154092 research outputs found
Sort by
Exploring the role of generative AI in science teacher education programs: a qualitative study
Get PDFThe introduction of transformative generative open AI (GenAI) has impacted science education, presenting opportunities for students and teachers to enhance teaching and learning efficiency. Equally GenAI poses challenges, including risks such as plagiarism and superficial engagement with content. Science teacher education programs play a key role in the way these opportunities are realized and how challenges are dealt with through educating the future generation of science teachers. Science teacher educators face the challenge to remodel their teaching program to showcase how GenAI is used appropriately. Their student teachers face the challenge of working with GenAI in their own learning, but also in their classroom teaching where their students in secondary education might be using GenAI. This interview study explored how science teacher educators and student-science teachers in the teacher training programs of the four technical universities in The Netherlands envisage the potential impact of GenAI on university science teacher education. Few of the teacher educators had actually used GenAI, compared to the number of student teachers that had used GenAI. Potential uses for GenAI in science teacher education and for science teaching in general were identified, as well as desired new learning goals. A strong need for a policy on the use of GenAI was expressed, including a need for clear guidelines and rules. The conclusion presents possible design characteristics for science teacher education to benefit from the advent of GenAI and circumvent associated risks
Rethinking Computing Systems in the Era of Climate Crisis:A Call for a Sustainable Computing Continuum
Get PDFThe advancement and widespread adoption of computing technology has yielded services that could help mitigate the climate crisis. However, the retirement of obsolete equipment, the consumption of rare earth materials, and the escalating energy demands associated with massive data processing and cloud infrastructures have raised new environmental dilemmas. Existing design and development methodologies primarily focus on fulfilling functional requirements and improving performance. In this article, we argue that these methodologies must be augmented with sustainability considerations encompassing energy efficiency, material usage, longevity, and upgradability. Solutions at different layers of the system stack, from the physical to the application layer, must be integrated. Moreover, there should be a strong focus on the transparency of sustainability metrics across the whole computing continuum. Building on fruitful discussions at the International Lorentz Workshop on Future Computing for Digital Infrastructures, we advocate novel approaches in the design, development, and operation of the computing continuum.</p
Safety Challenges in Battery Swapping Operations of Electric Underground Mining Trucks
Get PDFRecently, the global landscape of public transportation has witnessed a transformative shift towards sustainable and efficient modes of mobility, with particular emphasis on electric vehicles (EVs) and their integration into industrial applications. The mining industry, including the underground mining of the mineral resources sector is following this trend. However, underground mines are critical environments and the adoption of EVs needs to be carefully analysed. This study investigates the associated hazards and risks of adopting EVs (such as dumpers and loaders) focusing on the swapping battery operations. First, current hazards related to battery swapping are identified—21 in total, occurring in 25 instances. After, risks are assessed and associated with specific hazards. Finally, possible measures and solutions for reducing the impacts of these risks on the performance of the EVs are offered
Exploring the relation of livability mapping and flood exposure analysis by combining remote sensing and citizen science
