CONDITIONS OF MONOTONE APPROXIMATION OF RAMSEY CURVES AND THEIR MODIFICATIONS

The algorithm for approximating the experimental data of the Ramsey curve and its modifications has been developed, which provides a monotonic increase of the approximating function in the interval [0;\infty) and an existence of a given number of inflection points. The Ramsey curve belongs to the family of logistic curves that are widely used in modeling of limited increasing processes in various subject fields. The classical Ramsey curve has two parameters and has a left constant asymmetry. It is also known that its three-parameter modification provides the possibility of displacement along the axes of ordinate. The extensive practical use of the Ramsey curve with both two and more parameters for approximating experimental dependences is restrained by the frequent loss by this curve of the logistic shape when approximating without additional restrictions on the relationships between its parameters. The article discusses modifications of the Ramsey curve with three and five parameters. The first and second derivatives of the studied modifications of the Ramsey function have a special structure. They are products of polynomial and exponential functions. This allows using Sturm's theorem on the number of polynomial roots in a given interval to control the shape of the approximating curve. It has been shown that with an increase in the number of parameters for the modified curve, the number of possible combinations of restrictions on the values of the parameters ensuring the preservation of its like shape increases significantly. The solution to the approximation problem in this case consists of solving a sequence of conditional global optimization problems with different constraints and choosing a solution that provides the smallest approximation error. Also, the studies of the accuracy of estimating the parameters of the Ramsey curve in accordance with the accuracy of the experimental data have been carried out. In order to simulate the presence of measurement errors, the values of a normally distributed random variable with a mathematical expectation equal to zero and different values of the standard deviation for different series of computational experiments were added to the values of the deterministic sequence. Computational experiments have shown a significant sensitivity of the values of the Ramsey function parameters to the measurement accuracy of experimental data.

An Image Encryption Algorithm Using Logistic Map with Plaintext-Related Parameter Values

This paper deals with a plaintext-related image encryption algorithm that modifies the parameter values used by the logistic map according to plain image pixel intensities. The parameter values are altered in a row-wise manner, which enables the usage of the same procedure also during the decryption. Furthermore, the parameter modification technique takes into account knowledge about the logistic map, its fixed points and possible periodic cycles. Since the resulting interval of parameter values achieves high positive values of Lyapunov exponents, the chaotic behavior of the logistic map should be most pronounced. These assumptions are verified by a set of experiments and the obtained numerical values are compared with those reported in relevant papers. It is found that the proposed design that uses a simpler, but well-studied, chaotic map with mitigated issues obtains results comparable with algorithms that use more complex chaotic systems. Moreover, the proposed solution is much faster than other approaches with a similar purpose.

Synthesis and characterization of perovskite-supported CoNi catalyst for CO oxidation via exsolution

The introduction of perovskite oxide as catalysts alternative has increased the worldwide interest due to its advantages such as its versatility to accommodate different transition metals. This study set out to evaluate the catalytic activity of CO oxidative perovskite catalysts (LCCNTO), fabricated via solid-state method and reduced under various reducing condition for the exsolution of the active metals, Cobalt-Nickel (CoNi) from the perovskite lattice. The effect of reducing parameter modification towards the catalytic activity of the fabricated LCCNTO was discussed in terms of CO conversion and CO2 production rate. Through the light-off test, the sample that reduced with the longest deration (S2T10H6-R5H5) showed the highest CO conversion of 45.45% and CO2 production rate of 0.1409 × 10−4 mol s− 1g−1 at the reaction temperature of 500 °C. Not only that, it was discovered that by controlling the reducing duration, the initiate temperature for the reaction to occur was lowered from 360 °C (S2T10H6-R5H3) enabling the reaction to occur at lower temperature at 280 °C in S2T10H6-R5H5. Under the same reducing temperature, the CO2 production of sample reduced for 200 minutes (S2T10H6-R5H3) started at 360 °C but as the reducing duration increased to 300 minutes (S2T10H6-R5H5), the CO oxidation initiated at a much lower temperature of 280 °C. Although LCCNTO catalyst still suffer from similar deterioration as the other reported base metal catalyst, but tuning the reducing duration given to a sample, it greatly affects the initiation temperature for the reaction to occur.

