Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.

Nowadays, companies and governments collect data along every phase of a product/service lifecycle. The data is acquired and stored in various and continuous ways, contributing to a large and unique data fingerprint for every product/service in use. Thus, establishing policies, processes and procedures (P3) around data and subsequently enacting those to compile and use such data for effective management and decision-making is extremely important. Data governance (DG) plays an essential role in a dynamic environment with multiple entities and actors, complex IT infrastructures, and heterogeneous administrative domains. Indeed, not only is it beneficial for existing products/services, but it can also support appropriate adjustments during the design of new ones. This research provides an overview of the existing literature and current state-of-the-art in the domain of data processing and governance aiming to evaluate existing approaches and to investigate their limitations. To this extent, this study introduces a novel approach for data governance as a service (DGaaS), which provides a framework for (private or public) organizations that facilitate alignment with their vision, goals and legal requirements. It is flexible and adaptable to the needs of the different sectors, and aims at increasing the value of data and minimize data processing related costs and risks.

During the last decade, artificial intelligence (AI) has enabled key technological innovations within the modern dementia and frailty healthcare and prevention landscape. This has boosted the impact of technology in the clinical setting, enabling earlier diagnosis with improved specificity and sensitivity, leading to accurate and time-efficient support that has driven the development of preventative interventions minimizing the risk and rate of progression. Background/Objectives: The rapid ageing of the European population places a substantial strain on the current healthcare system and imposes several challenges. COMFORTage is the joint effort of medical experts (i.e., neurologists, psychiatrists, neuropsychologists, nurses, and memory clinics), social scientists and humanists, technical experts (i.e., data scientists, AI experts, and robotic experts), digital innovation hubs (DIHs), and living labs (LLs) to establish a pan-European framework for community-based, integrated, and people-centric prevention, monitoring, and progression-managing solutions for dementia and frailty. Its main goal is to introduce an integrated and digitally enabled framework that will facilitate the provision of personalized and integrated care prevention and intervention strategies on dementia and frailty, by piloting novel technologies and producing quantified evidence on the impact to individuals’ wellbeing and quality of life. Methods: A robust and comprehensive design approach adopted through this framework provides the guidelines, tools, and methodologies necessary to empower stakeholders by enhancing their health and digital literacy. The integration of the initial information from 13 pilots across 8 European countries demonstrates the scalability and adaptability of this approach across diverse healthcare systems. Through a systematic analysis, it aims to streamline healthcare processes, reduce health inequalities in modern communities, and foster healthy and active ageing by leveraging evidence-based insights and real-world implementations across multiple regions. Results: Emerging technologies are integrated with societal and clinical innovations, as well as with advanced and evidence-based care models, toward the introduction of a comprehensive global coordination framework that: (a) improves individuals’ adherence to risk mitigation and prevention strategies; (b) delivers targeted and personalized recommendations; (c) supports societal, lifestyle, and behavioral changes; (d) empowers individuals toward their health and digital literacy; and (e) fosters inclusiveness and promotes equality of access to health and care services. Conclusions: The proposed framework is designed to enable earlier diagnosis and improved prognosis coupled with personalized prevention interventions. It capitalizes on the integration of technical, clinical, and social innovations and is deployed in 13 real-world pilots to empirically assess its potential impact, ensuring robust validation across diverse healthcare settings.

This study introduces an Integrated Information System (IIS) for the management, analysis, and visualization of Cultural Heritage (CH) data. Emphasizing the critical importance of digital technologies for the preservation, accessibility, and understanding of CH assets, the article presents a comprehensive case study of the Holy Aedicule of the Holy Sepulchre. It highlights the necessity for a unified system that efficiently integrates diverse data types and analytical methods. The proposed web-based platform is designed to support various stakeholders by providing advanced tools for data management, analytics, and visualization, thereby facilitating the sustainable preservation and educational utilization of CH assets. Through this framework, the study contributes to the advancement of CH management practices, addressing both current challenges and anticipating future needs.