Volume 12 | Issue 5

 Abstract

This abstract explores the intricate world of mutual funds, delving into the nuances of navigating familiarity and assessing the investment aspects associated with these financial instruments. Mutual funds have become a popular choice for investors seeking diversification and professional management of their portfolios. However, understanding the complexities and risks involved in mutual fund investments is crucial for making informed decisions. The abstract discusses the importance of familiarity with different types of mutual funds, including equity funds, bond funds, and hybrid funds, to align investment objectives with risk tolerance and return expectations. It highlights the need for investors to conduct thorough research and due diligence before selecting a mutual fund that suits their financial goals. Furthermore, the abstract delves into the critical aspects of assessing mutual fund investments, such as performance evaluation, expense ratios, fund manager expertise, and historical returns. It emphasizes the significance of Analyzing these factors to make sound investment choices and achieve long-term financial objectives. Overall, this abstract serves as a guide for investors looking to navigate the complex landscape of mutual funds, providing insights into how to leverage familiarity and assess key investment aspects to make informed and strategic investment decisions

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Mutual Funds, Gender Differences, Age Groups, Income Levels, Investment Weightage, Investment Perspectives, Investment Methods, Investment Duration, Mutual Fund Types, Investment Benefits

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The Unani System of Medicine traces its origins to Egypt and its sister civilization, Mesopotamia, with its earliest roots often attributed to Hermes (Idris A. S.). While it initially took on a more scientific form under the influence of Hippocrates in ancient Greece, it underwent further refinement and development by Arab physicians. Upon reaching Iran and other Persian countries, the Unani System experienced significant enhancement and improvement, thanks to the substantial contributions of Persian physicians. They played a crucial role in not only translating existing texts but also composing original works, which were subsequently translated into various languages and served as textbooks in European schools during the Dark Ages in Europe. Here, we explore the contributions of several physicians written in Persian language and provide brief accounts of their remarkable achievements in this article.

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Mesopotamia, Unani System of Medicine, Persian physicians

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The Permanent Magnetic Synchronous Motor (PMSM) has garnered increasing attention in various high-performance applications such as electric vehicles, owing to its advantageous features including high power density, power factor, and efficiency. This paper introduces a novel approach for speed control of Permanent Magnet Synchronous Machines (PMSM) drives, utilizing fuzzy logic-based control techniques. PMSM torque and speed control has relied on Field Oriented Control (FOC). However, advancements in vector control techniques have expanded the application of PMSM motors into domains where previously only DC drives were feasible. The proposed fuzzy logic-based speed controller is meticulously examined with MATLAB/SIMULINK under various operating conditions, including sudden changes in load demand and frequent alterations in speed, such as sudden speed reversals. Through rigorous analysis and simulation, this approach aims to demonstrate its effectiveness in maintaining precise speed control and robust performance in dynamic operating environments. By combining the advantages of PMSM technology with the adaptability and robustness of fuzzy logic control, this research seeks to contribute to the advancement of high-performance electric drive systems, particularly in applications requiring agile and responsive motor control.

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PMSM, PWM, Fuzzy Logic Controller, Vector Control

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Shingles caused by the reactivation of the varicella-zoster virus, often presents with painful skin rashes and blisters. While antiviral medications are commonly used for treatment, herbal remedies have gained attention for their potential to alleviate symptoms and promote healing. These study explores the development and efficacy of a novel herbal cream formulated specifically for shingles relief. The cream comprises a blend of natural ingredients known for their anti-inflammatory, analgesic, and antiviral properties, including extracts of licorice root, Neem,Tulsi, Turmeric, Aloevera gel , Rose oil, and St. John's worth.The research methodology involved a randomized controlled trial with participants diagnosed with shingles. The cream was applied topically to affected areas, and outcomes such as pain reduction, lesion healing, and overall symptom relief were assessed over a defined period..Preliminary results indicate promising outcomes, with a significant reduction in pain and faster healing observed among participants using the herbal cream compared to controls. The cream's tolerability and safety profile were also favorable, with no reported adverse effect. .In conclusion, this herbal cream shows potential as a complementary or alternative treatment for shingles, offering symptomatic relief and aiding in the recovery process. Further studies are recommended to validate these findings and explore the mechanisms underlying the cream's therapeutic effects.

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Key words: licorice, Shingles, varicella zoster virus.

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This survey investigates the growing imperative for effective abnormal event detection in video monitoring applications, propelled by the ubiquitous deployment of surveillance cameras in both public and private spaces. Recognizing the challenges posed by labour-intensive human-based surveillance, the study focuses on the current landscape of state-of-theart methodologies in machine learning and deep learning for abnormal event detection. By expanding on prior research, the survey meticulously assesses the techniques' applications within the context of surveillance videos, scrutinizing datasets utilized and providing a nuanced analysis of their strengths and limitations. Through an exhaustive literature review, the paper aims to shed light on the principal challenges inherent in abnormal event detection, distilling key insights to guide future research endeavours in this critical domain. Moreover, the survey contributes to the discourse on bolstering security through intelligent video surveillance systems by synthesizing existing knowledge. In a parallel exploration, the review delves into the realm of crime prediction employing machine learning and deep learning techniques. Examining over 150 articles, the paper elucidates the diverse algorithms applied in predicting crime occurrences, offering insights into patterns and trends. Access to datasets used by researchers is provided, along with an analysis of prevalent approaches and factors influencing criminal activities. The paper identifies potential gaps and proposes future directions to enhance prediction accuracy. As a comprehensive reference for researchers, this overview serves to consolidate the multifaceted landscape of crime prediction using machine learning and deep learning approaches, facilitating advancements in this field.

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Video Monitoring, Surveillance Cameras, Artificial Intelligence , Machine Learning, Deep Learning, Scrutinizing Datasets, Crime Prediction, Threat Detection, Crowd Surveillance.

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Six Sigma is a data-driven methodology aimed at improving processes by reducing variability and defects. This paper explores the origins, principles, methodologies, and applications of Six Sigma. It also examines the benefits, challenges, and future trends of Six Sigma in various industries.

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Six Sigma, DMAIC, DMADV, process improvement, variability reduction, quality management

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The internet is a network of networks that's connects computers all over the world. Education became more effective by using internet. The teaching and learning of today is making use of the internet in so many forms and ways. Therefore computer awareness is very much essential for teachers as well as learners' .On this present study of the data, the following conclusions were evolved. They are primarily with type of school, gender, medium of instruction, subject, locality of school and training in internet .It is concluded that the internet awareness away the higher secondary school student is found to be different, internet awareness same male student are found to be same that of female student, English medium student internet awareness formal to be same that of Tamil medium, internet awareness rural student are found to be same that of urban student. It is concluded that computer science student awareness are forward to be higher away science and mathematics students.

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Internet, Training, Awareness.

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Assessing the effect of transparent communication on employee job fulfilment and retaining in Pune's higher secondary education area is a appreciated effort that can make available insights into the changing aspects of organizational communication and its impact on employee good fortune and organizational success. This study explores the impact of transparent communication on employee job satisfaction and retention in Pune's higher secondary education sector. Transparent communication is important for nurturing trust, relationship and organisational commitment, yet its specific effects within educational surroundings endure underexplored. Utilizing a mixed-methods approach, qualitative interviews and quantitative surveys were conducted among employees across various higher secondary institutions in Pune. Introductory analysis indicates a positive relationship between transparent communication and both job satisfaction and holding. Employees observing transparent communication practices within their institutions information, higher levels of job satisfaction and are more tending to remain with their organizations. Qualitative visions expose the significance of transparent communication in construction of trust, encouraging inclusivity and improving organizational values.

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Assessing the Effect of Transparent Communication on Employee Job Satisfaction and Retention in Pune's Higher Secondary Education Sector.

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Apple, Carrot, Beetroot, Probiotic juice, Fruit, Vegetable, Non dairy products, Vegan, Physicochemical characteristics

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Left ventricular remodeling (LVR) may result from rheumatic mitral stenosis (RMS) and may continue even after valve repair. Determining the best time for intervention can be aided by identifying markers for early structure and function in rheumatic heart disease individuals who are susceptible to unfavorable long-term circulation following surgery. The purpose of this study was to determine if preoperative global left ventricular long-axis strain (LVGLS) and mechanical discretization (MD) factors might be used to predict postoperative unfavorable left ventricular rupture (LVR).

