Steady flow of power-law fluids in a 1:3 planar sudden expansion

The laminar flow of inelastic non-Newtonian fluids, obeying the power-law model, through a planar sudden expansion with a 1:3 expansion ratio was investigated numerically using a finite volume method. A broad range of power-law indices in the range 0.2 n 4 was considered. Shear-thinning, Newtonian and shear-thickening fluids are analyzed, with particular emphasis on the flow patterns and bifurcation phenomenon occurring at high Reynolds number laminar flows. The effect of the generalized Reynolds numbers (based on power-law index, n, and the in flow channel height, h) on the main vortex characteristics and Couette correction are examined in detail in the range varying from 0.01 Regen 600. Values for the critical generalized Reynolds number for the onset of steady flow asymmetry and the appearance of a third main vortex are also included. We found that the shear-thinning behavior increases the critical Regen, while shear-thickening has the opposite effect. Comparison with available literature and with predictions using a commercial software (FluentR 6.3.26) are also presented and discussed. It was found that both results are in good agreement, but that our code is able to achieve converged solution for a broader range of flow conditions, providing new benchmark quality data

IMPROVEMENT OF OIL PAN BY USING VALUE ENGINEERING METHODOLOGY

Valueengineeringisaproventoolforreducingcostsandincreasingthevalueoftheproducts.Thishasadirectbearingonimprovedcompetitivepositioninthemarketplaceandincreasedprofitmargins.ThisprojectdescribestheimplementationofthistoolforanoilpandesigninICengine.ValueengineeringfortheOilpanaimsinreducingthecomplicatedmanufacturingbyvirtueoftheprocessandalsobyeffectiveutilizationofrawmaterial,whichdirectly resultsinreductionofmaterial&processcost.Thesimplifiedmanufacturingprocesswilleffectivelyimprovetheproductionratebyreducingthetimetakentomanufacturetheproduct.Tofindthebest possible alternative from the choices we have incorporated a toolnamed as Decision Matrix. Decision Matrix gives the mostappropriate result and is even easy to use. Theselected alternative method is modeled with Pro Engineer tool and is simulatedinanalysistoollikeNASTRANtojustifytheachievedvibrationproperties

Aathangarai Oram Novel Life's Thoughts

Everyone's affairs should be conducted in the hope of creativity. For that, scientists, engineering experts and the government should plan and carry out studies related to the construction of dams. The innovation of "Athankarai Oram" of God's grace emphasizes how fundamental and necessary it is to have the mental participation of the people who have been living there for ages. This novel explains how we can deal with the challenge that growing cities and urban demands pose to natural beauty and prosperity, and the nature of human hearts that have been so broken along the path of civilization. Babies cry when they leave their mother's womb and land on the ground. Then the child feels a pain. The news that the government has ordered the construction of a dam and is going to resettle the people living on the banks of the river hundreds of miles away is causing pain in the hearts of the riverside villagers of Sindur. To leave the place of residence and migrate is a sin for the people of Sindoor. The division increased the bond between them. The innovation 'Aathangarai Oram' clarified the answer to the question of how we can overcome the challenge posed to natural beauty and resources by growing cities and the needs of cities. Even if the government mobilizes people for the welfare of the people and fights morally without violence, its plans will be fulfilled with the ferocity of the government. Those who opposed it till the end will get self-satisfied that 'this is the last dam' and then go to rally against another such attempt. Based on this realistic situation, this novel called 'Aathangarai Oram', which is very well written, is seen as the best Indian literature written in Tamil. This innovation was seen to broaden people's minds and stimulate thought while emphasizing aesthetic sense and humanistic qualities

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

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

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

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors

Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET

Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize intrusion detection, which controls the system to detect additional security issues. Monitoring is essential to avoid attacks and provide extra protection against unauthorized access. Although the current solutions have been designed to defeat the attack nodes, they still require additional hardware, have considerable delivery delays, do not offer high throughput or packet delivery ratios, or do not do so without using more energy. The capability of a mobile node to forward packets, which is dependent on the platform's life quality, may be impacted by the absence of the network node power source. We developed the Safe Routing Approach (SRA), which uses behaviour analysis to track and monitor attackers who discard packets during the route discovery process. The attacking node recognition system is made for irregular routing node detection to protect the controller network's usual properties from becoming recognized as an attack node. The suggested method examines the nearby attack nodes and conceals the trusted node in the routing pathway. The path is instantly assigned after the initial discovery of trust nodes based on each node's strength value. It extends the network's life span and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy consumption, network performance, and detection of attack nodes, the suggested approach is contrasted with AIS, ZIDS, and Improved AODV. The findings demonstrate that the recommended strategy performs superior in terms of PDR, residual energy, and network throughput