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Concentrating solar thermal systems are widely used for industrial heat production. The state-of-the-art methods use different concepts among which tracking Fresnel mirrors or parabolic troughs. In the market however there are very few devices operating at intermediate to medium temperatures. In order to fill this gap, an innovative concentrating solar thermal system is studied in the frame of the Hellenic — German cooperation project “SCoSCo”. The proposed device concentrates the solar radiation to the receiver using a micro-mirror array. The scope of this paper is to present the optical simulation results of this innovative reflector, and compare the results obtained by different software packages. The optical performance assessed using Solstice ray-tracing software, yields a theoretical optical efficiency from 0.7 to 0.9. In order to evaluate the annual energy yield of the prototype, solar radiation data measurements in Patras, Greece for 2016 and were used as input. A three dimensional thermal model is developed in the COMSOL software to estimate the temperature distribution at the receiver. It was found that temperatures , ranged from 115 to 225 °C. The annual energy yield, calculated using the CARNOT toolbox ranges from 683 to 729 kWh.

The magnitude of soil settlement depends on several variables such as the compression index, Cc, and recompression index, Cr, which are determined by a consolidation test. This laboratory test is time consuming and labor intensive, thus efforts to correlate the compressibility indexes and soil index properties have been made. In this study, soil compressibility prediction models are enhanced by the support vector machine (SVM) algorithm, and the performances of those correlations are tested via field verification in terms of settlement calculation. The field verification portion of the study consists of identifying sites with borings, consolidation tests, and measured settlement from overlying load. Soil layers, within the influence zone of settlement, are tested to obtain soil index parameters. Once a complete soil profile has been established, a series of settlement analyses were performed. Settlement predictions were made based on both predicted and measured Crfor each soil layer. An additional settlement prediction was made using a rule of thumb equating Ccto Cr. The predicted settlements were then compared to the measured settlement taken in close proximity. Upon conclusion, using the Crand Cccorrelations provides comparable settlement predictions compared to measured settlement and the predicted Cr(using a correlation from predicted Cc) exhibits the strongest settlement predictions. The predicted settlements are lower than the measured settlements for both site locations. This leads one to believe that the influence zone of settlement may be deeper than originally considered.

Hashtag hijacking is a form of cyber content attack that arises in modern social media microblogging platforms. It occurs when a hashtag is used for a different purpose than the one originally intended, such as tagging messages with undesirable content and surfacing this content to a target audience. In marketing campaigns, hashtag hijacking can have a significant impact on a company’s brand and/or the success of a marketing campaign. Despite the practical importance of hashtag hijacking and its numerous citations in the popular media, there has been little academic literature dealing with this emerging topic. This paper presents a first study of this phenomenon from a practical yet academically oriented perspective. Namely, it provides (1) a taxonomical classification of the different types of hashtag hijacking attacks; (2) a socio-technical data-mining methodology for detecting it at an early stage; and (3) a proposed response strategy informed by the traditional marketing literature.

This is a follow-up sensitivity study on r-mode gravitational wave signals from newborn neutron stars illustrating the applicability of machine learning algorithms for the detection of long-lived gravitational wave transients. In this sensitivity study, we examine three machine learning algorithms (MLAs): artificial neural networks, support vector machines, and constrained subspace classifiers. The objective of this study is to compare the detection efficiencies that MLAs can achieve to the efficiency of the conventional (seedless clustering) detection algorithm discussed in an earlier paper. Comparisons are made using two distinct r-mode waveforms. For the training of the MLAs, we assumed that some information about the distance to the source is given so that the training was performed over distance ranges not wider than half an order of magnitude. The results of this study suggest that we can use the machine learning algorithms as part of an investigative stage in the pipeline that would be able to provide very fast and solid triggers for further, and more intense, investigation. [ABSTRACT FROM AUTHOR]