Volume 13 | Issue 4

 Abstract

With the evolution of cellular networks beyond 4G, addressing critical challenges such as increased capacity,higher data rates,reduced latency, and improvedQuality of Service (QoS) has become essential. To meet these demands, significant advancements in cellular network architecture are required. This paper provides a comprehensive survey of the fifth-generation (5G) cellular network architecture, highlighting key technologies that contribute to its development. Among these technologies, Massive Multiple-Input Multiple-Output (MIMO) and Device-to-Device (D2D) communication are particularly emphasized, as they play a crucial role in enhancing network efficiency, improving spectral utilization, and enabling seamless connectivity in dense urban environments. Beyond the core 5G architecture, this paper also explores emerging technologies that are fundamental to optimizing network performance. These include interference management strategies,spectrumsharingthroughcognitiveradio,ultra-densenetworks(UDN), multi-radio access technology (Multi-RAT),full-duplexradios,and millimeter-wavecommunication.The integrationof cloud-based Radio AccessNetworks (C-RAN) and Software-DefinedNetworks (SDN) further enhances the flexibility, scalability, and automation of 5G infrastructure, making it more adaptable to dynamic traffic demands. A probable 5G network architecture is also proposed, incorporating essential elements such as small cell access points, network cloud integration, and the Internet of Things (IoT) to ensure efficient connectivity across various applications. Furthermore, the paper conducts an extensive survey of global research projects on 5G, examining the contributions of leading research groups and institutions worldwide. These studies offer valuable insights into the ongoing innovations and technological breakthroughs that are shaping the future of next-generation wireless communication.

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 Keywords

5G Network Architecture, Massive MIMO, Device-to-Device (D2D) Communication, Ultra-Dense Networks (UDN), Cognitive Radio and Spectrum Sharing, Cloud-Based Radio Access Networks (C-RAN) and Software-Defined Networks (SDN).

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Introduction: Mobile advertising is one of the key factors determining consumer purchases to generate profit. This study aimed to describe the consumers' attitudes toward mobile advertising, exploring the four dimensions of Entertainment, Credibility, Irritation, and Personalization. Methodology: This study utilized a quantitative descriptive design. It determined a total of 129 teaching and non-teaching personnel of one of the private schools in Qatar as respondents through simple stratified random sampling. Results: The findings of this study indicate that the consumers' attitude towards mobile advertising, when taken collectively, was negative; the highest dimension was Entertainment, while the lowest was Irritation. Discussion: The results found that there are no significant differences on the consumers attitude towards mobile advertising when grouped according to age and sex. Recommendations: Based on the results of this study, the researchers recommend that marketers must improve the credibility of their mobile advertising, as well as personalize them to consumer preference, all while reducing irritating factors in order to receive a positive attitude from consumers and ensure consumer purchase.

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Consumer Attitude, Marketing, Mobile Advertising, Mobile Devices, SMS

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Bastar District, Chhattisgarh, is renowned for its rich tribal art and craft traditions, including Dhokra metalwork, terracotta, wrought iron, woodcraft, and Kosa silk textiles, practiced by indigenous communities like the Gond, Maria, and Muria tribes. These crafts, rooted in ancient techniques and cultural narratives, face a critical decline due to economic pressures, lack of market access, and modernization. This research paper examines the historical significance, current challenges, and socio-economic impacts of Bastar's vanishing art forms. Using a mixed-methods approach, including field surveys, artisan interviews, and secondary data analysis, the study identifies key factors contributing to the decline and proposes actionable strategies for preservation. Findings underscore the urgent need for government intervention, NGO support, and global market integration to sustain these cultural treasures.

