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This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabil-ities, making it well-suited for tuning the controller. The proposed approach utilizes a master-slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control sys-tems performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a compre-hensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.

The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice., Funding Agencies|National Natural Science Foundation of China Program [62271316]; National Key Research and Development Project of China [2019YFB1802703]; Fundamental Research Funds for the Central Universities; Shanghai Key Laboratory of Digital Media Processing [18DZ2270700]; Research Grants Council, Hong Kong, SAR, China [25215020]; ELLIIT; KAW Foundation

Electric drones as an autonomous mode of transport are scaling up to transform last-mile goods delivery, raising an urgent need for assessing impacts of drone transport from a systems perspective. In this paper, we conduct systems analyses to assess the environmental, economic, and delivery time impact of large drones for delivery scenarios to pick-up centers between mid-size cities predominantly in rural areas, and deliveries within city limits compared with electric and diesel trucks. Results show that large drones have lower emissions than diesel trucks for deliveries in rural areas and that drones dont compete with electric trucks, mainly due to the high energy demand required for take-off and landing for each delivery. Furthermore, we show that electric drones are an economically more cost-effective option than road-bound transport modes such as diesel and electric trucks due to the high degree of automation, and also provide the fastest delivery times. Our analysis provides unique insights that drones can address rapid electrification and emergency applications due to low costs, high flexibility, and fast operations. However, for regulators and practitioners to realize it as an emission-friendly option it is necessary to determine the optimal size of drones, particularly for use cases in urban areas, avoid very low landings for deliveries, and have home deliveries instead of pick-up points., Funding Agencies|We sincerely thank the drone manufacturers, logistics companies, and County Insurance in Sweden for their inputs on quantitative values assumed in this paper.

The quest for eco-friendly materials with anticipated positive impact for sustainability is crucial to achieve the UN sustainable development goals. Classical strategies of composite materials can be applied on novel nanomaterials and green materials. Besides the actual technology and applications also processing and manufacturing methods should be further advanced to make entire technology concepts sustainable. Here, they show an efficient way to combine two low-cost materials, cellulose and zinc oxide (ZnO), to achieve novel functional and "green" materials via paper-making processes. While cellulose is the most abundant and cost-effective organic material extractable from nature. ZnO is cheap and known of its photocatalytic, antibacterial, and UV absorption properties. ZnO nanowires are grown directly onto cellulose fibers in water solutions and then dewatered in a process mimicking existing steps of large-scale papermaking technology. The ZnO NW paper exhibits excellent photo-conducting properties under simulated sunlight with good ON/OFF switching and long-term stability (90 minutes). It also acts as an efficient photocatalyst for hydrogen peroxide (H2O2) generation (5.7 x 10(-9) m s(-1)) with an envision the possibility of using it in buildings to enable large surfaces to spontaneously produce H2O2 at its outer surface. Such technology promise for fast degradation of microorganisms to suppress the spreading of diseases., Funding Agencies|Knut and Alice Wallenberg Foundation; VINNOVA (Digital Cellulose Center); Vetenskapradet [2016-05990]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoeping University [2009-00971]; aForsk Foundation [17-433]

In industrial applications where the physical parameters are highly interconnected, keeping the process flow steady is a major concern for the operators. This is caused by the sensitivity of system to the process dynamics. As a result, a slight adjustment to a control parameter can significantly affect the efficiency of the system and thus impact the financial gain. Paper pulp production is an example of such a process, where operators continuously investigate the potential of changes in the process and predict the consequences of an adjustment before making a decision. Process parameter adjustments prescribed by simulated control models cannot be fully trusted as the external disturbances and the process inherent variabilities cannot be fully incorporated into the simulations. Therefore, to assess the viability of a strategy, operators often compare the situation with the historical records and trends during which the processes in the plant ran steadily. While previous research has mostly focused on developing advanced control models to simulate complex pulp production process, this work aims to support operators analytical reasoning by provision of effective data visualization. The contributions of our design study include a domain problem characterization and a linked-view visual encoding design, which aims to enhance operator's mental models independent of particular users or scenarios. Finally, by reflecting on the advantages of our choice of task abstraction technique, inherited from the ecological interface design framework [5], we reason for the generalizability of our approach to similar industrial applications., Funding agencies: The Sweden’s Innovation Agency (Vinnova) under the project EXPLAIN (2021-04336) and the Knutand Alice Wallenberg Foundation (grant KAW 2019.0024).

The millimeter-wave (mmWave) and sub-terahertz (sub-THz) bands have received much attention in recent years for wireless communication and high-resolution imaging radar applications. The objective of this paper is to provide an overview of recent developments in the design and technical implementation of GW-based antenna systems and components. This paper begins by comparing the GW-transmission line to other widely used transmission lines for the mmWave and sub-THz bands. Furthermore, the basic operating principle and possible implementation technique of the GW-technology are briefly discussed. In addition, various antennas and passive components have been developed based on the GW-technology. Despite its advantages in controlling electromagnetic wave propagation, it is also widely used for the packaging of electronic components such as transceivers and power amplifiers. This article also provided an overview of the current manufacturing technologies that are commonly used for the fabrication of GW-components. Finally, the practical applications and industry interest in GW technology developments for mmWave and sub-THz applications have been scrutinized., Funding Agencies|European Union - Marie Sklodowska-Curie [766231WAVECOMBEH2020-MSCA-ITN-2017]

Accurate localization is a part of most autonomous systems. GNSS is today the go to solution for localization but is unreliable due to jamming and is not available indoors. Inertial navigation aided by visual measurements, e.g., optical flow, offers an alternative. Traditional feature-based optical flow is limited to scenes with good features, current development of deep neural network derived dense optical flow is an interesting alternative. This paper proposes a method to evaluate the result of dense optical flow on real image sequences using traditional feature-based optical flow and uses this to compare six different dense optical flow methods. The results of the dense methods are promising, and no clear winner amongst the methods can be determined. The results are discussed in the context of how they can be used to support localization.

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.

In man-made energy systems like the electricity system, new concepts have the potential to influence and shape the development of the system. Sometimes the influence leads to a positive development and in other cases the new concept may lead into disadvantageous pathways. In this paper we argue that when a new concept is introduced, it may give rise to an unhinged paradox. An unhinged paradox implies that introducing a new concept, such as a new governance or management model, might lead to unintended consequences where some parts or the whole system become more unstable, or less resilient or unhinged . The transition of energy systems includes many "wicked " problems, i.e., aspects that are difficult to foresee the outcome of. The need for a rapid transition with an urgent need to implement new concepts together with a lack of or delayed feedback loops may give rise to wicked problems and unhinged systems. This unhinged paradox is likely to be found even beyond the scope of energy systems and will be further discussed in this paper in relation to the deregulation of the energy market, improved energy efficiency and energy flexibility.

