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As the world continues to urbanise, cities face increasing pressure to become more sustainable, efficient and livable. Sustainable smart cities are emerging as a promising solution to this challenge, leveraging technology and data to improve urban systems and services while reducing environmental impact. This chapter provides an overview of the concept of sustainable smart cities and its implications for urban development. It explores the key features of sustainable smart cities, including their focus on technology, data and citizen engagement and the challenges they are facing in terms of infrastructure, data management, social equity, environmental sustainability, governance and regulations. The chapter also highlights the implications of sustainable smart cities for urban planners, policymakers and other stakeholders, emphasising the need for collaborative approaches that engage citizens and stakeholders in the design and implementation of smart city initiatives.

A computational fluid dynamics based parametric analysis for shell and helical tube heat exchanger (SHTHE) using CuO/water and Al₂O₃/water nanofluids is the main purpose of the present work. The parameters having impact on the performance of a heat exchanger have been studied in depth. As the solid nanoparticle shows higher thermal conductivity compared to liquid particles, inclusion of this nanoparticle into the base fluid significantly enhances the thermal conductivity of the liquid. Incorporation of nanofluid in the heat exchanger can increase its performance.

The simulation is performed in Solid-Works flow simulation, and the performance of SHTHE is observed by varying the pitch of helical tube from 0.013 to 0.018 m and coil diameter from 0.0813 to 0.116 m, keeping the other parameters constant. The tube side and shell side flow rate is kept as 2 LPM.

The results indicate that the effectiveness of the heat exchanger increases with the increase of pitch and coil diameter. The maximum effectiveness of 0.5022 for CuO/water and 0.4928 for Al₂O₃/water nanofluid is observed at a pitch of 0.018 m and the coil diameter of 0.116 m.

It is observed that CuO/water nanofluid shows better performance compared with Al₂O₃/water nanofluid. For a coil diameter of 0.116 m and pitch of 0.018 m, the SHTHE with CuO/water nanofluid shows 1.82% greater effectiveness compared to the effectiveness with Al₂O₃/water nanofluid.

An electrical power distribution network is expected to deliver uninterrupted power supply to the customers. The disruption in power supply occurs whenever there is a fault in the system. Therefore, fast fault detection and its precise location are necessary to restore the power supply. Several techniques are proposed in the past for fault location in distribution network but they have limitations as their fault location accuracy depends on system conditions. The purpose of this paper is to present a travelling wave-based fault location method, which is fast, accurate and independent of system conditions.

This paper proposes an effective method for fault detection, classification and location using wavelet analysis of travelling waves for a multilateral distribution network embedded with distributed generation (DG) and electric vehicle (EV) charging load. The wavelet energy entropy (WEE) is used for fault detection and classification purpose, and wavelet modulus maxima (WMM) of aerial mode component is used for faulted lateral identification and exact fault location.

The proposed method effectively detects and classifies the faults, and accurately determines the exact fault location in a multilateral distribution network. It is also found that the proposed method is robust and its accuracy is not affected by the presence of distributed generation and electric vehicle charging load in the system.

Travelling wave based method for fault location is implemented for a multilateral distribution network containing distributed generation and electric vehicle load. For the first time, a fault location method is tested in the presence of EV charging load in distribution network.

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.