Eindhoven University of Technology

The global hospitality industry is fast-turning sustainable and environmentally friendly. Behaviour-driven energy conservation is an emerging green hotel operation strategy to support this change. The long-stay accommodation services have gained momentum in the hospitality sector since the COVID-19 pandemic. However, the characteristics of long-stay hotel guests are often overlooked in sustainable interventions. Based on an empirical survey in China, this study aims to explore the factors driving energy-saving behaviours of long-stay hotel guests and to compare their effects on guests for different visiting purposes (leisure, business, and extended-stay resident). The analysis indicates that attitude, personal norm and place attachment present a direct contribution to energy-saving behaviour. Besides, the results support that attitude and personal norm connect environmental values and energy-saving behaviour. Both altruistic and biospheric values have positive effects, while egoistic values seem to play a negative role. Biospheric values have stronger impact on attitude and personal norm of business guests. Place attachment has a stronger influence on extended-stay residents while its contribution to energy-saving behaviours of business guests is smaller than other guests. Besides, leisure guests are more sensitive to moral obligations. This research sheds novel lights on the psychological perspectives of the observed heterogeneity of energy-saving behaviours of hotel guests with different visiting purposes. The findings provide hotel operators with a novel theoretical reference for targeted energy-saving interventions to promote energy-saving actions of long-term hotel guests. The study, therefore, can contribute to sustainable tourism policymaking and behaviour-driven hotel energy management.

Triangle count is a frequently used network statistic, possessing high computational cost. Moreover, this task gets even more complex in the case of signed networks which consist of unbalanced and balanced triangles. In this work, we propose a fast I ncremental T riangle C ounting (ITC) algorithm for counting all types of triangles, including balanced and unbalanced. The proposed algorithm updates the count of different types of triangles for newly added nodes and edges only instead of recalculating the same triangle multiple times for the entire network repeatedly. Thus, the proposed ITC algorithm also works for dynamic networks. The experimental results show that the proposed method is practically efficient having run time complexity of $O(m k_{\text{max}})$ , where $m$ represents the number of edges and $k_{\text{max}}$ represents the maximum degree of the given signed network.

The slowing-down of Moore’s law is shifting the computing paradigm towards solutions based on quantum and neuromorphic computing elements. Unlike conventional digital computing, neuromorphic computing is based on analog devices. In this work, we propose a three-terminal neuromorphic organic device () capable of providing both analog computing and memory in a single device by tuning its conductance. The availability of three-terminal devices enables the independent tuning of the, preventing write sneak path issues typical of the two-terminal memristor crossbar array. The conductance relaxes exponentially with a measured time constant of 2.9 h, furthermore, it can be operated at 51 MHz, corresponding to an estimated energy efficiency of 0.1 pJ per multiply-accumulate (MAC) operation. To demonstrate the ’s computing capabilities, a 3 $\times$ 3 crossbar array has been successfully used to perform edge detection and blurring on an image with 128 $\times$ 64 pixels.

Atrial fibrillation (AF) is an insidious disease. Many long-term wearable electrocardiogram (ECG) monitoring devices have been used to monitor AF. The accuracy of detectors used to classify AF/sinus rhythm is already very high on the public database. Due to the significant individual differences and interference from other arrhythmias (e.g., premature beats), the performance of the developed AF detectors can degrade when tested on wearable ECGs. To tackle this, we proposed to use domain-adversarial (DA) learning strategy to minimize feature distribution between the annotated public ECG database (the MIT-BIH AF database) and unlabeled dynamic ECG recordings to improve AF recognition accuracy. DA algorithms based on the shifted window Transformer (DA-ST) and residual neural network (DA-RN) were proposed and validated on the 2021 China Physiological Signal Challenge (CPSC) database including four datasets. The accuracies were 93.85%, 89.78%, 91.93%, and 87.35% on the four datasets when using DA-ST. The corresponding results were 96.67%, 92.25%, 90.58%, and 89.46% when utilizing DA-RN. Importantly, these results demonstrated superior performance compared to the results obtained without DA. The proposed method was validated on 12 wearable long-term recordings, consisting of 4 recordings with premature beats, 4 recordings of AF with premature beats, 2 recordings of sinus rhythms, and 2 recordings of AF. The average results were 98.67% (DA-ST) and 97.89% (DA-RN), proving that the proposed method could provide reliable AF detection for dynamic ECG recordings with significant individual differences.

