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Ginzburg Landau Model for Nanofluid Convection in the Presence of Time Periodic Plate Modulation
Get PDFHere we study thermal modulation effect on nanofluid convection and discuss heat and mass transfer in the layer. The non-uniform time periodic boundary conditions of the system are considered. A weak non-linear stability analysis has been performed and obtained heat and mass transfer coefficients as a function of the system parameters. The Ginzburg Landau model was employed to derive nanofluid convective amplitude at different stages of flow disturbances and modulation. Slow variations of time scale shows that thermal modulation impact on transport phenomenon for the case of out phase modulation (OPM) and (lower boundary modulation) LBM. Also the effect of IPM (in-phase modulation) is observed low effect on Nu and which are similar to un-modulation case. It is also justified that LBM restuls are similar to gravity modulation results. It is found that thermal modulation and concentration Rayleigh numbers are either stabilize or destabilize the system. Further, GL model shows better results on regulation of transport proces
Importance of Antiviral and Antibacterial Face Mask Used in Pandemics: An Overview
Get PDFCommunity-wide mask wearing may contribute to the control of COVID-19 by reducing the amount of emission of infected saliva and respiratory droplets from individuals with subclinical or mild COVID-19. In this work, a brief review is presented on face masks and related things. First, the size of microorganisms in relation to PM2.5 and PM10 is given for an approximate estimate of the sizes of objects that needs to be filtered. In continuation, the principles of filtration of objects by the network of fibers (woven, non-woven, knitted, etc.) are given. Common fibers used for making face mask is presented along with various fabric structure and their manufacturing. Additionally, advancements like the treatment of fibers in terms of coated fabrics, nano-particle finishes, and green synthesized nano-particle coatings have been explained in view of their anti-bacterial and anti-viral properties. The classification of the face masks based on their fabric make-up has been given which has been extended to classification based on the barrier properties and various efficiencies of the face masks. The characterization of face masks like particle filtration efficiency, bacterial filtration efficiency, breathing resistance, flash resistance, and flame resistance are also include
Localization of mixed intracranial hemorrhages by using a ghost convolution-based YOLO network
Get PDFIntracranial hemorrhage (ICH) is a serious medical condition that must be diagnosed in a stipulated time through computed tomography (CT) imaging modality. However, the neurologist must initially confirm the specific type of hemorrhage to prescribe an effective treatment. Although conventional image processing and convolution based deep learning models can effectively perform multiclass classification tasks, they fail to classify if a CT input image contains multiple hemorrhages in a single slice and takes a lot of time to make the final predictions. To overcome these two difficulties, we proposed a novel YOLOv5x-GCB model that can be able to detect multiple hemorrhages with limited resources by employing a ghost convolution process. The advantage of ghost convolution is that it produces the same number of feature maps as vanilla convolution while using less expensive linear operations. Another feature of the proposed model is that it uses the mosaic augmentation technique throughout the training to improve the accuracy of mixed hemorrhage detection. A brain hemorrhage extended dataset containing 21,132 slices from 205 positive patients was used in training and validating the proposed model. To test the robustness of the proposed model, we created a separate dataset with the existing segmentation data, which are available in PhysioNet. As a result, the proposed model achieved an overall precision, recall, F1- score, and mean average precision of 92.1%, 88.9%, 90%, and 93.1%, respectively. In addition to these metrics, other parameters were used in evaluating the proposed model and checking its lightweight capability in terms of memory size and computational time. Results showed that our proposed model can be used in real-time clinical diagnosis by using either embedded devices or cloud services
"Effect of Additives in the Hydroamination/Cyclization of Aminoalkenes Catalyzed by a Binaphtholate Yttrium Catalyst"
