Volume 13 | Issue 1 |

 Abstract

This study investigates the impact of porous inserts in forced convection systems on heat transfer enhancements. ANSYS Fluent 2021 is used to study the effects of varying porous layer thickness and Reynolds number on pressure drop and heat transfer rates in pipes. Porous media of varying radius ratios of R = 0.8, 0.6, 0.4, and 0.2 were analyzed under constant temperature conditions. The results from the study found that increasing the thickness of the porous medium generally enhances the peak axial velocity, shifts it closer to the pipe wall, and improves overall heat conduction efficiency. A significant temperature gradient near the pipe wall was observed, indicating an increase in the Nusselt number and overall heat transfer efficiency. Also, there exists an optimal porous thickness beyond which no significant increase in the Nusselt number occurs. The study found that the influence of porosity on heat transfer is less pronounced compared to that of porous layer thickness. The Performance Evaluation Criteria (PEC) indicates that porous media inserts substantially enhance the overall heat transfer.

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 Keywords

Porous Media, Porous Layer Thickness, Thermal Dispersion, Nusselt number

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Biomedical waste (BMW) management is critical for hospital hygiene and preventing the spread of infections. Housekeeping staff, who handle and dispose of biomedical waste, must possess adequate knowledge of proper management practices. This study aimed to assess the level of knowledge regarding BMW management among housekeeping staff in selected hospitals of Ujjain City, Madhya Pradesh, and to identify gaps that need to be addressed through training. Methods: A quantitative research approach using a quasi-experimental pre-test and post-test design was employed. A total of 300 housekeeping staff from various hospitals in Ujjain were selected through stratified random sampling. Data were collected using a structured questionnaire covering demographic details, general knowledge, waste segregation, handling procedures, legal knowledge, and hospital-specific practices. Knowledge levels were evaluated before and after the intervention (training program). Data were analyzed using descriptive and inferential statistics. Results: The pre-test results showed that 20% of participants had good knowledge, 50% had moderate knowledge, and 30% had poor knowledge. Following the intervention, the percentage of participants with good knowledge increased to 50%, while those with poor knowledge decreased to 10%. The t-value was 24.02, with a p-value of < 0.05, confirming that the training significantly improved the participants' knowledge.Conclusion:The study demonstrated that training significantly enhanced the biomedical waste management knowledge of housekeeping staff. However, challenges remain in consistent waste segregation and PPE usage, which require ongoing training and support. The findings underscore the importance of regular educational interventions to improve compliance with BMW management practices, ensuring better hospital hygiene and reduced health risks.

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 Keywords

Biomedical Waste Management, Housekeeping Staff, Hospital Hygiene, Training, Waste Segregation, PPE, Knowledge Assessment, Ujjain City.

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This study investigates the development of advanced feature engineering tools and techniques to uncover potential associations between polycystic ovary syndrome (PCOS) and gynecological cancers, such as ovarian cancer. By leveraging variables derived from ultrasound imaging and metabolic data, this research establishes a systematic approach to feature extraction, transformation, and selection for predictive modelling. Key ultrasound variables, such as follicle count and ovarian volume, are integrated with metabolic indicators, including glucose levels and hormonal profiles, to construct a comprehensive dataset. These features are then processed using methodologies like recursive feature elimination, correlation analysis, and principal component analysis (PCA) to identify the most significant predictors. Machine learning models, including logistic regression and random forests, are trained on the engineered features to evaluate the predictive accuracy and robustness of the approach. The results highlight the pivotal role of combining multimodal datasets in achieving high predictive performance, with random forests achieving an F1-score of 0.87. Furthermore, this research emphasizes the importance of feature engineering in medical diagnostics, offering insights into the complex interrelations between PCOS and ovarian cancer. The findings advocate for continued advancements in data integration and model development to support personalized healthcare interventions.

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 Keywords

PCOD/PCOS, Gynecological cancer, Machine Learning, Deep Learning

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Abstract--The rapid advancement of technology, particularly machine learning (ML) and the Internet of Things (IoT), is revolutionizing agriculture by optimizing crop production and ensuring sustainability. This paper presents a web-based platform developed to assist farmers in selecting suitable crops, predicting fertilizer requirements, and diagnosing plant diseases. The platform uses data from Kaggle, ML models implemented in Flask, and ReactJS for the frontend. Farmers can input NPK values and city names to receive insights on temperature, humidity, and fertilizer requirements, while also benefiting from disease prediction through image uploads and SMS alerts. The system aims to improve farming practices, promote sustainability, and enhance productivity [3], [4].

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Keywords--Machine Learning, Precision Agriculture, ReactJS, Flask, Crop Prediction, Disease Detection, SMS Alerts, Fertilizer Recommendation

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'Causality' and 'freedom' are central to discussions in Philosophy of action. Causality and freedom have been addressed from ontological as well as ethical perspectives. Ontologically, causality refers to the dynamics of changes signifying the way there is continual success of cause and effect. Needless to say that cause and effect are relative terms .That which is a cause in relation to the effect can be seen as the effect in relation to its antecedent which is the cause. The relationship between cause and effect is one of necessity such that given the cause effect can be predicted in advance and given the effect the cause can be inferred retrospectively. Causal necessity lives no room for human freedom, Therefore moral judgments cannot be passed on the causal process in the state of nature. 'Freedom' on the other hand is a moral concept. Freedom of will makes sense only in human domain, because human beings are endowed with autonomy of will. Given the circumstances, different individual are free to act differently. Causality and freedom do not go hand in hand. State of nature is understood and explained in terms of causal laws whereas it is the freedom of will which defines the distinctiveness of human action. The distinction between action and event is fundamental. Event is causally determined whereas action is determined by free will. In other words an event is caused whereas action is willed. Morality makes sense only in the domain of human actions, because human agents hare autonomy of will. The 'will' can be used and abused as well on account of which the moral judgments are rendered meaningful. The dissertation is an exercise to a undertake analysis of the notion of 'causality' and 'freedom' in Buddhist Philosophy.

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Causality, Freedom, Determinism, Samsara, Nirvana, Law of karma, suffering.

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Ayurvedic medicine is an ancient system that originated in India thousands of years ago and has been used for prevention and treatment of heart disease with the support of cardiovascular health. Heart-related illnesses are mainly caused by imbalances in the three doshas, and treating CVDs can be difficult when trying to restore their balance. Historically, herbal treatments have been used to cure a wide range of illnesses in many traditional medical systems, demonstrating their importance in human healthcare. Various Ayurvedic herbs like Ashwagandha, Arjuna, and Guggulu are scrutinized for their roles in circulating blood, lowering cholesterol, and controlling blood pressure. But a review of clinical studies and case reports indicates that if used in conjunction with conventional therapies, Ayurvedic treatments may promote heart health. More research and clinical trials are necessary to comprehend their efficacy in contemporary cardiology. The paper notes that integrating ayurvedic medicine with conventional treatments may provide comprehensive management of heart disease.

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Ayurveda, Heart Disease, Cardiovascular Health, Herbal Medicine, Arjuna, Ashwagandha, Guggulu.

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Various essential elements are crucial for the survival and well-being of human beings. other beings for their survival, agriculture plays a crucial role in the development of the economy of India. The major impediment to food security is population growth resulting in increasing demand for food. To increase the supply, farmers need to grow more on the same land. Tech can help farmers produce more through crop yield prediction. 1. The primary goal of this. paper is to predict 3. Applying crop yield. rainfall, crop, meteorological conditions, area, production, and yield variables, which have threatened the long-term sustainability of agriculture (Mahmood et al., 2017). Crop yield forecasting: a decision-making tool that employs machine learning and deep learning techniques to assist in making informed choices regarding agricultural production

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 Keywords

Deep Learning, Machine Learning.

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In light of the growing importance of environmental sustainability, the transition to renewable energy sources like solar energy has become crucial. Solar products, which harness solar energy for various applications, hold significant potential in addressing energy needs while minimizing environmental impact. However, the widespread adoption of these products is heavily influenced by consumer awareness and understanding of their benefits and functionality. This study examines how demographic variables such as age, monthly family income, and educational qualification impact consumer awareness of solar products. Using a descriptive and exploratory approach, the research gathered data from 120 participants in Haryana through a structured questionnaire. The analysis, which includes Chi-square tests and descriptive statistics, reveals significant associations between consumer awareness and demographic factors. Younger consumers, those with higher income, and individuals with higher educational qualifications are more likely to be informed about solar products. The findings emphasize the need for targeted education and outreach to enhance awareness, promoting the adoption of solar energy solutions for a more sustainable future.

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 Keywords

Exploring, Awareness, Consumers, Solar, and Products.

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The protection of personal identification numbers and passwords is a significant barrier for cybersecurity. Deceptive login pages soliciting personal information deceive billions of users daily. A variety of nefarious approaches are employed to deceive individuals into accessing harmful websites, such as phishing emails, clickjacking, malware, SQL injection, session hijacking, man-in-the-middle attacks, denial of service, and cross-site scripting. The offender creates a fraudulent yet convincingly comparable website to deceive victims into divulging their credentials. Researchers have offered many security solutions to mitigate these vulnerabilities; however, these methods are both ineffective and susceptible to error. We introduce and implement a client-side defence system that employs machine learning to detect phishing attempts and recognise fraudulent web sites. Our machine learning algorithm serves as a proof of concept for the Google Chrome plugin PhishCatcher, which classifies URLs as either trustworthy or suspicious. The random forest classifier evaluates a login page for authenticity after acquiring four web properties. The precision and validity of the extension were evaluated on multiple real-world web applications. The findings exhibited a precision and accuracy rate of 98.5% when evaluated on 400 authentic URLs and 400 identified phishing URLs. We assessed the latency of our technique using forty phishing URLs. We improved Random Forest by integrating XGBOOST, a technique that evaluates datasets through forest trees or ensembles of estimators to optimise features more efficiently and achieve superior accuracy.

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 Keywords

IOT devices, Support Vector Machine (SVM) and Random Forest (RF).

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This theoretical conceptualization evaluated the importance of green leadership in bringing about a sustainable change in the educational institutions particularly focusing on the psychological factors influencing the stakeholder behaviour. This framework discusses the Theory of Planned Behaviour, Value-Belief-Norm theory, and Social Identity Theory. This paper also identifies the key characteristics of green leaders that include sustainable knowledge, visionary thinking, effective communication and emotional intelligence. Simultaneously, the challenges are also identified that are faced by the green leaders in implementing the sustainable change.

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 Keywords

Green leaders, sustainable change, psychological factors, stakeholders, educational institutions

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