The escalating global freshwater crisis, exacerbated by climate change, industrialization, and population growth, necessitates sustainable and energy-efficient desalination technologies. While conventional methods like Reverse Osmosis (RO) and thermal distillation are widely adopted, they pose significant challenges in terms of energy consumption, operational complexity, and environmental impact. This research introduces and evaluates Low Pressure Distillation (LPD) as a promising alternative to overcome the limitations of existing systems. LPD capitalizes on the thermodynamic principle that lowering ambient pressure reduces the boiling point of water, allowing evaporation at significantly lower temperatures. This study investigates the theoretical and empirical underpinnings of LPD, developing a mathematical model based on energy balance and Rohsenow's correlation to simulate mass and heat transfer under sub-atmospheric conditions. Simulation parameters—such as vacuum pressures (0.1–0.05 atm), varying heat inputs (500–1500 W), and surface area—are explored to estimate freshwater yield and energy efficiency. The findings demonstrate that LPD achieves substantial energy savings (up to 50%) compared to conventional methods while maintaining effective desalination performance. Additionally, integration with low-grade heat sources like solar thermal energy is shown to be feasible and scalable for decentralized applications. Graphical analysis and validation against experimental data confirm the model's accuracy and practical relevance. Overall, LPD presents itself as a sustainable, modular, and environmentally friendly solution for water-stressed regions, especially where access to conventional infrastructure or grid power is limited. This study lays a foundational framework for the development and deployment of next-generation desalination systems optimized for low-energy, off-grid environments.

Read More...

In this paper will discuss on analysis and design of ground level reservoir of rectangular shaped for storage of water for drinking purposes. Accordingly the population forecasting, volume of GLR, location, lateral soil pressures & water pressures, wind and earthquake load are considered. The design of GLR wall faces horizontal & lateral forces due to soil & water pressure acts as cantilever, using LSM considering load of dead load, live load, & Seismic load for safe & economical design.

Read More...

This paper presents the development of a machine-learning–based recommendation system that predicts and suggests products to users based on browsing history, purchase patterns, and seasonal buying trends. The system analyses user–item interactions, extracts meaningful behavioural features, and recommends products through similarity-based ML techniques. Seasonal demand is included to enhance prediction accuracy. The proposed model demonstrates that even a lightweight, interpretable ML approach can generate relevant and personalized product recommendations suitable for small e-commerce datasets.

Read More...

Expansive black cotton soils pose severe challenges to infrastructure due to their low bearing capacity and high swell–shrink behaviour. This study investigates the efficacy of randomly distributed sisal fiber reinforcement as a sustainable stabilization technique. Sisal fiber was mixed with black cotton soil at 0%, 0.25%, 0.5%, 0.75%, 1.0%, and 1.25% by dry weight. The optimum fiber content of 1.0% increased soaked CBR by 551.5% (1.71% → 11.14%) and unsoaked CBR by 220.9% (2.54% → 8.15%). At this dosage, plasticity index reduced by 48.8%, free swell index by 44.6%, and swelling pressure by 44.8%. Compaction characteristics exhibited a modest 5.3% decrease in maximum dry density and 9.9% increase in optimum moisture content. Durability tests under 28-day exposure to water, acid (pH 4), leachate, and freeze–thaw cycles revealed excellent freeze–thaw resistance (only 10.2% CBR loss), good performance under water and leachate (24.9–31.5% loss), and moderate resistance in acidic conditions (40.1% loss). Most degradation occurred within the first 14 days. The results confirm that 1.0% sisal fiber reinforcement transforms weak black cotton soil into a competent, durable subgrade material suitable for pavements across diverse Indian climatic zones while offering significant environmental and economic advantages over conventional stabilizers.

Read More...

