Volume 12 | Issue 6

 Abstract

The Panchamahabhuta theory in Ayurveda, which comprises Ether, Air, Fire, Water, and Earth, is foundational for understanding human anatomy, physiology, and clinical practice. This research examines the correlations of these elements within the human body by comparing classical Ayurvedic texts like the Charaka Samhita and Sushruta Samhita with modern anatomical and physiological sciences. The study aims to bridge traditional and contemporary medical paradigms, emphasizing Ayurveda's holistic approach that integrates body, mind, and spirit. Each element is linked to specific bodily structures and functions: Ether to cavities and consciousness, Air to movement and neural activity, Fire to metabolism and vision, Water to fluids and cohesion, and Earth to structural integrity and stability. This chapter explores these manifestations from the cellular to the systemic level, noting, for instance, the association of bones with the Earth element and digestive enzymes with Fire. It also highlights the clinical relevance of the Panchamahabhuta theory, using case studies to show how imbalances in these elements cause health issues and how Ayurvedic treatments can restore balance. By integrating Ayurvedic concepts with modern medical understanding, the study enhances Ayurvedic practice and provides valuable insights for contemporary medicine, promoting a more holistic and effective healthcare approach.

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Panchamahabhuta, Five Elements, Panchmahabhut Theory, Panchmahabhut chikitsa

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In today's world, ensuring the safety of women is paramount. To address this concern, we present a novel women safety device equipped with advanced technologies. The device integrates an ESP32 microcontroller, NEO-6M GPS module, I2C LCD display, push buttons, relay for pepper spray activation, and a DC motor for anesthesia control. These components work together to provide comprehensive safety features. In conclusion, our women safety device represents a significant advancement in personal security technology. By combining cutting-edge features with user-friendly design, it empowers women to navigate the world with confidence and peace of mind.

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Injection,Pepper spray,GPS,Google Firebase

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 Abstract

UTTRAKHAND KE MHILAO KI STHITI : DASHA AUR DISHA (POLICE BALO KE VISHESH SHANDARBH ME )

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UTTRAKHAND KE MHILAO KI STHITI : DASHA AUR DISHA (POLICE BALO KE VISHESH SHANDARBH ME )

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The focus of the project is OCR. The acronym for this is "Optical Character Recognition." An input device called OCR is used to read printed text. Character by character, OCR optically reads the text, transforms it into a machine-readable code, stores it in the system memory, and then creates a document out of it. Optical character recognition (OCR) technology was employed to convert printed text into editable text. OCR is a very useful and common method in many applications. Techniques for segmenting and preparing text can affect the accuracy of OCR. Text retrieval from the image may occasionally be difficult due to its complex backdrop, style, size, and orientation variations. This technology can identify text contained within a digital picture. Text recognition in scanned documents and images is a widely used feature. OCR can be used to turn an image or a physical paper document into a text-rich, readable electronic version.

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Flutter Framework, Dart, Flutter, Android.

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One of the most significant dental conditions that adds to the worldwide burden of chronic illness is periodontal disease. This study aimed at finding the awareness and motivation surrounding periodontal health among an educated demographic class not affiliated with the medical profession. Through an online survey, responses from individuals outside the medical field, aged between 20 and 60, were collected to gauge their understanding of periodontal health. Initial findings suggested a nuanced panorama of awareness among this cohort. A questionnaire set of 20 was created on Google forms and forwarded with link on social media. 500 people of various professions including teachers, engineers, data scientist, pilot etc. were included in survey and result were collected in the digital pie diagram. It was a 6 month survey during June to December 2023.The result were in percentage which revealed that the respondents' knowledge of the value of dental care, particularly periodontal health care, is only minimal.

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 Keywords

- Survey, Periodontal health, Awareness, Motivation, Oral health perspectives, Preventive measures.

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In the sector of community control and safety, maintaining an accurate and stable community surroundings gives massive challenges that often exceed conventional tracking strategies. This look at examines the effectiveness of net scanners, with a particular cognizance on their capacity to enhance safety, optimize performance, and make certain compliance. The studies method consists of deploying advanced network scanning gear to find out gadgets, analyze open ports, and determine network vulnerabilities. These tools file targeted records about community settings and device interactions, enabling you to become aware of potential protection dangers and activity. By analyzing dynamic characteristics along with network traffic, tool conduct, and protocol utilization, the scanner can locate subtle anomalies that traditional strategies may also pass over. Extensive information sets, inclusive of actual network configurations and hobby logs, are cautiously amassed, pre-processed, and used to assess the tools. Benchmark analyses are in comparison to standard manual strategies, conventional community control software, and other automatic scanners to evaluate the effectiveness of the network scanner. In conclusion, the combination of advanced community scanners is a promising manner to improve community safety, overall performance control, and compliance. This research contributes to ongoing efforts to broaden the extra sturdy and adaptive network management answers had to keep a steady and efficient virtual infrastructure in modern day complicated network environments.