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NA

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There are a lot of inexpensive, tiny sensor nodes in Wireless Sensor Networks (WSN) that can send data. limited memory, limited processing power, low power supply, and short communication range are the limitations of wireless sensor nodes. Due to these limitations, this network is susceptible to numerous attacks, particularly the bwdos_sinkhole_attack attack. A type of attack known as a "bwdos sinkhole attack" involves the hacked node attempting to draw in network traffic by promoting its bogus routing update. A bwdos_sinkhole_attack attack has the ability to initiate spoofing, selective sharing, and removing or modifying routing data, among other impacts. It might also be used to send fictitious data to the base station. In this paper, time-varying in wireless sensor networks, bwdos_sinkhole_attack attack detection technique based on snapshot-based Neighbour-controlled Traffic-centric (TSNT) technology is used to address these issues and enhance service quality. The base station monitors the traffic using the TSNT algorithm, and it keeps track of the list of sensor devices the packet has passed through over time. The presence of bwdos_sinkhole_attack is identified from this list using a snapshot of the WSN captured at different times. In parallel, the base station receives assistance from a reliable outside source within the WSN network to identify the bwdos_sinkhole_attack attack through message digest hash-based data integrity verification. The experimental outcome demonstrates that, in comparison to other current techniques, the suggested method efficiently detects bwdos_sinkhole_attack.

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Network vulnerability, Bwdos_sinkhole_attack attack, Fake data, Dropping, Service quality

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Water body identification and monitoring are crucial in Earth observation and environmental monitoring efforts. These bodies, including natural lakes, rivers, reservoirs, and human-made ponds, hold significant ecological and practical value, impacting ecological balance, resource management, and disaster risk reduction. While methods like NDWI and MNDWI have been traditional tools for delineating water bodies from satellite data, the rise of machine learning offers new avenues for accuracy enhancement. This project focuses on using machine learning for water body extraction from remote sensing data, aiming to improve detection accuracy beyond traditional methods. Through model evaluation, the study seeks to advance current practices, benefiting resource management, environmental conservation, and disaster readiness. The trained model shows steady improvement over 60 epochs, starting with an accuracy of 63.39% and a loss of 0.5331 in the first epoch, eventually reaching approximately 81.14% accuracy and a loss of 0.1505 by the final epoch. This progression indicates the model's ability to learn and adapt, capturing underlying data patterns effectively. Validation accuracy also increases consistently, reflecting the model's ability to generalize well. The convergence of training and validation metrics signifies the model's stability and reliability in making predictions on unseen data.

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Water body extraction, machine learning, remote sensing, deep learning, environmental monitoring

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Primary education of the children provides the foundation for all other higher levels of education as reported by National Policy on Education-1986 and it highlights the measures in the direction of making primary education free and compulsory for all adopting innovative programme of incorporating Mid-Day Meals and Black Board Operations in the Primary schools. The Govt. of India has launched a Nation-wide mission oriented project of Sarva Shiksha Abhiyan (SSA) in the motto of making primary education free and compulsory for all with support services of quality teaching learning materials, attractive school environment equipped with all health and sanitary facilities, incentives, scholarships, study materials, uniforms, aids and training services to teachers of the Primary schools etc. Further, the Act of Right to Education of Children-2009 (RTE Act) has been enforced throughout the nation to give a boost to SSA in the vision of making quality primary education accessible for all with more parental and institutional involvement. In this context, a study was conducted to assess the level of parental awareness about the Right to Education Act of Children in Puri District taking a self made tool comprising 26 items covering different features of the Right to Education Act-2009 enacted by the Govt. Of India applied on a sample of 100 parents of the 6th class students reading in different Primary Schools of Puri District of Odisha. Adopting suitable statistical techniques i.e. Mean, SD and 't' test, the result of the study were examined on the grounds of the Gender, level of Education and locality of habitation of the sampled parents and it was found that significantly male parents had better awareness than the female parents about the Right to Education Act of Children establishing role of the factor of gender influencing their awareness. Further the study also revealed that the locality of habitation of parents had positive impact on level of awareness of parents about Right to Education Act of Children causing to have better awareness with the parents of urban locality than the Parents of Rural locality. Similarly the study revealed that the factor of education had positive role in influencing the level of awareness of parents about Right to education Act of Children causing parents having matriculate level of education to have better awareness about Right to Education Act of Children than the parents having below matriculate level of education. The study therefore implied that parental level of education is important factor for shaping their awareness about right to education Act of children and therefore all parents urged to have higher level of education. Parents of the rural locality and female parents should be educated and made aware about the features, benefits and implications of right to education Act of their children through mass media, TV, printed materials and organising orientation programmes, meetings and seminars etc in villages and schools. Parental awareness about Right to Education Act of children can lead towards the success of SSA project.

