The effects of nitrogen deficiencies on the lipid and protein contents of Spirulina platensis

Nitrogen deficiencies were studied in Spirulina platensis (Cyanophyceae) with the aim of determining the effects of the 50 and 100% deficient nitrogen on the lipid and protein contents of the cell under laboratory conditions. S. platensis cultures were grown in Spirulina medium and kept at the constantroom temperature of 26 ± 2°C, illuminated with fluorescent lamps at an irradiance level of 80 ìmol m-2 s-1 with photoperiod 16:8 (L:D) and aerated continuously. In the Spirulina biomass harvested at the stationary phase, 67.4, 53.5, 5.6% protein and 5.78, 13.66, 17.05% lipid were recorded for the groups of control, 50% N(-) and 100% N(-), respectively. The highest lipid content and 1.00 gL-1 dry-weight were recorded from the culture to which treated 100% N(-).Key words: Spirulina platensis, lipid, nitrogen deficiencies, protein

Jointly learning trajectory generation and hitting point prediction in robot table tennis

This paper proposes a combined learning framework for a table tennis robot. In a typical robot table tennis setup, a single striking point is predicted for the robot on the basis of the ball's initial state. Subsequently, the desired Cartesian racket state and the desired joint states at the striking time are determined. Finally, robot joint trajectories are generated. Instead of predicting a single striking point, we propose to construct a ball trajectory prediction map, which predicts the ball's entire rebound trajectory using the ball's initial state. We construct as well a robot trajectory generation map, which predicts the robot joint movement pattern and the movement duration using the Cartesian racket trajectories without the need of inverse kinematics, where a correlation function is used to adapt these joint movement parameters according to the ball flight trajectory. With joint movement parameters, we can directly generate joint trajectories. Additionally, we introduce a reinforcement learning approach to modify robot joint trajectories such that the robot can return balls well. We validate this new framework in both the simulated and the real robotic systems and illustrate that a seven degree-of-freedom Barrett WAM robot performs well

Morphometric properties of the tensor fascia lata muscle in human foetuses

Background: In neonatal and early childhood surgeries such as meningomyelocele repairs, closing deep wounds and oncological treatment, tensor fasciae lata (TFL) flaps are used. However, there are not enough data about structural properties of TFL in foetuses, which can be considered as the closest to neonates in terms of sampling. This study’s main objective is to gather data about morphological structures of TFL in human foetuses to be used in newborn surgery. Materials and methods: Fifty formalin-fixed foetuses (24 male, 26 female) with gestational age ranging from 18 to 30 weeks (mean 22.94 ± 3.23 weeks) were included in the study. TFL samples were obtained by bilateral dissection and then surface area, width and length parameters were recorded. Digital callipers were used for length and width measurements whereas surface area was calculated using digital image analysis software. Results: No statistically significant differences were found in terms of numerical value of parameters between sides and sexes (p > 0.05). Linear functions for TFL surface area, width, anterior and posterior margin lengths were calculated as y = –225.652 + 14.417 × age (weeks), y = –5.571 + 0.595 × age (weeks), y = –4.276 + 0.909 × age (weeks), and y = –4.468 + 0.779 × age (weeks), respectively. Conclusions: Linear functions for TFL surface area, width and lengths can be used in designing TFL flap dimensions in newborn surgery. In addition, using those described linear functions can also be beneficial in prediction of TFL flap dimensions in autopsy studies

Position-dependent mass models and their nonlinear characterization

We consider the specific models of Zhu-Kroemer and BenDaniel-Duke in a sech$^{2}$-mass background and point out interesting correspondences with the stationary 1-soliton and 2-soliton solutions of the KdV equation in a supersymmetric framework.Comment: 8 Pages, Latex version, Two new references are added, To appear in J.Phys.A (Fast Track Communication

Spectrum generating algebras for position-dependent mass oscillator Schrodinger equations

The interest of quadratic algebras for position-dependent mass Schr\"odinger equations is highlighted by constructing spectrum generating algebras for a class of d-dimensional radial harmonic oscillators with $d \ge 2$ and a specific mass choice depending on some positive parameter $\alpha$. Via some minor changes, the one-dimensional oscillator on the line with the same kind of mass is included in this class. The existence of a single unitary irreducible representation belonging to the positive-discrete series type for $d \ge 2$ and of two of them for d=1 is proved. The transition to the constant-mass limit $\alpha \to 0$ is studied and deformed su(1,1) generators are constructed. These operators are finally used to generate all the bound-state wavefunctions by an algebraic procedure.Comment: 21 pages, no figure, 2 misprints corrected; published versio

Solution of spin-boson systems in one and two-dimensional geometry via the asymptotic iteration method

We consider solutions of the $2\times 2$ matrix Hamiltonian of physical systems within the context of the asymptotic iteration method. Our technique is based on transformation of the associated Hamiltonian in the form of the first order coupled differential equations. We construct a general matrix Hamiltonian which includes a wide class of physical models. The systematic study presented here reproduces a number of earlier results in a natural way as well as leading to new findings. Possible generalizations of the method are also suggested.Comment: 13 pages, 5 figures. Please check "http://www1.gantep.edu.tr/~ozer/" for other studies of Nuclear Physics Group at University of Gaziante

USING DEEP LEARNING TO GENERATE FRONT AND BACKYARDS IN LANDSCAPE ARCHITECTURE

The use of artificial intelligence (AI) engines in the design disciplines is a nascent field of research, which became very popular over the last decade. In particular, deep learning (DL) and related generative adversarial networks (GANs) proved to be very promising. While there are many research projects exploring AI in architecture and urban planning, e.g., in order to generate optimal floor layouts, massing models, evaluate image quality, etc., there are not many research projects in the area of landscape architecture - in particular the design of two-dimensional garden layouts. In this paper, we present our work using GANs to generate optimal front- and backyard layouts. We are exploring various GAN engines, e.g., DCGAN, that have been successfully used in other design disciplines. We used supervised and unsupervised learning utilizing a massive dataset of about 100,000 images of front- and backyard layouts, with qualitative and quantitative attributes, e.g., idea and beauty scores, as well as functional and structural evaluation scores. We present the results of our work, i.e., the generation of garden layouts, and their evaluation, and speculate on how this approach may help landscape architects in developing their designs. The outcome of the study may also be relevant to other design disciplines

A new approach to the exact solutions of the effective mass Schrodinger equation

Effective mass Schrodinger equation is solved exactly for a given potential. Nikiforov-Uvarov method is used to obtain energy eigenvalues and the corresponding wave functions. A free parameter is used in the transformation of the wave function. The effective mass Schrodinger equation is also solved for the Morse potential transforming to the constant mass Schr\"{o}dinger equation for a potential. One can also get solution of the effective mass Schrodinger equation starting from the constant mass Schrodinger equation.Comment: 14 page