Applied Science and Engineering Journal for Advanced Research

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

2024-11-10T11:17:50+0530

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

2024-11-11T16:25:07+0530

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

2024-11-18T12:43:34+0530

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.