Volume 13 | Issue 4 |

 Abstract

The fashion industry is under growing pressure to embrace sustainable practices due to its substantial environmental effect. This case study investigates the relationship among modern technology and conventional reuse practices to build an environmentally friendly fashion future. This study investigates how cutting-edge technologies like artificial intelligence (AI)-driven fashion, electronic fashion, and cryptocurrencies for supply chain transparency, when combined with reuse techniques like upcycling and textile-to-textile recycling, can minimise waste, cut carbon emissions, and develop a circular economy. An exhaustive investigation of well-known companies and creative startups is part of the report. The results show that although conventional approaches are still important, technology is becoming more and more important in helping to scale and improve the efficacy of environmentally friendly methods in the fashion industry. This study provides useful information to fashion industry stakeholders who want to promote sustainability by combining innovation and reuse in a balanced way.

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 Keywords

1. Upcycling 2. Textile Recycling 3. AI driven fashion 4. Electronic fashion 5. Environmentally friendly fashion 6. Reduce waste

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Faulty software detection is a critical task in software development because it can highly impact the quality and reliability of software systems. In this paper, we propose a novel approach for faulty software detection using machine learning and the XGBoost algorithm. Our approach utilizes a dataset of Python code with control structures of basic such as if, else, and do while loops, and applies the XGBoost algorithm to diagnose faults in the code. We evaluate the performance of our approach using metrics such as accuracy, precision, recall, F1-score, and ROC AUC score, and compare it with existing research. Our results show that the XGBoost algorithm is highly effective in diagnosing faults in Python code, and our approach achieves state-of-the-art results on many machine learning challenges. We also provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, our approach scales beyond billions of examples using far fewer resources than existing systems.

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 Keywords

LSTM, Machine Learning, XGBoost, Python, Fault software detection, Deep Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Computer Science, Cyber Security

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This study presents the chemical synthesis and physico-chemical characterization of iron oxide nanoparticles using the co-precipitation method. Iron salts (FeCl? and FeSO?) were reacted under controlled pH and temperature to obtain nanoscale magnetite (Fe?O?) particles. The synthesized nanoparticles were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and vibrating sample magnetometry (VSM). XRD patterns confirmed the crystalline nature of the nanoparticles, while SEM revealed a spherical morphology with an average particle size below 50 nm. FTIR spectra validated the presence of Fe-O bonds, and VSM analysis showed superparamagnetic behavior. These results demonstrate the suitability of the co-precipitation method for synthesizing magnetic nanoparticles with potential applications in biomedical and environmental fields.

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Iron oxide nanoparticles, synthesis, co-precipitation, SEM, XRD, FTIR, magnetic properties

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Big Data refers to the massive volume of structured and unstructured data that is generated at high velocity from various sources. With the digitalization of industries, Big Data has become a pivotal component in modern computing, analytics, and decision-making. This study focuses on the principles of Big Data, its characteristics, processing techniques, and wide-ranging applications across sectors like healthcare, finance, e-commerce, and transportation. Big Data technologies like Hadoop and Spark have enabled real-time analytics and predictive modeling, facilitating data-driven strategies and innovations. This paper explores the ecosystem of Big Data and how it empowers businesses and research with deeper insights.

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 Keywords

Big Data, Volume, Hadoop, Apache Spark, Data Lakes, Data Mining, Data Analytics, Cloud Computing

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This paper explores how open economy policies have reshaped international relations in Latin America and the Caribbean. Since the 1980s, liberalization reforms have boosted trade and foreign investment but also deepened inequalities and environmental pressures. Case studies of Mexico, Brazil, Chile, and Caribbean nations reveal varied outcomes and growing geopolitical complexity, especially with China's rising influence. The paper calls for inclusive, sustainable strategies and stronger regional cooperation to navigate a multipolar global order.

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 Keywords

Open Economy, Trade Liberalization, FDI, Latin America, Geopolitics, Regional Integration, Sustainable Development

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Inadequate agriculture production laid at the heart of India's development problems in the late nineteenth and early twentieth century's. The rural sector, comprising agriculture and ancillary activities such as animal husbandry, forestry and fishing was the foundation of the colonial economy. It employed about three-quarters of the workforce and produced well over half of national income between the 1860s and 1940s. However, there were also sever productivity constraints, linked to the problems of labor utilization, as well as an endemic scarcity of capital and a lack of investment in irrigation and other capital inputs, creating in turn a shortage of productive land. World Wars are also responsible for the scarcity of agricultural production in India. Governmental measures, such as grant of subsidies for sinking wells, interest free takkavi loans for purchase of seeds, manures etc., were adopted by the Government for the encouragement of agriculture. This paper focuses on the measures taken by the Indian Government for the development of Indian Agriculture.

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Keywords Agricultural Finance, Cooperative Societies, Agricultural Development Programmes, Irrigation etc.,

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Daru is analysed as a descriptive poem about characters in a play. It is assumed to have derived from Dhruva songs, which are musical compositions about different elements of nature. Chapter 32 in Natya Sastra mentions about Dhruva songs.

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Dhruva, Daru.

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Plant diseases significantly impact crop yield and food security worldwide, making their early detection a priority in sustainable agriculture. Manual identification of plant diseases is often inaccurate, labor-intensive, and time-consuming. In recent years, deep learning particularly Convolutional Neural Networks (CNNs)--has emerged as a powerful tool for image-based disease detection and classification. This review consolidates insights from recent studies and surveys to present a comprehensive understanding of methodologies, including CNN and transfer learning techniques, datasets used, comparative performances, current challenges, and opportunities for future development. The integration of artificial intelligence with precision agriculture holds the potential to revolutionize the monitoring and management of plant health, thereby enhancing crop quality and yield. . To detect plant diseases, we are utilizing image processing with a Convolution neural network (CNN). A convolutional neural network (CNN) is a form of artificial neural network that is specifically intended to process pixel input and is used in image.

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CNN

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Creative Commons Attribution 4.0 and The Open Definition

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The present study explores the digital marketing strategies in the B2C automobile industry concerning the possible influences on purchasing decisions. As the automotive market continues its evolutionary journey, understanding consumer behaviour and the role of digital channels therein has become a very critical requirement for marketers and stakeholders alike. This study seeks to establish the degree by which a multi-digit marketing strategy, for example, social media advertising, online review, and e-mail marketing, impacts the decision-making of car buyer. This study used a mixed-methods approach in which the survey part put emphasis on quantitative data sets initially, then qualitative analysis through the consumers' interviews on the mentioned phenomenon. This research study's findings have provided insightful outcomes regarding changes within the automobile industry and increased the relevance of digital touch points for the customer journey. Digital marketing not only impacts brand awareness and consideration but also plays an essential role in the final purchasing decision stage, when information on the web and through social proof becomes increasingly important in the consumer's decision-making process. Such factors require detailed analysis so that the keys of successful techniques in digital marketing, for the automobile sector, can be found out those keys may also be the message personalization, mobile optimizations, coordination between online marketing, and other offline marketing practices. That insight can guide automobile manufacturers and dealerships in making more targeted and impactful digital campaigns that improve customer engagement and push out sales.

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 Keywords

Digital Marketing Consumer Behavior Buying Decisions Online Reviews, Social Media Advertising etc.

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Creative Commons Attribution 4.0 and The Open Definition