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Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

The main focus of this paper is how a manufacturing organization can secure the capabilities of effectively executing a series of improvements of various sizes and innovativeness from small to large and incremental to more radical at the manufacturing plant, so that the speed of improvement as a whole at the plant can be kept high. A recent study on a Toyota plant in Japan has shown that a group of production engineers located at the plant plays an important role in ensuring the mentioned kind of capabilities. Those engineers are considered trusted experts in improvement of various sizes and innovativeness. They also coordinate those improvements so that the speed of improvement at the plant level can be kept high. This study raises a question of how other manufacturing companies do to ensure these capabilities, given that the group of the production engineers playing the role is likely a specific organization design to this specific company. For the question, three manufacturing plants producing vehicles in Swedish are studied. The paper discusses implications drawn from a cross-case analysis of those plants including the Toyota plant, in terms of the main focus of this paper. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Whether leaders and their teams agree or not on perceptions of leadership has been found to impact follower well-being and performance. Less is known about how agreements or disagreements play a role in relation to safety and leadership training. The present study examined the effects of leaders’ and followers’ perceptual distance on safety leadership prior to a leadership safety training. Forty-eight leaders and a total of 211 followers from the paper industry completed surveys before and after training. Polynomial regression with response surface analyses revealed that the agreement between leaders and their followers regarding safety leadership before training was positively related to training outcomes including safety leadership and followers’ safety self-efficacy. Line managers who overrated themselves on safety leadership before training had less favorable training outcomes. Our findings suggest that 360-degree feedback may not be sufficient for motivating leaders to change their behaviors during leadership training.

The hydrolate, or hydrosol, is the residue of essential oil (EO) distillation traditionally used in perfumery. Evidence that also this product exerts anti-microbial activity promoted a study on Monarda citriodora EO and hydrolate against thirty representative strains of bacteria and fungi. Additionally, the sole hydrolate was tested in four moulds responsible for the spoilage of paper artworks. The micro-broth dilution test served to assess EO (concentrations between 0.0078% and 4%) and hydrolate (concentrations between 12.5% and 50%) suppressiveness vs untreated control. EO and hydrolate were analysed (GC–MS), resulting in thymol being the major terpenic compound of both products (19.6% and 66.4% in the respective spectra). EO suppressed all micro-organisms, although resistant Pseudomonas aeruginosa strains required up to 4% EO as minimum inhibitory concentration (MIC). Hydrolate suppressed all micro-organisms, except three P. aeruginosa strains where even the highest concentration (50% hydrolate) failed to reach the MIC. In each strain, the concentration needed to obtain 50% inhibition (IC50) was calculated by means of an exponential decay function. Median IC50 was 0.06% (EO) and 7.35% (hydrolate), while six IC50 EO data were shown to be outliers. In non-outlier data, IC50 hydrolate and EO were significantly correlated (r = 0.73**), and IC50 hydrolate was approximately a hundred times higher than IC50 EO. Despite higher concentration needed for the same inhibitory effect, the amount of terpenes supplied with hydrolate was lower than with EO. This means a higher hydrolate effectiveness that was likely due to the hydrophilic environment promoting higher terpene availability. Lastly, hydrolate exhibited promising results in the control of fungal growth on paper artworks, suppressing the four tested strains at concentrations of 25–50%.

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

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).

The transition towards a circular economy necessitates the development of tools for evaluating circularity. This paper proposes an approach with three important indicators—industrial waste, energy use, and IT material waste—to solve the challenge of evaluating circularity in the Swedish automobile industry. Each indicator includes a number of scores and sub-scores allowing for a thorough evaluation of circularity at product level. Qualitative and quantitative data are gathered to calculate scores for individual aspects of the indicators, which are then combined into an overall product score using a weighted average, and associated with an accuracy measurement reflecting the quality of the input data. The sensitivity of circularity indicator values to data availability underscores the importance of accuracy metrics, while placing a priority on simplicity, usefulness, and adaptability. This framework incorporates knowledge from scientific literature, business reports, and sustainability specialists. The result is an overall product circularity score. Such a scorecard can be crucial in accelerating the transition to circularity and sustainability since it highlights areas that need improvement. The framework's development incorporates feedback from sustainability experts, while interviews with industry professionals and data testing ensure its practicality for companies. The framework is designed to be adaptable and scalable to suit industry requirements. Companies are encouraged to adopt it as an internal tool and take ownership to drive circularity initiatives effectively., QC 20240215

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).

