Aligarh Muslim University

This study investigated the age-dependent compressive strength (fc), static modulus of elasticity (Es), and microstructure of geopolymer concrete (GPC) incorporating varying proportions of fly ash (FA) and ground granulated blast furnace slag (GGBFS). GGBFS was introduced as a partial replacement for FA at levels of 0%, 20%, 40%, 60%, and 80%. Testing was conducted at six ages: 7, 28, 60, 90, 150, and 180 days. Additionally, an ordinary portland cement concrete (OPCC) mix with the same binder content was also studied for comparison. Experimental results showed that all GPC mixes exhibited consistent gains in fc and Es with age. The inclusion of GGBFS significantly enhanced early-age and long-term fc and Es. Optimal performance was observed in mixes with 40% and 60% GGBFS, which surpassed OPCC strength after 90 days. Microstructural analysis using scanning electron microscopy and energy dispersive spectroscopy revealed that increasing GGBFS content improved the matrix compactness by enhancing the formation of calcium-alumino-silicate-hydrate (C─ A─ S─ H) gel in synergy with sodium-alumino-silicate-hydrate (N─ A─ S─ H) gel, which resulted in superior mechanical properties. Additionally, new predictive models for fc and Es were developed, incorporating the effects of age and GGBFS content, and validated against experimental data and published literature. Further, the sustainability aspect of GPC in terms of CO2 reduction revealed that all GPC mixes demonstrated significantly better sustainability performance compared to OPCC. Overall, this study provides a comprehensive understanding of the mechanical and microstructural behavior of GPC with FA and GGBFS, offering valuable insights for its application in sustainable construction and guiding the design of optimized GPC mixes.

The present study introduces FPGA(Field-Programmable Gate Array)-based Real-Time Multi-Class Vehicle Classification using Millimeter wave Radar (mmWave radar), which overcomes the limitations of conventional sensors such as LiDAR and cameras, which are sensitive to adverse weather and lighting conditions. On a hardware-software platform, the implementation of multi-class vehicle classification demonstrated its effectiveness. Within the realm of multi-class vehicle classification applications, the FPGA-based PYNQ-ZU (Python Productivity for Zynq) serves as an efficient embedded architecture. The reliability and accuracy of this method are improved, rendering it a promising solution for autonomous vehicles and advanced driver assistance systems (ADAS) in a variety of driving scenarios. We employed 3D point cloud data produced by mmWave radar via a PC, then transformed it into 2D point cloud images by top-view filtration methods. This method demonstrated greater efficacy in feature extraction with VGG-16. Multiple machine learning models were employed for classification tasks on both hardware and software platforms, achieving 100% accuracy with the Random Forest (RF) algorithm.

Artificial intelligence reading environments, in which users upload texts, pose interpretive questions, and receive computationally generated syntheses, are rapidly embedding themselves in scholarly and educational practice. Existing debates frame these tools in terms of cognitive enhancement or displacement. This paper argues that both miss the nature of the transformation. The central issue is structural: AI systems reconfigure the cognitive and hermeneutic architecture through which interpretation unfolds. Drawing on Gadamer's and Ricoeur's hermeneutics, Ong's history of reading, and Star's infrastructure studies, the paper demonstrates that AI-mediated reading introduces a triadic interpretive structure where the hermeneutic tradition has assumed a dyadic one. Between reader and text now stands a computational intermediary that organizes which aspects of the text become visible, in what form, and according to what prior schema of relevance. In this configuration, core interpretive operations previously performed by the reader are redistributed to computational processes that pre-structure the encounter before the reader's own horizon of understanding has formed. The paper develops this claim through five interlocking concepts: the fourth epoch of reading, the triadic reading architecture, synthetic dialogue, algorithmic hermeneutics, and interpretive infrastructure. It argues that AI reading environments generate a form of engagement termed synthetic dialogue, which reproduces the external form of classical dialogic reading while inverting its philosophical conditions, privileging coherence and closure over the provisional openness required for genuine hermeneutic dialogue. Through algorithmic hermeneutics, computational systems construct relevance and structure interpretive pathways. These processes introduce path-dependent trajectories within reading sessions. When embedded at institutional scale, these mechanisms produce collective interpretive convergence that masquerades as organic intellectual agreement. The paper conceptualizes this condition as interpretive infrastructure and identifies at its core a process of double concealment: the simultaneous hiding of computational operations and the interpretive plurality they foreclose. In response, it proposes critical algorithmic literacy as a hermeneutic disposition oriented toward making this infrastructure visible and negotiable, defined by three practices: recognizing pre-structured pathways, recovering sealed alternatives, and suspending premature interpretive closure.