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The electrification and digitalization of our society has propelled the demand for energy storage solutions. High-end technologies have been developed to satisfy the requirements of demanding applications, such as electromobility and portable consumer electronics, which also increasingly find markets for less demanding applications. These markets include grid and domestic energy storage, as well as Internet of Things (IoT). However, using high-end technologies for low-end applications is a waste of resources that puts unnecessary stress on the supply lines. Thus, more low-cost cost and environmentally friendly alternative technologies are sought, among which renewable biobased materials derived from agriculture and forestry play a prominent role. The dominant chemical constituents in plants, cellulose and lignin, exhibit some intriguing electrochemical and colloidal properties. Cellulose has been found to efficiently stabilize various electronic materials, whereas lignin can be used as an electronic material itself. Lignocellulosic materials also open for papermaking as an alternative manufacturing approach. Taking the step to using papermaking methods is, however, a bit far from the technology readiness level, as the vast majority of the research on paper electrodes is based on nanocellulose. The material properties of such nanopapers are indeed extraordinary, but the lack of large-scale production methods for nanopapers is a serious challenge. To circumvent this obstacle and find a shortcut to the realization of paper electrodes, this thesis has turned to conventional papermaking techniques. Fibres are essentially different to nanofibrils by their difference in size, and the papermaking process requires careful composition of the formulations. Thus, as the research on nanopaper electrodes cannot be directly translated into conventional papermaking techniques, this calls for separate studies on fibre-based systems. This thesis is based on four separate works carrie

The electrification and digitalization of our society has propelled the demand for energy storage solutions. High-end technologies have been developed to satisfy the requirements of demanding applications, such as electromobility and portable consumer electronics, which also increasingly find markets for less demanding applications. These markets include grid and domestic energy storage, as well as Internet of Things (IoT). However, using high-end technologies for low-end applications is a waste of resources that puts unnecessary stress on the supply lines. Thus, more low-cost cost and environmentally friendly alternative technologies are sought, among which renewable biobased materials derived from agriculture and forestry play a prominent role. The dominant chemical constituents in plants, cellulose and lignin, exhibit some intriguing electrochemical and colloidal properties. Cellulose has been found to efficiently stabilize various electronic materials, whereas lignin can be used as an electronic material itself. Lignocellulosic materials also open for papermaking as an alternative manufacturing approach. Taking the step to using papermaking methods is, however, a bit far from the technology readiness level, as the vast majority of the research on paper electrodes is based on nanocellulose. The material properties of such nanopapers are indeed extraordinary, but the lack of large-scale production methods for nanopapers is a serious challenge. To circumvent this obstacle and find a shortcut to the realization of paper electrodes, this thesis has turned to conventional papermaking techniques. Fibres are essentially different to nanofibrils by their difference in size, and the papermaking process requires careful composition of the formulations. Thus, as the research on nanopaper electrodes cannot be directly translated into conventional papermaking techniques, this calls for separate studies on fibre-based systems. This thesis is based on four separate works carrie

The electrification and digitalization of our society has propelled the demand for energy storage solutions. High-end technologies have been developed to satisfy the requirements of demanding applications, such as electromobility and portable consumer electronics, which also increasingly find markets for less demanding applications. These markets include grid and domestic energy storage, as well as Internet of Things (IoT). However, using high-end technologies for low-end applications is a waste of resources that puts unnecessary stress on the supply lines. Thus, more low-cost cost and environmentally friendly alternative technologies are sought, among which renewable biobased materials derived from agriculture and forestry play a prominent role. The dominant chemical constituents in plants, cellulose and lignin, exhibit some intriguing electrochemical and colloidal properties. Cellulose has been found to efficiently stabilize various electronic materials, whereas lignin can be used as an electronic material itself. Lignocellulosic materials also open for papermaking as an alternative manufacturing approach. Taking the step to using papermaking methods is, however, a bit far from the technology readiness level, as the vast majority of the research on paper electrodes is based on nanocellulose. The material properties of such nanopapers are indeed extraordinary, but the lack of large-scale production methods for nanopapers is a serious challenge. To circumvent this obstacle and find a shortcut to the realization of paper electrodes, this thesis has turned to conventional papermaking techniques. Fibres are essentially different to nanofibrils by their difference in size, and the papermaking process requires careful composition of the formulations. Thus, as the research on nanopaper electrodes cannot be directly translated into conventional papermaking techniques, this calls for separate studies on fibre-based systems. This thesis is based on four separate works carrie

The electrification and digitalization of our society has propelled the demand for energy storage solutions. High-end technologies have been developed to satisfy the requirements of demanding applications, such as electromobility and portable consumer electronics, which also increasingly find markets for less demanding applications. These markets include grid and domestic energy storage, as well as Internet of Things (IoT). However, using high-end technologies for low-end applications is a waste of resources that puts unnecessary stress on the supply lines. Thus, more low-cost cost and environmentally friendly alternative technologies are sought, among which renewable biobased materials derived from agriculture and forestry play a prominent role. The dominant chemical constituents in plants, cellulose and lignin, exhibit some intriguing electrochemical and colloidal properties. Cellulose has been found to efficiently stabilize various electronic materials, whereas lignin can be used as an electronic material itself. Lignocellulosic materials also open for papermaking as an alternative manufacturing approach. Taking the step to using papermaking methods is, however, a bit far from the technology readiness level, as the vast majority of the research on paper electrodes is based on nanocellulose. The material properties of such nanopapers are indeed extraordinary, but the lack of large-scale production methods for nanopapers is a serious challenge. To circumvent this obstacle and find a shortcut to the realization of paper electrodes, this thesis has turned to conventional papermaking techniques. Fibres are essentially different to nanofibrils by their difference in size, and the papermaking process requires careful composition of the formulations. Thus, as the research on nanopaper electrodes cannot be directly translated into conventional papermaking techniques, this calls for separate studies on fibre-based systems. This thesis is based on four separate works carrie

This special section is composed of substantially extended handpicked papers from the IEEE Nordic Circuits and Systems Conference (NorCAS) 2022 that took place in October 2022 in Oslo, Norway.

