Flow through a circular tube with a permeable Navier slip boundary

For Newtonian fluid flow in a right circular tube, with a linear Navier slip boundary, we show that a second flow field arises which is different to conventional Poiseuille flow in the sense that the corresponding pressure is quadratic in its dependence on the length along the tube, rather than a linear dependence which applies for conventional Poiseuille flow. However, assuming that the quadratic pressure is determined, say from known experimental data, then the new solution only exists for a precisely prescribed permeability along the boundary. While this cannot occur for conventional pipe flow, for fluid flow through carbon nanotubes embedded in a porous matrix, it may well be an entirely realistic possibility, and could well explain some of the high flow rates which have been reported in the literature. Alternatively, if the radial boundary flow is prescribed, then the new flow field exists only for a given quadratic pressure. Our primary purpose here is to demonstrate the existence of a new pipe flow field for a permeable Navier slip boundary and to present a numerical solution and two approximate analytical solutions. The maximum flow rate possible for the new solution is precisely twice that for the conventional Poiseuille flow, which occurs for constant inward directed flow across the boundary

Deep reinforcement learning for drone navigation using sensor data

Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios

Expression of Multiple Resistance Genes Enhances Tolerance to Environmental Stressors in Transgenic Poplar (Populus × euramericana ‘Guariento’)

Commercial and non-commercial plants face a variety of environmental stressors that often cannot be controlled. In this study, transgenic hybrid poplar (Populus × euramericana ‘Guariento’) harboring five effector genes (vgb, SacB, JERF36, BtCry3A and OC-I) were subjected to drought, salinity, waterlogging and insect stressors in greenhouse or laboratory conditions. Field trials were also conducted to investigate long-term effects of transgenic trees on insects and salt tolerance in the transformants. In greenhouse studies, two transgenic lines D5-20 and D5-21 showed improved growth, as evidenced by greater height and basal diameter increments and total biomass relative to the control plants after drought or salt stress treatments. The improved tolerance to drought and salt was primarily attributed to greater instantaneous water use efficiency (WUEi) in the transgenic trees. The chlorophyll concentrations tended to be higher in the transgenic lines under drought or saline conditions. Transformed trees in drought conditions accumulated more fructan and proline and had increased Fv/Fm ratios (maximum quantum yield of photosystem II) under waterlogging stress. Insect-feeding assays in the laboratory revealed a higher total mortality rate and lower exuviation index of leaf beetle [Plagiodera versicolora (Laicharting)] larvae fed with D5-21 leaves, suggesting enhanced insect resistance in the transgenic poplar. In field trials, the dominance of targeted insects on 2-year-old D5-21 transgenic trees was substantially lower than that of the controls, indicating enhanced resistance to Coleoptera. The average height and DBH (diameter at breast height) of 2.5-year-old transgenic trees growing in naturally saline soil were 3.80% and 4.12% greater than those of the control trees, but these increases were not significant. These results suggested that multiple stress-resistance properties in important crop tree species could be simultaneously improved, although additional research is needed to fully understand the relationships between the altered phenotypes and the function of each transgene in multigene transformants

Modelling and Verification of Timed Robotic Controllers

Designing robotic systems can be very challenging, yet controllers are often specified using informal notations with development driven primarily by simulations and physical experiments, without relation to abstract models of requirements. The ability to perform formal analysis and replicate results across different robotic platforms is hindered by the lack of well-defined formal notations. In this paper we present a timed state-machine based formal notation for robotics that is informed by current practice. We motivate our work with an example from swarm robotics and define a compositional CSP-based discrete timed semantics suitable for refinement. Our results support verification and, importantly, enable rigorous connection with sound simulations and deployments.</p

A Survey of University Students’ Preferences for Midwifery Care and Community Birth Options in 8 High‐Income Countries

Background: Midwifery care is associated with positive birth outcomes, access to community birth options, and judicious use of interventions. The aim of this study was to characterize and compare maternity care preferences of university students across a range of maternity care systems and to explore whether preferences align with evidence- based recommendations and options available. Methods: A cross-sectional, web-based survey was completed in 2014-2015 by a convenience sample of university students in 8 high-income countries across 4 continents (n=4,569). In addition to describing preferences for midwifery care and community birth options across countries, socio-demographic characteristics, psychological factors, knowledge about pregnancy and birth, and sources of information that shaped students’ attitudes towards birth were examined in relation to preferences for midwifery care and community birth options. Results: Approximately half of the student respondents (48. 2%) preferred midwifery-led care for a healthy pregnancy, 9.5% would choose to birth in a birthing center, and 4.5% preferred a home birth. Preference for midwifery care varied from 10.3% among women in the United States to 78.6% among women in England. Preferences for home birth varied from 0.3% among US women to 18.3% among Canadian women. Women, health science students, those with low childbirth fear, who learned about pregnancy and birth from friends (compared to other sources, e.g. the media) and those who responded from Europe were significantly more likely to prefer midwifery care and community birth. High confidence in knowledge of pregnancy and birth was linked to significantly higher odds of community birth preferences and midwifery care preferences Conclusions: It would be beneficial to integrate childbirth education into high school curricula, to promote knowledge of midwifery care, pregnancy and childbirth and reduce fear among prospective parents. Community birth options need to be expanded, to meet demand among the next generation of maternity service users