Volume 13 | Issue 8 |

 Abstract

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Crack closure, MRCC, Knotted SMP, Construction

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Creative Commons Attribution 4.0 and The Open Definition

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This study examines the conundrum of smart governance in Punjab, India, where digital illiteracy is still prevalent. Despite significant advancements in digital infrastructure and e-governance platforms, such as the introduction of e-District portals, mobile governance apps, and BharatNet initiatives, a sizable section of Punjab's population is still not able to take advantage of these developments because of their low level of digital literacy. The study looks at how this digital divide, which is characterized by socioeconomic constraints, gender differences, and rural-urban divides, reduces the goals of e-governance like accountability, transparency, and citizen participation and prevents equitable access to public services. An in-depth examination of digital service adoption, infrastructure preparedness, and citizen engagement in Punjab is provided in this paper through a qualitative methodology based on secondary data from government reports, policy documents, and surveys. Results show that while educated and urban populations adjust to digital platforms well, rural residents, especially women and the elderly, find it difficult to use them because they lack the necessary knowledge and abilities. Underutilization of digital services, a decline in trust in governance, and a persistent reliance on middlemen are the results of this. The report suggests community-based training centers, women-focused programs, hybrid service models, public-private partnerships, and inclusive digital literacy programs in regional languages as ways to close the digital divide. According to the study, the promise of smart governance will not be realized unless efforts are made to empower digitally marginalized groups, which will exacerbate already-existing disparities in civic empowerment and service delivery.

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Creative Commons Attribution 4.0 and The Open Definition

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Smart governance, e-governance, digital illiteracy, Punjab, digital divide, digital literacy, citizen engagement.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Assess, Knowledge, STP and transportation of sick neonates

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Creative Commons Attribution 4.0 and The Open Definition

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Investigating the level of Heavy Metal contamination in the Ganganagar district's agricultural area was the goal of this study. The study was carried out in 2014 during the post-monsoon season. Using an Atomic Absorption Spectrophotometer (AAS), the concentrations of Cd, Cr, Cu, Mn, Ni, Pb, and Zn in the research area's water, soil, and a few crops were measured. One of the biggest problems facing the globe today is the poisoning of plants and water by heavy metals, which needs to be addressed. Excessive levels of heavy metals pose a serious risk to plant and animal life. The overuse of pesticides, fertilizers, irrigation, atmospheric deposition, and industrial effluents are some of the causes of heavy metal contamination in many ecosystems. There were notable differences in the concentrations of certain heavy metals in the soil, water, and specific crop samples. In some of the samples, the levels of some heavy metals were higher than the maximum allowable levels. Because urban dust concentrations were linked to short-term emissions in the urban region, whereas soil concentrations mostly reflected the long-term effects of urban pollution, there was no correlation between the concentrations of heavy metals in urban dust and soil. The non-carcinogenic risk of heavy metals in urban dust and soil for children was generally at a safe level in the majority of cities. The main cause of the overall health risk posed by heavy metals in urban dust and soil was lead. Therefore, it is recommended that frequent heavy metal monitoring be done in order to lessen the negative effects of the metals.

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Creative Commons Attribution 4.0 and The Open Definition

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Atomic Absorption Spectrophotometer; Heavy metals; Maximum permissible concentrations.

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Creative Commons Attribution 4.0 and The Open Definition

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Diabetes mellitus (DM) represents a significant global health challenge, impacting 537 million adults across the globe. Among its various complications, diabetic retinopathy (DR) stands out as the primary cause of preventable blindness. The traditional manual screening for DR is costly, invasive, and reliant on skilled ophthalmologists, rendering it impractical for widespread screening in areas with limited resources. This research introduces a deep learning-driven automated framework for DR detection utilizing retinal fundus images. We assessed DenseNet121, EfficientNet-B4, and an Ensemble model that integrates both architectures using the EyePACS dataset. The preprocessing steps included green-channel extraction, Contrast Limited Adaptive Histogram Equalization (CLAHE), and comprehensive data augmentation to enhance lesion visibility and address class im- balance. The Ensemble model achieved an accuracy of and a Cohen's Kappa of surpassing earlier techniques, including the Symmetric CNN (SCNN) method. This study highlights the promise of AI-enhanced, non-invasive retinal screening for the early detection of DR and the facilitation of telemedicine-based healthcare services.

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Diabetes Mellitus, Diabetic Retinopathy, Retinal Fundus Images, Deep Learning, DenseNet, EfficientNet, Ensemble Learning

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This paper presents the synthesis of samples such as, BiFeO3, BiFe0.95Pb0.05O3, BiFe0.85Pb0.15O3, BiFe0.75Pb0.25O3 and BiFe0.65Pb0.35O3 multiferroic ceramics via solution combustion method (SCM). These BiFeO3, BiFe0.95Pb0.05O3, BiFe0.85Pb0.15O3, BiFe0.75Pb0.25O3 and BiFe0.65Pb0.35O3 ceramic materials were formulated by using metal nitrates and urea as an initial starting precursors. The temperature dependence of dielectric constant shows a dielectric anomalies at various temperatures in BiFe0.95Pb0.05O3, BiFe0.85Pb0.15O3, BiFe0.75Pb0.25O3 and BiFe0.65Pb0.35O3 ceramics at 1 kHz, 3 kHz and 5 kHz frequencies. Each of these powdered samples were ground in an acetone medium, then calcined and sintered at elevated temperatures before being pelletized.

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Multiferroics, Pure BiFeO3, Pb doped BiFeO3, SCM, Dielectric, applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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With the exponential growth of video surveillance systems in public and private spaces, automatic video anomaly detection has become a critical research area. Manual monitoring of surveillance footage is labor-intensive and prone to human error. Deep learning, particularly neural networks, has revolutionized video anomaly detection through robust spatiotemporal feature learning. This survey presents a comprehensive analysis of recent advancements in neural network-based methods for anomaly detection in video surveillance. We discuss foundational approaches, evaluate cutting-edge techniques such as autoencoders, GANs, transformers, and skeletal-based models, and highlight key datasets and performance metrics. Comparative analyses demonstrate the trade-offs across architectures and detection paradigms. The paper concludes with future research directions in privacy-preserving models, multimodal fusion, and explainable AI for real-world deployments.

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Creative Commons Attribution 4.0 and The Open Definition

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Video anomaly detection, deep learning, surveillance systems, neural networks, spatiotemporal modeling, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, autoencoders, generative adversial networks (GANs), transfer learning, skeleton-based detection.

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This project introduces a non-invasive method to predict an individual's blood group using fingerprint images. A deep learning model was trained on a dataset containing fingerprint samples labeled with different blood groups. We used Convolutional Neural Networks (CNN) along with techniques like ReLU activation and max pooling to extract and learn important image features. A user-friendly HTML interface allows users to log in and upload their fingerprint image. The trained model then analyzes the image and predicts the blood group. This system provides a quick, contactless alternative to traditional blood testing. It demonstrates the use of biometric data and AI in healthcare, aiming to improve accessibility and speed in emergency medical situations. Overall, it reflects the potential of deep learning in modern medical applications.

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Creative Commons Attribution 4.0 and The Open Definition

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Blood group prediction, Fingerprint recognition, Convolutional Neural Network (CNN), Deep learning, Non-invasive diagnostics.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Occupational Safety Package, Petroleum Factories, Refinery Staff, Workplace Hazards, Knowledge Assessment, Pre-test and Post-test, Safety Education, Industrial Safety, Bina Refinery, Hazard Awareness.

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Creative Commons Attribution 4.0 and The Open Definition