Abstract
DIn the digital transformation of industrial sectors, data is a high-value business asset. How companies manage data between systems within the organization or through networks of business partners impacts their competitive factor. Technological maturity may imply several adversities, such as the lack of interoperability standards for simple and transparent data exchange. This paper presents an architecture that enables secure exchanges of supply chain orders between textile and clothing companies. This architecture is based on Electronic Business (eBIZ) 4.0 and International Data Spaces (IDS) frameworks, fostering trust and widespread adoption of platforms in the industry sector, particularly when handling sensitive supply chain information. The architecture was implemented and validated in 3 use cases with Enterprise Resource Plannings (ERPs) from the same vendor, different vendors, and communication from a ERP to a Web portal. Implementing the proposed architecture impacted efficiency, transparency, and accountability within the supply chain network. The lead times for purchases, provisioning, and the number of additional information requests in the ordering were reduced. In subcontracting, a reduction in non-conformities and an overall improvement in delivery times were verified. Moreover, logistics operations and communication with subcontractors were optimized, leading to faster order reception and reducing informal contacts.

Abstract
In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicability and reproducibility are not easy to achieve due to the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment via the same frameworks, code, programming languages, dependencies, and so on. We propose a framework, known as SciRep, that supports the configuration, execution, and packaging of computational experiments by defining their code, data, programming languages, dependencies, databases, and commands to be executed. After the initial configuration, the experiments can be executed any number of times, always producing exactly the same results. Our approach allows the creation of a reproducibility package for experiments from multiple scientific fields, from medicine to computer science, which can be re-executed on any computer. The produced package acts as a capsule, holding absolutely everything necessary to re-execute the experiment. To evaluate our framework, we compare it with three state-of-the-art tools and use it to reproduce 18 experiments extracted from published scientific articles. With our approach, we were able to execute 16 (89%) of those experiments, while the others reached only 61%, thus showing that our approach is effective. Moreover, all the experiments that were executed produced the results presented in the original publication. Thus, SciRep was able to reproduce 100% of the experiments it could run. © 2025 The Authors

Abstract

Abstract
Since the publication of the 2020 paper, Finding the Gaps About Uses of Immersive Learning Environments: A Survey of Surveys, the landscape of immersive learning environments (ILEs) has continued to evolve rapidly. This update aims to revisit the gaps identified in that previous research and explore emerging trends. We conducted an extensive review of new surveys published after that paper's cut date. Our findings reveal a significant amount of new published reviews (n = 64), more than doubling the original corpus (n = 47). The results highlighted novel themes of usage of immersive environments, helping bridge some 2020 research gaps. This paper discusses those developments and presents a consolidated perspective on the uses of immersive learning environments.

Abstract
Background and objectives: Timely treatment of pediatric obstructive sleep apnea (OSA) can prevent or reverse neurocognitive and cardiovascular morbidities. However, whether distinct phenotypes exist and account for divergent treatment effectiveness remains unknown. In this study, our goal is threefold: i) to define new data-driven pediatric OSA phenotypes, ii) to evaluate possible treatment effectiveness differences among them, and iii) to assess phenotypic information in predicting OSA resolution. Methods: We involved 22 sociodemographic, anthropometric, and clinical data from 464 children (5-10 years old) from the Childhood Adenotonsillectomy Trial (CHAT) database. Baseline information was used to automatically define pediatric OSA phenotypes using a new unsupervised subject-based association network. Follow-up data (7 months later) were used to evaluate the effects of the therapeutic intervention in terms of changes in the obstructive apnea-hypopnea index (OAHI) and the resolution of OSA (OAHI < 1 event per hour). An explainable artificial intelligence (XAI) approach was also developed to assess phenotypic information as OSA resolution predictor at baseline. Results: Our approach identified three OSA phenotypes (PHOSA1-PHOSA3), with PHOSA2 showing significantly lower odds of OSA recovery than PHOSA1 and PHOSA3 when treatment information was not considered (odds ratios, OR: 1.64 and 1.66, 95 % confidence intervals, CI: 1.03-2.62 and 1.01-2.69, respectively). The odds of OSA recovery were also significantly lower in PHOSA2 than in PHOSA3 when adenotonsillectomy was adopted as treatment (OR: 2.60, 95 % CI: 1.26-5.39). Our XAI approach identified 79.4 % (CI: 69.9-88.0 %) of children reaching OSA resolution after adenotonsillectomy, with a positive predictive value of 77.8 % (CI: 70.3 %-86.0 %). Conclusions: Our new subject-based association network successfully identified three clinically useful pediatric OSA phenotypes with different odds of therapeutic intervention effectiveness. Specifically, we found that children of any sex, >6 years old, overweight or obese, and with enlarged neck and waist circumference (PHOSA2) have less odds of recovering from OSA. Similarly, younger female children with no enlarged neck (PHOSA3) have higher odds of benefiting from adenotonsillectomy.

Abstract
In this paper, we introduce a novel approach for rigorously verifying safety properties of state machine specifications. Our method leverages an auto-active verifier and centers around the use of action functions annotated with contracts. These contracts facilitate inductive invariant checking, ensuring correctness during system execution. Our approach is further supported by the Why3-do library, which extends the Why3 tool's capabilities to verify concurrent and distributed algorithms using state machines. Two distinctive features of Why3-do are: (i) it supports specification refinement through refinement mappings, enabling hierarchical reasoning about distributed algorithms; and (ii) it can be easily extended to make verifying specific classes of systems more convenient. In particular, the library contains models allowing for message-passing algorithms to be described with programmed handlers, assuming different network semantics. A gallery of examples, all verified with Why3 using SMT solvers as proof tools, is also described in the paper. It contains several auto-actively verified concurrent and distributed algorithms, including the Paxos consensus algorithm.