Skin Uncertainty in Multi-Layered Commingled Reservoirs with Non-Uniform Formation Damage

Imperial Users onl

Greening Libraries for a Sustainable Future: A Comparative Analysis of Green and Traditional Library Practices

This paper explores the future of libraries by comparing traditional library practices with emerging green library practices. With an increasing emphasis on environmental sustainability, libraries are evolving to incorporate more eco-friendly practices to reduce their ecological footprint. This paper examines the definition of green libraries and highlights their significance in promoting sustainability and environmental awareness within communities. The paper reviews the differences between traditional libraries and green libraries, including the adoption of renewable energy sources, integration of natural features, use of smart building technologies, workshops and education programs, collaborations with community organizations, and advocacy for sustainable policies and initiatives. By analyzing these differences, the paper provides a comparative analysis of green libraries and traditional libraries. The paper presents case studies of successful green libraries in Indian contexts to showcase their achievements and impact. It also discusses the potential benefits of green libraries, such as improved energy efficiency, reduced waste, increased community engagement, and enhanced reputation. This paper highlights the growing trend towards green libraries and their potential to transform the future of libraries. By comparing traditional and green library practices, it offers insights and implications for library professionals, policymakers, and researchers, emphasizing the need for further research and innovation in this evolving field

Use of Information Visualization Techniques for Collection Management in Libraries: A Conceptual Review

This paper presents a conceptual review exploring the application of information visualization techniques in the context of collection management in libraries. Collection management plays a crucial role in ensuring libraries offer relevant and diverse resources to meet the information needs of users. Information visualization, with its ability to visually represent complex data, has emerged as a powerful tool for enhancing collection management practices. Drawing upon a comprehensive literature review, this paper examines the theoretical foundations, benefits, challenges, and practical applications of information visualization techniques in library collection management. It discusses various visualization methods, such as charts, graphs, and maps, and explores their potential in assessing collection composition, analyzing usage patterns, and supporting decision-making processes. The paper highlights the benefits of information visualization in improving user engagement, optimizing resource allocation, and facilitating data-driven decision making. It also addresses challenges related to data integration, technology infrastructure, and ethical considerations. Through real-world case studies and examples, this conceptual review provides insights into successful implementations of information visualization in collection management. The paper concludes by emphasizing the potential of information visualization techniques to transform collection management practices in libraries, enhancing the accessibility, relevance, and impact of library resources

Enhancing Plagiarism Detection: The Role of Artificial Intelligence in Upholding Academic Integrity

Plagiarism poses a significant threat to academic integrity, requiring effective measures for its detection and prevention. This paper explores the efficacy of plagiarism detection tools in upholding academic integrity, with a specific focus on the use of artificial intelligence (AI) technologies. The abstract introduces the concept of plagiarism and its impact on scholarly work. It highlights the importance of reliable and accurate plagiarism detection methods and emphasizes the role of AI in enhancing the effectiveness of such tools. The abstract briefly outlines the main points covered in the paper, including the use of AI techniques such as text matching algorithms and natural language processing, the application of machine learning in plagiarism detection, and the challenges and advancements in cross-language detection. The abstract concludes by emphasizing the importance of promoting ethical scholarship and academic integrity in educational institution

Observations on Factors Affecting Performance of MapReduce based Apriori on Hadoop Cluster

Designing fast and scalable algorithm for mining frequent itemsets is always being a most eminent and promising problem of data mining. Apriori is one of the most broadly used and popular algorithm of frequent itemset mining. Designing efficient algorithms on MapReduce framework to process and analyze big datasets is contemporary research nowadays. In this paper, we have focused on the performance of MapReduce based Apriori on homogeneous as well as on heterogeneous Hadoop cluster. We have investigated a number of factors that significantly affects the execution time of MapReduce based Apriori running on homogeneous and heterogeneous Hadoop Cluster. Factors are specific to both algorithmic and non-algorithmic improvements. Considered factors specific to algorithmic improvements are filtered transactions and data structures. Experimental results show that how an appropriate data structure and filtered transactions technique drastically reduce the execution time. The non-algorithmic factors include speculative execution, nodes with poor performance, data locality & distribution of data blocks, and parallelism control with input split size. We have applied strategies against these factors and fine tuned the relevant parameters in our particular application. Experimental results show that if cluster specific parameters are taken care of then there is a significant reduction in execution time. Also we have discussed the issues regarding MapReduce implementation of Apriori which may significantly influence the performance.Comment: 8 pages, 8 figures, International Conference on Computing, Communication and Automation (ICCCA2016

The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

Application of somatic hybridization for the improvement of horticultural crops

Somatic hybridization (SH) using protoplast fusion is a promising tool to produce symmetrical and asymmetrical polyploidy somatic hybrids in many agricultural crops. The technique of SH could facilitate conventional breeding by providing of novel lines so as to use them as elite breeding materials in conventional crosses for both scion and rootstock improvement. Further, SH can overcome those problems associated with sexual hybridization viz., sexual incompatibility, nucellar embryogenesis, and male/female sterility. Successful exploitation of SH in horticultural crops mainly comes from transfer of resistance genes for biotic and abiotic stresses from related species in several horticultural crops, viz., citrus, potato, brinjal, tomato, mango, avocado, banana, strawberry, pear, cherry etc. Unlike transgenic technology, SH is not affected by legal formalities and able to transfer uncloned multiple genes. However, certain boundaries and limitations of SH restricts its use over sexual hybridization but, envisage of new genomic technologies providing better insight into the plant genomes will increase the potentiality of SH in betterment of agriculture