Volume 12 | Issue 12 |

 Abstract

Constructing embankments on soft clay deposits poses significant challenges in geotechnical engineering, especially in terms of ensuring stability and controlling settlement to meet design requirements. The observational method is commonly used in such cases, allowing for adjustments to the design during construction. Matsuo (1977) proposed a construction control chart that combines vertical and lateral displacements, making it particularly useful for predicting the failure of embankments on soft ground during construction. Soil properties, loading history, and environmental conditions all have a substantial impact on these predictions, even with small variations. To evaluate this, the paper compares field observations from a case history with results from finite element analysis (FEA) conducted using PLAXIS 2D. The finite element analysis shows a good agreement with the observed field data. The analysis of results shows that 22% and 36% of reduction in maximum settlement and lateral displacements respectively. Finally, the stability of the embankment is assessed using Matsuo's construction control chart which agrees with the construction stages.

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 Keywords

Embankment, Soft clay, Finite element Analysis, Stability

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 Abstract

Agricultural produce drying is a critical step in preserving food and reducing post-harvest losses. The drying methods often rely on fossil fuels, making them costly and environmentally harmful. To address these issues, a multipurpose solar dryer is designed specifically for agricultural purposes. This innovative system utilizes solar energy to efficiently dry various agricultural products, including fruits, vegetables, grains, and herbs. The design incorporates solar collectors, a heat exchanger, and a drying chamber, all powered by renewable energy sources. This solar dryer offers significant advantages such as reduced operating costs, minimal environmental impact, and improved product quality. Its versatility and adaptability make it a valuable tool for farmers in diverse geographical regions, aiding in the reduction of food waste and enhancing the sustainability of agricultural practices. Experimental testing and performance evaluation demonstrate the effectiveness of the multipurpose solar dryer in reducing moisture content, preserving nutritional quality, and extending the shelf life of dried products. The results highlight the potential of solar drying technologies in sustainable agriculture and food processing industries, contributing to food security and economic development in rural communities. This paper underscores the potential of solar drying technologies in addressing food security challenges, especially in rural areas where post-harvest losses are prevalent. By harnessing renewable energy sources like solar power, the solar dryer not only contributes to sustainable agricultural practices but also aligns with global efforts to mitigate climate change. Overall, the multipurpose solar dryer offers a viable solution for improving food preservation, reducing waste, and promoting economic resilience in agricultural sector.

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 Keywords

Solar Dryer, Multipurpose, Portable, Design, Drying, Collector & Heat exchanger

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 Abstract

A significant societal issue, particularly among youngsters, is cyberbullying. "The deliberate, repeated, and hostile use of information technology to harm or harass other people" is the definition of cyberbullying. It has increased in popularity since the creation of social media platforms like Twitter and Facebook. The Internet is the sole foundation of contemporary society. People today have a hard time imagining life without the internet. People have been utilizing social networking sites in recent years to exchange information, thoughts, and ideas. Different types of content, including text, images, audio, and video data, may be used in these exchanges. Giving users the opportunity to manage the messages posted on their own private space is a significant problem in today's online social networks (OSNs), since it prevents the broadcast of inappropriate information. As of right present, OSNs hardly support this need. As a result, social media academics are placing more and more emphasis on the automatic identification of messages that engage in cyberbullying. The use of rules and norms, human moderators, and blacklists based on offensive language are some of the traditional strategies for combating cyberbullying. In order to automatically identify cyberbullying behaviors, a principled learning framework must be created. A flexible rule-based system that enables users to tailor the filtering criteria to be factual to their walls and a soft classifier powered by machine learning that automatically labels messages in content-based filtering help achieve this. Server can learn the words and save them in the database based on this filtering. Before the message can be exchanged, the server might review the words at the moment of transmission and prohibit.

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 Keywords

Cyberbullying, social media, information technology, harassment, social networking sites, inappropriate content, content moderation, machine learning, rule-based system, soft classifier, offensive language, user control, filtering, human moderators, blacklists, automatic identification, server review, privacy.

