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Recent studies show that energy consumption of battery electric vehicles (BEVs) increases in traffic congestion. Therefore, it is important to consider the effect of link flow on BEV energy consumption. The flow-dependent energy consumption changes the route choice and user equilibrium conditions. In this paper, some shortcomings of available BEV flow-dependent energy consumption user equilibrium models are shown first. Then, “sufficient” as well as “sufficient and necessary” user equilibrium based on the generalized travel time of each path and sub-path penalties are defined and modeled for flow-dependent energy consumption. While it is difficult to solve the sufficient and necessary model, the sufficient model can be solved directly with commercial solvers for small to medium-sized networks by generating all paths. An iterative algorithm is also presented to generate paths as required to solve the problem for larger networks. Numerical examples demonstrate the model and proposed algorithm, and analyze the impact of flow-dependent energy consumption on equilibrium conditions.

The success of open innovation has been widely acknowledged in both industrial practice and academic research. However, we understand much less about the limits of open innovation. A better understanding of this downside is nevertheless very important as there are still many organizations that struggle when trying to implement an open innovation strategy. At the same time, some research has pointed to decreasing returns to openness, or more generally to the compex relationship between open innovation and (financial) performance. This is why we call for more attention to failures, risks, and costs — and even the “dark side” — in the context of open innovation. In this editorial, we will further introduce this perspective, describe the articles published in this special issue, and offer some suggestions for future research. Overall, we believe that a better understanding of the limits of openness will allow for a more balanced view, thereby further expanding the frontiers of open innovation practice and theory.

Sensing customer needs capabilities generally help firms to utilize customer feedback. Yet, as research linking micro-economics to industrial dynamics has shown, a strong focus on such feedback may prevent an adequate response to more promising market developments. We analyse this tension for firms providing customized services, whose innovativeness heavily depends on customer input. By drawing upon an NK search model, we simulate various interactive search strategies. Our simulations result in hypotheses concerning the strategies’ relation to innovation-based turnover. We use survey data from 292 firms to substantiate our expectations empirically. Both our simulation and regression results point to a positive influence on turnover of sensing customer needs and of customer feedback. While the interaction effect is positive for non-customizing service providers, it is negative for their customizing counterparts. The latter group may fail to exploit their inventiveness, as they concentrate on tuning new services to the specific needs of existing customers rather than turning them into more widely marketable innovations.

Brainport Eindhoven is uitgegroeid tot de economische motor van Nederland, en heeft zich getransformeerd van een regio die sterk afhankelijk was van Philips tot een divers en snel groeiend ecosysteem van deeptech-ondernemerschap. Wat verklaart het succes van Brainport en wat kunnen andere regio’s daarvan leren ?

Brainport Eindhoven is uitgegroeid tot de economische motor van Nederland, en heeft zich getransformeerd van een regio die sterk afhankelijk was van Philips tot een divers en snel groeiend ecosysteem van deeptech-ondernemerschap. Wat verklaart het succes van Brainport en wat kunnen andere regio’s daarvan leren ?

Synthetically designed biomaterials strive to recapitulate and mimic the complex environment of natural systems. Using natural materials as a guide, the ability to create high-performance biomaterials that control cell fate, and support the next generation of cell- and tissue-based therapeutics, is starting to emerge. Supramolecular chemistry takes inspiration from the wealth of noncovalent interactions found in natural materials that are inherently complex, and using the skills of synthetic and polymer chemistry, recreates simple systems to imitate their features. Within the past decade, supramolecular biomaterials have shown utility in tissue engineering and the progress predicts a bright future. On this 30th anniversary of the Netherlands Biomaterials and Tissue Engineering society, we briefly recount the state of supramolecular biomaterials in the Dutch academic and industrial research and development context. This review provides the background, recent advances, industrial successes and challenges, as well as future directions of the field, as we see it. Throughout this work, we notice the intricate interplay between simplicity and complexity in creating more advanced solutions. We hope that the interplay and juxtaposition between these two forces can propel the field forward. Supramolecular biomaterials based on noncovalent interactions hold the ability to rebuild some of the complexity of natural biomaterials in synthetic systems. While still in its infancy, the field is currently vigorously moving from fundamental experiments toward applications and products in the tissue engineering and regenerative medicine arena. Herein, we review the current state of the field in the Netherlands. While supramolecular biomaterials have incredible potential, systematic studies, balancing complexity and simplicity, efficient translation, and enhanced performance are all required for success of these strategies. As we move the field toward commercial solutions for clinical patients, we must also pay homage and remember the fundamental studies that allow these jumps in innovation.

The requirement of social distancing during the COVID-19 pandemic has presented significant challenges for high-rise buildings, which heavily rely on elevators for vertical transportation. In particular, the need for social distancing has reduced elevator capacity typically by at least two-thirds or as much as over 90% the normal amount. This reduction is a serious concern, as reduced elevator capacities cause large queues to build up in lobbies, which makes social distancing difficult and results in large wait times. The objective of this study is to safely manage the elevator queues by proposing simple, technology-free interventions that drastically reduce the waiting time and length of lobby queues. We use mathematical modeling, epidemiological expertise, and simulation to design and evaluate our interventions. The key idea is to explicitly or implicitly group passengers that are going to the same floor into the same elevator as much as possible. In the Cohorting intervention, we attempt to find passengers going to the same floor as the first person in the queue. In the Queue Splitting intervention, we create a different queue for different groups of floors. Based on simulation and analytical findings, Cohorting and Queue Splitting can significantly reduce queue length and wait time, while also maintaining safety from viral transmission in otherwise crowded elevators, building lobbies, and entrances. These interventions are generally accessible for many buildings since they do not require programming the elevators, and rely on only using signage and/or a queue manager to guide passengers.

We study a special class of cooperative games with transferable utility (TU), called (Formula presented.) -attribute games. Every player in an (Formula presented.) -attribute game is endowed with a vector of (Formula presented.) attributes that can be combined in an additive fashion; that is, if players form a coalition, the attribute vector of this coalition is obtained by adding the attributes of its members. Another fundamental feature of (Formula presented.) -attribute games is that their characteristic function is defined by a continuous attribute function (Formula presented.) —the value of a coalition depends only on evaluation of (Formula presented.) on the attribute vector possessed by the coalition, and not on the identity of coalition members. This class of games encompasses many well-known examples, such as queueing games and economic lot-sizing games. We believe that by studying attribute function (Formula presented.) and its properties, instead of specific examples of games, we are able to develop a common platform for studying different situations and obtain more general results with wider applicability. In this paper, we first show the relationship between nonemptiness of the core and identification of attribute prices that can be used to calculate core allocations. We then derive necessary and sufficient conditions under which every (Formula presented.) -attribute game embedded in attribute function (Formula presented.) has a nonempty core, and a set of necessary and sufficient conditions that (Formula presented.) should satisfy for the embedded game to be convex. We also develop several sufficient conditions for nonemptiness of the core of (Formula presented.) -attribute games, which are easier to check, and show how to find a core allocation when these conditions hold. Finally, we establish natural connections between TU games and (Formula presented.) -attribute games.

