Volume 12 | Issue 4 |

 Abstract

Introduction-.Ovarian Haemorrhagic cyst also known as functional cyst is fluid filled sac that bleeds.Though minor cyst resolves on its own ,cysts of more than 10mm size may require surgical intervention so as to avoid complications. Patient's main concerns-abdominal pain and per vaginal spotting and menta symptoms of deliriums and delusion .Diagnosis-Ovarian Haemorrhagic cyst Intervention- Based on individualisation Opium 200 single dose was prescribed Outcomes- The patient showed marked improvement in the symptoms without experiencing any adverse effects.. The individual curative response of the case was assessed using Modified Naranjo criteria for homoeopathic case reporting, casual attribution (MONARCH) which had a score of 12. Conclusion- Homoeopathy has the potential to treat Ovarian Haemorrhagic Cyst. Its further role should be evaluated with more controlled trials.

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Individualised Homoeopathy, ovarian haemorrhagic cyst ,A case report.

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This paper examines the burgeoning field of sustainability in pharmaceutical manufacturing, focusing on research efforts to promote environmentally friendly practices and reduce the industry's environmental impact. The research encompasses various aspects, including green chemistry, solvent selection, energy-efficient processes, waste management, life cycle assessment, green packaging, and green analytics. By exploring alternative synthetic routes, solvent options, and energy-saving technologies, pharmaceutical companies aim to minimize waste generation, energy consumption, and greenhouse gas emissions. Additionally, strategies for sustainable waste management, life cycle assessment, and green packaging design are explored to mitigate environmental pollution and resource depletion. Collaboration between academia, industry, and regulatory agencies is emphasized to foster innovation and knowledge sharing in sustainable drug development and manufacturing. This abstract highlights the importance of these research endeavors in advancing sustainability within the pharmaceutical industry, ultimately contributing to a more environmentally responsible approach to drug production.

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 Keywords

Component, formatting, style, styling, insert. sustainability, pharmaceutical manufacturing, green chemistry, solvent selection, energy-efficient processes, waste management, life cycle assessment, green packaging, green analytics, collaboration.

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Historic assets create a focal point that people can relate to their past and are familiar with - giving a sense of place. Our built heritage represents the very best of our past. It also provides a huge resource that can play an important role in the future of our towns, cities and rural areas in terms of the stimulus provided to regeneration and the promotion of sustainable development. Evidence from across the country demonstrates that 'Heritage Works' is a valuable asset that has an important role to play as a catalyst for regeneration, redevelopment, rehabilitation, revitalization. Working with heritage assets brings a unique set of issues to the development process, such as understanding the special conservation, planning, funding and construction matters associated with them. The process also requires specialist knowledge, where expertise in any of these areas is lacking, projects can fail. The study has concentrated on listing buildings, as these heritage assets are most commonly associated with regeneration projects. Heritage assets are a key component of sustainable development. The re-use of such assets can be used to boost local economies, attract investment, highlight local distinctiveness and add value to neighbouring properties.

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Heritage, Conservation, Critical study, Muziris, Paliam Nalukettu

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UNESCO's World Heritage Site Great Himalayan National Park Conservation Area (GHNPCA) is highly undulating and difficult terrain to cover by ground and hence along with Remote Sensing and GIS based study, an attempt was also made to study the structure of the vegetation through profile diagrams. Stratified random sampling was performed. Materials used mainly vegetation map, Survey of India maps, hypsometer, graph paper, measuring tape and graph paper. Size of plots was determined based on species area curve. Observations recorded for the months of May and November. For each vegetation type a plot of 25?10 m was laid along the gradient. Sample Plot has been drawn on graph sheets at an appropriate reduced scale (1 mm = 20 cm). Slope was obtained using hypsometer. A total of 66 sample plots were laid. Of these 46 were in communities with trees and shrubs and 20 were for grasslands. The sub tropical ground flora is quite disturbed and subjected to grazing and fire. Under broad leaved forest, regeneration of Aesculus indica is quite good with the presence of different age group plants. Ground flora is very rich because of thick humus and moist conditions under Broadleaf Mixed with Conifers Forests. In Secondary Scrub; Eco-development zone, been put to lot of biotic pressure. Secondary scrubs are found intermixed with agriculture. A very interesting community of Viburnum was located on way to Maror from Shakti. Subtropical riverain forests have Alnus nitida groth in narrow belts. Pure disturbed as well as undisturbed patches of Hippophae salifolia are found under Temperate Riverain Forest. Alpine Scrub, Betula utilis forms the top story, first story is formed by Rhododendron companulatum. On steep slopes extensive grasslands are there. Alpine grasslands are found mixed with alpine scrub. Terraces with orchards are very common sites under Habitation /Agriculture/ /Orchard. Rocks with scattered growth of grasses in alpine zone above tree line. The effort will be useful, for mounting an information base, which will enable conservation practices in future.

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Keywords: Conservation, profile diagram, sample plot, vegetation

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In this attempt of research work synthesis, hardness and tribological properties of aluminium metal matrix composites (AMMCs) are produced as Al-7068 matrix metal and reinforcements as TiC particulates through stir casting. Experiments were conducted by varying weight fraction of TiC (0%, 3%, 6%, 9% and 12%). Wear characteristics of Al-TiC composites have been investigated under dry sliding. Dry siding wear tests have been carried out using pin-on-disk wear tester at different loads (2 Kg & 3 Kg) and different speeds (200RPM, 400RPM, 600RPM). The tests were carried out making possible combination of load and speed with each weight fraction of TiC. The results indicate that on addition of TiC reinforcement in Al-7068 Alloy has improved the wear performance. The effect of load indicates that as load increases the wear increases and while speed increases the wear performances decreases. The hardness of the composite materials was measured using Brinell hardness tester and found that as weight fraction increases the hardness of the composite increases.

