Smart Cities are undergoing rapid transformation driven by advanced tools, technologies, and significant investments worldwide. These cities aim to balance growth with environmental sustainability, emphasizing reduced carbon footprints, lower harmful emissions, and optimized energy consumption. Despite progress, Smart Cities face critical challenges, including traffic congestion, water scarcity, energy inefficiencies, waste management, limited citizen participation, and complex IT infrastructure maintenance. These challenges, although varying in scope and scale, are common across regions. This paper examines key challenges encountered during the development, operation, and maintenance of Smart Cities and explores how ICT and digital technologies address these issues effectively. High-impact technologies such as IoT, AI, Machine Learning, Blockchain, Data Analytics, Digital Twin, 5G, and Cloud Computing play a pivotal role in delivering innovative solutions. Using a systematic literature review and primary data from our research, the study highlights the application of these technologies in resolving operational and developmental challenges. Case studies and insights from industry professionals underscore the transformative potential of digital tools while acknowledging certain limitations. The findings emphasize the importance of prioritizing solutions for pressing issues in Smart Cities and offer recommendations for enhancing the deployment of these technologies. Future plans are discussed to achieve full-scale implementation and address current limitations, fostering continuous innovation and improvement in Smart City services.
As Artificial Intelligence (AI) continues to infiltrate various sectors, from healthcare to finance, the ability to trust AI-driven decisions becomes crucial. Machine learning (ML) models, though highly accurate, often operate as "black boxes," making it difficult to understand their decision-making processes. This lack of transparency creates significant challenges in critical areas like medical diagnosis and financial transactions, where understanding the reasoning behind decisions is vital. In particular, ensemble models like Random Forests and Deep Learning algorithms, while improving prediction accuracy, exacerbate the issue of interpretability. This paper reviews the current challenges in explaining ML predictions and explores existing approaches to Explainable Artificial Intelligence (XAI). Through an extensive literature review of research from reputable sources, we identify key gaps in current methods and provide insights into opportunities for future development. While some algorithms, such as Decision Trees and KNN, offer built-in interpretability, there is no universal solution for explaining the outcomes of complex models. The paper proposes a conceptual framework for developing a common approach to XAI that can address these challenges, providing clarity and consistency in decision explanations. Finally, the paper outlines future research directions to improve the interpretability and adoption of AI models in various sectors.
The COVID-19 pandemic forced the closure of physical retail stores, compelling brands to explore innovative ways to engage with customers. Live Streaming emerged as a powerful tool, enabling brands to maintain customer access and foster engagement even during lockdowns. This study explores the potential of livestreaming as a viable medium for direct-to-consumer communication and interaction. This paper presents a conceptual framework for using live streaming in marketing, developed through focus group discussions and model analysis based on the ABCD listing methodology. The study examines the dynamics of livestreaming within retail and ecommerce, focusing on its ability to engage both external customers and internal stakeholders. The research reveals that engagement should extend beyond customers to include business partners and internal employees, demonstrating that live streaming is not only a tool for retail sales but also a platform for broader organizational engagement. The findings suggest that livestreaming is a long-term asset for the retail and e-commerce industries, even beyond the pandemic’s impact. The study acknowledges the widespread business disruptions caused by the pandemic, which led to decreased demand and hindered operations globally. The limitations also highlight the gaps in business interruption insurance policies, emphasizing the need for businesses to adapt and reconsider existing policies. The pandemic's economic damage remains largely unaddressed. This paper highlights the latest trends in live streaming, offering valuable insights into its application as an integrated marketing communication channel. It underscores the importance of adapting to new technologies and approaches for sustained customer engagement and business resilience.
Central Public Sector Enterprises (CPSEs) are integral to India’s economic framework, providing critical goods and services. To ensure long-term sustainability, CPSEs must consistently generate reasonable profits. A higher dispersion in investment returns indicates greater risk. Therefore, this study analyses the industry-wise dispersion of investment returns in Indian CPSEs over the period from 2010-11 to 2019-20. Secondary data was utilized for the study. Dispersion in investment returns was assessed using the Coefficient of Variation (CV). Additionally, a paired t-test was employed to identify significant changes in average dispersion across the selected period, and one-way ANOVA was used to examine differences in investment returns among various industries. The findings show significant variability in the rate of investment returns across industries, with some industries displaying better consistency in returns during the first sub-period compared to the second. The study also revealed significant differences in investment returns across industries, underscoring the impact of each industry’s performance on the overall investment returns of CPSEs. This study provides valuable insights into the dispersion of investment returns in CPSEs, offering a clearer understanding of the inherent risks across different industries and their cumulative effect on the financial health of CPSEs.
3D printers are gaining significant popularity in diverse fields, especially for prototyping and product development. However, physical 3D printers are expensive, bulky, and challenging to transport, which poses difficulties for researchers who need access to these devices for testing algorithms and prototypes. This research proposes an alternative solution by leveraging the CoppeliaSim simulator to create a virtual 3D printer model. Using the popular and affordable Ender 3D printer specifications, we build a detailed simulation of the printer within CoppeliaSim, a powerful open-source robotics simulation environment. The process is simplified through a step-by-step guide, allowing researchers to quickly create the model, control it with LUA scripting, and simulate printing tasks such as drawing a square on the print bed. All relevant project files and code are made available on GitHub, enabling researchers to easily download and integrate the model into their own work. The simulation provides an accessible platform to test 3D printing algorithms, analyse 3D print files, and explore printer functionality without the physical constraints of a real printer. Researchers can further adapt the model by modifying physical parameters or designing custom 3D printers. This method offers a cost-effective, flexible, and practical solution for 3D printing research, allowing faster iteration and experimentation, particularly for those with limited resources or space.