Society faces existential challenges, such as global warming, demographic change and digital innovations. Standards help solving problems and reaping opportunities. They have long been recognised as important, from technological, business, and economic perspectives, but clear evidence about their societal impact is still missing.Against this background, the XXM Partners commissioned this project to get a clearer view of how standards impact society. The project was carried out by researchers from Eindhoven University of Technology and Rotterdam School of Management, Erasmus University between November 2021 and November 2022. We conducted a pre-study of 48 standards, reviewed academic literature on standards’ impact and its measurement. We carried out eight in-depth case studies of particular standards with 86 expert interviews, relevant documents, and other sources. In doing so, we reached all five goals that were agreed in the project contract:Goal 1: Overview over dimensions of standards impactsSocietal impact has many dimensions, which relate, e.g., to public health, education, or the environment. Our literature review identified the 17 United Nations Sustainable Development Goals (SDGs) as the most suitable framework for our project. The SDGs are goals, agreed upon in global consensus, on which society should focus and improve. A growing global movement has formed around them to address societal challenges.In a pre-study, we conducted desk research to explore potential impacts of 48 standards. They cover the breadth of subject areas, and include standards with societal, environmental, technical and/or business purposes. We find that these standards can be linked to all SDGs. Often one standard affects multiple SDGs, going beyond intended impacts.Goal 2: Overview over scientific literature on standards’ impactsStandards are important for society. This is widely recognised in academic literature. However, there is relatively limited research on how they impact society. Most of this limited research focuses on two standards: ISO 9001 (quality management systems) and ISO 14001 (environmental management systems). Our review reveals a research focus on business/economic (both standards) and environmental (ISO 14001) impacts. Some evidence exists on non-environmental societal impacts (e.g., workplace safety), but this remains limited.We extended our review to other fields, especially addressing best practice for impact measurement. We compared multiple approaches, selected the Logic Model for understanding impact, and adapted it to standards. This model helps understand impact by tracing it in five steps: (1) inputs (including standards), (2) activities for standard implementation, (3) outputs (a situation reflecting what a standard prescribes), (4) outcomes (changes for stakeholders), and eventually (5) long-term impact.Goal 3: Evidence of standards’ societal impacts – empirical researchUnder the project team’s guidance, master students carried out eight in-depth studies in Norway (two studies), Sweden (two studies), Finland (two studies), Denmark and Austria. Standards were selected in consultation with the XXM Partners. They cover a broad range of important areas, e.g., greenhouse gas (GHG) emissions reporting (ISO 14064-1), clinical trials for medical devices (ISO 14155), and information security management (ISO 27001).Our studies led to findings about (1) specific societal changes caused by the investigated standards, (2) what drives standards’ impacts in general, and (3) measuring standards’ impact (see Goal 4 below).Findings about standards’ specific societal changesTwo standards in our study are particularly successful in creating positive impact:• EN 16516 (emissions of construction products into indoor air) promotes the availability of low-emission construction products, thereby contributing to healthy indoor air.• ISO 14155 (clinical investigations for medical devices) contributes to safer clinical trials and availability of innovative medical devices for patients.In the other cases, we observe strong potential for positive societal impact. However, this is not achieved, e.g., due to standards not being implemented at large scale, not meeting potential users’ expectations, and/or competing with other standards. In two cases (ISO 14064-1, GHG emissions accounting; ISO 14044, life-cycle assessment), foreseeable abuse of the standard may even provide opportunities for greenwashing.All investigated standards have broader (potential) impacts on the SDGs than identified by ISO. E.g., ISO 14155 does not only contribute to SDG 3 (“Good Health and Wellbeing”), but also to SDG 9 (“Industry, Innovation and Infrastructure”) and the institutional aspect of SDG 16 (“Peace, Justice and Strong Institutions”).Findings about drivers behind standards’ impacts: importance of their ecosystemsAll investigated standards are deeply embedded in their ecosystems. A remarkable finding concerns the large extent, to which (mis)alignment with this context drives their impact. This also makes it challenging to isolate the standard’s impact from that of its entire ecosystem.Where we observed positive impact, this was largely due to a standard serving a clear purpose in its ecosystem (e.g., meeting market needs, supporting certification, alignment with regulatory requirements). In the cases where potentials for positive impact have not yet been realised, our data show how this is driven by misaligned ecosystems and standards. We have recommendations for how to address this (see Goal 5 below)Goal 4: Applied methodology for demonstrating societal impactsIn the course of the project, we developed a methodology for measuring standard’s societal impacts. It consists of a six-step process, which is based on four essential frameworks/tools:1. Stakeholder analysis,2. A checklist for analysing a standard’s ecosystem,3. The logic model for standards,4. The SDGs as a framework for classifying impact.We recommend this methodology for systematically studying further standards’ impacts.Goal 5: Recommendations to National Standard Bodies (NSBs)Our results have clear implications for the XXM Partners, ISO/IEC, CEN/CENELEC, and their members. Based on these implications, we provide recommendations on (1) communicating standards’ impacts to policymakers and other stakeholders, and (2) enhancing standards’ positive impact. Section 9.5 (p.102) specifies these recommendations in detail.Communicating standards’ impact• To policymakers: Emphasise standards’ functions as essential instruments for change in their respective ecosystems.• To other stakeholders: Focus on standards’ contributions to the SDGs, while acknowledging that the full potential is not yet achieved.• Ensure standards’ inclusion in frameworks for measuring societal impact: Engage in dialogue with the impact measurement community. Ensure that increasingly influential approaches to measuring societal impact include standards. This would support clearer communication about standards’ impacts and contribute to frameworks’ accuracy.Enhancing standards’ positive impact• Already address societal impacts during the standardisation process. We provide two tools, which may be used for this.• Improve standards’ relevance and quality in line with their ecosystems’ expectations (positioning standards relative to competing and/or complementary standards, clarity of requirements, quality of translations, withdrawing unused standards).• Need for future research in six directions: (1) Studies at the level of standard families and/or ecosystems. (2) Large-scale replication. (3) Understanding standard competition. (4) Reflecting ongoing developments in impact measurement. (5) Effects of societal change on standards’ role in society. (6) Starting from the “grand- societal challenges”.ConclusionOur work makes a novel contribution to knowledge about standards’ impacts. By doing so, we offer concrete insights, which the XXM Partners can use in communicating the benefits of standards. We also identify key factors affecting standards’ societal impacts, and offer tools and methods that are relevant for practitioners and researchers alike. The XXM Partners can use these insights in their work to further maximise standards’ value to society, and limit any potential negative effects.

