Vittorio Astarita

Intelligent healthcare monitoring in geographically isolated communities requires solutions that can operate under limited digital infrastructure while maintaining privacy, regulatory compatibility, and public confidence. Selecting an appropriate system architecture is therefore particularly important in areas where connectivity and computational resources are not always reliable. This study develops a fuzzy Delphi-based framework to assess and rank five privacy-aware healthcare monitoring architectures for such environments. Expert judgments were collected for the candidate architectures and processed through the fuzzy Delphi procedure to derive consensus-based fuzzy scores and establish their final priorities. The framework was applied to the Calabria region in Southern Italy, where dispersed settlements and uneven digital connectivity provide a relevant setting for examining healthcare monitoring solutions designed for remote communities. The findings identified "edge-based local processing" (A2) as the most suitable architecture, with a final weight of 0.4, followed by "differential privacy–enhanced platforms" (A5) with a weight of 0.25. "Federated learning architectures" (A3) is ranked third (0.18), while "Blockchain-enabled monitoring" (A4) (0.09) and "centralized AI monitoring" (A1) (0.05) occupied the fourth and fifth positions, respectively. The ranking indicates a clear preference for architectures that reduce dependence on continuous connectivity and limit the external transmission of sensitive healthcare data. Comparison with results reported in previous assessments showed a high level of agreement in the relative priorities of the alternatives, providing additional support for the consistency of the proposed fuzzy Delphi-based evaluation.

The increase in road travel has a significant impact on road safety, fuel consumption, and environmental pollution. Continuous efforts have been made to increase road safety and reduce fuel consumption and pollution. One of the factors that is of great importance in this regard is driver behavior. In this study, an advanced artificial intelligence model using convolutional neural networks is used to accurately classify driving behaviors under speed threshold criteria. In this modeling, a dataset with 370 data points is used with five inputs. The input data for modeling and predicting driver behaviors include fuel flow rate/hour, engine speed, speed, acceleration, and grade. Four classes (labels) are considered: normal (class 1), moderate (class 2), aggressive (class 3), and dangerous (class 4). The obtained results indicate that the convolutional neural network model achieved very high classification performance during the modeling process. In addition to classification accuracy, the proposed framework contributes by establishing a structured behavior classification scheme based on telematics data and speed-threshold criteria, which can support intelligent safety monitoring, eco-driving assessment, and transportation management applications. Finally, the most important factors that affect driver behavior classification are the speed of cars and engine speed.

This study examines the potential of satellite internet technologies to enhance crisis management in urban evacuation scenarios in Italy, with a specific focus on the Starlink system as a case study. In emergency situations, traditional mobile and WiFi networks often become inaccessible, significantly impairing timely communication and coordination. Reliable connectivity is therefore imperative for effective rescue operations and public safety. This research analyzes how satellite-based internet can provide robust, uninterrupted connectivity even when conventional infrastructures fail. The study discusses operational advantages such as rapid deployment, broad coverage, and scalability during disasters, as well as key constraints including line-of-sight requirements, environmental sensitivity, and regulatory challenges. Empirical findings from the deployment of Starlink during an actual urban evacuation event in Italy indicate that latency dropped below 200 ms and sustained upload/download speeds averaged approximately 50 Mbps—up to three times faster than ground networks in disrupted zones. By evaluating both benefits and limitations, this paper provides a comprehensive understanding of the integration of satellite internet services within Italian emergency response systems, aiming to improve the performance of urban evacuation strategies.

Smart mobility has ushered in advanced sensing technologies. These, together with high‑level data analytics, are revolutionizing how we analyze driving behavior. Excellent performance in dealing with real-world, high-technology complexities for machine learning has made wide enthusiasm to utilize them to study driver behavior. This article gives a thorough overview of the important machine learning methods—especially clustering and classification techniques—that help analyze complex driving behaviors, predict fuel and energy use, and improve vehicle safety systems. The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. Also, this review discusses the integration of clustering and classification techniques with hybrid deep learning models, and examines their applications in eco-driving, energy forecasting, and intelligent transport systems while offering novel findings that contribute to more sustainable mobility. Emphasis is placed on how these methods transform vast, heterogeneous driving data into actionable insights that support real-time monitoring and personalized feedback for eco-driving and smart transportation applications. Finally, current benefits and barriers, and future research opportunities and challenges in integrating machine learning into intelligent transportation systems are reviewed. The potential to advance to safer, better, and more sustainable forms of mobility is emphasized.