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Aluminium composite, Al-7068, Tribological properties, Dry sliding Wear, Titanium Carbide, Stir casting, Hardness.

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This research work titled "A Study on Workers Participation in Management and its Impacts of Dhanalakshmi Ceramics at Karur", was done with the main objective of knowing the opinion of respondents towards Workers participation in Management. To compile the profile of the respondent. To analyze the participation of workers in decision-making. To relate workers' participation with organizational efficiency. To study the relationship between the employer and employee. The Primary data has been used to collect data with the help of a Questionnaire. The sample size is 120. Tools used in this study Chi-Square test, ANOVA, and the major findings are the majority 70.8% of respondents strongly agree that I feel motivated to actively participate in management decisions at my workplace. The majority 53.3% of respondents strongly agree that I feel that my opinions and suggestions are valued in decision-making processes at work. The suggestions for the betterment of the company are Proper motivation factors that have to be followed to increase the level of participation of workers. Employers and workers should agree on the objectives of the industry.

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Organizational effectiveness, Employee satisfaction, Workers participation, Decision-making.

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This project aims to address the crucial task of Alzheimer's disease (AD) classification through the implementation of two distinct deep learning models: Convolutional Neural Network (CNN) and Transfer Learning using Densenet201. Leveraging a diverse dataset comprising four classes - Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented - sourced from open-access repositories, our study seeks to compare the efficacy of these models in accurately diagnosing different stages of Alzheimer's. The CNN model will be developed from scratch, while Densenet201, a pre-trained model, will be fine-tuned for optimal performance. Evaluating and comparing these models will provide insights into their respective strengths and weaknesses, aiding in the determination of the superior approach for AD classification. This research contributes to the advancement of diagnostic methodologies, potentially enhancing the early detection of Alzheimer's Disease.

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Alzheimer's Disease, Deep Learning, CNN, DenseNet201.

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In response to society's need for personal data, a number of online information platforms have evolved as a crucial business model. In these platforms, a service provider purchases raw data from data suppliers and subsequently provides value-added services to data customers. The data trading layer presents a significant challenge for data consumers, too, namely how to confirm that the service provider is indeed obtaining and processing their data. Furthermore, data contributors often are reluctant to provide sensitive personal information to data consumers or to reveal their true identity. A method for identifying contributors that is based on honesty is given. A user can submit an assessment to the system after purchasing a product to see if contributors are permitted. Data contributors cannot personally identify others under Data Verification and Privacy Preservation, even though they must legally provide their own data. Furthermore, it is required of the service provider to accurately gather and manage data. Furthermore, critical raw data and the personally identifiable information of data providers are appropriately safeguarded. For data verification and privacy protection, we also used two more data services, and we closely evaluated their effectiveness on two real-world datasets.

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Emergency demand response (EDR); Advanced Encryption Standard(AES);Access control (AC); Fair and Privacy-Preserving Deep Learning (FPPDL).

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The technology allows users to with the assistance of algorithms, users of the technology may predict whether or not they will develop diabetes mellitus and whether or not they will acquire cancer. This system uses a number of classification models, including association rules, logistical regression, artificial neural networks, decision trees, and Naive Bay. The accuracy of every model in the project is then determined using the Random Forest technique. The project is a smartphone application made to predict whether or not a person's class is at risk for diabetes and cancer. The dataset used is a Pima Indians Diabetes Data Set, which contains information on individuals, some of whom develop diabetes. We are investigating four popular classifiers for sickness risk prediction. These algorithms include Bay Naive, Regression Logistic, Artificial Neural Network, and Decision Tree. Subsequently, these algorithms are merged with bagging and boosting procedures to improve each model's solidity. Finally, the Random Forest algorithm is applied. The purpose of this research is to assess diabetes and cancer risk without requiring blood work or hospital stays for any individual. Encouraging and improving human health is another goal of the study.

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Extreme Learning Machine(ELM); Decision Tree (DT);Random Forest(RF);Convolution Neural Network(CNN); Confusion Matrix(CM);Artificial neural network(ANN);Super Vector Machine(SVM).

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This project aims to advance avian identification through the utilization of audio signals, employing a comprehensive approach that integrates with deep learning techniques. The study focuses on developing three distinct models: Ridge Classifier, Support Vector Machine (SVM) Classifier, and Artificial Neural Network (ANN). By converting audio signals into numerical data using standard Python libraries, we aim to create robust models capable of accurately identifying bird species based on their distinct audio signatures. The Ridge and SVM classifiers, representing classical ML paradigms, will leverage advanced feature extraction methodologies. Simultaneously, the ANN model, a deep learning approach, will harness the power of neural networks to capture intricate patterns within the audio data. This interdisciplinary fusion of ML and deep learning techniques is poised to significantly enhance avian species identification, contributing to biodiversity monitoring and conservation efforts.

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 Keywords

Bird Spices Classification; Audio Signal Processing; SVM, Ridge Classifier; Artificial Neural Network.

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