Universidad Complutense de Madrid

Schizophrenia and autism share neurobiological mechanisms and overlapping clinical features, often resulting in the emergence of autistic traits in early stages of psychosis. The PANSS Autism Severity Score (PAUSS) provides a rapid measure of autistic features within the standard PANSS assessment. We aimed to determine the prevalence of autistic features in first-episode psychosis (FEP), characterise their clinical, cognitive, and functional profile, and examine their impact on 2-year outcomes. A total of 328 FEP patients were included from the PEPs multicentre cohort, followed for 2 years. Autistic features were rated using PAUSS (cut-off ≥ 30), yielding autistic (n = 38) and non-autistic (n = 290) groups. Sociodemographic, clinical, cognitive, and functional variables were analysed. Longitudinal analyses examined symptomatic remission rates and trajectories of psychopathology and functioning using logistic regression and mixed-model ANOVA. The autistic group represented 11.6% of the sample. At baseline, they exhibited lower birth weight, greater medication side effects, higher general psychopathology and depressive severity, and poorer global functioning. Cognitively, they showed significant deficits in working memory, social cognition, and cognitive reserve compared to the non-autistic group. Over 2 years, this group was 3.6 times less likely to achieve symptomatic remission and consistently exhibited higher symptom severity and lower functioning across all follow-ups. Autistic features in FEP identify a subgroup with a possible distinct profile of neurodevelopmental markers, greater cognitive and functional impairments, and poorer clinical outcomes. Early identification may guide more personalised interventions, although further research is needed to refine PAUSS specificity and develop targeted, tailored treatments.

• Transport impedance with Current-modulated Hydrogen flow-rate Spectroscopy (CH2S). • Effective diffusivities and mass transport resistances of hydrogen measured with CH2S. • GDL substrate and MPL layers operando transport properties differentiated. • Transport properties compared for passive-PEMFC and conventional-PEMFC • Current density and active area effects on hydrogen transport in passive PEMFC. Current-modulated Hydrogen flow-rate Spectroscopy (CH2S) is the mass-transport impedance that relates cell current ( I ˜ ) and hydrogen flow rate ( Q ˜ H 2 ) in the anode of a proton exchange membrane fuel cell (PEMFC). The transfer function ( H = n F Q ˜ H 2 / I ˜ ) provides kinetic information about transport processes in the anode of a PEMFC under working conditions. CH2S can differentiate hydrogen transport in the gas diffusion layer substrate (GDLS) and in the microporous layer (MPL) of the anode. Using an analytical model and the distribution of relaxation times formalism, the effective, operando , hydrogen diffusivities and mass transport resistances of the GDLS and MPL are obtained. Here CH2S is used to study mass transport in a PEMFC anode fed with quasi-static hydrogen and air atmospheres, or passive PEMFC (p-PEMFC). The anode is a commercial gas diffusion electrode, with Pt/C based catalyst layer, a hydrophobic carbon black MPL, and a woven carbon cloth GDLS. The results show larger mass transport losses in the GDLS of the p-PEMFC compared with a convective PEMFC (c-PEMFC) fed with gases under forced convection. The MPL, however, presents same mass transport losses in both cell types, with operando water saturation close to 100%. Increasing current density, up to 180 mA cm -2 , has little impact on GDLS transport properties but improves them in the MPL attributed to thermal activation and drier conditions in the anode by water electroosmotic dragging. Larger active area size increases transport losses in p-PEMFC due to more difficult passive elimination of water from the cell by natural forces. Changes in the p-PEMFC design are proposed to mitigate mass transport losses and bring its efficiency closer to the c-PEMFC.

The European Union has recently introduced a suite of foundational digital regulations—the Cyber Resilience Act, the Artificial Intelligence Act, the Radio Equipment Directive, the NIS 2 Directive, and the Cybersecurity Act—that directly affect the engineering of software-intensive systems. While these instruments aim to enhance trust and security, their overlapping scopes generate a complex compliance landscape that software development must address at the design, implementation, and deployment stages. This paper examines the cross-regulatory impact of such EU cybersecurity legislation from a software engineering perspective, aiming to provide a set of guidelines and recommendations for implementing a compliance-by-design approach. We analyse and compare the five legal instruments, focusing on how their obligations intersect with each other. We then translate their regulatory requirements into actionable artefacts, ranging from architectural constraints and security controls to organisational processes, using a legal engineering approach. Finally, we propose a compliance-by-design lifecycle pattern that integrates regulatory alignment into requirements engineering, system design, and testing. To demonstrate applicability, we evaluate three representative use cases: an AI-enabled power plant, an autonomous drone delivery platform, and an AI-powered clinical decision support system. These examples demonstrate that multiple regulatory regimes often govern software-based systems. We conclude with practical recommendations for suppliers, deployers, and policymakers towards an integrated compliance framework to promote compliance-aware software engineering. Our findings indicate that the European digital landscape is shifting compliance from a post-hoc audit exercise to a design-time engineering principle. Embedding compliance early into the software development lifecycle not only supports regulatory alignment but also improves system resilience and trustworthiness.