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Cervical cancer is the fourth leading cause of cancer in women worldwide. Early detection of cervical intraepithelial neoplasia (CIN) can improve patient survival .This paper proposes a deep learning-based technique for determining whether a cervical cancer diagnosis is good or bad. By harnessing the power of transfer learning, the framework combines the benefits of pre-trained models like ResNet50, MobileNet, and DenseNet with the capabilities of CNNs. Our architecture is trained on a comprehensive database of cervical cancer images, comprising both online sources and magnification data-generated images. Using this model, our body can identify cervical cancer as positive or negative, which shows the effectiveness of CNNs and changes the study by checking medical images.

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 Keywords

Cervical Cancer, Deep Learning, CNN, Transfer Learning, ResNet50, MobileNet, DenseNet

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In this project, a computerized greenhouse monitoring and control system is considered to control the environmental and climatic parameters for optimal plant growth. This system leverages sensor fusion of multiple sensors (temperature, humidity, soil moisture, and light intensity) for constant monitoring of the greenhouse environment. An embedded microcontroller reads the data and automatically controls devices such as fans, sprinklers, and lights (depending on the conditions needed for optimal plant growth). The automation minimizes the need for Human interference, saves water, and increases Energy efficiency. This system can be scaled for different greenhouse sizes and customized for various plant requirements.

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 Keywords

Greenhouse, Sensor, Actuator, Environmental Monitoring, Controlling, IoT etc

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Animal husbandry is a vital component of agriculture that faces mounting challenges due to increasing demand, climate change, and the necessity for sustainable practices. Artificial Intelligence (AI) offers innovative solutions to optimize operations, improve animal welfare, and promote environmental sustainability. This paper examines how AI can revolutionize animal husbandry, discussing key applications, benefits, and the challenges associated with its adoption.

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AI Artificial Intelligence Animal Husbandry

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 Abstract

This survey paper explores the progress made in using neural networks for diagnosing Autism Spectrum Disorder (ASD), a condition marked by challenges in social interaction, communication, and behavior. Timely diagnosis is crucial for effective intervention; however, traditional diagnostic methods can be subjective, lengthy, and may not provide the accuracy needed for early detection. As machine learning and artificial intelligence (AI) advance, neural networks present promising opportunities to automate and enhance the accuracy of ASD diagnosis, thereby reducing reliance on conventional assessment techniques. The paper systematically reviews recent studies on the use of neural networks for ASD, focusing on the analysis of data from behavioral assessments, speech patterns, and imaging data, including MRI and other imaging modalities. Key research is assessed based on the neural network architectures employed, the sources of datasets, and the performance metrics used, offering insights into the strengths and weaknesses of different approaches. Our analysis highlights significant challenges in current research, such as limited data availability, model overfitting, and the difficulty of translating complex neural network models into tools that can be easily interpreted in clinical settings. The importance of developing models that generalize effectively across diverse populations and provide reliable insights in real-world scenarios is underscored. Moreover, this survey points out critical gaps in the existing literature and proposes avenues for future research. It recommends the creation of self-updating datasets as a means to ensure that models remain relevant and accurate as new data emerges. Additionally, combining various data types--such as behavioral, speech, and imaging data--could improve diagnostic accuracy and offer a more comprehensive understanding of ASD characteristics. In summarizing the current landscape of neural network applications for ASD diagnosis, this survey seeks to inform future developments aimed at creating more robust, adaptable, and accessible diagnostic solutions.