The vegetation gross primary productivity and carbon sequestration efficiency of vegetation exhibits different spatiotemporal heterogeneity at different spatial and temporal scales, and the sensitivity of carbon sequestration to climate change varies among different vegetation types. This study takes at Buxa hills as an example, and based on remote sensing MODIS images, different vegetation types, gross primary productivity (GPP) and net primary productivity (NPP) were obtained. The carbon utilization efficiency (CUE) values of vegetation were calculated, and the spatiotemporal distribution characteristics of GPP, NPP and CUE of different vegetation types were analyzed. The impact of climate change on vegetation CUE was discussed and the contributions of climate change and human activities to CUE was decomposed. The results showed that GPP showed an increasing trend, while CUE showed a decreasing trend, and the order of GPP size for different vegetation types was: forest>shrub>grassland>cultivated vegetation, the order of CUE size was cultivated vegetation>shrubs>grasslands>forests. GPP was higher in summer, while CUE was higher in spring and winter, and lower in summer. The GPP of different vegetation types shows a increasing trend, with the order of grassland>cultivated vegetation>shrub>forest. While the CUE of different vegetation types shows a decreasing trend, with the order of grassland>shrub>cultivated vegetation>forest. Below an altitude of 700m, there is a larger vegetation CUE, while above an altitude of 1700m, there is a significant increase in vegetation CUE. The vegetation CUE from May to August has the greatest correlation with temperature during the same period, showing negative and positive correlations with temperature and precipitation, respectively. There are differences in different periods, especially in winter vegetation CUE was positively correlated with precipitation and temperature. Meanwhile, human activities also have a certain impact on carbon utilization efficiency. These studies provide a basis for regional vegetation protection and carbon sequestration function assessment.

Read More...

The disposal of waste tyres poses a serious environmental challenge due to their non-biodegradable nature and increasing generation worldwide. Incorporating waste tyre aggregate in concrete has emerged as a sustainable alternative that addresses both solid waste management and conservation of natural aggregates. This study investigates the feasibility and performance of concrete in which a portion of conventional fine aggregate is replaced with processed waste tyre aggregate. Emphasis is placed on evaluating the mechanical properties of tyre aggregate concrete under varying replacement levels. Experimental results indicate that the inclusion of waste tyre aggregate leads to a reduction compressive strength compared to conventional concrete. Higher replacement ratios adversely affect strength parameters, optimized proportions show potential for non-structural and semi-structural applications such as pavements, barriers, and vibration-damping elements. The present study deals with the comparative study of untreated and acetic acid treated rubber aggregate on the mechanical properties of concrete. The study highlights that with proper mix design and proportioning, waste tyre aggregate can be effectively utilized in concrete, contributing to sustainable construction practices by reducing environmental pollution and promoting resource efficiency.

Read More...

Educational institutions worldwide face unprecedented challenges in ensuring examination integrity within digital learning environments [21]. We present ProctoAI, a novel multi- modal artificial intelligence framework that combines five in- dependent detection mechanisms to identify academic dishonesty during remote assessments. Our implementation integrates facial biometric verification through deep convolutional networks [1], visual attention analysis via eye-gaze estimation [22], multiple- entity detection using contemporary object recognition models [4], acoustic pattern recognition for unauthorized verbal communication [23], and behavioral fingerprinting through unsupervised learning techniques [8]. Evaluation across 500 simulated examination scenarios demonstrates our framework achieves 94.7 percent classification accuracy while reducing false alarm rates to 3.2 percent, representing substantial improvements over conventional monitoring approaches [17]. The framework operates as a decision-support tool, providing human supervisors with intelligent alerts and comprehensive audit trails rather than automated disciplinary actions [24].

Read More...

Agricultural waste management and access to modern farming equipment are major challenges for farmers, especially in rural areas. Improper disposal of agricultural waste leads to environmental pollution, while the high cost of farming machinery limits productivity for small and marginal farmers. To address these issues, this project proposes an integrated digital solution consisting of an Android application for farmers and a web application for service providers. The system enables farmers to manage and recycle agricultural waste by connecting them with recyclers and also allows them to rent farming equipment at affordable rates. The web application supports equipment owners and recycling agencies in managing services and transactions. This solution promotes sustainable farming, resource sharing, and a circular agricultural economy while reducing costs and improving productivity.

Read More...

This work presents a CNN-based approach for automated video genre classification to address the growing need for scalable multimedia organization. Key frames are extracted from videos, preprocessed, and used to train a CNN model capable of learning visual patterns relevant to genre prediction. Optimization techniques such as data augmentation, batch normalization, and dropout are applied to improve generalization and reduce overfitting. The model is evaluated using standard performance metrics including accuracy, precision, recall, and F1-score, demonstrating clear improvements over traditional machine learning and handcrafted feature-based methods. Experimental results show that the proposed approach achieves reliable and consistent classification performance across multiple video genres. The system demonstrates strong potential for integration into real-world applications, including personalized recommendation systems, automated metadata generation, content indexing, and large-scale digital media management.

Read More...