Innovations in education need to be accelerated to address the grand challenges in our society more adequately. We indicate the need for more collaboration with external stakeholders and for self-reflection. We refer to Challenge-Based Learning, an educational concept developed in engineering education, as a step in transforming education in response to societal changes, where students develop new capacities and are empowered to gain more ownership of their own learning. We highlight the role of design and designers in developing the capacities needed by students and teachers, and detail the necessary changes in the role and responsibilities of learners and teachers.

Mobility-as-a-Service (MaaS) can be considered the latest innovative technological solution to sustainable transportation. It offers travelers access to a pre-paid bundle of transportation modes on a single app with the additional convenience of the integration of planning, booking, and payment. Capturing the impact of this new technology on travel behavior remains a challenge, given the scarce number of comprehensive MaaS pilots. In this study, a stated adaptation experiment was designed to determine travelers’ propensity toward using this new service. Such knowledge sheds light on the potential of MaaS to alter daily travel patterns.

An intelligent transportation system (ITS) is one of the core elements of smart cities, enhancing public safety and relieving traffic congestion. Detection and classification of critical vehicles, such as police cars and ambulances, passing through roadways form crucial use cases for ITS. This paper proposes a solution for detecting and classifying safety-critical vehicles on urban roadways using deep learning models. At present, a large-scale dataset for critical vehicles is not publicly available. The appearance scarcity of emergency vehicles and different coloring standards in various countries are significant challenges. To cope with the mentioned drawbacks and to address the unique requirements of our smart city project, we first generate a large-scale critical vehicle dataset, combining images retrieved from various sources with the support of the YOLO vehicle detection model. The classes of the generated dataset are: fire truck, police car, ambulance, military police car, dang erous truck, and standard vehicle. Second, we compare the performance of the Vision in Transformer (ViT) network against the traditional convolutional neural networks (CNNs) for the task of critical vehicle classification. Experimental results on our dataset reveal that the ViT-based solution reaches an average accuracy and recall of 99.39% and 99.34%, respectively.

Humans increasingly interact with AI systems, and successful interactions rely on individuals trusting such systems (when appropriate). Considering that trust is fragile and often cannot be restored quickly, we focus on how trust develops over time in a human-AI-interaction scenario. In a 2x2 between-subject experiment, we test how model accuracy (high vs. low) and type of explanation (human-like vs. not) affect trust in AI over time. We study a complex decision-making task in which individuals estimate jail time for 20 criminal law cases with AI advice. Results show that trust is significantly higher for high-accuracy models. Also, behavioral trust does not decline, and subjective trust even increases significantly with high accuracy. Human-like explanations did not generally affect trust but boosted trust in high-accuracy models.

Publisher Copyright: © 2023 The Authors Most studies published in Technovation adopt a theory-driven approach toward empirical phenomena. In this editorial, we call for a more instrumental approach, one that effectively serves the needs of practitioners in the field of technological innovation as well as scholars seeking to increase their impact on innovation practice. In this respect, design science (DS) methodologies have emerged in various adjacent domains such as information systems, operations management, and entrepreneurship. Inspired by Simon's “The Sciences of the Artificial”, DS operates at the interface of problem-solving design and explanatory science to create and test solutions as artifacts. Whereas DS work can result in various kinds of artifacts, tools for practitioners are the most promising ones. We first provide several examples of DS work resulting in widely used tools and then identify various challenges in the technological innovation domain that call for tools addressing these challenges. Subsequently, we provide practical guidance on how to prepare manuscripts about designing and testing tools which are likely to have a major impact on practice. Non

Dementia is a growing societal challenge putting pressure on care systems across Europe. Providing supporting technology for people living with dementia, referring to both people with dementia and their caregivers, is an important strategy to alleviate pressure. In this paper, we present lessons learned from the Interreg NWE Project Certification-D, in which we evaluated technological products with people living with dementia, using a Living Lab approach. Living Labs were set up in five different countries to conduct field evaluations at the homes of people living with dementia. Via an open call products from small to medium enterprises across northwestern Europe were selected to be evaluated in the Living Labs. In this paper, we describe the setup of and reflection on Living Labs as multi-stakeholder collaboration networks to evaluate technological products in the context of dementia. We reflect on the experiences and insights from the Living Lab researchers to execute and operate the Living Labs in such a sensitive setting. Our findings show that Living Labs can be used to conduct field evaluations of products, that flexibility is required to adopt a Living Lab in various care settings with different stakeholder compositions and expertise, and that Living Lab researchers serve as both a linking pin and buffer between people living with dementia and companies and thereby support the adoption of technological products. We close the paper with a proposal of best practices to encourage inclusivity in, and scalability of, Living Labs in the context of dementia. ispartof: International Journal Of Environmental Research And Public Health vol:20 issue:3 pages:1673- ispartof: location:Switzerland status: published

A large body of literature explores the role of context, structure, actors, and outcomes of open innovation (OI), yet pays little attention to the mechanisms underlying these relationships. In this review paper, we synthesize the OI literature using a context-mechanism-outcome approach to identify and classify the various mechanisms observed in empirical OI studies. Our findings demonstrate that the OI literature draws on a wide variety of mechanisms originating from the fields of management, sociology, economics, and psychology. The fifteen mechanisms most frequently observed in the literature fall into four categories: governance and policies; environmental dynamics and interactions; knowledge, skills, and capabilities; and learning by doing. Moreover, by examining the levels of analysis of these mechanisms, we observe substantial differences in how these mechanisms operate at the individual, project, firm, network, and society level. Finally, we identify various avenues for future research arising from our synthesis of the literature.

Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but such marketplaces are seldom commercially viable. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially not of neglected research topics that may advance such marketplaces toward commercialization. This study provides an overview of the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR) approach to examine 133 academic articles and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.

Carsharing-facilitating neighbourhood refers to a development scheme to combine carsharing, sustainable transportation-residential planning and housing features to promote less private car use and improve residential environments. Since this concept is new, little is known about residents’ reactions. This study focuses on the individuals who indicated zero-interest in living in such neighbourhoods to discuss the influential factors and possible improvements for the concept. The analysis is based on a stated choice experiment conducted in densely populated areas in the Netherlands. According to the survey results, 25.4% of the respondents indicated zero-interest. A binary logistic regression was applied to understand internal influencing factors on individuals’ zero-interest. Accordingly, respondents’ social-demographics, travel habits and present housing conditions have significant influences on their zero-interest. Particularly, zero-interest residents are more likely to be males, have full-time jobs or not work, have no child, live in medium-sized cities, own large gardens, have no driving licence, often travel by private cars or metros rather than bikes. Besides, based on the unobserved factors indicated by these individuals, several issues can also lead to zero-interest, such as objection to top-down governance, doubt of the necessities to provide shared-cars by neighbourhood management and lack of disability design.

Over the last years, cameras have become an integral component of modern cyber-physical systems due to their versatility, relatively low cost and multi-functionality. Camera sensors form the backbone of modern applications like advanced driver assistance systems (ADASs), visual servoing, telerobotics, autonomous systems, electron microscopes, surveillance and augmented reality. Image-based control (IBC) systems refer to a class of data-intensive feedback control systems whose feedback is provided by the camera sensor(s). IBC systems have become popular with the advent of efficient image-processing algorithms, low-cost complementary metal–oxide semiconductor (CMOS) cameras with high resolution and embedded multiprocessor computing platforms with high performance. The combination of the camera sensor(s) and image-processing algorithms can detect a rich set of features in an image. These features help to compute the states of the IBC system, such as relative position, distance, or depth, and support tracking of the object-of-interest. Modern industrial compute platforms offer high performance by allowing parallel and pipelined execution of tasks on their multiprocessors.The challenge, however, is that the image-processing algorithms are compute-intensive and result in an inherent relatively long sensing delay. State-of-the-art design methods do not fully exploit the IBC system characteristics and advantages of the multiprocessor platforms for optimising the sensing delay. The sensing delay of an IBC system is moreover variable with a significant degree of variation between the best-case and worst-case delay due to application-specific image-processing workload variations and the impact of platform resources. A long variable sensing delay degrades system performance and stability. A tight predictable sensing delay is required to optimise the IBC system performance and to guarantee the stability of the IBC system. Analytical computation of sensing delay is often pessimistic due to image-dependent workload variations or challenging platform timing analysis. Therefore, this thesis explores techniques to cope with the long variable sensing delay by considering application-specific IBC system characteristics and exploiting the benefits of the multiprocessor platforms. Effectively handling the long variable sensing delay helps to optimise IBC system performance while guaranteeing IBC system stability.

This study aims to determine whether innovation depends on long-term patterns of interactions in technology and science, using patents in nanotechnology. The previous literature has distinguished between a technology’s degree of novelty (science-base) and degree of technological impact, highlighting that links to the science-base will lead to more breakthrough inventions, due to distant recombinations. We extend the debate by distinguishing two types of science linkages, “direct and strong science-base” and “indirect and more diverse science-base.” To find long-term patterns, we developed an empirical strategy to study nanotechnology patents through the metaphor of “sleeping beauties”, e.g., delayed recognition and high impact. We show that sleeping beauties occur more frequently in nanotechnology than in the general population of patents, so nanotechnology is an emerging technology that may delay breakthrough inventions. As expected, both types of science linkages create a higher impact. Contrary to expectations, however, neither “direct and strong science-base” (proxied by university ownership) nor “indirect and more diverse science-base” (proxied by the non-patent literature) are significant. Although this emerging technology has many science linkages, these do not cause a delayed impact. Control variables of IPC application class and company ownership do matter. We conceptualize that these are typical characteristics when firms combine several technologies for industrial innovation. Among other contributions, we propose that the non-patent literature should not be considered a proxy for science linkages in general, but instead this reflects a search amongst various types of codified as well as informal technological and scientific knowledge. We suggest that the long-term patterns of delayed recognition and high impact in science-based technologies may require the firms to recombine multiple technologies, specializations, and industrial applications in order to do industrial invention.

Scalar transport (e.g. heat or chemical species) in laminar flows is key to many industrial activities and fluid stirring by flow reorientation is a common way to enhance this process. However, "How best to stir?" remains a major challenge. This study aims to contribute to existing solutions by the development of a dedicated flow-control strategy for the fast heating of a cold fluid via a hot boundary in a representative case study. Fluid deformation acts as the "thermal actuator" via which the flow affects heat transfer yet this may both enhance and diminish heat exchange between fluid parcels and thus restricts the beneficial impact of flow. Moreover, the impact of fluid deformation on the global fluid heating is primarily confined to the proximity of the moving boundary that drives the flow. These insights make incorporation of thermal behaviour essential for effective flow-based enhancement strategies and expose fluid mixing, the conventional approach in industry, as potentially sub-optimal. The notion that global heating encompasses two concurrent processes, i.e. increasing energy content and thermal homogenisation, yields the relevant metrics for the formulation of the control problem as the minimisation of a dedicated cost function. This facilitates step-wise determination of the "best" flow reorientation from predicted future evolutions of actual intermediate states and thereby paves the way to (real-time) regulation of scalar transport by flow control. Performance analyses reveal that this "adaptive flow reorientation" significantly accelerates fluid heating and thus is superior to conventional periodic schemes (designed for efficient fluid mixing) both in consistency and effectiveness. The controller in fact never selects these periodic schemes and achieves the same superior performance for all flow conditions irrespective of whether said mixing occurs., Comment: 34 pages, 17 figures

This study considers dynamic parking management in the era of driving automation that users of privately-owned automated vehicles (AVs) come to a downtown area to conduct various activities and choose between three parking options: outside parking, on-street parking, and cruising-as-substitution-for-parking. In the latter, AVs cruise in downtown until summoned by their users after concluding their activities. Given the distribution of users’ activity time in downtown, we propose a system of ordinary differential equations to model AVs’ parking choice and capture the impact of cruising-as-substitution-for-parking on traffic congestion. With the proposed model, we further investigate dynamic time-based tolling and parking provision to optimize the system performance. Results of our numerical experiments demonstrate the validity of our model and the potential of dynamic tolling and parking provision on managing downtown parking of AVs.