Let (Ax)x∈R be a measurable, weakly stationary random field, i.e. E[Ax]=E[Ay], Cov(Ax,Ay)=K(x−y), ∀x,y∈Rd, with covariance function K:Rd→R. Assuming only that the integral covariance function wt≔∫{|z|≤t}K(z)dz is regularly varying (which encompasses the classical assumptions found in the literature), we compute [Formula presented] for D,L⊆Rd belonging to a certain class of compact sets. As an application, we combine this result with existing limit theorems to obtain multi-dimensional limit theorems for non-linear functionals of stationary Gaussian fields, in particular proving new results for the Berry's random wave model. At the end of the paper, we also show how the problem for A with a general continuous covariance function K can be reduced to the same problem for a radial, continuous covariance function Kiso. The novel ideas of this work are mainly based on regularity conditions for (cross) covariograms of Euclidean sets and standard properties of regularly varying functions.

Additive Manufacturing (AM) has shown rapid development during the last decades. More commonly known as 3D printing, this technology offers significant benefits for the fabrication of physical models with complex geometries. This technology started as a valuable tool for prototyping purposes but is considered for general manufacturing due to the production cycle shortening and less (if none) material waste. Out of seven possible AM technologies, this research covers three of them, namely: Fused Deposition Modeling (FDM), Stereolithography (SLA), and Digital Light Processing (DLP). Although, ABS thermoplastic material is a thoroughly researched AM material, a new resin material also called ABS is an interesting object for research. Hence, the aim of this paper is to acquire the tensile properties of this material and to compare them to the “original” ABS thermoplastic material.

Air pollution is a primary concern, causing around 7 million premature deaths annually, with traffic-related sources contributing 23 %–45 % of emissions. While several studies have surveyed vehicle emission models, they are either outdated or focus on specific data-driven models. This paper systematically reviews vehicle emission prediction models, comparing traditional approaches with data-driven emission models. The traditional emission models can be divided into average-speed, modal, and other models, noting their reliance on empirical assumptions and parameters that may not be universally applicable. In contrast, we delve into data-driven models utilizing dynamometer and on-road test data for time-series and spatial-temporal predictions. The application of these models is discussed across various scenarios, highlighting the progress and gap. We observed that traditional models, primarily estimating total traffic emissions in study regions, lack micro-level detail crucial for tailored decisions. The direct link between road emission model accuracy and input data quality poses challenges in disaggregating on-road vehicle emission inventories. Due to unique transportation instruments, traffic fleet components, and patterns, exploring the effects of emission-reduction policies in specific cities or regions is urgent. Vehicle characteristics, environmental conditions, traffic scenarios, and prediction scales are common effect factors, while instantaneous driving profiles prove effective in model calibration. In data-driven models, ANN outperforms in estimating emissions and performance of low-power diesel engines with errors not exceeding 5 %. However, no single data-driven method performed excellently in predicting all pollutants. Besides, integrated methods utilizing LSTM, GRU, and RNN outperform individual models. To enhance prediction accuracy considering the inherent connectivity of road networks and spatiotemporal variation patterns of vehicle emissions, GCN is

Customer-to-Manufacturer (C2M) is a strategy in smart manufacturing where customers collaborate with manufacturers for customized product development on an online platform. The platform enables the shift from the traditional manufacturing process, which is driven by research and marketing, toward a customer-centric product development process. However, a challenge arises as customers lack technical knowledge to communicate their product specifications effectively, creating a semantic gap. This paper proposes a soft prompt-based network structure that utilizes pretrained language models to bridge the semantic gap on the C2M platform. To address limited customer needs data and imbalanced classes, a large corpus of product review texts is used to establish a mapping between reviews and product specifications. A smaller set of customer needs text is then employed to adapt this mapping to the target customer needs-product specifications relationship, thereby closing the semantic gap. The experimental results demonstrate the effectiveness of the proposed model adaptation operation and the prompting structure. Additionally, the experiments highlight the robustness of the proposed method against variations in training data size, thereby mitigating the challenges posed by imbalanced classes. The proposed method could potentially bring innovation to product customization and C2M platform development. By bridging the semantic gap, companies can better integrate customers in the co-design process and effectively translate customer needs into actionable product specifications. © 2024 Elsevier B.V.