CaPTure: Calcium PeakToolbox for analysis of in vitro calcium imaging data

ABSTRACTBackgroundCalcium imaging is a powerful technique for recording cellular activity across large populations of neurons. However, analysis methods capable of single-cell resolution in cultured neurons, especially for cultures derived from human induced pluripotent stem cells (hiPSCs), are lacking. Existing methods lack scalability to accommodate high-throughput comparisons between multiple lines, across developmental timepoints, or across pharmacological manipulations.ResultsWe developed a scalable, automated Ca2+ imaging analysis pipeline called CaPTure (https://github.com/LieberInstitute/CaPTure). This method detects neurons, classifies and quantifies spontaneous activity, quantifies synchrony metrics, and generates cell- and network-specific metrics that facilitate phenotypic discovery. The method is compatible with parallel processing on computing clusters without requiring significant user input or parameter modification.ConclusionCaPTure allows for rapid assessment of neuronal activity in cultured cells at cellular resolution, rendering it amenable to high-throughput screening and phenotypic discovery. The platform can be applied to both human- and rodent-derived neurons and is compatible with many imaging systems.

Electronic Differential System Based on Adaptive SMC Combined with QP for 4WID Electric Vehicles

This study investigates an adaptive differential control system for 4WID (4-wheel-independent-drive) electric vehicles. The novel adaptive system will maneuver the independently operating hub motors without the help of any conventional steering mechanism. The control system consists of a hierarchical structure to confront the vehicle stability condition, which includes a novel SMC (sliding mode control) with a fuzzy algorithm parameter modification to achieve the required virtual control signal at the top level, and a quadratic programming-based torque allocation algorithm at the bottom-level controller. The proposed controller was tested through Simulink/Carsim simulation and experiments. All the test cases showed the advantages of the proposed method over some of the currently existing 4WID control strategies.

Fractional Derivative Modification of Drude Model

A novel, two-parameter modification of a Drude model, based on fractional time derivatives, is presented. The dielectric susceptibility is calculated analytically and simulated numerically, showing good agreement between theoretical description and numerical results. The absorption coefficient and wave vector are shown to follow a power law in the frequency domain, which is a common phenomenon in electromagnetic and acoustic wave propagation in complex media such as biological tissues. The main novelty of the proposal is the introduction of two separate parameters that provide a more flexible model than most other approaches found in the literature. Moreover, an efficient numerical implementation of the model is presented and its accuracy and stability are examined. Finally, the model is applied to an exemplary soft tissue, confirming its flexibility and usefulness in the context of medical biosensors.

A microstructure-based parameterization of the effectivetransverse isotropic elasticity tensor of snow, firn, and porous ice

<p>Effective elastic properties of snow, firn, and porous ice are key for<br>various applications and influenced by ice volume fraction and<br>different types of anisotropy. The geometrical anisotropy of the ice-matrix created by temperature gradient metamorphism in low-density<br>snow and firn and the crystallographic anisotropy commonly created<br>upon deformation in high-density, porous ice. Towards a quantitative-distinction of the impact of the different anisotropies on elasticity,<br>we derived a parametrization for the effective elasticity tensor over<br>the entire range of volume fractions as a function of density and<br>geometrical anisotropy. We employed FEM simulations on 395 X-ray<br>tomography microstructures of Lab, Alpine, Arctic, and Antarctic<br>samples. We employed an empirical two-parameter modification of the<br>anisotropic Hashin Shtrikman bounds to obtain a closed-form<br>parametrization accounting for density, anisotropy, and the correct<br>limiting behavior for bubbly ice. We compare our prediction to<br>previous parametrizations derived in limited density regimes and we<br>utilize the Thomson parameter to compare the geometrical-elastic<br>anisotropy to the crystallographic-elastic anisotropy of<br>monocrystalline ice. Our results suggest that a coupled treatment of<br>geometrical and crystallographic effects would be beneficial for a<br>careful interpretation of acoustic measurements in deep firn.</p>