Purpose: Among a multitude of initiatives that can contribute to decarbonize freight transport (McKinnon, 2018), electrification has surficed as a main strategy (Li et al, 2022). The transition towards electrification has commenced, but in particular for long-haul transportation it is slow. Obstacles to the transition include the price of the new technology, lack of infrastructure and lack of policy incentives. In addition, the technology is new to all actors. This new-ness, together with the acknowledged complexity of the transition to electrified freight proposes learning challenges for all involved actors, for example vehicle manufacturers, haulier firms, logistics service providers and shippers. The necessary knowledge to navigate and operate the freight transport system cannot reside in one single orchestrator of the supply chain, but must be shared among actors, which in turn highlight the need for interaction with the purpose to share knowledge and experiences, and to jointly build new knowledge. This paper aims to address how learning among actors in the supply chain can contribute to accelerate the electrification of freight transport. Design / Methodology / Approach: This conceotual paper presents a framework for describing and analysing learning between actors in a supply chain. The framework takes a stance in a model for organisational learning (Crossan, Lane & White, 1999) adapted to the context of inter-organisational learning. This model will be adapted to the context of electrification of freight based on findings from a range of ongoing research projects into electrification of freight transport. Findings: The paper presents a model for describing and analysing learning between organisations in a supply chain. The model is conceptual and is intended to be used for analysing multi-actor learning processes in the context of electrification of freight. Practical implications: The results of the paper include suggestions for how learning between a

Changing trains is a crucial part of many rail journeys, and it is important that arrival and departing trains are allocated close to each other for a swift interchange. Today the platform allocation of trains is often based on local traditions, where trains of the same type to the same destination depart from the same track. In this paper we address the problem of using the platform tracks in the best way, balancing crossing train paths with easy interchanges at the same platform. We use a RailSys model of the station in Norrköping, Sweden, to assess three platform allocation strategies with respect to crossing train paths and train changes at the same platform. Moreover, the capacity utilisation is calculated by a timetable compression-based method. The results show that a platform allocation maximising the changes at the same platform leads to more crossing train paths and higher capacity utilisation. Future work includes more accurate modelling of train connections, assessment of delays through simulation, and a cost-benefit analysis to find the best balance between easy interchanges and conflicting train paths., Funding agencies: This research is a result of a collaboration between KTH Royal Institute of Technology and Linköping University and has been funded by Trafikverket (the Swedish Transport Administration) through the strategic research initiative “Excellensområdesprogram - Järnvägsforskning 2021-2030” (Dnr. TRV 2023/30667), and through the project “Kapacitet i nätverk 2 (KAIN 2)” (Dnr. TRV 2020/72702). We would like to thank Trafikverket, for providing timetable data from the Swedish railway network.

Policymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR frameworks predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies., Funding Agencies|Linkoping University

This paper delves into the dyadic relationships between Science Parks (SPs) and universities from the perspective of SPs. It explores various dimensions, including organizational functions, co-location, collaboration, management team activities, partnerships, and connections with university students and senior academics. A survey of 120 European SPs underscores the significance of having the University-Industry Liaison Office within the SP, fostering increased collaboration with the local university, providing career opportunities for university students, and promoting alumni network activities. Additionally, the proximity of universities and research institutions within a 50 km radius positively impacts the relationships between SPs and universities. Additionally, the paper offers several managerial implications. Establishing communication channels between SP management and universities fosters an environment that boosts the open exchange of ideas, collaborative discussions, and problem-solving. The alignment of SPs and universities' goals and objectives, particularly in areas such as research themes, industry partnerships, technology transfer, and talent development, further solidifies the mutually advantageous nature of these relationships, establishing a strong foundation for their enhancement. Within the SP environment, universities can closely collaborate with businesses, start-ups, and entrepreneurs, promoting innovation, commercializing research findings, and incubating spin-off ventures., Funding: Chalmers University of Technology

Remanufacturing has been modelled extensively from an economic perspective. However, there are relatively few studies focusing on models that consider the implementation difficulties in industry, such as data availability and model transparency. This paper aims to identify how remanufacturing initiations can be economically assessed with models particularly suitable for industry. These models should contribute to disseminating the know-how about initiating remanufacturing to transit industries towards more circular product flows. To fulfil this aim, a systematic literature review was conducted. The search resulted in a total of 964 papers published till August 2021. The results show that assessment models could be categorised into a two-dimensional framework indicating the level of computer support needed to handle the models and the level of data requirements to use the models, both of which are considered essential in the industrial implementation of the models. The review revealed and structured a further need for research on assessing remanufacturing initiation profitability, especially on economic assessment models designed to assist industries without previous remanufacturing knowledge in such an initiation., Remometer, Mistra REES (Resource-Efficient and Effective Solutions)

This conceptual paper argues that there are unrealized benefits from a cross-fertilization between research on sustainable tourism and sustainability transitions studies. With the aim to stimulate such cross-fertilization, the paper identifies three contemporary streams in sustainability transitions studies that are particularly relevant for sustainable tourism: deep transitions, transitions in practice, and the geography of transitions. These three streams present complementary perspectives, which can help reach a more nuanced understanding of transition processes in the tourism sector. The paper concludes that while research on sustainable tourism can benefit from theories, concepts, and frameworks from research on sustainability transitions, research on sustainable tourism can support further conceptual developments in sustainability transitions studies., The future of event and meeting industry for transport effective society

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

Policymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR frameworks predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies., Funding Agencies|Linkoping University

This paper delves into the dyadic relationships between Science Parks (SPs) and universities from the perspective of SPs. It explores various dimensions, including organizational functions, co-location, collaboration, management team activities, partnerships, and connections with university students and senior academics. A survey of 120 European SPs underscores the significance of having the University-Industry Liaison Office within the SP, fostering increased collaboration with the local university, providing career opportunities for university students, and promoting alumni network activities. Additionally, the proximity of universities and research institutions within a 50 km radius positively impacts the relationships between SPs and universities. Additionally, the paper offers several managerial implications. Establishing communication channels between SP management and universities fosters an environment that boosts the open exchange of ideas, collaborative discussions, and problem-solving. The alignment of SPs and universities' goals and objectives, particularly in areas such as research themes, industry partnerships, technology transfer, and talent development, further solidifies the mutually advantageous nature of these relationships, establishing a strong foundation for their enhancement. Within the SP environment, universities can closely collaborate with businesses, start-ups, and entrepreneurs, promoting innovation, commercializing research findings, and incubating spin-off ventures., Funding: Chalmers University of Technology

Remanufacturing has been modelled extensively from an economic perspective. However, there are relatively few studies focusing on models that consider the implementation difficulties in industry, such as data availability and model transparency. This paper aims to identify how remanufacturing initiations can be economically assessed with models particularly suitable for industry. These models should contribute to disseminating the know-how about initiating remanufacturing to transit industries towards more circular product flows. To fulfil this aim, a systematic literature review was conducted. The search resulted in a total of 964 papers published till August 2021. The results show that assessment models could be categorised into a two-dimensional framework indicating the level of computer support needed to handle the models and the level of data requirements to use the models, both of which are considered essential in the industrial implementation of the models. The review revealed and structured a further need for research on assessing remanufacturing initiation profitability, especially on economic assessment models designed to assist industries without previous remanufacturing knowledge in such an initiation., Remometer, Mistra REES (Resource-Efficient and Effective Solutions)