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Parental Awareness, RTE Act-2009

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Mamang Dai is one of the influential women writers from North-East region. Her work is a voyage to explore the dark corner of this region. Dai's home state, Arunachal Pradesh, is home to a variety of tribes, traditions and folk beliefs. Each tribe has its own rules and everyone is expected to abide by them. Dai's uses myths and superstitions through the life of 'Pinyar', the protagonist in her interconnected tale Pinyar, the Widow from the novel The Legends of Pensam (2006). The present study is an attempt to explore the incidents which highlight the presence of myths and superstitions and their effect on human world.

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Myth, tradition, folk-belief, effect, human.

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The idea that theory, text and literariness have a triangular interrelationship or the thesis that the three have, or should have, autonomous existence - have been the subject of debate among philosophers and literary critics ever since the traditional liberal humanist approach to English studies was challenged by a plethora of theories and perspectives during the post war years, particularly 1970 onwards. It appears that theoretical positions about literature were never explicitly formulated by liberal humanists, at least in Britain, and that everything remained implicit, yet a widely current body of theoretical work existed from the beginning within literacy studies the world war. Aristotle's Poetics, which is perhaps the earliest work of theory, is about the nature of literature itself, in spite of its title. Aristotle developed for the first time a 'reader-centered' approach to literature since his concept of tragedy sought to describe how it affected the audience by arousing in them sympathy for and empathy with the plight of the protagonist, resulting into what he calls 'catharsis'. Without going back to the history of literary theoretisation in detail, suffice it would be to say that Sir Philip Sidney's is the first prominent name in English writing about literature, who wrote his Apology for Poetry (1580). Expanding Ovid's credo of 'decere delictendo' - to teach by delighting - Sidney espouses the Horatian dictum, that a poem is 'a speaking picture, with this end, to teach and delight'. This revolutionary idea of literature or literary text having as its primary aim the giving of pleasure to the reader, as advocated by Sidney was about literature in general, and not about individual works or writers. Critical theory, in fact, long pre-dates the literary criticism of individual works.

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TRIANGULATION OF THEORY, TEXT AND LITERARINESS : A STUDY IN FEMINISM AND GENDER PERSPECTIVES

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The increasing concerns regarding water scarcity and the overuse of fertilizers, leading to biomagnification, are particularly pronounced in arid regions with low rainfall. These challenges necessitate innovative solutions for efficient water management in agriculture. This paper introduces an integrated micro irrigation and fertigation system designed to automate and optimize irrigation and fertigation processes in water-scarce environments, providing precision tailored to specific plant needs. The Micro Irrigation and Fertigation system represents a pioneering solution that meticulously tailors water distribution to the specific needs of each plant. The seamlessly integrated fertigation system ensures optimal dispersion of Nitrogen, Phosphorus, and Potassium (NPK) over the field at regular intervals, fostering maximum crop growth and health at different stages of plant development. This holistic approach, combining micro irrigation with targeted fertigation, enhances water efficiency and facilitates precise nutrient delivery, promoting overall sustainability. The system features a user-friendly interface (UI) and a robust backend infrastructure, ensuring efficient data management for irrigation and fertigation in agricultural fields. The notable automation reduces the need for manual intervention, maximizing agricultural productivity and reflecting a strong commitment to environmental sustainability. This micro irrigation and fertigation system mitigates challenges related to unequal water distribution among field crops and excessive fertilizer application through a comprehensive approach that integrates advanced technology, data analytics, and efficient hardware.

