Eindhoven University of Technology

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer “all or nothing” approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly.

We demonstrate an approach to integrate multiple transducers on the tip of an optical fiber, leading to multiplexed and multiparameter sensing. The sensor consists of four photonic crystals that are placed on the tip of a cleaved 125 $\mu$ m diameter four-core fiber. Each photonic crystal has a different design which ensures that the sensitivities to temperature and refractive index of the solutions are different, enabling the simultaneous measurement of temperature and concentration. The sensor was calibrated in solutions of deionized water and ethylene glycol in a temperature range of $25~{^\circ }$ C to $65~{^\circ }$ C by fitting using a two-dimensional second order polynomial. Non-linear least square estimation was applied to determine concentration and temperature based on test data. The estimation has a high coefficient of determination where both parameters have a $R^{2}\gt 0.998$ . The root mean square errors were found to be $RMSE_\mathbf {T}=0.45~^\circ$ C and $RMSE_\mathbf {c}=0.20\%$ for the temperature and concentration estimation respectively. The demonstrated sensor represents a scalable platform for multiplexed sensing on the tip of an optical fiber, with unprecedented spatial density. It could open the way to referenced biosensing to multiple analytes.

Assessment in (physical) education remains challenging, both from a pedagogical and motivational perspective. How can teachers assess students in a more motivating way while teaching in contemporary—performance grade-driven—physical education? The scarce evidence available does not provide sufficient basis to conclude that the presence of grading in itself negatively impacts students’ motivational functioning in physical education. It is important, next to grades, to inform students about goals and to provide step-by-step guidance in their learning progress. This is important because when students are well-informed about their progress in learning and aware of their effectiveness in the task at hand, judgments of the quality of their learning such as grades are potentially no surprise to these students, and therefore may not affect students’ motivational functioning negatively. This chapter aims to provide insights in the potential contributions and consequences of various functions of assessment to students’ motivation. We discuss why teachers assess in general and in physical education, and why this is challenging. Moreover, we discuss the relation between assessment and students’ motivation in general and in physical education. Finally, we end this chapter with practical recommendations and suggestions for future research.

Local energy communities (LECs) represent a collaborative approach to managing energy resources, where community members share and optimise the use of distributed energy resources (DERs). Hence, they require multiple objective functions to optimise a set of objectives, including economic, environmental, social and technical considerations while addressing the diverse interests of community members and stakeholders. Due to the complexity of LECs and the unpredictability of DERs, real‐time operations in LECs often deviate significantly from day‐ahead scheduling. To tackle these challenges, this paper presents a stochastic multi‐objective optimization framework designed to improve day‐ahead scheduling by accounting for forecasting errors in DERs. The proposed method employs advanced scenario generation techniques, including multivariate copulas and quantile forecasting, to capture uncertainties in load demand and renewable production without relying on prior distribution assumptions. The results demonstrate significant improvements in energy bill savings, grid management and user comfort, highlighting the effectiveness of the proposed optimization framework using a real‐world dataset from a living lab in the Netherlands.

Despite the substantial progress reported in the job‐crafting literature, knowledge about how proactive leaders encourage daily job‐crafting behaviours in their followers remains limited. This study explores how proactive leaders foster daily job‐crafting behaviours among their followers. Grounded in role modelling theory, we propose a multilevel dual‐process model that connects leaders' proactive personalities with followers' daily job crafting through two mechanisms: leaders' own job crafting (informative function) and their empowering behaviours (inspirational function). We further hypothesize that proactive leaders employ more empowering strategies when interacting with proactive followers. To validate these hypotheses, we collected daily diary data from 96 leader‐follower dyads over 10 consecutive workdays. The results show that proactive leaders not only engage in job crafting themselves but also exhibit empowering behaviours towards proactive followers, enhancing followers' job‐crafting activities. This indicates that the confluence of proactive traits in both leaders and followers amplifies a leadership style that emphasizes empowerment, granting followers greater autonomy in their job‐crafting endeavours.

