Simulations are essential in physics education but remain difficult for non-programmers to design and adapt. Existing tools often limit customisation, hindering teachers and students from tailoring experiments to their needs. This paper presents Physics4All, a domain-specific language (DSL) built with JetBrains MPS to democratise the creation of physics simulations. Physics4All introduces domain-specific constructs—worlds, objects, forces, dimensions, and vectors—expressed in familiar mathematical notation. Its modular generation pipeline supports multiple targets (Java and JavaScript), enabling simulations to run across platforms without altering models. Key innovations include implicit unit conversion, reusable forces and objects, and live type checking for correctness. Beyond the educational domain, these features illustrate generalisable DSL engineering principles for modularity, abstraction, and reusability. We evaluated Physics4All through a metrics-based comparison with a GPL baseline and an empirical case study involving secondary school teachers and educational technology developers. The comparison highlighted substantial reductions in implementation effort, while the case study confirmed high suitability, expressiveness, and productivity, with usability and maintainability identified as areas for improvement. Compared to widely used tools such as PhET, Algodoo, and COMSOL, Physics4All offers greater customisation while remaining accessible to non-programmers. The results demonstrate how DSLs can expand the reach and impact of simulation-based education while contributing to broader discussions on domain-specific language engineering.

Low-code/no-code (LCNC) platforms are increasingly promoted for healthcare applications, enabling non-technical professionals to prototype digital solutions. In regulated domains, however, compliance with the General Data Protection Regulation (GDPR) is critical, and it is unclear whether LCNC platforms provide adequate support for such requirements. This paper introduces a requirements-driven evaluation framework that operationalises five GDPR provisions—data minimisation (Art. 5), lawfulness of processing (Art. 6), consent (Art. 7), privacy by design/default (Art. 25), and security of processing (Art. 32)—into concrete modelling tasks. The framework is applied in a comparative study of two leading LCNC platforms, Mendix and OutSystems, using a benchmark chronic disease management application. Findings show that Mendix offers more accessible support for non-technical users, particularly for consent and privacy-by-default, while OutSystems provides greater flexibility in data handling at the cost of higher configuration effort. The study contributes a structured framework for linking legal obligations to model-based development tasks and provides practical insights for selecting LCNC platforms in GDPR-regulated healthcare contexts.

Abstract Modelling is an important activity in software development and it is essential that students learn the relevant skills. Modelling relies on dedicated tools and these can be complex to install, configure, and use—distracting students from learning key modelling concepts and creating accidental complexity for teachers. To address these challenges, we believe that modelling tools specifically aimed at use in teaching are required. Based on discussions at a working session organised at MODELS 2023 and the results from an internationally shared questionnaire, we report on requirements for such modelling tools for teaching. We also present examples of existing modelling tools for teaching and how they address some of the requirements identified.

This short paper presents preliminary results on the evaluation of a Classification Algorithms Framework Domain-Specific Language (CAF DSL) that was previously developed. In our previous work, a domain-specific language to provide a framework for choosing-evaluating classification algorithms but with a system-level and simplified interface approach was presented. However, the evaluation that took place was restricted to data scientists and language engineers. In this work, the evaluation of the same DSL but by finance-accountant professionals (domain-experts) took place in a small scale. The DSL was presented and feedback regarding its usability and learnability took place. Future plans consist of scaling this attempt further and extending it to different domains such as health care. The long-term plan consists of bringing AI – Classification algorithms to professionals and to the wider public.

In the world of information technology and the Internet, which has become a part of human life today and is constantly expanding, Attention to the users' requirements such as information security, fast processing, dynamic and instant access, and costs savings has become essential. The solution that is proposed for such problems today is a technology that is called cloud computing. Today, cloud computing is considered one of the most essential distributed tools for processing and storing data on the Internet. With the increasing using this tool, the need to schedule tasks to make the best use of resources and respond appropriately to requests has received much attention, and in this regard, many efforts have been made and are being made. To this purpose, various algorithms have been proposed to calculate resource allocation, each of which has tried to solve equitable distribution challenges while using maximum resources. One of these calculation methods is the DRF algorithm. Although it offers a better approach than previous algorithms, it faces challenges, especially with time-consuming resource allocation computing. These challenges make the use of DRF more complex than ever in the low number of requests with high resource capacity as well as the high number of simultaneous requests. This study tried to reduce the computations costs associated with the DRF algorithm for resource allocation by introducing a new approach to using this DRF algorithm to automate calculations by machine learning and artificial intelligence algorithms (Autonomic Dominant Resource Fairness or A-DRF).

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings.

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users' tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRFS.