The effect of mode and context on survey results: analysis of data from the Health Survey for England 2006 and the Boost Survey for London.

BACKGROUND: Health-related data at local level could be provided by supplementing national health surveys with local boosts. Self-completion surveys are less costly than interviews, enabling larger samples to be achieved for a given cost. However, even when the same questions are asked with the same wording, responses to survey questions may vary by mode of data collection. These measurement differences need to be investigated further. METHODS: The Health Survey for England in London ('Core') and a London Boost survey ('Boost') used identical sampling strategies but different modes of data collection. Some data were collected by face-to-face interview in the Core and by self-completion in the Boost; other data were collected by self-completion questionnaire in both, but the context differed. Results were compared by mode of data collection using two approaches. The first examined differences in results that remained after adjusting the samples for differences in response. The second compared results after using propensity score matching to reduce any differences in sample composition. RESULTS: There were no significant differences between the two samples for prevalence of some variables including long-term illness, limiting long-term illness, current rates of smoking, whether participants drank alcohol, and how often they usually drank. However, there were a number of differences, some quite large, between some key measures including: general health, GHQ12 score, portions of fruit and vegetables consumed, levels of physical activity, and, to a lesser extent, smoking consumption, the number of alcohol units reported consumed on the heaviest day of drinking in the last week and perceived social support (among women only). CONCLUSION: Survey mode and context can both affect the responses given. The effect is largest for complex question modules but was also seen for identical self-completion questions. Some data collected by interview and self-completion can be safely combined

Multi-phase ecological change on Indian subcontinent from the late Miocene to Pleistocene recorded in the Nicobar Fan

Modern grasslands on the Indian subcontinent, North and South America, and East Africa expanded widely during the late Miocene - earliest Pleistocene, likely in response to increasing aridity. Grasses utilizing the C4 photosynthetic pathway are more tolerant of high temperatures and dry conditions, and because they induce less C isotope fractionation than plants using the C3 pathway, the expansion of C4 grasslands can be traced through the δ13C of organic matter in soils and terrigenous marine sediments. We present a high-resolution record of the elemental and isotopic composition of bulk organic matter in the Nicobar Fan sediments from IODP Site U1480, off western Sumatra, to elucidate the timing and pace of the C3-C4 plant transition within the ∼1.5 × 106 km2 catchments of the Ganges/Brahmaputra river system, which continue to supply voluminous Himalaya-derived sediments to the Bay of Bengal. Using a multi-proxy approach to correct for the effects of marine organic matter and account for major sources of uncertainty, we recognize two phases of C4 expansion starting at ∼7.1 Ma, and at ∼3.5 Ma, with a stepwise transition at ∼2.5 Ma. These intervals appear to coincide with periods of Indian Ocean and East Asian monsoon intensification, as well as the expansion of Northern Hemisphere glaciation starting at ∼2.7 Ma. Our data from the deep sea for a multi-phased C4 expansion on the Indian subcontinent are in agreement with terrestrial data from the Indian Siwaliks

Pilot Sensitivity to Simulator Flight Dynamics Model Formulation for Stall Training

A piloted simulation study was performed in the Cockpit Motion Facility at the National Aeronautics and Space Administration Langley Research Center. The research was motivated by the desire to reduce the commercial transport airplane fatal accident rate due to in-flight loss of control. The purpose of this study, which focused on a generic T-tail transport airplane, was to assess pilot sensitivity to flight dynamics model formulation used during a simulator stall recognition and recovery training/demonstration profile. To accomplish this, the flight dynamics model was designed with many configuration options. The model options were based on recently acquired static and dynamic stability and control data from sources that included wind tunnel, water tunnel, and computational fluid dynamics. The results, which are specific to a transport airplane stall recognition and recovery guided demonstration scenario, showed the two most important aerodynamic effects (other than stick pusher) to model were stall roll- off and the longitudinal static stability characteristic associated with the pitch break

Multi-attribute decision making on mitigating a collision of an autonomous vehicle on motorways

Autonomous vehicles have the potential to improve automotive safety, largely by removing human error as a possible cause of collisions. However, it cannot be guaranteed that autonomous vehicles will be able to eliminate all collisions. Therefore, automotive safety will continue to be a necessity for automotive design. This paper proposes a decision making system which selects the least severe collision for an autonomous vehicle to take, when facing multiple imminent and unavoidable collisions on a motorway. The novel decision making system developed combines simulation results and multi-attribute decision making (MADM) methods. The simulator includes models of vehicle dynamics and the manoeuvre trajectory path. MADM methods are used to decide which vehicle(s) the autonomous vehicle should collide with, based on the severity of collisions. Severity of collisions is calculated in the simulator using the following variables: impact velocity between autonomous vehicle and vehicle ahead, impact velocity between vehicle behind and autonomous vehicle, manoeuvre acceleration and time-to-collision. Various MADM methods are investigated and three methods are selected including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytical Hierarchy Process (AHP), and the Analytical Network Process (ANP). Various collision scenarios are defined and tested in order to understand the impact that small changes in parameters of the autonomous vehicle and vehicles ahead and behind have on the decision made. The analysed decision making results are promising and lead to the conclusion that MADM methods can be successfully applied in autonomous vehicles