This conceptual paper argues that there are unrealized benefits from a cross-fertilization between research on sustainable tourism and sustainability transitions studies. With the aim to stimulate such cross-fertilization, the paper identifies three contemporary streams in sustainability transitions studies that are particularly relevant for sustainable tourism: deep transitions, transitions in practice, and the geography of transitions. These three streams present complementary perspectives, which can help reach a more nuanced understanding of transition processes in the tourism sector. The paper concludes that while research on sustainable tourism can benefit from theories, concepts, and frameworks from research on sustainability transitions, research on sustainable tourism can support further conceptual developments in sustainability transitions studies., The future of event and meeting industry for transport effective society

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

Changing trains is a crucial part of many rail journeys, and it is important that arrival and departing trains are allocated close to each other for a swift interchange. Today the platform allocation of trains is often based on local traditions, where trains of the same type to the same destination depart from the same track. In this paper we address the problem of using the platform tracks in the best way, balancing crossing train paths with easy interchanges at the same platform. We use a RailSys model of the station in Norrköping, Sweden, to assess three platform allocation strategies with respect to crossing train paths and train changes at the same platform. Moreover, the capacity utilisation is calculated by a timetable compression-based method. The results show that a platform allocation maximising the changes at the same platform leads to more crossing train paths and higher capacity utilisation. Future work includes more accurate modelling of train connections, assessment of delays through simulation, and a cost-benefit analysis to find the best balance between easy interchanges and conflicting train paths., Funding agencies: This research is a result of a collaboration between KTH Royal Institute of Technology and Linköping University and has been funded by Trafikverket (the Swedish Transport Administration) through the strategic research initiative “Excellensområdesprogram - Järnvägsforskning 2021-2030” (Dnr. TRV 2023/30667), and through the project “Kapacitet i nätverk 2 (KAIN 2)” (Dnr. TRV 2020/72702). We would like to thank Trafikverket, for providing timetable data from the Swedish railway network.

This paper delves into the dyadic relationships between Science Parks (SPs) and universities from the perspective of SPs. It explores various dimensions, including organizational functions, co-location, collaboration, management team activities, partnerships, and connections with university students and senior academics. A survey of 120 European SPs underscores the significance of having the University-Industry Liaison Office within the SP, fostering increased collaboration with the local university, providing career opportunities for university students, and promoting alumni network activities. Additionally, the proximity of universities and research institutions within a 50 km radius positively impacts the relationships between SPs and universities. Additionally, the paper offers several managerial implications. Establishing communication channels between SP management and universities fosters an environment that boosts the open exchange of ideas, collaborative discussions, and problem-solving. The alignment of SPs and universities' goals and objectives, particularly in areas such as research themes, industry partnerships, technology transfer, and talent development, further solidifies the mutually advantageous nature of these relationships, establishing a strong foundation for their enhancement. Within the SP environment, universities can closely collaborate with businesses, start-ups, and entrepreneurs, promoting innovation, commercializing research findings, and incubating spin-off ventures., Funding: Chalmers University of Technology

In this paper least squares and maximum likelihood estimates under univariate and multivariate linear models with a priori information related to maximum effects in the models are determined. Both loss functions (the least squares and negative log-likelihood) and the constraints are convex, so the convex optimization theory can be utilized to obtain estimates, which in this paper are called Safety belt estimates. In particular, the complementary slackness condition, common in convex optimization, implies two alternative types of solutions, strongly dependent on the data and the restriction. It is experimentally shown that, despite of the similarity to the ridge regression estimation under the univariate linear model, the Safety belt estimates behave usually better than estimates obtained via ridge regression. Moreover, concerning the multivariate model, the proposed technique represents a completely novel approach., This is a technical report and has not been externally reviewed.

The field of 2D materials is a relatively young and rapidly growing area within materials science, which is concerned with atomically thin states of matter. Because of their intrinsic 2D morphology, 2D materials have exceptionally high surface to weight or surface to volume ratio. This renders them excellent candidates for surface-sensitive applications such as catalysis and energy storage, which can aid us in the transition to a more sustainable society. 2D materials are also interesting because they show properties intrinsically different from those of their 3D counterparts, expanding the attainable property space within materials science. A 2D material can be synthesised by either a bottom-up or top-down approach. The focus here is on the latter, where the 2D material is derived by either mechanical exfoliation or selective etching of a 3D nanolaminated parent phase. A 3D laminate can typically be assigned to one of two types, depending on the type of interlayer bonding: van der Waals (vdW) or chemical bonding. In a vdW bonded phase, the constituent layers are kept together into their 3D form by rather weak vdW forces, while in the latter type, the layers are bound more strongly by chemical interactions (i.e., covalent, ionic and metallic bonds). The first 2D materials were derived from vdW-phases, which can be exfoliated by mechanical methods. In a chemically bound laminated phase, the inter layer bonding is stronger, and more complex methods are required for exfoliation of these phases into 2D. This thesis concerns the computational study and development of novel 2D materials through exploration of 3D nanolaminated structures, assessment of their phase stability, and potential for conversion into 2D. The 2D derivatives are in turn studied through prediction of dynamical stability, termination configuration, and evaluation of electronic properties. Paper III and IV each addresses a family of van der Waals structures. The family of 3D materials studied in Paper, Financial support from the Swedish Research Council, VR (grant number 2019-05047 and 2022-06099), the Göran Gustavsson Foundation for Research in Natural Sciences and Medicine, and the Knut and Alice Wal-lenberg (KAW) foundation has made this work possible. Simulations has been carried out using resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and Swedish National Infrastructure for Computing (SNIC), using the NSC, PDC and HPC2N computer clusters.

Policymakers must find efficient public transport solutions to promote sustainability and provide efficient urban mobility in the course of urban growth. A growing number of research papers are applying Geographically weighted regression (GWR) to model the relationship between public transport demand and its influential factors. However, few studies have considered the rapid development of journey inference from ticket transaction data. Similarly, the potential of GWR to analyze spatio-temporal changes that reflect changes in transportation supply and thus provide a measure for evaluating the local success of transport supply changes has yet to be exploited. In this paper, we use inferred journeys from smart card inferences as the dependent variable and analyze how public transport demand responds to a set of explanatory variables, emphasizing transport supply. Consequently, GWR and its successor Multiscale Geographically Weighted Regression (MGWR) are applied to analyze the spatially varying impact of transport supply changes for seven consecutive time frames between autumn 2017 and spring 2020, allowing conclusions about local changes in transport demand, as well as the benchmarking of transport supply changes. The (M)GWR frameworks predictive power is evaluated by training the model with past transport supply data and testing the model with data from the following consecutive years. The conducted analyses reveal that the (M)GWR model, using inferred journeys and transport supply data, can retrospectively predict the impact of transport supply changes on travel behavior and thus provides conclusions about the success of transport policies., Funding Agencies|Linkoping University