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Web Scanning, Anomalous conduct and worm.

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Blindness is a form of disability or inability to see. Lack of vision is only a disadvantage if the activity being pursued by the person who is blind is one that requires sight. Blind people may face troubles only due to inaccessible infrastructure and social challenges. Navigating around places and using pedestrian access signs are one of the biggest challenges for a blind person, especially the one with zero vision. For a physically challenged person, gaining independence may the most valuable asset and a person with visual impairment can lead an independent life in an environment which is designed with required adaptations for them. This paper presents a real-time object detection system using YOLO (You Only Look Once) based deep learning algorithm to help visually impaired people in their daily life. A Cobotic Spectacle is a cutting-edge product designed specifically for visually impaired individuals. The system seamlessly integrates face motion recognition, object identification and text detection functionalities using YOLO Algorithm. YOLO uses convolution neural networks (CNN) and single forward propagation through a neural network for object-detection in real-time. In this proposed system, YOLOv8, a real-time Object detection algorithm trained on the Common Object in Context (COCO) dataset is used to identify the object present before the person which produce high rate of accuracy in object identification. The system delivers the output through synthesized voice, enhancing visual perception. The face motion recognition module promotes improved social interaction, while object identification increases environmental awareness. This integrated solution combines edge processing and cloud-based analysis for portability, real-time processing, and computational efficiency. A Cobotic Spectacle empowers visually impaired individuals, providing a state-of-the-art smart spectacle system that enhances visual perception, promotes independence, and improves their overall quality of life.

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Visually impaired, Object detection, YOLOv8, Text to Speech, CNN

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This paper conducts a thorough, in-depth analysis of Bapsi Sidhwa's narrative techniques and their portrayal of women in literature. Sidhwa's unique storytelling methods serve as a lens through which to explore the complexities of women's experiences, offering readers an in-depth understanding of female characters and their roles within the literary landscape. Through a meticulous analysis of Sidhwa's works, this study aims to uncover the nuances of her narrative craft and its impact on the representation of women in literature. Drawing upon key concepts from narrative theory and feminist literary criticism, this paper examines how Sidhwa constructs women's narratives, delving into aspects such as character development, plot structure, and narrative voice. By closely examining Sidhwa's use of language, symbolism, and imagery, this study seeks to unravel the layers of meaning embedded within her narratives, shedding light on the diverse ways in which women's stories are crafted and conveyed. Furthermore, this analysis considers the broader socio-cultural contexts in which Sidhwa's works are situated, exploring how her narratives reflect and respond to issues of gender, identity, and power. Through a comparative approach that spans across Sidhwa's literary corpus, this paper highlights the evolution of her narrative techniques and their implications for the representation of women in literature. Ultimately, this study aims to contribute to a deeper understanding of Sidhwa's contributions to women's literature and to underscore the significance of narrative craft in shaping the portrayal of women in literary discourse. By examining the intricacies of Sidhwa's storytelling methods, this paper seeks to illuminate the richness and complexity of women's narratives in literature, offering new insights into the ways in which female experiences are depicted and interpreted within the literary canon.

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Narrative Techniques, Complexities, Imagery, Feminism, Portrayal, Structure, Craft.

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Overweight and obesity in children have become much more common in recent decades; now, one-third of children in the United States suffer from one of these conditions. Early childhood obesity raises the likelihood of obesity later in life. There is proof that family-based behavioral treatment is effective in helping people control their weight and achieve better health outcomes. There is evidence of health hazards associated with obesity, such as metabolic syndrome. In recent times, there has been a rise in the prevalence of type 2 diabetes (T2D) in young people, who are typically obese and have a family history of the disease. Obesity and type 2 diabetes in young people are linked to lower income and membership in ethnic minorities. Since most young people with T2D do not attain ideal glycemic control, they are at an increased risk of developing health issues in the future. T2D and obesity The number of children and teenagers who are overweight or obese is still rising, and the yearly healthcare expenses associated with obesity for patients aged 6 to 17 have risen to $127 million. These days, type 2 diabetes and impaired glucose tolerance are being identified in overweight children and adolescents. They also exhibit early indicators of the insulin resistance syndrome and cardiovascular risk. Youth cardiovascular risk and the onset of type 2 diabetes have been linked to a number of risk factors. These include increased body fat, particularly in the abdomen, insulin resistance, ethnicity (children of African American, Hispanic, and Native American descent are more at risk), and puberty onset. Although the exact mechanism by which these factors raise risk is unknown, their combined effects seem to be the result.

