Comparative Analysis of Mathematical Models for Blood Flow in Tapered Constricted Arteries

Pulsatile flow of blood in narrow tapered arteries with mild overlapping stenosis in the presence of periodic body acceleration is analyzed mathematically, treating it as two-fluid model with the suspension of all the erythrocytes in the core region as non-Newtonian fluid with yield stress and the plasma in the peripheral layer region as Newtonian. The non-Newtonian fluid with yield stress in the core region is assumed as �i� Herschel-Bulkley fluid and �ii� Casson fluid. The expressions for the shear stress, velocity, flow rate, wall shear stress, plug core radius, and longitudinal impedance to flow obtained by Sankar �2010� for two-fluidHerschel-Bulkleymodel and Sankar and Lee �2011� for two-fluid Casson model are used to compute the data for comparing these fluid models. It is observed that the plug core radius, wall shear stress, and longitudinal impedance to flow are lower for the two-fluid H-B model compared to the corresponding flow quantities of the two-fluid Casson model. It is noted that the plug core radius and longitudinal impedance to flow increases with the increase of the maximum depth of the stenosis. The mean velocity and mean flow rate of two-fluid H-B model are higher than those of the two-fluid Casson model

Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration

As the integration of Internet of Things devices with cloud computing proliferates, the paramount importance of privacy preservation comes to the forefront. This survey paper meticulously explores the landscape of privacy issues in the dynamic intersection of IoT and cloud systems. The comprehensive literature review synthesizes existing research, illuminating key challenges and discerning emerging trends in privacy preserving techniques. The categorization of diverse approaches unveils a nuanced understanding of encryption techniques, anonymization strategies, access control mechanisms, and the burgeoning integration of artificial intelligence. Notable trends include the infusion of machine learning for dynamic anonymization, homomorphic encryption for secure computation, and AI-driven access control systems. The culmination of this survey contributes a holistic view, laying the groundwork for understanding the multifaceted strategies employed in securing sensitive data within IoT-based cloud environments. The insights garnered from this survey provide a valuable resource for researchers, practitioners, and policymakers navigating the complex terrain of privacy preservation in the evolving landscape of IoT and cloud computingComment: 33 page

Neuronal glucose transporter isoform 3 deficient mice demonstrate features of autism spectrum disorders.

Neuronal glucose transporter (GLUT) isoform 3 deficiency in null heterozygous mice led to abnormal spatial learning and working memory but normal acquisition and retrieval during contextual conditioning, abnormal cognitive flexibility with intact gross motor ability, electroencephalographic seizures, perturbed social behavior with reduced vocalization and stereotypies at low frequency. This phenotypic expression is unique as it combines the neurobehavioral with the epileptiform characteristics of autism spectrum disorders. This clinical presentation occurred despite metabolic adaptations consisting of an increase in microvascular/glial GLUT1, neuronal GLUT8 and monocarboxylate transporter isoform 2 concentrations, with minimal to no change in brain glucose uptake but an increase in lactate uptake. Neuron-specific glucose deficiency has a negative impact on neurodevelopment interfering with functional competence. This is the first description of GLUT3 deficiency that forms a possible novel genetic mechanism for pervasive developmental disorders, such as the neuropsychiatric autism spectrum disorders, requiring further investigation in humans

Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection, which capitalizes on the relationships between labelled and unlabeled data to enhance accuracy. The work begins by investigating data augmentation and preprocessing techniques to address the challenges of image quality and feature variations. Techniques such as image cropping, resizing, contrast adjustment, normalization, and data augmentation are explored to optimize feature extraction and improve the overall quality of retinal images. Moreover, apart from detection and diagnosis, this work delves into applying ML algorithms for predicting the risk of developing DR or the likelihood of disease progression. Personalized risk scores for individual patients are generated using comprehensive patient data encompassing demographic information, medical history, and retinal images. The proposed Semi-Supervised Graph learning algorithm is rigorously evaluated on two publicly available datasets and is benchmarked against existing methods. Results indicate significant improvements in classification accuracy, specificity, and sensitivity while demonstrating robustness against noise and outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced datasets, common in medical image analysis, further enhancing its practical applicability.Comment: 13 pages, 6 figure

Mathematical Analysis of Blood Flow Through Stenosed Arteries With body Acceleration.