Integrating building information modeling (BIM) with emerging blockchain technology has gained considerable attention in addressing design collaboration problems like poor data security, weak traceability, and low transparency in off-site construction. However, blockchain implementations in off-site construction design, especially with openBIM standards, currently lack standardized workflow management and methods for ensuring data completeness within the blockchain environment. To bridge these gaps, this paper presents a novel blockchain-enabled platform-as-a-service (PaaS), specifically tailored for off-site construction to enhance interoperability and collaborative design efficiency using openBIM standards. The proposed PaaS offers two significant intellectual contributions: (1) a data contract system that manages openBIM workflow standardization and (2) a data escrow system that ensures the completeness and integrity of openBIM data. The data contract system utilizes an innovative asynchronous smart contract algorithm to streamline workflow checks, while the data escrow system introduces a new blockchain-driven data-checking strategy to ensure openBIM completeness at the data level. Besides, a PaaS architecture is developed to clarify the interaction logic between these two systems. The PaaS is validated and evaluated in openBIM-based design collaborations from a real-world project, with results indicating improvements: a 38% time-saving in design issue coordination and a 32% reduction in design data delivery time. Furthermore, the platform received favorable feedback from project participants, validating the approach and confirming the PaaS as a valuable tool for enhancing design collaboration in off-site construction. © 2024

The success of deep learning on image classification and recognition tasks has led to new applications in diverse contexts, including the field of medical imaging. However, two properties of deep neural networks (DNNs) may limit their future use in medical applications. The first is that DNNs require a large amount of labeled training data, and the second is that the deep learning-based models lack interpretability. In this paper, we propose and investigate a data-efficient framework for the task of general medical image segmentation. We address the two aforementioned challenges by introducing domain knowledge in the form of a strong prior into a deep learning framework. This prior is expressed by a customized dynamical system. We performed experiments on two different datasets, namely JSRT and ISIC2016 (heart and lungs segmentation on chest X-ray images and skin lesion segmentation on dermoscopy images). We have achieved competitive results using the same amount of training data compared to the state-of-the-art methods. More importantly, we demonstrate that our framework is extremely data-efficient, and it can achieve reliable results using extremely limited training data. Furthermore, the proposed method is rotationally invariant and insensitive to initialization. © 2024 The Authors

This paper examines the chaos caused by introducing advanced language models, specifically ChatGPT, to art. Our focus is on the potential impact of ChatGPT on art creation and collaboration. We explore how it has been utilized to generate art and assist in creative writing and how it facilitates collaboration between artists. This exploration includes an investigation into the use of AI in creating art, music, and literature, emphasizing ChatGPT's role in generating poetry and prose and its ability to provide valuable suggestions for sentence structure and word choice in creative writing. We conduct case studies and interviews with diverse artists and AI experts to understand the benefits and challenges of using ChatGPT in the creative process. Our findings reveal that artists find ChatGPT helpful in generating new ideas, overcoming creative blocks, and improving the quality of their work. It enables remote collaboration between artists by providing a real-time communication and idea-sharing platform. However, ethical concerns relating to authorship ownership and authenticity have emerged. Artists fear using ChatGPT may lead to losing their artistic identity and ownership of their work. While our data suggests that ChatGPT holds the potential to transform the art world, careful consideration must be given to the ethical implications of AI in art. We recommend future research to focus on developing guidelines for the responsible use of AI in art, safeguarding artists’ rights, and preserving artistic authenticity. © 2024

In this paper, a high-sensitivity digital flow meter, featuring a low-power Thermo-resistive Micro Calorimetric Flow (TMCF) sensor fabricated by CMOS-MEMS technology, is presented for respiratory monitoring and neonatal ventilator-assisted ventilation. The packaging of the TMCF sensor has been meticulously optimized to achieve a 1.5-fold increase in sensitivity, resulting in a high sensitivity of 76.5 mV/SLM in the linear range of −7.5SLM to 7.5SLM, and its heating power is only 2.23 mW. The developed digital flow meter is quantized into a 14 bit output within the flow measurement range of −40SLM to 40SLM, providing a high resolution down to 0.005SLM. With a remarkable response time of less than 11.5 ms, corresponding to a bandwidth of 13.8 Hz, and equipped with a 2 kHz data update rate, our proposed digital flow meter excels in capturing abrupt changes in breathing and high-frequency respiratory signals. Furthermore, boasting a noise level below 0.0025SLM, the sensor system demonstrates the capability to identify subtle variations in respiratory flow. Based on its outstanding performance, the high-sensitivity digital flow meter effectively identifies simulated abnormal respiratory patterns, including apnea, polypnea, and hypopnea, and successfully performs closed-loop ventilation measurements for neonates on a ventilator. Therefore, it holds promising potential as a crucial sensing component with high precision, high sensitivity, and low power consumption in respiratory monitoring and neonatal ventilation therapy. © 2024 Elsevier B.V.

