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

Hybrid Link Prediction Model

In network science several topology--based link prediction methods have been developed so far. The classic social network link prediction approach takes as an input a snapshot of a whole network. However, with human activities behind it, this social network keeps changing. In this paper, we consider link prediction problem as a time--series problem and propose a hybrid link prediction model that combines eight structure-based prediction methods and self-adapts the weights assigned to each included method. To test the model, we perform experiments on two real world networks with both sliding and growing window scenarios. The results show that our model outperforms other structure--based methods when both precision and recall of the prediction results are considered

Link prediction methods and their accuracy for different social networks and network metrics

Currently, we are experiencing a rapid growth of the number of social–based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches that could provide a better prediction accuracy in social networked structures extracted from large–scale data about people and their activities and interactions, the existing methods are not comprehensively analysed. Presented in this paper, research focuses on the link prediction problem in which in a systematic way, we investigate the correlation between network metrics and accuracy of different prediction methods. For this study we selected six time–stamped real world social networks and ten most widely used link prediction methods. The results of our experiments show that the performance of some methods have a strong correlation with certain network metrics. We managed to distinguish ’prediction friendly’ networks, for which most of the prediction methods give good performance, as well as ’prediction unfriendly’ networks, for which most of the methods result in high prediction error. The results of the study are a valuable input for development of a new prediction approach which may be for example based on combination of several existing methods. Correlation analysis between network metrics and prediction accuracy of different methods may form the basis of a metalearning system where based on network characteristics and prior knowledge will be able to recommend the right prediction method for a given network at hand

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

The detection of lubricating oil viscosity changes in gearbox transmission systems driven by sensorless variable speed drives using electrical supply parameters

Lubrication oil plays a decisive role to maintain a reliable and efficient operation of gear transmissions. Many offline methods have been developed to monitor the quality of lubricating oils. This work focus on developing a novel online method to diagnose oil degradation based on the measurements from power supply system to the gearbox. Experimental studies based on an 10kW industrial gearbox fed by a sensorless variable speed drive (VSD) shows that measurable changes in both static power and dynamic behaviour are different with lube oils tested. Therefore, it is feasible to use the static power feature to indicate viscosity changes at low and moderate operating speeds. In the meantime, the dynamic feature can separate viscosity changes for all different tested cases

Offshore Code Comparison Collaboration, Continuation: Phase II Results of a Floating Semisubmersible Wind System: Preprint

Offshore wind turbines are designed and analyzed using comprehensive simulation tools that account for the coupled dynamics of the wind inflow, aerodynamics, elasticity, and controls of the turbine, along with the incident waves, sea current, hydrodynamics, and foundation dynamics of the support structure. The Offshore Code Comparison Collaboration (OC3), which operated under the International Energy Agency (IEA) Wind Task 23, was established to verify the accuracy of these simulation tools [1]. This work was then extended under the Offshore Code Comparison Collaboration, Continuation (OC4) project under IEA Wind Task 30 [2]. Both of these projects sought to verify the accuracy of offshore wind turbine dynamics simulation tools (or codes) through code-to-code comparison of simulated responses of various offshore structures. This paper describes the latest findings from Phase II of the OC4 project, which involved the analysis of a 5-MW turbine supported by a floating semisubmersible. Twenty-two different organizations from 11 different countries submitted results using 24 different simulation tools. The variety of organizations contributing to the project brought together expertise from both the offshore structure and wind energy communities. Twenty-one different load cases were examined, encompassing varying levels of model complexity and a variety of metocean conditions. Differences in the results demonstrate the importance and accuracy of the various modeling approaches used. Significant findings include the importance of mooring dynamics to the mooring loads, the role nonlinear hydrodynamic terms play in calculating drift forces for the platform motions, and the difference between global (at the platform level) and local (at the member level) modeling of viscous drag. The results from this project will help guide development and improvement efforts for these tools to ensure that they are providing the accurate information needed to support the design and analysis needs of the offshore wind community

Plausible role of estrogens in pathogenesis, progression and therapy of lung cancer

Malignant neoplasms are among the most common diseases and are responsible for the majority of deaths in the developed world. In contrast to men, available data show a clear upward trend in the incidence of lung cancer in women, making it almost as prevalent as breast cancer. Women might be more susceptible to the carcinogenic effect of tobacco smoke than men. Furthermore, available data indicate a much more frequent mutation of the tumor suppressor genep53 in non-small cell lung cancer (NSCLC) female patients compared to males. Another important factor, however, might lie in the female sex hormones, whose mitogenic or carcinogenic effect is well known. Epidemiologic data show a correlation between hormone replacement therapy (HRT) or oral contraceptives (OCs), and increased mortality rates due to the increased incidence of malignant tumors, including lung cancer. Interestingly, two types of estrogen receptors have been detected in lung cancer cells: ERα and ERβ. The presence of ERα has been detected in tissues and non-small-cell lung carcinoma (NSCLC) cell lines. In contrast, overexpression of ERβ is a prognostic marker in NSCLC. Herein, we summarize the current knowledge on the role of estrogens in the etiopathogenesis of lung cancer, as well as biological, hormonal and genetic sex-related differences in this neoplasm