The mathematical analysis discusses the pulsatile flow of blood through stenosed narrow artery with Body acceleration,treating blood as Herschel-Bulkley fluid

Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection

Huge diagrams have unique properties for organizations and research, such as client linkages in informal organizations and customer evaluation lattices in social channels. They necessitate a lot of financial assets to maintain because they are large and frequently continue to expand. Owners of large diagrams may need to use cloud resources due to the extensive arrangement of open cloud resources to increase capacity and computation flexibility. However, the cloud's accountability and protection of schematics have become a significant issue. In this study, we consider calculations for security savings for essential graph examination practices: schematic extraterrestrial examination for outsourcing graphs in the cloud server. We create the security-protecting variants of the two proposed Eigen decay computations. They are using two cryptographic algorithms: additional substance homomorphic encryption (ASHE) strategies and some degree homomorphic encryption (SDHE) methods. Inadequate networks also feature a distinctively confidential info adaptation convention to allow the trade-off between secrecy and data sparseness. Both dense and sparse structures are investigated. According to test results, calculations with sparse encoding can drastically reduce information. SDHE-based strategies have reduced computing time, while ASHE-based methods have reduced stockpiling expenses

Mathematical Analysis of Carreau Fluid model for Blood Flow in Tapered Constricted Arteries

The pulsatile flow of blood through a tapered constricted narrow artery is investigated in this study, treating the blood as Carreau fluid model. The constriction in the artery is due to the formation of asymmetric stenosis in the lumen of the artery. The expressions obtained by Sankar (2016) for the various flow quantities are used to analyze the flow with different arterial geometry. The influence of various flow parameters on the velocity distribution, wall shear stress and longitudinal impedance to flow is discussed. The velocity of blood increases with the increase of the power law index and stenosis shape parameter and it decreases considerably with the increase of the maximum depth of the stenosis. The wall shear stress and longitudinal impedance to flow decrease with the increase stenosis shape parameter, amplitude of the pulsatile pressure gradient, flow rate, power law index and Weissenberg number. The estimates of the percentage of increase in the wall shear stress and longitudinal impedance to flow increase with the increase of the angle tapering and these increase significantly with the increase of the maximum depth of the stenosis. The mean velocity of blood decreases considerably with the increase of the artery radius (except in arteriole), maximum depth of the stenosis and angle of tapering and it is considerably higher in pulsatile flow of blood than in the steady flow of blood

Statistical signal processing approach to segment primary components from pathological phonocardiogram

Cardiac disorders has become pretty common in the current world. Despite of the availability of many advanced techniques like electrocardiography (ECG), Echocardiography and Carotid pulse, listening to the heart sounds has become one of the orthodox approach which is being performed from long ago, often named as auscultation methodology or Phonocardiogram. This methodology is the primary tool for the health care physicians to screen the patients for heart pathology. However, to master, it needs a lot of experience and knowledge. Yet the non-availability of advanced techniques at every door step and its cost made this orthodox approach to survive. The proposed study is to make the health care physicians to diagnose the pathology using phonocardiography in an effective manner. The study uses the statistics of the signal information in the form of variance. The proposed technique uses filtering and decimation as preprocessing method to limit the low frequency noises/disturbances and to concentrate only on the components of interest (i.e. S1 and S2). The preprocessed signal is wavelet analyzed and synthesized followed by principal component analysis to extract necessary features which resembles the information of S1 and S2. A proposed splitting algorithm is processed to the featured signal to separate the phonocardiogram signal into series of cardiac cycles and energy envelope is calculated for the same featured signal. By using the information of the cardiac cycles and energy envelopes, segmentation of S1 and S2 from pathological phonocardiogram is performed. The results show that the proposed technique does not rely on any time-frequency parameter which effects the performance of the study. Hence a novel technique based on statistical analysis has been proposed to detect the primary components (S1 and S2) from pathological phonocardiogram with less computational effort and better accuracy

Correlation between tunneling magnetoresistance and magnetization in dipolar coupled nanoparticle arrays

The tunneling magnetoresistance (TMR) of a hexagonal array of dipolar coupled anisotropic magnetic nanoparticles is studied using a resistor network model and a realistic micromagnetic configuration obtained by Monte Carlo simulations. Analysis of the field-dependent TMR and the corresponding magnetization curve shows that dipolar interactions suppress the maximum TMR effect, increase or decrease the field-sensitivity depending on the direction of applied field and introduce strong dependence of the TMR on the direction of the applied magnetic field. For off-plane magnetic fields, maximum values in the TMR signal are associated with the critical field for irreversible rotation of the magnetization. This behavior is more pronounced in strongly interacting systems (magnetically soft), while for weakly interacting systems (magnetically hard) the maximum of TMR (Hmax) occurs below the coercive field (Hc), in contrast to the situation for non-interacting nanoparticles or in-plane fields (Hmax=Hc). The relation of our simulations to recent TMR measurements in self-assembled Co nanoparticle arrays is discussed.Comment: 21 pages, 8 figures, submitted to Physical Review