With the Netherlands having the world's highest cycling rates, many see its current cycling policies as a model for the transition to sustainable transportation systems. Understanding these high cycling rates, however, requires understanding the geographic, historical, and institutional context in which social movements, working with government actors, helped stop a rapid decline in cycling rates between the 1950s and the 1970s in the Netherlands. This article uses historical sources and interviews with activists and government actors to show how social movements supported cycling by helping reverse the negative effects of rapid motorization. These social movements worked with government actors to implement three specific innovations: the woonerf, the bottleneck memorandum, and the car-restricted city center. This article contributes to transitions literature by looking beyond the relationship between enterprise and the state and demonstrating how social movements within a specific institutional context and with broad public support can advance sustainable transportation innovations.

Carsharing-facilitating neighbourhood is an emerging concept in urban development that combines carsharing, sustainable transportation-residential planning, and attractive housing to reduce private car use and improve neighbourhood quality. To investigate residents’ preferences for such neighbourhoods, a stated choice experiment was designed that systematically varied attributes of carsharing-facilitating neighbourhoods to elicit their utility for people with a particular socio-demographic profile. The survey was conducted among residents who currently live in densely populated urban areas in The Netherlands. In total, 610 valid responses were obtained for analysis. To derive the utility of carsharing-facilitating neighbourhoods of a particular profile, a mixed logit model was estimated. Results indicate that the utility of a carsharing-facilitating neighbourhood primarily depends on carsharing cost, required carsharing booking time, green space density, housing costs, housing type, housing size and housing building year. Besides, the utility varies with socio-demographic characteristics, such as age, household composition, net monthly income, work status and education level. The estimated results can help policymakers and real estate developers understand the contribution of particular factors to the utility of carsharing-facilitating neighbourhoods, and determine target groups to develop implementation strategies.

This study explores a tourists’ preference for tour mode bundle, defined as a set of preferable travel modes for multiple trips in a tour, under Mobility-as-a-Service (MaaS). In the context of tourism, MaaS could improve transportation sustainability by providing integrated information about sustainable transportation modes. To develop a systematic understanding of tourists’ preference for tour mode bundle, we design a tour-based stated preference (SP) choice experiment based on bundle configuration of travel modes in MaaS including a shared van as a paratransit mode in combination with the existing bus and taxi services. A web-based SP survey is conducted for 331 individuals who visited Jeju Island in Korea. Two mixed logit models are estimated to reveal differences between the trip- and tour-based mode choice situations. The estimation results reveal that the trip sequence significantly influences the preference for travel mode in a tour mode bundle, which has significant cross effects with individual and travel context attributes. A shared van is the most preferred in both a trip and a tour. Persistence to using the same mode and synergy of using different modes are captured through significant cross effects of sequence-specific travel modes in a tour mode bundle. Based on these findings that can only be identified through tour-based choice situations, MaaS can be more effectively designed in tourism.

Regions with carbon emission regulations bear the risk of "carbon leakage'' if local producers shift production capacity to an unregulated region. We investigate the problem for a producer subject to geographically asymmetric emission regulation with uncertain future emission price. The producer has two ex-ante options to lower its compliance cost: investing in clean production technology in the regulated region and building production capacity in the unregulated region. The producer determines its production quantities ex-post, after emission price uncertainty is resolved. We study two anti-leakage policies, Border Tax (BT) and Output-Based Allocation (OB), where the former adopts a "stick'' approach that penalizes offshore production and the latter adopts a "carrot'' approach that grants free emission allowances for production in the regulated region. First, we show that the emission price uncertainty can exert opposing effects in the absence of an anti-leakage policy: When the expected emission price is low (high), a higher uncertainty aggravates (mitigates) carbon leakage. Second, through a comprehensive comparison, we highlight that while both BT and OB are able to reduce carbon leakage, BT has a stronger effect in both the regulated and unregulated regions in multiple dimensions, especially when the carbon leakage risk is high. Third, we find that a higher emission price uncertainty weakens the effect of both BT and OB. We therefore suggest that emission price uncertainty should be accounted for when formulating anti-leakage policies. Finally, we extend our analysis to a competitive case and find that the superiority of BT relative to OB is enhanced.

We investigate the benefits of on-site printing at remote geographic locations, where access to spare parts is intermittent and supplies are replenished at fixed intervals. Organizations typically have no solution to spare parts shortages other than expensive expediting orders, or waiting for a part to arrive with the next replenishment. We investigate whether on-site three-dimensional (3D) printing of spare parts can bring relief. Our work extends dual-sourcing literature with fixed order cycles by considering two emergency supply options: expediting regular parts and 3D printing lower quality parts. We model the replenishment and emergency supply decisions as a Markov decision process and find that the optimal inventory control policy consists of two thresholds that control when to expedite, when to print and when to wait for regular parts via the next replenishment. We apply our model to a case study of the Royal Netherlands Army (RNLA) for her United Nations peacekeeping mission in Mali. Our results show that on-site 3D printing, much more so than expediting, leads to large operational cost savings through on-site inventory reductions and increased asset availability, thus increasing the ability of the RNLA to operate in remote locations. These results extend to many other organizations that operate in remote locations, for example, those in the mining and offshore industry.

The Brainport-Eindhoven region has developed into a leading location for deeptech entrepreneurship in Europe. Against all odds, it has transformed itself from a region that heavily depended on the multinational company Philips, into a diverse and fast-growing deeptech ecosystem. While this success has not gone unnoticed, there is not yet a clear account of how and why the Eindhoven region emerged as a global hotspot for deeptech innovation and entrepreneurship. Moreover, such an account might provide an exemplary model of a collaborative ecosystem, one that provides an alternative to the “winner-takes-all” entrepreneurial culture of Silicon Valley. This essay explores the performance of the Eindhoven region in terms of three structural conditions. First, the focus on deeptech R&D and entrepreneurship appears to be deeply rooted in the region’s history as well as strong competencies in systems engineering, design thinking, and multidisciplinary collaboration. Second, a collaborative approach to regional policy gives industrial, academic, and governmental actors an equivalent position in its “triple helix” governance. Finally, the Eindhoven region benefits from a systemic approach toward co-locating R&D and entrepreneurial activities on five campuses. Overall, the huge complexity of deeptech systems and products apparently requires a truly collaborative approach at all levels of the entrepreneurial ecosystem.

