Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems incorporate new components that are capable of generating better decision making (humidity/temperature/soil sensors, drones for plague detection, smart irrigation, etc.) and also include novel processes for crop control (reproducible environmental conditions, proven strategies for water stress, etc.). At the same time, advances in model-driven development (MDD) simplify software development by introducing domain-specific abstractions of the code that makes application development feasible for domain experts who cannot code. XMDD (eXtreme MDD) makes this way to assemble software even more user-friendly and enables application domain experts who are not programmers to create complex solutions in a more straightforward way. Key to this approach is the introduction of high-level representations of domain-specific functionalities (called SIBs, service-independent building blocks) that encapsulate the programming code and their organisation in reusable libraries, and they are made available in the application development environment. This way, new domain-specific abstractions of the code become easily comprehensible and composable by domain experts. In this paper, we apply these concepts to a smart agriculture solution, producing a proof of concept for the new methodology in this application domain to be used as a portable demonstrator for MDD in IoT and agriculture in the Confirm Research Centre for Smart Manufacturing. Together with model-driven development tools, we leverage here the capabilities of the Nordic Thingy:53 as a multi-protocol IoT prototyping platform. It is an advanced sensing device that handles the data collection and distribution for decision making in the context of the agricultural system and supports edge computing. We demonstrate the importance of high-level abstraction when adopting a complex software development cycle within a multilayered heterogeneous IT ecosystem.

Background: In biomedical imaging research, experimental biologists generate vast amounts of data that require advanced computational analysis. Breakthroughs in experimental techniques, such as multiplex immunofluorescence tissue imaging, enable detailed proteomic analysis, but most biomedical researchers lack the programming and Artificial Intelligence (AI) expertise to leverage these innovations effectively. Methods: Cinco de Bio (CdB) is a web-based, collaborative low-code/no-code modelling and execution platform designed to address this challenge. It is designed along Model-Driven Development (MDD) and Service-Orientated Architecture (SOA) to enable modularity and scalability, and it is underpinned by formal methods to ensure correctness. The pre-processing of immunofluorescence images illustrates the ease of use and ease of modelling with CdB in comparison with the current, mostly manual, approaches. Results: CdB simplifies the deployment of data processing services that may use heterogeneous technologies. User-designed models support both a collaborative and user-centred design for biologists. Domain-Specific Languages for the Application domain (A-DSLs) are supported through data and process ontologies/taxonomies. They allow biologists to effectively model workflows in the terminology of their field. Conclusions: Comparative analysis of similar platforms in the literature illustrates the superiority of CdB along a number of comparison dimensions. We are expanding the platform’s capabilities and applying it to other domains of biomedical research.

We review our experience with integrating Artificial Intelligence (AI) into healthcare systems following the Model-Driven Development (MDD) approach. At a time when AI has the potential to instigate a paradigm shift in the health sector, better integrating healthcare experts in the development of these technologies is of paramount importance. We see MDD as a useful way to better embed non-technical stakeholders in the development process. The main goal of this review is to reflect on our experiences to date with MDD and AI in the context of developing healthcare systems. Four case studies that fall within that scope but have different profiles are introduced and summarised: the MyMM application for Multiple Myeloma diagnosis; CNN-HAR, that studies the ability to do AI on the edge for IoT-supported human activity recognition; the HIPPP web based portal for patient information in public health; and Cinco de Bio, a new model driven platform used for the first time to support a better cell-level understanding of diseases. Based on the aforementioned case studies we discuss the characteristics, the challenges faced and the postive outcomes achieved.

Cancer misinformation is becoming an increasingly complex issue. When a person or a loved one receives a diagnosis of possible cancer, that person, family and friends will try to better inform themselves in this area of healthcare. Like most people, they will turn to their clinician for guidance and the internet to better verse themselves on the topic. But can they trust the information provided online? Are there ways to provide a quick evaluation of such information in order to prevent low-quality information and potentially dangerous consequences of trusting it? In the context of the UL Cancer Research Network (ULCan), this interdisciplinary project aims to develop the Health Information Portal for Patients and Public (HIPPP), a web-based application co-designed with healthcare domain experts that helps to improve people navigate the health information space online. HIPPP will be used by patients and the general public to evaluate user-provided web-based health information (WBHI) sources with respect to the QUEST framework and return a quality score for the information sources. As a web application, HIPPP is developed with modern extreme model-driven development (XMDD) technologies in order to make it easily adaptable and evolvable. To facilitate the automated evaluation of WBHI, HIPPP embeds an artificial intelligence (AI) pipeline developed following model-driven engineering principles. Through co-design with health domain experts and following model-driven engineering principles, we have extended the Domain Integrated Modelling Environment (DIME) to include a graphical domain-specific language (GDSL) for developing websites for evaluating WBHI. This GDSL allows for greater participation from stakeholders in the development process of both the user-facing website and the AI-driven evaluation pipeline through encoding concepts familiar to those stakeholders within the modelling language. The time efficiency study conducted as part of this research found that the HIPPP evaluation pipeline evaluates a sample of WBHI with respect to the QUEST framework up to $98.79\%$ faster when compared to the time taken by a human expert evaluator.

Here we present a new approach to training and operationalizing segmentation models for de-arraying Tissue Micro Arrays (TMAs). The scarcity of large, high-quality datasets in sensitive domains such as human tissue samples, coupled with strict privacy regulations to protect doner interests, poses significant obstacles to training robust and generalised segmentation models. To address these challenges, we introduce a new Low-Code/No-Code (LCNC) Domain-Specific Language (DSL) integrated into the Cinco de Bio (CdB) platform. The DSL consists of multiple Service-Independent Building Blocks (SIBs), each providing a distinct functionality essential to creating a pipeline. LCNC enables biologists to train and deploy de-arraying models without writing code. Our methodology incorporates a domain-specific data augmentation technique that generates pseudo-synthetic samples from a minimal set of real data. It also leverages AutoML techniques, including Neural Architecture Search (NAS) and hyperparameter optimisation, to automate the model development process. Furthermore, we present an architectural update to the Cinco de Bio platform, adopting a “Model as Data” paradigm that treats neural network models as dynamic, versioned data assets that can be used as inputs to SIBs. This work provides a practical solution to the challenges of distribution shift and data scarcity in sensitive health domains, where building sufficiently sized datasets to train generalise robust models is infeasible. The proposed LCNC DSL and accompanying pipeline enables domain experts to effectively leverage Artificial Intelligence (AI) technologies and tailor them to their own data.