Energy efficiency has gained a lot of prominence in recent debates on urban sustainability and housing policy due to its potential consequences for climate change. At the local, national and also international level, there are numerous initiatives to promote energy savings and the use of renewable energy to reduce the environmental burden. There is a lot of literature on energy saving and other forms of energy efficiency in housing. However, how to bring this forward in the management of individual housing organisations is not often internationally explored. An international research project has been carried out to find the answers on management questions of housing organisations regarding energy efficiency. Eleven countries have been included in this study: Germany, the United Kingdom (more specifically: England), France, Sweden, Denmark, the Netherlands, Switzerland, Slovenia, the Czech Republic, Austria and Canada. The state of the art of energy efficiency in the housing management of non-profit housing organisations and the embedding of energy efficiency to improve the quality and performance of housing in management practices have been investigated, with a focus on how policy ambitions about energy efficiency are brought forward in investment decisions at the estate level. This paper presents the conclusions of the research
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The project focuses on highlighting the importance of the work-life balance within the hospitality industry, in 5-star hotels. Moreover, it is tailored to deliver a concrete and efficient implementation plan of a 5-day work week system instead of the current 6-day work week model system within a 5 star hotel in Abu Dhabi, United Arab Emirates. Several aspects were taken in consideration when developing the project such as, financial implications, stakeholder management, resistance management and organizational change.
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The primary aims of this study were (1) to evaluate whole-body mechanical efficiency (ME) in a large group of chronic obstructive pulmonary disease (COPD) patients with a wide range of degrees of illness and (2) to examine how ME in COPD is related to absolute work rate and indices of disease severity during exercise testing. A total of 569 patients (301 male patients; GOLD stage I: 28, GOLD stage II: 166, GOLD stage III: 265, and GOLD stage IV: 110) with chronic obstructive pulmonary disease (COPD) were included in the data analysis. Individual maximal workload (watt), peak minute ventilation ((Equation is included in full-text article.)E, L/min body temperature and pressure, saturated), and peak oxygen uptake ((Equation is included in full-text article.)O2, mL/min standard temperature and pressure, dry) were determined from a maximal incremental cycle ergometer test. Ventilatory and metabolic response parameters were collected during a constant work rate test at 75% of the individual maximal workload. From the exercise responses of the constant work rate test, the gross ME was calculated. The mean whole-body gross ME was 11.0 ± 3.5% at 75% peak power. The ME declined significantly (P < .001) with increasing severity of the disease when measured at the same relative power. Log-transformed absolute work rate (r = .87, P < .001) was the strongest independent predictor of gross ME. Body mass was the single other variable that contributed significantly to the linear regression model. Gross ME in COPD was largely predicted by the absolute work rate (r = .87; P < .001) while indices of the severity of the disease did not predict ME in COPD.
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Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