No full textAbstract: A series of various solvents and additives were tested in enantioselective hydroamination/ cyclization reactions of aminoalkenes catalyzed by a binaphtholate yttrium catalyst. The functional group tolerance of the catalyst and the influence on the reaction rate and enantioselectivity was studied. Some weakly coordinating polar solvents, such as Et2O, MTBE, and chlorobenzene led to slightly increased reaction rates
compared to the less polar solvent benzene, presumably due to a better stabilization of the polar transition state. Stronger binding solvents and additives, such as THF, DMAP, pyrrolidine, n-propylamine, and 1-phenylethylamine, decrease the reaction rate and diminish the enantioselectivity of the hydroamination
product. Some additives, such as THF, Et2O, MTBE, chloro- and bromobenzene, as well as (+)-sparteine resulted in slightly higher enantioselectivities in the cyclization of the model substrate C-(1-allylcyclohexyl)- methylamine, although this observation was not generally true for other aminoalkene substrates. The reaction rates and enantioselectivities were de pressed in the presence of (-sparteine using the (R)-binaphtholateοΏ½ligated catalyst. In case of C-(1-llylcyclohexyl)methylamine, the enantioselectivity was switched from 76% ee favoring the (S)-enantiomer of the hydroamination product when using (+)-sparteine to 22% ee in
favor of the (R)-enantiomer when (-sparteine was used. The rates of cyclization of aminoalkenes and the resulting enantioselectivities significantly depend on substrate concentration with the highest rate (13.6 h and enantioselectivity (68% ee) observed in dilute conditions (0.05 M) compared to a concentrated solution (0.5 M, 5.0 h 1
, 35% ee) for 2,2-dimethylpent-4-enylamine. These observations indicate that the reaction mechanism is shifted in favor of a slower, less enantioselective catalytic cycle involving a higher coordinate species when higher substrate concentrations or stronger binding additives are present
Measurement and optimization of power consumption and aerosol emissions in magnetic field assisted electrical discharge machining of Inconel 718
No full textProcessing of Inconel 718 is still big challenge for manufactures to process with better machining performance and less
environmental effect. Therefore, the present study was aimed to develop a sustainable magnetic field-assisted electrical
discharge machining (MEDM) for machining of Inconel 718 to reduce power consumption, aerosol emissions and surface
defects. Experiments were conducted at different levels of current, pulse on time, pulse off time and voltage with and
without magnetic field assistance and the experimental results for power consumption, aerosol emissions, metal removal
rate (MRR) and surface roughness were collected and analyzed. The power consumption, surface roughness, recast layers,
microcracks, microvoids and microglobules were found to be reduced with improved MRR in the MEDM. A hybrid
teaching learning-based optimization algorithm was proposed and optimized the process parameters in MEDM. With the
proposed methodology, the MRR, surface roughness, power consumption and aerosol emissions reached 82%, 80%, 87%
and 68% of the specified targets, respectively. The optimum process parameters were 4 A of current, 106 ls of pulse on
time, 79 ls of pulse off time, 38 V of voltag
Multifunctional properties of gadolinium doped annealed zinc oxide nanoparticles
No full textIn the present study, Zn1βx Gdx O (x = 0.02) nanoparticles have been synthesized by chemical route (Sol-Gel Method) using polyvinyl alcohol (PVA) as chelating agent. These materials are annealed from 500 Β°Cβ1000 Β°C with a step size of 100 Β°C temperature. The multifunctional properties of the prepared materials have been studied in view of their structural, morphological, optical, magnetic, and mechanical characteristics. X-ray diffraction (XRD) studies have shown that ZnO (zinc oxide) nano powders are crystallized in the Wurtzite hexagonal structure, and the structural parameters have been determined. Transmission Electron Microscopy (TEM) study conducted on a sample annealed at 900 Β°C revealed nanoparticles size as βΌ22 nm. Spherical nanoparticles with irregular particle morphology was observed from SEM (Scanning Electron Microscopy) images. Energy dispersive spectroscopy (EDS) accorded the existence of elementary components of the prepared samples. The characteristic peaks of ZnO are evident from Fourier transform infrared spectroscopy (FTIR) study. The bandgap energies from UVβvisible studies levied to decrease from 3.31 eV to 3.13 eV with annealing temperatures. Near band edge emission exhibited redshift from Photoluminescence (PL) study with the increase in annealing temperature. The Dilute Magnetic Semiconductor (DMS) nature was observed on the studied materials, making them promising materials for multifunctional spin-based applications. The study of Mechanical properties suggests that these materials may find applications as cutting fluids, nanofillers and nanolubricant additives