Ports are actively pursuing greater operational efficiency to effectively handle the increasing global flow of goods, while striving to improve the energy efficiency of their operations to comply with new environmental regulations. As a result, innovation-leading ports have begun to recognize the potential of digital twins to overview, coordinate and optimize port processes, resulting in energy savings, and reductions of costs and of CO2 emissions. While digital twins have gained momentum in other domains such as smart manufacturing and aerospace, their adoption in ports has been comparatively slow. This can be explained, among other things, by the multi-stakeholder nature of the port and the high complexity of the often interconnected port processes. Thus, this thesis, grounded in the context of ports, discusses what constitutes a digital twin, proposes characteristics to assess the maturity of existing digital twins, and introduces and evaluates mathematical models to support a key port process, which can be used as components of a digital twin for the port. The thesis is composed of three papers: Paper 1 is based on an extensive literature review, through which digital twins among different domains are studied in depth in order to transfer insights from these to the port domain. The resulting discussion of what constitutes a port’s digital twin and the requirements that a port’s digital twin must fulfil, together with a discussion of use cases of how port digital twins can contribute to energy savings, form the basis of Paper 1. Paper 2 discusses how digital twins’ maturity can be assessed within six maturity levels and presents milestones for their implementation. Notably, Interoperability is identified as the highest maturity level, as the numerous stakeholders and their respective digital twins must work together to reach a coordinated system of systems performance. Using this assessment demonstrates that only a few innovation-leading ports have developed so, Hamnar strävar aktivt efter ökad operativ effektivitet för att hantera den ökande globala varuflödet, samtidigt som de strävar efter att förbättra energieffektiviteten. Som ett resultat har ledande hamnar börjat se potentialen hos digitala tvillingar för att skapa överblick samt koordinera och optimera processer i hamnen. Målet med användningen av digitala tvillingar är energibesparingar samt minskning av kostnader och CO2-utsläpp. Medan digitala tvillingar har använts inom andra områden såsom tillverknings-, flyg- och rymdindustrin, har införandet i hamnar varit jämförelsevist långsamt. Detta kan förklaras, bland annat, av hamnens många olika involverade aktörer och den höga komplexiteten i de ofta sammanlänkade hamnprocesserna. Därför fokuserar denna avhandling, med utgångspunkt i hamnkontexten, vad som utgör en digital tvilling, presenterar egenskaper för olika mognadsnivåer hos befintliga digitala tvillingar, och introducerar samt utvärderar matematiska modeller som kan bli delkomponenter i en digital tvilling för hamnen. Avhandlingen består av tre artiklar: Artikel 1 bygger på en omfattande litteraturöversikt, inom vilken digitala tvillingar för olika områden studeras ingående för att överföra insikter från dessa till hamndomänen. Detta resulterar i en presentation av vad som utgör en hamns digitala tvilling och de krav som en hamns digitala tvilling måste uppfylla, tillsammans med en diskussion om möjliga sett på vilka hur hamnens digitala tvillingar kan bidra till energibesparingar. Artikel 2 presenterar ett ramverk för hur mognaden hos digitala tvillingar kan bedömas baserat på sex mognadsnivåer och presenterar milstolpar för deras implementering. Noterbart är att interoperabilitet identifieras som den högsta mognadsnivån, eftersom de många intressenterna och deras respektive digitala tvillingar måste koordineras för att nå en fungerande system-av-systemnviå. Genom att använda denna bedömning visar det sig att endast några få innovationsledande hamnar hittil

Remanufacturing has been modelled extensively from an economic perspective. However, there are relatively few studies focusing on models that consider the implementation difficulties in industry, such as data availability and model transparency. This paper aims to identify how remanufacturing initiations can be economically assessed with models particularly suitable for industry. These models should contribute to disseminating the know-how about initiating remanufacturing to transit industries towards more circular product flows. To fulfil this aim, a systematic literature review was conducted. The search resulted in a total of 964 papers published till August 2021. The results show that assessment models could be categorised into a two-dimensional framework indicating the level of computer support needed to handle the models and the level of data requirements to use the models, both of which are considered essential in the industrial implementation of the models. The review revealed and structured a further need for research on assessing remanufacturing initiation profitability, especially on economic assessment models designed to assist industries without previous remanufacturing knowledge in such an initiation., Remometer, Mistra REES (Resource-Efficient and Effective Solutions)

In this paper least squares and maximum likelihood estimates under univariate and multivariate linear models with a priori information related to maximum effects in the models are determined. Both loss functions (the least squares and negative log-likelihood) and the constraints are convex, so the convex optimization theory can be utilized to obtain estimates, which in this paper are called Safety belt estimates. In particular, the complementary slackness condition, common in convex optimization, implies two alternative types of solutions, strongly dependent on the data and the restriction. It is experimentally shown that, despite of the similarity to the ridge regression estimation under the univariate linear model, the Safety belt estimates behave usually better than estimates obtained via ridge regression. Moreover, concerning the multivariate model, the proposed technique represents a completely novel approach., This is a technical report and has not been externally reviewed.

Detailed longitudinal dynamics simulations may be used to predict the energy performance of road vehicles. However, including uncertainty in the operating conditions often implies high computational costs. Model-based formulations, in conjunction with statistical methods, may obviate this limitation by directly accounting for stochasticity, thus eliminating the need for simulating large populations of driving and operating cycles. To this end, leveraging directly the methods of stochastic calculus, this work presents a novel theory of longitudinal vehicle dynamics and energy consumption, where the vehicle’s speed varies stochastically depending on the characteristics of the operating environment. In particular, the proposed formulation, consisting of stochastic differential equations (SDEs) governing the longitudinal motion of road vehicles, inherently accounts for the statistical variation connected with uncertainties in the driver’s behavior and road properties, including, e.g., topography and legal speed. A Fokker-Planck partial differential equation (PDE) that describes the time evolution of the joint probability density function (PDF) of the vehicle’s speed, position, and road parameters is also derived from the SDEs established in the paper. The SDE and Fokker-Planck-based approaches enable statistical estimation of important quantities like speed fluctuations, instantaneous power requests, and energy consumption. The developed models may be used to assess the energy performance of road vehicles for different combinations of road transport missions. This is applicable at the early stages of the development, virtual testing, and certification processes, without the need to perform computationally expensive simulations, as corroborated by the virtual experiments conducted in the paper.

