On the quantum and classical scattering times due to charged dislocations in an impure electron gas

We derive the ratio of transport and single particle relaxation times in three and two - dimensional electron gases due to scattering from charged dislocations in semiconductors. The results are compared to the respective relaxation times due to randomly placed charged impurities. We find that the ratio is larger than the case of ionized impurity scattering in both three and two-dimensional electron transport.Comment: 4 pages, 3 figure

Giant Magnetic Moments of Nitrogen Stabilized Mn Clusters and Their Relevance to Ferromagnetism in Mn Doped GaN

Using first principles calculations based on density functional theory, we show that the stability and magnetic properties of small Mn clusters can be fundamentally altered by the presence of nitrogen. Not only are their binding energies substantially enhanced, but also the coupling between the magnetic moments at Mn sites remains ferromagnetic irrespective of their size or shape. In addition, these nitrogen stabilized Mn clusters carry giant magnetic moments ranging from 4 Bohr magnetons in MnN to 22 Bohr magnetons in Mn_5N. It is suggested that the giant magnetic moments of Mn_xN clusters may play a key role in the ferromagnetism of Mn doped GaN which exhibit a wide range (10K - 940K) of Curie temperatures

Phase transition and hybrid star in a SU(2) chiral sigma model

We use a modified SU(2) chiral sigma model to study nuclear matter at high density using mean field approach. We also study the phase transition of nuclear matter to quark matter in the interior of highly dense neutron stars. Stable solutions of Tolman-Oppenheimer-Volkoff equations representing hybrid stars are obtained with a maximum mass of 1.69 $M_{\odot}$, radii around 9.3 kms and a quark matter core constituting nearly 55-85 % of the star radii.Comment: 19 pages, 9 figures, accepted for Mod. Phys. Letts.

Friction force on slow charges moving over supported graphene

We provide a theoretical model that describes the dielectric coupling of a 2D layer of graphene, represented by a polarization function in the Random Phase Approximation, and a semi-infinite 3D substrate, represented by a surface response function in a non-local formulation. We concentrate on the role of the dynamic response of the substrate for low-frequency excitations of the combined graphene-substrate system, which give rise to the stopping force on slowly moving charges above graphene. A comparison of the dielectric loss function with experimental HREELS data for graphene on a SiC substrate is used to estimate the damping rate in graphene and to reveal the importance of phonon excitations in an insulating substrate. A signature of the hybridization between graphene's pi plasmon and the substrate's phonon is found in the stopping force. A friction coefficient that is calculated for slow charges moving above graphene on a metallic substrate shows an interplay between the low-energy single-particle excitations in both systems.Comment: 13 pages, 5 figures, submitted to Nanotechnology for a special issue related to the NGC 2009 conference (http://asdn.net/ngc2009/index.shtml

Primary Carcinosarcoma of Ovary an Unusual Tumor Case Report with Review of Literature

Primary ovarian carcinosarcoma is a rare biphasic tumor. There is variable admixture of both malignant epithelial and stromal component seen in this tumor. We report a case of a primary carcinosarcoma of ovary in a 72‑year‑old post‑menopausal female presenting with the complaint of abdominal distension. Staging laparotomy was done for this patient, and final histopathology was reported as the carcinosarcoma of ovary. The epithelial and sarcomatous components showed immunohistochemical positivity for their respective markers.Keywords: Malignant mixed Mullerian tumor, ovary, primary carcinosarcom

Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning.

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management

The medial entorhinal cortex is necessary for temporal organization of hippocampal neuronal activity.

The superficial layers of the medial entorhinal cortex (MEC) are a major input to the hippocampus. The high proportion of spatially modulated cells, including grid cells and border cells, in these layers suggests that MEC inputs are critical for the representation of space in the hippocampus. However, selective manipulations of the MEC do not completely abolish hippocampal spatial firing. To determine whether other hippocampal firing characteristics depend more critically on MEC inputs, we recorded from hippocampal CA1 cells in rats with MEC lesions. Theta phase precession was substantially disrupted, even during periods of stable spatial firing. Our findings indicate that MEC inputs to the hippocampus are required for the temporal organization of hippocampal firing patterns and suggest that cognitive functions that depend on precise neuronal sequences in the hippocampal theta cycle are particularly dependent on the MEC

Preliminary investigation of the antioxidant, anti-diabetic, and anti-inflammatory activity of Enteromorpha intestinalis extracts

Marine algae are a promising source of potent bioactive agents against oxidative stress, diabetes, and inflammation. However, the possible therapeutic effects of many algal metabolites have not been exploited yet. In this regard, we explored the therapeutic potential of Enteromorpha intestinalis extracts obtained from methanol, ethanol, and hexane, in contrasting oxidative stress. The total phenolic (TPC) and flavonoids (TFC) content were quantified in all extracts, with ethanol yielding the best values (about 60 and 625 mg of gallic acid and rutin equivalents per gram of extract, respectively). Their antioxidant potential was also assessed through DPPH•, hydroxyl radical, hydrogen peroxide, and superoxide anion scavenging assays, showing a concentration-dependent activity which was greater in the extracts from protic and more polar solvents. The α-amylase and α-glucosidase activities were estimated for checking the antidiabetic capacity, with IC50 values of about 3.8 μg/mL for the methanolic extract, almost as low as those obtained with acarbose (about 2.8 and 3.3 μg/mL, respectively). The same extract also showed remarkable anti-inflammatory effect, as determined by hemolysis, protein denaturation, proteinase and lipoxygenase activity assays, with respectable IC50 values (about 11, 4, 6, and 5 μg/mL, respectively), also in comparison to commercially used drugs, such as acetylsalicylic acid