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TYPE-2 DIABETES MELLITUS AND OBESITY

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The advent of blockchain technology has spurred innovation across various sectors, including crowdfunding. Traditional crowdfunding platforms often encounter challenges related to centralization, high fees, and lack of transparency. In response, this paper presents the design and implementation of a decentralized crowdfunding marketplace built on the Ethereum blockchain, utilizing Next.js for the front end, Hardhat for development and testing, and Solidity for smart contract programming. The proposed system aims to mitigate the limitations of centralized crowdfunding platforms by leveraging the inherent features of blockchain technology. By employing Ethereum smart contracts, the platform enables secure and transparent transactions between backers and project creators. Next.js is utilized to develop a user-friendly frontend interface, ensuring accessibility and responsiveness across devices. The project's architecture emphasizes decentralization, enabling creators to initiate crowdfunding campaigns with customizable parameters such as funding goals, durations, and rewards. Backers can participate in campaigns and engage in governance processes through decentralized decision-making mechanisms facilitated by Ethereum smart contracts. Furthermore, the integration with popular Ethereum wallets ensures seamless user interaction, enhancing the platform's usability and accessibility. All transactions and campaign details are recorded immutably on the Ethereum blockchain, providing auditable and transparent records.

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Cryptocurrency, Blockchain

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Orally disintegrating films (ODFs) have garnered significant attention in recent years as a promising drug delivery system. This comprehensive review explores the formulation, advantages, challenges, and future prospects of ODFs in pharmaceutical and Nutraceutical applications. Key features such as composition, formulation techniques, manufacturing considerations, taste-masking and Evaluation parameters are discussed. The strengths and weaknesses of ODFs are analyzed, along with potential solutions to address formulation complexity and stability challenges. The review concludes with insights into ongoing research efforts and the potential impact of ODFs on drug delivery innovation.

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Orally disintegrating films, drug delivery, formulation, Advantages

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 Abstract

BaWO4 crystallizes into a space group I41/a tetragonal scheelite structure. Nanocrystalline BaWO4 can be produced from a variety of salts, and the synthesis process, reaction temperature, precursor concentration, solution pH, and reaction duration all affect the shape of the particles. The synthesis process and the level of crystal lattice distortion determine the optical characteristics of BaWO4. This is a wide band gap semiconductor with blue luminescence and is an excellent host material for doping. Long luminescence life periods, large Stokes/anti-Stokes shifts, crisp band emissions, and exceptional photo-stabilities are characteristics of rare earth activated tungstate luminous materials.

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BaWO4; structural properties; optical properties; doping, photoluminescence

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This article reported to understand the transformative real-time impact of data analytics and artificial intelligence on the Property and Casualty insurance industry. The article investigated how these advanced technologies help to personalize insurance offerings, perform risk assessment, fasten claims processing, and improve customer engagement. Various critical factors such as data security, algorithmic bias, and model transparency are critically reviewed, with AI implications for customer trust and regulatory compliance. Reviewing various research analyses of benefits and challenges, this research work generates a valuable overview regarding the potential of data analytics and AI implications on property and casualty insurance by maintaining ethical considerations and regulatory guidelines.

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Property and Casualty insurance, data analytics, artificial intelligence, personalized insurance, risk assessment, claims processing, customer engagement, data security, algorithmic bias, model transparency, regulatory guidelines.

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A competition has been set up due to globalization and the entry of the new markets has been made possible with the opening of the markets. Issues of cross-cultural conflicts have also arisen in this new vogue due to professionals of the different nationalities working collectively. An analysis of the various research articles shows that the prior knowledge of the existing cultural influence and impact on the cordial relations of the projects must be looked into steps like the training on acquiring the skills in working and in managing the groups in the multicultural conditions . Conceptual framework, research theme and methodological approaches have been discussed.

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Globalization, Cross-cultural conflicts, Multiculturalism, Conflict, Culture.

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With the increase in complexity of System-on-Chip (SoC) designs to enable greater logic integration driven by Moore's law and beyond Moore principles, the process of verification of the design becomes increasingly complex. To facilitate faster and more efficient verification, a standard methodology like the Universal Verification Methodology (UVM) is essential. UVM provides a library of SystemVerilog (SV) base classes, which can be extended to meet specific requirements. Verification is a crucial component in chip design and fabrication process, necessitating thorough testing at both the block and full-chip levels. This paper focuses on the block-level verification of an IP, General Purpose Input/Output (GPIO) Controller. Utilizing the UVM architecture, a constrained random testbench has been developed to create a reusable and robust verification environment. The testbench supports multiple tests to ensure comprehensive design coverage, aiming for 100% code coverage. Advanced SystemVerilog features, including assertions, coverpoints, covergroups, and multithreading, are integrated into the testbench to enhance its effectiveness.