Metabolic theories in ecology interpret ecological patterns at different levels (individuals, populations, communities) through the lens of metabolism, often applying allometric scaling with rates of energy use described as power functions of body mass. However, the application of metabolic theory at higher levels requires a sound theory for metabolism at the individual level. In this thesis, metabolic theory has been developed and applied in three different contexts; 1) growth of individual organisms under food limitation, 2) life-history theory for age and size at maturity for individual organisms, and 3) population growth of marine mammals exposed to bioaccumulative toxicants through their diet. In the first context, a new mechanistic model for individual growth was developed, based on an energy balance that expresses growth as the net result of energy assimilation from food and various metabolic costs. The model can account for effects of body composition and cellular-level growth patterns, but foremost it considers a potential trade-off between regulated maintenance and growth under food limitation. The model was successfully calibrated and validated against empirical data for an insect (house cricket) under both unlimited and limited food conditions. Interestingly, the empirical calibration indicated that the energy per unit body mass that an organism allocates to maintenance of body structures may increase as the organism grows and may also be upregulated under food limitation. In Paper I, the maintenance-growth model (MGM), is presented, derived and demonstrated via numerical simulations and comparisons with available growth data. In Paper II and III, MGM is calibrated and evaluated against collected data for house crickets growing under unlimited and restricted food supply, respectively. In the second context (Paper IV), it was investigated how models for individual growth and mortality can be combined with life-history theory to model plastic responses in, Funding agencies: The Swedish research council, grant number 2018-05523, Viltforskningsanslaget, Swedish Environmental Protection Agency, and the BONUS program BaltHealth (Art. 185).

Felix Klein’s Erlangen Programme of 1872 introduced a methodology to unify non-Euclidean geometries. Similarly, geometric deep learning (GDL) constitutes a unifying framework for various neural network architectures. GDL is built from the first principles of geometry—symmetry and scale separation—and enables tractable learning in high dimensions. Symmetries play a vital role in preserving structural information of geometric data and allow models (i.e., neural networks) to adjust to different geometric transformations. In this context, spheres exhibit a maximal set of symmetries compared to other geometric entities in Euclidean space. The orthogonal group O(n) fully encapsulates the symmetry structure of an nD sphere, including both rotational and reflection symmetries. In this thesis, we focus on integrating these symmetries into a model as an inductive bias, which is a crucial requirement for addressing problems in 3D vision as well as in natural sciences and their related applications. In Paper A, we focus on 3D geometry and use the symmetries of spheres as geometric entities to construct neurons with spherical decision surfaces—spherical neurons—using a conformal embedding of Euclidean space. We also demonstrate that spherical neuron activations are non-linear due to the inherent non-linearity of the input embedding, and thus, do not necessarily require an activation function. In addition, we show graphically, theoretically, and experimentally that spherical neuron activations are isometries in Euclidean space, which is a prerequisite for the equivariance contributions of our subsequent work. In Paper B, we closely examine the isometry property of the spherical neurons in the context of equivariance under 3D rotations (i.e., SO(3)-equivariance). Focusing on 3D in this work and based on a minimal set of four spherical neurons (one learned spherical decision surface and three copies), the centers of which are rotated into the corresponding vertices of a regular, Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP); National Academic Infrastructure for Supercomputing in Sweden (NAISS) partially funded by the Swedish Research Council through grant agreement no. 2022-06725, and by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre.

Train dispatchers play a vital role for safe and efficient railway operations. Their role includes several tasks, such as controlling train movements, maneuvering railway infrastructures (e.g., switches and signals), communicating with train crew and maintenance contractors. Dispatching centers operate around the clock, resulting in a high risk of fatigue for dispatchers, especially during night shifts. Moreover, dispatchers’ cognitive performance deteriorates when workload levels are too high. Although workload is a subjective measure, it is correlated with task load, i.e., the number of tasks executed in a given period of time. A preventive approach against too high workload levels is to account for the task load already during the scheduling phase. Currently, shifts are scheduled manually, which is a very complex task that cannot guarantee a satisfying quality both in terms of high efficiency and balanced workload levels. The aim of this thesis is to create a scheduling framework, based on mathematical optimization models, that supports shift planners in their work. The framework helps in automatizing the scheduling process and improves the quality of the resulting shifts. Our initial approach is to use our models to figure out the required staffing levels and produce baseline schedules, and then improve these by increasing their attractiveness. To achieve this goal, we conduct three related studies considering the case of Malm¨o dispatching center. The studies result in different optimization models for shift scheduling of train dispatchers. We present the complete results in the following three papers: In the first paper, we build an optimization model for one-day shift scheduling, where the objective function is to minimize the number of needed dispatchers. Moreover, we analyze the impact on the computation time of different parameters, such as the number of geographical areas and how these are combined. The results of the experiments are obtained within accep

Train dispatchers play a vital role for safe and efficient railway operations. Their role includes several tasks, such as controlling train movements, maneuvering railway infrastructures (e.g., switches and signals), communicating with train crew and maintenance contractors. Dispatching centers operate around the clock, resulting in a high risk of fatigue for dispatchers, especially during night shifts. Moreover, dispatchers’ cognitive performance deteriorates when workload levels are too high. Although workload is a subjective measure, it is correlated with task load, i.e., the number of tasks executed in a given period of time. A preventive approach against too high workload levels is to account for the task load already during the scheduling phase. Currently, shifts are scheduled manually, which is a very complex task that cannot guarantee a satisfying quality both in terms of high efficiency and balanced workload levels. The aim of this thesis is to create a scheduling framework, based on mathematical optimization models, that supports shift planners in their work. The framework helps in automatizing the scheduling process and improves the quality of the resulting shifts. Our initial approach is to use our models to figure out the required staffing levels and produce baseline schedules, and then improve these by increasing their attractiveness. To achieve this goal, we conduct three related studies considering the case of Malm¨o dispatching center. The studies result in different optimization models for shift scheduling of train dispatchers. We present the complete results in the following three papers: In the first paper, we build an optimization model for one-day shift scheduling, where the objective function is to minimize the number of needed dispatchers. Moreover, we analyze the impact on the computation time of different parameters, such as the number of geographical areas and how these are combined. The results of the experiments are obtained within accep

With advances in artificial intelligence, machine learning, and cognitive modelling, unmanned aircraft are expected to actas human-like wingmen in the near future. For fluent and effective manned–unmanned teaming, synthetic wingmen must beable to account for and adapt to their partners’ intent with little or no communication. To enable such abilities, it becomescrucial to identify the requirements that makes intent explainable to synthetic wingmen, necessitating approaches to describeand analyse intent from a human-centric perspective. To address this issue, this paper reports on findings from using WorkDomain Analysis to design and analyse models of situated intent with six levels of cognitive control (frames, effects, values,generic, implementations, and physical). Through a literature review and seven subject matter expert interviews, a synthesizedmodel was designed to represent fighter pilots’ intent in a manned–unmanned teaming scenario. Using the synthesized modelas the context, a transfer of control and a link loss situation were further described and analysed. Experiences show that WorkDomain Analysis can provide a practical and applicable means to model situated intent, particularly since designed modelscan be re-utilised to model intent in similar situations. Furthermore, the model analyses show the importance of accountingfor fighter pilots’ adopted frames since small variations of the framing of the situations can propagate throughout the modelresulting in conflicting or inconsistent intent. The paper concludes that synthetic wingmen must be able to reason about all six levels of cognitive control, requiring a more holistic approach to make intent explainable., Funding: VINNOVA; Swedish Defence Material Administration [2017-04884,2017-04884,2017-04884,2017-04884]; NFFP (National Aviation Research Programme) - VINNOVA (Swedish Governmental Agency for Innovation Systems); Swedish Armed Forces, The Swedish Defence Material Administration and NFFP (National Aviation Research Pro- gramme), which is funded by VINNOVA (Swedish Governmental Agency for Innovation Systems, 2017-04884,2017-04884,2017- 04884,2017-04884), the Swedish Armed Forces, and the Swedish Defence Material Administration.

