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Paper-based packaging is a complex multi-material composed of paper (fibers), plastics, and metals, making efficient material recycling complicated. Currently, most of the fibers are recycled into new paper products while the residual material is commonly incinerated. Therefore, to improve the circularity and reduce the fossil dependency chemical recycling is needed. In this study, recycling of the residual materials was evaluated by thermal and catalytic pyrolysis. First, screening tests were performed using five reject materials and four catalysts in an analytical scale and then, a selection of catalyst and reject materials were evaluated in lab-scale followed by a techno-economic assessment. Experimental results indicated that the conversion was more efficient if the reject had high content of plastics compared to fibers, leading to products with increased heating value, higher hydrocarbon yield and less reactive oxygenates compared to the rejects with low plastic ratio. In the thermal pyrolysis 54 % of the weight of the feedstock and 70 wt% of the carbon in the feedstock ended up in a solid organic product (wax) which contained hydrocarbons and alcohols. In the analytical catalytic pyrolysis, HZSM-5 gave the best result in terms of cracking, deoxygenation, and aromatization. Ex-situ catalytic pyrolysis using HZSM-5 resulted in an improved quality of organic liquid with reduced hydrocarbon length as well as deoxygenated and aromatic compounds. The yield of the organic liquid was up to 19 wt% and contained mainly monoaromatics. The techno-economic evaluation showed, for processing 100,000 tons year−1 residual material, the total plant investment and the annual profit are about 29 and 12 million Euros, respectively, if no incentive for treating the residual material., Funding: Packforsk Owners Association

Paper-based packaging is a complex multi-material composed of paper (fibers), plastics, and metals, making efficient material recycling complicated. Currently, most of the fibers are recycled into new paper products while the residual material is commonly incinerated. Therefore, to improve the circularity and reduce the fossil dependency chemical recycling is needed. In this study, recycling of the residual materials was evaluated by thermal and catalytic pyrolysis. First, screening tests were performed using five reject materials and four catalysts in an analytical scale and then, a selection of catalyst and reject materials were evaluated in lab-scale followed by a techno-economic assessment. Experimental results indicated that the conversion was more efficient if the reject had high content of plastics compared to fibers, leading to products with increased heating value, higher hydrocarbon yield and less reactive oxygenates compared to the rejects with low plastic ratio. In the thermal pyrolysis 54 % of the weight of the feedstock and 70 wt% of the carbon in the feedstock ended up in a solid organic product (wax) which contained hydrocarbons and alcohols. In the analytical catalytic pyrolysis, HZSM-5 gave the best result in terms of cracking, deoxygenation, and aromatization. Ex-situ catalytic pyrolysis using HZSM-5 resulted in an improved quality of organic liquid with reduced hydrocarbon length as well as deoxygenated and aromatic compounds. The yield of the organic liquid was up to 19 wt% and contained mainly monoaromatics. The techno-economic evaluation showed, for processing 100,000 tons year−1 residual material, the total plant investment and the annual profit are about 29 and 12 million Euros, respectively, if no incentive for treating the residual material., Funding: Packforsk Owners Association

Traditionally, two different techniques have been used to enhance the spectral operating range of a photodetector, i.e., either combining materials of different bandgaps or combining semiconductor materials to form tertiary alloys. However, these methods involve multiple synthesis/processing steps and complex clean room-based fabrication. Addressing this, a simple and novel step in device architecture is demonstrated by surface engineering of Cu buffer layer in between a paper substrate and Cu2S to develop a highly responsive photodetector with tunable operating range. Cu2S nanoparticles were grown directly on the surface of Cu pre-deposited paper substrate using facile hydrothermal technique to obtain Ag/Cu2S/Ag - metal semiconductor metal (MSM) configuration on Cu/paper substrate. The morphological characterization revealed the uniform distribution of Cu2S nanoparticles on the surface of cellulose microfibers and the structural studies confirmed the formation of highly crystalline cubic - Cu2S with Cu-S stretching vibrations. In comparison to the pristine Ag/Cu2S/Ag on paper photodetector, the buffer-based Ag/Cu/Cu2S/Ag photodetector displayed excellent response from visible to NIR region. A superior responsivity and detectivity of 27 mA/W and 2.5 ×108 Jones respectively are observed for Cu buffer/Cu2S photodetector. The obtained response of the paper/Cu/Cu2S device can be attributed to the synergistic combination of Cu buffer layer induced reduction of defect sites and LSPR induced transfer of electrons from the Cu metal to Cu2S. The paper-based photodetector displayed a fast response time of 0.09 sec, and excellent mechanical stability and durability for 1000 bending cycles. The surface engineering of a buffer layer in the photodetector architecture certainly bridges a technological gap and presents a new strategy for development of high-performance optoelectronic devices. © 2022 Elsevier B.V.

