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Sea-level rise was a key factor changing environments during the Early Holocene in Northwest Europe. It affected Mesolithic hunter-gatherer communities by inundating large areas in the current North Sea, commonly referred to as Doggerland. In this paper we present novel inundation models for the southern North Sea providing visualisations of lateral inundation driven by sea-level rise and relate it to frequency analysis of radiocarbon dates from archaeological sites. These improve on previous studies that relied on bathymetric data, which includes post-inundation overprints of Holocene sedimentation and erosion, and thus significantly underestimates the timing of inundation in some areas. We constructed a paleoDEM (a composite elevation grid of the top of the Pleistocene) for the eastern part of the southern North Sea; and sea level surfaces that combine relative sea-level curves from glacio-isostatic adjustment models optimised for Britain and southern Scandinavia respectively. We corrected our paleoDEMs for tectonic background basin subsidence, and in the inundation modelling account for pre-compaction elevation of peat in coastal areas. We evaluated the impact of these model components on our results and describe the possible inundation history of Doggerland. We suggest earlier inundation than predicted by previous models, showing significant area loss around 10.5–10 ka cal BP. Palaeogeographic changes are compared with archaeological radiocarbon data using a dates-as-data approach. Composite Kernel Density Estimate and permutation tested Summed Probability Distributions are used as a proxy for the visibility, nature and intensity of human activity. Results indicate key periods of growth and decline recorded in the dataset, as well as regional differences in growth rate, some correlating with inundation phases. Chiefly, we find elevated growth rates around 10.5–10 ka in northwest Germany and the Netherlands, contemporaneous with the abovementioned phase of extensive area loss, and moreover, with changes in culture and practices on Early Mesolithic sites. However, the spatiotemporal distribution of archaeological data is significantly influenced by accessibility and preservation of sediments of a certain age. We discuss the importance of inundation modelling and sediment data in understanding how landscape taphonomy affects archaeological patterning, especially in dates-as-data approaches. [ABSTRACT FROM AUTHOR]

The REpowerEU plan prioritizes combining electrification with low-carbon hydrogen for a green energy shift. However, uncertainties surrounding the future hydrogen market and its supply costs impede the establishment of its value chain. Without definite plans for hydrogen production and infrastructure, investing in low-carbon hydrogen industrial processes is risky. Simultaneously, the absence of solid market interest poses challenges in deploying a hydrogen backbone. In recent years, European gas Transmission System Operators (TSOs) in Belgium, Netherlands, and Germany, have introduced various roadmaps for their prospective national hydrogen network. Despite the infrastructure proposals, detailed quantitative scenario analysis for the future hydrogen supply chain is still missing. This paper presents a hydrogen infrastructure model that employs Mixed Integer Linear Programming (MILP) for investment and operational cost optimization at detailed spatiotemporal resolution. A regionalization method is proposed to allocate the potential hydrogen consumption in different industrial sectors across various clusters within a country, represented as the prospective hydrogen demand nodes. The model assesses extreme supply scenarios to examine the robustness of the resulting network infrastructure and compare the system-levelized cost of hydrogen. A real-life case study focusing on the potential Belgian hydrogen supply chain showcases the model's capabilities and outputs. • Regionalization of the industrial hydrogen consumption derived from a national-scale energy system model. • MILP cost optimization model for hydrogen infrastructure design. • Comparative analysis of diverse extreme low-carbon hydrogen supply scenarios. • In-depth case study focusing on the prospective Belgian hydrogen backbone. [ABSTRACT FROM AUTHOR]

To clarify and elaborate on the choices that were made in the development of the Patient Scale of the Patient and Observer Scar Assessment Scale 3.0 (POSAS 3.0), based upon the rich information obtained from patients during focus groups and pilot tests. The discussions described in this paper are a reflection of the focus group study and pilot tests that were conducted in order to develop the Patient Scale of the POSAS3.0. The focus groups took place in the Netherlands and Australia and included 45 participants. Pilot tests were performed with 15 participants in Australia, the Netherlands, and the United Kingdom. We discussed the selection, wording and merging of 17 included items. Additionally, the reason for exclusion of 23 characteristics are given. Based upon the unique and rich material of patient input obtained, two versions of the Patient Scale of the POSAS3.0 were developed: the Generic version, and the Linear scar version. The discussions and decisions taken during the development are informative for a good understanding of the POSAS 3.0 and are indispensable as a background for future translations and cross-cultural adaptations. • The Patient Scale was meant to be concise, readily assessable, quick, and user-friendly instrument assessing the key attributes of scar quality. • Two versions have been developed, the Generic version (total of 16 items), and the Linear scar version (total of 17 items). • Eight of the identified characteristics were merged into three included items. • Twenty-three characteristics were excluded during different phases of the development process for multiple reasons. [ABSTRACT FROM AUTHOR]

Hydraulic structures can be a promising place for tidal energy extraction due to the high flow velocities, easy access to the power grid and easy access for maintenance. However, quantification of the impacts of a tidal power plant in a hydraulic structure is not straight forward. In 2015 a pilot plant consisting of an array of five Tocardo tidal turbines was installed in the Eastern Scheldt Storm Surge Barrier in the Netherlands. This pilot was accompanied by monitoring studies to verify that the operation of the plant had no adverse impact on the barrier and its surroundings. This paper presents the assessment of the hydraulic impact of the tidal power plant in the storm surge barrier based on an analysis of water level and current measurements, combined with numerical modeling and followed by an assessment of the environmental impact with emphasis on the effects on the intertidal areas in the estuary. This validation approach by a pilot plant is imperative to understand the interaction between tidal turbines and the hydraulic structure on the local scale. This understanding gives extra credibility to the predictions of the extrapolated large-scale and large array assessments which will always be fully numerical. [ABSTRACT FROM AUTHOR]

The spatial concentration of services and facilities poses a challenge to providing sufficient social and economic opportunities in rural areas. Accessibility evaluations based on spatial data potentially overlook individual heterogeneity in needs, desires and abilities, leading to mismatches with how accessibility is perceived. Using data from a self-administered survey in the Netherlands, this paper examines individual factors that explain differences between accessibility to daily activities calculated from spatial data and perceived levels of accessibility in rural areas. Accessibility is perceived as high on average, while 12% of the population is estimated to experience low levels of accessibility at least to some extent. Perceived accessibility correlates poorly with spatial accessibility, but is rather moderated by individual factors. Car mobility appears to be a major contributor to perceiving sufficient levels of accessibility in rural areas. However, the lowest levels of perceived accessibility seem to be more associated with social disadvantages than with transport disadvantages. The integration of relatively easy-to-obtain individual characteristics into spatial accessibility measurements may already significantly close the gap with perceived accessibility. With more explicit consideration of the individual dimension when constructing or interpreting accessibility indicators based on spatial data, the validity of their use in planning practice will increase and therefore facilitate successful accessibility-based planning. • Aggregated spatial accessibility measures likely overlook inequalities in perceived accessibility. • Integrating relatively easily obtainable individual factors may already significantly close the gap between spatial and perceived accessibility. • 12% of the Dutch rural population is estimated to experience low levels of accessibility at least to some extent. • The lowest levels of perceived accessibility in Dutch rural areas are more related to social than to transport disadvantages. [ABSTRACT FROM AUTHOR]

This paper highlights the evolution and impact of the SWAP model (Soil – Water – Atmosphere – Plant), which was initiated by R.A. Feddes and colleagues fifty years ago, in 1974. Since then, the SWAP model has played a crucial role in the advancement of agrohydrology. This paper highlights some major advances that have been made, especially focussing on the last fifteen years. The domain of the SWAP model deals with the simulation of the soil water balance in both unsaturated and saturated conditions. The model solves the Richards equation using the water retention and hydraulic conductivity functions as described by the Van Genuchten – Mualem equations. Bimodal extensions of the Van Genuchten - Mualem relationships have been implemented, as well as modifications near saturation and addressing hysteresis. An important sink term in the Richards equation is root water uptake. Crop development plays an important role in a robust simulation of root water uptake. That is why a link has been made with the dynamic crop growth model WOFOST. Instead of using a prescribed crop development, a distinction between potential and actual crop development is calculated by reducing the potential photosynthesis as a result of water or oxygen stress. Since the early days of SWAP, empirical and macroscopic concepts have been used to simulate root water uptake. Recently two process-based concepts of root water uptake and oxygen stress have also been implemented. Another important sink-source term in the Richards equation is the interaction with artificial drains. In SWAP, drainage can be simulated by either using prescribed or simulated drain heads and simulation of controlled drainage with subirrigation is possible. Finally, we briefly elaborate on three studies using SWAP: water stresses in agriculture in the Netherlands, regional water productivity in China, and controlled drainage with subirrigation. We finish discussing promising developments for the near future. • Advances in soil-vegetation-atmosphere transfer modelling in the SWAP model. • Coupling of soil water dynamics with dynamic crop growth model. • Process-based root water uptake and oxygen uptake functions. • Climate-adaptive drainage with subirrigation. [ABSTRACT FROM AUTHOR]