Objectives To quantify inconsistent self-reporting of chronic conditions between the baseline (2011–2015) and first follow-up surveys (2015–2018) in the Canadian Longitudinal Study on Aging (CLSA), and to explore methods to resolve inconsistent responses and impact on multimorbidity. Methods Community-dwelling adults aged 45–85 years in the baseline and first follow-up surveys were included ( n = 45,184). At each survey, participants self-reported whether they ever had a physician diagnosis of 35 chronic conditions. Identifiable inconsistent responses were enumerated. Results 32–40% of participants had at least one inconsistent response across all conditions. Illness-related information (e.g., taking medication) resolved most inconsistent responses (>93%) while computer-assisted software asking participants to confirm their inconsistent disease status resolved ≤53%. Using these adjudication methods, multimorbidity prevalence at follow-up increased by ≤1.6% compared to the prevalence without resolving inconsistent responses. Discussion Inconsistent self-reporting of chronic conditions is common but may not substantially affect multimorbidity prevalence. Future research should validate methods to resolve inconsistencies.

Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations. This paper builds on quantum recurrent neural networks (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models that preserve invariance to time-warping transformations of the (classical) input-output sequences. The model, referred to as time warping-invariant QRNN (TWI-QRNN), augments a QRNN with a quantum-classical adaptive gating mechanism that chooses whether to apply a parameterized unitary transformation at each time step as a function of the past samples of the input sequence via a classical recurrent model. The TWI-QRNN model class is derived from first principles, and its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.

This paper presents a data pre-processing algorithm to tackle class imbalance in classification problems by undersampling the majority class. It relies on a formalism termed Presumably Correct Decision Sets aimed at isolating easy (presumably correct) and difficult (presumably incorrect) instances in a classification problem. The former are instances with neighbors that largely share their class label, while the latter have neighbors that mostly belong to a different decision class. The proposed algorithm replaces the presumably correct instances belonging to the majority decision class with prototypes, and it operates under the assumption that removing these instances does not change the boundaries of the decision space. Note that this strategy opposes other methods that remove pairs of instances from different classes that are each other’s closest neighbors. We argue that the training and test data should have similar distribution and complexity and that making the decision classes more separable in the training data would only increase the risks of overfitting. The experiments show that our method improves the generalization capabilities of a baseline classifier, while outperforming other undersampling algorithms reported in the literature.

In assemblies of identical molecules or chromophores, electronic excitations can be described as excitons, bound electron‐hole pairs that can move from site to site as a pair in a coherent manner. The understanding of excitons is crucial when trying to engineer favorable photophysical properties through structuring organic molecular matter. In recent decades, limitations of the concept of an exciton have become clear. The exciton can hybridize with phonon and photons. To clarify these issues, the exciton is discussed within the broader context of the gauge properties of the electromagnetic force.

Stimulating plastic waste valorisation is suggested as an important way to address the growing waste problem in low‐income countries. However, policy interventions have not led to substantial waste valorisation, and the reasons for this have not been thoroughly analysed. We address this through a qualitative study of plastic waste in urban Zambia, which is representative of the policy and practice challenges in African plastic waste management. Using extensive data gathered through interviews, site visits and stakeholder meetings, we first conduct a business ecosystem analysis which provides a holistic view on value creation, capture, and destruction processes across all actors involved in the plastics lifecycle. Next, we map the barriers to value creation and capture by the system's main actors. Aggregation of these barriers reveals a low‐value trap, in which individual actors are disincentivized to increase waste valorisation activities. Finally, we analyse the reasons why policies aimed at waste valorisation have failed to break through this status quo. We find that policies have insufficiently addressed the barriers that keep the low‐value trap in place. Hence, they have not acted effectively on the root causes of systemic stagnation. By combining a business ecosystems analysis with an identification of barriers facing the individual actors in that ecosystem, our study is able to show why substantial plastic waste valorisation has not emerged despite policy incentives. Our analysis points toward concrete policy actions aimed at value redistribution and value increase, as key leverage points in the system to increase valorisation.