Underwater surveillance using passive sonar and track-before-detect technology requires accurate models of the tracked signal and the background noise. However, in an underwater environment, the signal channel is time-varying and prior knowledge about the spatial distribution of the background noise is unavailable. In this paper, an autoregressive model that captures a time-varying signal level caused by multi-path propagation is presented. In addition, a multi-source model is proposed to describe spatially distributed background noise. The models are used in a Bernoulli filter track-before-detect framework and evaluated using both simulated and sea trial data. The simulations demonstrate clear improvements in terms of target loss and improved ability to discern the target from the noisy background. An evaluation of the track-before-detect algorithm on the sea trial data indicates a performance gain when incorporating the proposed models in underwater surveillance and tracking problems.

Dynamically tunable reflective structural colors are attractive for reflective displays (electronic paper). However, it is challenging to tune a thin layer of structural color across the full red-green-blue (RGB) basis set of colors at video rates and with long-term stability. In this work, this is achieved through a hybrid cavity built from metal-insulator-metal (MIM) "nanocaves" and an electrochromic polymer (PProDOTMe(2)). The reflective colors are modulated by electrochemically doping/dedoping the polymer. Compared with traditional subpixel-based systems, this hybrid structure provides high reflectivity (>40%) due to its "monopixel" nature and switches at video rates. The polymer bistability helps deliver ultralow power consumption (& AP;2.5 mW cm(-2)) for video display applications and negligible consumption (& AP;3 & mu;W cm(-2)) for static images, compatible with fully photovoltaic powering. In addition, the color uniformity of the hybrid material is excellent (over cm(-2)) and the scalable fabrication enables large-area production., Funding Agencies|Swedish Research Council [201806768]; Swedish Foundation for Strategic Research [EM16-0002]; UK EPSRC [EP/L027151/1, EP/N016920/1]; MC2 at the Chalmers University of Technology; Swedish Foundation for Strategic Research (SSF) [EM16-0002] Funding Source: Swedish Foundation for Strategic Research (SSF)

At Linköping university, a model to facilitate impact and bridge the gap between research, education, and business creation, has been developed. It is named “ComICIR”, which stands for Commercialization of Innovative Challenges from Industry and Research. The model allows researchers, firms, and students to work in a co-creation process that are built on the following five steps: (1) research validation, (2) idea generation, (3) idea validation, (4) idea evaluation and, (5) innovation strategy. In the paper, we describe the model and analyse how challenges and ideas could be developed and experientially based pedagogical approaches could be adjusted in order to benefit the regional ecosystem of research, education and industry and contribute to reaching increased impact of innovative ideas and ventures. Our main finding is that CBL is beneficial but requires close cooperation between teachers and innovation support actors. Flexibility is needed to fit the purpose of the course as well as the needs of the challenge providers. Hence, challenges need to be categorized and qualified to take into account the aim and scope of the challenge as well as its degree of development as this affects how the challenges should be written and treated to get the best outcome.

Attacks against Supervisory Control and Data Acquisition (SCADA) systems operating critical infrastructures have largely appeared in the past decades. There are several anomaly detection systems that model the traffic of request-response mechanisms, where a client initiates a request to a server and the server sends back a response later. However, many modern SCADA protocols also allow server-driven traffic without a paired request, and anomaly detection for server-driven traffic has not been well-studied. This paper provides a comprehensive understanding of server-driven traffic across different protocols, such as MMS, Siemens S7, S7-plus, and IEC 60870-5-104 (IEC-104), with traffic analysis. The analysis results show that the common postulation of periodicity and correlation within SCADA traffic holds true for most of the analyzed datasets. The paper then proposes a Multivariate Correlation Anomaly Detection (MCAD) approach for server-driven traffic that presents complicated correlations among flows. The proposed approach is compared with a univariate correlation anomaly detection approach designed for SCADA and a general purpose anomaly detection approach based on neural network techniques. These approaches are tested with an IEC-104 dataset from a real power utility with injected timing perturbations resulting from a Stuxnet-like stealthy attack scenario. The detection accuracy of MCAD outperforms the compared methods and the time-to-detection performance is promising., Funding Agencies|Swedish Civil Contingencies Agency (MSB) through the RICS project

The Industry 4.0 concept, invariably more intertwined with the Circular Economy paradigm, has been increasingly applied outside the manufacturing domain. As a side-effect, Industry 4.0 technologies are disruptively reshaping, under the threefold Triple Bottom Line perspective, supply chains and different contexts such as innovation management and societal development. The eleven papers published in this special issue aim at contributing to the emerging research stream about the sustainable development of I4.0-CE-driven supply chains. In particular, four main macro-topics emerged: (i) I4.0-driven Circular Supply Chains (CSCs); (ii) Life Cycle Information in CE ecosystems; (iii) Waste management process optimisation; (iv) Drivers, challenges, opportunities, and barriers for I4.0-CE-driven supply chains. In the studies included in this special issue collection, novel conceptual frameworks and methodologies are proposed, and some practical cases are also conducted, promoting relevant advances in the twofold field of I4.0-CE, triggering the green and digital twin transition towards a supply chain and social dimension. Nevertheless, the analysis of the papers constituting the special issue reveals promising research opportunities necessary to investigate and explore this emerging domain in its specific facets. More quantitative studies are needed to understand better the dynamics in CSC, CE ecosystems and Industrial Symbiosis networks. Finally, developing specialised circular and sustainable technologies based on intelligent architectures and adopting data-driven approaches could support the adoption of circular practices not only within the companies but also outside them in the CSC and social dimensions., Author´s last draft is self-archived on the author's personal homepage: https://ylifin.wordpress.com/publications

Training a deep neural network is computationally expensive, but achieving the same network performance with less computation is possible if the training data is carefully chosen. However, selecting input samples during training is challenging as their true importance for the optimization is unknown. Furthermore, evaluation of the importance of individual samples must be computationally efficient and unbiased. In this paper, we present a new input data importance sampling strategy for reducing the training time of deep neural networks. We investigate different importance metrics that can be efficiently retrieved as they are available during training, i.e., the training loss and gradient norm. We found that choosing only samples with large loss or gradient norm, which are hard for the network to learn, is not optimal for the network performance. Instead, we introduce an importance sampling strategy that selects samples based on the cumulative distribution function of the loss and gradient norm, thereby making it more likely to choose hard samples while still including easy ones. The behavior of the proposed strategy is first analyzed on a synthetic dataset, and then evaluated in the application of classification of malignant cancer in digital pathology image patches. As pathology images contain many repetitive patterns, there could be significant gains in focusing on features that contribute stronger to the optimization. Finally, we show how the importance sampling process can be used to gain insights about the input data through visualization of samples that are found most or least useful for the training., The fulltext is published under Creative Common license Attribution 4.0https://creativecommons.org/licenses/by/4.0/No changes have been made to the publication.

Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.

Due to high demand and capacity consumption, railway timetables are often sensitive to disturbances. To maintain punctual operations, it is important that timetables are robust, and methods are needed that make them robust without consuming too much capacity. In this paper, we demonstrate how a policy change in the form of new timetable planning rules can be used to achieve more robust timetables. We present the use of the rules in a real-world case from 2019, when our rules were applied for the Swedish Southern mainline. In this paper, we describe how a new policy for scheduling trains can be applied, and we discuss implications observed when going from research to practice. We also describe how the proposed rules affect train paths and runtimes. The outcome of the rules is measured in a comprehensive evaluation of the traffic performance based on empirical operational data. The results from this study show that practical knowledge is necessary when developing a policy, as well as when developing a timetabling model. Insights, given to us by experienced timetable planners, can be used to enhance optimisation models and make the models more applicable in the real world. The main contribution of this paper is to show that it is possible to increase timetable robustness with a minor policy change based on previously presented research results. Even with relatively small timetable modifications, we can learn from the operational data that the new rules had the intended effect and that overall punctuality can be increased., Funding Agencies|Trafikverket (The Swedish Transport Administration)

A case study of the use of reflections within the Applied physics and electrical engineering program at Linköping University is presented. Reflections have been used for several years and they are done at four stages in the program, in terms of reflections at the end of the Introductory course in year one, design-implement experiences in year three and five, and a reflection document that is the last component of the Master’s thesis. In the first three stages a project model is used to support the planning and execution of the project, and in the project model the project work ends with a reflection. In the reflection document connected to the Master’s thesis the student reflects upon both the thesis work itself and the entire education program, according to the sections and subsections of the CDIO Syllabus. The paper describes how the reflections are integrated in the program. Experiences from student perspective are collected in a small-scale study via interviews with students from year one and year five.

Value-based selling (VBS) has become an increasingly important selling practice in contemporary business-tobusiness (B2B) markets, but currently most VBS studies are limited to exploratory and descriptive case studies. Therefore, the purpose of this paper is to develop a theoretical toolbox that provides a foundation for more theory-driven research on VBS and thereby advances both the theoretical and managerial understanding of VBS. Specifically, we consider equity theory, framing theory, mental accounting, social exchange theory, signaling theory, agency theory, game theory, and institutional theory in terms of their implications for VBS. We provide a brief overview of each theory and its applications, highlight the key implications and potential research questions for VBS, and summarize our insights into a research agenda. This paper contributes to the contemporary VBS literature by developing a theory-driven research agenda. It also provides managerial insights on how different theories can inform decision-making for the implementation and management of VBS in B2B markets., Funding Agencies|Jan Wallander and Tom Hedelius Foundation [W19-0018]

Understanding the microscopic mechanisms of regeneration of cellulose is prerequisite for engineering and controlling its material properties. In this paper, we performed coarse-grained Martini 3 molecular dynamics simulations of cellulose regeneration at a scale comparable to the experiments. The X-ray diffraction (XRD) curves were monitored to follow the structural changes of regenerated cellulose and trace formation of cellulose sheets and crystallites. The calculated coarse-grained morphologies of regenerated cellulose were backmapped to atomistic ones. After the backmapping we find that the regenerated coarse-grained cellulose structures calculated for both topology parameters of cellulose I beta and cellulose II/III, are transformed to cellulose II, where the calculated XRD curves exhibit the main peak at approximately 20-21 degrees, corresponding to the (110)/(020) planes of cellulose II. This result is in good quantitative agreement with the available experimental observations., Funding Agencies|Knut and Alice Wallenberg foundation through the Wallenberg Wood Science Center at Linkoping University

This paper presents a novel mathematical model, integrating timetabling and vehicle scheduling problems for electric buses. The objective is to minimize the number of buses while satisfying constraints concerning routing and charging, including design choices for where to install charging stations. The aim of the paper is to illustrate and discuss the effects of solving the timetabling and vehicle scheduling of electric buses (including where to install charging infrastructure) separately, compared to solving them jointly in one single step. For that purpose, we perform tests with: i) given timetable, that is, solving only the vehicle scheduling problem, ii) fixed headways for each line, and iii) variable headways. A small test case based on actual bus lines from Va center dot stra Fro center dot lunda, Gothenburg, Sweden, is used. From the numerical experiments, we verify that combining the two planning steps can significantly reduce the number of vehicles needed., Funding Agencies|Swedish Energy Agency [40377-1]

Remote attestation (RA) is emerging as an important security mechanism for cyber-physical systems with strict security requirements. Trusted computing at large and Trusted Execution Environments (TEEs) in particular have been identified as key technologies to enable RA since they ideally allow retaining some element of control over remote devices despite them being compromised at the OS level. Unfortunately, sometimes it is claimed that TEEs provide RA support without really substantiating how this support is provided. In this paper we build the assurance arguments for RA to carefully map how secure RA depends on underlying security properties and how these in turn can be provided by TEE capabilities. We base our security analysis of RA on existing literature on security requirements for RA and use Goal Structuring Notation (GSN) as the method to build the security arguments. Our analysis identifies the set of TEE properties (as described in the GlobalPlatform standard) that are needed to support RA, and which goals that cannot be mapped to TEE implementations, and therefore, require other forms of evidence for RA to be trusted at the top level., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

A common research process in visualization is for visualization researchers to collaborate with domain experts to solve particular applied data problems. While there is existing guidance and expertise around how to structure collaborations to strengthen research contributions, there is comparatively little guidance on how to navigate the implications of, and power produced through the socio-technical entanglements of collaborations. In this paper, we qualitatively analyze reflective interviews of past participants of collaborations from multiple perspectives: visualization graduate students, visualization professors, and domain collaborators. We juxtapose the perspectives of these individuals, revealing tensions about the tools that are built and the relationships that are formed — a complex web of competing motivations. Through the lens of matters of care, we interpret this web, concluding with considerations that both trouble and necessitate reformation of current patterns around collaborative work in visualization design studies to promote more equitable, useful, and care-ful outcomes.

This paper proposes a temporal graph neural network model for forecasting of graph-structured irregularly observed time series. Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph. This is achieved by introducing a time-continuous latent state in each node, following a linear Ordinary Differential Equation (ODE) defined by the output of a Gated Recurrent Unit (GRU). The ODE has an explicit solution as a combination of exponential decay and periodic dynamics. Observations in the graph neighborhood are taken into account by integrating graph neural network layers in both the GRU state update and predictive model. The time-continuous dynamics additionally enable the model to make predictions at arbitrary time steps. We propose a loss function that leverages this and allows for training the model for forecasting over different time horizons. Experiments on simulated data and real-world data from traffic and climate modeling validate the usefulness of both the graph structure and time-continuous dynamics in settings with irregular observations.