This paper studies an integrated train timetabling and rolling stock circulation planning problem with stochastic demand and flexible train composition (TRSF). A novel stochastic integer programming model, which is formulated on a space-time underlying network to simultaneously optimize the train timetable and rolling stock circulation plan with flexible train composition, is proposed by explicitly considering the random feature of passenger distribution on an urban rail transit line. To solve this problem efficiently, the proposed model is decomposed into a master problem and a series of sub-problems regarding different stochastic scenarios. We further prove that each sub-problem model is equivalent to its linear programming relaxation problem, by proving that the coefficient matrix of each linear programming relaxation model is totally unimodular. Then, the classical Benders decomposition algorithm is applied to the studied problem. Based on the model characteristics, both single-cut and multi-cut methods with some speed-up techniques are developed to solve the proposed model in a novel and effective way. Numerical experiments are conducted on small-scale cases and large-scale cases derived from Shanghai Metro Line 17, and the results show that solving the stochastic problem can extract gains in efficiency and the value of stochastic solution tends to be high. © 2024 Elsevier B.V.

In a multi-party machine learning system, different parties cooperate on optimizing towards better models by sharing data in a privacy-preserving way. A major challenge in learning is the incentive issue. For example, if there is competition among the parties, one may strategically hide their data to prevent other parties from getting better models. In this paper, we study the problem through the lens of mechanism design and incorporate the features of multi-party learning in our setting. First, each agent's valuation has externalities that depend on others' types and actions. Second, each agent can only misreport a type lower than his true type, but not the other way round. We provide the optimal truthful mechanism in the separable utility setting, as well as necessary and sufficient conditions for truthful mechanisms in general cases. Finally, we propose an algorithm to find the desirable mechanism that is truthful, individually rational, efficient and weakly budget-balanced, and analyze the computational complexity of the algorithm. © 2024 Elsevier B.V.

With the increasing adoption rate of Internet of Things (IoT) devices in smart home applications, it is vital to safeguard the privacy and security of information, communications among IoT devices and the underlying infrastructure. In open communication scenarios, an adversary can easily tamper with data transmitted by IoT communication devices. This paper proposes a softwarized lightweight authentication key agreement scheme for smart home applications in Software-Defined IoT (SDIoT) environment. The proposed scheme comprises registration, authentication, and key selection phases. Devices are registered in the registration phase after the successful completion of validation checks, while sessions’ keys are granted to the registered devices in the authentication phase. In the final phase, controllers classify IoT devices based on pre-defined parameters and impose class-specific access control based on classification. The security of the proposed scheme is validated using the widely accepted AVISPAs verification tool against a strong adversary. Simulation results demonstrate that our proposed scheme outperforms existing schemes in terms of running time, computation complexity, and energy consumption. It is found that the proposed scheme has significant cost-effectiveness. © 2024 Elsevier B.V.

Existing multi-agent reinforcement learning methods employ a paradigm of centralized training with decentralized execution (CTDE) to learn cooperative policy among agents via coordination. However, within continuous destruction conditions, the inclusion of information from dead agents significantly undermines the ability to effectively learn cooperative policies in multi-agent systems. In this paper, we first analyze the bias introduced by dead agents under the CTDE paradigm and how it affects cooperation among agents. Following this, we propose q-learning based downsizing adaptive policy (QDAP) framework for cooperative multi-agent reinforcement learning. QDAP actively discerns relevant values from dead agents and utilizes an innovative approach to convert historical trajectories into weighting factors, thereby aiding remaining active agents in learning more appropriate cooperative policies. Moreover, we extend our proposed framework into the CTDE paradigm, facilitating seamless adaptation with the methods of value decomposition. Experimental results demonstrate that QDAP significantly improves learning speed and achieves superior cooperation performance on challenging Starcraft II micromanagement benchmark tasks. © 2024 Elsevier B.V.

Ultrasonic bulk wave inspection of defects in safety–critical components with complex external geometries, such as turbine blades is challenging. While ultrasonic phased array imaging can yield high-resolution subsurface images, a commercial phased array probe can hardly be mounted on irregular external boundaries to perform in-situ imaging. In fact, a component with irregular shapes, as a highly reverberant body, is capable of generating elastic random diffuse or coda wavefields. The diffuse wavefields can be utilized to reconstruct Green's functions between any two passive receiving points. In this paper, an ultrasonic passive array imaging method using the diffuse reverberation resulting from complex boundaries is implemented to image internal defects. The method involves the utilization of active piezoelectric actuators to excite elastic diffuse waves within the component, which are received by a laser vibrometer scanning at multiple points. A passive full matrix capture (FMC) of array signals is extracted for defect imaging using the total focusing method. The proposed method is evaluated by the numerical simulations, and the effects of centre frequency, bandwidth, and source excitation methods on the imaging performance are investigated. An experiment using a turbine blade-like structure is conducted to further evaluate the imaging method. © 2024

