The Statistical Properties of Superfluid Turbulence in 4^4He from the Hall-Vinen-Bekharevich-Khalatnikov Model

We obtain the von K\'arm\'an-Howarth relation for the stochastically forced three-dimensional Hall-Vinen-Bekharvich-Khalatnikov (3D HVBK) model of superfluid turbulence in Helium ($^4$He) by using the generating-functional approach. We combine direct numerical simulations (DNSs) and analyitcal studies to show that, in the statistically steady state of homogeneous and isotropic superfluid turbulence, in the 3D HVBK model, the probability distribution function (PDF) $P(\gamma)$, of the ratio $\gamma$ of the magnitude of the normal fluid velocity and superfluid velocity, has power-law tails that scale as $P(\gamma) \sim \gamma^3$, for $\gamma \ll 1$, and $P(\gamma) \sim \gamma^{-3}$, for $\gamma \gg 1$. Furthermore, we show that the PDF $P(\theta)$, of the angle $\theta$ between the normal-fluid velocity and superfluid velocity exhibits the following power-law behaviors: $P(\theta)\sim \theta$ for $\theta \ll \theta_*$ and $P(\theta)\sim \theta^{-4}$ for $\theta_* \ll \theta \ll 1$, where $\theta_*$ is a crossover angle that we estimate. From our DNSs we obtain energy, energy-flux, and mutual-friction-transfer spectra, and the longitudinal-structure-function exponents for the normal fluid and the superfluid, as a function of the temperature $T$, by using the experimentally determined mutual-friction coefficients for superfluid Helium $^4$He, so our results are of direct relevance to superfluid turbulence in this system.Comment: 12 pages, 3 figure

Face Biometric Cloud Authentication Access Using Extreme Learning Class Specific Linear Discriminant Regression Classification Method

The Extreme Learning Class Specific Linear Discriminant Regression Classification used in this proposed system aims at improving the accuracy and recognition rate of the face biometric identification for secured cloud access. The accuracy is improved by maximizing and minimizing the reconstruction error. The between class reconstruction error (BCRE) and within-class reconstruction error (WCRE) are the two values simultaneously increased and decreased for every sample to provide improved accuracy. By selecting the suitable value of WCRE, the learned projection matrix for the discriminant subspace is identified. The class specific representation is implemented for the label created in feature vector to further improve the efficiency of identifying a face. Based on the classification results given by the proposed EL-CSLDRC method, an efficient access of secured data from the big data cloud system is promoted

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment

Precoding-Based Network Alignment For Three Unicast Sessions

We consider the problem of network coding across three unicast sessions over a directed acyclic graph, where each sender and the receiver is connected to the network via a single edge of unit capacity. We consider a network model in which the middle of the network only performs random linear network coding, and restrict our approaches to precoding-based linear schemes, where the senders use precoding matrices to encode source symbols. We adapt a precoding-based interference alignment technique, originally developed for the wireless interference channel, to construct a precoding-based linear scheme, which we refer to as as a {\em precoding-based network alignment scheme (PBNA)}. A primary difference between this setting and the wireless interference channel is that the network topology can introduce dependencies between elements of the transfer matrix, which we refer to as coupling relations, and can potentially affect the achievable rate of PBNA. We identify all possible such coupling relations, and interpret these coupling relations in terms of network topology and present polynomial-time algorithms to check the presence of these coupling relations. Finally, we show that, depending on the coupling relations present in the network, the optimal symmetric rate achieved by precoding-based linear scheme can take only three possible values, all of which can be achieved by PBNA.Comment: arXiv admin note: text overlap with arXiv:1202.340

Novel indole-2-carboxylic acid analogues: Synthesis and a new light in to their antioxidant potentials

Two series of novel indole-2-carboxylic acid derivatives is reported. In the first series, N-substituted derivatives (3a-h) were synthesized via acylation of indole-2-carboxylic acid followed by aldol condensation reaction. Whereas, in the second series, indole-2-carboxamides (5a-g) were synthesized through conversion of acid to its acid chloride followed by coupling of substituted anilines. Structures of the newly synthesized compounds were confirmed by elemental analysis and spectral IR, 1H NMR and mass data and were screened for antioxidant activity. Among the first series, compound 3g showed higher antioxidant activity and whereas, in the second series compounds 5b and 5c exhibited potential antioxidant activity. Compounds 3g, 5b and 5c exhibited for its enhanced antioxidant activity

Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent Topic, Sentiment and Emotion Attributes

This paper presents a large, labelled dataset on people's responses and expressions related to the COVID-19 pandemic over the Twitter platform. From 28 January 2020 to 1 Jan 2021, we retrieved over 132 million public Twitter posts (i.e., tweets) from more than 20 million unique users using four keywords: "corona", "wuhan", "nCov" and "covid". Leveraging natural language processing techniques and pre-trained machine learning-based emotion analytic algorithms, we labelled each tweet with seventeen latent semantic attributes, including a) ten binary attributes indicating the tweet's relevance or irrelevance to the top ten detected topics, b) five quantitative emotion intensity attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from barely noticeable to extremely high intensity), and c) two qualitative attributes indicating the sentiment category and the dominant emotion category the tweet is mainly expressing. We report the descriptive statistics around the topic, sentiment and emotion attributes, and their temporal distributions, and discuss the dataset's possible usage in communication, psychology, public health, economics, and epidemiology research.Comment: Updated with the complete 2020 data (28 Jan 2020-1 Jan 2021