Search

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.

The CO2 concentration in the atmosphere is increasing and threatening the earth's climate. Selective CO2 capture at large point sources will help to reduce the CO2 emissions to the atmosphere. Biochar with microporous structure could be a potential material to capture CO2. The impact of feedstock type, pyrolysis temperature and steam activation of biochars were evaluated for CO2 adsorption capacity. Pine sawdust biochars were produced at 550 °C, and steam activated for 45 min at the same temperature after completing the pyrolysis (PS550 and PSS550). Paper mill sludge biochars were produced at 300 and 600 °C (PMS300 and PMS600). The CO2 adsorption capacity of biochars was tested at 25 °C using a volumetric sorption analyzer. Pine sawdust biochars showed significantly higher CO2 adsorption capacity than paper mill sludge biochars due to high surface area and microporosity. Pine sawdust biochars were then evaluated for dynamic adsorption under representative post-combustion flue gas concentration conditions (15% CO2, 85% N2) using a breakthrough rig. Both materials showed selective CO2 uptake over N2 which is the major component along with CO2 in flue gas. PSS550 had slightly higher CO2 adsorption capacity (0.73 mmol g^-1 vs 0.67 mmol g^-1) and CO2 over N2 selectivity (26 vs 18) than PS550 possibly due to increase of microporosity, surface area, and oxygen containing basic functional groups through steam activation. Pine sawdust biochar is an environmentally friendly and low-cost material to capture CO2.

QC 20201026

Scope Hepatitis B virus (HBV) infection reactivation is associated with high morbidity and mortality in patients with haematologic malignancy and/or haematopoietic stem cell transplantation (HSCT). However, information on this issue is limited. The scope of this position paper is to provide recommendations on HBV screening, monitoring, prophylaxis, treatment and vaccination in the patients described above. Methods These recommendations were developed from one meeting of experts attended by different Italian scientific societies as well as from a systematic literature review (of articles published through December 31, 2016) on HBV infection in haematologic patients and in patients who underwent haematopoietic stem cell transplantation published in the same issue of the journal. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess each recommendation's quality. Questions addressed These recommendations provide the answers to the following questions: (a) HBV screening and monitoring: Who should be screened before chemotherapy? Which screening tests should be used? Should HBV-DNA detection be used to monitor HBV reactivation before starting antivirals? What is the best timeline to monitor HBV reactivation? (b) Prophylaxis in HBsAg-positive patients: Which antiviral drugs should be used to treat HBsAg-positive patients? How long should antiviral prophylaxis be provided to HBsAg-positive patients? (c) Prophylaxis in patients with resolved HBV infection: Which patients with resolved HBV infection should receive antiviral prophylaxis? Which antiviral drug should be used? How long should antiviral prophylaxis be provided? (d) HBV infection management strategy in autologous (auto-HSCT) and allogeneic HSCT (allo-HSCT): Which HSCT recipients should receive antiviral prophylaxis? Which antiviral drug should be used? How long should antiviral prophylaxis be provided? (e) Choice of antiviral drugs in the treatment of HBV reac

This review summarizes our current understanding of nonalcoholic fatty liver disease (NAFLD), a multi-factorial systemic disease resulting from a complex interaction between a specific genetic background and multiple environmental/metabolic “hits”. The role of gut microbiota, lipotoxicity, inflammation and their molecular pathways is reviewed in-depth. We also discuss the epidemiology and natural history of NAFLD by pinpointing the remarkably high prevalence of NAFLD worldwide and its inherent systemic complications: hepatic (steatohepatitis, advanced fibrosis and cirrhosis), cardio-metabolic (cardiovascular disease, cardiomyopathy, arrhythmias and type 2 diabetes) and neoplastic (primary liver cancers and extra-hepatic cancers). Moreover, we critically report on the diagnostic role of non-invasive biomarkers, imaging techniques and liver biopsy, which remains the reference standard for diagnosing the disease, but cannot be proposed to all patients with suspected NAFLD. Finally, the management of NAFLD is also reviewed, by highlighting the lifestyle changes and the pharmacological options, with a focus on the innovative drugs. We conclude that the results of ongoing studies are eagerly expected to lead to introduce into the clinical arena new diagnostic and prognostic biomarkers, prevention and surveillance strategies as well as to new drugs for a tailored approach to the management of NAFLD in the individual patient.