Get PDFEnvironmental hazards are key determinants of urban liveability, shaping the safety, health, and resilience of residents. This study investigates the intersection of urban livability and flood exposure by integrating remote sensing, citizen science, and AI-driven analysis across three African countries: Ghana, Kenya, and Mozambique. Using Sentinel-1 satellite imagery, open geospatial datasets, and advanced deep learning techniques, a citizen-derived perceived livability index was created which was then combined with rapid flood exposure modelling through FastFlood. The results reveal that areas with the lowest livability scores -characterized by poor housing conditions, limited service access, and minimal green spaces- are also consistently the most exposed to frequent and severe flooding. In Nairobi, for instance, approximately 35% of built-up areas are flood-prone, with informal settlements like Kibera and Mathare facing disproportionate risks. Citizen science efforts validated the flood models, underscoring the critical role of local knowledge in capturing fine-scale flood dynamics invisible to remote sensing alone. The project demonstrates that liveability and environmental risk are deeply interrelated, and contribute to worsening urban vulnerability. By combining community mapping with scalable Earth Observation methods, this work delivers actionable methods for urban planners, humanitarian organizations, and local policymakers. Our results stress the importance of planning strategies that prioritize investments in flood mitigation, nature-based solutions, and resilient infrastructure for the most at-risk communities. Such communities are often omitted in official data. The needs and views of such vulnerable communities need to be included in supporting sustainable and inclusive urban development under increasing climate pressures
Fully Bio-Based Epoxy Resins from Liquefied Wood for Chemically Recyclable Wood Coatings
Get PDFEpoxy resins are widely used in the coatings industry, yet their petroleum-based origin and crosslinked structures pose challenges for sustainability and recyclability. This study explores a cradle-to-cradle approach for bio-based epoxy wood coatings using the heavy fraction of liquefied wood (LW) as a renewable curing agent. LW, a lignin-like compound rich in aromatic structures, acts as a hydroxyl donor and reacts with biobased glycerol diglycidyl ether (GDE). This article presents the molecular characterization of LW by different techniques. It demonstrates that the resulting coating has a performance comparable to commercial bisphenol A epoxy-amine systems, and shows that the crosslinked product of LW and GDE can be depolymerized and recycled as aromatic polyol using the same liquefaction process. Importantly, this work highlights that the recycling process does not require removing the coating from the wood matrix. Instead, the coated wood, including both the wood and coating, can be recycled together through the same liquefaction process used to produce the LW. This LW-epoxy platform demonstrates the feasibility of creating wood coatings with improved recyclability, contributing to more sustainable practices in the coatings industry.</p
Isobaric Vapor-Liquid Equilibrium of Methylcyclohexane + Toluene with Gamma-Valerolactone as a Biobased Entrainer and 1-Methylpyrrolidin-2-one as a Conventional Entrainer
Get PDFThe isobaric vapor-liquid equilibrium (VLE) data of the binary mixture of methylcyclohexane (1) + toluene (2) at 101.3 kPa; the pseudoternary mixture of methylcyclohexane (1) + toluene (2) + gamma-valerolactone (GVL) (3) with the entrainer-to-feed ratio (E/F) = 1 (mass basis) at 50, 80, and 100 kPa, and E/F = 2 and 3 at 100 kPa; and the pseudoternary mixture of methylcyclohexane (1) + toluene (2) + 1-methylpyrrolidin-2-one (NMP) (3) with E/F = 1 at 100 kPa were measured using a Fischer Labodest VLE602 ebulliometer. The reliability of the experimental VLE data was tested and confirmed by Van Ness and Fredenslund thermodynamic consistency tests. The experimental results indicate that the presence of GVL and NMP increases the relative volatility of methylcyclohexane to toluene; therefore, both entrainers remove a close-boiling behavior in the mixture. Non-random two-liquid (NRTL) and universal quasi chemical (UNIQUAC) thermodynamic models were applied in the experimental data correlation to obtain the optimum binary interaction parameters. For the mixture involving GVL, the experimental VLE data were accurately correlated by NRTL and UNIQUAC. However, NRTL has more accurate results compared with UNIQUAC. For the mixture containing NMP, both the UNIQUAC and NRTL models show favorable regression results.</p
First Order Methods with Non-Euclidean Geometry
Get PDFThe thesis investigates various first-order optimization methods across both de- terministic and stochastic frameworks.Part I addresses Stochastic Convex Optimization problems, specifically the complementary composite setting where the objective function combines a smooth function with a strongly-convex regularization term. Our accelerated framework, is demonstrated to be optimal for this class of problems. As applications, our accelerated algorithm also delivers sharp convergence rates when applied to synthetic data generation in differential privacy .Part II explores acceleration methods under relaxed standard assumptions. We develop an accelerated algorithm that replaces convexity with star-convexity, proving it to be nearly optimal for lp norms. In parallel, we examine another setting where the goal is to minimize the maximum of smooth convex functions through two distinct approaches. Our algorithms achieve sharp convergence rates compared to existing methods in the literature.<br/
