Abstract

Abstract
Validation is a central activity when developing formal specifications. Similarly to coding, a possible validation technique is to define upfront test cases or scenarios that a future specification should satisfy or not. Unfortunately, specifying such test cases is burdensome and error prone, which could cause users to skip this validation task. This paper reports the results of an empirical evaluation of using pre-trained large language models (LLMs) to automate the generation of test cases from natural language requirements. In particular, we focus on test cases for structural requirements of simple domain models formalized in the Alloy specification language. Our evaluation focuses on the state-of-the-art GPT-5 model, but results from other closed- and open-source LLMs are also reported. The results show that, in this context, GPT-5 is already quite effective at generating positive (and negative) test cases that are syntactically correct and that satisfy (or not) the given requirement, and that can detect many wrong specifications written by humans. © The Author(s) 2026.

Abstract
Analyzing time series of counts often encounters the challenge of missing data, which can significantly hinder the accuracy and reliability of statistical models. This study addresses this issue by employing Poisson first-order integer-valued au-toregressive (PoINAR) models in conjunction with the Gibbs sampler with data augmentation. This method is particularly effective as it accounts for both the mechanisms behind missing data and the intrinsic serial correlation within the time series. Two distinct approaches to data augmentation are explored and compared in this work and illustrated using both simulated and real data. © 2026 Author(s).

Abstract

Abstract
Atlantic salmon populations have declined in many regions and are affected by several natural and anthropogenic factors throughout their lives. We investigated the role of environmental drivers and the effect of dam construction on the trend in catches of spawning adults of a migratory population currently at risk. For this purpose, we examined the salmon catches from 1914 to 2020 in the Minho River (NW Portugal, SW Europe), located at the southern limit of this species' distribution. There was a decline in catches over time with an inverse and significant relationship between the trend in catches and lagged temperature. Delayed effects of this type may indicate temperature influences on survival during early life history stages. Similarly, the trend in catches decreased with the increasing number of dams. A forecast model built for the period before the construction of the first major dam in this river (before 1955), including lagged temperature, resulted in a decreasing trend in the number of catches. This demonstrates that catches would have declined due to temperature effects even without dam construction. This does not diminish the role of dams in the observed decline; rather, it reveals that temperature-driven declines would have occurred independently. Nonetheless, efficient management and conservation of this imperiled population require further detailed biological information on the number of returning spawning adults and salmons' survival throughout their life cycle.

Abstract
Background: – In the pursuit of understanding current improvements that enhance nursing care leveraging emerging technologies, this study focused on answering “How has artificial intelligence been integrated into electronic health records, with an emphasis on nursing practice?” Methods: – This scoping review was conducted after the methodology proposed by the Joanna Briggs Institute and structured according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. The study protocol was registered on the Open Science Framework platform (DOI: 10.17605/OSF.IO/D96TY). Searches were performed across 7 databases, in addition to grey literature and manual reference screening. Results: – A total of 74 studies were included. A variety of artificial intelligence technologies were identified, particularly traditional supervised learning and natural language processing. Artificial intelligence contributed to clinical decision-making, risk anticipation, workload reduction through documentation automation, and the enhancement of documentation quality by improving its accuracy, completeness, and consistency. Discussion: – The adoption of these technologies demonstrates promising potential to optimize nursing documentation, support clinical decisions, and strengthen patient safety, thereby promoting a more efficient and evidence-based nursing practice. However, effective implementation requires attention to data quality, interoperability, and increased active engagement of nurses in the development and use of such technologies.