It is the result of the increased urbanization levels and the simultaneous development of the metropolitan infrastructures that have resulted in the unprecedented growth of global energy demands. The current cities consume over 75 percent of the total energy being produced globally leading to the critical challenges related to peak-load management, grid resilience, carbon emissions, and integration of renewable energies. Green technologies of energy storage have become the key facilitators of dealing with these issues in order to enable effective capture, storage, and redistribution of energy. The paper will critically review the neo trends in sustainable energy storage of urban infrastructures with an analysis of electrochemical, mechanical, thermal, and hybrid energy storage models. The paper also assesses how smart grids, urban microgrids and decentralized energy networks can help increase resilience levels to energy at the urban level. In the review, there is a lot of literature to map the development of storage technologies, and method frameworks are built to analyze the performance, lifecycle viability, and integration capability. On the one hand, experimental outcomes and comparative modeling demonstrate the differences in the performances of different technologies in relation to energy density, environmental impact, expenses, and scalability. Based on the analysis, it is shown that, although the use of lithium-ion batteries will remain in the short-term storage, the long-term storage will be characterized by flow batteries, compressed-air energy storage (CAES), hydrogen storage, and thermal batteries, which are expected to dominate in the future city-scale application. It is shown in a multi-criteria analysis that hybrid storage architectures are the best to use with smart urban grids. The paper wraps up by giving the main policy considerations, technical challenges, and gaps in research, which should be resolved to achieve fully sustainable, scalable, and smart energy storage ecosystems in new urban city settings of the next generations.
Grace Ndlovu, Samuel Johnson· International Journal of Mod...· 0 citations
Artificial Intelligence (AI), Machine Learning (ML), and autonomous intelligent systems are transforming predictive decision-making across industries such as manufacturing, healthcare, finance, transportation, cybersecurity, and smart cities. Traditional centralized machine learning models often struggle to adapt to dynamic and uncertain environments. This paper proposes an Agent-Based Machine Learning Framework for Autonomous Predictive Decision Systems (ABML-APDS) that integrates distributed intelligent agents, collaborative learning, reinforcement learning, predictive analytics, and explainable AI into a unified architecture. The framework enables autonomous agents to collect data, engineer features, exchange knowledge, optimize predictions, and continuously improve decision-making with minimal human intervention. It incorporates explainable decision mechanisms to enhance transparency, trust, and interpretability while supporting supervised, unsupervised, deep, and reinforcement learning models. Continuous learning and decentralized agent collaboration improve adaptability, scalability, fault tolerance, computational efficiency, and real-time responsiveness. The proposed framework provides an intelligent and scalable foundation for next-generation autonomous predictive systems supporting Industry 5.0, cyber-physical systems, IoT, smart manufacturing, precision healthcare, and AI-driven digital transformation.
Grace Ndlovu, Samuel Johnson· International Journal of Mac...· 0 citations
The rapid development of artificial intelligence (AI), particularly tools like ChatGPT, has significantly transformed digital content creation and creative writing. Traditionally viewed as a deeply human activity requiring imagination, emotion, and originality, creative writing is now increasingly supported by AI systems capable of generating coherent and stylistically diverse text. This paper examines ChatGPT’s role in shaping the future of creative writing by analyzing its applications, advantages, limitations, and ethical concerns. AI-assisted tools help writers with idea generation, plot development, drafting, and experimenting with different writing styles, improving both productivity and creativity. However, challenges such as originality, authorship, intellectual property, and overreliance on AI remain critical concerns. While AI can enhance the writing process, it may also impact traditional notions of creativity and human expression. The study concludes that the future of creative writing will likely involve a collaborative relationship between humans and AI. Rather than replacing human creativity, tools like ChatGPT act as supportive technologies that expand creative possibilities. Ethical and responsible use of AI will be essential to maintain authenticity and artistic value in literature as its role continues to grow in education, publishing, and digital storytelling.
Grace Ndlovu· International Journal of Inn...· 0 citations
The high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.
Grace Ndlovu· International Journal of Mod...· 0 citations
The study highlights the potential of serverless computing as a sustainable backbone for next-generation IoT–ML systems, offering guidelines for building carbon-aware and cost-efficient inference pipelines for real-world applications.
Silvia Diallo, Grace Ndlovu· International Journal of Art...· 0 citations
The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning, and predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions.
I. Yusuf, Grace Ndlovu· International Journal of Com...· 0 citations
Stronger regulations, improved design, and better clinical validation are recommended to enhance digital mental health applications effectiveness, and the most effective approach is a hybrid model combining digital tools with professional care.
Grace Ndlovu, Samuel Johnson· International Journal of Inn...· 0 citations
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