A narrowband Terahertz (THz) sensor is designed for biomedical sensing applications. The step impedance-based resonator (SIR) based square rings are used to design the top layer. Initially the analysis is carried out by considering the refractive index (n) value from 1 to 2 with step size 0.2. The peak resonant frequency (PRF) occurred at 2.1487THz with 99.9
This study investigates the performance of self-compacting geopolymer concrete (SCGC) incorporating fly ash (FA) and silica fume (SF) as aluminosilicate precursors, with emphasis on mechanical behaviour, durability, microstructural characteristics, environmental impact, and machine-learning-based performance prediction. Four geopolymer SCC mixes were prepared using SF to substitute FA at 0, 5, 10, and 15% by weight and an OPC-based SCC of the same class of strength was prepared to serve as a benchmark. Fresh properties were assessed as per EFNARC standards after which compressive, split tensile and flexure strength were done at intervals of 28, 90, and 180 days. Sorptivity, rapid chloride permeability (RCPT) and ultrasonic pulse velocity (UPV) were used to determine durability, whereas scanning electron microscopy (SEM) was used to measure the microstructural evolution. Findings reveal that SF is a considerable improvement to fresh and hardened geopolymer SCC, the best performance being found at a 10% replacement. The G10 mix recorded the best compressive strengths of 65.3 MPa at 180 days, which was 21% higher than the FA only geopolymer mix, and tensile and flexural strengths were 14-18% higher compared to FA only geopolymer mix. The performance of durability increased significantly, as sorptivity was reduced by about 21% and RCPT was lower than 1000 Coulombs (extremely low permeability) and the largest values of UPV were the highest, which shows the presence of a dense and uniform internal structure. Refined pore structure and well-developed the formation of aluminosilicate gels in the G10 mix had been confirmed by SEM observations. Parameters of mix, fresh properties, and curing age were used as inputs to develop machine learning models (KNN, SVM, Decision Tree, and Random Forest). Among them the predictive accuracy of the Random Forest model (R2 = 0.94) with the least error showed excellent performance forecasting and mix optimization. It was found by Life Cycle Assessment (LCA) that geopolymer SCC mixtures had less environmental impact through global warming potential (30-45% less than OPC-SCC) and energy (around 20-25% less than OPC-SCC) and the G10 mix had the lowest environmental impact. Overall, the study demonstrates that FA-SF geopolymer SCC with 10% SF replacement provides a good combination of workability, strength, durability, and sustainability that is proven in experimental, microstructural, data-driven modelling, and environmental analysis.
Alkali Activated Concrete (AAC) is a relatively novel type of binder concrete that has garnered significant attention in recent decades because to its environmental benefits and characteristics. The fabrication of alkali-activated concrete involves the utilization of industrial by-products, such as ground granulated blast furnace slag and fly ash, alongside activators. This study investigates the effects of fly ash, ground-granulated blast furnace slag (GGBS), and lime on the workability, compressive strength, durability, and microstructure of AAC. This study involved the complete substitution of cement with varying quantities of fly ash, GGBS, and lime (with a constant 10
With the cyber-physical systems championing modern smart grids, securing real-time energy data and transactions is of the essence. Traditional methods such as Signature-Based Intrusion Detection Systems (SB-IDS) are static, thereby missing zero-day threats and incapable of dynamic adaptation, achieving only 85–88
This study investigates regional bias in large language models (LLMs), an emerging concern in AI fairness and global representation. We evaluate ten prominent LLMs: GPT-3.5, GPT-4o, Gemini 1.5 Flash, Gemini 1.0 Pro, Claude 3 Opus, Claude 3.5 Sonnet, Llama 3, Gemma 7B, Mistral 7B, and Vicuna-13B using a dataset of 100 carefully designed prompts that probe forced-choice decisions between regions under contextually neutral scenarios. We introduce FAZE, a prompt-based evaluation framework that measures regional bias on a 10-point scale, where higher scores indicate a stronger tendency to favor specific regions. Experimental results reveal substantial variation in bias levels across models, with GPT-3.5 exhibiting the highest bias score (9.5) and Claude 3.5 Sonnet scoring the lowest (2.5). These findings indicate that regional bias can meaningfully undermine the reliability, fairness, and inclusivity of LLM outputs in real-world, cross-cultural applications. This work contributes to AI fairness research by highlighting the importance of inclusive evaluation frameworks and systematic approaches for identifying and mitigating geographic biases in language models.
