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 CO2, 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. © 2025

Abstract
A fast response to faults in large-scale photovoltaic power plants (PVPPs), which can occur on hundreds of components like photovoltaic panels and inverters, is fundamental for maximizing energy generation and reliable system operation. This work proposes using a Graph Neural Network (GNN) combined with a digital twin for synthetic fault data scenario generation for fault location in PVPPs. It shows that GNN can adapt to system changes without requiring model retraining, thus offering a scalable solution for the real operating PVPPs, where some parts of the system may be disconnected for maintenance. The results for a real PVPP show the GNN outperforms baseline models, especially in larger topologies, achieving up to twice the accuracy in a fault location task. The GNN's adaptability to topology changes was tested on the simulated reconfigured systems. A decrease in performance was observed, and its value depends on the complexity of the original training topology. It can be mitigated by using several system reconfigurations in the training set.

Abstract
Recently, there has been growing attention on the decarbonisation of maritime transport, particularly regarding the landside operations at ports. This has spurred the development and implementation of strategies and policies aimed at enhancing the environmental performance of port activities. Among these strategies, the electrification of port infrastructure is emerging as a potential industry standard for the future. However, there remains a significant gap in understanding the patterns of electricity consumption in ports and how to forecast them accurately. To address this gap, this paper provides a review of the current literature on electricity demand in ports, examining practical applications, methodologies employed, and their key limitations. The findings indicate that, despite its importance in supporting the electrification process, electricity demand forecasting in ports has not received substantial attention in either industry or academic research, and there are no clearly established policies to support port authorities in obtaining the necessary data. Finally, the paper outlines potential directions for future research and how port authorities or local government agencies can contribute to these efforts.

Abstract
To achieve carbon neutrality by 2050, commercial buildings have installed photovoltaic systems to reduce carbon emissions and operational costs. Nevertheless, PV generation does not always match the building’s energy demand profile, therefore storage systems are needed to store excess energy and supply it when necessary. This paper presents a Mixed Integer Linear Programming optimisation algorithm designed to schedule the operation of the electric storage system, aiming to minimise the building’s energy-related costs. An annual hourly simulation of the optimised system was performed to assess the cost reduction. To prevent excessive operation of the electric storage system, an approach to penalise low energy charging was studied, with results showing a significant increase in the system’s lifespan.

Abstract
Widespread fleet electrification is concentrating electricity demand at commercial depots that face volatile prices, tight feeder limits and scarce chargers. This paper proposes a forecast-aware mixed-integer linear program (MILP) that co-optimises vehicle charging, battery-energy-storage dispatch and photovoltaic self-consumption. The model minimises energy cost plus state-of-charge (SOC) penalties, while enforcing charger exclusivity, battery-health bounds and continuous priority weights. It is evaluated on a 48-interval weekday data set comprising 20 electric vehicles, two 11?kW chargers, half-hourly solar forecasts, factory-load predictions and Iberian day-ahead prices. Relative to an uncontrolled first-come/first-served baseline, the optimiser cuts total charging expenditure by 49?%, inceases SOC compliance from 35?% to 65?%, increases PV self-consumption from 33.4?% to 35.5?% and lowers grid-attributed CO2 emissions by 66?%. A modest rise in instantaneous demand is held within transformer limits through strategic battery discharge. These results confirm that predictive scheduling transforms depot charging from a passive load into a cost-optimal, carbon-aware asset and motivate future extensions that embed stochastic forecasts, vehicle-to-grid services. route-energy coupling and Keywords. EV fleet charging; mixed-integer linear programming; battery energy self-consumption; predictive scheduling

Abstract
Data-based approach for diagnosis of thyroid disorders is still at its early stage. Most of the research outcomes deal with binary classification of the disorders, i.e. presence or not of some pathology (cancer, hyperthyroidism, hypothyroidism, etc.). In this paper we explore deep learning (DL) models to improve the multi-class diagnosis of thyroid disorders, namely hypothyroid, hyperthyroid and no pathology thyroid. The proposed DL models, including DNN, CNN, LSTM, and a hybrid CNN-LSTM architecture, are inspired by state-of-the-art work and demonstrate superior performance, largely due to careful feature selection and the application of SMOTE for class balancing prior to model training. Our experiments show that the CNN-LSTM model achieved the highest overall accuracy of 99%, with precision, recall, and F1-scores all exceeding 92% across the three classes. The use of SMOTE for class balancing improved most of the model’s performance. These results indicate that the proposed DL models not only effectively distinguish between different thyroid conditions but also hold promise for practical implementation in clinical settings, potentially supporting healthcare professionals in more accurate and efficient diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.