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Automated Irrigation, Optimized Fertigation, User-friendly Interface(UI),unequal water distribution

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The integration of Internet of Things (IoT) technology in grass cutting processes revolutionizes traditional lawn maintenance practices by enabling real time monitoring, data-driven decision-making, and enhanced efficiency. This paper explores the design and implementation of an IoT-enabled grass cutter system aimed at optimizing lawn care operations. The proposed system comprises smart sensors embedded within the grass cutter, which continuously collect environmental data such as grass height, moisture levels, and weather conditions. These sensors transmit data to a centralized IoT platform, where it is processed and analysed in real-time. Machine learning algorithms are employed to derive insights from the data, facilitating predictive maintenance scheduling, optimal cutting patterns, and resource allocation. Furthermore, the IoT platform interfaces with a user-friendly mobile application, allowing homeowners or maintenance professionals to remotely monitor grass cutting progress, adjust settings, and receive alerts or notifications regarding maintenance tasks. This seamless integration of IoT technology not only enhances operational efficiency but also promotes sustainability by minimizing resource usage and reducing environmental impact.

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Grass Cutter, IoT, Internet of Things, Real Time Monitoring

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Color blindness is a type of disease in which a person cannot differentiate between some colors like red-green blue-yellow. A color blindness test helps to detect the correct type of color blindness. If you cannot pass the test you suffer from color vision deficiency. It is also called color blindness. Most color blindness tests can specify that children who have problems identifying colors such as red-green and blue-yellow can be treated easily if they are below 5 years. This could be A characteristic in specific activities in school and at home. Color blindness is a very common condition: out of 10 people one color blindness patient has some level of color blindness of visual impact, however there are many tasks in daily life that require the ability of color recognition and visual discriminate. Color blindness cannot be cured. Today, technology gets up with solutions to help individuals with color blindness to see the image and distinguish between the different yet the most basic colors using some algorithms.

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Color Vision Deficiency, Color Correction, Recoloring, Accessibility.

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Vision Vigil is a video classifier, leveraging state-of-the-art machine learning model and generative AI to tackle the challenges in categorizing digital multimedia. The project's primary objective is to efficiently distinguish between Safe for Work (SFW) and Not Safe for Work (NSFW) content, providing users with preemptive content warnings. By integrating a ML model and LLM model, Vision Vigil ensures accurate classification, offering a proactive approach to content analysis. This innovative system goes beyond conventional video categorization, empowering diverse domains such as entertainment, security, education, and healthcare. Vision Vigil's adaptability enables seamless integration into various applications, offering a versatile solution for platforms prioritizing user safety. Through its sophisticated technology, Vision Vigil stands as a sentinel, cautioning viewers about the nature of video content and contributing to a more secure and informed digital landscape. In an era dominated by digital multimedia, Vision Vigil emerges as a crucial tool for enhancing content moderation and user experience.

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Video Classification, Audio Analysis, Text Classifier, Machine Learning, Multimedia Content, Deep Learning, Content Categorization, Generative AI, Gemini API Feature Extraction, Neural Networks, Sentiment Analysis, Image Recognition, Pattern Recognition, Content Moderation, Digital Media, Audio Segmentation, Video Analytics, Speech-to-Text, Image Classification, Language Models, Predictive Modeling, Information Retrieval, Topic Modeling, Audio Clustering, Document Classification, Image Segmentat

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The integration of renewable energy sources like photovoltaic (PV) solar panels and wind turbines (WT) into the smart grid is highly beneficial due to the inherent advantages of these resources. Solar and wind energy are not only environmentally friendly and sustainable but also widely available and cost-effective. By leveraging the capabilities of the smart grid, which includes advanced communication, control, and automation technologies, the efficiency and reliability of renewable energy systems can be significantly enhanced. This integration supports the transition to a cleaner energy landscape, reduces dependence on fossil fuels, and helps mitigate the impacts of climate change by lowering greenhouse gas emissions. The hybrid system approach, combining both solar and wind energy, further improves energy stability and availability, compensating for the intermittent nature of these renewable sources.This comparative study aims to evaluate the efficiency and effectiveness of these MPPT methods in optimizing the power output from the hybrid renewable energy system. Particle Swarm Optimization (PSO) is known for its robustness and fast convergence, potentially offering superior performance in tracking the maximum power point under varying environmental conditions. On the other hand, the P&O method is simpler and more widely used, but it may not perform as well in rapidly changing conditions. By implementing and simulating these techniques in MATLAB/SIMULINK, the study provides insights into their practical applications and performance metrics, guiding the development of more efficient renewable energy systems.

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Hybrid system, Photovoltaic system, Wind Power Energy System

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