Volume 11 | Issue 10 |

 Abstract

The cam shaft and its associated parts control the opening and closing of the two valves. The associated parts are push rods, rocker arms, valve springs and tappets. It consists of a cylindrical rod running over the length of the cylinder bank with a number of oblong lobes protruding from it, one for each valve. The camshaft is driven by the crankshaft through timing gears cams are made as integral parts of the camshaft and are designed in such a way to open and close the valves at the correct timing and to keep them open for the necessary duration. A common example is the camshaft of an automobile, which takes the rotary motion of the engine and translates it in to the reciprocating motion necessary to operate the intake and exhaust valves of the cylinders. In this work, a camshaft is designed for multi cylinder engine and 3D-model of the camshaft is created using modeling software solid works. The model created in solid works is imported in to ANSYS. After completing the element properties, meshing and constraints the loads are applied on camshaft for three different materials namely aluminum alloy, forged steel and cast iron. For that condition the results have been taken has displacement values and von miss's stresses for the static state of the camshaft. After taking the results of static analysis, the model analysis and harmonic analysis are done one by one. Finally, comparing the three different materials the best suitable material is selected for the construction of camshaft.

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 Keywords

Camshaft, model analysis, 3D-model and harmonic analysis

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 Abstract

Oil spills are a very dangerous occurrence for marine ecosystem is affected and the marine life-forms' existence gets unnecessarily threatened. Oil destroys the insulating ability of fur-bearing mammals, such as sea otters, and the water repellency of a bird's feathers, thus exposing these creatures to the harsh elements. Without the ability to repel water and insulate from the cold water, birds and mammals will die from hypothermia, as a result, it becomes important to employ various oil spill cleanup methods. In our system we may automatically detect & cleaning the oil in the sea water by using a modern technique. In this paper, we present a CNN architecture for semantically segmenting SAR images into multiple classes. The proposed CNN is specifically designed to run on remote embedded systems, which have very limited hardware capability and strict limits on power consumption. Even if the performance in terms of results accuracy does not represent a step forward compared with previous solutions, the presented CNN has the important advantage of being able to run on remote embedded systems with limited hardware resources while achieving good performance. The presented CNN is compatible with dedicated hardware accelerators available on the market due to its low memory footprint and small size. So that the amount of data that needs to be transmitted to ground and processed on ground can be substantially reduced, which will be greatly beneficial in significantly reducing the amount of time needed for identification of oil spills from SAR / real-time images and send SMS alert. And IoT devices equipped with GPS modules are deployed in strategic locations to monitor for oil spills. These devices are capable of collecting GPS location data.

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 Keywords

CNN architecture, SMS alert, Ecological disasters and SAR images

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 Abstract

Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms and deep learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three phase filtration algorithm to achieve the best segmentation results.

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 Keywords

Lymphoblastic, Convolutional Neural Network, microscopic images and Leukemia

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 Abstract

Meyp??u is the physical manifestation of emotions. The feelings and emotions of the experiencer are portrayed through bodily expressions. Uvakai or Love is the most important and indispensable Meyp??u of akam genre poetry, binding the other seven meyp??u-s to create a holistic homogenous platter of emotions. These different shades of emotions converge, diverge and intersperse with Uvakai meyp??u, namely Nakai, A?ukai, I?ivaral, Maru?kai, Accam, Perumitam and Veku?i. To sum up, a combination of these different meyp??u-s in relation to the most important Uvakai meyp??u, offers ample scope to be presented on stage as part of the Bharatanatyam repertoire.

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To preserve and manage the environment, ecological indicators can be used to monitor ecosystems. A chosen few species or groups of species can be used as proxies for more general trends because it is impractical to monitor every component of an ecosystem. The biological components chosen for this function are known as bioindicators. Bioindicators are creatures that are used to keep an eye on the environment's health, the presence of pollution and how it affects ecosystems, the development of environmental cleanup efforts and the testing of new substances, as well as to spot changes in the environment's natural features.

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Biodiversity; Bird population; Nanjarayan lake; Climate change; Conservation; Ecosystem.