Traditionally, two different techniques have been used to enhance the spectral operating range of a photodetector, i.e., either combining materials of different bandgaps or combining semiconductor materials to form tertiary alloys. However, these methods involve multiple synthesis/processing steps and complex clean room-based fabrication. Addressing this, a simple and novel step in device architecture is demonstrated by surface engineering of Cu buffer layer in between a paper substrate and Cu2S to develop a highly responsive photodetector with tunable operating range. Cu2S nanoparticles were grown directly on the surface of Cu pre-deposited paper substrate using facile hydrothermal technique to obtain Ag/Cu2S/Ag - metal semiconductor metal (MSM) configuration on Cu/paper substrate. The morphological characterization revealed the uniform distribution of Cu2S nanoparticles on the surface of cellulose microfibers and the structural studies confirmed the formation of highly crystalline cubic - Cu2S with Cu-S stretching vibrations. In comparison to the pristine Ag/Cu2S/Ag on paper photodetector, the buffer-based Ag/Cu/Cu2S/Ag photodetector displayed excellent response from visible to NIR region. A superior responsivity and detectivity of 27 mA/W and 2.5 ×108 Jones respectively are observed for Cu buffer/Cu2S photodetector. The obtained response of the paper/Cu/Cu2S device can be attributed to the synergistic combination of Cu buffer layer induced reduction of defect sites and LSPR induced transfer of electrons from the Cu metal to Cu2S. The paper-based photodetector displayed a fast response time of 0.09 sec, and excellent mechanical stability and durability for 1000 bending cycles. The surface engineering of a buffer layer in the photodetector architecture certainly bridges a technological gap and presents a new strategy for development of high-performance optoelectronic devices. © 2022 Elsevier B.V.

In the past few decades, we saw a proliferation of scientific articles available online. This data-rich environment offers several opportunities but also challenges, since it is problematic to explore these resources and identify all the relevant content. Hence, it is crucial that they are appropriately annotated with their relevant concepts so to increase their chance of being properly indexed and retrieved. In this paper, we present R-Classify, a web tool that assists users in identifying the most relevant concepts according to a large-scale ontology of research areas in the field of Computer Science.

Renin-angiotensin-aldosterone system (RAAS) inhibition is a mainstay of the pharmacological treatment of heart failure with reduced ejection fraction (HFrEF). In the last years RAAS blockade has been improved by the introduction of the Angiotensin Receptor-Neprilysin Inhibitor (ARNI) sacubitril/valsartan, that combines RAAS inhibition with the block of neprilysin, boosting the positive effects of natriuretic peptides. The PARADIGM-HF trial demonstrated a significant advantage of sacubitril/valsartan over enalapril on the reduction of cardiovascular (CV) mortality and heart failure hospitalizations rates. Then, several randomized clinical trials and observational studies investigated its role in different clinical settings and its efficacy has been fully recognized in the most recent HFrEF European and USA guidelines. The effects of sacubitril/valsartan on major CV outcomes are associated with reduction of NT-proBNP levels and reverse cardiac remodeling and mitral regurgitation, recognized as one of the mechanistic effects of the drug explaining the favorable prognostic effects. A careful evaluation of patients’ clinical profile is relevant to implement the use of ARNI in the clinical practice and to obtain the maximal treatment efficacy. The present Position Paper reports the opinion of the Italian Society of Cardiology on the optimal blockade of the RAAS system in HF patients with the aim of fostering widespread implementation of scientific evidence and practice guidelines in the medical community.

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing., Conference name: 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023; Conference date: 22 November 2023 through 24 November 2023; Conference code: 198427; All Open Access, Gold Open Access

High quality and productivity requirements prevail in aircraft manufacturing. To ensure these standards are met, production processes are largely automated and digitalized. However, high product complexity leads to many manual processes which are much more prone to errors and insufficient productivity. To support workers effectively, this paper proposes a concept for workers in aircraft manufacturing, which connects different Digital Assistance Technologies using a Digital Twin. The concept results from the analysis of influencing factors in manual work processes, the investigation of workers in the production environment and a survey on the technology acceptance of different Digital Assistance Technologies.

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing.

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing., Conference name: 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023; Conference date: 22 November 2023 through 24 November 2023; Conference code: 198427; All Open Access, Gold Open Access

The rapid growth of the electric vehicle (EV) market has led to an increased demand for batteries, necessitating sustainable solutions for their entire lifecycle. This paper presents a conceptual framework addressing the second life stage of EV batteries. The framework comprises three primary elements: Value Proposition, Value Creation and Delivery, and Value Capture, each further divided into sub-elements encompassing various aspects of second life batteries. Developed through a literature review and validated through workshops with partnering companies, the framework provides a holistic view of second life batteries management. The study's findings identify distinct product/service offerings, target customer differentiation, key activities, ownership options, channels and customer relationships, resources and capabilities, partners and suppliers, cost structures, and revenue models for second life batteries. The paper's theoretical contribution lies in offering a framework that guides researchers and practitioners in developing sustainable strategies for battery production, use, and recycling. Practical implications include providing a tool for companies to evaluate and improve their practices in terms of sustainability and value. By fostering sustainable and profitable strategies, this paper contributes to the literature on sustainable battery production and the circular economy, while also benefiting the EV industry and society.

