Abstract Following the period marked by the spread of the COVID-19 virus, tourists and enthusiasts flocked to museums and archaeological parks, highlighting persistent and emerging challenges concerning the enhancement of cultural heritage. This study proposes a novel framework that leverages context-aware recommender systems (CARS) to personalize the cultural experience based on the contextual conditions in which it occurs. The framework has been specifically developed and tested within the archaeological sites of Paestum and Pompeii. The evaluation process was conducted in two phases. The first phase focused on assessing the accuracy and reliability of the proposed context-aware approach, using a dataset comprising 972 user ratings collected from visitors to the Pompeii archaeological park. The second phase measured user satisfaction through a large-scale, in situ evaluation conducted directly within the archaeological parks. This phase involved nearly 2000 participants and aimed to assess the real-world usability and perceived satisfaction of the platform through direct interaction in cultural settings. The results confirm the effectiveness of the approach, with over 90% of respondents expressing a high level of satisfaction.

The growing intricacy in the administration of solar systems necessitates novel strategies to guarantee efficiency and reliability. This research presents a sophisticated methodology for developing a Digital Twin for the solar system at the University of Salerno campus. The primary aim is to create a dynamic digital model that facilitates real-time monitoring of the system's operational conditions and enhances predictive maintenance techniques. The integration of sophisticated technologies, including the Internet of Things (IoT), Building Information Modeling (BIM), and Geographic Information Systems (GIS), enabled the creation of a virtual replica of the physical system. The ThingsBoard platform centralizes and analyzes the real-time data collected by IoT sensors, while Autodesk Revit and Dynamo create a parametric BIM model that enables a comprehensive and interactive depiction of the system. Georeferencing and spatial analysis, executed using ArcGIS, enhance the Digital Twin, facilitating a thorough comprehension of the interactions between the system and its environmental surroundings. The findings indicate that the Digital Twin facilitates prompt anomaly identification, failure prevention, and the simulation of predictive maintenance scenarios. This approach enabled the optimization of operational management and a substantial reduction in maintenance expenses. The method shows how the Digital Twin can be used to effectively manage solar power plants and can serve as a model for managing other important infrastructures. This helps promote sustainable and resilient management.

The employment of Information and Communication Technologies helps Cultural Heritage enhancement and allows users’ cultural experience improvement. In particular, multimedia content employment enables better involvement through Storytelling techniques aimed at capturing the users’ attention. Moreover, to guarantee the personalization of the cultural experience, Digital Storytelling requires integrating tools for identifying the users’ preferences in the specific environment in which the cultural experience evolves. Therefore, this paper aims to introduce an architecture in which a Context-Aware Recommender System is able to provide personalized paths in which each Point of Interest is presented according to Digital Storytelling techniques. The proposed architecture is validated via two experimental phases: the first aimed to evaluate the accuracy of the recommender system, and the second based on user experience. Obtained results are promising.

Spa treatments may mistakenly be considered palliative compared to traditional medicines; however, this is not the case. Mineral/thermal waters are medicines for all intents and purposes and should be analyzed and used as such. The difference in spa treatments compared to other medicines is the greater complexity with which they are delivered. Patients must follow a course of treatment that can last up to a couple of weeks, during which the effects of the therapy gradually go into evidence. Both inside and outside the spa facility, having patient monitoring could be a valuable tool to measure the effectiveness of treatment and possibly even intervene with personalized care based on the parameters detected. New technologies and paradigms such as the Internet of Things can offer a valuable tool to improve spa care through active monitoring of patients, both inside and outside the facilities, by going to measure what are the key parameters (i.e., heart rate, blood oxygenation, etc.) to track the progress of the therapy accurately and precisely during treatment. In particular, wearable devices (smartwatches or smart bands) can perform constant and non-invasive monitoring of the patient's status and the therapy itself. Therefore, the work aims to define a framework based on the Internet of Things paradigm for intelligent analysis of spa treatments to manage patients correctly.

The will to travel leads humans to discover new places and enjoy new adventures. However, tourists usually need help knowing what to visit and, avoiding time issues, in which order to explore several Points of Interest (POIs). In this field, new technologies can help tourists to improve their experiences and select the visiting path according to personal preferences. Therefore, the employment of Recommender Systems allows the personalization of the experience through the appropriate POIs’ selection. Moreover, RSs’ analysis could take advantage of contextual information that suits the personalization in the specific environment where the elaboration happens, providing users with even more specific and tailored paths. This paper aims to design personalized visiting paths combining a Context-Aware Recommender System (CARSs) and a mathematical model to maximize the number of visited POIs in the available time. The proposed approach is tested through a prototype, obtaining promising results.

Cultural Heritage Buildings need to be preserved through interventions and actions that ensure accessibility and availability to present and future generations. The diffusion o f new technologies, including low-cost sensors and devices, has introduced many possibilities and strategies to monitor environments, including Historical and Cultural value buildings. Based on the Internet of Things (IoT) paradigm, modern devices and sensors can collect and manage helpful information to build a Digital Twin of the surrounding environment. In this scenario, reproducing a Digital Twin of Cultural Heritage Buildings could be crucial to monitoring, managing, and performing action aiming to protect them. One of the challenges in recent years is the automatic analysis of collected information. This paper introduces a novel approach to protecting Buildings using Heritage Building Information Modeling (HBIM), which leverages a sensor network and Deep Learning techniques to analyze sensor data. A case study related to the Archeological Park of Pompeii will be presented, where real-time collected data is managed and shared via a web-cloud-IoT platform. Then, collected data are analyzed through a Generative Adversarial Network (GAN) to prevent future issues related to Cultural Heritage Buildings, particularly humidity inside structures, which is crucial for ancient building preservation.