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Keywords: Tofacitinib, UV Spectrophotometer, Melting Point, Assay Method, Validation, Accuracy, Linearity, Ruggedness, Precision.

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Abstract : The research on the chemical characterization of super heavy elements in the field of radiochemistry is fundamental and intriguing area of research. Understanding these elements can provide valuable information into the structure, properties and behavior of these elements. Transactinides (atomic no >104 are newest members of the periodic table. Recently, the seventh row of the periodic table was completed by the addition of latest addition of last artificial element oganesson (Og, Z =118). In this paper we will discuss the characteristic and properties of this heavy element.

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synthetic element,short half life

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Brain tumors are common, requiring general medical providers to have a basic understanding of their diagnosis and management. The most prevalent brain tumors are intracranial metastases from systemic cancers, meningiomas, and gliomas, specifically, glioblastoma. Central nervous system metastases may occur anywhere along the neuroaxis, and require complex multidisciplinary care with neurosurgery, radiation oncology, and medical oncology. Meningiomas are tumors of the meninges, mostly benign and often managed by surgical resection, with radiation therapy and chemotherapy reserved for high-risk or refractory disease. Glioblastoma is the most common and aggressive malignant primary brain tumor, with a limited response to standard-of-care concurrent chemoradiation. The new classification of gliomas relies on molecular features, as well as histology, to arrive at an "integrated diagnosis" that better captures prognosis. This manuscript will review the most common brain tumors with an emphasis on their diagnosis, oncologic management, and management of medical complications. Early diagnosis and treatment are important in diminishing the morbidity and mortality rates in the latter group and in providing better palliative management in the former. Advances in neuroradiology, particularly the development of CT, have made early diagnosis possible, given clinical awareness of the syndromes of brain tumour on the part of the physician. New methods of accurate CT-controlled surgery for glioma have also been introduced. Advances in adjuvant methods of radiotherapy, with radiation sensitizers or interstitial implantation, and of chemotherapy by tailoring the drug regimen to an individual patient and by targetting agents specifically to brain tumour, are being sought while the feasibility of fresh modes of immunotherapy has been tested. These developments hold some hope that results for malignant glioma may improve in the forseeable future.

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 Keywords

Introduction, risk factors, pathology ,diagnosis, treatment, reasearch

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In an era characterized by the unprecedented growth of data, Python has firmly established itself as the leading programming language and toolset for data science. This abstract delves into the mutually beneficial relationship between Python programming and data science, shedding light on their central roles in unearthing valuable insights from the ever-expanding realm of data and driving innovation across diverse domains. This abstract serves as an entry point for exploring the transformative potential of Python within the dominion of data science. It emphasizes Python's role as a bridge between raw data and actionable insights, facilitating the extraction of knowledge that empowers individuals and organizations to make data-informed decisions. In an increasingly data-centric world, Python programming and data science stand as vital pillars of innovation, ushering in a new era of discovery and progress.

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Python ,Data Science,NumPy

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In the era of digital communication, social media platforms have become vital, facilitating global connectivity. However, they also confront severe challenges, such as the proliferation of hate speech and offensive content. Hate speech poses substantial risks by targeting individuals or groups based on attributes like religion, race, or gender. Addressing this issue necessitates a combination of technological, community-driven, and policy-oriented solutions. Due to the lengthy posts, it would be beneficial to identify the specific span of text containing hateful content to assist site moderators with removing hate speech. This paper investigates the application of machine learning and deep learning techniques, specifically Bidirectional Long Short-Term Memory (BiLSTM) networks, for automating the identification of hate speech and offensive content. Using BiLSTM in conjunction with GloVe embedding's, we achieved a considerable F1-score of hate span detection, marking a significant improvement over previous methods. Our automated system represents a critical step toward a safer and more inclusive online environment. By seamlessly integrating machine learning and deep learning, it enhances our ability to detect and combat hate speech effectively. This research underscores the importance of technological advancements in addressing complex social challenges within the digital realm. While significant progress has been made, ongoing efforts in technological innovation, community engagement, and policy development are necessary to create a more respectful and harmonious online space.

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 Keywords

hate speech, hate span, embedding's, Bi-LSTM

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