Industrial manual assembly, especially within the area of large-scale assembly, often lacks process monitoring since the feedback about performed assembly tasks is only generated by the worker. Optical sensor systems thereby offer the potential to monitor assembly states automatically and derive the required information without intervening the work in progress. With the growing trend of customization, not only the assembly process itself, but also the monitoring system must be adapt- able at short notice. However, most machine vision systems, as they are today, are commonly task-specific solutions and are therefore hard to be transferred to another inspection task or other work objects. To lower the barriers on applying machine vision into varying environments, this paper introduces an automated CAD-based sensor planning and implementation pipeline. An analysis and derivation of common constraints in assembly design is laid out, followed by a method of generating and optimizing inspection features and sensor poses. A strategy to implement the image processing pipeline based on the derived features is presented and applied on an assembly use case., Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events., This research was performed under the CAISR+ project funded by the Swedish Knowledge Foundation

Smart manufacturing (SM) enhances the competitiveness of manufacturing companies by promoting automation and overall equipment effectiveness (OEE), targeting to produce 100% qualified products fully automatically. One of the key challenges to the SM initiatives is the continuous demand fluctuations in the specification and quantity, especially when a new product variant comes to the production line. Reconfigurable manufacturing (RM) system provides cost-effective, rapid response to abrupt market changes. It provides a solution by its flexibility in repurposing tools, adding machines, and modifying software to rapidly respond to changing demands at low unit costs. The ability of SM technologies through self-programming and cloud computation may significantly complements RM initiatives. There is increasing evidence that SM and RM may augment each other through their complementary strengths, leading to the new paradigm of smart reconfigurable manufacturing (SRM). To highlight the complementary strengths, this paper investigates the converging trend of RM and SM based on natural language processing, e.g., topic modeling and semantic embedding. Key characteristics and industrial use cases are subsequently summarized to systematically delineate the new SRM paradigm and illustrate its advantages and feasibility in practice., QC 20240229

The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events., This research was performed under the CAISR+ project funded by the Swedish Knowledge Foundation

A kind of extended codes of linear codes C with respect to a vector u, denoted by C‾(u), was introduced by Sun, Ding and Chen (2024) [58]. In this paper, we investigate the weight distribution of the extended codes C‾(u) of projective two-weight codes C over finite fields. As a by-product, several families of linear codes with a few weights are presented, and six infinite families of distance-optimal linear codes are constructed in this paper. © 2024 Elsevier B.V.

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum. The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge–cloud computing models and the other for edge–cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing c

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems., Full text: CC BY-NC-ND license

Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative for monitoring and preserving these ecosystems. This paper introduces Unsupervised Curriculum Learning (UCL) to seagrass classification using the DeepSeagrass dataset. UCL progressively learns from simpler to more complex examples, enhancing the model's ability to discern seagrass features in a curriculum-driven manner. Experiments employing state-of-the-art deep learning architectures, convolutional neural networks (CNNs), show that UCL achieved overall 90.12 % precision and 89 % recall, which significantly improves classification accuracy and robustness, outperforming some traditional supervised learning approaches like SimCLR, and unsupervised approaches like Zero-shot CLIP. The methodology of UCL involves four main steps: high-dimensional feature extraction, pseudo-label generation through clustering, reliable sample selection, and fine-tuning the model. The iterative UCL framework refines CNN's learning of underwater images, demonstrating superior accuracy, generalization, and adaptability to unseen seagrass and background samples of undersea images. The findings presented in this paper contribute to the advancement of seagrass classification techniques, providing valuable insights into the conservation and management of marine ecosystems. The code and dataset are made publicly available and can be assessed here: https://github.com/nabid69/Unsupervised-Curriculum-Learning—UCL., Validerad;2024;Nivå 2;2024-09-09 (hanlid);Full text license: CC BY

Circular value propositions enable companies to build business models based on value preservation and circular resource flows. Circular value propositions also allow people to live more sustainably by facilitating increased utilization and resource efficiency. However, despite potential, transitions to circular business models remain low. One reason may be that circular value propositions are less attractive because they are often not developed with a user-centered approach and thus fail to meet people’s everyday needs. Through interviews with 13 companies and a literature study, this paper explores how companies consider user needs and involve users during development of circular value propositions. It also identifies conditions and actions that aid development of attractive circular offers. The findings are synthesized in a framework describing six enabling conditions and a definition of user-centered circular value propositions useful for circular economy practice and research., The authors would like to thank the interview participants for so openly sharing their experiences and valuable insights. The authors also acknowledge the two anonymous reviewers for their feedback which contributed to enhancing this manuscript. The research presented in this paper is part of the project Game-Based Approaches to Create Innovative Circular Value Propositions ( https://www.ri.se/en/what-we-do/projects/circular-co-creation-games ), which has received funding from the Swedish research council FORMAS , grant number 2021-02521 .

