Chronic Heart Failure (CHF) represents a significant global health concern due to its high morbidity and mortality rates. Effectively addressing this challenge requires scalable technology solutions to shift Heart Failure (HF) care from episodic reactive treatment to continuous personalized management. As digital health technologies advance, integrating Artificial Intelligence (AI) and the Internet of Things (IoT) into CHF care enables the development of scalable monitoring, prevention, and management strategies and real-time Clinical Decision Support Systems (CDSSs). This Systematic Literature Review (SLR) analyzes 67 peer-reviewed studies published between January 2021 and May 2024, selected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to evaluate the technological and clinical impacts of AI-enabled systems in CHF and broader HF care. The review identifies emerging trends, discusses dataset characteristics and clinical relevance, identifies IoT integration patterns, gaps, and deployment barriers, and highlights opportunities for improving the integration of AI/IoT systems into HF care workflows. The studies are organized into four clinical application domains: HF detection, phenotyping and classification, risk stratification, and other miscellaneous applications. Our findings highlight the progress in AI/IoT synergy; however, challenges remain in dataset heterogeneity and coverage, reproducibility, benchmarking practices, and clinical workflow integration, particularly as IoT integration is often limited or insufficiently explored. Our primary recommendations emphasize the use of multimodal datasets, the adoption of interpretable modeling approaches, and stronger interdisciplinary collaboration to improve clinical applicability and support integration into real-world settings.

In online auctions, fraudulent behaviours such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behaviour-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviours, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, demonstrating how smart contracts enforce transparent auction rules without trusted intermediaries. Simulations confirm the effectiveness of the proposed model: the dynamic penalty mechanism reduces the profitability of shill bidding while keeping penalties low for honest bidders. Performance evaluation shows that the system introduces only moderate gas and latency overhead, keeping transaction costs and response times within practical bounds for real-world use. The approach provides a practical method for behaviour-based fraud prevention in decentralised systems where trust cannot be assumed.

Most engineering courses include fundamental practice activities to be performed by students in computer labs. During lab sessions, students work on solving exercises with the help of teaching assistants, who often have a hard time for guaranteeing a timely, optimized, and "democratic" support to everybody. This paper presents a learning environment to improve the experience of the lab sessions participants, both the students and the teaching assistants. In particular, the environment was designed, implemented, and experimented in the context of a database course. The application designed to support the learning environment stores all the events occurring during a SQL practice lab, i.e., task progression, query submissions, error feedback, assistance requests and interventions, and it provides information useful both for use on-the-fly and for later analysis. Thanks to the analysis of these data, the application dynamically provides teaching assistants with a graphical interface highlighting where assistance is most needed, by considering different factors such as the progression rate, the percentage of correct solutions, and the difficulties in solving the current exercise. Furthermore, the stored data allow teachers later on to analyze and to interpret the behavior of the students during the lab, and to have insights on their main mistakes and misconceptions. After describing the environment, the interfaces, and the approaches used to identify the students' teams that need timely assistance, the paper presents the results of different analyses performed using the collected data, to help the teacher better understand students' educational needs.

During the last decade, network providers are faced by a growing problem regarding the distribution of bandwidth and computing resources. Recently, the mobile edge computing paradigm was proposed as a possible solution, mainly in consideration of the provided possibility of transferring service demands at the edge of the network. This solution heavily relies on the dynamic allocation of resources, depending on the user needs and network connection, therefore it becomes essential to correctly predict user movements and activities. This paper proposes an unsupervised methodology to define meaningful user locations from noninvasive user information, captured by the user terminal with no computing or battery overhead. The data is analyzed through a conjoined clustering algorithm to build a stochastic Markov chain to predict the users’ movements and their bandwidth demands. Such a model could be used by network operators to optimize network resources allocation. To evaluate the proposed methodology, we tested it on one of the largest public community’s labeled mobile and sensor dataset, developed by the “CrowdSignals.io” initiative, and we present positive and promising results concerning the prediction capabilities of the model.

Due to the increasing spread of location-aware applications, developers interest in user location estimation has grown in recent years. As users spend the majority of their time in few meaningful places (i.e., groups of near locations that can be considered as a unique place, such as home, school or the workplace), this paper presents a new energy efficient method to estimate user presence in a meaningful place. Specifically, instead of using commonly used but energy hungry methods such as GPS and network positioning techniques, the proposed method applies a Machine Learning algorithm based on Decision Trees, to predict the user presence in a meaningful place by collecting and analyzing: a) user activity, b) information from received notifications (receipt time, generating service, sender-receiver relationship), and c) device status (battery level and ringtone mode). The results demonstrate that, using 20 days of training data and testing the system with data coming from 14 persons, the accuracy (percentage of correct predictions) is 89.40% (standard deviation: 8.27%) with a precision of 89.04% and a recall of 89.40%. Furthermore, the paper analyzes the importance of each considered feature, by comparing the prediction accuracy obtained with different combinations of features.