New and interesting lichen records from the Ural Mountains, Russia

Ten species of lichenized ascomycetes are reported from the Urals. Aspicilia spermatomanes, Fuscidea praeruptorum, Lepra excludens, L. monogona, Metamelanea caesiella and Pertusaria amarescens are new to Russia while Bryobilimbia ahlesii, Lecanora orosthea, L. rouxii and Tephromela grumosa are new for the Urals. Our records considerably extend the ranges or fill gaps in the formerly disjunctive distributions of these species. The morphology, secondary chemistry and ecology of the species are discussed.

Lichens and allied fungi from the Pechenga district and surroundings (Lapponia Petsamoënsis, Murmansk Region, Russia)

168 species of lichens are specified for the Pechenga district and surroundings. Microcalicium ahlneri and Placidium norvegicum are new for the Murmansk Region. 18 species are new for Lapponia Petsamoënsis. Stereocaulon dactylophyllum is included into the Red Data Book of the Russian Federation. Caloplaca diphyodes, Dermatocarpon meiophyllizum, Haematomma ochroleucum, Phlyctis argena and Stereocaulon dactylophyllum are included into the Red Data Book of the Murmansk Region

Using machine learning technologies to solve the problem of classifying infrasound background monitoring signals

It is widely known that among sound signals generated by natural and anthropogenic phenomena, the most long-lived are waves of frequency less than 20 Hz, called infrasound. This property allows tracking at a distance by infrasound monitoring the occurrence of high-energy events on regional scales (up to 200–300 km). At the same time, the separation of useful infrasound signals from background noise is a non-trivial task in real-time and post-facto signal processing. In this paper we propose a new method for classification of specific signals in infrasound monitoring data using Shannon permutation entropy and vectors of frequency distribution of occurrence frequencies of permutations of consecutive sample values of rank 3 (number of permutation elements). To evaluate the validity of the proposed entropy-based classification method, two machine learning methods — random forest method and classical neural network approach — implemented in Python language using Scikit-lean, TensorFlow and Keras libraries were used. The classification quality was evaluated against the traditional frequency-based method of class extraction based on Fourier transform. Recognition was performed on the prepared infrasound monitoring data in the Altai Republic. The results of computational experiment on the separation of 5 classes of signals showed that classification by the proposed method gives the same results of recognition by neural network with in comparison with frequency classification of the original data; the recognition accuracy was 51–58 %. For the random forests method, the recognition accuracy of frequency classes was slightly higher: 51 % vs. 45 % for classes using the permutation entropy method. The analysis of the results of the computational experiment shows sufficient competitiveness of the method of classification by permutation entropy in the recognition of infrasound signals. In addition, the proposed method is much easier to implement for inline signal processing in lowconsumption microcontroller systems. The next step is to test the method at infrasound signal registration points and as part of the infrasound monitoring data processing system for real-time event detection

Toward a warmer Arctic Ocean: Spreading of the early 21st century Atlantic Water warm anomaly along the Eurasian Basin margins

We document through the analysis of 2002–2005 observational data the recent Atlantic Water (AW) warming along the Siberian continental margin due to several AW warm impulses that penetrated into the Arctic Ocean through Fram Strait in 1999–2000. The AW temperature record from our long-term monitoring site in the northern Laptev Sea shows several events of rapid AW temperature increase totaling 0.8°C in February–August 2004. We hypothesize the along-margin spreading of this warmer anomaly has disrupted the downstream thermal equilibrium of the late 1990s to earlier 2000s. The anomaly mean velocity of 2.4–2.5 ± 0.2 cm/s was obtained on the basis of travel time required between the northern Laptev Sea and two anomaly fronts delineated over the Eurasian flank of the Lomonosov Ridge by comparing the 2005 snapshot along-margin data with the AW pre-1990 mean. The magnitude of delineated anomalies exceeds the level of pre-1990 mean along-margin cooling and rises above the level of noise attributed to shifting of the AW jet across the basin margins. The anomaly mean velocity estimation is confirmed by comparing mooring-derived AW temperature time series from 2002 to 2005 with the downstream along-margin AW temperature distribution from 2005. Our mooring current meter data corroborate these estimations

Investigation of the Behavior of Hydrogen in the Aluminum Alloy in the Manufacture of Small Pigs at the Aluminum Plant UC RUSAL

В статье приведены результаты исследований динамики насыщения алюминия и его сплавов водородом в технологической схеме от алюминиевого электролизера до литейного конвейера в условиях Саяногорского алюминиевого завода ОК РУСАЛ. Показано, что одним из основных источников насыщения расплава алюминия водородом является его взаимодействие с влагой воздуха при открытых переливах металла в процессе его движения от электролизера до литейного конвейера. По результатам обследования предложены технические решения, направленные на снижение концентрации водорода в расплаве, которые составят предмет дальнейших исследованийThe results of studies of aluminum saturation dynamics and its alloys with hydrogen in the technological scheme of the electrolytic aluminum to the casting assembly line in a steel plant RUSAL. It was shown that one of the basic aluminum melt saturation hydrogen source is its interaction with moisture of air in open metal modulations during its movement from the electrolyzer molds. According to a survey of proposed technical solutions to reduce the hydrogen concentration in the melt during further investigation

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

Transverse momentum spectra of charged particles in proton-proton collisions at s=900\sqrt{s} = 900 GeV with ALICE at the LHC

The inclusive charged particle transverse momentum distribution is measured in proton-proton collisions at $\sqrt{s} = 900$ GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region $(|\eta|<0.8)$ over the transverse momentum range $0.15<p_{\rm T}<10$ GeV/$c$. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for $|\eta|<0.8$ is $\left<p_{\rm T}\right>_{\rm INEL}=0.483\pm0.001$ (stat.) $\pm0.007$ (syst.) GeV/$c$ and \left_{\rm NSD}=0.489\pm0.001 (stat.) $\pm0.007$ (syst.) GeV/$c$, respectively. The data exhibit a slightly larger $\left<p_{\rm T}\right>$ than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET.Comment: 20 pages, 8 figures, 2 tables, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/390

The ALICE experiment at the CERN LHC

ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 161626 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008