A critical shift is required to meet the challenges reflected in the sustainable development goals. Policymakers in the EU are betting on a transition from a linear to a circular economy to make this happen. Public procurement is vital in this context, e.g., accounting for 20% of Swedish GDP, with circular public procurement being critical to the transition. Until now, the research focus in this area has been narrower, concentrating on, e.g., IT equipment or furniture. However, plastic medical products have not been addressed despite generating substantial environmental impact and offering a tremendous upside. To enable the application of circular economy strategies in this context, changes are required on two ends: the design of the products and the criteria under which the products are procured. Indicators are the central basis of decision-making in any purchasing decision and are considered powerful enablers and drivers of the transition to a circular economy. This paper reviews circular indicators as a starting point toward guidelines for the circular design and procurement of plastic products in the healthcare sector. Based on this, critical factors for developing a comprehensive indicator for the design and procurement of circular plastic medical products are identified., QC 20240605

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems., Full text: CC BY-NC-ND license

Fault tree analysis is a system malfunction hazard evaluation quantitative and qualitative procedure. The method is well-known and widely used, especially in the safety systems domain, where it is a mandatory integral part of the so-called "Hazard Evaluation"documentation. This paper proposes an alternative or complementary deductive fault analysis method: it uses system topology to build a hypergraph representation of the system to identify component criticality and support loss of functionality probability evaluation. Once automated, the proposed method seems promising when the system engineers explore the different architectures. They may have indication about architecture's reliability without continuous feedback from the system safety team. The system safety team must check the solution once the engineers select the final architecture. They can also use the proposed method to validate the correctness of the fault tree analysis.

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum. The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge–cloud computing models and the other for edge–cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing c

Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative for monitoring and preserving these ecosystems. This paper introduces Unsupervised Curriculum Learning (UCL) to seagrass classification using the DeepSeagrass dataset. UCL progressively learns from simpler to more complex examples, enhancing the model's ability to discern seagrass features in a curriculum-driven manner. Experiments employing state-of-the-art deep learning architectures, convolutional neural networks (CNNs), show that UCL achieved overall 90.12 % precision and 89 % recall, which significantly improves classification accuracy and robustness, outperforming some traditional supervised learning approaches like SimCLR, and unsupervised approaches like Zero-shot CLIP. The methodology of UCL involves four main steps: high-dimensional feature extraction, pseudo-label generation through clustering, reliable sample selection, and fine-tuning the model. The iterative UCL framework refines CNN's learning of underwater images, demonstrating superior accuracy, generalization, and adaptability to unseen seagrass and background samples of undersea images. The findings presented in this paper contribute to the advancement of seagrass classification techniques, providing valuable insights into the conservation and management of marine ecosystems. The code and dataset are made publicly available and can be assessed here: https://github.com/nabid69/Unsupervised-Curriculum-Learning—UCL., Validerad;2024;Nivå 2;2024-09-09 (hanlid);Full text license: CC BY

Circular value propositions enable companies to build business models based on value preservation and circular resource flows. Circular value propositions also allow people to live more sustainably by facilitating increased utilization and resource efficiency. However, despite potential, transitions to circular business models remain low. One reason may be that circular value propositions are less attractive because they are often not developed with a user-centered approach and thus fail to meet people’s everyday needs. Through interviews with 13 companies and a literature study, this paper explores how companies consider user needs and involve users during development of circular value propositions. It also identifies conditions and actions that aid development of attractive circular offers. The findings are synthesized in a framework describing six enabling conditions and a definition of user-centered circular value propositions useful for circular economy practice and research., The authors would like to thank the interview participants for so openly sharing their experiences and valuable insights. The authors also acknowledge the two anonymous reviewers for their feedback which contributed to enhancing this manuscript. The research presented in this paper is part of the project Game-Based Approaches to Create Innovative Circular Value Propositions ( https://www.ri.se/en/what-we-do/projects/circular-co-creation-games ), which has received funding from the Swedish research council FORMAS , grant number 2021-02521 .

Corrosion-induced leakage in flanged gasketed joints is a critical issue in various industries, with implications for safety, environmental compliance, and economic sustainability. This review paper examines the mechanisms and factors contributing to corrosion-related failures in these joints, clarifying the diverse range of materials, operating conditions, and gasket types that influence their susceptibility to degradation. The paper investigates the key corrosion processes, such as pitting, crevice, and galvanic corrosion, that can initiate and propagate in the joint’s critical areas. It explores the role of environmental factors, including microorganisms, temperature, flow, and chlorination, in accelerating the corrosion process. Additionally, the influence of gasket materials and design on corrosion susceptibility is investigated, highlighting the importance of selecting appropriate materials and sealing technologies. Furthermore, it reviews various corrosion monitoring techniques that can help identify corrosion early, ensuring the integrity and reliability of flanged gasketed joints.

In this paper, we present a comprehensive study on the implementation of machine vision-enabled in-process quality inspection systems in machining operations. Our objective is to enable zero-defect manufacturing by maximizing efficiency and effectiveness in production processes. We utilize machine vision technology as a transformative tool, playing a vital role in our comparative analysis which reveals its superiority over traditional in-situ approaches in both ideal and challenging real-world scenarios. Through simulation, we demonstrate how machine vision improves the performance of in-line process systems. We also discuss substantial challenges of implementation, such as managing environmental contamination, optimizing machine coordination, accommodating a range of part sizes, and configuring effective coolant delivery systems. Our in-depth analysis of essential factors includes the robustness of machine vision equipment, operator training for machine vision technology, and cost-benefit analysis of its implementation. The research emphasizes machine vision's crucial role in transforming manufacturing setups and enhancing advanced automation systems. The study underscores the immense potential of machine vision in in-process quality inspection to significantly reduce production costs and time, fostering higher manufacturing sustainability and competitive advantage in the era of Industry 4.0.