A method for obstacle detection using the sound that a drone naturally emits is proposed. The sound emitted from a vehicle, ego-noise, is often considered a complicating factor for mission fulfilment, without purpose. The idea in this paper is to utilise this ego-noise for obstacle detection, being the first to perform practical experiments of this. Adding a few microphones to the vehicle, the ego-noise is utilised as the sound source for echolocation. The method consists of auto-correlating the received signals to estimate echo delays, using the known array geometry and signal propagation speed to relate delays to distances, and then beamforming to position targets. A proof-of-concept has been constructed, and promising results are presented for experiments in a controlled environment.

Modular deep learning has been proposed for the efficient adaption of pre-trained models to new tasks, domains and languages. In particular, combining language adapters with task adapters has shown potential where no supervised data exists for a language. In this paper, we explore the role of language adapters in zero-shot cross-lingual transfer for natural language understanding (NLU) benchmarks. We study the effect of including a target-language adapter in detailed ablation studies with two multilingual models and three multilingual datasets. Our results show that the effect of target-language adapters is highly inconsistent across tasks, languages and models. Retaining the source-language adapter instead often leads to an equivalent, and sometimes to a better, performance. Removing the language adapter after training has only a weak negative effect, indicating that the language adapters do not have a strong impact on the predictions.

Detailed longitudinal dynamics simulations may be used to predict the energy performance of road vehicles. However, including uncertainty in the operating conditions often implies high computational costs. Model-based formulations, in conjunction with statistical methods, may obviate this limitation by directly accounting for stochasticity, thus eliminating the need for simulating large populations of driving and operating cycles. To this end, leveraging directly the methods of stochastic calculus, this work presents a novel theory of longitudinal vehicle dynamics and energy consumption, where the vehicle’s speed varies stochastically depending on the characteristics of the operating environment. In particular, the proposed formulation, consisting of stochastic differential equations (SDEs) governing the longitudinal motion of road vehicles, inherently accounts for the statistical variation connected with uncertainties in the driver’s behavior and road properties, including, e.g., topography and legal speed. A Fokker-Planck partial differential equation (PDE) that describes the time evolution of the joint probability density function (PDF) of the vehicle’s speed, position, and road parameters is also derived from the SDEs established in the paper. The SDE and Fokker-Planck-based approaches enable statistical estimation of important quantities like speed fluctuations, instantaneous power requests, and energy consumption. The developed models may be used to assess the energy performance of road vehicles for different combinations of road transport missions. This is applicable at the early stages of the development, virtual testing, and certification processes, without the need to perform computationally expensive simulations, as corroborated by the virtual experiments conducted in the paper.

Optimal task and motion planning (TAMP) has seen an increase in interest in recent years. In this paper we propose methods for using numerical optimal control to improve upon a feasible solution to a TAMP rearrangement problem. The methods are extensions of existing improvement methods for pure motion planning. The first method poses an optimal control problem (OCP) to simultaneously improve all motions in the plan. The second method, which we denote multiple finite horizons (MFH), takes inspiration from finite horizon control and poses a sequence of finite horizon OCPs involving variables for the positions of temporary placements of movable objects as well as motions in the plan, such that after solving each problem a feasible plan is maintained and the plan cost is non-increasing after each step. The methods are evaluated on a TAMP problem for tractor-trailers in numerical experiments, and the results show that both methods improve the plan for the evaluated problems. The results also show that MFH can reduce the computation time compared to the first method, and that on one example problem it achieves plans of similar or better quality as when all the motions are optimized at the same time provided that the horizon length is sufficiently long.

In this era of ubiquitous surveillance and online presence, protecting facial privacy has become a critical concern for individuals and society as a whole. Adversarial attacks have emerged as a promising solution to this problem, but current methods are limited in quality or are impractical for sensitive domains such as facial editing. This paper presents a novel adversarial image editing framework called StyleAdv, which leverages StyleGAN's latent spaces to generate powerful adversarial images, providing an effective tool against facial recognition systems. StyleAdv achieves high success rates by employing meaningful facial editing with StyleGAN while maintaining image quality, addressing a challenge faced by existing methods. To do so, the comprehensive framework integrates semantic editing, adversarial attacks, and face recognition systems, providing a cohesive and robust tool for privacy protection. We also introduce the ``residual attack`` strategy, using residual information to enhance attack success rates. Our evaluation offers insights into effective editing, discussing tradeoffs in latent spaces, optimal edits for our optimizer, and the impact of utilizing residual information. Our approach is transferable to state-of-the-art facial recognition systems, making it a versatile tool for privacy protection. In addition, we provide a user-friendly interface with multiple editing options to help users create effective adversarial images. Extensive experiments are used to provide insights and demonstrate that StyleAdv outperforms state-of-the-art methods in terms of both attack success rate and image quality. By providing a versatile tool for generating high-quality adversarial samples, StyleAdv can be used both to enhance individual users' privacy and to stimulate advances in adversarial attack and defense research., Funding: This work was supported by the Swedish Research Council (VR) and the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology., Funding Agencies|Swedish e-Science Research Centre (SeRC); Swedish National Graduate School in Computer Science (CUGS); Swedish Research Council (Vetenskapsradet) [2018-04147]; Swedish Agency for Economic and Regional and Growth (Tillvaxtverket)

Purpose – This paper aims to further our understanding of policy–practice gaps in organizations from an organizational learning perspective. The authors conceptualize and analyze policy–practice gaps in terms of what they label the dual challenge of organizational learning, i.e. the organizational tasks of both adapting ongoing practices to prescribed policy demands and adapting the policy itself to the needs of practice. Specifically, the authors address how this dual challenge can be understood in terms of organizational learning and how an organization can be managed to successfully resolve the dual learning challenge and, thereby, bridge policy–practice gaps in organizations. Design/methodology/approach – This paper draws on existing literature to explore the gap between policy and practice. Through a synthesis of theories and an illustrative practical example, this paper highlights key conceptual underpinnings. Findings – In the analysis of the dual challenge of organizational learning, this study provides a conceptual framework that emphasizes the important role of tensions and contradictions between policy and practice and their role as drivers of organizational learning. To bridge policy–practice gaps in organizations, this paper proposes five key principles that aim to resolve the dual challenge and accommodate both deployment and discovery in organizations. Research limitations/implications – Because this is a conceptual study, empirical research is called for to explore further and test the findings and conclusions of the study. Several avenues of possible future research are proposed. Originality/value – This paper primarily contributes by introducing and elaborating on a conceptual framework that offers novel perspectives on the dual challenges of facilitating both discovery and deployment processes within organizations.

