Study of noise reduction characteristics of composite fiber-reinforced panels, interior panel configurations, and the application of the tuned damper concept

The application of fiber reinforced composite materials, such as graphite epoxy and Kevlar, for secondary or primary structures developing in the commercial airplane industry was investigated. A composite panel program was initiated to study the effects of some of the parameters that affect noise reduction of these panels. The fiber materials and the ply orientation were chosen to be variables in the test program. It was found that increasing the damping characteristics of a structural panel will reduce the vibration amplitudes at resonant frequencies with attendant reductions in sound reduction. Test results for a dynamic absorber, a tuned damper, are presented and evaluated

Weak approximation for del Pezzo surfaces of low degree

We prove, via an “arithmetic surjectivity” approach inspired by work of Denef, that weak weak approximation holds for surfaces with two conic fibrations satisfying a general assumption. In particular, weak weak approximation holds for general del Pezzo surfaces of degrees 1 or 2 with a conic fibration

The circulatory impact of dust from dust profile assimilation

We present results from a reanalysis of temperatures, dust columns and dust vertical profiles focussing on the assimilation, distribution and transport of dust in the martian atmosphere. The assimilation of dust vertical information in particular is a valuable technique which has been shown to be of vital importance to a successful assimilation of the martian atmosphere, with the vertical representation of the dust distribution having a critical effect on assimilation results generally. Atmospheric dust is a key driver of the martian circulation. Dust-induced heating and cooling is a potential feedback mechanism for dust lifting, for example, and can modify the circulation to either enhance or suppress dust storm activity. Accurately representing its complex spatial and temporal distribution is therefore crucial for understanding Mars’ atmospheric dynamics and transport

Useful applications of earth-oriented satellites - Systems for remote-sensing information and distribution, panel 8

Problems and potential use of data gathered by remote sensing from satellites or aircraf

Effectiveness of a targeted telephone-based case management service on activity in an Emergency Department in the UK: a pragmatic difference-in-differences evaluation.

BACKGROUND: This study evaluates the effectiveness of a targeted telephone-based case management service that aimed to reduce ED attendance amongst frequent attenders, known to disproportionately contribute to demand. Evidence on the effectiveness of these services varies. METHODS: A 24-month controlled before-and-after study, following 808 patients (128 cases and 680 controls (41 were non-compliant)) who were offered the service in the first four months of operation within a UK ED department. Patients stratified as high-risk of reattending ED within 6 months by a predictive model were manually screened. Those positively reviewed were offered a non-clinical, nurse-led, telephone-based health coaching, consisting of care planning, coordination and goal setting for up to 9 months. Service effectiveness was estimated using a difference-in-differences (DiD) analysis. Incident rate of ED and Minor Injury Unit (MIU) attendances and average length of stay in intervention recipients and controls over 12 months after receiving their service offer following ED attendance were compared, adjusting for the prior 12-month period, sex and age, to give an incidence rate ratio (IRR). RESULTS: Intervention recipients were more likely to be female (63.3% versus 55.4%), younger (mean of 69 years versus 76 years), and have higher levels of ED activity (except for MIU) than controls. Mean rates fell between periods for all outcomes (except for MIU attendance). The Intention-to-Treat analysis indicated non-statistically significant effect of the intervention in reducing all outcomes, except for MIU attendances, with IRRs: ED attendances, 0.856 (95% CI: 0.631, 1.160); ED admissions, 0.871 (95% CI: 0.628, 1.208); length of stay for emergency and elective admissions: 0.844 (95% CI: 0.619, 1.151) and 0.781 (95% CI: 0.420, 1.454). MIU attendance increased with an IRR: 2.638 (95% CI: 1.041, 6.680). CONCLUSIONS: Telephone-based health coaching appears to be effective in reducing ED attendances and admissions, with shorter lengths of stay, in intervention recipients over controls. Future studies need to capture outcomes beyond acute activity, and better understand how services like this provide added value

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling

Greedy heuristics may be attuned by looking ahead for each possible choice, in an approach called the rollout or Pilot method. These methods may be seen as meta-heuristics that can enhance (any) heuristic solution, by repetitively modifying a master solution: similarly to what is done in game tree search, better choices are identified using lookahead, based on solutions obtained by repeatedly using a greedy heuristic. This paper first illustrates how the Pilot method improves upon some simple well known dispatch heuristics for the job-shop scheduling problem. The Pilot method is then shown to be a special case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the Pilot method, MCTS methods use random completion of partial solutions to identify promising branches of the tree. The Pilot method and a simple version of MCTS, using the $\varepsilon$-greedy exploration paradigms, are then compared within the same framework, consisting of 300 scheduling problems of varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS reaches better or same results as the Pilot methods in this context.Comment: Learning and Intelligent OptimizatioN (LION'6) 7219 (2012

Planetary wave reanalysis using satellite data

A key motivation to use data assimilation for plane-tary science is in order to recover information about day-to-day atmospheric variability, or ‘weather’. Whilst there is no immediate need for a regular weather forecast in most planetary science, data assimilation offers the prospect of a systematic rea-nalysis of past and present spacecraft data. This is especially valuable when, as is often the case, a planet is being observed from only one or two orbital platforms at any one time and synoptic-scale weather system may translate and change signifi-cantly between satellite passes. Observations are often sparse and incomplete. This leads to problems of aliasing and potential ambiguity in a convention-al data analysis