Engineering changes (ECs) are new product development activities addressing external or internal challenges, such as market demand, governmental regulations, and competitive reasons. The corresponding EC processes, although perceived as standard, can be very complex and inefficient. There seem to be significant differences between what is the 'officially' documented and the executed process. To better understand this complexity, we propose a data-driven approach, based on advanced text analytics and process and data mining techniques. Our approach sets the first steps toward an automatic analysis, extracting detailed events from an unstructured event log, which is necessary for an in-depth understanding of the EC process. The results show that the predictive accuracy associated with certain EC types is high, which assures the method applicability. The contribution of this article is threefold: 1) a detailed model representation of the actual EC process is developed, revealing problematic process steps (such as bottleneck departments); 2) homogeneous, complexity-based EC types are determined (ranging from 'standard' to 'complex' processes); and 3) process characteristics serving as predictors for EC types are identified (e.g., the sequence of initial process steps determines a 'complex' process). The proposed approach facilitates process and product innovation, and efficient design process management in future projects.

This discussion article contains a reflection of the challenges I encountered while teaching technological forecasting to undergraduate students. I group these challenges into three groups: the inherent uncertainty of the forecasting process, the lack of appropriate learning materials, and existing methodological inconsistencies. I discuss how these challenges appeared in my classroom, and whenever possible, how I tried to solve them. I conclude by discussing some actions that the forecasting community could undertake to facilitate both teaching and research in this field.

Mobility as a Service (MaaS) is an innovative concept, enabled by the ubiquitous availability of digital technologies. Many cities around the world envision MaaS as a way to improve sustainable transportation by reducing the use of fossil fuel-based transportation modes. However, MaaS may also trigger users of environmentally-friendly transportation modes to switch to less sustainable modes, such as taxi and ridesharing. The aim of this study is to explore how different bundling and pricing schemes of MaaS contribute to improving sustainable transportation. In order to analyze individuals’ intended choice of MaaS, a stated portfolio choice experiment was developed. A mixed logit model is estimated to investigate the bundle of transportation modes individuals configure when subscribing to MaaS. We explore empirical issues such as which transportation modes they prefer to include in the bundle, and how pricing schemes affect the composition of the bundle. Furthermore, scenario analyses are conducted to assess the impact of pricing schemes on sustainable transportation.Results show that MaaS contributes to improving sustainable transportation in a non-linear manner as a function of decreasing monthly subscription fees and/or increasing length of the subscription.

Quantum dots (QDs) were suggested a decade ago as a means of spectral decoy to divert rocket attacks from flying objects such as satellites but have not yet found industrial applications. A possible reason is that these advanced nanomaterials are unstable in the long-Term under the forces exhibited on a flying object and the nanoparticles might agglomerate to form larger microparticles, which have a different, unwanted spectral footprint. This work proposes an on-board (in situ) real-Time synthesis of quantum dots on a satellite as a result of an interdisciplinary innovation that combines nanomaterial chemistry, heating and mixing with batch microfluidics, and microfabrication of a miniaturized chip. We employed a microchip as the in situ reaction chamber due to its capability of fast, compact synthesis. The implementation of the space micro-reactor, on the other hand, leads to other fundamental challenges that need to be solved such as robust pump-free fluid motion and quick synthesis of quantum dots in less than 180 s which is equivalent to the typical time a rocket needs to reach the satellite from the ground. Additionally, quantum dots need to be tailored in the type of material and size to replicate the spectral signal of the to-be-protected space asset while the synthesis process needs to be robust and resilient to work reliably and automatically under the demanding conditions of a flying object. In this study, we selected cadmium selenide (CdSe) and lead selenide (PbSe) as the materials of choice. Our synthesis process aims to simplify and tailor CdSe and PbSe quantum dots to match the above demands. The produced quantum dots were characterized by their wavelength emission spectra and high-Angle annular dark-field scanning transmission electron microscopy (HAADF STEM) images. Results show that both CdSe and PbSe quantum dots have been successfully synthesized within a maximum of 10 minutes. While the chosen CdSe-based synthesis was unable to reach IR-emitting sizes within several minutes, we managed to meet all other research targets. The synthesized PbSe QDs were able to reach the mock-spectrum and could produce an emission wavelength peak from 1200 to 1400 nm. This work finally provides insights into the limits of batch synthesis in a microfabricated nanodot chip, such as the need to improve reproducibility and enhance particle growth. Based on this learning, we propose a self-priming flow microchip synthesis that can capitalize on mixing as a key asset to overcome those limits.

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation.KeywordsTraffic flow Disturbance Time-varying OD Route choice Network disruption Dynamic travel time

This research studies a production-recycling inventory model for postconsumer recycled content products made from a mix of virgin and recycled materials with specific proportions. The proportion is known as the postconsumer recycled content level. We study a closed-loop supply chain (CLSC) where a recycling facility and a supplier respectively ship the recycled and virgin raw materials to a manufacturer. The manufacturer mixes them based on the proportion to produce these products for a retailer. The recycling facility recycles the used products collected by a material recovery facility. The paper develops a joint economic lot-size model (JELS) to maximize the chain-wide total profit. A branch-and-bound algorithm is developed, along with the convex analysis to find the exact solution. To coordinate the CLSC, we present models called disproportionate profit-sharing (DPS) and proportional profit-sharing (PPS) operating under quantity discount. The DPS would give more powerful members, a higher proportion of the extra profits resulting from the JELS. However, the PPS guarantees equitable increases in the individual profits accruing to all members from the JELS. Whereas the DPS works properly in vertically integrated CLSCs and decentralized ones with dominant player(s), the PPS is appropriate for decentralized ones with no dominant players. Additionally, we prove the optimal level of postconsumer recycled content is often either at its maximum or minimum allowable. However, the zero-derivative points within the range can be optimum in a particular case. Consequently, we present decision trees for managers to determine the right profit-sharing method and the optimal postconsumer recycled content level.

Digital technology is fundamental to experimentation, learning, and the rate of innovation. Digital technology facilitates the rapid distribution of experimental design and debug information. However, we should consider how this fundamentally changes organizational learning and experimentation when managing the rate of product innovation. We address this issue by investigating what drives experimentation‐based learning in high‐tech product innovation and production. The longitudinal dataset in our study consists of 216 projects over a period of almost 5 years, involving thousands of digitally recorded design iterations and design debugs. Based on a time series linear regression analysis, we demonstrate that learning from an accumulation of completed projects drives learning in experimentation more than failure experience in successfully completed design debugs. Furthermore, we show that processing iterations and debugs rapidly enhances the speed of product innovation learning as this allows for short‐loop experimentation that restricts superstitious learning when conditions change over time. The results also show this can be achieved using digital tools as a source of agility.