Geometric error, mainly due to imperfect geometry and dimensions of machine components, is one of the major error sources of machine tools. Considering that geometric error has significant effects on the machining quality of manufactured parts, it has been a popular topic for academic and industrial research for many years. A great deal of research work has been carried out since the 1970 s for solving the problem and improving the machining accuracy. Researchers have studied how to measure, detect, model, identify, reduce, and compensate the geometric errors. This paper presents a thorough review of the latest research activities and gives an overview of the state of the art in understanding changes in machine tool performance due to geometric errors. Recent advances in measuring the geometrical errors of machine tools are summarized, and different kinds of error identification methods of translational axes and rotation axes are illustrated respectively. Besides, volumetric geometric error modeling, tracing, and compensation techniques for five-axis machine tools are emphatically introduced. Finally, research challenges in order to improve the volumetric accuracy of machine tools are also highlighted. © 2023 Chinese Society of Aeronautics and Astronautics

Environmental pollution and poor feed quality pose potential threats to aquatic organisms and human health, representing challenges for the aquaculture industry. In light of the rising demand for aquatic organisms, there is an urgent need to improve aquacultural production and protect the products from contamination. Char, a carbonaceous material derived through pyrolysis of organic carbon-rich biomass, has proven advantages in soil, air, and water remediation. While char's performance and the associated physicochemical characteristics depend strongly on the pyrolysis temperature, residence time, and feedstock type, char generally shows advantages in pollutant removal from the environment and livestock. This enables it to enhance the health and growth performance of livestock. Given the growing attention to char application in aquaculture in recent years, this review summarises major studies on three applications: aquacultural water treatment, sediment remediation, and char-feed supplement. Most of these studies have demonstrated char's positive effects on pollutant removal from organisms and aquacultural environments. Moreover, adopting char as fish feed can improve fish growth performance and the condition of their intestinal villi. However, due to insufficient literature, further investigation is needed into the mechanistic aspects of pollutants removal in aquatic organisms by char as a feed additive, such as the transportation of char inside aquatic organisms, the positive and negative effects of char on these products, and how char alters the gut microbiota community of these products. This paper presents an overview of the current application of char in aquaculture and highlights the research areas that require further investigation to enrich future studies. © 2024 Elsevier B.V.

Context: Bug bisection is a common technique used to identify a revision that introduces a bug or indirectly fixes a bug, and often involves executing multiple revisions of a project to determine whether the bug is present within the revision. However, many legacy revisions often cannot be successfully compiled due to changes in the programming language or tools used in the compilation process, adding complexity and preventing automation in the bisection process. Objective: In this paper, we introduce an approach to repair test cases of Java projects by performing dependency minimization. Our approach aims to remove classes and methods that are not required for the execution of one or more test cases. Unlike existing state-of-the-art techniques, our approach performs minimization at source-level, which allows compile-time errors to be fixed. Methods: A standalone Java tool implementing our technique was developed, and we evaluated our technique using subjects from Defects4J retargeted against Java 8 and 17. Results: Our evaluation showed that a majority of subjects can be repaired solely by performing minimization, including replicating the test results of the original version. Furthermore, our technique is also shown to achieve accurate minimized results, while only adding a small overhead to the bisection process. Conclusion: Our proposed technique is shown to be effective for repairing build failures with minimal overhead, making it suitable for use in automated bug bisection. Our tool can also be adapted for use cases such as bug corpus creation and refactoring. © 2024 Elsevier B.V.

Co-seismic landslides can cause devastating consequences in seismically active mountainous regions. Rapid assessment of co-seismic landslides is essential in effective emergency response. A dilemma that hinders the assessment is the presence of a significant bias, either overestimating or underestimating the actual landslide hazard. To overcome this dilemma, this paper proposes a two-phase strategy that involves constructing a hierarchical Bayesian model augmenting data from a new earthquake with historical co-seismic landslide inventories. Our findings demonstrate that even with limited and evenly distributed co-seismic data from the new earthquake, it is possible to achieve an unbiased and accurate assessment. The two-phase strategy can facilitate the rapid and unbiased assessment of co-seismic landslides, thereby informing policy-making and resource allocation. © 2024 Elsevier B.V.

