Zenaida Mourão

Abstract
Seaport cranes operate continuously and consume large amounts of energy while aiming to minimise containerships’ berthing time. Although previous studies have contributed to addressing the crane scheduling problem, most have focused exclusively on loading time, often overlooking the aspect of energy consumption. Furthermore, crane activity is typically modelled in a simplified manner—commonly assuming a fixed cycle duration or constant energy usage when handling a container—without accounting for the impact of variable container masses. In this study, an energy-aware quay crane scheduling formulation for container terminals is proposed, highlighting the importance of integrating an energy model into the scheduling problem. The optimisation problem is formulated as a Mixed Integer Linear Programming (MILP) model. The objective is to minimise total energy costs by reordering the sequence in which containers are handled, while respecting precedence constraints defined by the ship’s stowage plan. Two solution methods—a MILP approach solved using CPLEX and a genetic algorithm (GA)—are compared. The results indicate that, for larger containerships, the genetic algorithm provides a more efficient solution method. Moreover, incorporating detailed energy consumption models for electric cranes may significantly reduce energy costs during containership handling operations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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
Office workers spend a considerable part of their day at the workplace, making it vital to ensure proper indoor environmental quality (IEQ) conditions in office buildings. This work aimed to identify significant factors influencing IEQ and assess the effectiveness of an environmental intervention program, which included the introduction of indoor plants, carbon dioxide (CO2) sensors, ventilation, and printer relocation (source control), in six modern office buildings in improving IEQ. Thirty office spaces in Porto, Portugal, were randomly divided into intervention and control groups. Indoor air quality, thermal comfort, illuminance, and noise were monitored before and after a 14-day intervention implementation. Occupancy, natural ventilation, floor type, and cleaning time significantly influenced IEQ levels. Biophilic interventions appeared to decrease volatile organic compound concentrations by 30%. Installing CO2 sensors and optimizing ventilation strategies in an office that mainly relies on natural ventilation effectively improved air renewal and resulted in a 28% decrease in CO2 levels. The implementation of a source control intervention led to a decrease in ultrafine particle and ozone concentrations by 14% and 85%, respectively. However, an unexpected increase in airborne particle levels was detected. Overall, for a sample of offices that presented acceptable IEQ levels, the intervention program had only minor or inconsistent impacts. Offices with declared IEQ problems are prime candidates for further research to fully understand the potential of environmental interventions.

Abstract
The literature on the isothermal model gas flow is extensive, but the effect of temperature variation on the hydraulic characteristics has been rarely addressed. Additionally, the impact of hydrogen blending on the thermal condition of NG pipelines is also an emergent topic that requires new approaches to the gas flow problem formulation and resolution. In this paper, a model for the gas flow problem was developed to optimise the operation of natural gas distribution networks with hydrogen injection while maintaining pressure, gas flows, and gas quality indexes within admissible limits. The goal is to maximise the injection of hydrogen and investigate the influences of thermal variations in the gas blending. Also, this model enables the calculation of the maximum permitted volume of hydrogen in the network, quantifying the total savings in natural gas usage and carbon dioxide emissions in different temperature conditions.

Abstract
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. The model is designed to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe index (WI) and higher heating value (HHV)) within admissible limits. This study also presents the maximum injection allowable of hydrogen correlated with the gas quality index variation. The model has been applied to a case study of a gas network with four distinct scenarios and implemented using Python. The findings of the case study quantify the maximum permitted volume of hydrogen in the network, the total savings in natural gas, and the reduction in carbon dioxide emissions. Lastly, a sensitivity analysis of injected hydrogen as a function of the Wobbe index (WI) and Higher Heating Value (HHV) limits relaxation.