Abstract
Accurately imputing missing data is critical in time series analysis. The present work compares Foundation Model Chronos against Linear Interpolation, K-Nearest Neighbor Imputer, and Gaussian Mixture Model Imputer with three types of missing data patterns: random, short sequential chunks, and a long sequential chunk. These results confirm that for random missing values, KNN and interpolation yield the highest performance, while Chronos outperforms these on sequences. Indeed, however, for longer sequences of missing values, Chronos starts suffering from cascading errors which eventually allow the simpler imputation methods to outrank it. Another test with limited quantities of training data showed different tradeoffs for the different methods. Unlike KNN and interpolation, which smooth out the gaps, Chronos generates variable synthetic data. This can be beneficial in tasks which require control or simulation. The results highlight the strengths and weaknesses of the imputers and, therefore, offer practical insights into trade-offs between computational complexities, accuracy, and suitability for time series imputation scenarios.

Abstract

Abstract

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

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