3D mesh denoising is a crucial pre-processing step in many graphics applications. However, existing data-driven mesh denoising models, primarily trained on synthetic white noise, are less effective when applied to real-world meshes with the noise of complex intensities and distributions. Moreover, how to comprehensively capture information from input meshes and apply suitable denoising models for feature-preserving mesh denoising remains a critical and unresolved challenge. This paper presents a rotation-Equivariant model-based Mesh Denoising (EMD) model and a Realistic Mesh Noise Generation (RMNG) model to address these issues. Our EMD model leverages rotation-equivariant features and self-attention weights of geodesic patches for more effective feature extraction, thereby achieving SOTA denoising results. The RMNG model, based on the Generative Adversarial Networks (GANs) architecture, generates massive amounts of realistic noisy and noiseless mesh pairs data for data-driven mesh denoising model training, significantly benefiting real-world denoising tasks. To address the smooth degradation and loss of sharp edges commonly observed in captured meshes, we further introduce varying levels of Laplacian smoothing to input meshes during the paired training data generation, endowing the trained denoising model with feature recovery capabilities. Experimental results demonstrate the superior performance of our proposed method in preserving fine-grained features while removing noise on real-world captured meshes. © 2024 Elsevier B.V.

To link traceable flow calibrations of Coriolis meters at ambient conditions to flow measurements in cryogenic, liquid hydrogen (LH2) applications, physical effects of very low temperatures on the calibration factor must be satisfactorily predicted by temperature correction methods. In this paper, four correction models are investigated, which differ in the sense of which temperature effects they cover and how these effects are determined. These correction models were applied to three Coriolis meter designs – straight, arc and U-tube. As a reference value in the evaluation, we use the simulation results with the finite element model that incorporates temperature effects related to elastic material properties, thermal strains and thermal stresses. The best agreement (within ± 0.2 % for the curved tubes) is achieved by the correction model that considers the known temperature dependence of the tube elastic properties as well as the dimensional changes due to thermal strain., This work was supported through the Joint Research Project (JRP) “Metrology infrastructure for high-pressure gas and liquified hydrogen flows” (20IND11 MetHyInfra). This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. This project has also received funding from the Slovenian Research Agency (research core funding no. P2-0223) and the Ministry of Economic Affairs and Climate Policy of the Netherlands.

Sustainable materials are becoming increasingly important due to environmental concerns and the energy crisis. Non-wood resources such as bamboo are being explored as alternatives to wood-based materials to reduce deforestation. However, the chemical properties of these resources determine their usability. This study analyzed the chemical composition and solubility of Bambusa bambos (L.) Voss, a type of bamboo. The effects of age and height position (top, middle, and bottom) on the chemical composition and solubility were also considered. The study followed the standards of TAPPI (Technical Association of the Pulp and Paper Industry) to analyze holocellulose, lignin, and extractive content, and water (hot and cold) and caustic soda (1% NaOH) solubility. The results showed that the chemical composition, i.e., holocellulose, lignin, and extractive, increased while solubility, i.e., cold water, hot water, and NaOH, decreased with the ageing of B. bambos. The average holocellulose, lignin, and extractive contents of three-year-old B. bambos were 70.49%, 27.55%, and 4.54%, respectively. These values were within the range of previous studies, indicating that B. bambos has potential applications in various purposes. © 2024 The Authors

Circular design tools can help companies to move from linear to circular design practices and an increased uptake of such tools have potential to help realise the circular transition. Despite the existence of numerous circular design tools, companies often find it challenging to identify tools that align with their needs as the tool landscape is difficult to navigate. This paper offers a synthesized overview of 65 circular design tools that uniformly describes the tools’ main attributes. The tools are categorized into five families to make it easier to navigate the tool landscape and identify tools for specific needs. The findings offer a useful resource for the research community as well as companies seeking to e.g. design for value retention and extended product lifetimes. Implications for tool developers and practitioners are highlighted, advocating for more effective utilization of existing tools and the alignment of future tool development with circular design practices.

This manuscript introduces a perturbation-free extremum-seeking control strategy specifically designed to enhance the operational performance of wind turbines under optimal conditions. The strategy employed utilizes a perturbation-free extremum-seeking approach to ensure the wind turbine farm operates at maximum power efficiency while also reducing power output fluctuations. By employing this control system, the wind turbine generators are maintained at their optimal power output level, effectively navigating through disturbances such as tower shadow, wind shear, and the erratic nature of wind speeds without introducing external perturbations. The algorithm is adept at managing the blade pitch angles and the turbines’ rotational speed, ensuring a controlled and efficient operation. Furthermore, this paper outlines a perturbation-free method to optimize energy production and decrease fatigue loads, thereby extending the operational lifespan of a wind turbine. The superior operational efficiency of the wind turbine array generators achieved through this approach underscores its effectiveness. The robustness and effectiveness of this perturbation-free control system are validated through comprehensive simulation tests on an array comprising four wind turbine generators. The outcomes of these simulations solidly back the extremum-seeking control system's proficiency in reaching maximum power output without relying on perturbative methods, highlighting its innovative contribution to wind turbine efficiency optimization., Validerad;2024;Nivå 1;2024-06-28 (joosat);Full text license: CC BY-NC-ND

