Authors: Rohit Sunil Khedkara, Sharad Dhanvijay

Abstract: The escalating atmospheric CO₂ concentration and its contribution to global climate change have driven intensive research into carbon capture technologies. Ionic liquids (ILs) have emerged as promising alternatives to conventional amine-based absorbents, offering unique advantages including negligible vapor pressure, exceptional thermal stability, and tunable physicochemical properties through rational cation-anion design. This comprehensive review examines the full spectrum of ionic liquid applications in CO₂ capture, from fundamental absorption mechanisms to process-scale implementations. Physical absorption in conventional ILs, chemisorption in task-specific ILs incorporating amine, carboxylate, and amino acid functionalities, and IL-based mixed absorbents are systematically analyzed. Structure-property relationships governing CO₂ solubility—including the influence of cation alkyl chain length, anion basicity, and functional group incorporation—are critically evaluated against experimental and computational data. Supported ionic liquid membranes (SILMs) and ionic liquid-based mixed matrix membranes for CO₂ separation are reviewed, highlighting permeability-selectivity trade-offs and stability considerations. Process configurations including IL-based absorption-desorption cycles, membrane contactors, and hybrid systems are assessed for energy consumption and economic viability. Recent advances in computational screening, machine learning-guided IL design, and process intensification are presented. Key challenges including high viscosity, long-term stability under operating conditions, absorbent regeneration energy, and scale-up economics are addressed. Finally, future directions toward industrial implementation are discussed, emphasizing the integration of ILs with renewable energy sources and the development of sustainable, cost-effective capture technologies.

DOI: https://doi.org/10.5281/zenodo.19050263

 

Published by: vikaspatanker

Authors: Won Kuk Jin, Choe Jin Hyok

Abstract: Window design is a critical factor significantly influencing building aesthetics, daylighting performance, visual comfort, and energy consumption. Conventional energy-saving strategies often rely on reducing window area, which negatively impacts architectural aesthetics and user satisfaction. This study proposes a novel form-oriented design approach that enhances energy efficiency while maintaining the window area. Four window geometries—flat, polygonal, protruded, and curved—were compared under identical area and material conditions. Key performance indicators included U-value, Solar Heat Gain Coefficient (SHGC), cooling and heating loads, and daylighting performance. The analysis revealed that curved windows achieved the highest cooling performance with an 18.2% reduction in cooling load but exhibited a significant drawback with an 8.2% increase in heating load, indicating substantial winter heat loss. Protruded windows showed a minimal cooling load reduction of only 0.3% and a 3.6% increase in heating load. Polygonal windows demonstrated the most balanced performance, with a 7.1% reduction in cooling load and a 3.8% increase in heating load. These results suggest that in a cold climate like Pyongyang, winter heating performance has a greater impact on annual energy consumption than summer cooling performance, implying that window form selection should not be based solely on summer performance.

DOI: https://doi.org/10.5281/zenodo.19049364

Published by: vikaspatanker

Authors: Devansh Indrodiya, Shivangi Patel

Abstract: The integration of Large Language Models (LLMs) into modern search engines has significantly transformed digital discoverability, shifting search behavior from deterministic webpage ranking to probabilistic entity citation within AI-generated responses. Unlike traditional search engines that present ordered lists of hyperlinks, generative search systems synthesize contextual answers and selectively cite businesses based on semantic relevance, trust signals, review sentiment, and inferred user intent. This transformation challenges conventional Search Engine Optimization (SEO) strategies that were originally designed to optimize positional ranking rather than inclusion within generative responses. This paper introduces Generative Engine Optimization (GEO), a geospatial artificial intelligence framework designed to model, measure, and improve business visibility in generative search environments. The proposed framework integrates geospatial analysis, semantic entity recognition, and machine learning–based prediction models to evaluate discoverability within AI-generated responses. A monitoring system called GeoRank360 is developed to track business citations across multiple generative platforms and compute a unified metric termed the Generative Visibility Score (GVS), which incorporates citation frequency, semantic prominence, sentiment strength, entity consistency, and temporal stability. An empirical evaluation conducted across 100 local businesses, five generative search platforms, 500 query variations, and over 4,000 geo-grid coordinates reveals spatial visibility volatility ranging from 35% to 60%, substantially higher than fluctuations observed in traditional search rankings. Predictive modeling achieves up to 87.1% accuracy in forecasting generative citation outcomes. The results indicate that semantic relevance exerts greater influence than geographic proximity in determining visibility within generative search responses. The proposed GEO framework establishes a foundation for future research in generative search visibility modeling, semantic ranking analysis, and AI-driven local discovery systems.

Published by: vikaspatanker

Authors: G. Janaki, P. Sangeetha, S. Swetha

Abstract: A Diophantine triple is a set of three positive integer a,b, c such that the product of any two distinct elements is added to one, is a perfect square .This article investigates the existence of a specific Diophantine triple involving Centered Hexagonal Number ensuring the product of any two members of the added to the property D(n).

DOI: https://doi.org/10.5281/zenodo.19019965

Published by: vikaspatanker

Authors: Deepak Tomar, Kismat Chhillar

Abstract: Water resource management has become increasingly challenging due to rapid population growth, climate variability, urbanization, and rising agricultural demand. Traditional hydrological models often struggle to capture the complex and nonlinear interactions between environmental variables affecting water systems. Machine Learning (ML) offers powerful data-driven techniques that can analyze large and heterogeneous datasets to support efficient water management. This paper explores the role of machine learning in water resource management, highlighting its applications in hydrological forecasting, irrigation optimization, groundwater monitoring, and water quality assessment. Various ML algorithms such as Artificial Neural Networks, Random Forest, Support Vector Machines, and Deep Learning architectures are examined for their ability to model complex hydrological processes. The study also discusses current challenges including data availability, model interpretability, and integration with existing hydrological frameworks. The findings indicate that ML-based approaches can significantly enhance predictive accuracy, optimize resource utilization, and support sustainable water management strategies.

