The Blakiston’s Fish Owl (Ketupa blakistoni) at north-eastern limits of its range

New data on the distribution were the reported: Buksendya river (153º15’E, 59º12’N), Yama valley (152º59’E, 60º00’N) and Nayakhan river (158º15’E, 62º33’N), mostly single birds in late summer, autumn or early winter. Resident breeding pairs regularly occur only in the Chelomdzha and further to the west – in Inya and Ulbeya valleys, and upper heads of the Kava valley (Fig. 1). New observations in the Inya valley (July-August 1999) and in the Chelomdzha valley (July 2003) have proved that the Blakiston’s Fish Owl dwells in lush flood-plain woods along the middle and lower streams of both of these valleys. Currently, the Blakiston’s Fish Owl steadily occurs within the limits of Kava-Chelomdza forestry of the Magadansky State Reserve (Tarkhov & Potapov 1986), and, most likely, the Chelomdzha valley forms currently the north-eastern limit of the species range. In the Chelomdzha valley the regular duet singing of the Blakiston’s Fish Owl begins from early February. Usually the birds display in the evenings, 20-40 min after sunset. The longevity of evening vocalizations increases from 3-5 min in first week of February to 30-50 min in mid-March. The intervals between strophes vary from 14-55 s, 27 s on average (n = 48). The chicks hatched between 2nd and 5th of May. Daytime hours the parents spend nearby the nest in the crowns of larches. During intense chick’s growth the parents visit the nest 4-5 times in a night. Search for food and hunting takes from 40-60 min. According to photo documents, the parents feed the chicks with sculpins and graylings (18–30 cm in length). The parents spend midnight hours nearby the nest. Becoming 50 days old the chicks leave the nest and roams around supervised by the parents.Neue Erkenntnisse zur Verbreitung und Brutbiologie des Fischuhus (Ketupa blakistoni) an der nordöstlichen Arealgrenze werden hier dargestellt. Im Buksendya- (153º15’E, 59º12’N), Yama- (152º59’E, 60º00’N) und Nayakhan-Flusstal (158º15’E, 62º33’N) lebten meist Einzelvögel im Spätsommer, Herbst und Frühwinter. Brutpaare siedelten an Chelomdzha und westlich an Inya, Kava und Ulbeya. Neue Beobachtungen an Inya (Juli-August 1999) und Chelomdzha (Juli 2003) bestätigten ein regelmäßiges Vorkommen an Mittel- und Unterläufen. In den Wäldern des Kava-Chelomdza im Magadansky State Reserves kam die Art regelmäßig vor (Tarkhov & Potapov 1986). Das Chelomdzha-Flusstal bildet wahrscheinlich die nordöstliche Arealgrenze. Im Duett singende Partner wurden ab Anfang Februar gehört, meist 20-40 Min. nach Sonnenuntergang. Die Gesangsdauer nahm von 3-5 Min. Anfang Februar auf 30-50 Min. Mitte März zu. Die Pausen zwischen den Strophen schwankten zwischen 14-55 Sek. (Mittel: 27 Sek., n = 48). Jungvögel schlüpften zwischen dem 2. und 5. Mai. Die Eltern saßen tagsüber in Nestnähe in Lärchenkronen. Während der größten Wachstumsrate der Jungvögel wurden vier- bis fünfmal pro Nacht Fische von 18-30 cm Länge gefüttert. Die Mitternachtsstunden verbrachten die Eltern in Nestnähe. Nach 50 Tagen verließen die Jungen das Nest und wurden weiter von den Eltern betreut

Isospin dependence of mass-distribution shape of fission fragments of Hg isotopes

Using an improved scission-point model, the mass distributions are calculated for induced fission of even Hg isotopes with mass numbers A=174 to 196. With increasing A of a fissioning AHg nucleus the mass distribution evolves from symmetric for 174Hg, to asymmetric for isotopes close to 180Hg, and back to more symmetric for 192,194,196Hg. In the fissioning Hg isotopes their excitation energy weakly influences the shape of the mass distribution. In 180,184Hg, the mass distributions of fission fragments remain asymmetric even at high excitation energies

Gini coefficient as a life table function

This paper presents a toolkit for measuring and analyzing inter-individual inequality in length of life by Gini coefficient. Gini coefficient and four other inequality measures are defined on the length-of-life distribution. Properties of these measures and their empirical testing on mortality data suggest a possibility for different judgements about the direction of changes in the degree of inequality by using different measures. A new computational procedure for the estimation of Gini coefficient from life tables is developed and tested on about four hundred real life tables. The estimates of Gini coefficient are precise enough even for abridged life tables with the final age group of 85+. New formulae have been developed for the decomposition of differences between Gini coefficients by age and cause of death. A new method for decomposition of age-components into effects of mortality and composition of population by group is developed. Temporal changes in the effects of elimination of causes of death on Gini coefficient are analyzed. Numerous empirical examples show: Lorenz curves for Sweden, Russia and Bangladesh in 1995, proportional changes in Gini coefficient and four other measures of inequality for the USA in 1950-1995 and for Russia in 1959-2000. Further shown are errors of estimates of Gini coefficient when computed from various types of mortality data of France, Japan, Sweden and the USA in 1900-95, decompositions of the USA-UK difference in life expectancies and Gini coefficients by age and cause of death in 1997. As well, effects of elimination of major causes of death in the UK in 1951-96 on Gini coefficient, age-specific effects of mortality and educational composition of the Russian population on changes in life expectancy and Gini coefficient between 1979 and 1989. Illustrated as well are variations in life expectancy and Gini coefficient across 32 countries in 1996-1999 and associated changes in life expectancy and Gini coefficient in Japan, Russia, Spain, the USA, and the UK in 1950-1999. Variations in Gini coefficient, with time and across countries, are driven by historical compression of mortality, but also by varying health and social patterns.inequality, life expectancy, mortality, variability

