Recent studies show that charging stations are operated in an inefficient way. Due to the fact that electric vehicle (EV) drivers charge while they park, they tend to keep the charging station occupied while not charging. This prevents others from having access. This study is the first to investigate the effect of a pricing strategy to increase the efficient use of electric vehicle charging stations. We used a stated preference survey among EV drivers to investigate the effect of a time-based fee to reduce idle time at a charging station. We tested the effect of such a fee under different scenarios and we modelled the heterogeneity among respondents using a latent class discrete choice model. We find that a fee can be very effective in increasing the efficiency at a charging station but the response to the fee varies among EV drivers depending on their current behaviour and the level of parking pressure they experience near their home. From these findings we draw implications for policy makers and charging point operators who aim to optimize the use of electric vehicle charging stations.
This paper analyzes connectivity and efficiency of a SME network across two industries. These characteristics are likely to be different for networks of various industries. The concept of 'small worlds' is used to judge overall network efficiency. The actual network can be classified as one in which a small world is present. Visualization of the results shows a single core group in the network. It was found that non-profit as well as science actors were overrepresented in the core of the field.
In this paper we present a methodology to measure the energy consumption of software. The methodology is based on detailed monitoring of power usage of hardware components. We explain our lab setup after which we apply the methodology to different pieces of DNS resolver software. Through this case study we demonstrate some of the uses of our methodology, as it can be used to determine which software performs the tasks at hand in the most energy efficient way, what the influence of software configuration can be, etcetera.
Designing cities that are socially sustainable has been a significant challenge until today. Lately, European Commission’s research agenda of Industy 5.0 has prioritised a sustainable, human-centric and resilient development over merely pursuing efficiency and productivity in societal transitions. The focus has been on searching for sustainable solutions to societal challenges, engaging part of the design industry. In architecture and urban design, whose common goal is to create a condition for human life, much effort was put into elevating the engineering process of physical space, making it more efficient. However, the natural process of social evolution has not been given priority in urban and architectural research on sustainable design. STEPS stems from the common interest of the project partners in accessible, diverse, and progressive public spaces, which is vital to socially sustainable urban development. The primary challenge lies in how to synthesise the standardised sustainable design techniques with unique social values of public space, propelling a transition from technical sustainability to social sustainability. Although a large number of social-oriented studies in urban design have been published in the academic domain, principles and guidelines that can be applied to practice are large missing. How can we generate operative principles guiding public space analysis and design to explore and achieve the social condition of sustainability, developing transferable ways of utilising research knowledge in design? STEPS will develop a design catalogue with operative principles guiding public space analysis and design. This will help designers apply cross-domain knowledge of social sustainability in practice.
In this proposal, a consortium of knowledge institutes (wo, hbo) and industry aims to carry out the chemical re/upcycling of polyamides and polyurethanes by means of an ammonolysis, a depolymerisation reaction using ammonia (NH3). The products obtained are then purified from impurities and by-products, and in the case of polyurethanes, the amines obtained are reused for resynthesis of the polymer. In the depolymerisation of polyamides, the purified amides are converted to the corresponding amines by (in situ) hydrogenation or a Hofmann rearrangement, thereby forming new sources of amine. Alternatively, the amides are hydrolysed toward the corresponding carboxylic acids and reused in the repolymerisation towards polyamides. The above cycles are particularly suitable for end-of-life plastic streams from sorting installations that are not suitable for mechanical/chemical recycling. Any loss of material is compensated for by synthesis of amines from (mixtures of) end-of-life plastics and biomass (organic waste streams) and from end-of-life polyesters (ammonolysis). The ammonia required for depolymerisation can be synthesised from green hydrogen (Haber-Bosch process).By closing carbon cycles (high carbon efficiency) and supplementing the amines needed for the chain from biomass and end-of-life plastics, a significant CO2 saving is achieved as well as reduction in material input and waste. The research will focus on a number of specific industrially relevant cases/chains and will result in economically, ecologically (including safety) and socially acceptable routes for recycling polyamides and polyurethanes. Commercialisation of the results obtained are foreseen by the companies involved (a.o. Teijin and Covestro). Furthermore, as our project will result in a wide variety of new and drop-in (di)amines from sustainable sources, it will increase the attractiveness to use these sustainable monomers for currently prepared and new polyamides and polyurethanes. Also other market applications (pharma, fine chemicals, coatings, electronics, etc.) are foreseen for the sustainable amines synthesized within our proposition.
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.