Short association fibers (SAFs) in the superficial white matter play a key role in mediating local cortical connections but have not been well‐studied as innovations in whole‐brain diffusion tractography have only recently been developed to study superficial white matter. Characterizing SAFs and their relationship to long‐range white matter tracts is crucial to advance our understanding of neurodevelopment during the period from childhood to young adulthood. This study aims to (1) map SAFs in relation to long‐range white matter tracts, (2) characterize typical neurodevelopmental changes across these white matter pathways, and (3) investigate the relationship between microstructural changes in SAFs and long‐range white matter tracts. Leveraging a cohort of 616 participants ranging in age from 5.6 to 21.9 years old, we performed quantitative diffusion tractography and advanced diffusion modeling with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Robust linear regression models were applied to analyze microstructural features, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index (ODI). Our results reveal that both SAFs and long‐range tracts exhibit similar overall developmental patterns, characterized by negative associations of MD, AD, and RD with age and positive associations of FA, ICVF, ISOVF, and ODI with age. Notably, FA, AD, and ODI exhibit significant differences between SAFs and long‐range tracts, suggesting distinct neurodevelopmental trajectories between superficial and deep white matter. In addition, significant differences were found in MD, RD, and ICVF between males and females, highlighting variations in neurodevelopment. This normative study provides insights into typical microstructural changes of SAFs and long‐range white matter tracts during development, laying a foundation for future research to investigate atypical development and dysfunction in disease pathology.

Sweat provides a non-invasive alternative to blood draws, enabling glucose-concentration monitoring for both healthy individuals and diabetic patients. In our previous work, we demonstrated a strategy that accurately estimates blood glucose concentrations from sweat measurements. However, this method involves time-consuming simulations using a biophysical model, limiting its application to offline use. The goal of this study is to propose an approach that increases computational efficiency, thereby facilitating real-time estimation of blood glucose concentrations using sweat-sensing technology. To this end, we propose replacing the original biophysical model with the Local Density Random Walk (LDRW) model. This is justified because both models describe the pharmacokinetics of glucose transport through a convective-diffusion process. The performance of the LDRW model and the original biophysical model are compared in terms of estimation accuracy, computational efficiency, and model complexity, using seven datasets from the literature. The estimation of blood glucose concentrations using the LDRW model closely approximates that of the original model, with a root mean square difference of just 0.04 mmol/L between the two models' estimates. Remarkably, the LDRW model significantly reduces the average computational time to 2.6 s per data point, representing only 0.7% of the time required by the original method. Furthermore, the LDRW model demonstrates a smaller corrected Akaike Information Criterion value than the original method, indicating an improved balance between goodness of fit and model complexity. The proposed novel approach paves the way for the clinical adoption of sweat-sensing technology for non-invasive, real-time monitoring of diabetes. Graphical Abstract

Recent studies have used algorithm tailoring on digital platforms to provide household energy-saving advice. Such ‘recommender systems’ have successfully used the psychometric Rasch model as an advice algorithm, matching energy-saving measures in terms of their difficulty to consumers’ ability levels. While these previous studies indicated positive user experiences, tailored advice did not lead to higher savings overall; not even when also using persuasive nudges, such as displaying social norm percentages in the system. One possible reason for these results was that the system was used exploratively, allowing users to pick energy measures as they liked without tapping into goal setting or value-based motivational frames (e.g., signposts). In this study, 202 participants used and evaluated our ‘Saving Aid’ Rasch recommender system, choosing energy-saving measures they would like to perform at home. Through a 3 × 2-between subject design, we examined whether guided goal setting and signposts (kWh/Euro/CO2) affected user experience and energy savings. Following the signpost literature, we examined the moderation of these effects by user values, such as environmental concern (New Environmental Paradigm (NEP) score). A structural equation model analysis revealed that goal setting did not affect outcome variables, while signpost framing had varying effects, although these were not in line with prior expectations. Still, the overall system remains promising, with users achieving a 316 kWh yearly savings with the chosen recommendations.