• We proposed a delineating field parcel model (TSANet) based on satellite image time series. • TSANet learn the relevance of spatial-spectral-temporal feature representation. • TSANet is robust across space and time. • TSANet performs better than pervious methods. • The time series data from the main growing period has a greater impact. Satellite image time series (SITS), such as Sentinel-2 imagery, plays a crucial role in the delineation of agricultural fields by reducing the impacts of ambiguities due to the spatial arrangement of field boundaries. Existing delineate field parcel models rely extensively on spatial features derived from single-date imagery. However, several studies have exploited the potential of SITS to effectively tackle the complexities associated with the intrinsic consistency between agricultural fields and their boundaries. This paper proposes a novel Two-Stream Attention convolutional neural network (TSANet) to capture the subtle difference between agricultural fields from SITS. Specifically, a field temporal semantic stream is introduced to adaptively leverage the significance of spatial-spectral-temporal feature representation associated with the location of agricultural parcels, especially where transitions in crop types take place. Considering the consistency between field parcels and their boundaries, we developed a field boundary prediction stream to enhance the extraction of edge features, particularly for the extraction of small and irregular agricultural parcels. Moreover, a field parcel refining block is employed to further enhance the geometric accuracy of agricultural fields. We conducted experiments on Sentinel-2 images from the Netherlands. Results showed that our approach produced a better layout of agricultural fields, with an average F1-score of 0.91 than the existing 3D-UNet, U-TEA, and BiConvLSTM. In addition, through the analysis of both quantitative and qualitative results, the stronger robustness of the model compared to other algorithms has been verified by temporal transfer and large-scale spatial prediction. We compared the difference between SITS and the corresponding composite images, which further verified the influence of temporal variation on the proposed approach. This paper provides a general guide for delineating agricultural parcels using SITS. [ABSTRACT FROM AUTHOR]

This paper examines the neighbourhood-level spatial determinants of residential solar photovoltaic (PV) adoption in the Netherlands. We investigate the roles of place-specificities and spatial interaction effects by utilising high-resolution spatial data on 13,205 Dutch neighbourhoods. Unlike other studies, we use impact measures on the basis of a spatial Durbin model to distinguish between direct and indirect effects of the spatial determinants. We find significant neighbourhood-level spatial interaction effects in residential solar PV adoption. Where there is a spatial dependence, household income, property value, population density, housing type and household type are among the factors that influence residential solar PV adoption. • Spatial factors that influence residential solar photovoltaic (PV) adoption are identified. • Spatial econometric models are applied using high-resolution spatial data on 13,205 Dutch neighbourhoods. • Direct and indirect effects of the spatial determinants are distinguished. • Significant spatial interaction effects are identified. • Neighbourhood-level spatial differences influence solar PV adoption. [ABSTRACT FROM AUTHOR]

Hospitals in the Netherlands generate approximately 1.3 million kg of waste from the polypropylene (PP) wrapping paper (WP) used to wrap surgical instruments each year. The aim of this study was to develop a method to recycle WP waste into new medical devices. WP was recovered from Maasstad Hospital, Netherlands. The WP was melted into bars, granulated, and mixed with virgin material at different ratios and temperatures. Dog bones were injection-molded from volume (v.%) virgin, mixed (%R), and recycled (100%R) granulate, and a tensile testing machine was used to compare the material properties before and after ten disinfection cycles at the sterilization department. Then, 25 instrument openers were made from the 50%R material and circulated for four weeks. The data indicated no significant differences in the mechanical properties at different melting temperatures. For dog bones made from the 100%R, 50%R, and virgin granulate, the Young's moduli were 1021 (SD13), 879 (SD13), and 795 (SD14) MPa, and the strains were 8%, 12%, and 14%. Ten disinfection cycles did not significantly change the material properties. After one month, the openers did not show any deterioration or damage other than surface scratches. The results indicated that the initial WP melting temperature did not influence the mechanical properties. Although devices could be produced directly from the recycled WP granulate, increasing the recycled granulate in the mix ratio increased the strength and brittleness. It is feasible to recycle WP waste into a high-quality raw material for the injection molding of medical devices without using additives. This would allow hospitals to become more compliant with the circular economy enabling economically viable and circular processes that positively contribute to cleaner technical processes, sustainable products, and the reduction of medical waste. ⁃ Circular reprocessing of surgical waste into high quality raw material. ⁃ Enables cleaner processes for reduction of medical waste. ⁃ Increases sustainable development. ⁃ A new method to decrease the production of medical waste. ⁃ Decreases the shortage of base materials and costs. ⁃ Environmental and sustainability assessment: designs for products based on the "green" wishes of customers. [ABSTRACT FROM AUTHOR]

Recently, two new treatment techniques, i.e. proton therapy and MR-linac based radiotherapy (RT), have been introduced in Dutch RT centres with major impact on daily practice. The content and context of these techniques are frequently described in scientific literature while little is reported about the implementation phase. This process is complex due to a variety of aspects, such as the involvement of multiple stakeholders, significant unpredictability in the start-up phase, the impact of the learning curve, standard operating procedures under development, new catchment areas, and extensive training programs. Insight about implementation in daily care is utterly important for clinics that are about to introduce these new technologies in order to prevent that every centre needs to reinvent the wheel. This position paper gives an overview of the implementation of proton therapy and MR-linac based RT in two large academic RT centres in the Netherlands, i.e. Maastro and Radboudumc respectively. With this paper we aim to report our lessons learned, in order to facilitate other RT centres that consider introducing these and other new techniques in their departments. [ABSTRACT FROM AUTHOR]

In this paper, we present a digital tool named Diversity Perspectives in Social Media (DivPSM) which conducts automated content analysis of strategic diversity communication in organizational social media posts, using supervised machine-learning. DivPSM is trained to identify whether a post makes mention of diversity or a diversity-related issue, and to subsequently code for the presence of three diversity dimensions (cultural/ethnic/racial, gender, and LHGBTQ+ diversity) and three diversity perspectives (the moral, market, and innovation perspectives). In Study 1, we describe the training and validation of the instrument, and examine how it performs compared to human coders. Our findings confirm that DivPSM is sufficiently reliable for use in future research. In study 2, we illustrate the type of data that DivPSM generates, by analyzing the prevalence of strategic diversity communication in social media posts (n = 84,561) of large organizations in the Netherlands. Our results show that in this context gender diversity is most prevalent, followed by LHGBTQ+ and cultural/ethnic/racial diversity. Furthermore, gender diversity is often associated with the innovation perspective, whereas LHGBTQ+ diversity is more often associated with the moral perspective. Cultural/ethnic/racial diversity does not show strong associations with any of the perspectives. Theoretical implications and directions for future research are discussed at the end of the paper. • DivPSM can analyze how organizations communicate about diversity on social media • DivPSM codes whether a post includes cultural/ethnic/racial, gender, or LHGBTQ+ diversity • DivPSM identifies the underlying rationale for diversity management that the organization uses • In social media posts by large Dutch organizations, gender diversity is mentioned most often • Gender diversity is often communicated as a way to increase creativity and innovation [ABSTRACT FROM AUTHOR]

• A timber system is proposed for the seismic retrofit of URM buildings. • Analytical equations are derived for design against different failure modes. • Wall in-plane and out-of-plane responses, diaphragms and connections are addressed. • The proposed equations are applied to case-study buildings and structural members. • Good agreement between analytical calculations and experimental data is shown. An innovative timber retrofit was investigated at the EUCENTRE and at the University of Pavia, Italy, as part of a comprehensive research campaign on the seismic vulnerability of existing unreinforced masonry buildings subjected to induced seismicity in The Netherlands. The retrofit system consists of timber frames, connected to the masonry piers and to the floor diaphragms, and oriented-strand board sheathing nailed to the frames and to flexible timber diaphragms. Starting from the main critical aspects observed in experimental tests, the proposed solution was conceived to enhance the in-plane and out-of-plane capacities of masonry piers, improve the overall wall-to-diaphragm connections, increase the floor diaphragm stiffness and strength, and allow possible integration with energy efficiency upgrades. This paper, the first of a series of two, focuses on the conceptual bases of the seismic retrofit system and on the analytical equations that can be used for the design of its components. Moreover, a step-by-step design procedure is presented to guide the reader through the application of these equations. A companion paper [1] will discuss the validation of the analytical formulation with the experimental data from quasi-static cyclic and dynamic shake-table tests on building components and complete specimens. [ABSTRACT FROM AUTHOR]

