The integration ofi Low-Earth Orbit (LEO) satellite constellations into 5G Radio Access Networks (RAN) has emerged as a critical solution fior achieving global connectivity and low-latency communication. This paper addresses the unique challenges ofi RF link budget optimization in satellite-enhanced 5G RAN architectures. Key considerations include dynamic path loss, Doppler efifiects, and power constraints, all ofi which impact the quality ofi service (ǪoS). Techniques such as adaptive power control, beamfiorming, and machine learning-driven optimization are explored. Simulation results and use cases demonstrate how link budget optimization enables seamless integration ofi LEO systems into 5G RAN, improving efifiiciency and reliability.
The exponential growth of data across industries has led to the emergence of complex, multi-database systems, necessitating intelligent and efficient methods for data source selection and query generation. This research explores the transformative potential of Large Language Models (LLMs) in addressing these challenges. By leveraging their advanced natural language understanding and contextual reasoning capabilities, LLMs can dynamically select relevant data sources and generate optimized queries tailored to specific user inquiries and operational contexts. We propose a framework that integrates LLMs to streamline data retrieval and enhance decision-making processes across multiple domains.
The paper discusses the fundamentals of chatbot design, including key components, types of chatbots, and the importance of user experience and interaction design. It then delves into the structure and function of Large Language Models, their training, and their use cases in chatbot development. The paper explores the process of integrating LLMs with chatbot frameworks, highlighting the key steps and the services available for building chatbots, such as Amazon Bedrock, Microsoft LUIS, and Google Bard.Paper explains the fundamentals of chatbot design, provides an overview of large language models, discusses chatbot architecture for handling unstructured and structured data, and highlights the roles of AWS Titan and Anthropic Claude models in the development of retrieval-augmented generation systems.
The drilling of oil and gas wells also requires ensuring safety in the operation of the well construction systems and the protection of the environment and employees. During the drilling of the wells, eruptive manifestations and/or pressure increases in the well may occur (above the pressure value provided by the weight of the drilling fluid), which may lead to the production of oil fluid eruptions (which also contain rock particles and drilling fluid compounds) and cause fires or accidents human lives, as well as environmental pollution.
The rapid evolution of web applications across diverse domains like e-commerce, healthcare, and enterprise solutions necessitates architectures that are scalable, maintainable, and performance-efficient. Micro-Frontends (MFEs) have emerged as a modular alternative to monolithic frontends, enabling independent development, deployment, and testing of UI components. However, challenges such as dependency conflicts, static orchestration strategies, and inefficient module management limit the full potential of traditional MFE architectures. This research introduces Dynamic AI-Orchestrated Modular Architecture (DAIMA), a novel framework that leverages artificial intelligence to enhance the scalability and maintainability of large-scale applications.
This research investigates the energy efficiency of solar water heaters constructed from alternative materials, emphasizing their relevance in regions with inadequate electrical infrastructure and low financial capacity. The study identifies and evaluates alternative materials to reduce production and installation costs, making solar heating systems more accessible. The research highlights the significant environmental and economic benefits of solar heaters, including reduced CO₂ emissions and lower operating costs compared to traditional gas and electric systems. The prototype developed demonstrates a thermal efficiency of 58%, showcasing the potential of alternative materials in sustainable energy solutions.
The Internet of Things (IoT) has revolutionized digital connectivity by integrating sensors, actuators, and intelligent communication protocols to enable seamless interaction among devices. IoT architectures provide structured frameworks that facilitate device communication, while various protocols ensure interoperability, security, and efficiency in data exchange. This paper explores IoT system architecture, categorizing key layers such as perception, network, and application layers. Furthermore, it examines enabling technologies such as cloud computing, edge computing, artificial intelligence (AI), and blockchain that enhance IoT scalability, security, and efficiency.
The present study investigates the most important issues faced in reconciling the inventory between Oracle ERP Inventory and Oracle Cloud Warehouse Management System (WMS). The objective is to identify the reasons for discrepancies, including data accuracy, system integration, and transaction timing; and compare the impact these problems have on the overall efficiency and accuracy of inventory management.
The integration of Artificial Intelligence (AI) in project management has emerged as a disruptive paradigm, reshaping traditional methodologies and operational frameworks across various industries. Among these, the furniture manufacturing sector presents a compelling case for AI adoption due to its intricate production processes,dynamic supply chain dependencies, and the growing demand for customization. This paper delves into the multifaceted role of AI in optimizing project planning, resource allocation, workflow automation, and inventory management within furniture manufacturing.
Toys are instrumental in exploring the relationship between an animal's behaviour and its surroundings. By watching how pets engage with various toys, researchers can collect valuable insights into their cognitive functions, social interactions, and overall health. This paper explores the application of AI and machine learning (ML) models to analyze how different types of toys influence and transform the behaviour of pet dogs. By leveraging advanced analytical techniques, we aim to uncover patterns in canine interactions with toys, shedding light on their cognitive processes and emotional well-being.