The relationship between liver disease and pregnancy is of great clinical impact. Severe liver disease in pregnancy is rare; however, pregnancy-related liver disease is the most frequent cause of liver dysfunction during pregnancy and represents a severe threat to foetal and maternal survival. A rapid differential diagnosis between liver disease related or unrelated to pregnancy is required in women who present with liver dysfunction during pregnancy. This report summarizes the recommendation of an expert panel established by the Italian Association for the Study of the Liver (AISF) on the management of liver disease during pregnancy. The article provides an overview of liver disease occurring in pregnancy, an update on the key mechanisms involved in its pathogenesis, and an assessment of the available treatment options. The report contains in three sections: (1) specific liver diseases of pregnancy; (2) liver disease occurring during pregnancy; and (3) pregnancy in patients with pre-existing chronic liver disease. Each topic is discussed considering the most relevant data available in literature; the final statements are formulated according to both scientific evidence and clinical expertise of the involved physicians, and the AISF expert panel recommendations are reported.

The relationship between liver disease and pregnancy is of great clinical impact. Severe liver disease in pregnancy is rare; however, pregnancy-related liver disease is the most frequent cause of liver dysfunction during pregnancy and represents a severe threat to foetal and maternal survival. A rapid differential diagnosis between liver disease related or unrelated to pregnancy is required in women who present with liver dysfunction during pregnancy. This report summarizes the recommendation of an expert panel established by the Italian Association for the Study of the Liver (AISF) on the management of liver disease during pregnancy. The article provides an overview of liver disease occurring in pregnancy, an update on the key mechanisms involved in its pathogenesis, and an assessment of the available treatment options. The report contains in three sections: (1) specific liver diseases of pregnancy; (2) liver disease occurring during pregnancy; and (3) pregnancy in patients with pre-existing chronic liver disease. Each topic is discussed considering the most relevant data available in literature; the final statements are formulated according to both scientific evidence and clinical expertise of the involved physicians, and the AISF expert panel recommendations are reported.

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

Paper surfaces (Whatman filter papers, Kimwipes®, and Japanese Washi papers) were employed as support for gold nanoparticles (AuNPs) deposited by the seed-mediated growth method. The AuNP-modified paper or textile was brought into “weak” (or non-permanent) contact with a glassy carbon electrode and immersed into aqueous electrolyte media. Electrochemical responses for the Fe(CN)6[4−]/Fe(CN)6[3−] redox system in 0.1 M phosphate buffer (pH 7.0) and 1.0 M KCl solutions were investigated by voltammetry and impedance methods. Even for weakly contacted AuNP catalysts of relatively low density faster electron transfer was observed. Particle sizes of at least 20 nm diameter were required. There was no permanent contact from AuNP catalysts to the electrode and the process was therefore termed “weak contact catalysis”. The method is proposed as a rapid and novel catalyst screening tool with potential applications in fuel cell and sensor technologies.

Superhydrophobic alkyl ketene dimer (AKD) layers were successfully produced on top of untreated paper surfaces by a rapid expansion of supercritical CO2 solution (RESS) process. The new method resulted in a degree of hydrophobicity, as measured by contact angles of water droplets on AKD surfaces, dramatically higher, up to 173 degrees, compared to a conventional method consisting in melting AKD granules directly on the paper substrate, giving contact angles of around 109 degrees. Experiments were conducted to investigate the effects of varying pre-expansion pressure (100-300 bar), pre-expansion temperature (40 and 60 degrees C) and spraying distance (10 and 50 mm) on the properties of the treated surfaces. The surfaces were analyzed regarding AKD particle size, surface morphology and hydrophobicity with the aid of scanning electron microscopy (SEM) and contact angle measurements. The average AKD particle size after RESS processing was between 1 and 2 mu m depending upon the experimental conditions used, being slightly smaller when using higher pre-expansion pressure and temperature as well as shorter spraying distance.

A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints.

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.

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)

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