This paper proposes a shared multi-stakeholder PV system for traction substations and nearby residential loads to reduce the need for storage, AC grid exchange, and curtailment. The residential stakeholders offer both the base electrical load and the solar panels installation space needed by the traction stakeholder, who brings the peak load and investments to the former. Two case studies were conducted for one year in the city of Arnhem, The cy=Netherlands, using comprehensive and verified simulation models: A high-traffic and a low-traffic substation. The results showed a positive, synergetic benefit in reducing the PV system's excess energy and size requirement for any type of traction substations connected to any number of households. In one detailed example, the multi-stakeholder system suggested in this paper is shown to reduce curtailment by up to 80% in moments of zero-traction load. Generally, the direct load coverage of a PV system is increased by as much as 7 absolute percentage points to the single-stakeholder system when looking at energy-neutral system sizes. This multi-stakeholders system offers then an increase in the techno-economic feasibility of PV system integration in urban loads. • Shared PV for traction and residential loads profitable; less storage and curtailment. • Shared PV profitable at any traction traffic value or number of connected households. • Cost of energy always better in the shared PV system compared to a private system. [ABSTRACT FROM AUTHOR]

Sparsely populated areas tend to be poorly served by Fixed Line and Schedule (FLS) public transport systems as the operation of a regular bus line is not economically viable for such areas. Therefore, introducing a Demand Responsive Transport (DRT) to partially replace FLS can result in increasing mobility service accessibility and inclusion. In this paper, a mixed-integer linear problem (MILP) is proposed to design an integrated FLS and DRT network for a transport operator. Passengers behavior is implicitly incorporated in our proposed approach via a discrete choice model. In addition, a tailored Adaptive Large Neighborhood Search (ALNS) coupled with tabu search and simulated annealing are introduced. We test our algorithm on real instances from a public transport operator in the Netherlands. The proposed algorithm can solve the problem up to 170 times faster than the MILP within 4% to 10% gap. Our proposed resolution approach investigates the temporal and spatial feasibility of deploying these integrated mobility systems based on the service level and provides recommendations to public transport operators. • An integrated FLS and DRT integrated transport system is considered. • A choice-driven MILP mathematical model is presented. • An ALNS algorithm coupled with tabu search and simulated annealing is introduced. • Computational time and optimality gaps are reported for proposed methods. • Results are validated using a case in the Netherlands. [ABSTRACT FROM AUTHOR]

• A value-added model as a conceptual framework is useful to evaluate selection bias in the performance of small-scale healthcare providers • We do not find any evidence in favour of selection bias in nursing homes' outcomes after correcting correction for observable case-mix differences • While the estimates are imprecise due to the small scale of many nursing homes, we identify considerable differences in outcomes • Structure and processes indicators do not capture much of the variation in nursing homes' performances on health outcomes Measuring performance in healthcare remains a challenge. The use of health outcomes rather than structure and process indicators is considered as the way forward, but outcome-based results risk being biased by selection. Accounting for such selection bias is more difficult in settings with small-sized providers and low chances of resident health improvement, like in the case of nursing homes. In this paper we (i) measure the health outcomes of Dutch nursing homes in terms of mortality and avoidable hospitalizations among residents, (ii) we adopt a novel approach to test for selection bias and (iii) we examine the relationship between outcomes and other nursing home quality indicators and characteristics. Using administrative data from more than 110,000 residents, we estimate the performance of the 849 largest nursing homes in the Netherlands in the period 2015–2019. Controlling for an extensive set of observable case-mix variables, we first test for the presence of selection bias using a distance-based instrumental variable. We do not find any evidence for such a structural bias. While the wide confidence intervals of the estimates display considerable imprecision, our results do reveal substantial differences between top and bottom performing nursing homes. Because the outcome-based estimates turn out to be only weakly correlated with other quality indicators, we conclude that our mortality and avoidable hospitalization-based indicators provide important complementary information. When small sample issues and case-mix differences are adequately accounted for, outcome-based indicators can provide useful policy guidance for quality improvement in nursing homes. [ABSTRACT FROM AUTHOR]

Objectives: Involvement of patients and medical professionals in assessment of relative effectiveness (relative effectiveness assessment) contributes to an efficient and effective health technology assessment (HTA) process and supports acceptance and implementation of the outcome. This study aimed to analyze stakeholder involvement in assessing relative effectiveness and how the parties involved value this collaboration.Methods: This is a document analysis of all drug assessments completed in 2019 (20) by the public HTA agency of The Netherlands, enriched with semistructured interviews with employees of the HTA agency (18) and representatives of patient (5) and medical (11) associations involved in these assessments. Data were analyzed, coded, and categorized.Results: In almost half of the assessments, there was no coordination with the medical associations at the start of the relative effectiveness assessment and no patient associations involved in this phase. During the assessment procedure, patient and medical associations were always asked to comment on the draft report. Nevertheless, the strict 5-day deadline that the HTA agency uses as a response period often hampered a proper response and involvement. According to interviewees of the HTA agency, this leads to a great diversity in the substantive quality of their input. Patient and medical associations indicated that the HTA agency relies too much on "paper knowledge," which leads to a (perceived) lack of alignment with clinical practice.Conclusions: The limited involvement results in a lack of coordination and mutual trust. Optimizing involvement of patients and medical professionals in HTA practice requires effort from all parties involved. Procedural adjustments and better coordination, especially at the start of the assessment, would probably improve cooperation. [ABSTRACT FROM AUTHOR]

Reducing emissions caused by urban freight transportation is an increasingly important policy objective for transportation planners around the world. New and innovative ways of data collection provide new possibilities to analyze these issues. In this paper we present MASS-GT, a new multi-agent simulation system for urban goods transport. The empirical basis is provided by an exceptionally large dataset of truck trip travel diaries for The Netherlands that was collected from transportation management systems using an automated data collection interface. The dataset is very dense and includes information on vehicles, routes, and shipments carried. The strategic part of the model simulates the formation of individual shipments based on logistic processes at a strategic level, such as sourcing, distribution channel choice and shipment size choice. At tactical level disaggregate choices are simulated for tour formation, vehicle type- and time of day choice, based on observed distributions. The multi-agent approach allows to implement heterogeneous preferences and thus differentiated responses to new policies. We present an application of the model to study the impacts of urban consolidation centers (UCC) and zero emission zones. The freight transportation volumes transported to these UCC and their impact on logistic indicators are analyzed. Simulation results show that vehicle kilometers travelled within the wider region increase with the introduction of UCC, and at the same time the efficiency of deliveries increases as well. Thus the model allows to study trade-offs between regional and local systems that emerge from different behavioural responses to policies. • Simulation models should take into account the heterogeneity and logistic decision making behind urban freight demand. • MASS-GT is an empirical multi-agent simulation system for the analysis of urban freight transportation demand. • The empirical basis is provided with an exceptionally large and dense dataset of truck trip travel diaries. • To manage complexity the model is developed following an incremental development path. • Possibilities for strategic policy studies are illustrated with a zero emission zone case study. [ABSTRACT FROM AUTHOR]

The urge to reduce the dependence on natural gas for heating at the residential level has led to the deployment of different fossil fuel-free alternatives. In the Netherlands, two technologies are leading the transition: heat pumps, due to their high COP, and photovoltaic–thermal systems, due to their dual electric-thermal output. However, both represent a challenge for users and grid operators, aside from their stochastic behavior. Heat pumps alone can surpass a typical Dutch house's total energy and power consumption. Photovoltaic–thermal systems, as their only electric homologs, usually have a mismatch between generation and demand, causing energy injections to the grid. From the electric perspective, storage systems are a proven solution to reduce the energy exchange with the distribution network. This paper proposes four multi-carrier energy system configurations for a Dutch household, comprising different combinations of a photovoltaic–thermal system, a battery energy storage, a heat pump, and an underground water tank thermal energy system, providing analytical models for every component (including the thermal losses from the thermal storage to the ground), and the space heating and electrical demands. We determined the components' compatibility and evaluated the combinations considering their thermal performance, electrical performance, and equivalent CO 2 emissions. The results suggest that using a heat pump combined with a photovoltaic system and a battery provides the best trade-off. The photovoltaic–thermal system alone could not supply the thermal demand required for comfortable space heating nor reach temperatures high enough to charge the thermal storage. Combining the thermal storage with the heat pump allows a certain degree of flexibility for the heat pump activation at the cost of COPs between 0.8 and 1.38 when used to charge the thermal storage, thus increasing energy consumption and equivalent emissions considerably. [Display omitted] • Heat pumps and gas boilers have similar CO 2,eq emissions in Dutch houses. • A PV system can compensate for the CO 2,eq emissions of a heat pump in Dutch houses. • PVT cannot reach temperatures to charge a thermal storage system for space heating. • Thermal storage provides flexibility for heat pump activation but can reduce its COP. [ABSTRACT FROM AUTHOR]