Heart Disease Prediction using Deep Learning Techniques
Get PDFConsidering that heart attacks are one of the main causes of unexpected mortality, heart attack prediction is essential. With the help of treatment histories and current health
conditions, the healthcare industry generates substantial volumes of data each day that can be exploited to forecast future heart attacks that could affect a patient. Eventually,
when making decisions this suppressed information from the health care data can be employed. Researchers are concentrating on creating software that can aid doctors in
making decisions about a patient's health, including the diagnosis and outlook of heart disease. The major goal of this paper is to foresee the likelihood of having a heart attack
before it happens. By treating patients early, this can lower the risk to their lives, increase their chances of survival, and lower treatment costs. Through graphical representation of
the outcomes, comparative analysis of the accuracy of deep learning algorithms like Feedforward Neural Network, Long Short-Term Memory (LSTM) and Bidirectional LSTM will
be carried out
A COMPARATIVE ANALYSIS ON THE COMBINED MULTI LEVEL FUNCTIONALITY FRAMEWORK IN CLOUD ENVIRONMENT WITH ENHANCED DATA SECURITY LEVELS FOR PRIVACY PRESERVATION
Get PDFCloud Computing (CC) refers to a network of remote servers and user access via the Internet. Distributed data centers all around the globe are responsible for providing the infrastructure and hosting the servers that power cloud services. The dynamic user group handling and securing their sensitive information
using cryptography model is a challenging task as the user groups are continuously increasing. Encryption is a better answer for these kinds of problems, but allowing access to users in the cloud has its own set of challenges. From the client's point of view, when the information is saved in the cloud, it
should be crypted well to ensure that no other user can read it if they get access to it in any way. Using cloud storage comes with a number of potential drawbacks, including the lack of security for critical information. This study examines the considerations that should be made when choosing a cloud service provider, with a focus on the client's encryption needs and how, without them, the client runs the risk of either losing data or paying more than necessary to the cloud service provider. To enhance the privacy preservation and key management issues a combined framework that handles data encryption, key
management and distribution, cloud user group management is analyzed in this research that enhance the data security levels using cryptography model and the key management and distribution process with the dynamic cloud user groups. This research performs a comparative analysis by comparing the combined framework with traditional models. The proposed model when compared with the existing methods exhibit better security level
Empowering early diagnosis :leveraging machine learning for breast cancer detection
Get PDFWorldwide, breast cancer ranks as the second greatest cause of mortality for women. Research into breast cancer detection is essential due to the positive impact early identification and prompt treatment may have on patient outcomes. Analysis of mammograms and other medical data using machine learning algorithms has shown encouraging results in the identification of breast cancer. In this study, we survey the current top methods for detecting breast cancer via machine learning. In this article, we'll go through the many machine learning models used for breast cancer diagnosis, as well as the difficulties inherent in creating models that can be relied upon to accurately diagnose the disease. In addition, we point out the potential benefits of machine learning in breast cancer screening and diagnosis and suggest new avenues for
stud
A Review of DDoS Evaluation Dataset: CICDDoS2019 Dataset
Get PDFDDoS assaults are a danger to network security because they overwhelm target networks with malicious traffic, making them unusable as a result. One of the most significant concerns is the lack of a real-time DDoS attack detector that requires low processing power. When it comes to testing novel detection algorithms and approaches, good datasets are vital. After evaluating current datasets, the authors of this paper propose a new DDoS taxonomy. After that, we produce a new dataset, CICDoS2019 that addresses all of our issues. Third, we offer a new family detection and classification technique based on network flow features extracted from the created dataset, which we call FlowNet. Finally, we rank the most relevant feature sets for identifying distributed denial-of-service (DDoS) assaults