Volume 13 | Issue 4 |

 Abstract

Retinopathy of Prematurity (ROP) is a significant cause of visual impairment among preterm infants, but its impact extends beyond vision to broader neurodevelopmental outcomes.(1) This narrative review explores findings from an observational study conducted in Belagavi City, assessing the cognitive, language, motor, social-emotional, and adaptive behavior development of children with ROP at three years of age using the Bayley Scales of Infant and Toddler Development IV (BSID-IV). Results indicate that while social-emotional domains are relatively preserved, significant delays are evident in cognitive, language, and motor domains. Risk factors such as severe ROP, low birth weight, consanguinity, and maternal factors like preeclampsia were associated with poorer developmental outcomes. Early neurodevelopmental screening and targeted interventions are recommended to improve the developmental trajectories of these children.

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 Keywords

Keywords: Retinopathy of Prematurity, Neurodevelopment, BSID-IV, Cognitive Delay, Motor Delay, Belagavi

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 Abstract

The rise of autonomous and semi-autonomous machinery in industrial settings necessitates advanced safety mechanisms to ensure smooth operation while preventing collisions and protecting nearby workers. This project proposes an AI-based Extreme-Edge Collision-Avoidance System utilizing a Temporal Convolutional Network (TCN) deployed on an STM32 microcontroller. The system integrates multiple sensing technologies, including LIDAR, Camera Modules, Ultrasonic Sensors, and Infrared Sensors, to comprehensively monitor the machine's surroundings. At the system's core, the STM32 microcontroller processes real-time data from the sensors via a driver circuit, ensuring ultra-low-latency response. AI-based monitoring runs on a Raspberry Pi, analyzing time-series sensor data using the TCN model to detect potential hazards in real-time. The AI algorithm predicts collision risks and enhances situational awareness, ensuring timely interventions critical for industrial safety. Upon detecting an obstacle, the STM32 microcontroller triggers immediate corrective actions, controlling machinery operations while engaging an alarm and alert system to notify nearby workers. The integration of Raspberry Pi extends computational flexibility, supporting data logging, visualization, and remote monitoring, while enabling machine learning model updates. The system ensures robust performance in noisy and dynamic industrial environments by leveraging sensor fusion and AI. Extreme-edge processing minimizes latency, optimizes energy consumption, and maintains a compact memory footprint. Designed for seamless integration into industrial applications, this real-time collision avoidance system significantly enhances workplace safety, reduces accidents, and improves operational efficiency.

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 Keywords

Collision avoidance, Industrial safety, Edge computing, Ultrasound sensors, Temporal Convolutional Network (TCN), Machine learning, Low-power MCU, Raspberry Pi, Real-time processing, Sensor fusion, Acoustic noise robustness, Embedded systems, Proximity sensing, Smart manufacturing, Industrial automation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Automatic License Plate Recognition (ALPR) is an advanced computer vision technology that extracts vehicle registration numbers from images and videos for various applications, including security, traffic management, and automated parking systems. This research focuses on developing an efficient and accurate ALPR system leveraging Optical Character Recognition (OCR) and Fuzzy Searching techniques to enhance accuracy, even with noisy or unclear images. The system follows a structured approach where license plate images are first captured through a camera, processed using OCR to extract alphanumeric characters, and further refined with fuzzy searching algorithms to handle variations caused by lighting, font differences, and image distortions. The extracted license number is then matched against a database to identify the vehicle owner and generate real-time reports for security personnel, administrators, or authorities. A key contribution of this research is the integration of real-time analytics and a user-friendly dashboard for monitoring visitor logs, security access, and traffic trends. The proposed model is tested on a dataset of license plate images under varying environmental conditions to evaluate performance in terms of accuracy, processing time, and robustness. Experimental results demonstrate that our approach significantly improves recognition accuracy compared to traditional OCR-based models, particularly in challenging scenarios. This work has broad applications in smart parking systems, automated toll collection, vehicle entry management in organizations, and traffic surveillance. The future scope includes integrating deep learning-based recognition methods, expanding the dataset to accommodate multiple regional plate formats, and enhancing security features with blockchain-based data integrity.

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 Keywords

License Plate Recognition, OCR, Fuzzy Searching, Image Processing, ALPR, Smart Security, Automated Vehicle Identification.

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Creative Commons Attribution 4.0 and The Open Definition

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Federalism, as a system of governance, distributes power between a central authority and constituent political units, such as states or provinces. This research paper provides a detailed analysis of federalism, evaluating its pros and cons in contemporary governance. Drawing on scholarly literature and theoretical frameworks, the paper investigates how federalism promotes democracy, diversity, and accountability, while also exploring its challenges in coherence, efficiency, and governance. Additionally, the paper discusses relevant topics such as fiscal federalism, intergovernmental relations, and the role of federalism in managing crises and fostering innovation.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Federalism, Authority, Governance, power, state, intergovernmental

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In today's digital world, typing efficiency and accuracy are crucial for seamless communication. Users often struggle with slow typing speeds and frequent errors, especially in text-based interfaces. Next Word Prediction is a critical application of Natural Language Processing (NLP) that enhances user experience in text input systems by suggesting the next probable word based on the context. It focuses on implementing a Next Word Predictor using Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN) designed to handle sequential data efficiently. LSTMs are well-suited for capturing long-term dependencies in text, enabling context-aware and accurate predictions. The model is trained on a large text collection, allowing it to learn linguistic patterns, grammatical structures, and semantic relationships between words. Preprocessing steps include tokenization, embedding, and sequence generation, which prepare the data for training. The system is evaluated using perplexity and accuracy metrics to ensure performance, improving both typing speed and accuracy. LSTM-based Next Word Prediction models can be integrated into various applications, including virtual keyboards, chatbots, and assistive writing tools, enhancing user interaction.

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Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Linguistic Patterns, Grammatical Structures.

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT:- Brand loyalty plays a massive role by acting as a critical determinant of consumer behaviour thereby influencing purchase patterns and long-term brand attachment. Specially in an environment which is increasingly shaped by rapid dissemination of information and negative word-of-mouth (NWOM). Understanding the protective function of brand loyalty has become crucial in a world where negative word-of-mouth (NWOM) and the quick spread of information are shaping society more and more. The goal of this study, "Shielded by Loyalty: Analyzing Consumer Resistance to Negative Word-of-Mouth," is to investigate how consumer resistance to NWOM and brand loyalty are related. The study's goals are to determine how loyal consumers are to a brand, how much they are exposed to unfavorable word-of-mouth, and how loyalty levels relate to resistance behaviors. To determine whether loyalty serves as a protective barrier against adverse influences, special consideration is given to extremely devoted customers. A structured questionnaire that can be accessed by a wide range of people, including common and semi-literate consumers, is used to gather primary data. To analyze the data, statistical tools are used, with an emphasis on finding trends, correlations, and differences among various loyalty segments. It is anticipated that the results will show a positive correlation between resistance to NWOM and brand loyalty, emphasizing loyalty as a moderating factor in consumer decision-making. The study adds to the body of knowledge on consumer behavior and offers useful advice for brand strategists who want to improve customer retention and brand resilience against reputational risks.

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Key words:- Consumer Behaviour, Brand Loyalty, Negative Word-of-Mouth, Consumer retention.

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 Abstract

Mergers and acquisitions (M&A) have repeatedly emerged as a favored strategy for organizations seeking to expand beyond their organic capacities. In contrast to internal growth, which can be slow and limited by resource availability, mergers and acquisitions provide companies with a more rapid and significant means of expansion. Through these strategic agreements, corporations might penetrate new markets, expand their product offerings, gain technological competencies, or remove competition. M&A serves as a potent instrument for corporate restructuring, enabling firms to reorganize activities, optimize processes, and enhance overall efficiency. The incentives for mergers and acquisitions are primarily economic, typically based on the desire for enhanced profitability, expanded market share, and enduring sustainability. Companies may opt to merge with or purchase others in reaction to evolving industry dynamics, new consumer needs, or macroeconomic concerns. Every transaction is distinct, shaped by a confluence of strategic foresight, fiscal preparedness, and regulatory structures. This research seeks to examine the financial ramifications of M&A activities, specifically analyzing the impact on the financial performance of acquiring corporations before and after the merger or acquisition. The paper examines a series of chosen M&A deals in India throughout two notable periods to accomplish this objective. The research employs a series of essential financial ratios--namely profitability, liquidity, leverage, and efficiency ratios--to assess the performance of acquiring firms during these intervals. These financial indicators provide a thorough overview of the companies' operational and financial well-being. Additionally, the paired t-test at a 5% significance level is utilized to evaluate the statistical significance of the variations in financial performance pre- and post-merger. This meticulous methodology guarantees that the results are both sound and dependable, enhancing the comprehension of the actual financial implications of M&A selections inside the Indian business environment.

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 Keywords

Mergers and Acquisitions (M&A), Inorganic Growth, Financial Performance, Strategic Restructuring, Acquirer Company, Pre-Merger Analysis, Post-Merger Analysis, Financial Ratios, Statistical Analysis, Indian Corporate Sector, Profitability, Liquidity, Leverage

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Creative Commons Attribution 4.0 and The Open Definition

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In today's connected world, companies can do business in many countries. This has made corporate tax dodging a pressing issue. Big firms often move their profits between different places paying much less tax than you'd expect based on their work in a country. While these tax tricks might follow the rules, they often go against the idea of fairness that tax systems are built on. In India where public money is key to build roads run social programs, and keep the government going, these practices hit harder. To tackle these issues, India brought in the General Anti-Avoidance Rule (GAAR), which kicked off in 2017. GAAR aimed to give tax authorities the ability to overrule deals that, while within the law, were set up to dodge taxes. The thinking behind GAAR is straightforward but important: tax should be based on what's happening, not just what's on paper. But putting the rule into action hasn't been easy. It showed up after years of hold-ups and arguments, and even now many taxpayers and experts aren't sure how it'll work in real life. This research paper digs deep into the legal structure and real-world workings of GAAR in India's tax system. It looks at why it was created how it's been put into action (or not), and breaks down important cases and expert opinions that have shaped how people understand it. To give a wider view, the paper also stacks up India's GAAR against similar setups in other countries--Australia, the UK, and Canada--which have been using general anti-avoidance rules for longer. These side-by-side looks help show what's good and what's missing in India's approach. The research points out that GAAR could help stop aggressive tax planning, but it's got some problems right now. It's not clear enough, doesn't get used much, and courts haven't explained it well. If it's not used the same way all the time and there's no clear guidance, it might end up being just for show instead of doing something. The paper wraps up by saying that for India to tackle tax avoidance, it's not just about having tough laws written down. It's about how people understand those laws how they're explained, and how they're put into action. So, for GAAR to work well, there needs to be a good mix of legal power, the ability to run it, and people trusting that the tax system is fair.

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 Keywords

Corporate Taxation, Tax Avoidance, Tax Havens, GAAR, India,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Chronic Kidney Disease (CKD) is a significant global health issue with increasing prevalence and limited early detection methods. Traditional diagnostic practices often identify CKD at advanced stages. This project proposes a data-driven approach using machine learning algorithms to detect early-stage CKD from patient health records. The system utilizes medical datasets and applies classification models like Random Forest, SVM, and Logistic Regression to predict CKD occurrence. By integrating data analytics with healthcare diagnostics, this approach aims to improve early detection accuracy, enabling timely interventions and better patient outcomes.

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 Keywords

Chronic Kidney Disease (CKD) Kidney failure End-Stage Renal Disease (ESRD) Nephrology Kidney function Glomerular filtration rate (GFR) Proteinuria Creatinine levels Renal insufficiency Dialysis Kidney transplant ? Medical and Scientific Terms Uremia Hemodialysis Peritoneal dialysis Nephrons Electrolyte imbalance Hypertension Diabetes mellitus Renal biopsy Albuminuria eGFR (estimated GFR)

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Indigenous art form is a cultural practice related to the way of life of a traditional group. It includes painting, sculpture, music, dance, and more. This art form is passed down through generations. Rajbanshis are a group of ethnic people in the northern part of West Bengal, the Lower part of Assam, and some parts of Bangladesh, Nepal and Bhutan. They belong to the Royal family of Kamta / Kamrup Dynasty. Over time, they have mixed with other communities, yet some have preserved their identity through their traditional culture. Rajbanshi women initially represented themselves for the welfare of society and to welcome guests. Over a period, this representation evolved into an art form. K?ti g?na is a ritual based on the worship of God named "K?ti". Only women artists perform the song of K?ti along with the dance. The study will focus on the contributions of women artists, the process of acquiring this art form, and its cultural relevance. The research will follow qualitative research methodologies. The findings of this research Women perform around the K?ti deity. The song describes the K?ti deity, K?ti's birth story. In their performances, they describe the various parts of K?ti's body part like the hand, nose, ears, etc, which have been created. Then prays to K?ti for a son.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Kati Gana, Rajbanshi women, Women's art form, Rajbanshi

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Creative Commons Attribution 4.0 and The Open Definition