This paper explores the link between income, and wealth inequality and the ecological footprint in France, Netherlands, United States, and United Kingdom from 1962 to 2021. Based on theoretical considerations, we allow the relationship to vary over time. Our analysis provides some support for income inequality influencing ecological footprints, specifically through carbon emissions. Yet, we do not observe a significant effect on non‑carbon footprints. Notably, the link between income inequality and carbon emissions shifted from negative in the 1960s to positive from the late 1980s onwards. Over all our findings imply that economic inequality's impact on the environment is likely limited and context dependent. • We test the relationship between economic inequality and the ecological footprint. • The results indicate a weak relationship that varies over time. • Inequality only affects the carbon footprint and not the non‑carbon footprint. • Changes in consumption patterns may partially explain the time varying effects. [ABSTRACT FROM AUTHOR]

The main aim of this study was to evaluate the type, parameters, loss and complications of contact lenses (CLs) in the treatment of paediatric aphakia over a 10-year period. This retrospective multicentre chart review included the files of aphakic CL wearers under the age of 9 years old that were treated between 2008 and 2018. Patients with traumatic aphakia and scarring of the cornea were excluded. The following data were collected; demographic data, cataract type (congenital or secondary), CL type, parameters and wearing time, reason for replacement and discontinuation of the CLs, visual acuity (VA), prophylactic use of antibiotics (ABs), and complications. Ninety-one aphakic children (132 eyes) were fitted with soft CLs. The median age of cataract extraction was 10.50 weeks (interquartile range (IQR) 7,15) in the congenital cataract group and 112 weeks (IQR 41,285) in the secondary cataract group. At the initial fitting a silicone elastomer CL was fitted in 86 % and a silicone hydrogel CL in 12 %, the remaining 2 % were mixed CL types. The median CL power at baseline was + 29 D (IQR 25,32) and after 3 years of wear the median power had shifted significantly to + 20 D (IQR 17,26), P < 0.001. A total of 1083 extra CL replacements were needed of which 414 in the first year of wear. Of these 414 replacements almost half (46 %;n = 191) were due to loss of the CL. Complications developed in 8 (9 %) cases and 7 (8 %) patients discontinued CL wear. This paper confirms that paediatric aphakia can be successfully treated with soft CLs with low rates of complications and discontinuation encountered. Unscheduled CL replacements due to loss are a concern, especially in the first year, and are straining for both the care giver and medical system. Attentive care and clear information is advised during the first year of CL wear. [ABSTRACT FROM AUTHOR]

[Display omitted] • Scientometric analysis of MPs in soil ecosystems was performed. • A total of 2210 articles on MPs from 2012 to 2022 were analyzed. • Knowledge bases, trends, and hotspots in MP research were identified. • China was the top country in terms of publications and funding. • The migration of MPs from terrestrial to aquatic systems should be considered in the future. Soil microplastic (MP) pollution is a growing global environmental issue leading to a series of ecosystem problems and functional losses. However, understanding the advances and trends in soil MP pollution research proved to be difficult based on individual studies. Herein, CiteSpace visualization analysis software was used to sort the literature obtained on soil MP research from the Web of Science (WoS) TM core collection database, and bibliometric analysis of publication volume, country/region, keywords and other information was conducted. Results showed that soil microplastics (MP) research had much lower publication volume than hydrological system MP research, and was still in its developing stage. However, the number of research hotspots in soil MP research increased continuously since 2018. Countries such as China, the United States, the Netherlands and Australia have published more papers in this field than other countries, and collaboration among research institutions is strong. The source, pollution status, analysis methods and toxic effects of MPs in soils are current research hotspots. In the future, the focus of MP research in terrestrial ecosystems should shift from ecotoxicology to the impact of MPs on ecosystems and earth system feedback. The bibliometric analysis results of this study can serve as a reference for in-depth studies of the pollution status of MPs in soils. For MP soil pollution research, cross-background, cross-institution, cross-country cooperation and cross-disciplinary roles should be encouraged to accelerate the growth and diversification of the field. [ABSTRACT FROM AUTHOR]

• In this paper, the findings and conclusions of a complete failure investigation process and mitigation analysis are presented with respect to the switching of 33kV shunt reactors by means of vacuum circuit breakers. • Detailed description of the system under study as well as operational aspects are given. Next to that, the EMT model, its validation and the analysis results are discussed. • Description is given on the set-up of the on-site transient measurements and the method used for the analysis of the measurement data. Main findings and conclusions of the measurement campaign are presented as well. • The results of the desktop study are presented with respect to the application of RC surge suppressors as a remedy against the VCB multiple re-ignitions. Switching of (small) inductive currents by means of vacuum circuit breakers may lead to multiple re-ignitions. This is the result of the ability of the interrupter's quenching capability, which generates steep overvoltages that can stress the equipment insulation. Should no mitigation measures be applied, the equipment is exposed to a high risk of failure. In this paper, the findings and conclusions of a such a failure investigation and mitigation analysis are presented, referring to multiple cable connector failures in 33kV shunt reactor installations in the Netherlands. [ABSTRACT FROM AUTHOR]

This paper aims to understand how people's lifestyles are associated with their willingness to adopt a relatively new and innovative mobility solution, Mobility-as-a-Service (MaaS). The lifestyle is conceptualized as a combination of a mechanistic lifestyle manifested by an individual's activity-travel patterns and a psychographic lifestyle depicted by an individual's psychological traits. We propose a hierarchical latent variable and latent class model in which respondents are probabilistically allocated to one of the latent classes based upon mechanistic lifestyle, whereas psychographic lifestyle is incorporated in the model as values and personality traits exerting impact on attitudes which themselves are part of the utility function of MaaS subscription choice. The model is calibrated by the data emanated from a stated choice experiment and a lifestyle survey distributed among 1299 respondents in the Netherlands. The results confirm that psychographic lifestyles play a substantial role in people's decision to subscribe to MaaS. Having positive attitudes towards multimodal travel increases the propensity to adopt MaaS, where the attitudes are moderated by values and personality traits significantly. Moreover, mechanistic lifestyles, having non car-oriented modality lifestyle in particular, enable to segment the respondents to two latent classes showing their preference heterogeneity. [ABSTRACT FROM AUTHOR]

• Much support for policy measures taken to enable safe train travel. • The mediation model distinguishes indirect safety effects from direct effects. • Various policy measures have opposing indirect and direct effects on support. • Socio-demographic variables do not correlate strongly with support. • Latent class choice model provides a richer insight in policy measure support. To minimize the risk of becoming infected by the Coronavirus while traveling by train, the national government and the Dutch railways' operator (NS) in the Netherlands have taken several policy measures. These involve that passengers have to wear masks and guidelines are issued for working at home and teaching online. In addition, other policy measures, such as introducing a reservation system, were considered. To examine to what extent train travelers support policy measures and how these change their perception of becoming infected while traveling by train, a stated preference experiment is conducted. Respondents were asked to evaluate various combinations of policy measures, both whether they consider it safe to travel by train under the stated conditions and whether they would vote in favor of the policy package in a referendum. To analyze the data, a mediation choice model is developed, which allows disentangling the direct effect of the policy measures on support and the indirect effect mediated by infection safety perception. To illustrate this, the results show that implementing the policy measure teaching on campus with later starting times would decrease travelers' infection safety perception and therefore indirectly decrease its support. However, the positive direct effect on support suggests that travelers like this option better than teaching online, the guideline that applied at the time of data collection. The direct and indirect effects cancel each other out, indicating that this alternative policy measure would not count on more support than the guideline teaching online. Furthermore, this paper examines the heterogeneity in the support for policy measures by presenting and discussing the results of a Latent Class Choice Model. Amongst others, the results reveal that one class strongly supports the policy measure reservation system , while another class stongly opposes this measure, suggesting that implementing this measure is not trivial as suggested by its moderate effects at the aggregate level. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, public transport in many cities faces dramatic reduction of passenger demand. Various countermeasures such as social distancing and in-vehicle disinfection have been implemented to reduce the potential risks concerning infection, the effectiveness in promoting the use of public transport however remains unclear. Unlike the usual situation where time and cost are the main factors affecting travel decisions, the uncertainty hiding behind the behavior change of public transport users in a pandemic might be greatly affected by the control measures and the perception of people. This paper therefore aims to examine the effects of COVID-19 related countermeasures implemented in public transport on individuals' travel decisions. We explore the extent to which do policy countermeasures influence different groups of people on the use of public transport. An error component latent class choice model was estimated using the data collected in the Netherlands. Results show that the restrictions policy lifted by the Dutch central government have significant effect on individuals' transportation mode choice decision during the pandemic. The related measures adopted by the public transport sector, by contrast, present different effects on different people. The older and highly educated people are more susceptible to enforcement measures, whereas young and single Dutch citizens are more accessible to non-compulsory measures. Moreover, compared with other private modes, public transport is generally identified as a riskier option, and the average willingness to travel descends. Findings of this study are helpful for the authorities in designing and promoting effective policies in the context of pandemics. [ABSTRACT FROM AUTHOR]