This paper is dedicated to the ductile–brittle transition behaviour of the microalloyed structural steel NIOMOL 490 K. This steel grade is used for welded pressure vessels subjected to dynamic loads and operating at sub-zero temperatures. Therefore, it must have an acceptable toughness. Due to its importance for the safety assessment of pressure vessels, a characterization of this steel was carried out using the Charpy V-notch impact test in the temperature range between −60 °C and + 60 °C. The notches were located in parent material, heat affected zone and weld metal. In this paper, the tensile strength properties at ambient temperature and the nil ductility temperature in the temperature range from −60 °C to + 60 °C are presented.

This paper is dedicated to the ductile–brittle transition behaviour of the microalloyed structural steel NIOMOL 490 K. This steel grade is used for welded pressure vessels subjected to dynamic loads and operating at sub-zero temperatures. Therefore, it must have an acceptable toughness. Due to its importance for the safety assessment of pressure vessels, a characterization of this steel was carried out using the Charpy V-notch impact test in the temperature range between −60 °C and + 60 °C. The notches were located in parent material, heat affected zone and weld metal. In this paper, the tensile strength properties at ambient temperature and the nil ductility temperature in the temperature range from −60 °C to + 60 °C are presented.

In this paper, a system security problem is studied based on a two-person non-cooperative dynamic game. Security of the system is regarded in the context of observability. Namely a system is secure if it is not observable. Both the defender and attacker are assumed to have means to modify the dimension of unobservable subspace, which is set as the value function. The strategies and algorithms to minimize or maximize the value function are given respectively. Finally, examples are provided to demonstrate the effectiveness of the proposed algorithms and show some interesting game results, including accumulation mode, loop mode and locked mode., Part of ISBN 9781713872344QC 20240222

HALLO Hubs for Last Mile Delivery Solutions is an EIT (European Institute of Innovation & Technology) Urban Mobility project focusing on more sustainable last-mile delivery solutions in Barcelona, Spain and Stockholm, Sweden (Apr 2021 - Mar 2022). In Barcelona, the project has worked to establish micro hubs to support last-mile, cargo-bike delivery through a series of pilot actions implemented in municipalities on the border of the Low Emission Zone (LEZ). Two different cargo-bike box innovations have been developed and tested. In Stockholm, a micro hub has been piloted to support last-mile delivery of refrigerated goods by a light electric vehicle. A stakeholder dialogue has also been undertaken to inform the development of a roadmap for fossil-free logistics. The paper presents results from the project activities, discubes lessons learned, and reflects upon implications for cities., QC 20240201

Efficiently mastering the complexity of electrified powertrains by simultaneously meeting all functional and performance targets requires a highly integrated development approach. The present paper provides an overview of the simulation framework elaborated in the project VISION-xEV for supporting digital development to enable faster deployment of electrified/hybrid vehicles on the market. The research and innovation activities of VISION-xEV comprised the development of fast high-fidelity e-component models including the related parameterization workflows and consistent methodologies for virtual component and system integration. The methods resulting from VISION-xEV facilitate the increased utilization of virtual prototyping on component, subsystem and powertrain/vehicle level for supporting the efficient and effective development of all kinds of future electrified/hybrid vehicles., QC 20240201

The paper provides a methodology for measuring the adherence of Research & Innovation (R&I) actions in railways to the stakeholders' requirements and expected benefits. After collecting the stakeholders' requirements, the R&I objectives are extracted from high-level documents such as Europe's Rail Joint Undertaking Master Plan and Multi Annual Work Programme. Lastly, after standardization of the requirements and the technology concepts, a top-down analysis is performed. The results show that network management and vehicle technologies have the highest benefit for the stakeholders, and ERRAC and Regulatory Bodies' requirements are well addrebed. However, the end-users requirements are only partially fulfilled. The necessity of increasing multimodal transport and cooperation with other transport modes is shown, while stronger focus can be placed on the employees and education/training., QC 20240201

The demand for railway service facilities in Europe has been rapidly increasing, prompting for more conflicts in capacity requests by railway undertakings. In Sweden, many of these facilities do not have any automatic monitoring possibilities and the infrastructure manager does not have accurate or real-time information about vehicles occupying the tracks. This introduces challenges in having efficient capacity utilization in such facilities. This paper proposes a framework for capacity allocation for facilities which are currently unmonitored in general and for railway yards in specific. The framework is proposed upon abessment of the feasibility of emerging technologies in monitoring railway service facilities, as well as evaluating the current capacity allocation process and the suitability of different pricing principles as a basis for a charging scheme for capacity reservation., QC 20240206

This paper empirically explores the extent to which European cities are on track to sustainable development by examining the environmental performance of a large sample of cities observed during the last two decades. The paper builds on the Environmental Kuznets Curve framework and leverages satellite data to build a set of environmental indicators as varied and as granular as possible. Evidence suggests that European cities are, on average, on track to sustainable development, meaning that the contribution of their economic growth to environmental degradation is already negative or close to becoming so. The paper also examines Transnational City Networks as accelerators of environmental transition. The results suggest that cities participating in such networks anticipate the transition with respect to their peers.

