Reformulation of the strong-field approximation for light-matter interactions

We consider the interaction of hydrogen-like atoms with a strong laser field and show that the strong field approximation and all its variants may be grouped into a set of families of approximation schemes. This is done by introducing an ansatz describing the electron wave packet as the sum of the initial state wave function times a phase factor and a function which is the perturbative solution in the Coulomb potential of an inhomogeneous time-dependent Schr\"odinger equation. It is the phase factor that characterizes a given family. In each of these families, the velocity and length gauge version of the approximation scheme lead to the same results at each order in the Coulomb potential. By contrast, irrespective of the gauge, approximation schemes belonging to different families give different results. Furthermore, this new formulation of the strong field approximations allows us to gain deeper insight into the validity of the strong field approximation schemes. In particular, we address two important questions: the role of the Coulomb potential in the output channel and the convergence of the perturbative series in the Coulomb potential. In all the physical situations we consider here, our results are compared to those obtained by solving numerically the time-dependent Schr\"odinger equation.Comment: 19 pages, 9 figures, submitted for publicatio

The effect of natural antioxidant(s) on date palm (Phoenix dactylifera L.) in vitro

Date palm (Phoenix dactylifera L.) is one of the most valuable economic resources in the Middle East and North Africa that grow on monocotyledonous trees. To increase crop yield of palm trees, in vitro micro-propagation has become an attractive alternative for large-scale production of date palm. A problem that frequently damages tissues in the early micro-propagation is the brown color that advances in the callus culture due to the creation of quinones. Quinones seize plant cellular developments which lead to cellular decay. This study advocates the use of antioxidant factors found in spinach, kale and strawberries within various concentrations (50, 150 and 300 mg/L) with respect to the medium culture, in an attempt to reduce the level of total phenol and browning which occurs, and also to improve growth and development in different in vitro stages of date palm (P. dactylifera L.). The results indicate that better growth value of callus was achieved using 150 mg/L of kale concentration; allowing the total phenol level to be reduced to 0.9237 mg/g D.W, presenting a significant growth value in comparison to the other treatments in the embryonic callus stage. In the date palm’s somatic embryogenesis stage, the results show that the use of 50 mg/L of spinach, 50 mg/L of kale, 150 mg/L of strawberries, achieved a high number of somatic embryos and the total phenol level was reduced to 0.6167 mg/g D.W. Results from date palm shoot proliferation shows that high numbers of shoot (16.3) was achieved using 50 to 300 mg/L of kale; however, total phenol level was reduced to 0.04567 at 150 mg/L of spinach concentration. The fluctuation of reducing total phenol level in date palm was recorded when the explants were grown on medium supplemented with 50 mg/L of kale concentration.Keywords: Date palm, tissue culture, natural antioxidants, browning, quionones.African Journal of Biotechnology, Vol 13(31) 3366-337

Implementation of a machine learning algorithm for sentiment analysis of Indonesia’s 2019 presidential election

In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing “Jokowi and Prabowo.” We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%

Aningre (Aningeria spp.) tannin extract characterization and performance as an adhesive resin

International audienceAs aningre tree species are very abundant in central Africa, are rapid growing and have a very high percentage yield of tannin, aningre tannin extract was characterized using attenuated total reflectance Fourier transform (ATR-FT MIR) spectra in the 1800 and 600 cm(-1) range and Matrix Assisted Laser Desorption Ionisation Time of Flight (MALDI-TOF) mass spectrometry. These two characterization methods have proven that aningre tannin is a procyanindin/prodelphinidin composed of catechin, gallogatechin as well as galloylated catechin and gallocatechin units. Moreover, oligomers presenting the simultaneous combination of methoxy groups and linked glucose have been observed, these not having been observed before in bark and wood extracted tannins. Two resin formulations were developed with this extract. The Modulus of Elasticity (MOE) was studied by thermomechanical analysis and wood particleboards were prepared bonded with these resins

Time scaling with efficient time-propagation techniques for atoms and molecules in pulsed radiation fields

We present an ab initio approach to solve the time-dependent Schr\"odinger equation to treat electron and photon impact multiple ionization of atoms or molecules. It combines the already known time scaled coordinate method with a new high order time propagator based on a predictor-corrector scheme. In order to exploit in an optimal way the main advantage of the time scaled coordinate method namely that the scaled wave packet stays confined and evolves smoothly towards a stationary state the modulus square of which being directly proportional to the electron energy spectra in each ionization channel, we show that the scaled bound states should be subtracted from the total scaled wave packet. In addition, our detailed investigations suggest that multi-resolution techniques like for instance, wavelets are the most appropriate ones to represent spatially the scaled wave packet. The approach is illustrated in the case of the interaction of an one-dimensional model atom as well as atomic hydrogen with a strong oscillating field.Comment: 26 pages, 9 figures, to be published in Physical Review

Automated diagnosis of diabetes using entropies and diabetic index

Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals