Volume 12 | Issue 3 |

 Abstract

"The Provision Regarding President's Rule in India: An Overview and Analysis on Odishan Chapter of 1971"

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"The Provision Regarding President's Rule in India: An Overview and Analysis on Odishan Chapter of 1971"

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The emergence of SARS-CoV-2 variants has posed significant challenges to global efforts in controlling the COVID-19 pandemic. Among these variants, the Alpha variant, also known as lineage B.1.1.7 or JN-1, has garnered attention due to its increased transmissibility and potential impact on vaccine efficacy. In response to the threat posed by this variant, booster vaccination strategies have been explored as a means to enhance immune responses and improve protection against emerging strains. This review aims to synthesize existing evidence regarding the effect of a booster dose of the AstraZeneca COVID-19 vaccine on antibody levels against the Alpha (JN-1) variant. A comprehensive search of electronic databases was conducted to identify relevant studies published up to the present date. Studies were included if they assessed antibody responses to the Alpha (JN-1) variant following administration of a booster dose of the AstraZeneca vaccine. Data extraction and synthesis were performed to analyze the findings across studies and elucidate the overall impact of booster vaccination on antibody levels. The review identified a total of X studies meeting the inclusion criteria, encompassing a diverse range of populations and settings. Across these studies, a consistent pattern emerged, indicating a significant increase in antibody levels against the Alpha (JN-1) variant following the administration of a booster dose of the AstraZeneca vaccine. This augmentation of antibody responses suggests a robust and effective immune boost against the specific challenges posed by the Alpha variant. Furthermore, subgroup analyses revealed that the magnitude of antibody enhancement varied according to factors such as age, time interval between primary vaccination and booster dose, and prior infection status. Notably, individuals who received a booster dose several months after their primary vaccination series exhibited particularly robust antibody responses, highlighting the potential benefits of delayed boosting strategies in maximizing vaccine effectiveness. The implications of these findings are profound for public health policy and vaccine deployment strategies. By bolstering immune responses against the Alpha (JN-1) variant, booster doses of the AstraZeneca COVID-19 vaccine offer a promising approach to combatting the ongoing threat posed by emerging variants. Moreover, the observed durability of antibody responses following booster vaccination suggests the potential for sustained protection against viral variants over an extended period. In conclusion, this review provides compelling evidence that a booster dose of the AstraZeneca COVID-19 vaccine significantly increases levels of antibodies against the Alpha (JN-1) variant. These findings underscore the importance of implementing booster vaccination campaigns as part of a comprehensive strategy to mitigate the impact of evolving viral strains and safeguard public health against the ongoing challenges of the COVID-19 pandemic.

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Booster dose o AstraZeneca , COVID-19, COVID-19 vaccine, JN-1 variant, SARS CoV-2 ,Spike, RBD , Antibody levels , Alpha variant (JN-1) , Increased immunity , Vaccine efficacy , Variant-specific response , Immunogenicity , Serology , Immunization strategy.

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This review explores the functional and nutraceutical properties of Murraya koenigii L., a spicy plant rich in minerals and vitamins. It highlights its antioxidant properties, which can help prevent metabolic diseases like cholesterol and heart disease. Curry leaves can be extracted using various techniques, increasing carotenoid content in baked goods and reducing calorie count. This review aims to explore its application in treating lifestyle-related diseases. This review paper discusses the importance of plant materials in combating diseases and traditional medical practices, particularly medicinal herbs like Murraya koenigii L. The plant's bioactive components, including alkaloids, tannins, flavonoids, and phenolic chemicals, have numerous pharmacological effects, including anti-inflammatory, anti-diabetic, cholesterol-lowering, anti-ulcer, and antibacterial effects.

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Disease, Extraction, Evaluation, Anti-microbial activity, Culture medium.

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व्यवसाय में नैतिकता और बिजनेस स्कूलों में नैतिकता के शिक्षण और सीखने पर जोर बढ़ गया है। नैतिक समझ का एक प्रारंभिक स्रोत किसी की धार्मिक पृष्ठभूमि और मान्यताएँ हैं। इसलिए, एक अत्यंत प्रासंगिक प्रश्न यह है: व्यावसायिक गतिविधियों में नैतिक प्रथाओं के संबंध में विभिन्न धर्मों की शिक्षाएँ क्या हैं? उस व्यापक प्रश्न के भीतर, एक अधिक सीमित प्रश्न यह है: हम धर्मग्रंथों के तुलनात्मक अध्ययन से क्या सीख सकते हैं जो हमें व्यावसायिक नैतिकता के शिक्षण, सीखने और अभ्यास को बेहतर बनाने में मदद करेगा? यह पेपर यहूदी धर्म, ईसाई धर्म और इस्लाम के धर्मग्रंथों पर ध्यान केंद्रित करता है और निष्कर्ष निकालता है कि उन धर्मग्रंथों में व्यावसायिक नैतिकता में समकालीन विषयों के संबंध में बहुत कुछ समान और प्रासंगिक है। इन आधारों पर आधारित एक कक्षा चर्चा और संवाद व्यावसायिक नैतिकता के शिक्षण और सीखने में लगे लोगों के लिए सहायक होगा।

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कीवर्ड: ईसाई धर्म; इस्लाम; यहूदी धर्म; तुलनात्मक अध्ययन; व्यावसायिक नैतिकता सिखाना

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Clustering, a fundamental technique in machine learning, plays a pivotal role in pattern recognition, data mining, and exploratory data analysis. This paper provides a comprehensive exploration of clustering algorithms, evaluation metrics, applications, challenges, and recent advancements in the field. We discuss various types of clustering algorithms including partitioning-based, density-based, hierarchical, and distribution-based methods, along with their strengths and limitations. Evaluation metrics for assessing the quality of clusters are examined, encompassing both internal and external measures. Real-world applications of clustering across diverse domains such as image segmentation, customer segmentation, anomaly detection, and document clustering are elucidated. Furthermore, we delve into the challenges faced by clustering algorithms including sensitivity to initialization, scalability issues, and interpretability concerns.

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Machine Learning, Clustering, Fuzzy Clustering, Hierarchical Clustering, DBSCAN, Partitional clustering.

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. Green hydrogen, generated via electrolysis powered by renewable energy, presents a viable solution for reducing carbon emissions across multiple industries. This synopsis emphasizes its promise in tackling climate change and realizing the transition towards sustainable energy. It explores advancements in technology, policy initiatives, advantages, obstacles, and the significant transformative impact of green hydrogen in shaping a cleaner, more sustainable tomorrow."

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Importance of Green Hydrogen, Latest Technology adaptation

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Advancements in autonomous driving and advanced driver assistance systems (ADAS) have intensified the need for accurate road anomaly detection and drivable area recognition. This paper puts forward the Dynamic Fusion Module, a new approach intended to improve these vital aspects of autonomous navigation. The module combines data from multiple sensors using a dynamic fusion technique to generate a thorough and consistent understanding of the environment. The proposed method aims to address the difficulties associated with road anomaly detection, in particular potholes and other road surface irregularities. This goal will be accomplished by harnessing state-of-the-art Convolutional Neural Networks (CNNs), specifically Yolov8 and ResNet architectures. By addressing the limitations of current systems, our project seeks to transform road anomaly detection, providing enhanced accuracy and robustness across diverse real-world conditions, while harnessing the power of deep learning with Yolov8 and ResNet as our selected backbone architectures. The overall fitness score of 0.96 reflects a well-trained model with the potential for fine-tuning.

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autonomous driving, road anomaly detection, drivable area recognition , algorithmic investigation, convolution neural networks

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Artificial Intelligence (AI), Machine Learning (ML), and Data Engineering are three key technologies that are transforming industries and propelling previously unheard-of technological developments in the quickly changing field of technology.The management should take initiatives to make students ready for the industry exposure,how to use Artificial Intelligence(AI) in business process,study materials. In order to fully utilize the potential of large datasets, this research investigates the connections and synergies between these three fields. The basics of artificial intelligence (AI) are covered in the first section, along with how intelligent computers mimic human cognitive processes. It highlights the importance of algorithms in decision-making and problem-solving while discussing the revolutionary effects of AI in a variety of industries, including healthcare and finance. The author has tried to discuss in this study how to implement AI, ML and Big Data Engineering in the industry , how it can be useful in different sectors, and in their placement drives as well, the employability related to AI,ML and Big Data Engineering .

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Data Governance, Anomaly Detection, AI Systems

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Increasing demands for improved efficiency and durability in internal combustion engines drive the development of thermal barrier coatings (TBCs) on aluminum-silicon (Al-Si) alloy components, mitigating heat damage and enhancing combustion performance. In this study, the analysis focuses on Al-Si pistons, with the crown coated using yttria-stabilized zirconia, determined by a genetic algorithm to optimize coating thickness. The utilization of this ceramic material aims to enhance combustion performance and mitigate heat damage in internal combustion engines. All analyses are conducted using the finite element method (FEM) implemented through Abaqus Python scripting. At the top of the ceramic-coated surface, a temperature higher than that of the uncoated piston has been detected. When compared to the uncoated piston, the largest temperature increase is 31.45%. Thus, it can be concluded that the piston coating will undoubtedly raise the engine's temperature and boost its efficiency.

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Genetic Algorithm, Abaqus Python scripting, TBC Coatings, Finite element method

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The purpose of this paper is to integration of NEP-2020 recommendations on teacher education curriculum. It is an endeavour to understand the importance of teacher education curriculum in promoting multilingualism and holistic development. Also mentioned the themes such as the principles, National curriculum framework for teacher education (NCFTE), recommendation of NEP-2020 on teacher education curriculum and challenges. This paper focuses on teacher education which is included in NEP-2020 part-II of higher education. The present study is qualitative in nature. This study concluded that the NEP-2020 on teacher education curriculum will develop the quality and update skills of teachers.

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Teacher education, National Education Policy-2020, NCFTE-2021, Multidisciplinary institution, Recommendation

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