GigaGauss solenoidal magnetic field inside of bubbles excited in under-dense plasma

Magnetic fields have a crucial role in physics at all scales, from astrophysics to nanoscale phenomena. Large fields, constant or pulsed, allow investigation of material in extreme conditions, opening up plethora of practical applications based on ultra-fast process, and studying phenomena existing only in exotic astro-objects like neutron stars or pulsars. Magnetic fields are indispensable in particle accelerators, for guiding the relativistic particles along a curved trajectory and for making them radiate in synchrotron light sources and in free electron lasers. In the presented paper we propose a novel and effective method for generating solenoidal quasi-static magnetic field on the GigaGauss level and beyond, in under-dense plasma, using screw-shaped high intensity laser pulses. In comparison with already known techniques which typically rely on interaction with over-dense or solid targets, where radial or toroidal magnetic field localized at the stationary target were generated, our method allows to produce gigantic solenoidal fields, which is co-moving with the driving laser pulse and collinear with accelerated electrons. The solenoidal field is quasi-stationary in the reference frame of the laser pulse and can be used for guiding electron beams and providing synchrotron radiation beam emittance cooling for laser-plasma accelerated electron and positron beams, opening up novel opportunities for designs of the light sources, free electron lasers, and high energy colliders based on laser plasma acceleration.Comment: 15 pages, 9 figures. Main text (without abstract, References and Appendix): 12 page

Algorithm for decomposition of differences between aggregate demographic measures and its application to life expectancies, healthy life expectancies, parity-progression ratios and total fertility rates

A general algorithm for the decomposition of differences between two values of an aggregate demographic measure in respect to age and other dimensions is proposed. It assumes that the aggregate measure is computed from similar matrices of discrete demographic data for two populations under comparison. The algorithm estimates the effects of replacement for each elementary cell of one matrix by respective cell of another matrix. Application of the algorithm easily leads to the known formula for the age-decomposition of differences between two life expectancies. It also allows to develop new formulae for differences between healthy life expectancies. In the latter case, each age-component is split further into effects of mortality and effects of health. The application of the algorithm enables a numerical decomposition of the differences between total fertility rates and between parity progression ratios by age of the mother and parity. Empirical examples are based on mortality data from the USA, the UK, West Germany, and Poland and on fertility data from Russia.healthy life expectancy, life expectancy, parity progression

The optimal sequence compression

This paper presents the optimal compression for sequences with undefined values. Let we have $(N-m)$ undefined and $m$ defined positions in the boolean sequence $vv V$ of length $N$. The sequence code length can\u27t be less then $m$ in general case, otherwise at least two sequences will have the same code. We present the coding algorithm which generates codes of almost $m$ length, i.e. almost equal to the lower bound. The paper presents the decoding circuit too. The circuit has low complexity which depends from the inverse density of defined values $D(vv V) = frac{N}{m}$. The decoding circuit includes RAM and random logic. It performs sequential decoding. The total RAM size is proportional to the $$logleft(D(vv V) ight) ,$$ the number of random logic cells is proportional to $$log logleft(D(vv V) ight) * left(log log logleft(D(vv V) ight) ight)^2 .$$ So the decoding circuit will be small enough even for the very low density sequences. The decoder complexity doesn\u27t depend of the sequence length at all

Complexity of Nondeterministic Functions

The complexity of a nondeterministic function is the minimum possible complexity of its determinisation. The entropy of a nondeterministic function, F, is minus the logarithm of the ratio between the number of determinisations of F and the number of all deterministic functions. We obtain an upper bound on the complexity of a nondeterministic function with restricted entropy for the worst case. These bounds have strong applications in the problem of algorithm derandomization. A lot of randomized algorithms can be converted to deterministic ones if we have an effective hitting set with certain parameters (a set is hitting for a set system if it has a nonempty intersection with any set from the system). Linial, Luby, Saks and Zuckerman (1993) constructed the best effective hitting set for the system of k-value, n-dimensional rectangles. The set size is polynomial in k log n / epsilon. Our bounds of nondeterministic functions complexity offer a possibility to construct an effective hitting set for this system with almost linear size in k log n / epsilon

Angular dispersion boost of high order laser harmonics with Carbon nano-rods

Periodic surface gratings or photonic crystals are excellent tools for diffracting light and to collect information about the spectral intensity, if the target structure is known, or about the diffracting object, if the light source is well defined. However, this method is less effective in the case of extreme ultraviolet (XUV) light due to the high absorption coefficient of any material in this frequency range. Here we propose a nanorod array target in the plasma phase as an efficient dispersive medium for the intense XUV light which is originated from laser-plasma interactions where various high harmonic generation processes take place. The scattering process is studied with the help of particle-in-cell simulations and we show that the angular distribution of different harmonics after scattering can be perfectly described by a simple interference theory