What One Can Learn From the Cloud Condensation Nuclei (CCN) Size Distributions as Monitored by the BEO Moussala?

In this proceeding we report initial studies into the big data set acquired by the Cloud Condensation Nuclei (CCN) counter of the Basic Environmental Observatory (BEO) Moussala over the whole 2016 year at a frequency of 1 Hz. First, we attempt to reveal correlations between the results for CCN number concentrations on the timescale of a whole year (2016) as averaged over 12 month periods with the meteorological parameters for the same period and with the same time step. Then, we zoom into these data and repeat the study on the timescale of a month for two months from 2016, January and July, with a day time step. For the same two months we show the CCN size distributions averaged over day periods. Finally, we arrive at our main result: typical, in terms of maximal and minimal number concentrations, CCN size distributions for chosen hours, one hour for each month of the year, hence 24 distributions in total. These data show a steady pattern of peaks and valleys independent of the concrete number concentration which moves up and down the number concentrations (y-axis) without significant shifts along the sizes (x-axis).Comment: 6 pages, 4 figure, The 10th Jubilee Conference of the Balkan Physical Union (BPU10), 26-30 August, Sofia, Bulgari

Alignment of the ALICE Inner Tracking System with cosmic-ray tracks

37 pages, 15 figures, revised version, accepted by JINSTALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 micron in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10^5 charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.Peer reviewe

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods