Metallurgical and Materials Engineering (E-Journal)
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Seasonal Signal Attenuation in Free-Space Optical Links: A Case Study of Coastal Regions (Goa, Odisha, Kanyakumari)
Get PDFFree-Space Optics (FSO) presents a compelling alternative for high-bandwidth communication, offering rapid deployment and cost-effectiveness compared to traditional wired systems.
1 However, FSO systems are susceptible to atmospheric conditions, particularly in coastal regions characterized by significant seasonal variations in weather. 2 This study investigates the impact of seasonal signal attenuation on FSO links operating in three distinct coastal regions of India: Goa, Odisha, and Kanyakumari. Utilizing Differential Phase-Shift Keying (DPSK) modulation, the research evaluates the performance of FSO systems by analyzing key metrics such as bit error rate (BER) and link availability across different seasons, considering factors like rainfall, fog, and atmospheric turbulence. The findings of this study aim to provide valuable insights into the feasibility and reliability of FSO deployments in challenging coastal environments, contributing to the development of robust and resilient FSO communication systems for diverse applications
Adaptation of National Regulations on the Use of Fall Protection Equipment for Firefighters to European Standards
Get PDFWork at height during firefighting represents a distinct category of operational activity requiring specialized safety equipment and professional training. This task remains one of the most hazardous for firefighters, particularly under the current conditions and challenges in Ukraine. This study analyzes both domestic and international literature on personal protective equipment (PPE) used by firefighters for fall protection. A comprehensive review of the regulatory and legal framework highlights certain inconsistencies that require resolution and delineates the strengths and limitations of both national and European standards concerning firefighter safety belts. The national definition of the term "fire rescue belt" is examined and compared with interpretations in the normative documents of leading countries. Based on an analysis of academic and official sources, an authorial definition of the term "fire rescue belt" is proposed. This device is defined as a protective tool intended for securing fire and rescue personnel during firefighting operations and for self-rescue in situations that pose threats to health and safety, with consideration given to individual risk assessments. Unified quality indicators for firefighter belts have been identified, which are crucial for ensuring product reliability and suitability, directly influencing user safety. The findings of this research lay the groundwork for substantiating quality indicators and testing methodologies for firefighter belts and for revising national regulatory documents to align them with contemporary safety standards
Deep Learning-Based Approaches for Machine Interface Analysis Using MRI Images
Get PDFThe tumor is a lethal illness that initiates due to unregulated growing of cells in the organs of the body like the brain, iris, spleen, lungs, etc. It is crucial to make an early diagnosis. There are numerous medical imaging methods, including CT, PET, Ultrasound, and MRI and so on, but MRI is frequently approached modality for its less ionization and less radiation. Recently, DL techniques are more popular in medical imaging technology. The important contribution of the article is to compare the effectiveness of the deep learning types/techniques for detecting any tissue from the T1-weighted (T1w) MRI abnormal brain images. In this article, the most used DL methods like CNN, RNN, and DBN are used, and analyzed the performance of each method in terms of DSC (dice score coefficient), PPV (positive predictive value), and sensitivity by using the BraTS 2020 dataset. The results of this segmented image have obtained the scores of 0.89, PPV of 0.87, and sensitivity of 0.90 for the CNN method. The C.N.N-based method is more effective for the brain tumor detection than RNN and DBN techniques
Crime Story Analysis Using NLP and ML with Django along with Voice
Get PDFThis project develops an advanced crime prediction and analysis system that utilizes natural language processing (NLP) and machine learning techniques to process crime narratives and offer actionable insights. The system is designed to analyse crime stories by extracting crucial information such as potential suspects, motives, opportunities, and the likelihood of specific crime categories. It employs various machine learning models, including deep learning approaches, to understand and classify the narratives efficiently. The system incorporates speech recognition, enabling users to input crime stories through voice commands, while the text-to-speech functionality allows for an interactive and seamless user experience. This combination of technologies makes the system more intuitive for law enforcement personnel and investigators, allowing them to quickly gather and understand relevant data without needing extensive training on technical aspects. At its core, the system aims to assist law enforcement agencies in predicting crime categories, identifying patterns in criminal activities, and profiling suspects based on narrative-driven information. By analysing the context and relationships within the stories, the system helps investigators focus on high-priority suspects and critical elements in each case. The backend is developed using Django, providing a robust platform for web-based crime analysis, while the integration of NLP libraries such as spaCy and Transformers enables the system to perform advanced text processing and model inference. The project's implementation provides a cutting-edge tool for improving the speed, accuracy, and efficiency of crime analysis in law enforcement agencies
Corporate Sustainability in India: ESG Practices, Net-Zero Strategies, and the Path to a Green Economy
Get PDFThe context of this study is the incorporation of, Environmental, Social and Governance (ESG) actions and corporate tactics in order to facilitate net-zero emissions in India. With regulators and investors taking increasingly strong regulatory and investor perspectives, ESG adoption has emerged as a key engine of financial resilience, stakeholder trust, and long-term sustainability. However, challenges such as regulatory inconsistencies, capital constraints, and greenwashing risks hinder widespread implementation. Through secondary data from peer-reviewed articles, corporate sustainability reporting and government reports, this work analyzes leading trends in ESG adoption, industry benchmarks and obstacles to carbon neutrality. Results suggest that targeted approaches for sectors, green finance, and improved regulation of corporate sustainability are critical to progress in corporate sustainability. The paper suggests policy recommendations and an action plan to improve ESG compliance and to promote India's low-carbon transition, providing sustainable growth without compromising the country's net-zero ambition towards 2070
Hybrid Flamingo Search and Ant Colony Optimization for Real-Time Intrusion Detection in IoT Networks
Get PDFReal-time threat detection in Internet of Things (IoT) networks is addressed in this research using a unique hybrid method combining Ant Colony Optimization (ACO) and Flamingo Search Algorithm (FSA). IoT ecosystems' natural resource limits and diversity call for optimization strategies that may quickly identify hazards while reducing computing overhead. Our FSA-ACO hybrid builds an adaptable, resource-efficient detection framework by combining the global search powers of flamingo search with the local optimization power of ant colony algorithms. Experimental results show a 23% increase in detection accuracy and a 47% decrease in false positives when compared to conventional machine learning methods. With little computational cost, the proposed model identifies zero-day attacks with 96.8% accuracy, making it fit for use in resource-constrained IoT contexts. These findings draw attention to the possible use of bio-inspired hybridization to solve the developing security issues in complex IoT ecosystems
Telecom Customer Churn Forecasting Using Machine Learning: A Data-Driven Predictive Framework
Get PDFCustomer churn is a significant challenge for businesses, impacting both short-term profits and long-term sustainability. Accurately predicting churn is essential for companies focused on retaining valuable customers and reducing acquisition costs. This paper explores the development and evaluation of a Customer Churn Prediction Model using several Machine Learning (ML) algorithms, such as Logistic Regression, Random Forest, XGBoost, Decision Tree, K-Nearest Neighbors (KNN), and Deep Learning, implemented on the RapidMiner platform. The analysis uses a publicly available telecommunications dataset from Kaggle, containing customer demographics, service usage, and billing information. The study follows key stages in the data science process, including data preparation, feature engineering, model training, and evaluation. Model performance is measured using metrics like Relative Mean Squared Error, Absolute Error, and Correct Predictions. While Deep Learning achieved the highest accuracy, Logistic Regression was the most interpretable and reliable. The findings highlight the importance of AI/ML in churn prediction, helping businesses optimize strategies and improve customer retention
s Star p Star Connected Spaces And s Star p Star Compact Spaces in Topological Spaces
Get PDFThe existing article’s goal is to establish and explore the new idea of connected and compact spaces which is known as s*p*connected and s*p*compact spaces by using s*p*open sets and s*p*closed sets. Also, we study and discuss some basic properties and theorems of these topological spaces
Zero Budget Natural Farming in the Era of Climate Crisis: A Multidimensional Framework for Sustainable Agricultural Transformation in India
Get PDFThis paper presents a comprehensive analysis of Zero Budget Natural Farming (ZBNF) as a transformative paradigm in sustainable agriculture, integrating ecological, economic, and social dimensions to address the shortcomings of conventional chemical-intensive farming. Through a synthesis of advanced theoretical frameworks, empirical evidence, and policy analysis, this research demonstrates how ZBNF aligns with agroecological principles, responds to systemic challenges in conventional agriculture, and offers scalable solutions for climate resilience, food security, and rural livelihoods. The study incorporates multidimensional sustainability assessment methodologies, ecosystem services valuation, agroecological transition models, and novel integrative frameworks to provide a nuanced, evidence-based evaluation of ZBNF's prospects and challenges. Our theoretical innovation includes the development of the ZBNF Adaptive Transformation Model (ZATM), which integrates sustainability transitions theory, complex adaptive systems thinking, and political ecology perspectives to analyze ZBNF's multidimensional impacts across scales. Findings from our mixed-methods research suggest that while ZBNF shows significant potential for enhancing farmer autonomy, environmental regeneration, and climate resilience, challenges remain in yield optimization, scientific validation, and scalability across diverse agroecological contexts. The paper contributes to the scholarly discourse by developing an innovative theoretical synthesis positioning ZBNF within contemporary sustainability science, providing methodological advances for assessing agroecological transitions, and offering practical recommendations for researchers, practitioners, and policymakers engaging with ZBNF and similar approaches globally
A Scientometrics Review Of Option Pricing Research: Insights Into The Black-Scholes Model And Its Variants
Get PDFThe pricing of options is a key concept in finance, and the Black-Scholes model, which was introduced in 1973, is one of the most important contributions to quantitative finance. This model provides a solid base for the valuation purposes of European options under some assumptions like the log-normality of asset prices and the constancy of volatility of the underlying asset. This paper makes a comprehensive study of the area of option pricing, with a particular focus on the development of the Black-Scholes model and its associated modifications. This paper adopts a scientometric review to bring together the growing body of literature on option pricing models and their variations. This paper finds yearly dissemination of publications, top research outlets, co-occurrence network of keywords, cluster analysis, collaboration network of authors in option pricing, co-authorship Patterns in Option Pricing Research, network analysis of article citation, most cited research articles in OP, influential Countries in Option Prices Research, Subject Area distribution in Option Pricing Research, and key Trends in Option Pricing Research. To the best of our knowledge, no previous research has tried to analyse the papers published in the Black-Scholes model. This paper contributes by conducting a scientometric analysis of Black-Scholes option pricing models. The research identified many themes, and it provides future research direction in this area of study