Air pollution and health are closely connected. An efficient management of air quality involves the collecting and analysis of a wide variety of data types that make cyberinfrastructures especially important for this scientific field. In the new information-driven world, a cyberinfrastructure allows to bring people together, high performance computational platforms, data integration techniques, visualization, and analysis tools, with the aim to make the research more effective and efficient. Currently, there is an important requirement for the development of information networks that are specific to air quality management, promoting the exchange and integration of data and applications. The paper presents the developing of a cyberinfrastructure for air quality management to protect children's health in Romania developed by ROKIDAIR research project. This infrastructure combines already existing air quality monitoring systems with new modes of data processing and displaying to be accessed by the concerned parents of children.

Pollutants' data, meteorological data and medical data feeds the relational database with information from vulnerable urban areas (i.e., Targoviste and Ploiesti), which will help the running of algorithms between air quality, meteorology and health effects and later use of forecasted outputs to forecast health effects. Several computerbased tools were developed to facilitate the population of the database with speci ic data from various sources. One of these tools allows the automatic capturing of pollutants' concentrations from web-based of icial sources. The relational database structure integrates the ields for the required variables in the attributed data tables (PM 2.5 and carried compounds/metals sub-database, meteorological sub-database and medical sub-database). The main criteria in selecting the respiratory illnesses that are linked to atmospheric pollution for children are the wheezing. The medical database contains as main ields: the number of wheezing episodes, number of asthma attacks (with hospitalization), the response to inhalation medication, medication controller, eosinophil count, serum level of E immunoglobins (lgE), and residential address and school/kindergarten address of the children. The presented database structure and adjacent tools are expected to improve the current monitoring methodology of air pollutants, mainly respirable dusts, and their content in various compounds in correlation with children's health.

In order for automatic microinjection to serve biomedical and genetic research, we have designed and manufactured a PDMS-based sensor with a circular section channel using the microwire molding technique. For the very precise control of microfluidic transport, we developed a microfluidic pulse width modulation system (MPWM) for automatic microinjections at a picoliter level. By adding a computer-aided detection and tracking of fluid-specific elements in the microfluidic circuit, the PDMS microchannel sensor became the basic element in the automatic control of the microinjection sensor. With the PDMS microinjection sensor, we precise measured microfluidic volumes under visual detection, assisted by very precise computer equipment (with precision below 1 μm) based on image processing. The calibration of the MPWM system was performed to increase the reproducibility of the results and to detect and measure microfluidic volumes. The novel PDMS-based sensor system for MPWM measurements of microfluidic volumes contributes to the advancement of intelligent control methods and techniques, which could lead to new developments in the design, control, and in applications of real-time intelligent sensor system control.

SUMMARYThe paper deals with MHD flow in pipes with arbitrary wall conductivity under the influence of a transverse magnetic field. We employ the pseudospectral collocation method for obtaining a numerical solution of the problem. The numerical results are compared with analytical ones in the case of pipe with insulating walls. We notice that the magnetic field is slowing the motion.

Air pollution is an everyday issue, very relevant to public authorities, requiring control and monitoring to provide data for decision-making policies. The objective of this study was to evaluate the air quality in Ploiesti city, Romania and to observe the advantages and limitations of the some statistical methods used in forecasting air quality. Data for six air quality parameters collected at monitoring stations in Ploiesti during the 2013 year were statistically analyzed. Principal component analysis (PCA) was used to provide a relevant description in factors that can be explained in terms of different sources of air pollution. The measured pollutants data were statistically analyzed using the auto-regressive integrated moving average (ARIMA) method in order to assess the efficiency of using this method in forecasting the environmental air quality. The results revealed that ARIMA method has some limitations and do not produce satisfactory results for certain air pollutants such as PM10 and CO, even the forecasted period is short. By comparison, the ARIMA model obtained for NOx , NO2 , or O3 time series, provides good results, with relative errors around 5%.