Performance of a 28-inch ramjet utilizing gaseous hydrogen at a mach number of 3.6, angles of attack up to 12 deg, and pressure altitudes up to 110,000 feet

Combustion efficiency of gaseous hydrogen fuel for ramjets at very high altitudes and high distortio

Probabilistic Approach to Structural Change Prediction in Evolving Social Networks

We propose a predictive model of structural changes in elementary subgraphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network analyzed in short, one day-long time windows, and reveals distinctive patterns of evolution of connections on the level of local network topology. We argue that the network investigated in such short timescales is highly dynamic and therefore immune to classic methods of link prediction and structural analysis, and show that in the case of complex networks, the dynamic subgraph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server

Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

© 2013 IEEE. The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward

Base Flow Characteristics for Several Four-Clustered Rocket Configurations at Mach Numbers from 2.0 to 3.5

A generalized study of base flow phenomena has been conducted with four 500-pound-thrust JP-4 fuel-liquid-oxygen rocket motors installed in the base of a 12-inch-diameter cylindrical model. Data were obtained over a Mach number and nozzle pressure ratio range of 2.0 to 3.5 and 340 to 600, respectively. Base heat flux, gas temperature, and pressure were highest in the center of the cluster core and decreased in a radial direction. Although a maximum heat flux of 93 Btu per square foot per second was measured within the cluster core, peripheral heat fluxes were low, averaging about 5 Btu per square foot per second for all configurations. Generally base heat flux was found to be independent of Mach number over the range investigated. Base heat flux within the cluster core was decreased by increasing motor spacing, motor extension, a combination of increasing nozzle area ratio and decreasing exit angle and gimbaling the two side engines. Small amounts of nitrogen injected within the cluster core sharply reduced core heat flux

Analysis of Offshore Wind Energy Leasing Areas for the Rhode Island/Massachusetts Wind Energy Area

The National Renewable Energy Laboratory (NREL), under an interagency agreement with the Bureau of Ocean Energy Management (BOEM), is providing technical assistance to BOEM on the identification and delineation of offshore leasing areas for offshore wind energy development within the Atlantic Coast Wind Energy Areas (WEAs) established by BOEM in 2012. This report focuses on NREL's evaluation of BOEM's Rhode Island/Massachusetts (RIMA) WEA leasing areas. The objective of the NREL evaluation was to assess the proposed delineation of the two leasing areas and determine if the division is reasonable and technically sound. Additionally, the evaluation aimed to identify any deficiencies in the delineation. As part of the review, NREL performed the following tasks: 1. Performed a limited review of relevant literature and RIMA call nominations. 2. Executed a quantitative analysis and comparison of the two proposed leasing areas 3. Conducted interviews with University of Rhode Island (URI) staff involved with the URI Special Area Management Plan (SAMP) 4. Prepared this draft report summarizing the key findings

A satellite-based snow cover climatology (1985–2011) for the European Alps derived from AVHRR data

Seasonal snow cover is of great environmental and socio-economic importance for the European Alps. Therefore a high priority has been assigned to quantifying its temporal and spatial variability. Complementary to land-based monitoring networks, optical satellite observations can be used to derive spatially comprehensive information on snow cover extent. For understanding long-term changes in alpine snow cover extent, the data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensors mounted onboard the National Oceanic and Atmospheric Association (NOAA) and Meteorological Operational satellite (MetOp) platforms offer a unique source of information. <br><br> In this paper, we present the first space-borne 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985–2011. The objective of this study is twofold: first, to generate a new set of cloud-free satellite snow products using a specific cloud gap-filling technique and second, to examine the spatiotemporal distribution of snow cover in the European Alps over the last 27 yr from the satellite perspective. For this purpose, snow parameters such as snow onset day, snow cover duration (SCD), melt-out date and the snow cover area percentage (SCA) were employed to analyze spatiotemporal variability of snow cover over the course of three decades. On the regional scale, significant trends were found toward a shorter SCD at lower elevations in the south-east and south-west. However, our results do not show any significant trends in the monthly mean SCA over the last 27 yr. This is in agreement with other research findings and may indicate a deceleration of the decreasing snow trend in the Alpine region. Furthermore, such data may provide spatially and temporally homogeneous snow information for comprehensive use in related research fields (i.e., hydrologic and economic applications) or can serve as a reference for climate models

Mercury, silver and selenium in serum before and after removal of amalgam restorations: results from a prospective cohort study in Norway

Objective A prospective cohort study on changes of health complaints after removal of amalgam restorations was carried out at the request of the Norwegian Directorate of Health. The aim was to provide and evaluate experimental treatment to patients with health complaints attributed to dental amalgam fillings. Methods Patients (n = 32) with medically unexplained physical symptoms (MUPS), which were attributed to dental amalgam restorations had all their amalgam restorations removed and replaced with other dental restorative materials. Samples of blood were collected before and 1 year after removal of the fillings, and concentration of inorganic mercury (I-Hg), methylmercury (MeHg), silver (Ag) and selenium (Se) in serum was determined by inductively coupled plasma–sector field mass spectrometry. The comparison groups (one with MUPS but without attribution to amalgam [n = 28] and one group of healthy individuals [n = 19]) received no treatment. The participants responded to questionnaires at baseline and at follow-up after 1 and 5 years. Results Concentration of I-Hg and Ag in serum decreased significantly after removal of all amalgam restorations. Concentration of MeHg and Se in serum were not changed. Intensity of health complaints was significantly reduced after amalgam removal, but there were no statistically significant correlations between exposure indicators and health complaints. Conclusions Removal of all amalgam restorations is followed by a decrease of concentration of I-Hg and Ag in serum. The results support the hypothesis that exposure to amalgam fillings causes an increase of the daily dose of both I-Hg and Ag. Even though intensity of health complaints decreased after removal of all amalgam restorations there was no clear evidence of a direct relationship between exposure and health complaints

Inferring Actual Treatment Pathways from Patient Records

Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. This study aims to infer the actual treatment steps for a particular patient group from administrative health records (AHR) - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. We introduce Defrag, a method for examining AHRs to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability and compare against baselines. We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag method, and to compare Defrag to several baselines where it significantly outperforms non-NN-based methods. Defrag significantly outperforms several existing pathway-inference methods and offers an innovative and effective approach for inferring treatment pathways from AHRs. Open-source code is provided to encourage further research in this area