Research Academic Computer Technology Institute

This special issue includes diverse approaches that support co-creation and circularity in urban and peri-urban environments and illustrates how computational infrastructures, combined with community engagement and responsible innovation, can shape the next generation of efficient, sustainable, and resilient smart environments.

Security by design leads to resilient and robust computing systems and is especially important for cyberphysical systems, where safety is fundamental. It paves the way to standards enabling higher awareness and the increased adoption of digital systems.

Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka-Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka-Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge-Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Moreover, our method outperforms ARIMA models in terms of accuracy, showcasing its effectiveness for crypto market forecasting. The entire framework, including neural network training and Runge-Kutta integration, was implemented in MATLAB.

Graft-versus-Host Disease (GvHD) is the main cause of morbidity and mortality of allogeneic hematopoietic cell transplantation (allo-HCT). Conventional immunosuppressive pharmacotherapy remains the backbone of GvHD prevention and treatment with suboptimal outcomes especially for patients with refractory disease. Adoptive immunotherapy with regulatory T-cells (Treg) stands as an alternative approach that aims to restore immune tolerance and circumvent prolonged immunosuppression albeit preserving the beneficial Graft-versus-Leukaemia (GvL) effect. In this review, we summarise recent knowledge on Treg biology, clinical applications of various Tregs subtypes in the setting of GvHD and future endeavours of the field.

Accurate identification and management of medical devices is of particular importance to ensure patient safety and regulatory compliance within healthcare systems. This paper presents a comprehensive exploration of medical device data retrieval, focusing on the integration of web scraping and API technologies. The utilization of Unique Device Identifiers (UDIs) and the Global Medical Device Nomenclature (GMDN) system is emphasized to enhance device authentication, attribute verification, and accurate categorization. This paper introduces a state-of-the-art code implementation that combines web scraping techniques and API integration to address the challenges of retrieving and verifying device information. The code facilitates both access to data and for healthcare professionals and stakeholders to make informed decisions based on reliable and up-to-date information. This is a significant and defining advance in the field, offering a powerful solution that is innovative as well as vital. The paper concludes by discussing the potential impact of these developments on patient safety, regulatory compliance, and the overall advancement of healthcare technology. In addition, the importance of accurate device identification, the role of UDIs and GMDN, and the significance of the provided cutting-edge code are highlighted, providing valuable insights into the field of medical device data retrieval.

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences.

LoRa technology is a recent technology belonging to the Low Power and Wide Area Networks (LPWANs), which offers distinct advantages for wireless communications and possesses unique features. Among others, it can be used for localization procedures offering minimal energy consumption and quite long-range transmissions. However, the exact capabilities of LoRa localization performance are yet to be employed thoroughly. This article examines the efficiency of the LoRa technology in localization tasks using Time Difference of Arrival (TDoA) measurements. An extensive and concrete experimental study was conducted in a real-world setup on the University of Patras campus, employing both real-world data and simulations to assess the precision of geodetic coordinate determination. Through our experiments, we implemented advanced localization algorithms, including Social Learning Particle Swarm Optimization (PSO), Least Squares, and Chan techniques. The results are quite interesting and highlight the conditions and parameters that result in accurate LoRa-based localization in real-world scenarios in smart cities. In our context, we were able to achieve state-of-the-art localization results reporting localization errors as low as 300 m in a quite complex 8 km × 6 km real-world environment.

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features; collaborative, which processes user behavior patterns; and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

This paper presents a virtual trade platform to promote entrepreneurial spirit and provide a systemic approach to support businesses and consumers to successfully promote their products and services in exhibitions through virtual reality, overcoming problems of transport, cost, and weakness for their participation due to insularity. Several activities were developed in the 3D Virtual World, involving the entrepreneurial spirit with innovative activities and procedures, increasing opportunities and putting theoretical knowledge into practice. The S.A.A.T. online expo platform uses 3D virtual reality (VR) to enhance entrepreneurship focused on the agri-food domain and tourism since it is possible for SMEs to promote products and services by overcoming distance problems from potential customers and interested investors, easily. According to the above-mentioned, the online S.A.A.T. expo platform provides some of the main functions of a modern virtual exhibition, to improve the applications of VR in those fields.