Suspension model for blood flow through a tapering catheterized inclined artery with asymmetric stenosis

We intend to study a particle fluid suspension model for blood flow through an axially asymmetric but radially symmetric mild stenosis in the annular region of an inclined tapered artery and a co-axial catheter in a suitable flow geometry has been considered to investigate the influence of velocity slip at the stenotic wall as well as hematocrit, shape parameter. The model also includes the tapering effect and inclination of the artery. Expressions for the flow variables have been derived analytically and their variations with various flow parameters are represented graphically. The results for the different values of the parameters involved show that the impedance to flow increases with stenosis height, hematocrit and catheter radius. However, it decreases with the shape parameter, angle of inclination of artery and velocity slip at the stenotic wall. The present analysis is an extension of the work by Chakraborty et al. (2011) and also includes several theoretical models of arterial stenosis in the uniform, tapering and catheterized tubes, with the consideration of velocity slip or zero slip at the vessel wall. Finally, some biological implications of this theoretical modeling are included in brief

Diffusion of gases and liquids in nanoporous solids

The diffusion of gases and liquids under nanoconfinement is of fundamental importance for understanding various processes like catalysis, enhanced oil recovery, and CO₂ sequestration. The present contribution narrates how diffusion NMR was used as a quantitative method to study the transport of probe molecules in nanoporous solids when the fluids are subjected to variable pressure. Nanoporous glass (NPG) and zeolites have been considered for investigation in this work. NPG was selected as a model system to study confinement effects, diffusion, and partitioning of liquid water and liquid cyclohexane. Self-diffusion coefficients of methane were measured in several commercially-important zeolites as a function of pressure, with a specific focus on experimental reproducibility and minimization of error in the calculated diffusivities. To identify differences in transport properties on modification of zeolites with different silica to alumina ratios, methods such as ammonium-hexa-fluoro-silicate (AHFS) treatment, phosphoric acid treatment, steaming, and, lanthanum ion exchange were employed. Variable pressure of nitrogen gas and methane were applied to the NPG and zeolites samples, respectively and their self-diffusion coefficients were measured. One of the primary outcomes of the research was the development of a complete method for introducing gases into nanoporous solids, specifically zeolite catalysts, at variable pressure that yields highly reproducible diffusion coefficients. With these improvements, the NMR diffusometry experiments have revealed that CH4 at elevated pressure is a sensitive probe of both chemical and topological perturbations to zeolite catalyst structures, and thereby applicable to a variety of systems in which sorbents, reagents, and surfactants are sequestered in nanoporous hosts

Comparative evaluation of anti-diabetic activity of fresh juice and ethanolic extract of Sunderban mangrove Rhizophora mucronata Lam. leaves in animal model

Background: Mangrove flora possess compounds with potential medicinal values with unique bioactive components. Traditionally Rhizophora mucronata, a mangrove has been used extensively for the treatment of diabetes. Studies revealed that, the leaves of Rhizophora (Bhora) had promising anti-diabetic action in rat model.Methods: A comparative analysis of the anti-diabetic action of fresh juice and ethanolic extract of Rhizophora mucronata leaves was carried out in Streptozotocin induced diabetic model and the different biochemical parameters were evaluated.Results: Present research explored a comparative analysis of the anti-diabetic action of fresh juice and ethanolic extract of leaves of Rhizophora mucronata Lam. in Streptozotocin induced diabetic model. The ethanolic extract showed more potent effect in lowering the elevated blood sugar in the diabetic rats, 200mg/kg was the most effective dose for both the extracts. The ethanol extract was more beneficial having potent lipid lowering action along with anti-hyperglycemic property.Conclusions: This supports the scientific validation for using Rhizophora mucronata leaves in the treatment of diabetes as traditional folk medicine. Identification of the bioactive molecule is under process

Activating ZnO nanorods photoanodes in visible light by CdS surface sensitiser

Thin films of c-axis aligned uniform ZnO nanorods (NRs) were fabricated on to fluorine-doped tin oxide-coated soda lime glass substrate by a two-step chemical route. Thereafter ZnO NRs/CdS core shell structures were successfully synthesised by depositing CdS layer on top of vertically aligned ZnO NRs using less hazardous nanocrystal layer deposition technique. The presence of CdS in ZnO NRs/CdS core shell structures was confirmed by energy dispersive X-ray analysis. Examination of structure and morphology of the fabricated films by X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) revealed that both films have one-dimensional hexagonal wurtzite structure. Optical properties evaluated from ultraviolet-visible and photoluminescence spectra demonstrated better photo response of ZnO NRs/CdS core shell structure with respect to bare ZnO NR structure. Optical to chemical conversion efficiency of ZnO NRs/CdS photoanode was found to be similar to 1.75 times higher than bare ZnO NRs photoanode in photo electrochemical water splitting under visible light