Restoring nutrient circularity across scales is important for ecosystem integrity as well as nutrient and food security. As such, research and development of technologies to recover plant nutrients from various organic residues has intensified. Yet, this emerging field is diverse and difficult to navigate, especially for newcomers. As an increasing number of actors search for circular solutions to nutrient management, there is a need to simplify access to the latest knowledge. Since the majority of nutrients entering urban areas end up in human excreta, we have chosen to focus on human excreta and domestic wastewater. Through systematic mapping with stakeholder engagement, we compiled and consolidated available evidence from research and practice. In this paper, we present ‘Egestabase’ – a carefully curated open-access online evidence platform that presents this evidence base in a systematic and accessible manner. We hope that this online evidence platform helps a variety of actors to navigate evidence on circular nutrient solutions for human excreta and domestic wastewater with ease and keep track of new findings., We are grateful to all stakeholders who provided valuable feedback and inputs. Egestabase was developed as part of the project \"End-of-wastewater\", which has received funding from the Kamprad Family Foundation under grant agreement 20200021 . Early stakeholder engagement took place as part of the project \"Going circular\", which has received funding from the Swedish Research Council for Environment , Agricultural Sciences and Spatial Planning (Formas) under grant agreement 2019-02476 .

The IEEE 802.15.4 radio standard features the possibility for IEEE 802.15.4 nodes to run on batteries for several years. This is made possible by duty-cycling medium access control (MAC) protocols, which allow IEEE 802.15.4 nodes to leave their radios in energy-saving sleep modes most of the time. Yet, duty-cycling MAC protocols usually incur long routing delays since it may take a while until a particular forwarder becomes available for forwarding a packet. Opportunistic routing alleviates this problem by opportunistically using a currently available forwarder, rather than waiting for a particular forwarder. Among all opportunistic routing schemes, so-called dynamic switch-based forwarding (DSF) schemes are most promising from a security and practical perspective, but some security and reliability issues with them persist. In this paper, we propose secure multipath opportunistic routing (SMOR), a DSF scheme that improves on current DSF schemes in three regards. First, SMOR builds on a denial-of-sleep-resilient MAC layer. Current DSF schemes, by comparison, rest on MAC protocols that put the limited energy reserves of battery-powered IEEE 802.15.4 nodes at risk. Second, SMOR operates in a distributed fashion and efficiently supports point-to-point traffic. All current DSF schemes, by contrast, suffer from a single point of failure and focus on convergecast traffic. Third, SMOR duplicates packets on purpose and routes them along disjoint paths. This makes SMOR tolerant of compromises of single IEEE 802.15.4 nodes, whereas current DSF schemes lack intrusion tolerance. We integrated SMOR into the network stack of the Contiki-NG operating system and benchmarked SMOR against the Routing Protocol for Low-Power and Lossy Networks (RPL) with the Cooja network simulator. Indeed, SMOR turns out to improve on RPL’s delays by between 33.51% and 39.84%, depending on the exact configurations and network dynamics. Furthermore, SMOR achieves between 0.16% and 2.03% higher mean pack, This work was supported by the Swedish Foundation for Strategic Research [grant number 2017-045989 ].

This paper gives an overview of the ongoing Joint Research Project (JRP) 20IND11 “Metrology infrastructure for high pressure gas and liquefied hydrogen flows” (MetHyInfra), which will ensure traceability in the hydrogen distribution chain. For this purpose, very precise nozzles with well-defined geometries have been produced. In this project, Critical Flow Venturi Nozzles (CFVNs) will be traceably calibrated for the first time with hydrogen and pressures up to 100 MPa using a Coriolis Flow Meter (CFM) as a secondary standard. A CFM has been successfully calibrated with hydrogen against a gravimetric primary standard. Equations of State (EoS) are important for the high-pressure calibration of the nozzles, but also for Computational Fluid Dynamics (CFD) simulations. With regard to CFD, a numerical model has been developed to simulate high pressure hydrogen flow in the CFVN. In a parameter study, non-ideal nozzle shapes are investigated using a shape variation parameter. New Speed of Sound (SoS) measurements were conducted at temperatures from 273 to 323 K and pressures from 1 to 100 MPa. These new data were then used to develop a new EoS for normal hydrogen, optimized for gas phase calculations. In addition to gaseous hydrogen, the project has a strong focus on liquefied hydrogen. Here a three-pronged approach allows traceable measurements. Each of the approaches presented is based on a unique flow calibration principle and relies on independent traceability schemes. The results of the project will ensure traceable measurements and thus a higher level of confidence among end users. © 2024 The Authors, This work was supported by the Joint Research Project (JRP) \u201CMetrology infrastructure for high-pressure gas and liquified hydrogen flows\u201D. This project (20IND11 MetHyInfra) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. This project has received funding from the Ministry of Economic Affairs and Climate Policy of the Netherlands.