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 Keywords

Keywords - Autism Spectrum Disorder (ASD) - Neural Networks - Deep Learning - Automated Diagnosis - Artificial Intelligence in Healthcare - Medical Imaging - Neuroimaging - Neural Network Architectures - Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs) - Multimodal Data Analysis - Feature Extraction - Diagnostic Tools - Machine Learning Algorithms - Behavioral Analysis - Early Diagnosis - Precision Medicine - Data-driven Approaches - Medica

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As cloud computing becomes the backbone of modern digital infrastructure, ensuring data privacy and security in cloud environments has emerged as a critical concern for enterprises and individuals alike. This paper provides a comprehensive review of the current landscape of data privacy and security in the cloud, highlighting the key challenges, emerging threats, and advanced techniques designed to mitigate risks. With the exponential growth of cloud services, sensitive data faces diverse vulnerabilities, including data breaches, insider threats, and compliance challenges. The study explores state-of-the-art encryption methods, including encryption for data-at-rest, data-in-transit, and data-in-use, as well as innovative privacy-preserving technologies like homomorphic encryption and confidential computing. The discussion extends to the role of regulatory frameworks--such as GDPR, HIPAA, and CCPA--and their impact on data sovereignty and access control. Additionally, the paper examines the principles of Zero Trust Architecture and its application in enforcing stringent security measures in cloud-native environments. Best practices for incident response, data loss prevention, and secure DevOps (DevSecOps) are analysed to provide a holistic approach to cloud security. Finally, the review addresses the evolving landscape of threats and anticipates future advancements in privacy-preserving technologies, emphasizing the need for a proactive and adaptive security posture. This paper aims to offer a nuanced understanding of data privacy and security dynamics, equipping researchers and practitioners with the knowledge to navigate the complexities of cloud security and contribute to building a more secure cloud ecosystem.

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 Keywords

Cloud Computing, Data Privacy, Data Security, Encryption, Homomorphic Encryption, Zero Trust Architecture, Regulatory Frameworks

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Creative Commons Attribution 4.0 and The Open Definition

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Menopause is the end of menstruation.Menopause is the permanent cessation of menstruation at the end of reproductive life due to loss of ovarian follicular activity. A comparative study was designed to assess the problems faced and coping strategies adopted by women with natural and surgical menopause residing in selected villages of Bardoli Taluka With the objectives to 1) assess the problems faced and coping strategies adopted by women with natural and surgical menopause 2) compare the problems faced and coping strategies adopted by women with natural and surgical menopause 3) find out the correlation between problems faced and coping strategies adopted by women with natural and surgical menopause 4)find out the association of mean score of problems faced and coping strategies adopted by women with natural and surgical menopause with their selected demographic variables.

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 Keywords

Problems faced, Coping strategies adopted, Women, Natural and surgical menopause.

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Creative Commons Attribution 4.0 and The Open Definition

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In the recent time, Polylactic acid is used in fused deposition modeling for making drug carriers, medical tools, cups, automotive parts, packaging, bottles, outdoor applications in purpose of medical, automotive, textile, constructions fields. The limitations of polylactic acid (PLA) is low melting point, not the hardest or toughest plastic, high production cost. To overcome the above problem, ABS is the best suitable polymer. The ABS material normally used in unconventional method. It has the limitations of less dimensional stability, poor surface finish, low hardest property. So, ABS polymer is used in additive manufacturing method. Because, good dimensional stability, very high surface finish, light weight and suitable for vast range applications. Now a days, polymer parts made by additive manufacturing methods (AM) have multiple applications in various industries. Additive manufacturing has the many techniques, but we are using fused deposition modeling (FDM). FDM is a procedure that uses thermoplastic filament that has been parched to its melting point and then thrust out layer to form a 3D object. The aspects associated with 3D printing such as less material wastage, ease of manufacturing, less human involvement, less post processing and energy efficiency makes the process sustainable for industrial use. In this study investigate the effects of Fused Deposition Modeling (FDM) process parameters of material the tensile strength, impact strength and hardness test of three-dimensional (3D) printed Acrylonitrile butadiene styrene (ABS) test parts were determined using the ASTM D638, ASTM D256 and ASTM.

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 Keywords

ABS polymer, Fused Deposition Modeling, Binder Jetting and Manufacturing.

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Creative Commons Attribution 4.0 and The Open Definition