The aim of this study was to compare survival and growth of juvenile narrow-clawed crayfish (Astacus leptodactylus) and signal crayfish (Pacifastacus leniusculus) fed only with experimental pellets (45% of proteins, 6% of fat, 20% crude fibre) under controlled conditions. The animals were reared in 12 (50 l) circular plastic tanks, the narrow-clawed crayfish set-ups being designated A1 and A2, the signal crayfish set-ups P1 and P2. The initial stocking densities for each species were 600 (A1, P1) and 1,200 (A2, P2) juveniles stage 2 per square metre for each set-up in three replicates. The experiment lasted 92 days under continuous photoperiod (L:D 24:0; 100 LUX) conditions at a temperature of 22.0 ± 0.1°C and an oxygen saturation > 90%. One shelter (plastic tube) was provided for 2 crayfish in each set-up.The highest survival rate was obtained for signal crayfish from set-up P1 (47.5%), the lowest for narrow-clawed crayfish from set-up A2 (22.8%). Crayfish survival evidently depends on the stocking density: in set-ups A1 and P1 it was about 16% higher than in set-ups A2 and P2 (P < 0.05). Mortality was significantly higher (P < 0.05) in both species during the first 30 days of the experiment (48-77% of the total mortality) than during the subsequent two months of the experiment (< 20% of the total mortality).The final body size was the largest in narrow-clawed crayfish from set-up A1 (799 mg, 29.2 mm) and the smallest in signal crayfish from set-up P2 (534 mg, 26.5 mm). Specimens of narrow-clawed crayfish were larger than signal crayfish, and the specimens of both species in set-ups A1 and P1 were larger than those in set-ups A2 and P2. During the first 30 days of the experiment the specific growth rate (SGR) of both species in all set-ups was twice the value reported during the subsequent two months (P < 0.05). La survie des écrevisses dépend évidemment de la densité de départ: dans les lots A1 et P1, la survie est supérieure de 16 % par rapport à celle des lots A2 et P2 (P < 0,05). La mortalité a été significativement plus importante (P < 0,05) pendant les 30 premiers jours de l'expérience (48-77 % de la mortalité totale) que pendant les deux derniers mois (< 20 % de la mortalité totale). Key-words:Les écrevisses à pattes grêles du lot A1 ont présenté les tailles corporelles les plus grandes (799 mg ; 29,2 mm) alors que les plus petites ont été enregistrées dans le lot P2 (534 mg ; 26,5 mm). D'une façon générale, les écrevisses à pattes grêles étaient plus grandes que les écrevisses signal et les individus des lots A1 et P1 étaient aussi plus grands que ceux des lots A2 et P2. Pendant les 30 premiers jours de l'expérience, le taux de croissance spécifique (SGR) de chaque espèce (A1, A2, P1, P2) était plus important que celui mesuré dans les deux mois suivants (P < 0,05).

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Fourteen easily recognizable stages of embryonic, larval and juvenile development are described in order to provide a model for early ontogeny in bream (Abramis brama L.). The proposed developmental stages are applicable both in labo ratory studies and in field work, enabling quantitative description of the course and rate of bream early ontogeny and give the possibility to use statistical analysis (i.e. comparison of mean length-at-developmental stage). Early development of bream was studied in a wide range of rearing temperatures (13.5-34.0 0c) and food supply (four food regimes), the two most important environmental factors influencing fi sh survival and development. In the lowest and highest test temperatures, the same stage was ob served in fish of significantly smaller size. Fish reared under different feeding regi mes differed in size when they reached particular developmental stages. Generally, fish kept in disadvantageous conditions (i.e. semi-lethal temperature, poor quality of food) attained the successive developmental stages at smaller size.

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Acta Technologica Agriculturae 1/2016Dušan Páleš et al.The most effective way for determination of curves for practical use is to use a set of control points. These control points can be accompanied by other restriction for the curve, for example boundary conditions or conditions for curve continuity (Sederberg, 2012). When a smooth curve runs only through some control points, we refer to curve approximation. The B-spline curve is one of such approximation curves and is addressed in this contribution. A special case of the B-spline curve is the Bézier curve Rédl et al., 2014). The B-spline curve is applied to a set of control points in a space, which were obtained by measurement of real vehicle movement on a slope (Rédl, 2007(Rédl, , 2008. Data were processed into the resulting trajectory (Rédl, 2012;Rédl and Kučera, 2008). Except for this, the movement of the vehicle was simulated using motion equations (Rédl, 2003;Rédl and Kročko, 2007). B-spline basis functionsBézier basis functions known as Bernstein polynomials are used in a formula as a weighting function for parametric representation of the curve (Shene, 2014). B-spline basis functions are applied similarly, although they are more complicated. They have two different properties in comparison with Bézier basis functions and these are: 1) solitary curve is divided by knots, 2) basis functions are not nonzero on the whole area. Every B-spline basis function is nonzero only on several neighbouring subintervals and thereby it is changed only locally, so the change of one control point influences only the near region around it and not the whole curve.These numbers are called knots, the set U is called the knot vector, and the half-opened interval 〈u i , u i + 1 ) is the i-th knot span. Seeing that knots u i may be equal, some knot spans may not exist, thus they are zero. If the knot u i appears p times, hence u i = u i + 1 = ... = u i + p -1 , where p >1, u i is a multiple knot of multiplicity p, written as u i (p). If u i is only a solitary knot, it is also called a simple knot. If the knots are equally spaced, i.e. (u i + 1 -u i ) = constant, for every 0 ≤ i ≤ (m -1), the knot vector or knot sequence is said uniform, otherwise it is non-uniform.Knots can be considered as division points that subdivide the interval 〈u 0 , u m 〉 into knot spans. All B-spline basis functions are supposed to have their domain on 〈u 0 , u m 〉. We will use u 0 = 0 and u m = 1.To define B-spline basis functions, we need one more parameter k, which gives the degree of these basis functions. Recursive formula is defined as follows:This definition is usually referred to as the Cox-de Boor recursion formula. If the degree is zero, i.e. k = 0, these basis functions are all step functions that follows from Eq. (1). N i, 0 (u) = 1 is only in the i-th knot span 〈u i , u i + 1 ). For example, if we have four knots u 0 = 0, u 1 = 1, u 2 = 2 and u 3 = 3, knot spans 0, 1 and 2 are 〈0, 1), 〈1, 2) and 〈2, 3), and the basis functions of degree 0 are N 0, 0 (u) = 1 on interval 〈0, 1) Acta In this co...