Context: In the digital age, there is a notable increase in fraudulent activities perpetrated by social engineers who exploit individuals’ limited knowledge of digital devices. These actors strategically manipulate human psychology, targeting IT devices to gain unauthorized access to sensitive data. Objectives: Our study is centered around two distinct objectives to be accomplished through the utilization of a serious game: (i) The primary objective entails delivering training and educational content to participants with a focus on phishing attacks; (ii) The secondary objective aims to heighten participants’ awareness regarding the perils associated with divulging excessive information online. Methodology: To address these objectives, we have employed the following techniques and methods: (i) A comprehensive literature review was conducted to establish foundational knowledge in areas such as social engineering, game design, learning principles, human interaction, and game-based learning; (ii) We meticulously aligned the game design with the philosophical concept of social engineering attacks; (iii) We devised and crafted an advanced hybrid version of the game, incorporating the use of QR codes to generate game card data; (iv) We conducted an empirical evaluation encompassing surveys, observations, discussions, and URL assessments to assess the effectiveness of the proposed hybrid game version. Results: Quantitative data and qualitative observations suggest the “PhishDefend Quest” game successfully improved players’ comprehension of phishing threats and how to detect them through an interactive learning experience. The results highlight the potential of serious games to educate people about social engineering risks. Conclusion: Through the evaluation, we can readily arrive at the following conclusions: (i) Game-based learning proves to be a viable approach for educating participants about phishing awareness and the associated risks tied to the unnecessary disclosure, This paper is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 883054 (EU-HYBNET).

We use a deterministic model to study two competing viruses spreading over a two-layer network in the Susceptible–Infected–Susceptible (SIS) framework, and address a central problem of identifying the winning virus in a “survival-of-the-fittest” battle. Almost all existing conditions ensure that the same virus wins regardless of initial states. In the present paper, we ask the following question: can we systematically construct SIS bivirus networks with an arbitrary but finite number of nodes such that either of the viruses can win the survival-of-the-fittest battle, depending on the initial states? We answer this question in the affirmative. More specifically, we show that given almost any network layer of one virus, we can (using our proposed systematic four-step procedure) construct the network layer for the other virus such that in the resulting bivirus network, either of the two viruses can win the survival-of-the-fittest battle. Conclusions from numerical case studies, including a real-world mobility network that captures the commuting patterns for people between 107 provinces in Italy, illustrate and extend the theoretical result and its consequences., QC 20240924

Digital transformation (DT) triggers a fundamental technological shift in industry enabling the creation of smart and connected factories. DT is not only a technology-driven innovation approach: it also requires changes of the Business Model (BM). Despite this, there is a lack of comprehensive studies that examine how aspects of manufacturing digitalisation align with the Business Model Innovation (BMI) process. To address this gap, this paper analyses the DT journey of several Swedish medium and large firms through semi-structured interviews with staff involved in such process. The empirical findings reveal that the BM elements addressing value creation and strategy aspects are the most affected by DT. Additionally, this study identifies the links among such elements. The findings are compiled in a holistic framework that can serve as a blueprint for practitioners seeking to adopt digital technologies in their production environments. This holistic approach aims at supporting practitioners to understand the BMI process triggered by DT and consequently aligning their manufacturing strategy with the BM's components., QC 20240613

Circular design tools can help companies to move from linear to circular design practices and an increased uptake of such tools have potential to help realise the circular transition. Despite the existence of numerous circular design tools, companies often find it challenging to identify tools that align with their needs as the tool landscape is difficult to navigate. This paper offers a synthesized overview of 65 circular design tools that uniformly describes the tools’ main attributes. The tools are categorized into five families to make it easier to navigate the tool landscape and identify tools for specific needs. The findings offer a useful resource for the research community as well as companies seeking to e.g. design for value retention and extended product lifetimes. Implications for tool developers and practitioners are highlighted, advocating for more effective utilization of existing tools and the alignment of future tool development with circular design practices.