Drivers’ behavior is one of the most important factors affecting road transportation safety. In particular, studying this issue in relation to young people aged 25 and below becomes more sensitive because they are not experienced and there are some age-related elements. Moreover, a significant percentage of beginner drivers fall into this age category, which can lead to risky behavior. Overconfidence, indiscipline, careless driving, or speeding tendencies may contribute greatly to their vulnerability to hazards on the roads. Hence, there is a need for further research to establish the constraints and possible risks involved with young drivers to improve road safety. Hence, this study aims to analyze the potential hazards associated with youth driving behavior in order to facilitate the development of relevant remedies through a thorough understanding of their behavior for safe transportation on the roads. To achieve this goal, a multi-criteria decision-making approach has been used. The proposed approach uses measurement of options and ranking based on the compromise solution method in an intuitive fuzzy environment to evaluate and rank risks. In addition, through consultation with experts and experienced technicians, a selection of 17 potential hazards were identified from existing risk factors. These risks are classified into three groups: working on the phone, distractions, and non-compliance. The present stud shows that risky driving and driving in reverse represent the highest level of risk, while speeding represents the lowest level of risk among young drivers.

Roads represent very important infrastructure and play a significant role in economic, cultural, and social growth. Therefore, there is a critical need for many researchers to model crash injury severity in order to study how safe roads are. When measuring the cost of crashes, the severity of the crash is a critical criterion, and it is classified into various categories. The number of vehicles involved in the crash (NVIC) is a crucial factor in all of these categories. For this purpose, this research examines road safety and provides a prediction model for the number of vehicles involved in a crash. Specifically, learning vector quantization (LVQ 2.1), one of the sub-branches of artificial neural networks (ANNs), is used to build a classification model. The novelty of this study demonstrates LVQ 2.1’s efficacy in categorizing accident data and its ability to improve road safety strategies. The LVQ 2.1 algorithm is particularly suitable for classification tasks and works by adjusting prototype vectors to improve the classification performance. The research emphasizes how urgently better prediction algorithms are needed to handle issues related to road safety. In this study, a dataset of 564 crash records from rural roads in Calabria between 2017 and 2048, a region in southern Italy, was utilized. The study analyzed several key parameters, including daylight, the crash type, day of the week, location, speed limit, average speed, and annual average daily traffic, as input variables to predict the number of vehicles involved in rural crashes. The findings revealed that the “crash type” parameter had the most significant impact, whereas “location” had the least significant impact on the occurrence of rural crashes in the investigated areas.

Urban areas, with their dense populations and complex infrastructures, are increasingly susceptible to various risks, including environmental challenges and infrastructural strain. This paper delves into the transformative potential of digital twins—virtual replicas of physical entities—for mitigating these risks. It specifically explores the role of digital twins in reducing disaster risks, such as those posed by earthquakes and floods, through a comprehensive bibliometric-based literature review. Digital twins could contribute to risk reduction by combining data analytics, simulation, and predictive modeling by creating virtual replicas of physical entities and integrating real-time data streams to better address and manage risks in urban environments. In detail, they can help city planners and decision-makers analyze complex urban systems, simulate potential scenarios, and predict potential outcomes. This proactive approach allows both the identification of vulnerabilities and better implementation of targeted mitigation strategies to enhance urban resilience and sustainability. More informed decisions can be made relying on simulations, and it can also be possible to optimize resource allocation and better respond to emerging challenges. This work reviews the key publications in this domain, with the aim of finding relevant papers that can be useful to urban planners and policy-makers. The paper concludes by discussing the broader implications of these findings and identifying challenges in the widespread adoption of digital twin technology, including data privacy concerns and the need for interdisciplinary collaboration. It also outlines prospective avenues for future research in this emerging field.

The rapid growth of urbanization and the consequent rise in the urban population have created unprecedented challenges for cities. Traditional methods have proven insufficient in tackling these challenges, prompting the adoption of modern technology, such as the Internet and sensors, to enhance the delivery of services and amenities to urban residents. This has led to the emergence of smart cities across the globe. Recently, there has been a surge in the use of information technology (IT) and the Internet of Things (IoT) in smart cities, particularly for crisis management. At present, cities are leveraging modern technology, IT, and IoT to improve the quality of life for their residents. It is beyond dispute that modern technologies play a pivotal role in managing emergencies and crises in smart cities. As these technologies experience rapid growth in the coming years, we can anticipate their roles and impact in smart cities to expand even further. The present study aims to investigate the role of artificial intelligence (AI) in emergency and crisis management in smart cities. Additionally, the challenges and opportunities associated with the use of AI in this context are explored, and recommendations are provided to enhance the level of awareness among researchers regarding this topic for future investigations.