According to the latest findings from the World Health Organization (WHO), cardiovascular disease reigns supreme as the leading global cause of mortality. Detecting heart ailments at an early stage is of paramount importance, as managing the condition often necessitates proactive measures like lifestyle modifications and preventive medications. Failing to address the issue promptly may unleash a cascade of cardio- vascular complications, potentially culminating in heart attacks or other life-threatening events that demand immediate medical intervention and exhibit alarmingly high fatality rates.
This paper explores the empirical impact of Artificial Intelligence (AI) on Identity and Access Management (IAM), with a focus on how AI and Machine Learning (ML) are revolutionizing the security sector. These technologies are increasingly seen as transformative opportunities by developers of IAM solutions, who recognize their potential to provide clients with more efficient and secure systems. AI-driven analytics offer deeper contextual insights and perspectives, facilitating time-efficient operations for both technical and non-technical personnel. These technological advancements streamline IAM compliance processes by automating procedures and significantly accelerating the detection of abnormalities and potential security threats.
Despite Extract, Transform, Load (ETL) processed billions of records per day across most industries like finance, healthcare, and e-commerce, high data quality remains a heavy bottleneck. In this paper, we propose that in order to maintain a wide database base with Great Expectations (GX), an open-source Python tool, we need to utilize a scalable data quality framework. To remedy these, the framework tackles issues related to duplicate records, source bout table count disparities, column form validation, and null detection in essential columns.
Optimizing RF Link Budgets for Low-Earth Orbit (LEO) Systems in Satellite-Enhanced 5G RAN ArchitecturesPratik Jangale,
Aqsa Sayed
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The integration ofi Low-Earth Orbit (LEO) satellite constellations into 5G Radio Access Networks (RAN) has emerged as a critical solution fior achieving global connectivity and low-latency communication. This paper addresses the unique challenges ofi RF link budget optimization in satellite-enhanced 5G RAN architectures. Key considerations include dynamic path loss, Doppler efifiects, and power constraints, all ofi which impact the quality ofi service (ǪoS). Techniques such as adaptive power control, beamfiorming, and machine learning-driven optimization are explored. Simulation results and use cases demonstrate how link budget optimization enables seamless integration ofi LEO systems into 5G RAN, improving efifiiciency and reliability.
Leveraging Large Language Models for Intelligent Data Source Selection and Query Generation in Multi-Database Systems
Nikunj Agarwal
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The exponential growth of data across industries has led to the emergence of complex, multi-database systems, necessitating intelligent and efficient methods for data source selection and query generation. This research explores the transformative potential of Large Language Models (LLMs) in addressing these challenges. By leveraging their advanced natural language understanding and contextual reasoning capabilities, LLMs can dynamically select relevant data sources and generate optimized queries tailored to specific user inquiries and operational contexts. We propose a framework that integrates LLMs to streamline data retrieval and enhance decision-making processes across multiple domains.
Integrating Large Language Models: Enhancing Chatbot Capabilities for Training on Diverse Data Sources
Dr. Ranjith Gopalan, Mr. Abhishek Sen, Mr. Vishal S
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The paper discusses the fundamentals of chatbot design, including key components, types of chatbots, and the importance of user experience and interaction design. It then delves into the structure and function of Large Language Models, their training, and their use cases in chatbot development. The paper explores the process of integrating LLMs with chatbot frameworks, highlighting the key steps and the services available for building chatbots, such as Amazon Bedrock, Microsoft LUIS, and Google Bard.Paper explains the fundamentals of chatbot design, provides an overview of large language models, discusses chatbot architecture for handling unstructured and structured data, and highlights the roles of AWS Titan and Anthropic Claude models in the development of retrieval-augmented generation systems.
Modeling the Effects of Accidents on Employees During Blowout Preventer Testing
Al Gburi Hassan Ali Mosleh, Daniel Iancu, Timur Chiș
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The drilling of oil and gas wells also requires ensuring safety in the operation of the well construction systems and the protection of the environment and employees. During the drilling of the wells, eruptive manifestations and/or pressure increases in the well may occur (above the pressure value provided by the weight of the drilling fluid), which may lead to the production of oil fluid eruptions (which also contain rock particles and drilling fluid compounds) and cause fires or accidents human lives, as well as environmental pollution.
Implementing AI-Driven Micro-Frontend Architectures Using Reinforcement Learning and Graph Neural Networks for
Scalable and Maintainable Large-Scale Web ApplicationsMuthu Selvam, PrakasamVenkatachalam, Sathiskumar Meganathan, Thalapathi Rajasekaran R
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The rapid evolution of web applications across diverse domains like e-commerce, healthcare, and enterprise solutions necessitates architectures that are scalable, maintainable, and performance-efficient. Micro-Frontends (MFEs) have emerged as a modular alternative to monolithic frontends, enabling independent development, deployment, and testing of UI components. However, challenges such as dependency conflicts, static orchestration strategies, and inefficient module management limit the full potential of traditional MFE architectures. This research introduces Dynamic AI-Orchestrated Modular Architecture (DAIMA), a novel framework that leverages artificial intelligence to enhance the scalability and maintainability of large-scale applications.