Digitalisation in Local Housing Energy Systems:Co‐Creation and Digital Literacy in the Dutch Context
Get PDFThis article critically reflects on the digitalisation of local housing energy systems. It looks at two Netherlands‐based cases and their implementation, combined with the use of digital tools. From a socio‐technical angle, it is crucial to provide energy‐consumption dashboards with a two‐fold feedback loop for residents about their energy consumption. That enables users to make informed decisions and behavioural adjustments in daily energy usage. By proposing a framework, the article introduces two new analytical categories: digital literacy and co‐creation applied to the use of interactive digital tools. The aim is to unpack new challenges of the digitalisation process and the use of dashboards in relation to the two analytical categories. To do so, the article compares two different configurations of local socio‐spatial contexts. The analysis draws upon an archive of correspondence, official documents, survey results, participant observations, multiple rounds of group interviews from the funded projects, and new in‐depth expert interviews. The results reveal that inhabitants should accept the underlying technology that revolves around decentralised energy systems and be willing to pay their share of the investment costs. Furthermore, the authors discuss the reach of digital literacy and co‐creation as emerging urban planning dilemmas. The empirical evidence is that the scale of implementation, the type of engagement with residents (tenants vs. owners vs. communities), the degree of digital literacy, and the opportunities for co‐creation activities are essential features for a more inclusive digitalisation outcome
ERRATUM ‘Model-Based Cost-Utility Analysis of Combined Low-Dose Computed Tomography Screening for Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease’ [JTO Clinical and Research Reports Volume 6 Issue 5 (2025) 100813] (JTO Clinical and Research Reports (2025) 6(5), (S2666364325000293), (10.1016/j.jtocrr.2025.100813))
Get PDFIn the original published version of this article, an error was introduced during the copyediting process by the Suppliers. The text originally published “Several studies have investigated these diseases.” Has been corrected to “Some studies investigate specific diseases." This error bears no reflection on the article or its authors. The publisher apologizes to the authors and the readers for this unfortunate error.</p
Modelling Alcohol Consumption Patterns to Enable Policy Impact Assessment
Get PDFObjective To prevent harmful effects of alcohol use, various countries implement policies preventing excessive and heavy episodic drinking. To enable the evaluation of the impact of such policies on (future) drinking behaviour, we aimed to develop a model that predicts alcohol consumption patterns.Methods The model predicts alcohol use in three stages. First, a logistic submodel predicts probabilities of drinking any alcohol. Second, for drinkers, a submodel predicts the weekly consumption through a negative binomial distribution for the number of beverages. Finally, based on the predicted weekly consumption, a logistic submodel predicts probabilities of heavy episodic drinking. The distribution for the weekly consumption was calibrated, targeted to predict the prevalence of excessive and heavy episodic drinking accurately.Model parameters were estimated using Dutch individual-level cross-sectional survey data covering the years 2008-2022. The characteristics age, sex, education, calendar time and their interactions were used as predictors and the model accounts for trend breaks in the data. Model performance was assessed by comparing population-level predictions with observed data on which the model was calibrated (2014-2022).Results A comparison between predictions of the calibrated model and observed data shows that the prevalences of excessive (error <0.2 percent point (pp)) and heavy episodic drinking (error <0.1 pp) align, averaged over the years 2014 to 2022. Visual inspection using qq-plots and within-sample validation over time further indicates that the model fits well for predicting excessive and heavy episodic drinking, based on the predicted distribution for the weekly consumption.Conclusions We developed a model for alcohol consumption patterns based on Dutch data. This model enables evaluation of the impact of interventions on the (future) prevalence of excessive and heavy episodic drinking