The Internet of Things paradigm has introduced the use of countless smart devices into our daily lives. These devices are able to assist us in our daily tasks, making the environments we experience increasingly pervasive. The most significant examples are smart cities, home monitoring systems, smart grids, and smart agriculture. In these fields, one of particular interest is represented by Smart Buildings, which are intelligent environments able to manage energy optimally and provide the highest possible comfort. This paper presents an innovative approach to managing complex IoT-based environments in Smart Buildings. The proposed approach exploits data coming from the context and the situation in which the user is located by extracting knowledge to support the user and performing autonomous choices on Smart Buildings' management. In particular, the approach aims to exploit data from sensors in combination with contextual data acquired through graph structures to perform actions that increase the ability to manage business buildings. An application case study conducted in a real environment will be presented. The system prototype has obtained promising results.

In the Internet era, where information and communication technologies (ICT) allow data exchange, new tools able to select the correct data are needed. In this field, Recommender Systems have a prime location. The recommendation methods take many forms based on the information exploited in order to provide rating forecasts. However, all of them can choose the correct information to support users. Indeed, these methods allow for overcoming the information overload problem. This paper introduces the theoretical bases of a novel recommendation method defined Rating Singular Value Decomposition (RSVD). RSVD is a Content-Based method that exploits the Singular Value Decomposition properties in order to calculate rating forecasts. This method aims to elaborate the users and items profile to obtain matrices related to ones obtained in Collaborative Filtering methods that exploit Singular Value Decomposition. The accuracy of RSVD is compared with the accuracy of Collaborative Filtering methods, and a study on the sparsity problem is performed. The results obtained are promising.

The world of distance education is constantly expanding, enriching itself with tools and services to increase the ability to provide training content. Due to the new technologies, the training paths take on new appealing features; however, it remains complex to suggest the appropriate training path to the right student. In this scenario, the use of Recommender Systems (RSs) could be helpful. RSs could allow recommending personalized learning paths to students in order to improve their abilities and their knowledge. In particular, among Recommender Systems, some of them consider contextual information. This paper aims to describe a new approach that suggests learning paths to users taking advantage of recommendation techniques and introducing them through multimedia content. Moreover, the proposed approach aims to provide recommendations when ratings are unknown through the knowledge of profiles of users and items. The proposed approach has been tested through students of two courses with diverse characteristics.

In March 2020, due to the pandemic linked to the COVID-19 virus spreading, all in-person teaching activities planned at the University of Salerno were suspended. This sudden suspension has forced the academic world to rethink in a short time the didactics and to modify teaching styles and approaches, especially in those courses in which laboratory activities were planned. This article will be presented the experiences made in the Computer Networks and Protocols for the Internet of Things (IoT) course. The course includes a significant laboratory component where students must put into practice what they learned during the theoretical lessons. In particular, the laboratory exercises involve using specific equipment and devices that can build digital ecosystems for the control and management of well-defined operational contexts difficult to reproduce virtually. Therefore, to give continuity to the didactic activity, it was necessary to redesign the entire exercise system preserving the original educational objectives.

The world of Cultural Heritage finds significant ad-vantages from fusion with new technologies. For example, the use of Recommender Systems to analyze contextual information and the Digital Storytelling technique allows improving the experience of users who get in touch with artistic and cultural heritage. This paper aims to describe a new approach that suggests cultural-touristic paths exploit recommendation techniques and propose multimedia content. Moreover, the proposed approach aims to provide recommendations when ratings are unknown, using a novel approach that takes advantage of the user and item profile knowledge. The proposed approach has been tested through an application prototype. The test involved standard and expert users of the University of Salerno with promising results.

Italy offers a Cultural Heritage of considerable value to be protected. In fact, the artifacts and ancient buildings are affected by a natural deterioration linked to the flow of time. Sometimes the deterioration compromises the functionality of Cultural Heritage, driving them toward degradation. In this scenario, given the different critical points, the wide variability of the factors involved, and the wide range of possible treatments, intervene efficiently seems impossible. However, the spread of low-cost technology has led to the possibility of having various devices and sensors able to communicate and interact with each other and with humans: the Internet of Things (IoT). In this scenario, the IoT paradigm allows mapping the reality by defining a coherent virtual environment, which could help preserve Cultural Heritage. This paper aims to introduce an IoT-based system that combines three aspects: monitoring, predictive maintenance, and decision-making related to interventions to be implemented to preserve buildings belonging to Cultural Heritage. In order to test the proposed architecture, a prototype capable of interacting with expert users has been implemented and tested. The results of the experimental campaign are promising.

Summary Increasing populations and rapid large‐scale urbanization has created a demand to increase the quality of life through economic development, social stability, and better quality environments. These issues are addressed in the field of Smart Cities where, through the Internet of Things, efforts are being made to support added‐value services for the administration of the city and for citizens. The continuous exchange of information inevitably produces a huge amount of data, which demands analyses of data using unconventional methods within a Big Data context. How can we properly process these data? How can we properly use these data in order to increase the competitiveness and efficiency of services, and how could they contribute to social development? Services that could be useful in this field include Early Warning Systems. Information management environments, or more generally pervasive data contexts, may be supported by context representation approaches and enhanced through adopting probabilistic approaches such as Context Dimension Tree, Ontology, and Bayesian Network. The aim of this work is to introduce and explain a methodology for merging CDTs and Ontologies, and probabilistic approach based on BNs in order to help expert users handle emergencies and provide suggestions for improving the liveability of cities for their inhabitants.