Advances in 3D bioprinting have opened new possibilities for developing bioengineered muscle models that can mimic the architecture and function of native tissues. However, current bioengineering approaches do not fully recreate the complex fascicle-like hierarchical organization of the skeletal muscle tissue, impacting on the muscle maturation due to the lack of oxygen and nutrient supply in the scaffold inner regions. A key challenge is the production of precise and width-controlled independent filaments that do not fuse during the printing process when subsequently extruded, ensuring the formation of fascicle-like structures. This study addresses the limitation of filament fusion by utilizing a pluronic-assisted co-axial 3D bioprinting system (PACA-3D) creates a physical confinement of the bioink during the extrusion process, effectively obtaining thin and independent printed filaments with controlled shapes. The use of PACA-3D enabled the fabrication of skeletal muscle-based bioactuators with improved cell differentiation and significantly increased force output, obtaining 3 times stronger bioengineered muscle when compared to bioactuators fabricated using conventional 3D extrusion bioprinting techniques, where a single syringe containing the bioink is used. The versatility of our technology has been demonstrated using different biomaterials, demonstrating its potential to develop more complex biohybrid tissue-based architectures with improved functionality, as well as aiming for better scalability and printing flexibility.

Neutron interactions in a fusion power plant play a pivotal role in determining critical design parameters such as coil-plasma distance and breeding blanket composition. Fast predictive neutronic capabilities are therefore crucial for an efficient design process. For this purpose, we have developed a new deterministic neutronics method, capable of quickly and accurately assessing the neutron response of a fusion reactor, even in three-dimensional geometry. It uses a novel combination of arbitrary-order discontinuous Galerkin spatial discretization, discrete-ordinates angular and multigroup energy discretizations, arbitrary-order anisotropic scattering, and matrix-free iterative solvers, allowing for fast and accurate solutions. One, two, and three-dimensional models are implemented. Cross sections can be obtained from standard databases or from Monte-Carlo simulations. Benchmarks and literature tests were performed, concluding with a successful blanket simulation.

The sputtering and transport of tungsten (W) impurity in the EAST tokamak have been investigated by the nonlinear magnetohydrodynamic code JOREK. The hybrid kinetic-fluid model in JOREK enables us to study the impacts of the Larmor gyration, sheath acceleration and, W sputtering energy and D⁺ impinging energy on the W sputtering and transport, which are generally simplified and ignored in fluid transport codes. The simulated W gross erosion flux exhibits a reasonable agreement with the measured data obtained through spectroscopy diagnostics on EAST. By means of the kinetic model in JOREK, it is indicated that the gyration and sheath effects can enhance the W redeposition probability on divertor targets by around three times compared to the fluid treatment. Moreover, the Thompson energy distribution for sputtered W particles has been attempted to survey the influence of the W sputtering energy on the W transport and redeposition, which shows a small discrepancy in the mean free path and redeposition probability of W particles compared to the case with a fixed sputtering energy. The detailed analysis of the W sputtering under the Maxwellian velocity distribution has been conducted, revealing significantly higher W erosion and leakage compared to the monoenergetic case. Eventually, the combined effects of the Larmor gyration, sheath acceleration, W sputtering energy and D⁺ impinging energy on W transport and redeposition behaviors have been investigated under varying plasma scenarios. It is found that the prompt redeposition of W particles plays a dominant role in the entire W redeposition compared to the long-range redeposition.

