In this paper, we studied synchronous references (references in the same papers) and diachronous citations (citations to the papers) of the renowned theoretical physicist, Homi Jehangir Bhabha. We ...utilized the Science Citation Index (SCI) on CD from 1982 – 2006. We identified his coauthors, his most cited works, citation identity (authors whom he had cited) and citation image makers (authors citing him). Bhabha published his first paper in 1933 at the age of 24 and the last paper in 1966, the year of his demise. His 66 papers could be categorized into nine fields: Cosmic ray physics (18 papers); Elementary particle physics, and Field theory (14 papers each); Quantum electrodynamics (6 papers); Nuclear physics (4 papers); General, and Interaction of radiation with matter (3 papers each); and Mathematical physics and General physics (2 papers each). He collaborated with 18 authors in 18 papers; the remaining 48 papers were single-authored. His citation identity consisted of 212 different authors, of whom six were Nobel laureates. He received 328 citations to his works, his overall citation rate being 10.6 per cited paper and the highest citation rate was 21.2 to his papers in the field of Elementary particle physics. His most often cited paper was in Quantum electrodynamics with 54 citations. His image makers comprised 537 different authors. Bhabha has the distinction of being cited by at least two Nobel laureates, P.M.S. Blackett and H. Yukawa in their respective Nobel lectures.
Statistical characteristics of pressure drop fluctuations have been investigated experimentally in order to obtain fundamental data for flow pattern identification of two-phase flow in a horizontal ...pipe. The experimental results show that the horizontal flow of nitrogen gas (or air) -water mixtures exhibits peculiar features of statistical properties (PDF, PSD), that its flow patterns can be classified by using the statistical features, and that the pressure drop fluctuations measured at a short interval of pipe have high sensitivity to flow change. The results suggest that it is possible to identify the flow patterns of horizontal two-phase flow on the basis of the features of statistical parameters (standard deviation and coefficients of skewness and excess).
Gender identification from video is an emerging research field that aims to automatically classify the gender of individuals based on video data. Due to the numerous applications for this task, it ...has received a lot of attention, including surveillance, human-computer interaction, and targeted marketing. In this study, we propose a gender identification system that utilizes the Pelican optimizer algorithm in combination with a Support Vector Machine (SVM) classifier. The Pelican optimizer is a metaheuristic algorithm inspired by the hunting behaviour of pelicans and has shown promising results in solving optimization problems. Pelican optimizer algorithm (POA) is applied to optimize the SVM parameter selection process such as kernel function. The POA algorithm searches for an optimal subset of parameters that maximizes the classification performance of the SVM model after the application of preprocessing and feature extraction techniques such as Local Binary Pattern (LBP). Finally, the selected optimized parameters otherwise known as POA-SVM classifier learns a decision boundary based on the labeled training data. The POA-SVM model is trained to distinguish between male and female samples and generalize the classification to unseen video data. Experimental evaluations are conducted using a benchmark dataset consisting of video samples with labelled gender information. The effectiveness of the suggested system is contrasted with other cutting-edge gender identification techniques. The results demonstrate the effectiveness of the Pelican Optimization Algorithm-SVM system, showing improved accuracy of 95%, and sensitivity of 94.4% at a faster recognition rate in gender classification from video data.
ABSTRACT
We attempt to model and visualize the main characteristics of cracks produced on the surface of a desiccating crusted soil: their patterns, their different widths and depths and their ...dynamics of creation and evolution. In this purpose we propose a method to dynamically produce three‐dimensional (3D) quasi‐static fractures, which takes into account the characteristics of the soil. The main originality of this method is the use of a 3D discrete propagation of ‘shrinkage volumes’ with respect to 2D precalculated paths. In order to get realistic cracks, we newly propose to take into account a possibly inhomogeneous thickness of the shrinking layer by using a watershed transformation to compute these paths. Moreover, we use the waterfall algorithm in order to introduce in our simulation a hierarchy notion in the cracks appearance, which is therefore linked with the initial structure of the surface. In this paper, this method is presented in detail and a validation of the cracks patterns by a comparison with real ones is given.
Early myoclonic encephalopathy Kamate, Mahesh; Mahantshetti, Niranjana; Chetal, Vivek
Indian pediatrics,
09/2009, Volume:
46, Issue:
9
Journal Article
Peer reviewed
Early myoclonic encephalopathy (EME) is a rare malignant epileptic syndrome. The erratic myoclonus with or without focal motor seizures, onset before 3 months of age, and persistent suppression-burst ...pattern in electroencephalograph (EEG) are accepted as the diagnostic criteria for EME. We report an 11 month old infant with EME which was secondary to non-ketotic hyperglycinemia.
This paper proposes a variable thickness radome for application in millimeter-wave (mm-wave) radar at missile warhead, so as to figure out the radiation distortion caused by uniform thickness radome ...(UTR). By choosing a appropriate condition of wall thickness of radome, the adverse effect of field reflection and phase delay on radiation pattern is minimized. Moreover, the radome thickness is further optimized through simulation, by which the ripple variation of main beam is effectively mitigated and radiation pattern become more symmetric. Simulated results show that the radiation performance of the 24-GHz off-center antenna array with our proposed radome has been improved a lot in terms of beam symmetry and broadside gain level.
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A liquid droplet on a rigid polydimethylsiloxane (PDMS) substrate exhibits a higher receding contact angle (θr), therefore, recedes earlier than its softer counterpart. The ...three-phase contact line of a suspension droplet on a composite rigid-soft PDMS substrate can be selectively tuned wherein the contact line recedes on the rigid substrate sooner and approaches toward the softer side, with microparticles eventually being deposited in the softer substrate region.
A composite PDMS substrate containing soft cores of various shapes (circular and non-circular) surrounded by rigid matrices was fabricated by employing 3D printing and soft lithography. A sessile suspension droplet containing spherical microparticles was deposited on the composite substrate and evaporated under ambient conditions. The evaporation dynamics was recorded and analyzed.
Evaporation-induced patterning (in circular, triangular, and rectangular areas) with sizes ranging from microns to millimetres were obtained. For the first time, by varying the ratio of the rigid-soft regions in the PDMS substrate, we were able to obtain different deposition sizes and shapes from an identical droplet. Instead of using lithographically patterned substrate, our simple methodology by using 3D printing and soft lithography opened up a new avenue for patterning microparticles based on a rigid-soft composite substrate.
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of ...the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.
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