Daily Archives: May 25, 2026

Authors: Manuela Christy Dany S, Dr. Nithyalakshmi B

Abstract: Keeping global water resources clean is becoming harder every year. Industrial growth has pushed wastewater systems into a corner, and the usual treatment methods are starting to look worn out. They demand a lot of energy and still struggle with stubborn pollutants that refuse to break down. This review takes a close look at nanobubble (NB) technology as a more sustainable option. Nanobubbles are tiny, sub-micron gas cavities with an unusually long life in water, and that alone makes them interesting. They also bring unusual physicochemical properties, including high internal pressure and the formation of reactive oxygen species (ROS). The paper covers the basic mechanisms behind NBs, their contribution to aeration and flotation, and their strong performance in removing organic dyes, nutrients, heavy metals, and pathogens. Reported studies show that NB-based systems can push Chemical Oxygen Demand (COD) removal above 90% while using much less energy than conventional activated sludge treatment. The aim here is straightforward: give researchers and practitioners a clear view of the technical value and economic promise of NB technology in modern water purification.

Published by: vikaspatanker

Authors: R. Swetha

Abstract: Climate change poses one of the most formidable challenges to global ecological and socioeconomic stability in the twenty-first century. As atmospheric concentrations of greenhouse gases continue to rise, the scientific community has increasingly turned to Nature-Based Solutions (NbS) as a viable and cost-effective complementary strategy to conventional technological mitigation approaches. This report provides a systematic analysis of Nature-Based Solutions, examining their mechanisms, classifications, documented effectiveness, and real-world implementation challenges. NbS encompass a spectrum of ecosystem-centred interventions — including reforestation, wetland restoration, urban greening, and sustainable agricultural practices — that simultaneously deliver climate mitigation benefits while enhancing biodiversity and community resilience. Key findings of this report indicate that NbS possess the theoretical capacity to contribute between 10 and 12 gigatons of CO₂ equivalent reductions annually by 2030, representing approximately 30% of the mitigation required to limit global warming to 1.5°C. However, this potential is contingent upon significant upscaling of political commitment, financial investment, and cross-sector governance frameworks.

Published by: vikaspatanker

Authors: Pragati Sharma, Bhomik Nahariya, Aryan Rajput, Vansh

Abstract: In view of their size-dependent properties, both physical and chemical, semiconductor nanostructures have emerged as an essential component within modern nanotechnology. Novel device functionalities and adaptable electronic states are being established as possible by having the ability to accurately tune morphology, from zero-dimensional quantum dots to one-dimensional nanowires and two-dimensional thin films. The link between structural morphology and electronic characterization is demonstrated in this paper's assessment of sophisticated experimental methods for the growth and manufacturing of semiconductor nanostructures. Alongside top-down techniques such as lithography and etching, molecular beam epitaxy (MBE), chemical vapor deposition (CVD), atomic layer deposition (ALD), and laser ablation are also presented. In addition, it focuses on the ways in which defects, interfaces, and quantum confinement influence electronic states.

DOI: https://doi.org/10.5281/zenodo.20375584

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Authors: Prof. P.S. Togrikar, A.N.Jamdade, P.R.Shirke, H.J.Phadtare

Abstract: Traffic Sign Recognition (TSR) is an essential component of Advanced Driver Assistance Systems (ADAS) and intelligent transportation. This paper presents a cost-effective IoT-based TSR system using an ESP32-CAM for image acquisition and a backend server for processing. Due to limited edge-device capability, images are transmitted via a Telegram Bot for remote inference using the YOLOv3 deep learning model trained on the GTSRB dataset. To enhance robustness under real-world conditions such as occlusion and varying illumination, preprocessing techniques like CLAHE and data augmentation are applied. The system returns annotated results through a Telegram interface and a local GUI. Experimental results demonstrate high accuracy and reliable performance, validating the effectiveness of the proposed approach. The system also shows strong performance under partially occluded conditions, improving real-world applicability. Furthermore, the proposed architecture ensures low-cost deployment and scalability for smart transportation systems. This work highlights the potential of integrating IoT with deep learning for practical and accessible traffic monitoring solutions.