Objectives Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the ECG is challenging. The aim of this study is to develop and validate a convolutional neural network (CNN)-based model for risk stratification of suspected NSTE-ACS patients and to compare its performance with currently available prehospital diagnostic tools. Methods For this study, an internal training cohort and an external validation cohort were used, both consisting of suspected NSTE-ACS patients. A CNN (ECG interpretation by CNN (ECG-AI)) was trained and validated to detect NSTE-ACS. The diagnostic value of ECG-AI in detecting NSTE-ACS was compared with on-site ECG analyses by an EMS paramedic (ECG-EMS), point-of-care troponin assessment and a validated prehospital clinical risk score (prehospital History, ECG, Age, Risk factors and POC-troponin (preHEART)). Results A total of 5645 patients suspected of NSTE-ACS were included. In the external validation cohort (n=754), 27% were diagnosed with NSTE-ACS. ECG-AI had a better diagnostic performance than ECG-EMS (area under the curve (AUROC) 0.70 (0.66 to 0.74) vs AUROC 0.65 (0.61 to 0.70), p=0.045) for diagnosing NSTE-ACS. The overall diagnostic accuracy of preHEART was AUROC 0.78 (0.74 to 0.82) and superior compared with ECG-AI (p=0.001). Incorporating ECG-AI into preHEART led to a significant improvement in diagnostic performance (AUROC 0.83 (0.79 to 0.86), p<0.001). Discussion Correctly identifying patients who are at low risk for having NSTE-ACS is crucial for optimal triage in the prehospital setting. Recent studies have shown that these low-risk patients could potentially be left at home or transferred to a general practitioner, leading to less emergency department overcrowding and lower healthcare costs. Other studies demonstrated better overall diagnostic performance compared with our artificial intelligence (AI) model. However, these studies were aimed at a study population with a high prevalence of occlusive myocardial infarction, which could explain the differing levels of diagnostic performance. Conclusion Integrating AI in prehospital ECG interpretation improves the identification of patients at low risk for having NSTE-ACS. Nonetheless, clinical risk scores currently yield the best diagnostic performance and their accuracy could be further enhanced through AI. Our results pave the way for new studies focused on exploring the role of AI in prehospital risk-stratification efforts.

A novel [(NHC)2Cu]Br (Cu(I)-NHC) complex promotes CuAAC reactions in organic solvents, water, buffers and even in complex biological media in good yields. Encapsulation of the Cu catalyst in an amphiphilic...

Plasma-based nitrogen fixation presents a promising alternative to conventional methods for NOx synthesis, with gliding arc reactors demonstrating high efficiency under ambient conditions. Current-limiting resistors (CLRs) are commonly used in experimental research to ensure stable discharge operation; however, most reported studies focus solely on reactor performance, overlooking the impact of these resistors on the overall process. This study systematically investigates the influence of resistors in the circuit on NOx concentration and energy consumption (EC) in a 2D gliding arc system. Three CLRs and two current viewing resistors were tested, and the case with a 20 kΩ CLR achieved the highest NOx concentration of 5.80 vol%. A key reason is that the CLRs maintained a stable glow-like discharge regime, suppressing undesired transitions of the discharge mode in a gliding arc. Optical emission spectroscopy measurements indicated that increased CLR values reduced the electron density and plasma temperature, potentially explaining variations in the achieved NOx concentration. Additionally, a significant disparity in EC was observed when accounting for total dissipated power, leading to a maximum EC increase of 5.65 MJ molN–1. These findings highlight the need to report the EC of both the reactor and CLR when evaluating plasma-based NOx synthesis efficiency.

Collaborative innovation is increasingly employed to address grand societal challenges, including regional energy transition. However, the approach remains fraught with challenges. Bringing diverse stakeholders together in iterative, time-intensive innovation processes often triggers conflicts, requiring trade-offs between slower, participatory decision-making and faster, efficiency-driven approaches to meet deadlines. While prior research has identified key drivers, barriers, and best practices, the inherent dynamic complexity of collaborative innovation remains underexplored. Traditional linear models, which assume unidirectional causality among aspects like conflict, decision-making speed, and time pressure, overlook their nonlinear interactions, necessitating a systems perspective. Drawing on rich qualitative data from a longitudinal case study, we address this gap by constructing a causal loop model to analyze the behavioural patterns and processual dynamics of a collaborative innovation initiative for the regional energy transition. Our model captures how the interplay between conflict, decision speed, and time pressure can both facilitate and frustrate collaborative innovation. For example, reducing participation and avoiding conflicts may initially expedite decisions but inadvertently escalate conflicts. Similarly, deadlines that foster deliberation and efficiency can result in premature consensus. We conceptualize this sustained tension between participation and efficiency—manifesting as sequences of ‘fixes-that-fail’—as a balancing act rather than an either-or dilemma. By integrating insights from planning and organizational theories, our study advances the understanding of collaborative innovation and offers practical guidance for stakeholders navigating its complexities.

