Aircraft thrust control

An integrated control system for coaxial counterrotating aircraft propulsors driven by a common gas turbine engine. The system establishes an engine pressure ratio by control of fuel flow and uses the established pressure ratio to set propulsor speed. Propulsor speed is set by adjustment of blade pitch

Perioperative outcomes of a hydrocortisone protocol after endonasal surgery for pituitary adenoma resection

Adrenal insufficiency after transsphenoidal resection of pituitary adenoma (PA) can be seen in 1-12% of cases. In PA, the use of postoperative cortisol measurement and supplementation remains controversial. It is unclear whether postoperative cortisol supplementation has a measurable effect on improving outcomes in patients with pituitary adenoma undergoing endoscopic transsphenoidal surgery (ETS). The objective of the study was to evaluate a postoperative steroid treatment protocol in patients with PA undergoing ETS

State-of-the-Art Survey of Additive Manufacturing Technologies, Methods, and Materials

The rapid pace of development in additive manufacturing (AM) technology, as well as its interdisciplinary and international nature, makes it an extremely difficult subject to present clearly, concisely, and completely. Daily, more progress is being made toward the maturation of the technology and new applications are being found to utilize it. The purpose of this treatise is not to present a rundown of the “latest developments” in AM; it is a serious attempt to survey and present the mechanics, rationale, basic theory, purpose, practical applications, and limitations of additive manufacturing in the context of engineering and science. The mission of this project is to answer the question “what exactly is additive manufacturing?” In order to answer this, it is essential to look past the media celebration and “cool, revolutionary technology” label. These days it is common to see news items and articles in technical magazines praising 3D printing and AM as the “3rd Industrial Revolution” or a “miracle technology” that is going to solve all of the world’s problems; are these claims true or simple media hype? To find out just how well-developed and the technology really is and to provide a basis for future research, a very extensive literature review will be performed and the results will be summarized and organized into this paper. The best available references will be utilized, particularly peer- reviewed journal articles, original process patents, industry standards, recent technical conference proceedings, and books written by well-respected authorities on the subject.Ope

Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

Rule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call “causal forces.” Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF’s forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF’s rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals

Marked overlap of four genetic syndromes with dyskeratosis congenita confounds clinical diagnosis

Financial support provided by The Medical Research Council-MR/K000292/1, Children with Cancer- 2013/144 and Blood Wise-14032 (AJW, LC, SC, AE, TV, HT and ID). KMG is supported by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre

The effect of static scanning and mobility training on mobility in people with hemianopia after stroke: A randomized controlled trial comparing standardized versus non-standardized treatment protocols

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Selecting and Ranking Time Series Models Using the NOEMON Approach

Abstract. In this work, we proposed to use the NOEMON approach to rank and select time series models. Given a time series, the NOEMON approach provides a ranking of the candidate models to forecast that series, by combining the outputs of different learners. The best ranked models are then returned as the selected ones. In order to evaluate the proposed solution, we implemented a prototype that used MLP neural networks as the learners. Our experiments using this prototype revealed encouraging results.

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

Short-term Building Energy Model Recommendation System: A Meta-learning Approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency