Fate of Strong Cosmic Censorship Conjecture in Presence of Higher Spacetime Dimensions

Strong cosmic censorship conjecture has been one of the most important leap of faith in the context of general relativity, providing assurance in the deterministic nature of the associated field equations. Though it holds well for asymptotically flat spacetimes, a potential failure of the strong cosmic censorship conjecture might arise for spacetimes inheriting Cauchy horizon along with a positive cosmological constant. We have explicitly demonstrated that violation of the censorship conjecture holds true in the presence of a Maxwell field even when higher spacetime dimensions are invoked. In particular, for a higher dimensional Reissner-Nordstr\"{o}m-de Sitter black hole the violation of cosmic censorship conjecture is at a larger scale compared to the four dimensional one, for certain choices of the cosmological constant. On the other hand, for a brane world black hole, the effect of extra dimension is to make the violation of cosmic censorship conjecture weaker. For rotating black holes, intriguingly, the cosmic censorship conjecture is always respected even in presence of higher dimensions. A similar scenario is also observed for a rotating black hole on the brane.Comment: v3, 35 pages, 7 figures, Accepted in JHE

Wreath product of a semigroup and a Γ-semigroup

Let S = {a,b,c,...} and Γ = {α,β,γ,...} be two nonempty sets. S is called a Γ -semigroup if aαb ∈ S, for all α ∈ Γ and a,b ∈ S and (aαb)βc = aα(bβc), for all a,b,c ∈ S and for all α,β ∈ Γ. In this paper we study the semidirect product of a semigroup and a Γ-semigroup. We also introduce the notion of wreath product of a semigroup and a Γ-semigroup and investigate some interesting properties of this product

Anti-inflammatory and Analgesic Activity of Methanolic Extract of Medicinal Plant Rhodiola rosea l. Rhizomes

ABSTRACT Rhodiola rosea L. (Crassulaceae) have been used as traditional medicines that can increase someone's physical strength, work productivity, longevity and resistance to high altitude sickness, fatigue, depression, anaemia, gastrointestinal ailments, infections, and nervous system disorders. The objective of this study was to evaluate the anti-inflammatory and analgesic activities from the methanolic extract of the rhizomes of Rhodiola rosea. Crude methanolic extract of the herb Rhodiola rosea were prepared and analyzed for its pharmacological activity. Swiss albino mice are used for acute toxicity study and also analgesic property of the extract by using Tail flick, Acetic acid induced writhing reflex and tail immersion method. Male Wister rat model are used to detect anti-inflammatory activity of the extract using carrageenan induced rat paw edema. After experiment statistical analysis like one way anova (nonparametric), Dunnet's test was done. Results are plotted in graph and from this the effective activity of the plant is determined. The orally administered methanolic extract of Rhodiola rosea demonstrated a significant analgesic and anti-inflammatory in animal model. The findings in the study suggest that the methanolic extract of the herb Rhodiola rosea possesses analgesic and anti-inflammatory activities. This results may prove the fact that the herb may be used as analgesic and anti-inflammatory along with its adaptogenic properties

Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

This article focuses on the importance of the continuous collection of water parameters data from the sensors and also the prediction of water quality using the latest different Machine learning algorithms like Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, K-nearest Neighbour, XGBoost, Gradient Boosting and Naive Bayes. These Machine learning models are implemented and tested to validate and achieve a satisfactory result of water quality prediction in terms of different attributes like pH, hardness, Solids, Chloramines, Sulfate, Conductivity, organic carbon, trihalomethanes, Turbidity and potability.</p

Stacked neural nets for increased accuracy on classification on lung cancer

Lung cancer is regarded as one of the most lethal diseases endangering human survival. It is difficult to detect lung cancer in its early stages, because of the ambiguity in the lung regions in the medical images. Healthcare business is automating itself with the use of image recognition and data analytics, much as the computing sector has completely automated. This article proposes a novel architecture, the Stacked Neural Network (SNN), for the detection and classification of lung cancer using CT scan data. The goal of the proposed technique is to investigate the accuracy levels of different Neural Networks (NN) and determine the early stage of lung cancer. The most effective technique for processing medical images, classifying and detecting lung nodules, extracting features, and predicting the stage of lung cancer is deep learning. First, lung areas are extracted using image processing techniques. SNN is used for the segmentation process. Various neural network techniques are utilised for the classification process once the features are retrieved from the segmented pictures. The suggested methods' performances are assessed using F1-Measure, accuracy, precision, and recall metrics. 96% classification accuracy is shown in the testing findings, which is comparatively greater than other methods currently in use. Proposed algorithm is clearly supported for real-world clinical practice

Ultralight crystalline hybrid composite material for highly efficient sequestration of radioiodine

Abstract Considering the importance of sustainable nuclear energy, effective management of radioactive nuclear waste, such as sequestration of radioiodine has inflicted a significant research attention in recent years. Despite the fact that materials have been reported for the adsorption of iodine, development of effective adsorbent with significantly improved segregation properties for widespread practical applications still remain exceedingly difficult due to lack of proper design strategies. Herein, utilizing unique hybridization synthetic strategy, a composite crystalline aerogel material has been fabricated by covalent stepping of an amino-functionalized stable cationic discrete metal-organic polyhedra with dual-pore containing imine-functionalized covalent organic framework. The ultralight hybrid composite exhibits large surface area with hierarchical macro-micro porosity and multifunctional binding sites, which collectively interact with iodine. The developed nano-adsorbent demonstrate ultrahigh vapor and aqueous-phase iodine adsorption capacities of 9.98 g.g−1 and 4.74 g.g−1, respectively, in static conditions with fast adsorption kinetics, high retention efficiency, reusability and recovery