Central University of Odisha

Media played an important role in disseminating the anti-colonial sentiments during India's freedom movement. Many cultural shows, dramas, and local festivals were used as a medium to spread secret messages and unite people against the British. Women, particularly the courtesans (tawaifs), contributed significantly through their unique social and artistic positions. Heeramandi: The Diamond Bazaar (2024) dramatizes these contributions, fusing fact with fiction to demonstrate how artistic expression functioned as a vehicle for rebellion. This paper explores the portrayal of courtesans as agents of alternative media and evaluates the balance between historical accuracy and cinematic romanticization.

Game-Based Learning (GBL) has emerged as an innovative pedagogical approach to enhance students’ engagement and conceptual understanding in mathematics at the elementary level. The present study titled “Challenges of Mathematics Teachers while Implementing Game-Based Learning at Elementary Level in Odisha” seeks to explore the realities faced by mathematics teachers in integrating GBL into classroom practices. The study is guided by three major objectives: to identify the instructional strategies currently used by mathematics teachers at the elementary level; to examine teachers’ opinions regarding the effectiveness of game-based learning in promoting student engagement; and to investigate the specific challenges encountered during its implementation. Adopting a descriptive research design, the study focuses on understanding teachers’ experiences, perceptions, and classroom practices in the context of elementary schools in Odisha. The findings are expected to reveal the extent to which traditional and innovative instructional strategies coexist, teachers’ attitudes toward the pedagogical value of GBL, and barriers such as limited resources, lack of training, time constraints, and classroom management issues. The study aims to provide insights for policymakers, curriculum planners, and teacher educators to strengthen support systems and promote effective integration of game-based learning in mathematics classrooms.

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% to improve sustainability and pozzolanic reactivity. Mixes were evaluated at two binder contents (500 and 600 kg/m³) and two solution-to-binder (S/B) ratios (0.6 and 0.5), with assessments of fresh properties, mechanical performance, and microstructural characteristics. All formulations demonstrated satisfactory workability and cohesive rheological behavior. The early age strength development was significant, with selected RHA-based mixes achieving over 50% of their 28-day compressive strength within 3 days and exceeding 85% by 7 days. The optimal mix, incorporating 600 kg/m³ binder and 15% RHA at 0.5 S/B, exhibited a 26% strength gain over the control mix, with corresponding improvements in tensile and flexural strengths. The SEM analysis revealed dense gel matrices with interpenetrating N–A–S–H and C–A–S–H phases, reduced porosity, and improved gel continuity. The results demonstrate that ambient-cured SCGC based on a hybrid industrial-waste binder constitutes a promising, lower-clinker geopolymer binder system with enhanced fresh, mechanical, and microstructural performance, while long-term durability and structural behavior require further investigation.

With climate change becoming more front and center in everyday conversations, the need for tools that actually help people keep track of their carbon impact has never been greater. There are already a bunch of apps out there trying to solve this, but honestly, many of them just don't quite cut it. They can be clunky, too reliant on manual input, or fail to provide anything truly meaningful. That's the gap EcoPulse is aiming to close. EcoPulse is built with regular users in mind. It's a mobilefirst platform powered by AI, designed to help folks monitor their carbon footprint in three major areas: travel, food, and energy use. What makes it stand out is how it pulls data from multiple sources—GPS, usage logs, and image recognition—so it can automatically estimate emissions without constantly asking users to fill things out manually. It quietly works in the background, putting together a clearer picture of your environmental impact without being a hassle. Technically speaking, it's built using Flutter, which means it runs smoothly across both Android and iOS. All the backend work—data syncing, user auth, real-time updates—is handled through Firebase Firestore, which keeps things fast and secure. On the AI side, the app uses lightweight TensorFlow Lite models that run directly on your device. So when you snap a pic of a food item or scan a bill, it processes instantly—and your data doesn't have to leave your phone. But what really makes EcoPulse different is how personal and adaptive it is. The results are promising: it's more responsive, more accurate, and more flexible than most of the traditional tools. At the end of the day, EcoPulse isn't just another footprint tracker—it's a step toward making carbon awareness something that fits naturally into people's daily lives.