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The Vanishing Art and Craft of Bastar District, Chhattisgarh: Challenges and Preservation Strategies

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The word computational thinking has emerged as a significant catalyst in transforming the way pupil engage and comprehend mathematics in the dynamic landscape of education. Despite the conventional realm of mathematical problem solving the computational thinking provides an alternative view point that align with the requirement of digital world. The study investigates computational thinking (CT) as a transformative catalyst in mathematics instruction. The paper conducts a thorough literature review to analyze multiple definitions of CT across disciplines, defines its essential components relevant to mathematics, and studies efficient integration procedures for mathematical problem solving. The study shows that four critical CT components such as decomposition, pattern recognition, abstraction, and algorithms are consistent with mathematics education frameworks and can considerably improve students' problem-solving abilities. The study also investigates pedagogical approaches that use both digital (plugged) and non-digital (unplugged) activities to enhance CT skills. The findings show that problem-based learning is particularly successful for incorporating CT into mathematics education. The study contributes to our understanding of how computational thinking can be systematically integrated into mathematics classrooms to prepare students for 21st-century issues, implying that careful curriculum design and teacher training are required for successful implementation.

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Computational Thinking, Mathematics Education, Pedagogical approaches, Role of Computational thinking skills.

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The project aims to predict rice leaf diseases using the CNN machine learning model and integrate it with Internet of Things (IoT) technology. CNN is a powerful convolutional neural networK(CNN) known for image classification, and in this context, it will be trained on a dataset of rice leaf images to accurately identify and classify diseases. The IoT aspect involves deploying sensors in rice fields to collect real-time environmental data, which will be fed into the model for more precise disease predictions. This integration of CNN and IoT enhances the accuracy and efficiency of disease detection in rice crops, allowing for timely and targeted interventions to mitigate the impact of diseases on rice yields. This project presents a smart irrigation system that optimizes water usage by adjusting pump speed using PWM pulses, based on real-time environmental parameters such as temperature, humidity, and water level.

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ABSTRACT This manuscript offers an extensive analysis of ChemDraw, an innovative software for chemical structure depiction that was developed in 1985. Originally designed to overcome the challenges associated with manual chemical illustrations, ChemDraw has transformed into a vital resource for chemists, researchers, educators, and professionals in various industries. It enables the creation of high-quality two-dimensional and three-dimensional molecular representations, reaction mechanisms, and predictive modeling of properties. The paper delineates the historical progression of ChemDraw, significant version enhancements, and essential features such as NMR prediction, stereochemical analysis, SMILES generation, and chemical property estimation. The review underscores its wide-ranging applications within academic settings, pharmaceutical development, industrial chemistry, and chemical engineering. Notwithstanding its myriad advantages--including an intuitive user interface, cloud functionality, and educational applications--the software exhibits certain limitations, particularly in the management of intricate biomolecules, inorganic chemistry, and three-dimensional modeling. The manuscript concludes by emphasizing ChemDraw's ongoing advancement and its critical role in contemporary chemical communication and documentation.

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ChemDraw chemical drawing software structure editor molecular modeling chemical structure representation chemical diagram tool scientific illustration cheminformatics molecular design chemical reaction drawing organic chemistry software IUPAC naming NMR prediction spectral analysis SMILES generation chemical publishing molecular formula chemical equation editor chemistry visualization ? Uses of ChemDraw: Drawing chemical structures and reactions Predicting NMR, IR, and Mass s

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Thyroid nodules are widespread in the general population, and a sizable percentage of them ar e benign .Differentiating between benign and malignant nodules is crucial to preventing unnec essary treatments and ensuring timely treatment for malignancies .Thyroid nodule stiffness can be evaluated noninvasively with ultrasound elastography, most especially using Acoustic Radi ation Force Impulse (ARFI) imaging .This article examines the concepts, procedures, diagnosti c potential, and clinical applications of ARFI elastography in thyroid nodule assessment..