A low pressure discharge sustained in molecular hydrogen with help of the electron cyclotron resonance heating at a frequency of 2.45 GHz is simulated using a fully electromagnetic implicit charge- and energy-conserving particle-in-cell/Monte Carlo code. The simulations show a number of kinetic effects, and the results are in good agreement with various experimentally measured data such as electron density, electron temperature and degree of dissociation. The electron energy distribution shows a tri-Maxwellian form due to a number of different electron heating mechanisms, agreeing with the experimental data in the measured electron energy interval. The simulation results are also compared with output data of a drift-diffusion model and proximity is observed between the computational results for the plasma density at the location of experimental measurement. However, the fluid approximation fails to accurately predict radical density and electron temperature because of the assumption of a single electron temperature. Special attention is paid to the characteristics of hydrogen radicals, whose production is strongly underestimated by the fluid model, whereas it is well predicted by the model considered here. The energy distribution of such radicals demonstrates the presence of a relatively large number of energetic hydrogen atoms produced by the dissociation of molecular hydrogen. The new insights are of significance for practical applications of hydrogen plasmas.

Protoplast regeneration into plant cells and further into plants is an ongoing challenge in agricultural biotechnology. Inspired by mammalian tissue engineering, a strategic shift is proposed in plant tissue engineering to steer protoplast culture using fully synthetic materials‐based culture platforms. Here a supramolecular materials method to engineer modular culture methods for protoplasts is chosen to use. Supramolecular monomers as modular building blocks allow to make various hydrogel formulations and to study different protoplast cultures; including 2D cultures on top of supramolecular hydrogels, 2.5D cultures using supramolecular fibers in solution, and 3D cultures when encapsulated in bulk hydrogels or microgels. Importantly, the need is shown for bioactive functionalization of the supramolecular hydrogels with a peptide additive in 2D protoplast cultures. After 11 days, the bioactive hydrogel induced protoplast enlargement, which is absent on pristine hydrogels. The opposite effect is present for protoplasts cultured in 3D, showing plasmolysis as a result of the bioactive additive. Interestingly, in 2.5D lower bioactive additive concentrations in supramolecular fibers stimulated protoplast enlargement, demonstrated by similar morphological changes as in 2D. Finally, protoplast encapsulation in supramolecular microgels is showcased. This work demonstrates the potential to modularly engineer various synthetic platforms to facilitate cellular agriculture.

The implementation of Industry 4.0 technologies, especially big data infrastructures, enables easy access to large amounts of data from all stages of semiconductor manufacturing. In this study, we propose a novel methodology to reduce a large amount of sensor data collected from wire bonding machines, called machine signals, into a set of interpretable features relevant to the wire bond quality of chips. The methodology is applied to real-world operational machine signal data from the wire bonding process at NXP Semiconductors N.V. During the wire bonding process, several sensors actively monitor the process, generating signals as discrete multivariate time-series for each semiconductor device. The proposed methodology consists of the following steps. First, we extract features from the discrete multivariate time-series and train a baseline model with all features. Second, we use permutation feature importance to rank the relevance of signals and corresponding features with the goal of identifying the optimal signal and feature set. Finally, the classification performance when using the optimal signal and feature set is compared to the performance of the baseline model. We conclude that the dimensionality of the data can be significantly reduced without losing classification performance. The reduced dimensionality leads to highly interpretable classification results in a real-world wire bonding use case.