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The electromagnetic waves and its propagation through material medium described by maxwell’s equations. We have identified that electromagnetic waves propagate through carbon nanotubes according to electric hertz potential with solution of Helmholtz equation and satisfied by using the concept of Gaussian beam or wave. When monochromatic electromagnetic wave propagates through a hollow single wall carbon nanotube, its energy absorbed by walls of nanotubes just like a capacitor because of carbon nanotubes have metallic as well as semiconductor characteristic which is shown by density of state and lattice vector. It is verified by Helmholtz equation and Schrödinger’s wave equation. Thus, the electromagnetic waves can propagate through carbon nanotubes and carbon nanotubes absorb the energy as a capacitor.

Unfolded protein aggregation in cellular system is a problem causing various types of diseases depending on which type unfolded proteins aggregate. This phenomenon of aggregation may take place during production, storage, shipment or delivery in the cellular medium. In the present work, we studied a simplified and extended version of unfolded protein aggregation model by Lumry and Eyring using stochastic approach. We solved analytically the Master equation of the model for the probability distribution $P(x,t)$ of the unfolded protein population and the solution was found to be time dependent complex binomial distribution. In the large population limit $P(x,t)\sim \Lambda(x,t)\times Pois(x,t)$. Further, the distribution became Normal distribution at large population and mean of the distribution limit: $P(x,t)\sim\Lambda(x,t)\times N(\langle qx\rangle,\langle qx\rangle)$. The fluctuations inherent in the dynamics measured by Fano factor can have sub-Poisson, Poisson and super-Poisson at different situations., Comment: 9 pages, 1 figure

The widespread significance of Android IoT devices is due to its flexibility and hardware support features which revolutionized the digital world by introducing exciting applications almost in all walks of daily life, such as healthcare, smart cities, smart environments, safety, remote sensing, and many more. Such versatile applicability gives incentive for more malware attacks. In this paper, we propose a framework which continuously aggregates multiple user trained models on non-overlapping data into single model. Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose a novel smart contract which enable aggregation process over blokchain platform. The LNN model analyzes various static and dynamic features of both malware and benign whereas the smart contract verifies the malicious applications both for uploading and downloading processes in the network using stored aggregated features of local models. In this way, the proposed model not only improves malware detection accuracy using decentralized model network but also model efficacy with blockchain. We evaluate our approach with three state-of-the-art models and performed deep analyses of extracted features of the relative model.

Artificial Intelligence (AI) has recently shown its capabilities for almost every field of life. Machine Learning, which is a subset of AI, is a `HOT' topic for researchers. Machine Learning outperforms other classical forecasting techniques in almost all-natural applications. It is a crucial part of modern research. As per this statement, Modern Machine Learning algorithms are hungry for big data. Due to the small datasets, the researchers may not prefer to use Machine Learning algorithms. To tackle this issue, the main purpose of this survey is to illustrate, demonstrate related studies for significance of a semi-parametric Machine Learning framework called Grey Machine Learning (GML). This kind of framework is capable of handling large datasets as well as small datasets for time series forecasting likely outcomes. This survey presents a comprehensive overview of the existing semi-parametric machine learning techniques for time series forecasting. In this paper, a primer survey on the GML framework is provided for researchers. To allow an in-depth understanding for the readers, a brief description of Machine Learning, as well as various forms of conventional grey forecasting models are discussed. Moreover, a brief description on the importance of GML framework is presented., Comment: 22 pages, 8 figures, journal paper

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.

Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy. The blockchain ledger technology provides the decentralization of federated learning model without any central server. The proposed homomorphic encryption scheme encrypts and decrypts the gradients of model to preserve the privacy. More precisely, the proposed framework: i) train the local model by a novel capsule network to segmentation and classify COVID-19 images, ii) then use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, and finally the model is shared over a decentralized platform through proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing in the decentralized network. The conducted experimental resultsdemonstrate the performance of the proposed scheme., Comment: 15 Pages, 5 Tables, 11 Figures, Journal Paper, Elsevier format

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of CT scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients data, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients., Comment: arXiv admin note: text overlap with arXiv:2003.10849 by other authors

The study was conducted with the aim of exploring any possible relationship between gall stone disease and lipid profile. Patients of any age or gender admitted for cholecystectomy due to gall stones (n=100) were kept in the study group while those admitted for reasons other than gall stone disease (n=100) were included as controls. Patients with history of intake of any hypolipidemic agents and diabetics were excluded. The patients of both the groups were divided into 3 different age groups which were further sub-divided into male and female. Thorough medical histories of the patients were taken and estimation of serum lipid profile was done. The study group (with gall stone disease) was found to have significantly high value of serum triglycerides (p 20 years. A definite inverse relationship between serum high density lipoprotein and gall stone was also noted in both genders above 20 years of age. In this study high serum triglycerides, total cholesterol, serum LDL and low serum HDL are shown to be significantly related to gall stone disease., Bu çalışmada safrataşları ve dislipidemi ararsında bir ilişkinin olup olmadığı araştırılmıştır. Safrataşı nedeniyle hastaneye müracaat eden 100 kişi ile başka nedenlerle hastaneye gelen ve safrataşı tesbit edilen 100 hasta çalışmaya alınmıştır. Lipid azaltıcı ilaç kullanan hastalar çalışmaya alınmadı. Hastalar 3 farklı yaş grubuna ve erkek kadın olarak bir diğer alt gruba tanımlandılar. Çalışma grubunda (Safra taşı şikayeti ile gelen hastalar) bulunan bütün alt grup hastalarda serum trigliserid değerleri anlamlı derecede yüksek bulundu (

Crohn’s Disease is a chronic, idiopathic, transmural inflammatory disease affecting predominantly distal ileum, the common presentation include stricture and fistula formation. Free perforation in the peritoneal cavity is rare. To study the presentation and management of Crohn’s perforation. A retrospective study of 9 cases of perforative peritonitis later diagnosed due to Crohn’s disease on histopathological examination. Among the 9 patients, 8 were males and 1 was female. The ages of the patients ranged from 30 to 58 years, with mean age of 41.8 years. 6 patients were in the age group of 30–45 years. 8 patients were not known to be suffering from Crohn’s. Resection followed by anastomosis was done in 4 cases including the case of known Crohn’s, while resection follwed by end illeostomy with mucous fistula was done in remaining cases. Resected specimens were sent for histopathological examination in all cases. Though Crohn’s perforation is rare it should be kept in mind when dealing with single or multiple perforation of the small intestine even in the developing countries. Though the number of cases in our series are too few to come to a conclusion, we found that illeostomy sems a prudent alternative to traditional resection anastomosis.