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Thyroid, Ultrasonography, Elastrography, Nodules, ARFI

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In the contemporary Information Technology (IT) era, information has become an indispensable factor influencing various facets of human life, particularly in sectors such as healthcare, education, business, and governance. The capability to efficiently collect, store, process, and disseminate data, information, and knowledge is now recognized as a critical determinant of success in an increasingly digital and data-driven world. This paradigm shift has been driven by remarkable advancements in computing power, storage capabilities, and the widespread use of electronic devices, resulting in the exponential growth of data generation--often referred to as big data. This research also highlights the intricate processes involved in data collection, storage, knowledge generation, and dissemination. In doing so, it underscores the transformative role of IT in modern organizational structures. Additionally, it examines how data management systems and mining technologies empower stakeholders to derive meaningful conclusions, optimize business operations, and maintain a competitive advantage in an increasingly data-centric landscape. The findings of this study offer valuable insights that can inform the development of robust data management strategies. Moreover, they provide a foundation for leveraging data mining techniques to maximize organizational value and enhance decision-making capabilities. The conclusions drawn from this research are expected to contribute to the ongoing discourse on data management and mining technologies, providing practical guidance for organizations aiming to navigate the complexities of a data-driven world

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 Keywords

Information Technology (IT), Big data, Data warehouses, Centralized repositories, Data collection, Data storage, Data mining tools, Machine learning, Artificial intelligence, Data analysis

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The banking sector plays a pivotal role in the economic development of a country, with private sector banks emerging as key players in delivering customer-centric services. This review paper examines the service quality of private sector banks by analyzing existing literature and research findings. It explores key dimensions of service quality, such as reliability, responsiveness, assurance, empathy, and tangibles, with a focus on how these factors influence customer satisfaction and loyalty. The study also highlights the growing importance of technology-driven services and digital banking in shaping customer experiences. By synthesizing insights from various empirical studies, this paper identifies gaps in service delivery, best practices, and areas requiring improvement. The findings aim to provide valuable implications for bank managers, policymakers, and researchers seeking to enhance service quality and customer relationship management in the competitive landscape of private sector banking.

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Service Quality, Private Sector Banks

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Food wastage is one of the most critical challenges faced by society today, especially in regions where celebrations, events, and large-scale gatherings frequently occur. Despite the abundance of food produced and available globally, countless individuals from underprivileged communities remain unable to access even a single meal in a day. Addressing this glaring disparity, this paper introduces a socially-driven web-based platform designed to bridge the gap between excess food availability and those in need. The platform provides an innovative solution to mitigate food wastage by enabling seamless interaction between food donors and local NGOs. Built using user-friendly technologies like HTML, CSS, and JavaScript, the platform allows food donors to input essential details, such as their location, estimated food quantity, and contact information. The information is then made accessible to NGOs, who can take swift action to collect the surplus food and ensure its redistribution to communities in need.

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Food donation , NGO , Web App

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An AI-powered personalized learning platform is developed using Streamlit, Gemini API, and Python in a VS Code environment. It allows students to take interactive quizzes and receive instant feedback with explanations for both correct and incorrect answers. The system identifies learning gaps and offers focused insights to help students improve specific skills. With a smooth and responsive interface, the platform adapts to individual performance, making learning more effective and engaging. By combining intelligent feedback with real- time progress analysis, it creates a tailored educational experience that encourages continuous learning and helps students achieve better academic results

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AI-powered learning, personalized quizzes, Streamlit, Gemini API, and Python within a VS Code environment, the system focuses on learning gap analysis and skill-based improvement

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Background: Parkinson's disease is one of the main contributory factor that affect adult's quality of life. Aim: To evaluate the impact of the educational module on knowledge regarding Parkinson's disease and its prevention among adults. Method and Materials: A quantitative research approach with a pre-experimental one-group pre-test and post-test design was used for the study. The setting of the study was selected village in Indore. By using the non-probability purposive sampling technique 150 adults were selected. A self-structured questionnaire was used to assess the demographic data and knowledge regarding Parkinson's disease and its prevention among adults. The pretest knowledge was evaluated on day 1 and educational module was administered regarding Parkinson's disease and its prevention to the samples. On the 7th day after intervention post-test knowledge was evaluated. The data analysis was done using descriptive and inferential statistics. Result: In the pretest, 137 (91%) adults obtained poor grade, 8 (5%) participants obtained average grade, 5(4%) obtained good grade and none of the participants obtained excellent grade. In the posttest, none of the participant obtained poor grade, 5 (3%) participants obtained average grade, 33 (22%) participants obtained good grade and 112 (75%) participants obtained excellent grade. The intervention was very helpful in increasing the knowledge grade of the participants. A statistically significant association was found between marital status and any information about Parkinson's Disease and pretest knowledge grade (P>0.05), showing that the pretest knowledge grade is dependent on marital status and any information about Parkinson's Disease. Conclusion: The findings of the study showed information module was effective in increasing the knowledge of adults regarding Parkinson's disease and its prevention. Keywords: Knowledge, adults, Parkinson's disease, prevention.

