Preparation, Characterization and Structure Prediction of In2SnO3 and Spectroscopic (FT-IR, FT-Raman, NMR and UV-Visible) Study Using Computational Approach.

Unadulterated and scorch stage In2SnO3 nanopowder is effectively arranged with the doping proportion of 80-20% (In2O3-Sn) by simple sol-gel combustion direction. The material is characterized by XRD measurements and their geometrical parameters are compared with calculated values. The FT-IR and NMR spectra are recorded in both bulk and nanophase and FT-Raman spectrum is recorded in bulk phase and the fundamental frequencies are assigned. The optimized parameters and the frequencies are calculated using HF and DFT (B3LYP, B3PW91 and MPW1PW91) theory in bulk phase of In2SnO3 and are compared with its nanophase. The vibrational frequency pattern in nanophase gets realigned and the frequencies are shifted up and down little bit to the region of spectra when compared with bulk phase. The UV-visible spectrum is simulated and analyzed. The frontier molecular orbital analysis has been carried out and the values of the HOMO-LUMO bandgap (Kubo gap) explore the optical and electronic characteristics of the In2SnO3. Structural studies by XRD showed the crystallite sizes of the particles. The atomic arrangement in the grain boundary seems to be somewhat different from regular periodic arrangement whereas inside the grain there is a good periodic arrangement of atoms. Above 10 mol% Sn ions, 15 mol% Sn ions, 20 mol% Sn ions to 50 mol% Sn ions form correlated clusters, 20 mol% Sn ions which lead to broadening. These EPR spectra were formed to contain two different components, one from the single isolated ions and the other from the clusters. The transition is observed for different composition increase with decreasing grain size.

Nanostructured Metal Tellurides and Their Heterostructures for Thermoelectric Applications-A Review.

Telluride's and Selenides were assessed whether it is appropriate for thermoelectric effects. Previous researches showed that researchers strived to progress the performance of telluride based materials in creating structures where the entire dimensions are reduced, such as nanowires or thin films. Seebeck and Peltier coefficient was developed by means of Telluride thermoelectric devices. Epitaxial growth methods such as molecular beam epitaxy and metal organic chemical vapor deposition are some of the frequent methods of acquiring telluride thin films. Thermoelectric nano thin films and nanostructured materials should have the properties of insulation so that it can be used as energy storage devices and thermo electric generators. Conduction of electricity is usually convoyed by reversible and irreversible effects, such as electrical resistance and thermal conduction which is used to, Peltier refrigerators, generating electricity, renewable energies and its applications. Telluride films can be used in thermoelectric applications; these thermoelectric materials are mainly rare metals such as (Bi), (Te), (Pb) and (Sb). Thermal conductivity, figure of merit is advantageous factor of these energy storage devices. Thermoelectric cooler, thermoelectric generators are the powerful sources which can be eligible due to the use of telluride thin films. The thermal conductivity performance, figure of merit and Seebeck and Peltier coefficients of diverse materials were conferred.

Xanthogranulomatous oophoritis with Leydig cell hyperplasia masquerading as ovarian neoplasm - A case report.

ABSTRACT: Xanthogranulomatous oophoritis is an uncommon form of chronic inflammation of the ovary. Its clinical manifestations, imaging findings, and gross picture can mimic an ovarian neoplasm. Hilar cells, which are morphologically difficult to distinguish from testicular Leydig cells, secrete testosterone and they are mostly seen in the ovarian hilum. They can undergo hyperplasia or can transform into a tumor. We present a case of xanthogranulomatous oophoritis with Leydig cell hyperplasia, which mimicked an ovarian neoplasm.

Causal oscillations in the visual thalamo-cortical network in sustained attention in ferrets.

Sustained visual attention allows us to process and react to unpredictable, behaviorally relevant sensory input. Sustained attention engages communication between the higher-order visual thalamus and its connected cortical regions. However, it remains unclear whether there is a causal relationship between oscillatory circuit dynamics and attentional behavior in these thalamo-cortical circuits. By using rhythmic optogenetic stimulation in the ferret, we provide causal evidence that higher-order visual thalamus coordinates thalamo-cortical and cortico-cortical functional connectivity during sustained attention via spike-field phase locking. Increasing theta but not alpha power in the thalamus improved accuracy and reduced omission rates in a sustained attention task. Further, the enhancement of effective connectivity by stimulation was correlated with improved behavioral performance. Our work demonstrates a potential circuit-level causal mechanism for how the higher-order visual thalamus modulates cortical communication through rhythmic synchronization during sustained attention.

Neighborhood Rough Neural Network Approach for COVID-19 Image Classification.

The rapid spread of the new Coronavirus, COVID-19, causes serious symptoms in humans and can lead to fatality. A COVID-19 infected person can experience a dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness, according to a clinical report. A chest X-ray (also known as radiography) or a chest CT scan are more effective imaging techniques for diagnosing lung cancer. Computed Tomography (CT) scan images allow for fast and precise COVID-19 screening. In this paper, a novel hybridized approach based on the Neighborhood Rough Set Classification method (NRSC) and Backpropagation Neural Network (BPN) is proposed to classify COVID and NON-COVID images. The proposed novel classification algorithm is compared with other existing benchmark approaches such as Neighborhood Rough Set, Backpropagation Neural Network, Decision Tree, Random Forest Classifier, Naive Bayes Classifier, K- Nearest Neighbor, and Support Vector Machine. Various classification accuracy measures are used to assess the efficacy of the classification algorithms.

COVID-19: an In Silico Analysis on Potential Therapeutic Uses of Trikadu as Immune System Boosters.

Corona virus pandemic outbreak also known as COVID-19 has created an imbalance in this world. Scientists have adopted the use of natural or alternative medicines which are consumed mostly as dietary supplements to boost the immune system as herbal remedies. India is famous for traditional medicinal formulations which includes 'Trikadu'-a combination of three acrids, namely Zingiber officinale, Piper nigrum and Piper longum which have antioxidant properties that boost our immune system hence acting as a strong preventive measure. In this study, AutoDock 4.0 was used to study interaction between the phytocompounds of Trikadu with RNA-dependent polymerase protein and enveloped protein of the SARS-CoV-2 virus. Analysis of the results showed that coumarin, coumaperine and bisdemethoxycurcumin showed strong bonding interactions with both the proteins. We can conclude that Trikadu has the potential molecules; hence, it can be incorporated in the diet to boost the immune system as a preventive measure against the virus.

A cognitive IoT-based framework for effective diagnosis of COVID-19 using multimodal data.

The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.