Applied Science and Engineering Journal for Advanced Research

Innovative Waste Management: Incorporating CETP Sludge in Concrete for Sustainable Construction

Kumar C Sandeep — 2024-11-11

In response to growing concerns about environmental sustainability and waste management, the exploration of alternative construction materials has gained prominence. One such alternative is common effluent treatment plant (CETP) sludge, a by-product of industrial wastewater treatment that poses significant environmental challenges. This study aims to evaluate the feasibility of substituting conventional fine aggregate with CETP sludge in concrete mixtures, addressing waste disposal issues and enhancing the sustainability of concrete construction. The research investigates the physical, chemical, and mechanical properties of CETP sludge to determine its suitability as a partial replacement for fine aggregate. Concrete mixtures with varying percentages of CETP sludge (0%, 10%, 20%, 30%, 40%, 50%) will be prepared and evaluated for compressive strength, durability, and workability. The study examines the potential benefits and challenges of incorporating CETP sludge, including its environmental impact, cost-effectiveness, and regulatory compliance. Initial findings suggest that CETP sludge possesses properties that make it a promising candidate for partial fine aggregate replacement. Further investigation will focus on its effect on the fresh and hardened properties of concrete, determining the optimal replacement ratio for desired performance. Environmental assessments will also be conducted to gauge the overall sustainability of concrete mixtures containing CETP sludge. This study aims to provide a novel solution for the responsible disposal of CETP sludge and promote environmentally friendly alternatives in construction. The research will explore the specific mechanical and durability properties of concrete with 10% CETP sludge replacement, aiming to identify an optimal balance between environmental benefits and structural integrity. The outcomes will contribute valuable insights into sustainable construction practices, encourage waste utilization in a circular economy, and reduce the environmental footprint of concrete materials.

Security Considerations for the Application of Large Language Models in the Financial Sector

Michael J Reynolds — 2024-11-18

With the widespread application of large language models (LLMs) in the financial sector, their intelligent advantages have significantly enhanced the efficiency of business processes such as customer service, risk prediction, and compliance management. However, the security issues related to these models are becoming increasingly prominent, including data privacy and information leakage, bias and uncertainty in model output, and the risk of system attacks. This paper provides an in-depth analysis of these security concerns and proposes corresponding solutions, such as data encryption and protection technologies, model monitoring and validation mechanisms, as well as the strengthening of compliance and regulatory requirements. Finally, by examining a real-world case, the paper explores the future prospects and challenges of applying large language models in the financial sector, offering insights for further research and practice.

Application of Machine Learning in Predicting Extreme Volatility in Financial Markets: Based on Unstructured Data

Chenwei Gong — 2024-11-18

Sentiment analysis is an important tool for revealing insights and shaping our understanding of market movements from financial articles, news, and social media. Despite their impressive abilities in financial natural language processing (NLP), large language models (LLMs) still have difficulties in accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. This article introduces a simple and effective instruction-tuning method to solve these problems. We have made significant progress in financial sentiment analysis by converting small amounts of supervised financial sentiment analysis data into command data and using this approach to fine-tune a generic LLM. In experiments, our approach outperforms state-of-the-art supervised sentiment analysis models and widely used LLMs such as ChatGPT and LLaMAs, especially when numerical and contextual understanding is critical.

AI-Enhanced Security for Large-Scale Kubernetes Clusters: Advanced Defense and Authentication for National Cloud Infrastructure

Lin Li — 2024-11-21

This paper presents an AI-enhanced security framework for large-scale Kubernetes clusters, addressing the critical need for advanced defense and authentication mechanisms in national cloud infrastructures. The proposed system combines machine learning models for threats, policy creation, and intelligent resource allocation to provide security across the environment. An experiment simulating a 1,000-node Kubernetes cluster was used to evaluate the framework's performance over 30 days. The results showed a significant improvement over traditional security methods, including 99.97% threat detection accuracy, a false positive rate of 0.005%, and an 85% reduction in average response time to security threats. The framework exhibits excellent performance, maintaining consistent performance up to 10,000 nodes with only 7% degradation. Notably, the change resulted in a 27% improvement in overall stability throughout the trial. This research has a significant impact on the security of the country's airspace, providing effective protection against threats, insider attacks, and ongoing threats. The study concludes by discussing limitations and future research directions, emphasizing the need for real-world deployment and research on possible AI architectures. Better for limited spaces.

Exploring Material Selection and Applications for Embedded Carbon Reduction in the Built Environment

Jingwen He — 2024-11-29

The building and construction industry is a significant contributor to global greenhouse gas emissions, with 37% of total emissions attributed to this sector. While efforts have primarily targeted operational carbon emissions, which stem from building operations like heating and cooling, the urgent need to address embodied carbon—associated with the materials used and their life cycle—has gained attention. This paper explores the critical role of material selection and innovative practices in reducing embedded carbon in the built environment. It highlights collaborative models and international cooperation essential for decarbonizing building materials to achieve net-zero emissions by mid-century. The findings underscore that embodied carbon currently represents a growing proportion of a building's overall carbon footprint, necessitating proactive measures in the design and construction phases. By integrating life cycle assessments and prioritizing sustainable material choices, stakeholders can significantly diminish carbon emissions and align with global climate goals. Through case studies and best practices, this research advocates for a comprehensive approach to carbon reduction that encompasses both operational and embodied emissions in the built environment.

Effect of Conducting Particle on Spacers in Air Insulated Systems

C Jouhar — 2024-11-30

Gas Insulated System (GIS) has been known to be reliable for more than 40 years. One of the reasons is because the active components are installed inside sealed-enclosures that reduce the environmental stress. Gas Insulated systems require solid insulating materials to provide mechanical support for conductors. Hence the spacers used in GIS should be precisely designed to realize more or less uniform field distribution along their surfaces. GIS occupy an important position in the power system. Insulating spacers are important parts in GIS. GIS have been used for many years as a means to provide safe and reliable high volt-age electrical systems. Normally, the problems connected with the use of these systems are few, especially when lower voltage levels are considered. However, the presence of metallic contamination can seriously reduce the insulation performance of a GIS. The aim of this work is to investigate the effect of conducting particle on spacers in air insulated system. The spacers used for study are Poly Methyl Metha Acrylate (PMMA) and nylon.