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THE IMPACT OF EDUCATIONAL MODULE ON KNOWLEDGE REGARDING PARKINSON'S DISEASE AND ITS PREVENTION AMONG ADULTS

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The rapid increase in textual data in navigation tasks for devices like GPS or smart assistants creates challenges for managing and storing data in large-scale systems. Data deduplication, which reduces storage needs by eliminating duplicate data, offers a solution but raises security concerns. This paper introduces DEDUCT, a new method that combines cloud-side and client-side deduplication to achieve high data compression while protecting data privacy. Designed for devices with limited resources, such as IoT devices, DEDUCT includes lightweight preprocessing and safeguards against security risks like side-channel attacks. Testing on a navigation dataset shows that DEDUCT can compress data by up to 66%, significantly cutting storage costs while keeping data secure, making it an efficient choice for managing large-scale data systems.

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Data deduplication, IoT storage optimization, Cloud security, Cryptographic access control.

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This study examines the relationship between product quality and brand loyalty among Crocs consumers in Coimbatore. Using a structured questionnaire administered to 122 respondents, the study explores how factors such as durability, comfort, material quality, and design aesthetics influence consumer perceptions and loyalty. The findings reveal that durability and brand reputation are primary drivers of repeat purchases. The research underscores the role of consistent product quality in sustaining brand trust and offers actionable strategies for enhancing consumer satisfaction.

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Product Quality Brand Loyalty Durability Comfort Material Quality Design Aesthetics Brand Reputation Customer Satisfaction Consumer Behavior Repurchase Intention Footwear Industry Crocs Consumers Customer Trust Marketing Strategies User Experience

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One of the critical multimedia challenges in today's digital landscape is the rapid and effective creation of video content enriched with relevant metadata. This metadata is essential for improving discoverability, organization, and engagement, but the process of manually generating it is both time-consuming and inconsistent. To address this, we propose a Video Metadata Automation System that utilizes recent advancements in artificial intelligence--particularly large language models (LLMs)--to automatically generate key video metadata, including titles, descriptions, captions, and thumbnails. Our system employs an end-to-end automated framework that integrates techniques such as audio extraction, speech-to-text transcription, and video analysis. Using APIs for connection to high-performing LLMs, the extracted transcripts are used to create metadata in a structured and SEO-friendly format. The most suitable LLMs are evaluated and selected for optimal performance in this context. Furthermore, the system features integration with image generation models such as Stability AI's Stable Diffusion to generate thumbnails based on the video's context. This paper explores the effective utilization of open-source tools and state-of-the-art AI technologies, presenting a unified and modular solution that simplifies the video content creation pipeline. By automating the metadata generation process, our system significantly reduces the workload for creators, enhances video accessibility, and enables faster content publication with minimal manual intervention.

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Metadata Generation, Large Language Models (LLMs), Speech-to-Text, Audio Extraction.

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In modern urban mobility systems, real-time and easily accessible information is crucial for enhancing public transportation efficiency. "MeetRoute" presents a user-centric platform that enables commuters to instantly access comprehensive bus route information and live passenger occupancy through QR code scanning at bus stops. Upon scanning, users are redirected to a mobile-optimized website that displays a list of available bus routes, providing vital details such as bus number, starting and ending points, number of stops, seating capacity, and real-time crowd data captured via onboard cameras. This solution addresses common challenges like uncertainty in travel, overcrowding, and lack of real-time updates. By integrating computer vision technologies with smart web systems, MeetRoute bridges the information gap between commuters and transportation services. This paper discusses the platform's design architecture, implementation methodology, challenges, results, and future directions aimed at creating a sustainable, efficient, and commuter-friendly transportation ecosystem. The research highlights how MeetRoute improves commuter convenience, promotes the adoption of public transportation, and contributes to reduced traffic congestion and environmental impact.

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Public Transport, QR Code Navigation, Real-Time Passenger Monitoring, MeetRoute.

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 Abstract

Aadivasi Mahilao Ki Panchayati Raj Me Bhagidari Per Ek Samaji Adhayan

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Aadivasi Mahilao Ki Panchayati Raj Me Bhagidari Per Ek Samaji Adhayan

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Abstract The Upper Beas Basin of Kullu valley has experienced a rapid growth in number of visitors in the past few decades, i.e., from a mere 10,000 visitors in the 1960's (Singh, T.V 1989) to more than 28 lakh in2022. (HPTDC 2022). This has resulted in an uncontrolled and unplanned growth of tourism related infrastructure, crowding, congestion and pollution. All this is having a deep impact on the regions environment, economy and society, raising questions of long term sustainability of tourism in the region. As such, the paper tries to assess the impacts of tourism especially on the social and cultural aspect of the region. Socio-Cultural impact assessment is crucial for tourism sustainability because it helps identify and mitigate potential negative effects on local communities, cultures and traditions. By understanding these impacts tourism development can be managed in a way that benefits both visitors and the host communities, ensuring long-term sustainability and harmony between tourism and local cultures. The study clearly demonstrates positive and negative impacts on the socio-cultural aspects of the Upper Beas Basin. Among the negative impacts one sees changes in the occupation, social values, family structure etc. There is a weakening of community bonds, degeneration of social value systems, commercialization of culture, increase in crime and moral laxity. The positive impacts on the other hand were preservation and revival of certain culture and traditions, spread of new ideas (cultural exchange), increasing awareness of the significance of education and a desire to improve their living standards.

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Upper Beas Basin (UBB), Tourism, Sustainable tourism, Mountain, Socio-cultural impacts,

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To improve the seamless smart grid integration of renewable energy sources (RES), this research explores the concept of resilient control. The advantages of RES for the economy and environment have made their integration into power grids a top priority, but RES's intermittent nature presents significant challenges to grid flexibility, stability, and reliability. To reduce the uncertainties and disturbances related to RES, the paper examines efficient applications of sophisticated control strategies, such as adaptive control, robust control, and model predictive control. It also examines the importance of communication networks, demand-side management, and cyber-physical security in enhancing the resilience and functionality of smart grids. The challenges of incorporating renewable energy, including energy storage devices, grid interoperability, and legislative barriers, are also covered in detail. This paper provides recommendations for building resilient smart grids that can accommodate large rates of RES penetration while preserving stability and operational security. Initiatives to fill existing research gaps and policy recommendations are also made, emphasizing the value of backup plans and standardized procedures for grid dependability.

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Smart Grids, Renewable Energy Sources (RES), Resilient Control, Grid Stability, Grid Reliability

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Mineral exploration in India continues to face the dual challenge of balancing economic urgency with the complexity of its vast and geologically diverse terrain. This study introduces a spatial predictive modeling framework using Geographic Information Systems (GIS) to identify high-potential mineral prospecting zones across selected regions in India. By integrating multi-layered spatial datasets--ranging from lithological, structural, and topographical variables to satellite-derived remote sensing indices--and employing a Random Forest classification model, the research develops a high-resolution mineral potential map calibrated against known mineral occurrences. The results reveal statistically significant spatial patterns, highlighting the predictive value of factors such as proximity to fault lines, lithological transitions, and elevation gradients. This approach not only reduces exploratory costs and environmental disruption but also provides a replicable model for mineral intelligence in resource-rich but data-fragmented environments. Ultimately, the study supports a paradigm shift toward data-driven, precision exploration strategies within India's mineral governance landscape.

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Geographic Information Systems (GIS); Mineral Prospectivity Mapping; Spatial Predictive Modeling; Remote Sensing; Random Forest Classification; India; Mineral Exploration; Geospatial Data Analysis; Lithology; Fault Proximity

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