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Intellectual Property, Verification, UVM, SystemVerilog, General Purpose I/O controller, code coverage

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The "Online Crime Reporting System" is a comprehensive platform designed to facilitate efficient communication between citizens and law enforcement agencies. With users able to report incidents and track the status of their complaints, the system ensures transparency and accountability in addressing community concerns. Additionally, users can provide valuable feedback to enhance system effectiveness. Meanwhile, administrators have access to a dedicated dashboard for managing reported complaints, enabling timely resolution and response. By leveraging technology to streamline crime reporting processes and foster collaboration between citizens and authorities, the system aims to contribute to safer and more secure communities.

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administrators

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Public relations (PR) plays a pivotal role in shaping the reputation, credibility, and overall success of corporate organizations. This study delves into the multifaceted impacts of PR, exploring its influence on brand perception, stakeholder engagement, crisis management, and corporate communication strategies. Through a comprehensive review of existing literature and case studies, the research highlights how effective PR practices can enhance organizational transparency, foster trust, and drive business growth. It examines the strategic integration of PR within marketing, its role in digital and social media landscapes, and the importance of maintaining a consistent and positive public image. The findings underscore that robust PR initiatives are crucial for navigating the complexities of modern corporate environments, mitigating risks, and leveraging opportunities to build sustainable competitive advantages. By elucidating the tangible and intangible benefits of PR, this study provides valuable insights for organizations seeking to optimize their PR strategies and achieve long-term success.

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Public relation, Crisis management, Stakeholder engagement, Reputation protection, Trust building.

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Breast cancer imaging characteristics and patient-related data make up the study's extensive dataset. Tumor traits, patient demographics, and medical history are all part of these features, which comprise a variety of therapeutically significant aspects. Next, we use this dataset to train and test SVM and KNN models. We want to see how well they can distinguish between benign and malignant tumors. Classification accuracy, sensitivity, and specificity are just a few of the performance indicators used to assess each model's efficacy. You can learn a lot about the models' accuracy in breast cancer case classification and tumor type discrimination from these measures. This research highlights the strong performance of SVM and KNN algorithms in detecting breast cancer, which is important. This exemplifies the promise of machine learning methods to greatly improve medical diagnostics. More accurate and advanced breast cancer detection techniques are in the works, and this study hopes to add to the existing body of knowledge in the area. These techniques have the potential to enhance patient care by allowing for earlier identification, which in turn improves treatment results. In the end, the study aims to connect machine learning with clinical practice, providing useful insights that may guide the creation of personalized breast cancer diagnostic plans for patients. Researchers, physicians, and healthcare providers are working together to make these breakthroughs so that healthcare professionals can fight breast cancer better. Patients all across the globe will benefit from this.

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Machine learning, SVM, KNN, medical diagnostics, imaging data, early detection, and imaging.

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The Waste Food Management and Donation System organising and distributing leftover food is a critical component in addressing food waste and insufficient dietary intake. The current method makes a lot of effort, but it is not without flaws. In order to optimise food flow, maintain food safety, and make a concerted effort to reduce food waste from the start. By collaborating, utilising technology, and spreading the word, we can improve it. In this manner, we can guarantee that no one goes without food and that quality food is not wasted.

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Food waste management, food redistribution, food excess, food waste reduction, logistics, food safety, preventive techniques, technology, public awareness, sustainability, and food insecurity.

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Cerebral stroke is a life-threatening medical condition that requires immediate diagnosis and intervention to minimize its devastating effects. Timely and accurate classification of stroke subtypes is crucial for determining appropriate treatment strategies and improving patient outcomes. In this research, we explore the application of supervised learning approaches to classify cerebral stroke subtypes based on relevant medical data. The study involves the collection of a diverse dataset comprising anonymized patient records, including clinical indicators, medical history, risk factors, and diagnostic imaging results. The data is carefully pre-processed to handle missing values, normalize numerical features, and address any potential biases. The experimental results demonstrate the effectiveness of supervised learning approaches in accurately classifying cerebral stroke subtypes. The model with the highest performance metrics is identified, and its potential integration into clinical settings is discussed. Additionally, the research explores potential challenges and limitations of the proposed approach, along with potential strategies to improve model accuracy and generalizability.

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stroke, risk prediction, machine learning, data analysis

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