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Background
True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).
Results
Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.
Conclusions
Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.
An enzyme system organized in a flow device was used to mimic a reversible Controlled NOT (CNOT) gate with two input and two output signals. Reversible conversion of NAD⁺ and NADH cofactors was used to perform a XOR logic operation, while biocatalytic hydrolysis of p-nitrophenyl phosphate resulted in an Identity operation working in parallel. The first biomolecular realization of a CNOT gate is promising for integration into complex biomolecular networks and future biosensor/biomedical applications.
In any books about genetics it can still today be read that our genetic code is called “degenerate” because it is still believed that 43 = 64 triplets encode the 20 essential amino acids. Indeed we have to assume the inverse law, what means that 34 = 81 exact code positions are really effective for our genetic code and encode the amino acids, compiled to proteins. This very important discovery leads to two completely new results that are limits-overlooking: 1) 34 (=81) genetic code positions mean exactly the same number as there are stable and naturally existing chemical elements in our universe. This famous argument should now lead to some alternative, as well as new fundamental conclusions about our existence. 2) A genetic code positioning system shows that nature is much smarter than expected: mutations are made less dangerous than believed, because they won't be that easily able any more to cause severe damages in the protein-synthesis. This should also lead to some alternative views upon evolution of life.
In this study, a high-speed chemical imaging system was developed for visualization of the interior of a microfluidic channel. A microfluidic channel was constructed on the sensor surface of the light-addressable potentiometric sensor (LAPS), on which the ion concentrations could be measured in parallel at up to 64 points illuminated by optical fibers. The temporal change of pH distribution inside the microfluidic channel was recorded at a maximum rate of 100 frames per second (fps). The high frame rate allowed visualization of moving interfaces and plugs in the channel even at a flow velocity of 111 mm/s, which suggests the feasibility of plug-based microfluidic devices for flow-injection analysis (FIA).
We study the estimation of some linear functionals which are based on an unknown lifetime distribution. The observations are assumed to be generated under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. Under this setup a semi-parametric estimator of the survival function was introduced by the author. If the parametric model assumption is correct, it is known that the estimated functional which is based on this semi-parametric estimator is asymptotically at least as efficient as the corresponding one which rests on the nonparametric Kaplan–Meier estimator.
In this paper we show that the estimated functional which is based on this semi-parametric estimator is asymptotically efficient with respect to the class of all regular estimators under this semi-parametric model.
This article describes the fabrication, characterization and application of an epidermal temporary-transfer tattoo-based potentiometric sensor, coupled with a miniaturized wearable wireless transceiver, for real-time monitoring of sodium in the human perspiration. Sodium excreted during perspiration is an excellent marker for electrolyte imbalance and provides valuable information regarding an individual's physical and mental wellbeing. The realization of the new skin-worn non-invasive tattoo-like sensing device has been realized by amalgamating several state-of-the-art thick film, laser printing, solid-state potentiometry, fluidics and wireless technologies. The resulting tattoo-based potentiometric sodium sensor displays a rapid near-Nernstian response with negligible carryover effects, and good resiliency against various mechanical deformations experienced by the human epidermis. On-body testing of the tattoo sensor coupled to a wireless transceiver during exercise activity demonstrated its ability to continuously monitor sweat sodium dynamics. The real-time sweat sodium concentration was transmitted wirelessly via a body-worn transceiver from the sodium tattoo sensor to a notebook while the subjects perspired on a stationary cycle. The favorable analytical performance along with the wearable nature of the wireless transceiver makes the new epidermal potentiometric sensing system attractive for continuous monitoring the sodium dynamics in human perspiration during diverse activities relevant to the healthcare, fitness, military, healthcare and skin-care domains.
Shakedown analysis of Reissner-Mindlin plates using the edge-based smoothed finite element method
(2014)
This paper concerns the development of a primal-dual algorithm for limit and shakedown analysis of Reissner-Mindlin plates made of von Mises material. At each optimization iteration, the lower bound of the shakedown load multiplier is calculated simultaneously with the upper bound using the duality theory. An edge-based smoothed finite element method (ES-FEM) combined with the discrete shear gap (DSG) technique is used to improve the accuracy of the solutions and to avoid the transverse shear locking behaviour. The method not only possesses all inherent features of convergence and accuracy from ES-FEM, but also ensures that the total number of variables in the optimization problem is kept to a minimum compared with the standard finite element formulation. Numerical examples are presented to demonstrate the effectiveness of the present method.