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A numerical method has been developed to solve of transient Inverse Heat Transfer Problem (IHCP) occurring during transient heat transfer conditions. The method is derived to an optimization problem where the objective function to be minimized includes the quadratic difference of temperature signals measured and estimated in the given locations of a cylindrical bar. The particle Swarm optimization method is used to mind the minimum of the objective function. The Heat Transfer Coefficients (HTC) on selected surfaces of the work piece are estimated. The method has been parallelized and implemented on a Graphic Accelerators architecture. The applicability of the proposed technique is proven by computational tests based on cooling curves obtained during quenching experiments.

During the last decade, mobile phones have become an integral part of urban life. Today the number of mobile phones worldwide is over 1 billion units, and have reached a penetration of over 70% of total adult population in several countries. Mobile devices became an ’extension of human body’ and provide ubiquitous possibilities of nearly anytime/anywhere one-to-one communication. Their footprints in the mobile network could help to understand the socioeconomic aspects of urban life. In this paper, we are applying metrics by which mobility and life style characteristics of urban area can be evaluated. Our proposed approach relies on time series analysis of Call Data Records acquisited by a Hungarian Mobile Telecommunication Operator.

The relevant information of heat trasnfer properties of cooling media required to maintain the heat treatement processes are mostly available in off line sources. Thses data include the cooling curves recorded in quenchnats, predicted Heat Flux or Heat Trasnfer Coefficients and other thermophysical properties. The International Federation for Heat Treatment and Surface Engineering (IFHTSE) initiated a project aiming to collect and share cooling curves acquisited during the quenching processes. The Liquid Quenchant Database is a global project with more the 30 contributors from 14 countries is led by the. The Database of Liquid Quenchants is available on the webpage of the IFHTSE.

By carrying mobile phones, people leaves digital footprints in the mobile phone network that provides rich information about the transportation customs. To understand the behavior of urban environments this data, provided by a Hungarian mobile network operator, is analyzed. The behavior of the city parts has been analyzed using the number of mobile phone activities including calls, texting and data transfer. The proposed approach is to identify regions of a city with similar behavior in mobile phone activity. This paper demonstrates the introduced method using a public demonstration event as a case study. It is clear from the data that the demonstrators caused an increase in the mobile phone network load at the places of the demonstration, so it was possible to identify their path.

We share almost all segments of our lives on the web, including our to-do's, meetings, events related to work or leisure. Not only we share with the world where we are going to be, but also how we are feeling in a given situation and with whom we are attending a certain event. If we ignore the individual aspect and look at a city as a whole, the people, who work and enjoy their free time in the city produce a sensor network. This network is continuously reporting about the current status of the city, in some cases even about its near future. These pieces of information are scattered on social media platforms, but most importantly, they pile up in an unprocessed manner. By collecting this information about a city and especially, about its traffic as a unit, we can expect a relatively accurate picture. If we complement this with data from other sources — such as weather forecast data, which can significantly influence our activities — we can estimate the tendency of urban citizens' movement, predict which traffic junctions can become overladen. In our article, we use the data from latest time period to prove our concept.