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Authors: Prof. Shyam Prasad H R, Rakshitha V, Rohan K M, Naveen S, Supriya R S

Abstract: The increasing development of urban areas and architectural requirements often lead to the construction of multi- storey reinforced concrete (RC) structures that do not comply with the symmetric and uniform structure that was assumed in classical seismic design theory.Multi-storey reinforced concrete (RC) structures are commonly built with an eccentric and non- uniform structure due to urban developments and architectural requirements, which do not match the symmetric and uniform structure used in classic seismic design theory. In this study, the behaviour of a G+7 RC building with an L-shaped plan irregularity and soft-storey vertical irregularity is studied under three structural configurations: Bare Frame, Infill Wall Frame and Shear Wall Frame and modeled in ETABS. The response spectrum analysis has been done according to IS 1893 (Part 1):2016, gravity loads as per IS 875 (Parts 1-3):1987, RC design as per IS 456:2000, ductile detailing as per IS 13920:2016. These values of storey drift ratio, lateral stiffness, storey shear and storey displacement were extracted and compared. The Shear Wall structure reduced the roof level lateral displacement by ~99.9% and storey drift by ~99.8–99.9% compared to the Bare Frame, and provided around five times the stiffness. The displacement reduction for the Infill Wall model ranged from intermediate (62–78%). A clear performance hierarchy was generated: Shear Wall > Infill Wall > Bare Frame, with shear wall being the critical member in an irregular building in high seismic zone construction, to ensure safety.

DOI: http://doi.org/10.5281/zenodo.20375845

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Authors: Prof. Dr H R Divakar, Kavana S, K P Renuka Prasad, Manoj Kumar S R, Lipika K

Abstract: The traditional attendance system used in educational institutions is often time-consuming, error-prone, and vulnerable to proxy attendance. Existing hardware-based attendance solutions such as RFID systems require additional infrastructure, maintenance cost, and dedicated devices, making them less flexible and expensive for large-scale deployment. To overcome these limitations, this paper presents a Biometric Smart Attendance System that combines biometric verification, AI-based face verification, and GPS location validation to ensure secure, accurate, and reliable attendance management without using dedicated hardware components. The proposed system uses multi-factor authentication to verify student identity before marking attendance. Face verification technology identifies and authenticates students, while biometric authentication provides an additional layer of security. GPS-based location verification ensures that attendance can only be marked when the student is physically present within the authorized classroom premises. The system is developed using React, TypeScript, FastAPI, Python, DeepFace, OpenCV, and Supabase. Test results show face verification accuracy of 96% under normal lighting and overall attendance accuracy of 95%.

DOI: https://doi.org/10.5281/zenodo.20374000

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Authors: Dr. M. C. Padma, Bhoomika M, Faika Mehvish, Praveen Kumar R

Abstract: The rapid proliferation of deepfake videos—synthesised using Generative Adversarial Networks (GANs) and allied deep-learning techniques—poses grave risks to societal trust, democratic processes, and personal privacy. Existing detection approaches predominantly rely on frame-level spatial analysis and consequently fail to capture temporal inconsistencies that arise in manipulated sequences. This paper presents Deep Shield, a hybrid deep-learning framework that couples a ResNeXt convolutional neural network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) recurrent network for temporal sequence modelling. Each video frame is first preprocessed via face detection and alignment, after which ResNeXt encodes per-frame spatial embeddings that are subsequently fed into the LSTM to capture inter-frame inconsistencies. A fully connected classifier then labels the video as Real or Fake alongside a confidence score. The system is validated on three benchmark datasets—FaceForensics++, DFDC, and Celeb-DF—achieving detection accuracy exceeding 99 % together with precision, recall, and F1-score values above 99 %. The framework is wrapped in a Django-based web interface that allows nontechnical users to upload videos and obtain results in near real time. Robustness testing under compression artefacts, low-light conditions, and adversarial inputs confirms the generalisability of the approach.

DOI: http://doi.org/10.5281/zenodo.20375755

Published by: vikaspatanker