Here we report on the synthesis of discrete oligomers of alkyl‐bridged naphthalenediimides (NDIs) and study their molecular nanostructures both in bulk, in solution and at the solid‐liquid interface. Via an iterative synthesis method, multiple NDI cores were bridged with short and saturated alkyl‐diamines (C3 and C12) or long and unsaturated alkyl‐diamines (u2C33 to u8C100) at their imide termini. The strong intermolecular interaction between the NDI cores was observed by probing their photophysical properties in solution. In bulk, the discrete NDI oligomers preferentially ordered in lamellar morphologies, irrespective of whether a saturated or unsaturated spacer was employed. Moreover, both the molecular architecture as well as the crystallization conditions play a significant role in the nanoscale ordering. The long unsaturated alkyl chains lead preferably to folded‐chain conformations while their saturated analogues form stretched arrangements. At the solution‐solid interface, well‐defined lamellar regions were observed. These results show that precision in chemical structure alone is not sufficient to reach well‐defined structures of discrete oligomers, but that it must be combined with precision in processing conditions.

Objective After lowering the Dutch threshold for active treatment from 25 to 24 completed weeks’ gestation, survival to discharge increased by 10% in extremely preterm live born infants. Now that this guideline has been implemented, an accurate description of neurodevelopmental outcome at school age is needed. Design Population-based cohort study. Setting All neonatal intensive care units in the Netherlands. Patients All infants born between 24 0/7 and 26 6/7 weeks’ gestation who were 5.5 years’ corrected age (CA) in 2018–2020 were included. Main outcome measures Main outcome measure was neurodevelopmental outcome at 5.5 years. Neurodevelopmental outcome was a composite outcome defined as none, mild or moderate-to-severe impairment (further defined as neurodevelopmental impairment (NDI)), using corrected cognitive score (Wechsler Preschool and Primary Scale of Intelligence Scale-III-NL), neurological examination and neurosensory function. Additionally, motor score (Movement Assessment Battery for Children-2-NL) was assessed. All assessments were done as part of the nationwide, standardised follow-up programme. Results In the 3-year period, a total of 632 infants survived to 5.5 years’ CA. Data were available for 484 infants (77%). At 5.5 years’ CA, most cognitive and motor (sub)scales were significantly lower compared with the normative mean. Overall, 46% had no impairment, 36% had mild impairment and 18% had NDI. NDI-free survival was 30%, 49% and 67% in live born children at 24, 25 and 26 weeks’ gestation, respectively (p<0.001). Conclusions After lowering the threshold for supporting active treatment from 25 to 24 completed weeks’ gestation, a considerable proportion of the surviving extremely preterm children did not have any impairment at 5.5 years’ CA.

During evolution, animals have returned from land to water, adapting with morphological modifications to life in an aquatic environment. We compared the osteochondral units of the humeral head of marine and terrestrial mammals across species spanning a wide range of body weights, focusing on microstructural organization and biomechanical performance. Aquatic mammals feature cartilage with essentially random collagen fiber configuration, lacking the depth-dependent, arcade-like organization characteristic of terrestrial mammalian species. They have a less stiff articular cartilage at equilibrium with a significantly lower peak modulus, and at the osteochondral interface do not have a calcified cartilage layer, displaying only a thin, highly porous subchondral bone plate. This totally different constitution of the osteochondral unit in aquatic mammals reflects that accommodation of loading is the primordial function of the osteochondral unit. Recognizing the crucial importance of the microarchitecture-function relationship is pivotal for understanding articular biology and, hence, for the development of durable functional regenerative approaches for treatment of joint damage, which are thus far lacking.