Stock market prediction is a critical aspect of financial decision-making, traditionally relying on statistical models that often fail to capture complex market dynamics. With advancements in artificial intelligence (AI), machine learning (ML), and blockchain technology, predictive models have significantly improved accuracy and reliability. This paper proposes an AI-powered stock prediction system that integrates ML techniques, sentiment analysis, and blockchain security to enhance forecasting and data integrity. Historical stock data is analyzed using machine learning algorithms, and financial news and social media sentiment analysis are performed to improve prediction accuracy. Additionally, blockchain technology ensures data transparency and prevents manipulation by securely storing financial records and AI-generated predictions. The proposed system also features a user-friendly dashboard for real-time stock visualization and interactive analytics. Challenges such as data privacy, computational efficiency, and market volatility are discussed, along with future directions focusing on reinforcement learning, improved sentiment analysis, and multi-asset investment strategies. Integrating AI and blockchain in stock forecasting provides a secure and transparent approach to financial market analysis, aiding investors in making informed decisions.

Forests are essential in combating climate change by functioning as carbon sinks, sequestering atmospheric carbon dioxide and storing it in biomass and soil. India, possessing various forest ecosystems, holds substantial potential for carbon sequestration. Climate change, marked by increasing temperatures, altered precipitation patterns and extreme weather phenomena, jeopardizes forest biomass and carbon sequestration. This systematic analysis evaluates the effects of climate change on forest biomass and carbon sequestration in several forest types in India. The study evaluates different methodologies for biomass estimation, including destructive, non-destructive and remote sensing approaches. It highlights the contributions of diverse forest types such as Himalayan forests, tropical rainforests, deciduous forests, mangroves and agroforestry systems in carbon sequestration. The findings indicate that while Indian forests act as vital carbon reservoirs, deforestation, land-use changes and climate-induced stressors have generally decreased the carbon sequestration potential of these forests by reducing biomass accumulation and increasing ecosystem stress, although variability exists across forest types. Sustainable forest management, afforestation and climate-resilient strategies are essential to enhance carbon storage. Integrating remote sensing technologies, ecological modeling and policy frameworks like REDD+ can aid in better monitoring and conservation efforts. This review provides insights into future strategies to strengthen India’s forest carbon sequestration capacity and mitigate climate change impacts.

Abstract BackgroundCardiometabolic diseases (CMDs), including diabetes, hypertension, and metabolic syndrome, are rising sharply in South Asia’s urban populations. However, the influence of ethnic and regional variation within these urban settings remains poorly understood.ObjectiveTo systematically review the literature on ethnic and regional disparities in cardiometabolic risk among urban South Asian adults, with emphasis on subnational variability in India, particularly Odisha and Kalahandi district.MethodsThis review followed PRISMA 2020 guidelines. A comprehensive search was conducted across PubMed, Scopus, Cochrane, Ovid MEDLINE, Web of Science, Google Scholar, and grey literature (WHO, ICMR) for studies published between 2015 and 2025. Inclusion criteria focused on urban South Asian adults (≥18 years) with data stratified by ethnicity or region. Quality was assessed using the Newcastle–Ottawa Scale and AMSTAR-2. A narrative synthesis was conducted.ResultsThirteen studies were included. CMD prevalence varied by country, state, and district. Northern and western India showed higher CMD rates than eastern regions. In Odisha, metabolic syndrome prevalence ranged from 24% to 33.5%, with a pronounced gender gap in central obesity and pre-metabolic syndrome. Findings from Kalahandi district revealed a substantial burden of early-stage CMD risk, especially among young urban adults.ConclusionCardiometabolic risk among urban South Asians is shaped by complex regional and ethnic factors. Public health strategies must move beyond generalized models to adopt localized, culturally tailored interventions that address the specific needs of diverse urban populations.