• A later start of English as a foreign language (EFL) lessons is beneficial to pupils with DLD. • Pupils with more positive attitudes towards EFL lessons obtain higher scores. • In pupils with DLD, more EFL anxiety is associated with lower achievement. • Predictors of EFL achievement change over time. Like children with typical language development, their peers with developmental language disorder (DLD) are expected to learn English as a foreign language (EFL). For pupils without DLD, it is well-established that amount of informal exposure to English outside of the classroom, starting age of EFL instruction and motivation are strong positive predictors of EFL learning rate and/or achievement, whereas anxiety is negatively related to performance. This paper is the first attempt to investigate how these predictors of EFL performance operate in learners with DLD. Participants were nineteen Dutch-speaking 7th graders with DLD learning English as a school subject at a specialist education facility in the Netherlands. English receptive grammar and receptive vocabulary were measured twice, with a four-month interval. Foreign language learning motivation, anxiety and (length and amount of) informal exposure to and instruction in English were measured via questionnaires. The participants did not show any progress on English vocabulary and grammar. At Time 1, vocabulary and grammar scores were positively related to starting age of EFL instruction and negatively related to anxiety. For vocabulary, achievement was also positively predicted by attitudes towards English lessons. Only the relationship between starting age of instruction and vocabulary outcomes was visible at Time 2. Amount and length of informal exposure to English did not predict performance, which is in stark contrast to the patterns observed in EFL learners with typical language development. We conclude that children with DLD benefit from a later onset of foreign language lessons, whereas length and amount of out-of-school exposure to English are less important in the context of DLD, possibly due to difficulty with implicit learning. [ABSTRACT FROM AUTHOR]

[Display omitted] • Estimating API emissions to wastewater on city-scale is feasible. • Choice of excretion fraction impacts model performance substantially. • Model-based emission estimation can offer an informative alternative for monitoring. • Availability + accessibility of use data limit application to other contaminants. With increasing numbers of chemicals used in modern society, assessing human and environmental exposure to them is becoming increasingly difficult. Recent advances in wastewater-based epidemiology enable valuable insights into public exposure to data-poor compounds. However, measuring all >26,000 chemicals registered under REACH is not just technically unfeasible but would also be incredibly expensive. In this paper, we argue that estimating emissions of chemicals based on usage data could offer a more comprehensive, systematic and efficient approach than repeated monitoring. Emissions of 29 active pharmaceutical ingredients (APIs) to wastewater were estimated for a medium-sized city in the Netherlands. Usage data was collected both on national and local scale and included prescription data, usage in health-care institutions and over-the-counter sales. Different routes of administration were considered as well as the excretion and subsequent in-sewer back-transformation of conjugates into respective parent compounds. Results suggest model-based emission estimation on a city-level is feasible and in good agreement with wastewater measurements obtained via passive sampling. Results highlight the need to include excretion fractions in the conceptual framework of emission estimation but suggest that the choice of an appropriate excretion fraction has a substantial impact on the resulting model performance. [ABSTRACT FROM AUTHOR]

• Five propulsion systems and six energy carriers for regional trains are analyzed. • Dutch regional railway network with sixteen rail passenger services is considered. • Energy carrier production pathway is the main driver of energy use and emissions. • Battery-electric system running on green electricity offers net-zero emissions. • Hydrogen provides emissions reduction only if produced from green electricity. This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric, plug-in hybrid-electric, fuel cell hybrid-electric or battery-electric counterparts, combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels, liquefied natural gas (LNG), hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix, these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel, LNG, or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis, hydrogen negatively affected both aspects, irrespective of the prime mover technology. However, when produced via green electricity, it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations, with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains, allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies. [ABSTRACT FROM AUTHOR]

• Continuum damage and brick-to-brick modeling techniques are compared. • A masonry wall strengthened with bed joint reinforced repointing is considered. • For un-strengthened case, all models provide accurate predictions of crack pattern. • For strengthened case, the simplified brick-to-brick model provides the best results. • Modelling of pre-damage should be considered with care. Bed joint reinforced repointing is a retrofitting technique for unreinforced masonry structures that is commonly applied in the Netherlands to repair settlement-induced damage. Using this technique, the bed joints of masonry walls are reinforced with steel rebars that are embedded in a high strength repair mortar. Due to the increase of induced seismic events in the northern part of the Netherlands, an experimental study was carried out at Delft University of Technology to investigate the performance of this retrofitting technique for combined settlement and seismic loading. This paper aims to simulate the experimental results, with a focus on the comparison of different finite element modelling approaches for studying both un-strengthened and strengthened full-scale tested walls. To that end, three different models are investigated – comprising both macro (continuum) and simplified and detailed micro (brick-to-brick) modelling approaches. The bricks and mortar joints are modelled as one homogenous continuum in the macro model, whereas in the two brick-to-brick models these structural components are modelled separately, with the detailed model including interface elements to simulate the brick–mortar bonds. Nonlinear pushover analyses are subsequently carried out using all three modelling approaches, for both monotonic and cyclic loading cases. Based on these analyses, the detailed brick-to-brick model was found unsuitable to simulate the strengthened wall because cracks in the model mainly occur in the form of opening of the brick–mortar bond interfaces, while smeared cracking in the plane stress elements of the mortar joints is very limited. Similarly, the continuum damage model was found to be inaccurate when pre-existing damage in the experiment needed to be taken into account. The continuum damage model also showed lower axial stresses in the rebars, compared with the simplified brick-to-brick model, as the former does not allow for the direct assignment of material properties for the high strength repair mortar in the strengthened joints. [ABSTRACT FROM AUTHOR]

• 'Best practices' to analyse bicycle crash causes in correlational studies. • An abbreviated list of crash causation criteria was used. • Risk comparisons between two types of bicycles is the example given. • Controlling for age, gender, and exposure is required to analyse crash risk. The increasing number of bicycle crashes leading to injuries in the Netherlands is frequently related (e.g., in the media) to increased use of the electric bicycle. For this reason, policy makers face the challenge of selecting and implementing the most promising countermeasures including those focused on electric bicycles. It may be questioned, however, to what extent the electric bicycle itself is a (direct) cause of crashes leading to injuries or whether other factors are important for explaining the increased number of bicycle injury crashes. On the basis of an abbreviated list of criteria by Elvik (2011) , this paper illustrates the potential influence of factor inclusions, analysis selections, and data presentations on the general impression about crash causation with the electric bicycle as an example. The aim is to provide a 'best practice guide' by taking into account (1) a theoretical explanation of causal mechanisms, (2) control for confounders, and (3) a statistical association of sufficient strength and consistency in the expected direction. We conclude that an apparent increased risk of electric bicycles may be explained by factors such as age, exposure, health factors, and gender of the cyclist. A relatively simple analysis, by comparing fatality numbers of conventional and electric bicycles, showed that including or excluding these factors may lead to vastly different interpretations of fatality causes and the relative risk of electric bicycles compared to conventional bicycles. [ABSTRACT FROM AUTHOR]