With the development of inventory planning and the contradiction between land supply and demand, urban renewal development has been gradually replacing reconstruction in China's community redevelopment projects. Such projects need multiple stakeholders' engagement. However, China's patterns of public engagement with top-down governance are different from those in developed countries with bottom-up initiatives. This fact also indicates that such developed patterns are not suitable for the context of China. Meanwhile, research on micro rehabilitation is relatively new and requires further analytical work on development pattern analysis. Therefore, the protagonist status of different stakeholders and allowing them to participate in redevelopment projects are social issues that need to be solved urgently. This paper explores patterns of public engagement in community micro-rehabilitation projects in China. Eleven communities in Guangzhou are taken as cases through participatory observation, document analysis, and interviews. This paper analyses opinions and comments from different stakeholders and summarises their information delivery paths. The findings indicate four present patterns: single-threaded, representative feedback, property involvement, and external party service patterns. Through comparative analysis, the study highlights that the participating stakeholders in the four patterns involved the projects to different degrees. However, they are all still in the "Tokenism" degree, which is in the middle category of the ladder of engagement. Furthermore, an appropriate and sustainable pattern is put forward to provide a reference and research basis for improving public engagement in community micro-rehabilitation projects in China. © 2023

Human drivers usually have distinct driving patterns and preferences. Driver heterogeneity is crucial for modeling driving behaviors. This paper incorporates driver heterogeneity with data-driven approaches to predict car-following behaviors. A bi-level similarity-based car-following model is proposed to predict the vehicle's moving distance. In the upper level, drivers with similar driving patterns as the ego vehicle are identified using k-nearest neighboring (kNN) search. In the lower level, leveraging kNN model, candidate records are selected from the identified vehicles' trajectories and applied to predict the ego vehicle's moving distance, combining the driving pattern similarity measured in the upper level. By taking into account the driver heterogeneity, the proposed model is capable of identifying the most relevant driving situations, which leads to an improvement of prediction accuracy. Furthermore, the established bi-level structure largely shrinks the searching space of candidate records, which reduces the searching complexity and enhances computational efficiency. We quantitatively evaluate and compare the performance of the proposed model in terms of both prediction accuracy and computational efficiency using real-world vehicle trajectory data. © 2024 The Authors. Published by ELSEVIER B.V.

The current manufacturing climate is characterised by the offering of customisable or one-of-a-kind products at mass production prices while complying with sustainability demands. Consequently, modern manufacturing companies are employing more and more industrial robots to increase flexibility and productivity. Nonetheless, for manufacturing companies to achieve true sustainability, a Zero-Defect Manufacturing (ZDM) approach is needed. Such an approach implies applying emerging technologies for quality inspection and improvement, as no system is perfect or free from deviations. Science and Technology (S&T) roadmaps are employed as decision aids to improve the coordination of activities and resources in increasingly complex and uncertain environments. This paper presents a S&T roadmap to guide all-size companies towards ZDM through advanced robotics for in-line quality inspection. The Key Performance Indicators (KPIs) and progress metrics for robotic in-line quality inspection towards ZDM are outlined.

This paper discusses the bike-sharing system in the Czech Republic and selected neighboring states. The current problem of deteriorating air quality is driving cities to implement an efficient, more environmentally friendly approach. One of them is introducing a system of shared resources - shared bikes, scooters, cars, and others. Our goal is to address the issue of shared bikes, which are already widely used worldwide. However, it is necessary to point out that it is essential to have the bikes appropriately distributed in the stations, and their collection during the night at frequented stations must be ensured. The paper aimed is to analyze bike-sharing systems in the Czech Republic and foreign countries in Middle Europe, primarily in the Slovak Republic. The bike-sharing system in the Czech Republic and Slovak republic was analyzed mainly. The Poland bike-sharing system was added. The analysis of the bike-sharing providers was based on the available data. Based on the results, we can conclude that two (three) primary providers are in the selected states. These providers have different ways of bike storage. The vast difference is the number of bikes in the chosen town with the same population.

With the vigorous development of today’s wireless communication industry, the Internet of Vehicles (IoV), as one of its application scenarios, has received extensive attention from researchers. Ensuring the security of information transmission is one of the many problems to be solved in the IoV system. To fit the actual scene, this paper considers the impacts of both the channel characteristics of Nakagami-m fading and the distribution of the vehicles’ positions in the real road scenario of the IoV. According to the random distribution characteristics of vehicle terminal position, a system model with a transmitter base station, a legitimate vehicle terminal, and an eavesdropper is established. The approximate and asymptotic analytical expressions of the secrecy outage probability over the Nakagami-m fading channels are derived. Finally, the correctness of the proposed analysis models established in this paper is verified by Monte Carlo simulation and numerical analysis. The secrecy outage performance and the influencing factors of the considered model in the Nakagami-m fading environment are discussed and analyzed.