This paper is focused on fault detection and isolation of component-based multi-mode systems, i.e., systems that can be operated in different continuous modes. As the system mode changes, the structure of the system also changes which impacts diagnosability analysis and synthesis. To meet this challenge, diagnosis based on a structural approach is modified to also detect and isolate faults when modes change. Here, definitions for some important diagnosis concepts are extended to cover also multi-mode systems. Then, a method for hierarchical diagnosis of component-based systems is proposed. The method is exemplified on a Li-ion battery pack to show its effectiveness. Copyright (c) 2023 The Authors., Funding Agencies|Swedish Electromobility Center; Swedish Energy Agency

To comply with a circular economy (CE) and gain significant financial, environmental, and societal benefits, original equipment manufacturers (OEMs) tend to adopt circular strategies, also known as "Rs". With a growing tendency to develop new Rs in CE frameworks, OEMs can become disoriented. While remanufacturing is often ranked low compared to other Rs, remanufacturing can still retain the highest product value (pull the used product value up to the top of the Retained Value Hill (see graphical abstract)).The aim of this paper is to align remanufacturing in comparison to other value-retaining processes (VRPs) in terms of the retained product value. To fulfill this aim, data was collected through a literature study and interviews with remanufacturers. The results show that although remanufacturing tends to demand more resources to retain a high product value, it outperforms other VRPs and delivers better output quality, assured with a longer warranty, neutral product identity, and extended (doubled or tripled) product use time. This paper enriches the research on remanufacturing, VRPs, and CE frameworks by showing that OEMs can ride the Retained Value Hill highest and longest only with remanufacturing., Funding Agencies|Swedish Research Council [dnr 2021-01278]

Class-Incremental Learning is a challenging problem in machine learning that aims to extend previously trained neural networks with new classes. This is especially useful if the system is able to classify new objects despite the original training data being unavailable. Although the semantic segmentation problem has received less attention than classification, it poses distinct problems and challenges, since previous and future target classes can be unlabeled in the images of a single increment. In this case, the background, past and future classes are correlated and there exists a background-shift. In this paper, we address the problem of how to model unlabeled classes while avoiding spurious feature clustering of future uncorrelated classes. We propose to use Evidential Deep Learning to model the evidence of the classes as a Dirichlet distribution. Our method factorizes the problem into a separate foreground class probability, calculated by the expected value of the Dirichlet distribution, and an unknown class (background) probability corresponding to the uncertainty of the estimate. In our novel formulation, the background probability is implicitly modeled, avoiding the feature space clustering that comes from forcing the model to output a high background score for pixels that are not labeled as objects. Experiments on the incremental Pascal VOC and ADE20k benchmarks show that our method is superior to the state of the art, especially when repeatedly learning new classes with increasing number of increments.

This paper introduces a visual approach to sustainable business model innovation that helps business model designers conceptualise how sustainability can be integrated in business models. Drawings are used to illustrate four sustainable trajectories for business model innovation. The visualisation of trajectories helps to open the “black box” of sustainable value creation by enabling different understandings of value creation from a business model perspective. The paper also introduces and exemplifies the concept of “multi-lens” thinking for sustainable business model innovation, which entails combining insights from several perspectives.

Science Parks convey the idea that technological innovation originates in scientific research. A triple helix configuration provides all the necessary conditions for science parks to achieve their goals. This paper aims to investigate the existing interactions of science parks with triple helix actors related to the development of talent attraction activities. Talent is a necessary resource to meet the growing demands for innovation of park firms. In fact, a firm’s ability to innovate and, consequently, to be successful seems to be related to its capacity to find and retain employees with relevant skills. This study analyses five case studies on three Swedish Science Parks and demonstrates the importance of the relationship with stakeholders to ensure support and the adequate development of attracting talent to Science Parks. Also, this study showed that the studied parks carry out different activities to attract talent, and the involvement of government agents and the local university proved to be essential for developing such activities.

An observable trend nowadays is the change in the prime movers of mobile heavy machinery to electric alternatives to achieve more eco-friendly equipment. These solutions often require large and heavy batteries with limited capacity, making the research of more efficient components and the development of different system architectures an important topic of study. Hydraulic actuation is still a relevant application for these vehicles because of its reliability, controllability, and high power density. The electrification and digitalization of mobile machinery allow for innovative designs and control strategies to be implemented that take advantage of electro-hydraulic systems and their characteristics. Similar research has shown that a higher number of degrees of freedom allow for the system to operate with higher total efficiency. This paper introduces a novel actuation architecture that combines multiple fixed displacement hydraulic pumps and on/off directional valves to control the position and force of two hydraulic actuators for the working functions of a mobile machine. Each pump is powered by a variable speed electric drive so that each one can be operated independently, and together with the set of directional valves, allows the selection of different combinations of pumps and flow sharing between the actuators’ chambers to achieve the desired flow and pressure on each cylinder. The multi-pump system favours the use of smaller pumps, and the possibility of combining their flows reduces the need to operate the components at lower efficiency points such as partial displacement. At the same time, controlling the pumps’ flow through the variable-speed electric motors means that throttling valves are not needed. The development of this architecture will allow for its use in mathematical models to analyse its behaviour and efficiency and to obtain insights regarding points of improvement in the system architecture.

Privacy of machine learning models is one of the remaining challenges that hinder the broad adoption of Artificial Intelligent (AI). This paper considers this problem in the context of image datasets containing faces. Anonymization of such datasets is becoming increasingly important due to their central role in the training of autonomous cars, for example, and the vast amount of data generated by surveillance systems. While most prior work de-identifies facial images by modifying identity features in pixel space, we instead project the image onto the latent space of a Generative Adversarial Network (GAN) model, find the features that provide the biggest identity disentanglement, and then manipulate these features in latent space, pixel space, or both. The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID, which protects the individuals’ identity, while preserving as many characteristics of the original faces in the image dataset as possible. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions., This work is accepted and will soon be published open access. We are still waiting for doi etc.

