ACE College of Engineering, Trivandrum

The development of low-carbon and resource-efficient cementitious materials is essential to mitigate the environmental impacts associated with ordinary Portland cement production and natural aggregate depletion. In this context, one-part geopolymer systems, which eliminate hazardous liquid alkaline activators and energy-intensive thermal curing, represent a sustainable chemistry-driven alternative. This study investigates the fresh, mechanical, durability, and microstructural performance of ambient-cured one-part geopolymer concrete incorporating fly ash (FA), ground granulated blast furnace slag (GGBFS), and waste foundry sand (WFS). Fly ash and GGBFS were used as complete cement replacements in three binder proportions (70FA-30GGBFS, 50FA-50GGBFS, and 30FA-70GGBFS), while WFS replaced natural fine aggregate at levels of 10%, 20%, and 30%. Solid sodium hydroxide and sodium silicate were employed as dry alkaline activators. The results indicate that WFS incorporation up to 30% maintained adequate workability, with slump values ranging from 90 to 110 mm. The reactionbalanced 50FA-50GGBFS system exhibited the most favorable performance, achieving a 28-day compressive strength of 47.15 MPa, with satisfactory durability characterized by water absorption below 3% and chloride-induced mass loss below 2%. Microstructural analysis confirmed matrix densification associated with the coexistence of C-A-S-H and N-A-S-H gels. From a sustainable chemistry perspective, the proposed one-part geopolymer system emphasizes waste valorization, elimination of hazardous liquid activators, and energy-efficient ambient curing. The integration of fly ash, GGBFS, and waste foundry sand establishes a circular material pathway consistent with green chemistry principles and contributes directly to Sustainable Development Goals SDGs 9, 11, and 12.

Incorporating industrial by-products into concrete reduces the environmental impact ofcement production. This study evaluates sustainable ternary concrete mixes containing 10% fly ash, varying silica fume levels (0%, 6%, 12%, 18%, 24%), and 100% manufactured sand as fine aggregate to identify the optimal mix for enhanced mechanical and microstructural properties using scanning electron microscopy (SEM),energy-dispersive spectroscopy (EDS), thermogravimetric analysis (TGA), and machine learning (ML) assessment were done to streamline the experimental process. Compressive, split tensile, and flexural strengths, as well as ultrasonic pulse velocity, were measured at 7, 28, and 90 days. The mix with 10% fly ash, 12% silica fume, and 100% manufactured sand demonstrated the highest performance, with compressive strength increases of 18.61%, 16.85%, and 19.83% at each interval. Microstructural analysis revealed a dense C-S-H gel and uniform matrix, indicating improved hydrationand reduced porosity. Machine learning models (LASSO, Random Forest, Gradient Boosting, XGBoost, AdaBoost, and ANN) were applied to predict compressive strengthand to minimise the number of experimental trials. Gradient Boosting achieved the mostaccurate predictions, with an R2 of 0.9929 and minimal error, even with limited data. Both laboratory and machine-learning results confirm that concrete with 10% fly ash, 12% silica fume, and 100% manufactured sand provides a durable, high-performance solution for structural applications.

This study investigated the development of ambient-cured self-compacting geopolymer concrete ((SCGC)) using a neutral-grade sodium silicate solution as the sole activator. The binder system comprised ground granulated blast furnace slag (GGBFS), fly ash (FA), and micro silica (MS), with partial substitution of MS by rice husk ash (RHA) at 5–15

CodeSense is an educational platform powered by artificial intelligence, aimed at improving the learning experience of programming through intelligent code analysis, organized explanations, and performance assessments. Unlike conventional programming tools that mainly concentrate on executing code and detecting syntax errors, CodeSense helps learners grasp the underlying logic of their programs. This capability counters the tendency for shallow understanding and enhances problem-solving skills. The platform tackles this challenge by examining Java code submitted by users and offering in-depth explanations of essential elements such as methods, variables, and loops. It also assesses the time and space complexity of the code and illustrates these concepts through interactive visuals. Furthermore, CodeSense includes a compiler and debugger that pinpoints errors, clearly indicates the lines where they occur, and supplies helpful explanations along with recommended corrections. With its emphasis on user-friendliness and educational value, CodeSense is particularly beneficial for students and beginners looking to deepen their understanding of programming principles.

Modern CI/CD pipelines are fast but fragile, where even small syntax errors or logic bugs can break builds, delay deployments, and increase MTTR. THE AUTONOMOUS DEBUGGER is an AI powered self-healing system designed to automatically detect and fix build failures in GitHub Actions without human intervention, enabling continuous and reliable software delivery. The system uses an Agentic AI + RAG architecture to analyze error logs using Sentence Transformers and retrieve relevant faulty code from a ChromaDB vector database. Llama 3 (via Groq) then reasons over the error context and source code to generate accurate, syntax valid, and logically consistent patches. All fixes are validated in a secure sandbox environment before being auto-committed to the repository. This enables fully autonomous recovery of CI/CD pipelines, transforming DevOps into an AI driven, self-healing system that reduces downtime and accelerates software delivery.