Abstract
Software reliability is critical in ensuring that the digital systems we depend on function correctly. In software development, increasing software reliability often involves testing. However, for complex and critical systems, developers can use Design by Contract (DbC) methods to define precise specifications that software components must satisfy. Verification-Aware (VA) programming languages support DbC and formal verification at compile-time or run-time, offering stronger correctness guarantees than traditional testing. However, despite the strong guarantees provided by VA languages, their adoption remains limited. In this study, we investigate the barriers to adopting VA languages by analyzing developer discussions on public forums using topic modeling techniques. We complement this analysis with a developer survey to better understand the practical challenges associated with VA languages. Our findings reveal key obstacles to adoption, including steep learning curves and usability issues. Based on these insights, we identify actionable recommendations to improve the usability and accessibility of VA languages. Our findings suggest that simplifying tool interfaces, providing better educational materials, and improving integration with everyday development environments could improve the usability and adoption of these languages. Our work provides actionable insights for improving the usability of VA languages and making verification tools more accessible. © 2025 IEEE.

Abstract
Maintaining frequency stability is one of the biggest challenges facing future power systems, due to the increasing penetration levels of inverter-based renewable resources. This investigation experimentally validates the frequency provision capabilities of a real Polymer Electrolyte Membrane (PEM) hydrogen electrolyser (HE) using a power hardware-in-the-loop (PHIL) setup. The PHIL consists of a custom 3-level interleaved buck converter and a hardware platform for real-time control of the converter and conducting grid simulation, associated with the modelling of the future Iberian Peninsula (IP) and Continental Europe (CE) systems. The investigation had the aim of validating earlier simulation work and testing new responses from the electrolyser when providing different frequency services at different provision volumes. The experimental results corroborate earlier simulation results and capture extra electrolyser dynamics as the double-layer capacitance effect, which was absent in the simulations. Frequency Containment Reserve (FCR) and Fast Frequency Response (FFR) were provided successfully from the HE at different provision percentages, enhancing the nadir and the rate of change of frequency (RoCoF) in the power system when facing a large disturbance compared to conventional support only. The results verify that HE can surely contribute to frequency services, paving the way for future grid support studies beyond simulations.

Abstract
This work focuses on designing nonlinear control algorithms for dual half-bridge converters (DHBs). We propose a two-layer controller to regulate the current and voltage of the DHB. The first layer utilizes a change in the control variable to obtain a quasi-linear representation of the DHB, allowing for the application of simple linear controllers to regulate current and power flow. The second layer employs a nonlinear control allocation algorithm to select control actions that fulfill (pseudo) power setpoints specified by the first control layer; it also minimizes peak-to-peak currents in the DHB and enforces voltage balance constraints. We apply the DHB and this new control strategy to manage power flow in a hybrid energy storage system comprising of a battery and supercapacitors. Numerical simulation results demonstrate that, in comparison with state-of-the-art approaches, our control algorithm is capable of maintaining good transient behavior over a wide operating range, while reducing peak-to-peak current by up to 80%.

Abstract
The field of AI Ethics has recently gained considerable attention, yet much of the existing academic research lacks practical and objective contributions for the development of ethical AI systems. This systematic literature review aims to identify and map objective metrics documented in literature between January 2018 and June 2023, specifically focusing on the ethical principles outlined in the Ethics Guidelines for Trustworthy AI. The review was based on 66 articles retrieved from the Scopus and World of Science databases. The articles were categorized based on their alignment with seven ethical principles: Human Agency and Oversight, Technical Robustness and Safety, Privacy and Data Governance, Transparency, Diversity, Non-Discrimination and Fairness, Societal and Environmental Well-being, and Accountability. Of the identified articles, only a minority presented objective metrics to assess AI ethics, with the majority being purely theoretical works. Moreover, existing metrics are primarily concentrating on Diversity, Non-Discrimination and Fairness, with a clear under-representation of the remaining principles. This lack of practical contributions makes it difficult for Data Scientists to devise systems that can be deemed Ethical, or to monitor the alignment of existing systems with current guidelines and legislation. With this work, we lay out the current panorama concerning objective metrics to quantify AI Ethics in Data Science and highlight the areas in which future developments are needed to align Data Science projects with the human values widely posited in the literature.

Abstract
It is widely recognized that the well-being, health, and productivity of office workers can be influenced by indoor environmental quality (IEQ) conditions in the workplace. This study aimed to investigate associations between multi-domain IEQ in offices and workers' well-being, health, productivity, and perceived IEQ in 30 open office spaces (6 buildings) located in the urban area of Porto, Portugal. This cross-sectional study included 277 office workers and used a combination of methods to assess their perceptions and physiological responses. Data were collected through questionnaires (covering self-reported well-being, health, productivity, and IEQ satisfaction), pupillometry (autonomic nervous system activity), and concurrent monitoring of IEQ. Correlation, comparative, and regression methods were used to explore associations and differences between IEQ indicators and participants' outcomes. The findings showed that offices typically met acceptable IEQ standards. However, a higher prevalence of health problems and symptoms was observed in offices with higher levels of carbon dioxide (CO2), ozone (O3), particulate matter (PM10), and ultrafine particles (UFP). Interestingly, offices with higher COQ, PM2.5, and volatile organic compounds concentrations were linked to a reduced likelihood of participants reporting asthma, dry cough, and allergies. Additionally, thermal discomfort due to high temperatures, increased PM2.5, UFP, CO2, and O3, and low illuminance appear to reduce eye response in office workers. Higher CO2 and noise levels, and temperatures outside the comfortable range, were linked to lower productivity. The multi-domain analysis showed that perception of multiple IEQ factors significantly explained both self-reported productivity and overall satisfaction with work environment. Overall, ensuring proper IEQ and enhancing workers' satisfaction are essential for creating healthy and productive workplaces.

Abstract