Smart Cities are considered the intersection of technology and urban development, aiming to create sustainable, efficient, and citizen-centered environments using advanced tools such as the Internet of Things, artificial intelligence, and big data analytics. The major components of a smart city are smart buildings, smart transport, smart energy, smart health care, public safety and security, environmental and resource management, and smart technology. These components make the cities smart, transparent, and efficient. Information and communication technology is one of the most vital tools for modifying traditional cities into Smart Cities. Smart Cities are emerging as transformative urban ecosystems that leverage data, technology, and intelligent design to enhance sustainability, efficiency, and the quality of life for residents. Over 1,000 cities worldwide are developing or implementing smart city initiatives, with projected investments exceeding $1.5 trillion by 2025. Studies indicate Smart Cities can achieve up to 30% reductions in energy consumption and 20% improvements in public service efficiency. This paper presents a systematic review of 61 scholarly works, highlighting the benefits, challenges, and the central role of telecommunications in smart urban development. This systematic review dissects the dual nature of Smart Cities regarding their revolutionary capabilities and intrinsic challenges. The study points out important gaps in the literature, especially when it comes to long-term social effects, fair resource allocation, critical gaps in ethical governance, resource distribution policies, and their long-term implications on societal development, and ethical governance, underrepresentation of telecommunications as a central enabler, and propose future research directions to address these gaps.

Cryptocurrencies have become a significant asset class, attracting considerable attention from investors and researchers due to their potential for high returns despite inherent price volatility. Traditional forecasting methods often fail to accurately predict price movements as they do not account for the non-linear and non-stationary nature of cryptocurrency data. In response to these challenges, this study introduces the Helformer model, a novel deep learning approach that integrates Holt-Winters exponential smoothing with Transformer-based deep learning architecture. This integration allows for a robust decomposition of time series data into level, trend, and seasonality components, enhancing the model’s ability to capture complex patterns in cryptocurrency markets. To optimize the model’s performance, Bayesian hyperparameter tuning via Optuna, including a pruner callback, was utilized to efficiently find optimal model parameters while reducing training time by early termination of suboptimal training runs. Empirical results from testing the Helformer model against other advanced deep learning models across various cryptocurrencies demonstrate its superior predictive accuracy and robustness. The model not only achieves lower prediction errors but also shows remarkable generalization capabilities across different types of cryptocurrencies. Additionally, the practical applicability of the Helformer model is validated through a trading strategy that significantly outperforms traditional strategies, confirming its potential to provide actionable insights for traders and financial analysts. The findings of this study are particularly beneficial for investors, policymakers, and researchers, offering a reliable tool for navigating the complexities of cryptocurrency markets and making informed decisions.

Water quality monitoring is essential for protecting public health, sustaining ecosystems, and achieving sustainable development goal 6 (clean water and sanitation). While recent advances in artificial intelligence (AI), particularly machine learning, have improved the accuracy and responsiveness of water quality assessment, the opaque nature of many AI models limits transparency, stakeholder trust, and regulatory compliance. Explainable AI (XAI) offers a viable solution by enabling human-understandable insights into model behavior. This article presents a preferred reporting items for systematic reviews and meta-analyses-guided systematic review of 60 peer-reviewed articles (2011–2025), sourced from Scopus, to evaluate the evolution, application, and effectiveness of XAI in water quality monitoring. Key techniques such as Shapley additive explanations (SHAP), local interpretable model-agnostic explanations, and counterfactual reasoning have been applied across random forest, XGBoost, and long short-term memory models. Results indicate a surge in XAI adoption post-2022, with dominant use cases in groundwater prediction, surface water quality forecasting, and real-time monitoring in Internet-of-Things-enabled smart cities. While SHAP remains the most widely used method, multimodal and hybrid frameworks are emerging to address challenges such as data heterogeneity and model complexity. The review identifies persistent barriers including computational scalability, lack of standardized evaluation metrics, and limited deployment in low-resource settings. It proposes future research directions for integrating XAI with digital twins, causal inference, and edge computing to achieve robust, transparent, and equitable water management systems.

This study proposes a novel, integrated framework that combines Shannon Entropy Technique (SET), Data Envelopment Analysis (DEA), and Inverse DEA (IDEA) for the objective evaluation and ranking of equities, with a case application to the tourism sector of the Taiwan Stock Exchange (TWSE). SET is employed for data-driven variable selection to reduce dimensionality across five financial perspectives, enhancing the discriminatory power of DEA. DEA identifies efficient stocks, while IDEA ranks them based on adaptability to output perturbations, providing a dynamic and interpretable view of efficiency. Empirical analysis reveals that only five out of sixteen stocks achieved full efficiency. The IDEA-based ranking uncovered distinct adaptability profiles, with some firms requiring minimal input changes to accommodate output growth, indicating operational flexibility. Sensitivity analysis and model comparisons with super-efficiency and cross-efficiency DEA confirm the robustness and consistency of the proposed method, even under adverse scenarios. This work advances financial decision-making by offering a reproducible, biasminimized, and scenario-sensitive ranking model. Its transparent structure makes it especially valuable for investors, policymakers, and portfolio managers navigating volatile or crisis-prone markets.