Rapidly progressive interstitial lung disease (RP-ILD) clearly harms the prognoses of dermatomyositis/polymyositis (DM/PM) patients, however there is a dearth of numerical prevalence and therapy comparison in this field. Therefore, the purpose of this study was to determine the prevalence of RP-ILD in DM/PM patients and compare prognoses, including remission rate and survival data, between treatments. Studies with reports of RP-ILD in DM/PM patients and studies with definite remission and/or survival data of DM/PM-RP-ILD were included in the study. Data sources were Pubmed, Embase, and Cochrane Library without language restrictions. Two authors (WHL and WWQ) extracted independently the data. Estimates of the pooled effects were calculated using the Mantel-Haenszel technique (random effects). The prevalence meta-analysis included 18 papers with 6058 DM/PM patients, and 31 papers were analyzed for treatment effects, including remission rate, 6-month survival rate, 1-year survival rate, and 5-year survival rate. Database search yielded 1816 articles. In the DM/PM population, the combined prevalence of RP-ILD was 8.9% (95% CI, 5.8% to 12.1%). Patients with RP-ILD have a remission rate of 58.4% (95% CI, 47.3% to 69.4%), with biologic treatment with the highest remission rate, followed by triple therapy (defined as adding a third intravenous medication, including cyclophosphamide and immunoglobulin). Biologics therapy had the highest overall survival rate at six months (95% CI, 49.8% to 73.9%), followed by cDMARDs, plasma exchange, and triple therapy. The 1-year survival rate was 77.4% (95% CI, 66.7% to 88.1%), and triple therapy and cDMARDs had the best survival rates. The 5-year survival rate was 40.0% (95% CI, 10.0% to 69.9%). The prevalence of RP-ILD in DM/PM was approximately 8.9%, with a poor long-term prognosis. The use of biological agents appears to provide the best therapeutic outcomes, providing RP-ILD management with a novel evidence-based therapy. The use of

Ontologies play a pivotal role in knowledge representation, particularly beneficial for the Architecture, Engineering, and Construction (AEC) sector due to its inherent data diversity and intricacy. Despite the growing interest in ontology and data integration research, especially with the advent of knowledge graphs and digital twins, a noticeable lack of consolidated academic synthesis still needs to be addressed. This review paper aims to bridge that gap, meticulously analysing 142 journal articles from 2000 to 2021 on the application of ontologies in the AEC sector. The research is segmented through systematic evaluation into ten application domains within the construction realm- process, cost, operation/maintenance, health/safety, sustainability, monitoring/control, intelligent cities, heritage building information modelling (HBIM), compliance, and miscellaneous. This categorisation aids in pinpointing ontologies suitable for various research objectives. Furthermore, the paper highlights prevalent limitations within current ontology studies in the AEC sector. It offers strategic recommendations, presenting a well-defined path for future research to address these gaps. © 2023 The Author(s)

The paper examines the determinants of FDI profit repatriation in the Czech Republic and OECD countries as a group from 2013 to 2019. By applying the Lintner model about repatriation decisions at the national level, the paper finds the following findings. Firstly, the repatriation rate in the Czech Republic is higher than in most OECD countries. Secondly, financial development and investment opportunities negatively affect the payout ratio of OECD countries as a group, while the appreciation of host countries' currency and higher effective rates positively affect the payout ratio of OECD countries as a group. However, in the case of the Czech Republic, two factors that cause a higher payout ratio are financial development and anti-corruption enforcement. It implies that the Czech Republic should improve its financial market development and reduce the level of corruption to keep the profit from FDI in the country.

Recent research has focused on developing real-time sign language recognition systems (SLRSs) based on gesture recognition to classify hand motions into their equivalent meaning in spoken language, but no comprehensive system with all desirable features has been presented. The existence of different systems has hindered the process of selecting the most preferred system. Therefore, many researchers have compared and evaluated several recognition systems to identify the best one using multicriteria decision-making methods. These studies extended the fuzzy decision by opinion score method (FDOSM) using a single Likert scale under the Pythagorean fuzzy set (PFS) or one of its extensions. However, no comparative study has examined the influence of using multiple Likert scales with a single fuzzy set. Furthermore, the effect of employing multiple Likert scales on benchmarking results is a challenging task. Therefore, this paper examines the three Likert scales (five-, seven- and ten-point) under the same fuzzy environment. This paper extends FDOSM into PFSs based on the power Bonferroni mean (PBM) operator (named PFDOSM-PBM) to benchmark the real-time SLRS. The decision matrix is constructed based on 30 real-time SLRS-based wearable sensory devices and the 11 evaluation criteria. The results reveal that the five-point Likert scale is superior to other scales (i.e., seven- and ten-point) as it is flexible, easy to use and generates more accurate findings on the basis of uncertainty compared to other scales. Systematic ranking and comparative analysis are conducted to validate and evaluate the proposed method.

Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor generalization ability of existing underwater small object, this paper takes sea cucumber as research objects to research the small object recognition approach in complex underwater environment. In this paper, a sea cucumber dataset is established to solve the problem of lacking sea cucumber dataset in the real seabed environment. A deep learning model SO-YOLOv5 is proposed based on YOLOv5 for underwater small object recognition, which improves the recognition ability of the algorithm for different sizes of objects in complex environment. The proposed model embeds a large-scale feature extraction layer in structure to increase the detection ability of small objects by referring to the idea of the Bi-directional Feature Pyramid Network. SO-YOLOv5 fuses the features between deep and shallow layers and balances the feature information of different scales, and integrates the Coordinate Attention mechanism to enhance the sensitivity of the algorithm to the direction and position information. The experimental results illustrate that the mAP of the proposed approach achieves 95.47% for sea cucumber recognition in the complex seabed environment, which is 3.42%, 6.79%, and 5.46% higher than the traditional Faster R-CNN, SSD, and YOLOv5 algorithms, respectively. This research not only provides an effective approach for sea cucumber recognition in complex environment but also has certain reference significance for the recognition of small objects in other complex environments. In addition, the proposed approach has practical application value to improve intelligence level in aquaculture. © 2023 Elsevier B.V.

To provide direction and advice for future research on Industry 4.0 maintenance, we conducted a comprehensive analysis of 344 eligible journal papers published between 2013 and 2022. Our systematic literature review identifies key trends in advanced maintenance techniques and the consolidation of traditional maintenance concepts, which are driven by the increasing adoption of Industry 4.0 technologies and the need to optimize manufacturing systems' performance and reliability. In light of our findings, we highlight the importance of addressing sustainability factors, human aspects, and the implementation of environmental KPIs in future research. Building upon these insights, we introduce the Maintenance 5.0 framework, which emphasizes the integration of human-centered and AI-driven strategies for achieving efficient and sustainable maintenance in Zero-Defect Manufacturing (ZDM) systems. We propose a novel framework that links traditional and advanced maintenance policies for small and medium-sized enterprises (SMEs) to facilitate the adoption of Industry 4.0 technologies in the maintenance field. This work underscores the need for future research to bridge the gap between these policies, enabling a seamless transition for SMEs towards Industry 4.0 maintenance practices, while fostering sustainable and socially responsible operations.

Adaptation to climate change is impacted by a range of interrelated processes operating from local to global levels. There are often significant disconnects between different people's perceptions of responsibilities, capabilities and motivations, and divergent understandings of how the system works across actors, sectors and levels of governance. This results in misalignments of policies and practices, plus ineffective flows of resources and knowledge across the network of climate adaptation actors. As these disconnects are rooted in deep misunderstandings of the grounded realities of different actors, an experiential process of mutual discovery is required to build shared understanding and mutual respect. While it is common in the literature for people to talk about multi-level governance, most existing planning processes involve the production of separate plans at each individual level, based on the often-mistaken assumption that they will aggregate into an effective multi-level approach. This paper presents a new, multi-level integrated planning and implementation (MIPI) process, bringing together diverse actors from community, district, regional and national levels in the same workshop. The MIPI process creates a safe space that allows participants to interact directly in conducting systemic, cross-level analyses, as well as the multi-level integration of policies, plans and programs. The paper describes how the MIPI process was designed and facilitated in Ghana to address climate change, agricultural development and food security. This methodology has potential for much broader applicability to complex, multi-level planning and implementation processes.

Reinforcement Learning for EV charging optimization has gained significant academic attention in recent years, due to its ability to handle uncertainty, non-linearity, and real-time problem-solving. While the number of articles published on the matter has surged, the number of open-source environments for EV charging optimization remains small, and a research gap still exists when it comes to customizable frameworks for commercial vehicle fleets. To bridge the gap between research and real-world deployment of RL-based charging optimization, this paper introduces FleetRL as the first customizable RL environment for fleet charging optimization. Researchers and fleet operators can easily adapt the framework to fit their use-cases, and assess the impact of RL-based charging on economic feasibility, battery degradation, and operations., QC 20240307

This paper explores how vacuum foam-drying of a protein is influenced by formulation parameters by investigating the foam structure, physical properties of the foam, and the stability of the protein. Recombinant human bile salt-stimulated lipase was used as a model of a protein drug. The stability of the lipase was evaluated through activity measurements. Two disaccharides (sucrose and trehalose), strongly tending to an amorphous form, were used as matrix formers, and the physical properties were assessed through residual water content, glass transition temperature, and crystalline state. Moreover, some formulations included surfactants with different sizes and structures of the head group. The alkyl chain length was kept constant to only investigate the impact of the surfactant head group, in the presence of the lipase, on the foamability and surface coverage of the lipase. The study demonstrated that the lipase allowed for a dry, solid foam with a foam overrun of up to 2600 %. The wall thickness of the dry, solid foam was estimated to be 20–50 µm. Clear differences between sucrose and trehalose as matrix former were identified. The lipase showed no tendency to lose activity because of the drying and rehydration, despite a proportion of the lipase covering the surfaces of the dry material., 'Correspondence Address: D. Tristan Osanlóo; RISE Research Institutes of Sweden, Stockholm, Box 5604, SE-114 86, Sweden; The authors would like to thank Swedish Orphan Biovitrum for providing the lipase and Croda Nordica AB for the delivery of the surfactants. Dr. Stefan Ulvenlund (ENZA Biotech AB) is gratefully acknowledged for the delivery of surfactants and valuable discussions. This research was funded by the Swedish Governmental Agency for Innovation Systems (VINNOVA) and was carried out within the competence center NextBioForm (grant number 2018-04730).