Electrocatalysis, the foundation of electrical to chemical energy transformation, enables the mitigation of the electrical energy losses during reactions and the control of selectivity of the process to certain chemical products. The slow rate and the multi-step complexity of oxygen-associated reactions, namely oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), motivate the use of platinum group metals (PGM) catalysts, which significantly increase the price of the technologies due to the cost and scarcity of PGM-based materials. This thesis aims to fundamentally understand the electrocatalytical aspects of oxygen-associated reactions and their relevance to sustainable technologies by development of cheap and abundant materials. In this work, hydrothermal treatment routes are developed for synthesis of mesoporous MOx (M = Cr, Fe, Co, Ni, Ce) and NiCo2O4 as water-processable oxygen electrocatalysts. Firstly, anionic surfactant templated mesoporous NiO shows the lowest voltage loss with the highest turnover frequency for OER in consequence of the most accessible active sites among of nanoporous nickel (II) oxide (Paper I). It is observed that nickel and cobalt oxides are efficient bifunctional oxygen electrocatalysts compared to other investigated metal oxides. This stems from the lower voltage loss and by the presence of surface adsorbed hydroxyl species. In situ quantification shows that hydrogen peroxide is either the terminal product or the intermediate for ORR on meso-Cr2O3 and on other electrocatalysts, respectively (Paper II). In Paper IV, mesoporous NiO and NiCo2O4 are synthesized by using a template-free hydrothermal route, and NiCo2O4 performs more efficient bifunctional oxygen catalysts compared to NiO. It is found that ORR on mesoporous NiO and NiCo2O4 follow (2+1)e- and 4e-ORR path, with hydroxyl radical and hydroxyl ion as terminal products, respectively. Integrating the ORR and OER in electrochemical cells enables the study and developm

Deep brain stimulation (DBS) is an established method for symptom relief in movement disorders like Parkinson’s disease, essential tremor (ET), and dystonia. The therapy is based on implanting an electrode with four contacts in the deep brain structures where it provides electrical stimulation, mainly impacting the nerve tracts. Despite the evidence of DBS effectiveness, there are still questions regarding the optimal position of stimulation. With new technology, the possibility to customize the stimulation increases, which makes the programming session for each patient more complicated and tedious. Different computational models have been developed to estimate the anatomical impact of stimulation. Patient‐specific electric field simulations can be used to estimate the spatial extent of the stimulation and superimpose on patient magnetic resonance imaging (MRI) for anatomical analysis. MRI weighted with water diffusion can be used for reconstructions of nerve tracts, a process called tractography. Tractography utilizes the fact that water can move unrestricted along the nerve trajectories, but the diffusion is restricted in the perpendicular direction, i.e., the diffusion is anisotropic. For tremor, the dentato‐rubro‐thalamic tract (DRT) has gained interest. The electric conductivity has corresponding anisotropic characteristics as water diffusion in white brain tissue (nerve tracts). Diffusion MRI can therefore also be used to improve patientspecific simulations by including structure information, i.e., anisotropy. In this thesis, both a workflow for combining patient‐specific simulations with tractography of the DRT and a method for expanding the simulations with anisotropy were developed (Paper I). This was done using four patients with ET. The results show that including anisotropy will impact the simulation result in regions of dense nerve tracts (Paper I‐II). For the tractography, all patients’ estimated stimulation region intersected with the reconstructed DR

Nucleases are a group of enzymes that cleave the phosphodiester bonds in nucleic acids. As such, nucleases act as biological scissors that exhibit a plethora of fundamental roles, in prokaryotes and eukaryotes, dependent or non-dependent on their catalytic capability. Thus, differential status of nucleases between healthy and disease conditions might not be surprising, and can be deployed in disease detection. Specifically, there is growing body of research demonstrating the potential of nucleases as diagnostic biomarkers in several types of cancer. Biomarkers for early diagnosis are an immense need in the diagnostic landscape of cancer. In this sense, nucleases are promising biomolecules, and they possess a unique feature of catalytic activity that could be deployed for diagnosis and future therapeutic strategies. In this thesis we aim to demonstrate the use of nucleases as biomarkers associated to cancer, and the capability of oligonucleotide substrates for targeting a specific nuclease. The thesis work begins with comprehensive review of nucleases as promising biomarkers in cancer diagnosis (paper I). Then, we provide a methodological study in paper II, in which we propose a flexible approach for detection of disease associated nuclease activity using oligonucleotides as substrates. The probes utilized here are flanked with fluorophore at the 5’-end and a quencher at the 3’-end. Upon cleavage by nucleases, the fluorescent signal is increased in a proportional fashion to nuclease activity. This platform is suitable to implement in detection of any disease in which nuclease activity is altered. We have applied this method in paper III, by using 75 probes as substrates to screen breast cancer cells, along with controls, for nuclease activity. We have identified a probe (DNA PolyAT) that discriminates between BT-474 breast cancer cells and healthy cells based on nuclease activity profile associated with cell membrane. Next, we screened tissue samples from br

Purpose This paper aims to study the effects of different weather conditions on typical concrete work tasks productivity. Weather is one important factor that has a negative impact on construction productivity. Knowledge about how weather affects construction works is therefore important for the construction industry, e.g. during planning and execution of construction projects. Design/methodology/approach A questionnaire survey method is used involving means to perform pairwise comparisons of different weather factors according to the analytical hierarchical process (AHP). The survey also contains means to enable assessment of the loss in productivity for typical work tasks exposed to different weather types. The survey targets practitioners involved in Swedish concrete construction projects, and the results are compared with previous research findings. Findings The survey covers responses from 232 practitioners with long experience of concrete construction. The pairwise comparisons reveal that practitioners rank precipitation as the most important followed by wind and temperature. The loss in productivity varies significantly (from 0 to 100%) depending on the type of work and the type of weather factor considered. The results partly confirm findings reported in previous research but also reveal a more complex relationship between weather and productivity indicating several underlying influencing factors such as type of work, type of weather (e.g. rain or snow) and the intensity of each weather factor. Originality/value This paper presents new data about how 232 practitioners assess the effects of weather on construction productivity involving novel means to perform objective rankings such as the AHP methodology., Funding Agencies|Swedish construction industrys organization for research and development (SBUF); Cementa AB