Behavior-Driven Development (BDD) refers to an agile development practice to express the fulfillment of a requirement often depicted in a user story. BDD is meant to facilitate the understanding of how to properly execute requirements among role-divergent stakeholders in a software project. In that way, the development team avoids an excessive focus on coding at the early requirements definition stage and can focus on truly capturing the features and behaviors that are expected by the end-users. In BDD, user-driven scenarios are written in structured natural language following a defined template. Notwithstanding, not much attention has been placed in the literature in terms of defining/studying the quality aspects of the written BDD scenarios; therefore, practitioners tend to use the technique in an ad-hoc manner. In this study, we explore the quality attributes assigned to a well-written BDD scenario. We refine an existing framework by establishing formal definitions for each of the scenarios’ attributes, study their applicability through real BDD scenarios, and link them to the quality attributes appointed to user stories. We then develop and present an experimental Computer-Aided Software Engineering (CASE) tool that helps practitioners assess the quality of the BDD scenarios through the automated evaluation of a set of conforming quality attributes namely Uniqueness, Essentiality, Integrity, and Singularity. We further validate the framework and the tool by collecting two expert opinions.

This work explains the anomalously high runaway electron (RE) pitch angles inferred in the flat-top of dedicated Tokamak à Configuration Variable (TCV) experiments. Kinetic modelling shows that the resonant interaction between the gyromotion of the electrons and the toroidal magnetic field ripple will give rise to strong pitch angle scattering in TCV. The resulting increase in synchrotron radiation power losses acts as a RE energy barrier. These observations are tested experimentally by a magnetic field ramp-down which gradually reduce the resonant parallel momentum at which the REs interact with the ripple. Resulting changes in synchrotron emission geometry and intensity are observed using three multi-spectral camera imaging systems, viewing the RE beam at distinct spatial angles in multiple wavelength ranges. Experimental reconstructions of the RE distribution in momentum- and real-space are consistent with kinetic model predictions.

Phonon polaritons are promising for infrared applications due to a strong light-matter coupling and subwavelength energy confinement they offer. Yet, the spectral narrowness of the phonon bands and difficulty to tune the phonon polariton properties hinder further progress in this field. SrTiO3 – a prototype perovskite oxide - has recently attracted attention due to two prominent far-infrared phonon polaritons bands, albeit without any tuning reported so far. Here we show, using cryogenic infrared near-field microscopy, that long-propagating surface phonon polaritons are present both in bare SrTiO3 and in LaAlO3/SrTiO3 heterostructures hosting a two-dimensional electron gas. The presence of the two-dimensional electron gas increases dramatically the thermal variation of the upper limit of the surface phonon polariton band due to temperature dependent polaronic screening of the surface charge carriers. Furthermore, we demonstrate a tunability of the upper surface phonon polariton frequency in LaAlO3/SrTiO3 via electrostatic gating. Our results suggest that oxide interfaces are a new platform bridging unconventional electronics and long-wavelength nanophotonics.

Porous electrodes are performance-defining components in electrochemical devices, such as redox flow batteries, as they govern the electrochemical performance and pumping demands of the reactor. Yet, conventional porous electrodes used in redox flow batteries are not tailored to sustain convective-enhanced electrochemical reactions. Thus, there is a need for electrode optimization to enhance the system performance. In this work, we present an optimization framework to carry out the bottom-up design of porous electrodes by coupling a genetic algorithm with a pore network modeling framework. We introduce geometrical versatility by adding a pore merging and splitting function, study the impact of various optimization parameters, geometrical definitions, and objective functions, and incorporate electrode structures and flow field with well-defined geometries. Moreover, we show the need for optimizing electrodes for specific reactor architectures and operating conditions to design next-generation electrodes, by analyzing the genetic algorithm optimization for initial starting geometries with diverse morphologies (cubic and a tomography-extracted commercial electrode), flow field designs (flow-through and interdigitated), and redox chemistries (VO2+/VO2+ and TEMPO/TEMPO+). We found that for kinetically sluggish electrolytes with high ionic conductivity, electrodes with numerous small pores and high internal surface area provide enhanced performance, whereas for kinetically facile electrolytes with low ionic conductivity, low through-plane tortuosity and high hydraulic conductance are required. The computational tool developed in this work can guide the design of high-performance electrode materials for a broad range of operating conditions, electrolyte chemistries, reactor designs, and electrochemical technologies.