Gogte Institute of Technology (GIT) is a college in Belgaum, Karnataka, India, which is affiliated to Visvesvaraya Technological University.
Clay-based geopolymers have emerged as promising low-carbon alternatives to conventional Portland cement, driven by the need for sustainable construction materials. This review systematically synthesizes literature on clay-based geopolymers, focusing on mineralogy, activation methods, and microstructural influences on construction performance. The aim is to provide a comprehensive understanding of how clay mineralogy governs geopolymerization behavior, microstructural evolution, and engineering properties. The scope of this study includes a wide range of clay precursors, such as kaolinitic, illitic, smectitic, fibrous, and lateritic systems, along with their respective activation and treatment strategies. Emphasis is placed on establishing relationships between mineralogical characteristics, reaction mechanisms, and performance indicators, including mechanical strength, durability, and transport properties. In addition, the review highlights recent advances in characterization techniques and the growing role of statistical and reliability-based approaches in evaluating material performance. The significance of this work lies in integrating mineralogical insights with performance-based design to support the development of reliable and scalable geopolymer systems. Furthermore, the study discusses the potential of clay-based geopolymers in advancing sustainable and climate-resilient construction practices. Clay-based geopolymer binders align with UN Sustainable Development Goals 9, 11, 12, and 13, fostering sustainable infrastructure development and climate-resilient construction practices.
High-speed railways in frost regions face frost heave hazards to subgrades, which affect vehicle operation and the service performance of track structures. Existing methods for monitoring and inspecting frost heave in subgrades are characterized by high costs and low efficiency. For accurate simulation of interlayer mapping relationships for ballastless tracks under frost heave effects, a refined simulation model was developed. Subsequently, a feasibility zoning for frost heave in subgrades was proposed based on track irregularity management limits. Finally, a residual Convolutional Neural Network (CNN) combined with attention mechanism modules was employed to recognize vehicle acceleration. The results show that frost heave in subgrades can be divided into five zones, each corresponding to specific requirements such as enhanced monitoring, maintenance, and speed restrictions. As the deformation amplitude increases, the vehicle body acceleration frequency within the 0-15 Hz range exhibits an approximately linear rise. An increase in deformation wavelength, leads to a further increase in acceleration of the vehicle body's low-frequency components while gradual decrease is observed in the highfrequency components. Vehicle body acceleration can serve as the primary indicator. To enhance the recognition accuracy, the efficient channel attention (ECA), convolutional block attention module (CBAM), and an improved convolutional block attention module (ICBAM) mechanisms were incorporated. Network 3-4 incorporating the ECA attention module, achieved an accuracy of 93.89 % in identifying vehicle body acceleration. The convolutional neural network with the ICBAM module performed well in identifying bogie and axle box accelerations.
Supercapacitors are useful for storing and delivering more energy in smaller footprints. Developing high-energy-density supercapacitors enables more efficient utilization of energy, improved performance, and a means for flexibly addressing diverse energy storage requirements. The electrode materials and the techniques used for their fabrication play a significant role in obtaining the desired operating performance of supercapacitors. The present study employs a novel, simple, scalable, and low-cost fabrication technique to synthesize nickel-cobalt sulfide (NiCo2S4) nanostructures on commercial nickel foam. Prominent morphological and electrochemical characterization techniques were used to assess the structural and operating performance of the synthesized nanostructure. The synergistic effects of nickel and cobalt transition metals resulted in enhanced performance of the nanostructure. The synthesized nanostructure was further employed in a symmetric supercapacitor device. Surface morphological and electrochemical experiments showed an energy density value of 62.8 Wh/kg and retention capacity of 94
With increasing train operating speeds, track slabs in ballastless track systems are experiencing increased damage under high-speed train operations. To investigate the time-frequency characteristics of track slab stress under high-speed and ultra-high-speed conditions, in situ measurements and simulation analyses of track slab surface stress were conducted. The results reveal that measured track slab surface stress fluctuates in response to train passage. The strain curves exhibit a distinct double-peak pattern, and there are significant spatial variations in surface stress distribution across the track slabs. Under train loading, particular attention should be paid to the stress conditions at the central longitudinal positions of the slabs. A direct correlation between track slab stress frequency and train speed is observed, with stress frequency mainly falling within the 7-15 Hz range when the middle section of the train passes over the slab. The instantaneous maximum stress frequency occurs as adjacent bogies traverse the track slab. As train speed increases, track slab stress demonstrates a slight amplitude increase. When train speed is incrementally raised to 290 km/h, the maximum stress frequency increases from 20 Hz to 30 Hz. At speeds exceeding 300 km/h, track slab stress frequencies are primarily distributed within the 7-20 Hz and 20-45 Hz ranges. The 7-20 Hz range represents a continuous low-frequency distribution, while the 20-45 Hz range exhibits high-frequency transient distributions associated with axle spacing and bogie spacing. When train speed reaches 450 km/h, the instantaneous stress frequency of the track slab exceeds 45 Hz.
Artificial Intelligence (AI) is rapidly reshaping India’s education ecosystem, influencing pedagogy, assessment, administration, and personalized learning pathways. This study presents a comprehensive analysis of AI adoption across K-12 education, higher education, vocational training, and online learning platforms in India. Drawing upon peer-reviewed research, market intelligence reports, and government policy documents, including the National Education Policy 2020, the report identifies significant sectoral, regional, and institutional disparities in AI integration. Adoption rates range from 45% in online learning platforms to 15% in vocational training, revealing uneven technological diffusion. Urban metro institutions demonstrate substantially higher adoption compared to rural and government schools, underscoring a widening digital divide. Generative AI and adaptive learning systems emerge as dominant technologies, while infrastructure gaps, teacher training deficits, cost barriers, language diversity, and data privacy concerns remain critical constraints. Despite these challenges, India’s AI in education market is projected to grow at nearly 40% CAGR (Compound Annual Growth Rate), signalling strong policy momentum and commercial expansion. The study argues that equity-focused infrastructure investment, multilingual AI development, and large-scale teacher capacity building are essential to prevent a two-tier education system. Strategic, inclusive implementation can position India as a global leader in responsible and scalable AI-enabled education.