Modular deep learning has been proposed for the efficient adaption of pre-trained models to new tasks, domains and languages. In particular, combining language adapters with task adapters has shown potential where no supervised data exists for a language. In this paper, we explore the role of language adapters in zero-shot cross-lingual transfer for natural language understanding (NLU) benchmarks. We study the effect of including a target-language adapter in detailed ablation studies with two multilingual models and three multilingual datasets. Our results show that the effect of target-language adapters is highly inconsistent across tasks, languages and models. Retaining the source-language adapter instead often leads to an equivalent, and sometimes to a better, performance. Removing the language adapter after training has only a weak negative effect, indicating that the language adapters do not have a strong impact on the predictions.

A method for obstacle detection using the sound that a drone naturally emits is proposed. The sound emitted from a vehicle, ego-noise, is often considered a complicating factor for mission fulfilment, without purpose. The idea in this paper is to utilise this ego-noise for obstacle detection, being the first to perform practical experiments of this. Adding a few microphones to the vehicle, the ego-noise is utilised as the sound source for echolocation. The method consists of auto-correlating the received signals to estimate echo delays, using the known array geometry and signal propagation speed to relate delays to distances, and then beamforming to position targets. A proof-of-concept has been constructed, and promising results are presented for experiments in a controlled environment.

Optimal task and motion planning (TAMP) has seen an increase in interest in recent years. In this paper we propose methods for using numerical optimal control to improve upon a feasible solution to a TAMP rearrangement problem. The methods are extensions of existing improvement methods for pure motion planning. The first method poses an optimal control problem (OCP) to simultaneously improve all motions in the plan. The second method, which we denote multiple finite horizons (MFH), takes inspiration from finite horizon control and poses a sequence of finite horizon OCPs involving variables for the positions of temporary placements of movable objects as well as motions in the plan, such that after solving each problem a feasible plan is maintained and the plan cost is non-increasing after each step. The methods are evaluated on a TAMP problem for tractor-trailers in numerical experiments, and the results show that both methods improve the plan for the evaluated problems. The results also show that MFH can reduce the computation time compared to the first method, and that on one example problem it achieves plans of similar or better quality as when all the motions are optimized at the same time provided that the horizon length is sufficiently long.

With advances in artificial intelligence, machine learning, and cognitive modelling, unmanned aircraft are expected to actas human-like wingmen in the near future. For fluent and effective manned–unmanned teaming, synthetic wingmen must beable to account for and adapt to their partners’ intent with little or no communication. To enable such abilities, it becomescrucial to identify the requirements that makes intent explainable to synthetic wingmen, necessitating approaches to describeand analyse intent from a human-centric perspective. To address this issue, this paper reports on findings from using WorkDomain Analysis to design and analyse models of situated intent with six levels of cognitive control (frames, effects, values,generic, implementations, and physical). Through a literature review and seven subject matter expert interviews, a synthesizedmodel was designed to represent fighter pilots’ intent in a manned–unmanned teaming scenario. Using the synthesized modelas the context, a transfer of control and a link loss situation were further described and analysed. Experiences show that WorkDomain Analysis can provide a practical and applicable means to model situated intent, particularly since designed modelscan be re-utilised to model intent in similar situations. Furthermore, the model analyses show the importance of accountingfor fighter pilots’ adopted frames since small variations of the framing of the situations can propagate throughout the modelresulting in conflicting or inconsistent intent. The paper concludes that synthetic wingmen must be able to reason about all six levels of cognitive control, requiring a more holistic approach to make intent explainable., Funding: VINNOVA; Swedish Defence Material Administration [2017-04884,2017-04884,2017-04884,2017-04884]; NFFP (National Aviation Research Programme) - VINNOVA (Swedish Governmental Agency for Innovation Systems); Swedish Armed Forces, The Swedish Defence Material Administration and NFFP (National Aviation Research Pro- gramme), which is funded by VINNOVA (Swedish Governmental Agency for Innovation Systems, 2017-04884,2017-04884,2017- 04884,2017-04884), the Swedish Armed Forces, and the Swedish Defence Material Administration.

Detailed longitudinal dynamics simulations may be used to predict the energy performance of road vehicles. However, including uncertainty in the operating conditions often implies high computational costs. Model-based formulations, in conjunction with statistical methods, may obviate this limitation by directly accounting for stochasticity, thus eliminating the need for simulating large populations of driving and operating cycles. To this end, leveraging directly the methods of stochastic calculus, this work presents a novel theory of longitudinal vehicle dynamics and energy consumption, where the vehicle’s speed varies stochastically depending on the characteristics of the operating environment. In particular, the proposed formulation, consisting of stochastic differential equations (SDEs) governing the longitudinal motion of road vehicles, inherently accounts for the statistical variation connected with uncertainties in the driver’s behavior and road properties, including, e.g., topography and legal speed. A Fokker-Planck partial differential equation (PDE) that describes the time evolution of the joint probability density function (PDF) of the vehicle’s speed, position, and road parameters is also derived from the SDEs established in the paper. The SDE and Fokker-Planck-based approaches enable statistical estimation of important quantities like speed fluctuations, instantaneous power requests, and energy consumption. The developed models may be used to assess the energy performance of road vehicles for different combinations of road transport missions. This is applicable at the early stages of the development, virtual testing, and certification processes, without the need to perform computationally expensive simulations, as corroborated by the virtual experiments conducted in the paper.

In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology., Funding Agencies|Swedish e-Science Research Centre (SeRC); Swedish National Graduate School in Computer Science (CUGS); Swedish Research Council (Vetenskapsradet) [2018-04147]; Swedish Agency for Economic and Regional and Growth (Tillvaxtverket)

Purpose – This paper aims to further our understanding of policy–practice gaps in organizations from an organizational learning perspective. The authors conceptualize and analyze policy–practice gaps in terms of what they label the dual challenge of organizational learning, i.e. the organizational tasks of both adapting ongoing practices to prescribed policy demands and adapting the policy itself to the needs of practice. Specifically, the authors address how this dual challenge can be understood in terms of organizational learning and how an organization can be managed to successfully resolve the dual learning challenge and, thereby, bridge policy–practice gaps in organizations. Design/methodology/approach – This paper draws on existing literature to explore the gap between policy and practice. Through a synthesis of theories and an illustrative practical example, this paper highlights key conceptual underpinnings. Findings – In the analysis of the dual challenge of organizational learning, this study provides a conceptual framework that emphasizes the important role of tensions and contradictions between policy and practice and their role as drivers of organizational learning. To bridge policy–practice gaps in organizations, this paper proposes five key principles that aim to resolve the dual challenge and accommodate both deployment and discovery in organizations. Research limitations/implications – Because this is a conceptual study, empirical research is called for to explore further and test the findings and conclusions of the study. Several avenues of possible future research are proposed. Originality/value – This paper primarily contributes by introducing and elaborating on a conceptual framework that offers novel perspectives on the dual challenges of facilitating both discovery and deployment processes within organizations.