Metropolises in emerging markets are facing serious urban transport challenges. Understanding people’s travel preferences is crucial for designing effective sustainable urban policies. Little attention has been paid to studying travel preferences in multimodal transport systems in these markets. This study estimates the travel preferences in the metropolitan area of Beijing, which is notoriously plagued with high degrees of congestion. We administered a series of interwoven stated preference experiments on travel behavior. A semi-random sample of 2652 respondents participated in the experiments. The data were pooled and a scaled mixed logit model was used for estimation. The results provide rich information on how trade-offs are made among the manipulated attributes regarding travel time, cost, convenience, and reliability. Many findings deviate from results obtained in developed countries. A contrast standing out is that travelers in Beijing place much less weight on possible delays caused by traffic congestion.

Vaccination is a very effective measure to fight an outbreak of an infectious disease, but it often suffers from delayed deliveries and limited stockpiles. To use these limited doses of vaccine effectively, health agencies can decide to cooperate and share their doses. In this study, we analyze this type of cooperation. Typically cooperation leads to an increased total return, but cooperation is only plausible when this total return can be distributed in a stable way. This makes cooperation a delicate matter. Using cooperative game theory, we derive theoretical sufficient conditions under which cooperation is plausible (i.e., the core is non-empty) and we show that the doses of vaccine can be traded for a market price in those cases. We perform numerical analyses to generalize these findings and we derive analytical expressions for market prices that can be used in general for distributing the total return. Our results demonstrate that cooperation is most likely to be plausible in case of severe shortages and in case of sufficient supply, with possible mismatches between supply and demand. In those cases, trading doses of vaccine for a market price often results in a core allocation of the total return. We confirm these findings with a case study on the redistribution of influenza vaccines.

Currently, organizations face constant changes in the business landscape. Managing business models is essential for organizations to adapt to these changes and to remain competitive. Business model management can be defined as a process covering all phases of the business model lifecycle: ideation, design, implementation, operation, and monitoring and controlling. This study focuses on identifying methods related to the implementation and operation of the business model through their underlying business processes. For this purpose, we conduct a systematic literature review to identify the methods that structurally bridge business models and business processes. We categorize the selected approaches in a concept matrix according to their use for the implementation and operation of a business model. Finally, we conclude our paper by identifying knowledge gaps for future research and development of the topic of study.

Organizations innovate their business models in order to remain competitive in today’s dynamic business environment. To reduce uncertainty during the business model innovation process, organizations need to evaluate new and existing business models. One possible way to carry out this evaluation is through the use of business model performance indicators. Currently, organizations are still facing challenges in defining relevant indicators for their business models. The current literature lacks a complete picture of performance indicators for monitoring business model performance. Therefore, we conducted a systematic literature review to analyze which performance indicators are referred to in the academic literature related to business models. We contribute by synthesizing the identified indicators into a comprehensive catalog, categorized into relevant business model dimensions, and by providing possible directions for further research. Practitioners can use the catalog to select relevant performance indicators and tailor them to their organization’s specific business models.

Consumers are able to make use of a vast amount of mobility-related services, via mobile apps. For each of these apps, consumers are required to download, and sign-in separately. As a response to this complexity, Mobility-as-a-Service (Maas) platforms emerged which integrate multiple mobility service providers. These platforms present consumers with a variety of mobility service providers. Consumers then choose one of the mobility services. So far, little is known about consumers’ preference for service providers within MaaS platforms. We designed an online survey asking respondents about the influence of several service attributes on their preferences for mobility providers in MaaS platforms. Using factor analysis we identify the different consumers’ viewpoints regarding mobility service attributes. The results of our study can support the design of mobility services’ business models to achieve differentiation within MaaS platforms. This can improve adoption of such services and thereby help capture value for consumers, businesses and could contribute to livable cities.

The transportation sector is the largest contributor to global greenhouse gas emissions. Disruptive technological changes in this sector, such as alternative fuel vehicles, are crucial for emission reduction. We show how a cost-minimizing strategic transition plan to adopt electric trucks over time can be developed for a firm that owns and operates a fleet of diesel trucks. We consider the case in which the firm decides to invest in the charging infrastructure required to support this transition, either because the public charging infrastructure is currently inadequate or for strategic reasons. The congestion effect at the charging stations, the charging times, and the potential loss of productive driving time due to detours to reach charging stations are explicitly considered. By developing an independence property, we are able to model this problem as a linear integer program without specifying origins and destinations. We illustrate the resulting transition plan with realistic parameter configurations. Our results indicate that a firm with high transportation demand density over a given service region significantly benefits from adoption of electric trucks, while also enjoying substantial carbon emissions savings. High demand density also favors smaller battery capacity with shorter ranges under the optimized charging network capacity, even though larger battery capacity would increase productivity with extended ranges. Our analysis also offers insights for governments and regulators regarding the impact of several influential factors such as carbon cost, content of renewable energy in electricity mix, diesel engine efficiency, and subsidizing the charging infrastructure.Additionally, we present an extension to the model that allows for different modalities of partnership in the infrastructure investment; notably public-private and private-private partnerships. While in general our results suggest that such partnerships are beneficial to all involved, the amount and relative distribution of the potential gains depend on the topography and on the density of charging infrastructure.

De afgelopen vijf jaar heeft het programma Sociale Innovatie in de Energietransitie tientallen energieprojecten in Brabant ondersteund. De ervaringen zijn nu gepubliceerd in het boekverslag 'Waar een wil is, is een (om)weg'. Hierin staan maar liefst 34 lessen voor overheden, inwoners en marktpartijen op het gebied van de opwek van elektriciteit, energiebesparing en warmte.

De afgelopen vijf jaar heeft het programma Sociale Innovatie in de Energietransitie tientallen energieprojecten in Brabant ondersteund. De ervaringen zijn nu gepubliceerd in het boekverslag 'Waar een wil is, is een (om)weg'. Hierin staan maar liefst 34 lessen voor overheden, inwoners en marktpartijen op het gebied van de opwek van elektriciteit, energiebesparing en warmte.