DOI: https://doi.org/10.5281/zenodo.19019143

Published by: vikaspatanker

Authors: Akalya M, Mohammed Suhail Akthar J, Rohan P S, Dr R Karthik

Abstract: In Order To Detect And Track Wildlife In Real Time, Computer Vision Techniques Are Being Used More And More In Wildlife Monitoring. Modern YOLO Object Detectors (Yolov3, Yolov4, Yolov5, Yolov7, And Yolov8) Combined With Multiobject Tracking Algorithms, Specifically SORT And Deepsort, Are Assessed And Contrasted In This Study For Automated Wildlife Monitoring. Wildlife Camera Trap Datasets Are Used To Evaluate These Models' Performance, Taking Into Account Metrics Like Tracking Accuracy, Precision, Recall, Mean Average Precision (Map), And Inference Speed.According To Experimental Results, Deepsort Considerably Increases Tracking Stability By Lowering Identity Switches Through Appearance-Based Association, While Yolov8 Achieves The Best Detection Performance In Terms Of Map And AP@0.5. When Paired With Deepsort, Yolov5 Offers A Robust, Lightweight Baseline That Achieves High Tracking Accuracy (MOTA ≈ 94%) While Utilizing Computational Power Efficiently. Conversely, SORT, Which Has More Identity Switches And Only Uses Motion Cues. The Results Show The Trade-Offs Among YOLO Variants In Terms Of Detection Accuracy, Model Size, And Computational Cost. The Suggested YOLO + Deepsort Framework Shows Great Promise For Real-Time Wildlife Monitoring On Edge Devices Like Uavs And Field Cameras, Supporting Applications Like Habitat Analysis, Biodiversity Assessment, Antipoaching Surveillance, And Mitigating Conflicts Between Humans And Wildlife.

DOI: https://doi.org/10.5281/zenodo.19018863

 

Published by: vikaspatanker

Authors: Aswetha. M, Danwin shaju, Mrs. Sangeetha Priya

Abstract: The volume of unstructured texts has increased dramatically in recent years due to the internet and the digitization of information and literature. This onslaught of data will only grow, and it will come from new and unusual sources. Thus, it will be necessary to develop new and inventive approaches and tools to process and make sense of this data. Investors in the financial markets can now get information faster than ever before thanks to the expansion of communication channels, in addition to the online availability of news and reports in text format through providers like Reuters and Bloomberg. This contains a plethora of information that is often overlooked by financial market data. In order to measure the sentiment of a text, predictive and deductive methods are applied, these methods aim at extrapolating new feautures from big data.

DOI: https://doi.org/10.5281/zenodo.19018551

 

Published by: vikaspatanker

Authors: Vaishnav B.khokale, Aakash N.Nikam, Yash S. Patil, Aaditya S. Kshirsagar, Prof.S.S.Aher

Abstract: The continuous growth in population, urbanization, and technological advancement has resulted in a rapid increase in electrical energy demand. Conventional energy sources are not only limited but also responsible for environmental pollution. At the same time, a significant amount of human mechanical energy generated during physical activities such as gym workouts is wasted without any productive use. This research paper presents the concept of Harnessing Electricity from a Hybrid Green Gym, where human effort and solar energy are combined to generate electrical power. Mechanical energy produced during pedalling is converted into electrical energy using a generator, while solar energy acts as an additional and reliable source. The generated energy is stored in a battery system and can be used to operate small electrical loads. The proposed hybrid system ensures power availability during grid failures, power cuts, and environmental calamities. The system is eco-friendly, cost-effective, and suitable for decentralized energy generation. It also promotes physical fitness along with energy conservation.

Published by: vikaspatanker

Authors: Sagar Zodge, Rohit Vhawale, Vishal Vetal, Dikshant Zine

Abstract: This project presents a GPS and GSM based accident alert system that automatically detects accidents and sends emergency notifications. The system uses Arduino Uno and an accelerometer such as ADXL335 to detect sudden impacts. When an accident occurs, the location is obtained using the NEO-7M GPS Module and an alert message is sent through the SIM800 GSM Module to a predefined mobile number. This system helps provide faster emergency response and improves road safety .

 

 

Published by: vikaspatanker

Authors: Dr.S. Manikandan, Premkumar V, Thamarai Selvan T, Tharun M

Abstract: Independent navigation in dynamic and unfamiliar environments remains a major concern for individuals with visual impairments. Conventional mobility aids such as white canes and guide dogs offer limited spatial awareness and may not effectively detect obstacles at varying distances or heights. This paper introduces an Intelligent Shoe System for Blind Navigation that enhances user safety and mobility through real-time sensing and feedback mechanisms. The system embeds ultrasonic and infrared sensors within footwear to continuously monitor the surrounding environment and identify obstacles in the user’s path. A microcontroller processes sensor inputs and provides immediate feedback through vibration and audio cues, enabling intuitive and hands-free navigation. The design optionally incorporates GPS and wireless communication to support outdoor navigation, route assistance, and emergency alerts. Emphasis is placed on low power consumption, affordability, and comfort to ensure suitability for everyday use. Experimental results indicate improved obstacle detection performance and a noticeable reduction in collision incidents when compared to traditional assistive tools. The proposed intelligent shoe system aims to promote greater independence, confidence, and overall quality of life for visually impaired users.

DOI: https://doi.org/10.5281/zenodo.19017095

 

Published by: vikaspatanker