Removing snow in a city is an unavoidable task in Nordic countries like Sweden. A number of streets in an area need to be cleared of snow by a limited number of vehicles and the tours for the vehicles must be planned in order to minimize the time and/or cost. Since the amount of snow can vary significantly from one year to another, the plans/tours of one year cannot be used for the next year. Hence, new tours need to be planned each time. Snow removal can be done in rural or urban areas and in addition during snowfall or after a snowfall. In this thesis, we study urban snow removal after a snowfall. There are different relevant specifics of the urban snow removal problem. For instance, there are different types of streets which need different numbers of sweeps in order to remove the snow. In addition, some tasks must be done before other tasks can be started. This leads to precedence constraints. Furthermore, each vehicle needs a certain time to switch from a task to another task. The problem can be formulated as a huge time-indexed mixed integer programming which often is not directly solvable in practice. The contributions of this thesis include the study of different relaxations and heuristics to find feasible solutions and improve the bounds on the optimal objective function values which are discussed in five papers. Paper I deals with single vehicle snow removal. A branch-and-dive heuristic based on branch-and-bound principles is given in order to improve the solutions and bounds. In Paper II, feasible solutions for the snow removal problem with a limited number of identical vehicles are obtained. First, the work is broken down into smaller parts, one for each vehicle. Based on the obtained allocation, a feasible tour for each single vehicle snow removal is obtained. Finally, combined solution approaches and co-ordination of the vehicles to find a feasible solution for the original problem are discussed. In order to improve the computational efficiency, one, Snöröjning i en stad är ett oundvikligt problem i nordiska länder som Sverige. Ett antal gator i ett område behöver röjas från snö av ett begränsat antal fordon och turerna för fordonen måste planeras för att minimera tiden och/eller kostnaden. Eftersom snömängden kan variera avsevärt från ett år till ett annat kan ett visst års planer inte alltid användas nästa år. Därför måste nya turer kunna planeras vid behov. I denna avhandling studerar vi snöröjning i tätorter efter ett snöfall, vilket är annorlunda än snöröjning på landsbygden eller under pågående snöfall. Vid snöröjning i städer finns gator av olika bredd som behöver olika mycket arbete för att få bort snön. Dessutom måste vissa uppgifter utföras innan andra uppgifter kan påbörjas, såsom att röja en gata innan anslutande korsningar röjs. Dessutom behöver varje fordon ofta en viss tid för att byta från en uppgift till en annan uppgift, speciellt om de inte är närliggande. Problemet kan formuleras som en stor tidsindexerad blandad heltalsprogrammeringsmodell, som oftast inte är direkt lösbar med standardmetoder. Denna avhandling inkluderar studier av olika relaxationer och heuristiker för att hitta tillåtna lösningar och förbättra gränserna för det optimala målfunktions-värdet. Artikel I handlar om snöröjning med enstaka fordon. En så kallad branch-and-dive-heuristik baserad på trädsökningsprinciper används för att förbättra lösningarna och gränserna. I artikel II behandlas koordinationsproblemet att planera för flera fordon sam-tidigt i samma område. En sammansatt metod används. Först delas arbetet upp i mindre delar, en för varje fordon, och bra rundturer finnes för varje del. Därefter samordnas de olika turerna i tid och rum, vilket inte är så enkelt. Trots indelning finns alltid ett stort antal beröringspunkter där fordonen påverkar varandra. I artikel III utnyttjas det faktum att moderna stadsnätverk ofta innehåller delar, ofta bostadsområden, som är träd, det vill säga inte tillåter rundturer. I sådana o

Untill 1980 one of the main subjects of study in the theory of nonlinear water waves were the Stokes and solitary waves (regular waves). To that time small amplitude regular waves were constructed and the existence of large amplitude water waves of the same type was proved by using branches of water waves starting from a trivial (horizontal) wave and ending at extreme waves. Then in papers Chen & Saffman [7] and Saffman [32] numerical evidence was presented for existence of other type of waves as a result of bifurcations from a branch of ir-rotational Stokes waves on flow of infinite depth. It was demonstrated that the Stokes branch has infinitely many bifurcation points when it approaches the extreme wave and periodic waves with several crests of different height on the period bifurcate from the main branch. The only theoretical works dealing with this phenomenon are Buffoni, Dancer & Toland [4,5] where it was proved the existence of sub-harmonic bifurcations bifurcating from the Stokes branch for the ir-rotational flow of infinite depth approaching the extreme wave.The aim of this paper is to develop new tools and give rigorous proof of existence of subharmonic bifurcations in the case of rotational flows of finite depth. The whole paper is devoted to the proof of this result formulated in Theorem 5.4., Funding Agencies|Swedish Research Council (VR) [2017-03837]

Logistics actors such as the transport buyer, freight forwarder, and haulier are currently in a transition phase towards more sustainable practices typically referred to as Green Logistics Practices (GLPs). One of the more progressive GLPs is completion of freight transport using fossil-free fuels such as biomethane. Despite knowledge that fossil-free fuels are effective in reducing the amount of carbon emitted, logistics networks need to move faster in transitioning. This licentiate thesis looks at a successful implementation of biomethane in freight transport in Sweden with the purpose: understanding value creation in implementation of biomethane in freight transport from the point of view of the logistics actors. The research takes an abductive approach. The research is built upon a systematic literature review and literature study which lead to a series of interviews and eventually two different case studies of a regional and national network using biomethane. The systematic literature review and interview study of Paper 1 provided a knowledge base for understanding the status of GLP use and logistics actors’ roles. Paper 2, an embedded case study of the regional network, gives a better image of a network’s drivers and barriers in biomethane implementation including network dynamics. Paper 3 as a multiple case study including both the regional and national network allows for deeper understanding of a network’s dynamics, relationships and value propositions for value co-creation. Use of symbiotic relationships from biology inspired the analysis of the relationships between logistics actors and the value propositions which drives value co-creation. Building from the empirical data and analysis of both the individual papers and kappa, four value propositions were identified: communication, innovation, determination for success and perceived support. The analysis highlighted the weight of different value propositions may carry for the different actors in value cre, Allt fler logistika aktörer såsom logistikföretag, åkerier och transportköpare har börjat intressera sig för miljövänliga åtgärder, på engelska så kallade Green Logistics Practices (GLP). En av de mest progressiva åtgärderna är användning av fossilfria drivmedel såsom biogas. Även om använding av fossilfria drivmedel effektivt minskar koldioxidutsläppen och är viktigt för att uppnå klimatmålen så finns det en hel del utmaningar för företagen som ska implementera dessa drivmedel. I dagsläget försöker såväl företag som olika myndigheter hitta lösningar för att underlätta användning av fossilfria drivmedel. Denna licentiatavhandling studerar användningen av biogas för godstransporter i Sverige i två olika nätverk, ett regionalt och ett nationellt. Syftet är att öka kunskapen om värdeskapande och implementering av biogas i godstransporter utifrån logistikaktörernas perspektiv. För att uppfylla syftet har fallstudier använts som huvudsaklig metod där två nätverk, ett regionalt och ett nationellt studerats. Avhandlingen bygger på tre artiklar. De har fokuserat på använding av biogas som en av flera GLPs, drivkrafter och barriärer bland logistikaktörer för införande av biogas, samt värdeskapande vid införande av biogas. Kappan, som bygger på tre artiklarna, ha inspirerats av teorier grundad i biologiska symbiotiska relationer för att beskriva och förstå relationer mellan logistikaktörer och hur värde skapas. Ur analysen identifieras fyra faktorer av betydelse för värdeskapandet: kommunikation, innovation, beslutsamhet att lyckas och uppfattning av stöd. Inkluderade 3 papperna bidrar till kappan med ett djupare förståelse av aktörernas roll, deras relationer och värdeskapandet. Ett mönster som upprepas genom forskningen var vikten av kommunikation. Aktörer som kommunicerat bra hade stabila relationer och skapade därigenom värde tillsammans. Inom enskilda relationer var den aktör som var drivande ledde kommunikationen också den som drog mest nytta av värdeskapandet. Kappa