Train dispatchers play a vital role for safe and efficient railway operations. Their role includes several tasks, such as controlling train movements, maneuvering railway infrastructures (e.g., switches and signals), communicating with train crew and maintenance contractors. Dispatching centers operate around the clock, resulting in a high risk of fatigue for dispatchers, especially during night shifts. Moreover, dispatchers’ cognitive performance deteriorates when workload levels are too high. Although workload is a subjective measure, it is correlated with task load, i.e., the number of tasks executed in a given period of time. A preventive approach against too high workload levels is to account for the task load already during the scheduling phase. Currently, shifts are scheduled manually, which is a very complex task that cannot guarantee a satisfying quality both in terms of high efficiency and balanced workload levels. The aim of this thesis is to create a scheduling framework, based on mathematical optimization models, that supports shift planners in their work. The framework helps in automatizing the scheduling process and improves the quality of the resulting shifts. Our initial approach is to use our models to figure out the required staffing levels and produce baseline schedules, and then improve these by increasing their attractiveness. To achieve this goal, we conduct three related studies considering the case of Malm¨o dispatching center. The studies result in different optimization models for shift scheduling of train dispatchers. We present the complete results in the following three papers: In the first paper, we build an optimization model for one-day shift scheduling, where the objective function is to minimize the number of needed dispatchers. Moreover, we analyze the impact on the computation time of different parameters, such as the number of geographical areas and how these are combined. The results of the experiments are obtained within accep

A method for obstacle detection using the sound that a drone naturally emits is proposed. The sound emitted from a vehicle, ego-noise, is often considered a complicating factor for mission fulfilment, without purpose. The idea in this paper is to utilise this ego-noise for obstacle detection, being the first to perform practical experiments of this. Adding a few microphones to the vehicle, the ego-noise is utilised as the sound source for echolocation. The method consists of auto-correlating the received signals to estimate echo delays, using the known array geometry and signal propagation speed to relate delays to distances, and then beamforming to position targets. A proof-of-concept has been constructed, and promising results are presented for experiments in a controlled environment.

Felix Klein’s Erlangen Programme of 1872 introduced a methodology to unify non-Euclidean geometries. Similarly, geometric deep learning (GDL) constitutes a unifying framework for various neural network architectures. GDL is built from the first principles of geometry—symmetry and scale separation—and enables tractable learning in high dimensions. Symmetries play a vital role in preserving structural information of geometric data and allow models (i.e., neural networks) to adjust to different geometric transformations. In this context, spheres exhibit a maximal set of symmetries compared to other geometric entities in Euclidean space. The orthogonal group O(n) fully encapsulates the symmetry structure of an nD sphere, including both rotational and reflection symmetries. In this thesis, we focus on integrating these symmetries into a model as an inductive bias, which is a crucial requirement for addressing problems in 3D vision as well as in natural sciences and their related applications. In Paper A, we focus on 3D geometry and use the symmetries of spheres as geometric entities to construct neurons with spherical decision surfaces—spherical neurons—using a conformal embedding of Euclidean space. We also demonstrate that spherical neuron activations are non-linear due to the inherent non-linearity of the input embedding, and thus, do not necessarily require an activation function. In addition, we show graphically, theoretically, and experimentally that spherical neuron activations are isometries in Euclidean space, which is a prerequisite for the equivariance contributions of our subsequent work. In Paper B, we closely examine the isometry property of the spherical neurons in the context of equivariance under 3D rotations (i.e., SO(3)-equivariance). Focusing on 3D in this work and based on a minimal set of four spherical neurons (one learned spherical decision surface and three copies), the centers of which are rotated into the corresponding vertices of a regular, Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP); National Academic Infrastructure for Supercomputing in Sweden (NAISS) partially funded by the Swedish Research Council through grant agreement no. 2022-06725, and by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre.

Modular deep learning has been proposed for the efficient adaption of pre-trained models to new tasks, domains and languages. In particular, combining language adapters with task adapters has shown potential where no supervised data exists for a language. In this paper, we explore the role of language adapters in zero-shot cross-lingual transfer for natural language understanding (NLU) benchmarks. We study the effect of including a target-language adapter in detailed ablation studies with two multilingual models and three multilingual datasets. Our results show that the effect of target-language adapters is highly inconsistent across tasks, languages and models. Retaining the source-language adapter instead often leads to an equivalent, and sometimes to a better, performance. Removing the language adapter after training has only a weak negative effect, indicating that the language adapters do not have a strong impact on the predictions.

For virtually every area of science and engineering, state estimation is ubiquitous. Accurate state estimation requires a moderately precise mathematical model of the system, typically based on domain expertise. These models exist for a plethora of applications and available state estimators can generally produce accurate estimates. However, the models usually ignore hard-to-model phenomena, either due to the cost or the difficulty of modeling these characteristics. Further, the most widely used state estimator for nonlinear systems is still the extended Kalman filter (EKF), which may suffer from divergence for complex models, which essentially restricts the complexity of the usable models. Generally speaking, this thesis investigates ways of improving state estimation. Firstly, existing state-space models (SSMs) for target tracking are augmented with a Gaussian process (GP) in order to learn hard-to-model system characteristics online. Secondly, improved linearization-based state estimators are proposed that exhibit favorable robustness properties to the parameters of the noise processes driving the SSM. The first part of the thesis explores joint state estimation and model learning in partially unknown SSMs, where some a priori domain expertise is available, but parts of the model need to be learned online. Paper A combines a linear, a priori identified, SSM with an approximate GP. An EKF is applied to this GP-augmented SSM in order to jointly estimate the state of the system and learn the, a priori, unknown dynamics. This empirically works well and substantially reduces the prediction error of the dynamical model as compared to a non-augmented SSM. Paper B explores ways of reducing the computational complexity of the method of Paper A. Crucially, it uses a compact kernel in the GP, which admits an equivalent basis function (BF) representation where only a few BFs are non-zero at any given system state. This enables a method that is essentially computationally inv, Funding agency: The Wallenberg AI and Autonomous Systems and Software Program (WASP), funded by the Knut and Alice Wallenberg Foundation