The infrastructure occupation of many European railway networks appears to be at its limits, whilst recent public discussions aim to shift even more traffic towards the railway system. Thus, the question of the railway network's capacity is gaining increasing attention. In this paper, we assess the capacity of periodic railway networks on the strategic level, using a newly developed petri net (PN) based approach: the railway network utilization model (RNUM). The model builds upon individual lines which are connected to more complex networks. The network's emerging elementary circuits are then used for utilization analysis. To assess the networks capacity, the model is applied in the enumeration-based railway network capacity (RNC) framework. By evaluating the maximum utilization of different scenarios, the capacity of the periodic network is identified. This framework provides the minimal operable frequency of the network and corresponding train flows, as well as the average scheduled waiting times per train. It thereby assesses the capacity and the corresponding network effects, which are quantified in a network-specific macroscopic fundamental diagram for the railway network (MFD-R). Experiments on different generic networks demonstrate the RNC framework's applicability and reveal additional insights into the connections between train flow, waiting times and the total number of trains. The results show sensitivity between the networks throughput performance and the networks saturation. The evaluation also shows that the maximum utilization just emerges under very specific conditions and might result in unstable operations. Additionally, the results illustrate dependencies between network capacity and the network's structure. Furthermore, a case study performed at a real life railway network in the Netherlands proves the realism of the results. It is thus shown, that the presented model can contribute to prepare railway networks for tomorrows traffic and thus support the recent ambitions for eco-friendly transport. • Railway network capacity is determined by the interconnected critical constraints of its operations. • The interconnections of the operational constraints are impacted by the network's structure. • The network performance strongly depends on its saturation. • The maximum utilization of a railway network is only leveraged under specific conditions. [ABSTRACT FROM AUTHOR]

In order to accelerate the transition from carbon fuels to renewable energy sources, it is essential to extend our knowledge of the resources' availability to further improve or adjust the design of extraction devices. In the present paper, a first characterization of the tidal stream resource along the coast of The Netherlands is performed using a high-resolution unstructured grid implementation of the Thetis model. Extensive validation of the sea surface elevations was done by comparing with existing networks of tide gauges in the North Sea. The simulations from this study show that the highest tidal current intensities are generated mainly at Den Helder and Oost Vlieland, reaching values > 1.5 m s − 1 and power density estimates that are most frequently close to 300 W m − 2 and that can reach values ≥ 900 W m − 2 . Given the relatively reduced depths where these "hot spots" are found, most existing stream turbines will require further development to operate. Nevertheless, the existence of higher current intensities zones, along a commonly considered "low energy" coast, opens the door to include the tidal stream resource in near future plans to diversify the energy supply in The Netherlands. [Display omitted] • First tidal stream resource assessment for The Netherlands. • Identification of high energy sites for potential extraction. • Investigation on the persistence and magnitude of Dutch "hot-spots". [ABSTRACT FROM AUTHOR]

Arising the Nearly Zero Energy Buildings (NZEB) concept in Europe, the solar district heating systems (SDHS) present a potential solution to meet the buildings sector's European energy performance directive. Nevertheless, current practices face several technological and economical barriers to ensure service quality. In this context, our work presents a sustainability analysis (technical, economic, environmental, and social) for SDHS integration in the residential sector to meet the NZEB and positive energy building goals. This paper proposes an application of a machine learning model incorporating multi-objective optimization and multi-criteria decision making to facilitate a sustainability index for the decision-making stakeholders and policymakers. The proposed analysis application is illustrated through retrofitted residential communities with building energy rating (D) at different sizes (10, 25, 50, 100, and 500 houses) located in Emmen (Netherlands) and compared to a standard decentralized heat pump. The optimization results show the ability of SDHS to provide a solar fraction up to 95% in the community of 500 houses. Furthermore, achieving a NZEB status is only approved economically from a community size of 100 houses with a life cycle cost of 41 €/m2 and a payback period of 25 years. These results align with a substantial environmental and social improvement of 78.2% and 29.7%, respectively, compared to the decentralized heat pump. Overall, this study provides policy decision making with an evaluation for positive energy communities and suggests the SDHS integration to meet the global sustainability goals. [Display omitted] • Sustainability evaluation for solar district heating is proposed. • A multi-objective optimization based on a surrogate model is framed. • Five retrofitted solar residential communities located in the Netherlands are optimized. • Renewable energy fraction up to 95% can be achieved for the 500 houses. • Solar district heating is a competitive solution to attain Nearly Zero Energy Building. [ABSTRACT FROM AUTHOR]

• Irradiance based measure of solar yields in the Netherlands. • Estimates are on a regional, national, daily and annual basis. • Current annual yields for 2016 and 2017 were under and over estimated. • Sensitivity analysis for representativeness of photovoltaic system population. • Orientation, tilt and inverter to photovoltaic capacity ratio are important factors. This paper presents a classical estimation problem for calculating the energy generated by photovoltaic solar energy systems, on a daily, annual, regional and national basis. Our methodology relies on two data sources: PVOutput , an online portal with solar energy production measurements, and modelled irradiance data available for large parts of Africa and Europe, from the Royal Netherlands Meteorological Institute. Combining these, we obtain probability functions of observing energy production, given the irradiation. These are applied to a PV systems database, using Monte Carlo sampling , allowing daily and annual solar energy production to be calculated. These are, in turn, used to calculate solar energy production per municipality. As a case study, we apply this methodology to one country in particular, namely the Netherlands. By examining the variation in our estimates as a result of taking different subsets of PVOutput systems with certain specifications such as azimuth, tilt and inverter loading ratio, we obtain specific annual energy yields in the range of 877 - 946 kWh / kW p and 838 - 899 kWh / kW p for 2016 and 2017 respectively. The current method used at Statistics Netherlands assumes this to be 875 kWh / kW p , irrespective of irradiation, meaning the yields were underestimated in 2016 and overestimated in 2017. In the case of the Netherlands, this research demonstrates that an irradiation based measure of solar energy generation is necessary. More generally, this research shows that different types of open data sources may be combined to develop models that calculate the energy production of PV system populations. [ABSTRACT FROM AUTHOR]

Weather forecast models are essential for sustainable energy systems. However, forecast accuracy may not be the best metric for developing forecast models. A more or less conservative forecast may be preferred over pure accuracy. For example, forecasting accurately in times of energy-deprived situations may be more important than in times of excess wind power generation. This is not accounted for when learning a wind power forecast without system knowledge. Wind power forecasts directly impact system energy schedules. Therefore, to optimise system costs, our paper proposes a neural network structure for wind power forecasts directly considering varying energy system conditions. To train this neural network optimally, this paper models the system dispatch and their costs as an optimisation problem. Then, this paper connects the neural network with the optimisation model. In this connection, the implicit function theorem and the Karush–Kuhn–Tucker (KKT) conditions provide the system cost gradients during the training of the neurons. A case study using onshore and offshore weather data from Germany and The Netherlands showed forecast errors of system costs reduced by up to 10 % with high wind capacity. Wind curtailment was reduced by more than 20 % for individual cases but increased overall. Modified cases of the optimal power flow (OPF) with tighter line constraints led to a higher advantage of the proposed method. The approach led to more consistent forecast performance and reduced error variance by up to 70 %. • Neural networks for wind power forecasts considering varying energy system conditions • System dispatch and costs as an optimisation during the neural network training • The implicit function theorem and KKT conditions provide the system cost gradients • German and Dutch studies confirmed trained forecasts reduced system costs up to 10 % • More consistent forecast performance with reduced error variance by up to 70 %. [ABSTRACT FROM AUTHOR]

In this paper we explore the impact of the emerging COVID-19 pandemic on the governance of healthcare in the Netherlands. In doing so, we re-examine the idea that a crisis necessarily leads to processes of transition and change by focusing on crisis as a specific language of organizing collective action instead. Framing a situation as a crisis of a particular kind allows for specific problem definitions, concurrent solutions and the inclusion and exclusion of stakeholders. Using this perspective, we examine the dynamics and institutional tensions involved in governing healthcare during the pandemic. We make use of multi-sited ethnographic research into the Dutch healthcare crisis organization as it responded to the COVID-19 pandemic, focusing on decision-making at the regional level. We tracked our participants through successive waves of the pandemic between March 2020 and August 2021 and identified three dominant framings of the pandemic-as-crisis: a crisis of scarcity, a crisis of postponed care and a crisis of acute care coordination. In this paper, we discuss the implications of these framings in terms of the institutional tensions that arose in governing healthcare during the pandemic: between centralized, top-down crisis management and local, bottom-up work; between informal and formal work; and between existing institutional logics. • The COVID-19 pandemic is framed as a crisis of different kinds in healthcare. • Framing impacts decision-making and inclusion of stakeholders. • Ethnographic research provides insights into the consequences of these frames. [ABSTRACT FROM AUTHOR]

