplink of an ultra-dense network. Importantly, this chapter shows that the phenomena presented in – and the conclusions derived from – all the previous chapters also apply to the uplink, despite its different features, e.g. uplink transmit power control, inter-cell interference source distribution. System-level simulations are used in this chapter to conduct the study.
Chapter 9, using the new capacity scaling law presented in the previous chapter, explores three relevant network optimization problems: i) the small cell base station deployment/activation problem, ii) the network-wide user equipment admission/scheduling problem, and iii) the spatial spectrum reuse problem. These problems are formally presented, and exemplary solutions are provided, with the corresponding discussion on the intuition behind the proposed solutions.
Chapter 11 shows the benefits of dynamic time division duplexing with respect to a more static time division duplexing assignment of time resources in an ultra-dense network. As studied in previous chapters, the amount of user equipment per small cell reduces significantly in a denser network. As a result, a dynamic assignment of time resources to the downlink and the uplink according to the load in each small cell can avoid resource waste, and significantly enhance its capacity. The dynamic time division duplexing protocol is modelled and analyzed through system-level simulations in this chapter too, and its performance carefully examined.
Chapter 3 summarizes the modelling, derivations and main findings of probably one of the most important works on small cell theoretical performance analysis, which concluded that the fears of an inter-cell interference overload in small cell networks were not well-grounded, and that the network capacity – or in more technical words, the area spectral efficiency – linearly grows with the number of deployed small cells. This research was the cornerstone of much of the research that followed on small cells performance analysis.
Chapter 1 introduces the capacity challenge faced by modern wireless communication systems and presents ultra-dense wireless networks as an appealing solution to address it. Moreover, it provides background on the small cell concept – the fundamental building block of an ultra-dense wireless network – describing its main characteristics, benefits and drawbacks. This chapter also presents the structure of the book and the fundamental concepts required for its systematic understanding.
Chapter 6 brings attention to another important feature of ultra-dense networks, i.e. the surplus of the number of small cell base stations with respect to the amount of user equipment. Building on this fact and looking ahead at next generation small cell base stations, the ability to go into idle mode, transmit no signalling meanwhile, and thus mitigate inter-cell interference is presented in this chapter, as a key tool to enhance ultra-dense network performance and combat the previously presented caveats. Special attention is paid to the upgraded modelling and analysis of the idle mode capability at the small cell base stations.
Chapter 5 studies in detail – and also from a theoretical perspective – yet another and more important caveat towards a satisfactory network performance in the ultra-dense regime, i.e. that of the impact of the antenna height difference between the user equipment and the small cell base stations. Similarly as in the previous chapter, such antenna-related modelling upgrades, the new derivations in a three-dimensional space and the new obtained results are carefully presented and discussed in this book chapter for the better understanding of the readers. Moreover, several small cell deployment and configuration guidelines are provided to improve the network performance.