Reactive oxygen species (ROS) are an integral part of our lives. They perform essential functions in our biology and are, due to their high oxidative power, key molecules in advanced oxidation processes in industrial applications such as treatment of wastewater. Likewise, they play a crucial role as active intermediates in chemodynamic or photodynamic therapy of cancer. ROS, such as hydrogen peroxide (H2O2) or hydroxyl radicals (OH·), are derived from molecular oxygen (O2), and represent relatively more reactive oxidants with respect to parent O2. At the cellular level, ROS are produced primarily in mitochondria and among others take part in cell signaling and maintaining redox homeostasis. However, when exceeding a certain threshold, ROS can lead to oxidative stress and consequently to cardiovascular and neurodegenerative diseases, as well as cell death. Yet, the ability of modulating the generation of ROS externally gives the possibility to treat certain diseases as well. Advanced oxidation processes such as photoinduced generation of ROS and Fenton-like processes consisting of reactions of H2O2 with transition metal ions are particularly potent and tunable approaches of ROS generation. The aim of this thesis is to expand the range of applications for advanced oxidation processes, and it includes device and electrode fabrication and characterization for local ROS generation and delivery. In paper 1, we exploited naturally-sourced lignins, which share critical structural features with known photocatalysts, to photochemically reduce O2 to H2O2 with simultaneous degradation of the biopolymer, when irradiated with UV light. By adding electron donors, the autoxidation of the lignins was reduced and H2O2 generation partially increased. By showing the possibility to destructively photooxidize lignins to produce H2O2 we contribute to new developments of valorization of lignins and engineering solutions. In paper 2, we created an electro-Fenton device which electrochemical

High-performance electrocatalysts for the hydrogen evolution reaction are of interest in the development of next-generation sustainable hydrogen production systems. Although expensive platinum-group metals have been recognized as the most effective HER catalysts, there is an ongoing requirement for the discovery of cost-effective electrode materials. This paper reveals the prospects of two-dimensional (2D) noble metals, possessing a large surface area and a high density of active sites available for hydrogen proton adsorption, as promising catalytic materials for water splitting. An overview of the synthesis techniques is given. The advantages of wet chemistry approaches for the growth of 2D metals over deposition techniques show the potential for kinetic control that is required as a precondition to prevent isotropic growth. An uncontrolled presence of surfactant-related chemicals on a 2D metal surface is however the main disadvantage of kinetically controlled growth methods, which stimulates the development of surfactant-free synthesis approaches, especially template-assisted 2D metal growth on non-metallic substrates. Recent advances in the growth of 2D metals using a graphenized SiC platform are discussed. The existing works in the field of practical application of 2D noble metals for hydrogen evolution reaction are analyzed. This paper shows the technological viability of the "2D noble metals" concept for designing electrochemical electrodes and their implementation into future hydrogen production systems, thereby providing an inspirational background for further experimental and theoretical studies., Funding Agencies|Angpanneforeningens Forskningsstiftelse [21-112]

Additive manufacturing (AM) is a versatile manufacturing process which is gaining popularity in the automotive and aerospace industries. Through AM one can manufacture complex structures and combined with topology optimization (TO) a powerful design tool that provides great freedom in geometric form emerges. The goal of the research presented in this thesis is to develop new TO methods that consider specific properties related to AM for metals. In particular, anisotropy, non-homogeneity in the form of surface effects, and constraints on high-cycle fatigue (HCF) damage are treated. In the first paper of the thesis, an HCF constraint is introduced into a TO problem where the total structural mass is minimized. The HCF model is based on a continuous-time approach in contrast to more conventional cycle-counting approaches. It is based on the concept of a moving endurance surface, and a system of ordinary differential equations is used to predict the fatigue damage at every point in the design domain. The model is capable of handling arbitrary load histories, including most non-proportional loads. Gradient-based optimization is utilized, and the fatigue sensitivities are determined by the adjoint method. In the subsequent papers, several extensions are made to the original HCFconstrained TO problem: The HCF model is extended so that it is applicable not only to isotropic materials but also to transversely isotropic materials. The anisotropic properties are manifested in the constitutive elastic response and in the fatigue properties. Acceleration of fatigue and sensitivity analyses by extrapolation is introduced, making the treatment of an unlimited number of load cycles possible. Simultaneous optimization of build orientation and topology, considering stress- and HCF constraints, is performed. For better prediction of fatigue, especially for non-proportional loads, the original continuous-time HCF model is modified using a quadratic polynomial endurance function.

This paper explores the digitalization of business-to-business (B2B) customer journeys, which is recognized as a top research priority, but one that has not yet received substantial academic attention. Due to their complexity, customer journeys are particularly relevant in B2B settings, and this is further emphasized through the increasing use of digital technologies. This paper identifies three key dimensions of B2B customer journeys that are influenced by digitalization. These are the digitalization of touchpoints (i.e., adding digital touchpoints and transforming or facilitating touchpoints), the change of roles in digitalized journeys (i.e., introducing new roles, activating customers, and emphasizing collectivity), and the digitalization of the overall process (i.e., extending, enhancing, and supporting the process). The research is based on an in-depth case study of a B2B firm and four customers. Insights from the research add to the customer journey literature through the exploration of digita-lized B2B customer journeys, providing guidance for academics and practitioners.

PurposeWhilst green distribution alternatives for consumers have the potential to decrease environmental impact from logistics, retailers struggle to provide such alternatives. The purpose of this paper is to increase the understanding of the factors that hinder retailers from offering green distribution alternatives to consumers.Design/methodology/approachThe paper relies on a multiple case-study of three cases, with one retailer constituting each case. Semi-structured interviews with seven respondents and visits to the retailers checkouts were used for data collection.FindingsThe offering of green distribution alternatives is a complex task for retailers, with barriers related to six categories (organisational, financial, retailer-logistic service provider (LSP) market, retailer-consumer market, governmental and technological barriers) obstructing the way forward. A process towards offering green distribution services, including barriers and potential mitigation strategies, is suggested.Research limitations/implicationsThe study is limited to a Swedish context, and further research could consider how barriers would manifest themselves in countries with other characteristics.Practical implicationsA framework with barriers and mitigation strategies offers guidance for managers within e-commerce.Social implicationsThe greening of logistics is an important quest towards world-wide sustainability goals, and this paper contributes with an increased understanding of how to decrease environmental impact from e-commerce distribution.Originality/valueThe paper is one of few that takes the consumer side of the greening of logistics into account, thus contributing with valuable perspectives to this scarce body of literature., Funding Agencies|Vinnova (Swedens innovation agency) [2019-03197]