This paper describes how individuals update their retirement plans in response to substantial shocks in pension wealth during the Great Recession. Using a unique combination of survey and administrative panel data from before and after the Great Recession in the Netherlands, we investigate the causal relation between wealth and retirement expenditure goals. We separate 'pure' wealth effects from macro factors such as general pessimism. The estimates show that a negative shock in annuitized pension wealth of 100 euros reduced retirement expenditure goals with 24–33 euros. The effects are relatively large for old and high-income individuals. [ABSTRACT FROM AUTHOR]

• A UTAUT extension is proposed to analyze the user acceptance of ECS. • Direct and indirect effects on behavioral intention are tested in an integrated analysis. • Social influence represents the most important driver of behavioral intention. • Trust in ECS operators complying with hygiene requirements in response to the COVID-19 pandemic only has indirect impacts on behavioral intention. To reduce the externalities associated with the excessive use of carbon-fueled private vehicles, transport authorities and operators have recently been promoting one-way electric car-sharing services (ECS). Several studies attempted to identify the user acceptance and profiles of various car-sharing services, but there is a lack of consistent evidence of the psychological drivers of user acceptance. Based on an extension of the unified theory of acceptance and use of technology (UTAUT), this paper investigates the effects of six psychological constructs on behavioral intention to use ECS. Results from applying structural equation modeling to a survey with 656 respondents in the Netherlands show that social influence represents the most important driver of behavioral intention, followed by performance expectancy and personal attitude. It is also found that high satisfaction with the current means of transport for urban trips contributes to building trust in ECS companies, while car ownership has a negative indirect effect on behavioral intention. In response to the COVID-19 pandemic, the impact of hygienization measures on behavioral intention is considered. The result shows that respondents have a high degree of trust in ECS operators complying with cleaning requirements, which is translated into a lower degree of anxiety and consequently higher behavioral intention. [ABSTRACT FROM AUTHOR]

• Composite index of CE development in EU countries is proposed using MCDM methods. • CE development is positively correlated with country's socio-economic development. • CE progress has no immediate impact on environmental sustainability. • Environmental performance effects are realized subsequently, in two-year lag period. • Germany has the most developed CE, followed by the Netherlands, France and Austria. In order to ensure environmental sustainability, reducing the consumption of renewable resources, finding adequate substitutes for non-renewable resources, and reducing the generation of waste and pollution are necessities. The concept of circular economy one of the possible and good responses to improve the sustainability of the system, as it places particular emphasis on the reduction, reutilization and recycling of its elements. The paper presents a comparative analysis of the circular economy development in European Union countries based on the dataset encompasses 11 indicators, covering the seven-year period with biennial data. The analysis was performed using the integrated approach of the Principal Component Analysis and PROMETHEE with the aim of creating a composite index as a measure of the development of the circular economy at the national level. The ranking outcomes indicate that during the observed period Germany has the most developed circular economy, followed by the Netherlands, France and Austria. Additionally, the results of the research clearly indicate the positive correlation between the development of circular economies at the national level and the socio-economic development of the country, while progress in the circular economy has no immediate impact on environmental sustainability, but the effects are realized subsequently, with the intensity of the relationship increasing in two-year lag periods. [ABSTRACT FROM AUTHOR]

A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 mm yr - 1. We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets. [ABSTRACT FROM AUTHOR]

While in many regions the conventional bicycle has already been regarded as an environmentally friendly and healthy alternative to the car for daily commuting, societal and policy agendas are also increasingly promoting e-bike adoption. Adding to recent research on e-bike safety, satisfaction with travel and behavioral change, this paper reports on the impact of weather circumstances on the use of the e-bike in daily commuting in an e-cycling incentive program in the province of Noord-Brabant, the Netherlands. The impact of this incentive program was analyzed using a longitudinal design, and it combined travel patterns that were derived from individuals' GPS data over nine months, hourly observed meteorological data, and questionnaires on intended behavior and sociodemographic characteristics. The findings suggest that the presence of snow and ice, total precipitation, and high windspeed negatively affected the choice of commuting to work by e-bike, in this decreasing order of impact. Although the overall impact of air temperature on e-cycling was positive, the likeliness to commute by e-bike decreased at higher air temperatures. E-cycling under specific weather conditions was more likely if participants' intention to e-cycle under such weather conditions was stronger. Our study indicates that the benefits of the e-bike in daily commuting are underestimated in relation to adverse weather conditions. Respondents from households with one car only, therefore, have fewer alternatives in poor weather conditions: for these individuals, only total precipitation and the presence of relatively low air temperature impact e-cycling. In addition, reported gender and high wind speeds might have been expected to influence participation in e-cycling. [ABSTRACT FROM AUTHOR]

During the past decades various shared decision making (SDM) models have been developed to enable or study SDM in clinical practice. There can be a difference between how SDM is conceptualized in scientific literature and how it is understood and experienced by patients and healthcare professionals HCPs. Also, views of specific groups such as ethnic minorities have seldom been considered in the development of SDM. The aim of this study is therefore to investigate the values underlying SDM models that, according to the SDM models, steer the behavior of the actors involved (healthcare professionals (HCPs), patients, relatives and important others). Research papers in which SDM models have been conceptualized were included in this study. We used the SDM models recently analyzed by Bomhof-Roordink et al. (2019) (n=40). The research papers are currently analyzed using the reflexive thematic analysis and coded through the lens of Schwartz's value theory. We included 40 models in total, published in English between 1997-2019, coding revealed that predominantly the values universalism (equality, respect), self-direction (of both patients and HCPs), achievement (expertise, knowledge and skills) and security were reflected in the explanation of the models. In more recent models benevolence (supporting, helping) appeared to be an additional predominant value. Identifying and analyzing the underlying value structure of SDM models enhances our understanding of what values, according to the SDM models, steer the behavior of different actors involved in SDM in clinical practice. Furthermore, a description of the value structures underlying SDM enables a comparison of the values of the models with those of the actors involved in SDM in a specific context (e.g. SDM among ethnic minorities). A representation of the value structure underlying SDM can contribute to the alignment of the model with values of actors in SDM. [ABSTRACT FROM AUTHOR]

The integration of different types of knowledge in epistemically hierarchical settings remains one of the greatest challenges when developing standards for healthcare practices. In this article, we open up the notion of knowledge integration and empirically examine the various ways in which different types of knowledge interact and can be integrated. To allow us to focus on the diverse forms of knowledge as well as their interaction and integration, we combine Moreira's work on repertoires of evaluation with that of Dewulf and Bouwen on frame interactions. We examine the quest for knowledge integration by studying interactions in the case of the development of the COVID-19 vaccination guideline in the Netherlands, a prime example of the encounter of a wide range and diversity of knowledge that needs to be appraised and integrated into guideline recommendations. Drawing on ethnographic observations of more than 70 guideline development meetings between 2021 and 2022, we first map the different types of knowledge and reasonings used by the guideline developers and subsequently analyze their interactions. We identified eight knowledge interaction patterns, being disconnection, polarization, accommodation, incorporation, reconnection, reconciliation, passive juxtaposition, and kaleidoscopic integration. We hereby draw attention to the various possible knowledge interactions encompassed in the concept of "knowledge integration", especially to those in which integration is achieved while differences and incompatibilities are maintained. Finally, we discuss potential ways to facilitate fruitful knowledge interactions during collaborative work which include the ability to accept and sustain tensions between different types of knowledge and making more explicit use of frame or rather repertoire reflection. • The paper explores knowledge integration when developing healthcare standards. • COVID-19 vaccination guideline developers needed to combine diverse knowledge. • Different types of knowledge can interact and be integrated in a variety of ways. • Kaleidoscopic integration' enables integration without resolving differences. • Awareness of conversational practices can foster generative knowledge interactions. [ABSTRACT FROM AUTHOR]