The Shaheen Bagh Strike: Muslim Women and Political Protest in Contemporary India

The Shaheen Bagh protest in New Delhi highlighted the changing dynamics of Muslim women’s participation in socio-political movements in India. This paper argues how Muslim women proved themselves to be concerned citizens while protesting against the Citizenship Amendment Act (2019) and other forms of social discrimination. The paper analyses the Shaheen Bagh protest from an intersectional perspective to understand how Muslim women voiced their political opinions negotiating with gender and religion-based discrimination; they had to fight the multiple forms of patriarchy of Indian society while protesting against hypermasculine Hindutva politics. The Shaheen Bagh protest can be called a feminist strike of Third World women for the rights of their religious community in a particular socio-political context.La manifestation de Shaheen Bagh, à New Delhi, a mis en évidence l’évolution de la dynamique de la participation des femmes musulmanes dans les mouvements sociopolitiques en Inde. L’article soutient la façon dont les femmes musulmanes ont fait leurs preuves à titre de citoyennes engagées tout en s’opposant à un projet de loi pour modifier la loi sur la citoyenneté (« Citizenship Amendment Act », 2019) ainsi qu’à d’autres formes de discrimination sociale. Il analyse la manifestation de Shaheen Bagh selon une perspective intersectionnelle afin de comprendre la façon dont les femmes musulmanes ont exprimé leurs opinions politiques en composant avec la discrimination fondée sur le genre et sur la religion; elles ont dû lutter contre les multiples formes de patriarcat dans la société indienne tout en protestant contre les politiques hypermasculines de l’hindutva. La manifestation de Shaheen Bagh peut être qualifiée de grève féministe par des femmes du tiers-monde pour les droits de leur communauté religieuse dans un contexte sociopolitique particulier

Intuitionistic Type-2 Fuzzy Normed Linear Space and Some of Its Basic Properties

An intuitionistic fuzzy set is a more generalised tool than a fuzzy set for handling unpredictability as, in an intuitionistic fuzzy set, there is scope for considering a grade of non-membership, independent of the grade of membership, only satisfying the condition that their sum is less or equal to 1. The motivation of this paper is to introduce the notion of intuitionistic type-2 fuzzy normed linear space (IT2FNLS). Here, to each vector x, we assign two fuzzy real number valued grades, one for its norm and the other for the negation of its norm. A theorem of the decomposition of the intuitionistic type-2 fuzzy norm into a family of pairs of Felbin-type fuzzy norms is established. Also, we deal with Cauchyness and convergence of sequences in the IT2FNLS. Later on, in the finite-dimensional IT2FNLS, the completeness property and compactness property are explored. Finally, we define two types of intuitionistic type-2 fuzzy continuity and examine the relations between them

Reinforcement learning based effective communication strategies for energy harvested WBAN

This paper proposes effective communication strategies for Wireless Body Area Networks (WBANs) that consist of wearable or implantable sensor nodes placed in, on/around the human body to send body vitals to a sink. The main research challenges for communication strategy formulation include limited energy resources and varying link conditions. Though energy harvested sensor nodes partially address the problem of energy efficiency, finding an optimal balance between the energy constraint of the nodes and communication reliability is still challenging. Since data loss in such networks may prove to be fatal, it is important to investigate the problem prior to deployment and come up with effective communication strategies for initiating post-deployment operations. Hence, in this paper, the nodes are stochastically modeled as a Markov Decision Process. There is a need to adapt to the changing ambient conditions through exploration and exploitation. So, a modified Q-learning technique is proposed for post-deployment decision-making by the WBAN nodes subject to the dynamic ambient conditions. The effectiveness of the proposed strategy is validated through extensive simulation and compared with state-of-the-art works. The performance of the proposed approach is also verified with a real-life dataset. The results demonstrate that around 90% successful data delivery to sink could be made with the proposed scheme in the real-life scenario

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem

LVQ and HOG based speed limit traffic signs detection and categorization

This conference paper was presented in the International Conference on Informatics, Electronics and Vision, ICIEV 2014; Dhaka; Bangladesh; 23 May 2014 through 24 May 2014 [© 2014 IEEE] The conference paper's definite version is available at: http://dx.doi.org/10.1109/ICIEV.2014.6850741The proper identification of the traffic signs can ensure driving safety and can play a very important role in reducing the number of road accidents significantly. This paper represents a uniform way to detect the speed limit traffic signs and to confirm it by recognizing the sign's speed number. In this system, firstly the red color objects are segmented from an image using LVQ. Secondly, detected circular part is extracted from the color segmented image using bounding box and then Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of circular object and finally SVM classifier is applied to train the HOG features of each speed no. into their corresponding classes. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 381 images which contain 361 Speed Limit traffic sign and 30 Non- Speed Limit signs. It was found that the accuracy of recognition was 92.75% which indicates clearly the high robustness targeted by this system.Publishe