Magnetic resonance imaging (MRI) is one of the pillars of modern medical imaging, providing a non-invasive means to generate 3D images of the body with high soft-tissue contrast. Furthermore, the possibilities afforded by the design of MRI sequences enable the signal to be sensitized to a multitude of physiological tissue properties, resulting in a wide variety of distinct MRI modalities for clinical and research use. This thesis presents a number of advanced brain MRI applications, which fulfill, to differing extents, two complementary aims. On the one hand, they explore the benefits of a multimodal approach to MRI, combining structural, functional and diffusion MRI, in a variety of contexts. On the other, they emphasize the use of advanced mathematical and computational tools in the analysis of MRI data, such as deep learning, Bayesian statistics, and graph signal processing. Paper I introduces an anatomically-adapted extension to previous work in Bayesian spatial priors for functional MRI data, where anatomical information is introduced from a T1-weighted image to compensate for the low anatomical contrast of functional MRI data. It has been observed that the spatial correlation structure of the BOLD signal in brain white matter follows the orientation of the underlying axonal fibers. Paper II argues about the implications of this fact on the ideal shape of spatial filters for the analysis of white matter functional MRI data. By using axonal orientation information extracted from diffusion MRI, and leveraging the possibilities afforded by graph signal processing, a graph-based description of the white matter structure is introduced, which, in turn, enables the definition of spatial filters whose shape is adapted to the underlying axonal structure, and demonstrates the increased detection power resulting from their use. One of the main clinical applications of functional MRI is functional localization of the eloquent areas of the brain prior to brain surgery., Funding agencies: CENIIT (Center for industrial information technology) and LiU Cancer, as well as the ITEA/VINNOVA-funded projects IMPACT and ASSIST. Center for Medical Image Science and Visualization (CMIV) at Linköping University.

In this thesis, we focus on vulnerabilities and robustness of two wireless communication technologies: global navigation satellite system (GNSS), a technology that provides position-velocity-time information, and massive multiple-input-multiple-output (MIMO), a core cellular 5G technology. In particular, we investigate spoofing and jamming attacks to GNSS and massive MIMO, respectively, and the robust massive MIMO receiver against impulsive noises. In this context, spoofing refers to the situation in which a receiver identifies falsified signals, that are transmitted by the spoofers, as legitimate or trustable signals. Jamming, on the other hand, refers to the transmission of radio signals that disrupt communications by decreasing the signal to interference plus noise ratio (SINR) on the receiver side. The reason why we investigate impulsive noises is that the standard wireless receivers assume that the noise has Gaussian distribution. However, the impulsive noises may appear in any communication link. The difference between impulsive noises and standard Gaussian noises is that it is more likely to observe outliers in impulsive noises. Therefore, we question whether the standard Gaussian receivers are robust against impulsive noises and design robust receivers against impulsive noises. More specifically, in paper A we analyze the effects of distributed jammers on massive MIMO and answer the following questions: Is massive MIMO more robust to distributed jammers compared with previous generation's cellular networks? Which jamming attack strategies are the best from the jammer's perspective, and can the jamming power be spread over space to achieve more harmful attacks? In paper B, we propose a detector for GNSS receivers that is able to detect multiple spoofers without having any prior information about the attack strategy or the number of spoofers in the environment. In paper C and D, we design robust receivers for massive MIMO against impulsive noise. In paper C, w

With an emphasis on usability, this paper intends to create a digital museum guide app using the Flutter framework and Firebase web platform. In order to improve the visitor's experience, the app will make use of Geolocation, QR codes, and text-to-speech with translation. It will also provide pertinent audio tracks, activate them based on the user's location, and let users pause and resume audio for those around. The paper will examine how technology may enhance the museum-going experience and analyze the opportunities and challenges associated with developing a digital guide app that seeks to support the group experience.

In the era of Industry 4.0 and digitalization, planning solutions need to co-exist with each other and be able to manage higher complexity and with a higher performance. As the concept smart production planning and control is a part of industry 4.0, it is highly relevant to study and is in this paper explored on the four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning). This paper provides a framework for linking the four elements of smart PPC with data quality issues in state-of-the-art production planning and control environments. Maintaining a high standard of data quality in the business processes aids the organization to stay competitive in its market. Hence, our assumption is that a high level of data quality is needed in production planning and control for a high-performance outcome. The empirical part of our study results in a bar-chart of seven data quality problems and their occurrences together with their causes in PPC. According to the empirical data results, inaccurate data entries is the most common data quality problem related to PPC. The causes of the inaccurate data entries can be linked to human resources and organizational control. Future research should strengthen the validity of the proposed linkages between data quality problems and elements of smart PPC and implications on strategic, tactical, and operational planning levels.

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications., Funding Agencies|European Research Council; Amsterdam Institute for Advanced Metropolitan Solutions through the ALLEGRO [669792]; Center for Industrial Information Technology at Linkoeping University (CENIIT) [17.12]

Purpose Contingency studies within logistics and supply chain management have shown a need for longitudinal studies on fit. The purpose of this paper is to investigate the logistics strategy from a process of establishing fit perspective. Design/methodology/approach A large Swedish building contractor's logistics strategy process was analysed using a longitudinal single-case study for a period of 11 years (2008–2019). Findings The case study reveals three main constraints to logistics strategy implementation: a dominant purchasing organisation, a lack of incentives and diverging top-management priorities. This suggests that logistics strategy fit is not a conscious choice determined by contextual factors. Research limitations/implications Establishing fit is a continuous cycle of regaining fit between the logistics context and logistics strategy components. Fit can be achieved by a change to the logistics context or to logistics strategy components. Practical implications Logistics managers may need to opt for satisfactory fit in view of the costs incurred by changing strategy versus the benefits to be gained from a higher degree of fit. Originality/value This paper adopts a longitudinal case design to study the fit between the logistics context and strategy, adding to the body of knowledge on organisational design and strategy in logistics and supply chain management., Funding: Development Fund of the Swedish Construction Industry (SBUF); Lars Erik Lundberg Foundation for Research and Education; [13843]

We propose a simple and low-cost flow-through electrode for electrochemical cells used for instance in capacitive desalination. We have coated macro-porous carbon fiber papers with various loads of carbon microporous particles to combine both a high surface area and an open structure for good fluid dynamics. In this first study, we restrict our investigation to the charging/discharging behavior, the identification of side reactions, and the effect of geometry on the diffusion of ions. The electrochemical performance was first investigated by cyclic voltammetry and galvanic charge-discharge techniques. The specific capacitance increases by three orders of magnitude upon adding the carbon particles. Then, electrochemical impedance spectroscopy revealed the presence of charge transfer phenomena and modification in the mass transport by the diffusion process for the coated electrode., Funding Agencies|Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linkoping University; Linkoping University; [VR 2016-05990]; [KAW 2018.0058]; [P52023-1]; [2009-00971]