Shared automated vehicles (SAV) are expected to benefit the sustainable development of urban regions and alleviate the negative impacts brought by the increasing number of private cars. In this paper, we envision a future scenario where non-pooled SAVs replace private cars and provide public on-demand mobility services to satisfy the mobility needs of a city's residents. To help service providers make profitable fleet sizing and management decisions, we develop a mixed-integer non-linear programming model that considers the congestion effects and the mode choice of urban travellers in different income classes, between SAVs and bicycles. Our model optimises both strategic decisions (fleet size, initial fleet distribution, and service quality level) and operational decisions (trip assignment, vehicle routing, parking, and relocation). Travellers' preference for both transport modes is described through a binary logit model and congestion effects are described by dynamically varying travel times with respect to traffic flow in a non-linear fashion. In addition, we investigate two types of accept/reject mechanisms (mandatory vs. non-mandatory acceptance) which lead to an endogenously determined acceptance rate that can affect travellers' willingness to use SAV services. The computational challenge posed by the non-linear and non-convex nature of the model is addressed through reformulation and the use of outer-inner approximation methods combined with a breakpoint generation algorithm. We demonstrate the effectiveness of our proposed method in a case study of the city of Delft in The Netherlands, as well as a scaling analysis on three toy networks with various sizes and demand profiles. A sensitivity analysis of key parameters is carried out to assess system performance. Computational results indicate that fleet sizing decisions are influenced not only by the population's geographical distribution and land use patterns but also by the pricing strategy, unit operating costs of the SAV fleet, network congestion level, and traveller behaviour. When the price rate of using SAVs is low, the fleet sizing decisions can also be influenced by the trip accept/reject mechanism and the travellers' sensitivity to the service quality level. In addition, a low price of SAV service will attract more users but may not necessarily bring a higher profit because of the increased traffic congestion. • A mixed integer programming model for SAV fleet sizing and management is proposed. • Endogenous demand is incorporated using a binomial logit model. • An accept/reject mechanism and its impact are explored. • Traffic congestion is included using volume delay functions. • Impacts are estimated and managerial insights are extracted for SAV operators. [ABSTRACT FROM AUTHOR]

Cycling benefits human health and helps to mitigate environmental issues. However, limited evidence exists regarding how the built environment influences cycling volume under different time and weather conditions. In this paper, we employed a Gradient Boosting Decision tree method to analyze the non-linear and threshold effects of the multiscale built environment and microclimate on cycling volume. Results based on the multisource data of The Netherlands show that 27 out of the 28 variables have a non-linear and threshold effect on cycling volume. Temperature is found to be a dominant factor among all variables. At street level, slope is the most important factor, followed by the green view and sky view indexes. At neighborhood level, population density is the most important factor, followed by residential density, and the density of bus stops. These findings offer useful insights for planning a cycling-friendly urban built environment at different scales. [ABSTRACT FROM AUTHOR]

Patients with skeletal dysplasias usually experience health related problems in different parts and systems of the body. Therefore, they face challenges in multiple domains of functioning and health. To address these different domains, interdisciplinary care should be the standard for these patients. The basic algorithm of interdisciplinary care can be similar for patients with different skeletal dysplasias, as many of the problems and needs are generic within different age groups. With increased age the domains in which patients with skeletal dysplasia face challenges will change and the focus and frequency of the interdisciplinary care should change accordingly. Thorough understanding of the specific characteristics of different skeletal dysplasias is required to create an individualized efficient interdisciplinary screening and care program. This paper presents the current structure and rationale of the interdisciplinary screening and care program of the skeletal dysplasia expert center of the University Medical Center Utrecht in the Netherlands. It is presented here, tailored to osteogenesis imperfecta, but the structure of the program is generic for all skeletal dysplasias. [ABSTRACT FROM AUTHOR]

• The railway track longitudinal levels collected at high measurement frequency (monthly) in the Netherlands are analyzed. • The optimised DNN model using the Random Search algorithm is proposed. • The short-term and long-term prediction accuracy of the optimised DNN model is both high while the customization time is shortened. • The growth pattern and the location characteristics of track geometry can be predicted and the maintenance effect can be considered by the optimised DNN model. • The prediction results can advise the time and locations for track maintenance. Railway transportation becomes increasingly important due to the rapidly emerging needs of global trade, environmental protection, and high oil costs. Thus, railway infrastructure companies are required to provide higher reliability and safety for railway transportation. With the fast development of computer science, Deep Neural Network (DNN) models are developed for track geometry prediction to improve the efficiency of track maintenance. However, existing DNN models are still not widely adopted by railway companies because they are often needed to be re-programmed intensively by experienced engineers for customization. Therefore, an optimised DNN model using the Random Search algorithm is developed in this paper for track geometry prediction. The track longitudinal level measured for 2 years on a monthly basis is used to train and validate the prediction model. The results show short-term and long-term prediction accuracy of the optimised DNN model are both high while the customization time is shortened. The growth pattern and the location characteristics of track geometry can be predicted and the maintenance effect can be considered. The prediction results can advise the time and locations for track maintenance. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of student loans on students' (financial) behavior. For causal identification, we exploit quasi-experimental evidence using a nudge in the take-up of student loans in higher education in the Netherlands. We estimate an instrumental variable (IV) model with a first-stage Difference-in-Differences design. We find that a decline in the default student loan reduced monthly student borrowing by 141 euros. A one-euro decline in student loans reduced students' expenditures by 61 cents, but also led to a substantial increase of parental financial contributions (43 cents). Especially expenditures on leisure activities were affected. There is no evidence for increased labor earnings among students, on average. Self-reported indicators of academic performance do not worsen in response to the reform; students' GPA even improves. [ABSTRACT FROM AUTHOR]

This paper investigates the design of policy packages to enable carbon neutrality in the manufacturing sector by 2050, based on the case of the Netherlands. It first models potential pathways to net-zero emissions by 2050 in the Dutch manufacturing sector, and then evaluates the current policy framework's ability to catalyse the transition. The analysis shows that the Netherlands' credible commitment to raising carbon prices combined with ambitious technology support provides strong incentives for manufacturing to significantly decarbonise. However, even if the carbon levy sets an ambitious price trajectory to 2030, it is tempered by extensive preferential treatment to energy-intensive users, yielding a highly unequal carbon price across firms and sectors. Moreover, the country's technology support focuses on the cost-effective deployment of low-carbon options, which ensures least-cost decarbonisation in the short run but favours relatively mature technologies. The paper offers recommendations for policy adjustments to reach the country's carbon neutrality objective, including the gradual removal of exemptions, enhanced support for emerging technologies and greater visibility over future infrastructure plans. Other countries can learn from the Dutch experience when designing their own decarbonisation strategy for the manufacturing sector. • The Dutch policy package provides strong incentives for manufacturing to decarbonise. • Carbon pricing exemptions for energy-intensive users increase abatement costs. • Technology support favours short-term efficiency over long-term deep decarbonisation. • Dutch transition hinges on regulatory certainty and swift infrastructure deployment. • The Dutch case offers insights for other countries to design net-zero strategies. [ABSTRACT FROM AUTHOR]

This paper describes the long-term effects of practicing ten years reduced tillage on crop yields in a conventional and organic farming system context. Data were collected from the BASIS field experiment, which was established in the Netherlands in 2009 to investigate the effects of reduced tillage on crop yield and soil properties in a controlled traffic farming system on a sandy loam soil. The experiment is unique in its focus on root crops grown on ridges, planted crops and small seeded crops, including cash crops such as potato (Solanum tuberosum), carrot (Daucus carota subsp. Sativus), onion (Allium cepa) and sugar beet (Beta vulgaris). The objective of the present paper was to analyse the long-term effects of reduced tillage on the marketable yields of these crops, grown in a conventional (4-year rotation) and organic farming system (6-year rotation). Tillage treatments were conventional deep inversion tillage (CT, mouldboard plough, 23–25 cm depth), reduced tillage with sub-soiling (RTS, chisel plough, 18–20 cm depth), and reduced tillage without sub-soiling (RT). Our key finding is that the two reduced tillage systems, over a 10-year period, provided similar or even higher marketable yields than conventional tillage, for 12 (RTS) and 11 (RT) of the 13 crops grown. Reduced tillage resulted in lower yields in two crops: carrot (−13.4% in RTS and −15.2% in RT) and cabbage (−5.2% in RT). In both cases, yield losses could be partly related to negative effects of crop residues from the preceding cover crop. Our results provide evidence that yield levels of crops grown in RT in our experiment can generally compete with crop yields in ploughed systems. However, crop residue management and seedbed preparation remain a challenge in reduced tillage systems, requiring further attention in research and dissemination. • 10 year field trial of reduced tillage (RT) effect on crop yield on sandy loam soil. • 13 crops studied in an organic and conventional farming system. • Overall marketable crop yield was similar in RT vs deep inversion tillage. • Marketable carrot yield was significantly lower in RT vs deep inversion tillage. [ABSTRACT FROM AUTHOR]