Multi‐diagnostic multi‐model ensemble forecasts of aviation turbulence

Turbulence is one of the major weather hazards to aviation. Studies have shown that clear‐air turbulence may well occur more frequently with future climate change. Currently the two World Area Forecast Centres use deterministic models to generate forecasts of turbulence. It has been shown that the use of multi‐model ensembles can lead to more skilful turbulence forecasts. It has also been shown that the combination of turbulence diagnostics can also produce more skilful forecasts using deterministic models. This study puts the two approaches together to expand the range of diagnostics to include predictors of both convective and mountain wave turbulence, in addition to clear‐air turbulence, using two ensemble model systems. Results from a 12 month global trial from September 2016 to August 2017 show the increased skill and economic value of including a wider range of diagnostics in a multi‐diagnostic multi‐model ensemble

An experimental and analytical study of visual detection in a spacecraft environment, 1 July 1968 - 1 July 1969

Predicting star magnitude which can be seen with naked eye or sextant through spacecraft windo

EffiTest: Efficient Delay Test and Statistical Prediction for Configuring Post-silicon Tunable Buffers

At nanometer manufacturing technology nodes, process variations significantly affect circuit performance. To combat them, post- silicon clock tuning buffers can be deployed to balance timing bud- gets of critical paths for each individual chip after manufacturing. The challenge of this method is that path delays should be mea- sured for each chip to configure the tuning buffers properly. Current methods for this delay measurement rely on path-wise frequency stepping. This strategy, however, requires too much time from ex- pensive testers. In this paper, we propose an efficient delay test framework (EffiTest) to solve the post-silicon testing problem by aligning path delays using the already-existing tuning buffers in the circuit. In addition, we only test representative paths and the delays of other paths are estimated by statistical delay prediction. Exper- imental results demonstrate that the proposed method can reduce the number of frequency stepping iterations by more than 94% with only a slight yield loss.Comment: ACM/IEEE Design Automation Conference (DAC), June 201

Service Evaluation of 'Living Well with the Impact of Cancer' Courses

The aim of the Penny Brohn Cancer Care Living Well Service Evaluation was to measure the level of benefit that participants were receiving from the Penny Brohn Cancer Care(PBCC)Living Well course and to inform current and future service provision at PBCC. The Penny Brohn Whole Person Approach model(PB-WPA model), which underpins the Living Well course, was designed to support the ‘whole person’ and the course was intended to meet the needs of people with cancer, as identified by the National Cancer Survivorship Initiative (NCSI). The combined qualitative and quantitative results of the Living Well Service Evaluation have demonstrated, very clearly at times, that participants were highly satisfied with the course. The immediate benefit of attending was measurable, in terms of improved health related quality of life (HRQoL) and improved MYCaW (Measure yourself Concerns and Wellbeing) concerns and wellbeing. The evaluation results show that the Living Well course experience enabled the majority of participants to regain control over many aspects of their life, and to start taking responsibility for their health. The following aspects of the course were identified as the most helpful: -Specific units of ‘education and explanation’ about cancer and why healthy lifestyle changes to areas such as diet, exercise and relaxation are beneficial -Advice and education from medical doctors -The opportunity to share experiences with other participants For some, this empowerment led to long-term changes in exercise, food consumption,use of self-help techniques and the ability to communicate more freely and openly with family, friends and medical professionals. These improvements were reflected in the 12 month outcome data, where a sustained improvement in HRQoL and MYCaW concerns was reported by many clients. Such patient reported outcome measures (PROMs) are limited in what they can measure, thus qualitative data were also collected to ensure that participants were able to share their experiences (positive or negative) of the Living Well course, and their subsequent experiences of applying the education and techniques learnt on the course. A picture emerged that identified difficulties in sustaining lifestyle changes at around the 3-6 month follow-up. Participants who returned to PBCC within the 12 month follow-up period, however, were more likely to benefit by reporting a greater improvement in HRQoL and MYCaW scores, plus an improved understanding of how to make and maintain healthy lifestyle changes to suit their individual circumstances. In regards to the current NCSI priorities, it is hoped that the data reported in this evaluation go some way to informing the following: -Information and support from the point of diagnosis -Managing the consequences of treatment -Promoting recovery -Sustaining recovery -Supporting people with active and advanced disease -Improving survivorship intelligence Finally, this report demonstrates how a patient-centred model of support can be effectively evaluated to provide relevant, practical and evidence-based information to commissioners. Participant satisfaction: Participants were very satisfied with the course content, course delivery and resources provided which often exceeded their needs and expectations. Participant outcomes: The PB-WPA model successfully encompassed and supported all the types of concerns participants arrived with. The most frequently reported participant concerns were psychological and emotional,about their wellbeing and about their physical health. On average, participants experienced statistically and clinically significant improvements in their MYCaW concern and wellbeing scores, and total HRQoL scores,which remained improved over the 12 month follow-up. The aspects of HRQoL that were most likely to improve after attending the Living Well course were spiritual, emotional and functional wellbeing. Supporters had their own profile of concerns, namely psychological and emotional,supporter specific concerns and practical concerns. Concerns were as severely rated as those from participants with a diagnosis of cancer and also showed statistically significant average improvements throughout the 12 month follow-up. The small group of participants with metastatic disease reported significant improvements in their MYCaW concern scores, in line with the whole evaluation group,and a significantly greater improvement in HRQoL over 12 months compared to participants with primary cancer.Participants who returned for more support from PBCC were in more need of support than those who did not return. They were more likely to have poorer HRQoL at baseline and rate their concerns more severely. Participants who returned to PBCC experienced more improvement in HRQoL that was likely to be clinically significant. These participants also had a greater degree of improvement in their MYCaW concerns, compared to non-returners. Over half of the participants experienced new concerns over the 12 month follow-up period. Concerns were most frequently associated with psychological and emotional and physical issues. Furthermore, at 12 months, participants were still experiencing arange of health issues