Energy Efficiency of Solar Water Heater from Alternative MaterialsFernando Chichango, Luís Cristóvão, Gil Gabriel Mavanga,
Fabião Cumbe, Jorge Nhambiu
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This research investigates the energy efficiency of solar water heaters constructed from alternative materials, emphasizing their relevance in regions with inadequate electrical infrastructure and low financial capacity. The study identifies and evaluates alternative materials to reduce production and installation costs, making solar heating systems more accessible. The research highlights the significant environmental and economic benefits of solar heaters, including reduced CO₂ emissions and lower operating costs compared to traditional gas and electric systems. The prototype developed demonstrates a thermal efficiency of 58%, showcasing the potential of alternative materials in sustainable energy solutions.
Architectural Frameworks, Communication Protocols, and Enabling Technologies in the Internet of Things (IoT)
Abhay Mangalore
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The Internet of Things (IoT) has revolutionized digital connectivity by integrating sensors, actuators, and intelligent communication protocols to enable seamless interaction among devices. IoT architectures provide structured frameworks that facilitate device communication, while various protocols ensure interoperability, security, and efficiency in data exchange. This paper explores IoT system architecture, categorizing key layers such as perception, network, and application layers. Furthermore, it examines enabling technologies such as cloud computing, edge computing, artificial intelligence (AI), and blockchain that enhance IoT scalability, security, and efficiency.
Reconciliation of Inventory Between Oracle ERP Inventory and Oracle Cloud WMSSreenivasa Rao Sola
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The present study investigates the most important issues faced in reconciling the inventory between Oracle ERP Inventory and Oracle Cloud Warehouse Management System (WMS). The objective is to identify the reasons for discrepancies, including data accuracy, system integration, and transaction timing; and compare the impact these problems have on the overall efficiency and accuracy of inventory management.
AI-Driven Transformation in Furniture Production: Enhancing Supply Chains, Design, and Waste ReductionPratik Dahule
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The integration of Artificial Intelligence (AI) in project management has emerged as a disruptive paradigm, reshaping traditional methodologies and operational frameworks across various industries. Among these, the furniture manufacturing sector presents a compelling case for AI adoption due to its intricate production processes,dynamic supply chain dependencies, and the growing demand for customization. This paper delves into the multifaceted role of AI in optimizing project planning, resource allocation, workflow automation, and inventory management within furniture manufacturing.
Behavioural Insights through Play - AI and ML Models to Analyze the Transformation of Pet (Dogs) Behaviour with Toys
Hari Prasad Bomma
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Toys are instrumental in exploring the relationship between an animal's behaviour and its surroundings. By watching how pets engage with various toys, researchers can collect valuable insights into their cognitive functions, social interactions, and overall health. This paper explores the application of AI and machine learning (ML) models to analyze how different types of toys influence and transform the behaviour of pet dogs. By leveraging advanced analytical techniques, we aim to uncover patterns in canine interactions with toys, shedding light on their cognitive processes and emotional well-being.
Improving Compound Selection in Drug Discovery: A Quantitative Approach for Biased Data ModelingRohit Singh Raja
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According to the latest findings from the World Health Organization (WHO), cardiovascular disease reigns supreme as the leading global cause of mortality. Detecting heart ailments at an early stage is of paramount importance, as managing the condition often necessitates proactive measures like lifestyle modifications and preventive medications. Failing to address the issue promptly may unleash a cascade of cardio- vascular complications, potentially culminating in heart attacks or other life-threatening events that demand immediate medical intervention and exhibit alarmingly high fatality rates.
Exploring AI’s Influence on Identity and Access Management: An Empirical StudySurendra Vitla
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This paper explores the empirical impact of Artificial Intelligence (AI) on Identity and Access Management (IAM), with a focus on how AI and Machine Learning (ML) are revolutionizing the security sector. These technologies are increasingly seen as transformative opportunities by developers of IAM solutions, who recognize their potential to provide clients with more efficient and secure systems. AI-driven analytics offer deeper contextual insights and perspectives, facilitating time-efficient operations for both technical and non-technical personnel. These technological advancements streamline IAM compliance processes by automating procedures and significantly accelerating the detection of abnormalities and potential security threats.
Data Quality Framework-Using Great Expectations for ETL PipelinesSanjay Puthenpariyarath
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Despite Extract, Transform, Load (ETL) processed billions of records per day across most industries like finance, healthcare, and e-commerce, high data quality remains a heavy bottleneck. In this paper, we propose that in order to maintain a wide database base with Great Expectations (GX), an open-source Python tool, we need to utilize a scalable data quality framework. To remedy these, the framework tackles issues related to duplicate records, source bout table count disparities, column form validation, and null detection in essential columns.