In order to explore the determinants of vacant taxi drivers' customer-search behavior, this paper intends to calibrate a time-dependent Multinomial Logit (MNL) model by mining over 1.6 billion GPS records from about 8,400 taxis in Shanghai, China. First, based on the ordering points to identify the clustering structure (OPTICS) algorithm, the downtown area of Shanghai city is divided into 47 hotspots to identify the hot areas of customer delivery and searching. Then, by investigating a typical search delivery process of a vacant taxi, five candidate factors that may affect the customer-search behavior are summarized and defined. Using the maximum likelihood method, the significant factors are finally found. The results reveal that the relative passenger demand, the regional likelihood of pick-ups as well as the expected rate of return are the most significant factors influencing customer-search behavior. Although the impact of traffic situation (i.e., the en-route delay and traffic condition of the target hotspot) is not particularly significant, service providers and policymakers should still take full advantage of it to schedule taxi service and mitigate the traffic congestion caused by the circulation of vacant taxis. Besides, this paper also shows that the customer-search behavior of a vacant taxi driver varies with the time of day. Findings in this paper are expected to provide comprehensive insights about factors that should be considered in the future operation pattern of a taxi service system where human driver taxis and self-driving taxis are mixed.

The mobility solutions that currently dominate the mobility market have raised global challenges. Specifically, mass car ownership has led to traffic congestion, shortage of parking spaces, and sustainability issues. Recently, mobility solutions driven by technological advancements have emerged to address these issues via more efficient and sustainable use of resources. However, the wide range of mobility offerings has led to a scattered mobility market, and oversight is hard to grasp for travelers. Mobility-as-a-Service (MaaS) platforms aim to address this issue by integrating mobility services into a single platform. However, MaaS providers (operators) struggle to find sustainable business models. Additionally, research on shared mobility business models is limited, and there is little oversight in the scattered business model landscape. This report addresses this issue by summarizing the dominant business models in the mobility market through a systematic review of current initiatives and literature. It provides an overview of active MaaS business models and challenges and opportunities to integrate mobility services into MaaS. The types of mobility services reviewed in this study include bike-sharing, scooter-sharing, car-sharing, e-hailing, and MaaS platform providers. For each mobility service, the dominant operating mode and the main business model actors are identified and represented using the Service-Dominant Business Model Radar (SDBM/R). Furthermore, the value exchanges between the actors are mapped in Value Capture Diagrams. The report concludes with a discussion on the challenges and opportunities related to synthesizing shared mobility modes into MaaS and the expectations for its future.

Current societal challenges require technologies to be interconnected with one another along different steps of the value chain. They require interoperability. Said interoperability can be achieved by either vertical integration of products through one producer (monopoly) or through the adoption of open standards. However, agreement on a standard is often a difficult and lengthy process, in which companies’ interests (in terms of features, functionalities, etc.) might act as obstacles. We are thus faced with the dilemma between facilitating the market entry and diffusion of a new technology while avoiding market inefficiencies. To identify the enabling and disabling conditions that a technology in complex systems (i.e. with interoperability requirements and complex value chains) encounters, this paper will examine how different forms of technical alignment result in different stable configurations in the market. Three technologies will be analysed: Battery Electric Vehicles (BEVs), game consoles and payment systems at Points of Sale (POS).

We demonstrated the microtransfer molding of Norland Optical Adhesive 81 (NOA81) thin films. NOA81 nanogrooves and flat thin films were transferred from a flexible polydimethylsiloxane (PDMS) working mold. In the case of nanogrooves, the mold's feature area of 15 × 15 mm2 contains a variety of pattern dimensions in a set of smaller nanogroove fields of a few mm2 each. We demonstrated that at least six microtransfers can be performed from the same PDMS working mold. Within the restriction of our atomic force microscopy measurement technique, nanogroove height varies with 82 ± 11 nm depending on the pattern dimensions of the measured fields. Respective micrographs of two of these fields, i.e., one field designated with narrower grooves (D1000L780, case 1) and the other designated with wider grooves (D1000L230, case 2) but with the same periodicity values, demonstrate faithful transfer of the patterns. The designated pattern dimensions refer to the periodicity (D) and the ridge width (L) in the original design process of the master mold (dimensional units are nm). In addition, neither NOA81 itself (flat films) nor NOA81 nanogroove thin films with a thickness of 1.6 μm deteriorate the imaging quality in optical cell microscopy.

In knowledge-based economies, long-term wealth generation depends not only on human capital and research separately but also on the complementarity between research and human capital in the creation of innovation and the feedback effects of innovation into the economy. The interaction between education, research and innovation has gained prominence with the “Knowledge Triangle” concept. In this chapter, we present an explorative study of knowledge triangles of research-education-innovation within Dutch entrepreneurial ecosystems. Knowledge triangles do not evolve in a vacuum but are part of a broader set of interdependent actors and factors that, if coordinated in an adequate way, enable entrepreneurship within a particular territory. We focus on the role of regional governance (i.e. networks and leadership) in the knowledge triangle and the entrepreneurial ecosystem more broadly. This is reflected in the main question addressed in this chapter: How is the interaction between research [knowledge] and education [talent] coordinated [by networks and leadership] to promote entrepreneurship in regional ecosystems in the Netherlands?

High Pressure Direct Injection (HPDI) is a promising combustion concept for the medium- to heavy-duty industry to combat climate change. It uses a pilot diesel injection to ignite the main fuel consisting of Natural Gas (NG). Both fuels are injected directly in the combustion chamber using a dedicated HPDI injector. A significant reduction in carbon dioxide and Particulate Matter is achieved due to the use of the low carbon fuel NG. It is seen in literature that a small change in pilot injection can have profound consequences for the HPDI combustion. This research investigates the interaction between the pilot diesel and main NG injection. A relevant Computational Fluid Dynamics (CFD) simulation environment is setup for this purpose. It is observed that the main NG injection needs a certain pilot trigger to ignite. Furthermore, local conditions are derived to investigate driving factors of the ignition of NG on a fundamental level. A homogeneous reactor model is used to study Ignition Delay (ID) behavior by varying the initial temperature as well as concentrations of radicals H and OH. It is observed that both factors influence the ID. The initial temperature has to be higher than 1110 K in order to ignite the NG under enginelike conditions. It is also observed that species mole fractions H or OH encountered in the CFD simulation can reduce the ID up to 5.5 crank angle degrees at a speed of 1400 RPM.