The alternating least-squares algorithm for CDPCA

Clustering and Disjoint Principal Component Analysis (CDP CA) is a constrained principal component analysis recently proposed for clustering of objects and partitioning of variables, simultaneously, which we have implemented in R language. In this paper, we deal in detail with the alternating least-squares algorithm for CDPCA and highlight its algebraic features for constructing both interpretable principal components and clusters of objects. Two applications are given to illustrate the capabilities of this new methodology

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201

Integrative Whole Person Oncology Care in the UK

The term ‘whole person cancer care’ - an approach that addresses the needs of the person as well as treating the disease - is more widely understood in the UK than its synonym ‘integrative oncology”. The National Health Service (NHS), provides free access to care for all, which makes it harder to prioritise NHS funding of whole person medicine, where interventions may be multi-modal and lacking in cost-effectiveness data. Despite this, around 30% of cancer patients are known to use some form of complementary or alternative medicine (CAM). This is virtually never medically led, and usually without the support or even the knowledge of their oncology teams, with the exception of one or two large cancer centres. UK oncology services are, however, starting to be influenced from three sides; firstly, by well-developed and more holistic palliative care services; secondly, by directives from central government via the sustainable healthcare agenda; and thirdly, by increasing pressure from patient-led groups and cancer charities. CAM remains unlikely to be provided through the NHS, but nutrition, physical activity, mindfulness, and stress management are already becoming a core part of the NHS ‘Living With and Beyond Cancer’ agenda. This supports cancer survivors into stratified pathways of care, based on individual, self-reported holistic needs and risk assessments, which are shared between healthcare professionals and patients. Health and Wellbeing events are being built into cancer care pathways, designed to activate patients into self-management and support positive lifestyle change. Those with greater needs can be directed towards appropriate external providers, where many examples of innovative practice exist. These changes in policy and vision for the NHS present an opportunity for Integrative Oncology to develop further and to reach populations who would, in many other countries, remain underserved or hard-to-reach by whole person approaches

High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that Maximum Lik elihood inference is deeply related to Principal Component Analysis when the amp litude of the pattern components, xi, is negligible compared to N^1/2. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in xi/N^1/2. We stress that it is important to generalize the Hopfield model and include both attractive and repulsive patterns, to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size, N and of the amplitude, xi. The inference approach is illustrated on synthetic and biological data.Comment: Physical Review E: Statistical, Nonlinear, and Soft Matter Physics (2011) to appea

Mesoscopic Model for Free Energy Landscape Analysis of DNA sequences

A mesoscopic model which allows us to identify and quantify the strength of binding sites in DNA sequences is proposed. The model is based on the Peyrard-Bishop-Dauxois model for the DNA chain coupled to a Brownian particle which explores the sequence interacting more importantly with open base pairs of the DNA chain. We apply the model to promoter sequences of different organisms. The free energy landscape obtained for these promoters shows a complex structure that is strongly connected to their biological behavior. The analysis method used is able to quantify free energy differences of sites within genome sequences.Comment: 7 pages, 5 figures, 1 tabl