Volume 12 | Issue 7 |

 Abstract

This study explores the impact of mineral admixtures, such as fly ash, silica fume, and metakaolin, on the strength and durability of concrete. The primary focus is on M40 grade concrete, with fly ash replacing a portion of the cement according to IS10262:2009 standards. The replacement levels considered are 0%, 5%, 10%, 15%, and 20% for both silica fume and metakaolin. The investigation involves a comprehensive analysis of compressive strength, split tensile strength, and flexural strength at 7, 14, and 28 days, along with various durability tests. According to the test results, the incorporation of both silica fume and metakaolin has demonstrated an enhancement in the concrete's strength at specific replacement percentages.

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 Keywords

Comprehensive, flexural strength and atmosphere.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: Biogas is one of the promising renewable technologies which can have ability to convert animal, agriculture, municipal and industrial waste into a non-polluting form of energy. Biogas is a mixture of gases (mainly methane, CO2, traces of H2S, moisture, etc.) produced through the anaerobic digestion of biodegradable organic matter. The digested slurry produces in biogas plants as a byproduct is a better source of of nutrient enriched organic matter for use in agriculture. In India, the biogas development programme was started in 1981 and was one of the approaches to reduce the energy crisis in rural areas. At the national level the estimated potential of biogas power generation up to 2020 by off-grid project was only 7.34 MWe (Megawatt electrical). The highest potential of biogas generation is done by Tamil Nadu i.e., 1.97 MWe followed by Karnataka i.e., 1.57 MWe. Under the National Biogas and Manure Management Programme (NBMMP) total 50,56139 number of biogas plants were installed from 1981-82 to 2020-21 out of which Maharashtra state has the highest no. of biogas plants i.e., 9,24111 followed by Andhra Pradesh (5,58962), Karnataka (5,10916), Uttar Pradesh (4,40930) and Gujrat (4,35272). Recently, the government has emphasized several initiatives, including GOBAR-DHAN, New National Biogas and Organic Manure, Sustainable Alternative towards Affordable Transportation, and the waste-to-energy program. These initiatives aim to enhance the utilization of waste, promote cleanliness in villages and towns, and support the Swachh Bharat Mission and Atmanirbhar Bharat campaign.

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 Keywords

Keywords: Biogas, anaerobic digestion, Manure, Compressed biogas, GOARDHAN,

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The most essential and significant task in computer vision is object detection whose objective is to identify all the pertinent items in the image, along with their category and position. In recent times computer vision technologies have given precise and interesting results using convolution neural network (CNN) model to identify objects. This study has investigated the application of auditory perception in comprehending visual objects, highlighting the notable parallels between the senses of sight and hearing. In this work, real-time object detection assisted with a voice based an alert system has been created with the goal of conveying the user 18 what all is in the surroundings. This type of system can support the visually challenged persons for detecting objects at a particular distance in their vicinity which will help them move around safely, without crashing into any object. Another innovative aspect of this work is that it converts into Bengali regional language after translation for aid of those people who do not understand English. Performance of the suggested model for object detection was evaluated against contemporary techniques and the outcomes of the trial demonstrated its capacity to achieve a significant accuracy level.

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 Keywords

real-time object detection; convolution neural network; text-to-speech; voice feedback; visually impaired; deep learning.

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Creative Commons Attribution 4.0 and The Open Definition

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The World Economic Forum's 2024 Global Risk survey,highlighted concern towards increasing rates of dissemination of false news around the world as a significant concern in the digital age. In 2019, statistics report on fake news in India revealed that 51% of respondents are concerned about the twisting of facts and 32% worried about the impact of the manipulation of facts and the use of parody to spread the fake news.The present study focuses on the reasons for the spread of misleading propaganda in India and proposes mechanisms to combat such falsity using a non-doctrinal research methodology. The paper emphasized that significant challenges faced by individuals, society, economy, and the judiciary due to the widespread distribution of disinformation and misinformation by the intense media. It suggests that strengthening freedom of speech and expression may help to protect interest in society against dissemination of fake news in India.

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Creative Commons Attribution 4.0 and The Open Definition

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Fake News, Social-media, Misleading information, Misreport, False propaganda

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Creative Commons Attribution 4.0 and The Open Definition

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This study analyzes the monthly sunshine hours across five locations in Chhattisgarh, India, to determine if there are significant differences between them. The analysis is conducted using an ANOVA F-test in Microsoft Excel. The findings indicate no statically significant differences in sunshine hours among the locations.

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Creative Commons Attribution 4.0 and The Open Definition

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ANOVA, Excel, Renewable, Sunshine, Sun hours

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Creative Commons Attribution 4.0 and The Open Definition

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Lung cancer remains one of the leading causes of cancer-related deaths worldwide, necessitating the development of advanced diagnostic tools to improve early detection and treatment outcomes. This study introduces "DeepLungCareNet", a novel deep learning-based framework specifically designed for the prediction and classification of lung cancer from medical imaging data. Leveraging the power of convolutional neural networks (CNNs) and advanced image processing techniques, "DeepLungCareNet" aims to enhance the accuracy and reliability of lung cancer diagnosis. In this research, we utilized the IQ-OTH/NCCD Lung Cancer Dataset available from Kaggle, comprising thousands of labeled medical images, including computed tomography (CT) scans and X-rays. The dataset was preprocessed through a series of steps, including normalization, augmentation, and segmentation, to ensure optimal input quality for the neural network. Our model architecture was meticulously designed to capture intricate patterns and anomalies within the imaging data, utilizing multiple convolutional layers, pooling layers, and fully connected layers to extract and learn meaningful features. The performance of "DeepLungCareNet" was evaluated using various metrics, such as accuracy, precision, recall, F1-score, and support. Comparative analysis with existing state-of-the-art models demonstrated that "DeepLungCareNet" outperformed traditional methods, achieving superior results in both sensitivity and specificity. Additionally, we employed explainability techniques, such as Grad-CAM, to visualize and interpret the regions of interest that significantly contributed to the model's predictions, thus providing valuable insights for medical practitioners. Our findings underscore the potential of "DeepLungCareNet" as a robust tool for early lung cancer detection, offering promising implications for clinical practice and patient care. The proposed framework not only enhances diagnostic accuracy but also aids in reducing false positives and false negatives, ultimately contributing to better prognosis and treatment planning. Future work will focus on expanding the dataset, incorporating multi-modal data, and exploring transfer learning to further refine and validate the model's capabilities.

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 Keywords

DeepLungCareNet, lung cancer, machine learning, deep learning, medical imaging, convolutional neural networks (CNN), ResNet50, computed tomography (CT) scans, early cancer detection, Grad-CAM, diagnostic accuracy, privacy in AI, IQ-OTH/NCCD lung cancer dataset, Adam optimizer

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In today's medical landscape, understanding blood thinners is crucial for cardiac and neurological patients to mitigate the risk of life threatening complications like strokes or heart attacks. Knowledge of mechanisms, dosages, and potential side effects empowers patients, fostering better adherence and outcomes while minimizing adverse events. A study aimed to assess an individualized education program on blood thinners among 60 cardiac and neurological patients at a selected hospital. The objectives of the study were to assess the pretest knowledge regarding blood thinners among patients in a selected hospital, to determine the effectiveness of individualized education program on blood thinners among patients in a selected hospital and to determine the association between pretest knowledge score on blood thinners with their demographic variables in a selected hospital. Using a pre-experimental approach with non-probability purposive sampling, researchers evaluated patients' pre-test knowledge and demographic variables through structured questionnaires. And the data were collected and analyzed by using descriptive and inferential statistics. Results indicated varying levels of knowledge: 1.67% had average, 31.67% good, and 66.67% very good knowledge. Significant associations between pre-test knowledge and demographic variables were identified, highlighting the program's effectiveness in enhancing patient understanding of blood thinners.

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blood thinners, effectiveness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Crystal structure, dioxole, methyl, single crystal X-ray diffraction and phosphinate; T = 295 K; R factor = 0.0523; wR factor = 0.1154; data-to-parameter ratio = 9.08.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

BHARATIYA PARAMPARAYAM VIVAHA SAMSKARANAM TULANATMAKA SAMIKSANAM

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BHARATIYA PARAMPARAYAM VIVAHA SAMSKARANAM TULANATMAKA SAMIKSANAM

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rising demand for organic products online underscores the necessity for transparency and personalization. This project aims to develop an Android app featuring advanced rating and recommendation capabilities for organic items. Utilizing complex algorithms and machine learning, the app will analyze extensive data from trusted vendors and certification bodies, offering objective product scores based on purity, environmental impact, and ethical manufacturing. The app will provide personalized recommendations through user-friendly interfaces, enabling consumers to make informed choices aligned with their values. Users can rate, discuss, and evaluate products, enhancing the community-driven platform's knowledge and credibility. This innovative approach, integrating advanced analytics, user-centered design, and robust security, aims to revolutionize e-commerce by promoting sustainable purchasing practices and ensuring transparency in the supply chain. The app is expected to boost user engagement, simplify purchasing, and encourage eco-friendly behavior.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Organic Products, Machine Learning, Personalization, Transparency, Sustainability, Recommendation System, User Engagement, Supply Chain

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Creative Commons Attribution 4.0 and The Open Definition