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Introduction Prospective double-blind placebo-controlled randomized clinical trials (RCTs) are considered standard for the proof of the efficacy of oncologic therapies. Molecular methods have provided new insights into tumor biology and led to the development of targeted therapies. Due to the increasing complexity of molecular tumor characteristics and of the individuality of specific anti-tumor immune reactivity, RCTs are unfortunately only of limited use. Areas covered The historical methods of drug research and approval and the related practices of reimbursement by statutory and private health insurance companies are being questioned. New, innovative methods for the documentation of evidence in personalized medicine will be addressed. Possible perspectives and new approaches are discussed, in particular with regard to glioblastoma. Expert opinion Highly specialized translational oncology groups like the IOZK can contribute to medical progress and quick transfer 'from bench to bedside.' Their contribution should be acknowledged and taken into account more strongly in the development of guidelines and the reimbursement of therapy costs. Methodological plurality should be encouraged.

Introduction Prospective double-blind placebo-controlled randomized clinical trials (RCTs) are considered standard for the proof of the efficacy of oncologic therapies. Molecular methods have provided new insights into tumor biology and led to the development of targeted therapies. Due to the increasing complexity of molecular tumor characteristics and of the individuality of specific anti-tumor immune reactivity, RCTs are unfortunately only of limited use. Areas covered The historical methods of drug research and approval and the related practices of reimbursement by statutory and private health insurance companies are being questioned. New, innovative methods for the documentation of evidence in personalized medicine will be addressed. Possible perspectives and new approaches are discussed, in particular with regard to glioblastoma. Expert opinion Highly specialized translational oncology groups like the IOZK can contribute to medical progress and quick transfer 'from bench to bedside.' Their contribution should be acknowledged and taken into account more strongly in the development of guidelines and the reimbursement of therapy costs. Methodological plurality should be encouraged.

Objective: The Swedish Rheumatology Quality Register has implemented an internet-based method (PER) for registering patient-recorded outcome measures. The aim of this study was to compare the agreement between visual analogue scales (VASs) reported via PER and clinic-based reporting using paper forms.Methods: In a cross-sectional study (70 patients), the results of 79 registrations of VASs for global health, pain, and fatigue from PER were compared with corresponding clinic-based paper registrations. For patients with polyarthritis, 28-joint count Disease Activity Scores (DAS28) were computed. Patients with axial disease also completed Bath Ankylosing Spondylitis Disease Activity Index and Functional Index (BASDAI and BASFI) questionnaires. Mean differences and intraclass correlation coefficients (ICCs) were calculated. Agreement was visualized using Bland-Altman plots.Results: No statistically significant differences in VASs were found comparing PER and paper forms for VAS Global, VAS Pain, and VAS Fatigue (p=0.295, 0.463, and 0.288, respectively). ICCs for VAS Global, Pain, and Fatigue ranged from 0.889 to 0.952, indicating excellent agreement. Bland-Altman plots for VAS did not show any proportional bias. The mean difference for DAS28 calculated by VASs from paper vs PER was -0.02 (n=65, p =0.660), and the mean difference for BASDAI was 0.04 (n=11, p =0.742). ICCs for DAS28 and BASDAI were 0.962 and 0.985, respectively. Of the participating patients, 60% preferred PER.Conclusion: Internet-based reporting for patient-reported outcomes in a clinical setting resulted in similar data for VASs and corresponding disease activity scores to clinic-based reporting on paper forms.

Objective: The Swedish Rheumatology Quality Register has implemented an internet-based method (PER) for registering patient-recorded outcome measures. The aim of this study was to compare the agreement between visual analogue scales (VASs) reported via PER and clinic-based reporting using paper forms.Methods: In a cross-sectional study (70 patients), the results of 79 registrations of VASs for global health, pain, and fatigue from PER were compared with corresponding clinic-based paper registrations. For patients with polyarthritis, 28-joint count Disease Activity Scores (DAS28) were computed. Patients with axial disease also completed Bath Ankylosing Spondylitis Disease Activity Index and Functional Index (BASDAI and BASFI) questionnaires. Mean differences and intraclass correlation coefficients (ICCs) were calculated. Agreement was visualized using Bland-Altman plots.Results: No statistically significant differences in VASs were found comparing PER and paper forms for VAS Global, VAS Pain, and VAS Fatigue (p=0.295, 0.463, and 0.288, respectively). ICCs for VAS Global, Pain, and Fatigue ranged from 0.889 to 0.952, indicating excellent agreement. Bland-Altman plots for VAS did not show any proportional bias. The mean difference for DAS28 calculated by VASs from paper vs PER was -0.02 (n=65, p =0.660), and the mean difference for BASDAI was 0.04 (n=11, p =0.742). ICCs for DAS28 and BASDAI were 0.962 and 0.985, respectively. Of the participating patients, 60% preferred PER.Conclusion: Internet-based reporting for patient-reported outcomes in a clinical setting resulted in similar data for VASs and corresponding disease activity scores to clinic-based reporting on paper forms.

Objective: The Swedish Rheumatology Quality Register has implemented an internet-based method (PER) for registering patient-recorded outcome measures. The aim of this study was to compare the agreement between visual analogue scales (VASs) reported via PER and clinic-based reporting using paper forms.Methods: In a cross-sectional study (70 patients), the results of 79 registrations of VASs for global health, pain, and fatigue from PER were compared with corresponding clinic-based paper registrations. For patients with polyarthritis, 28-joint count Disease Activity Scores (DAS28) were computed. Patients with axial disease also completed Bath Ankylosing Spondylitis Disease Activity Index and Functional Index (BASDAI and BASFI) questionnaires. Mean differences and intraclass correlation coefficients (ICCs) were calculated. Agreement was visualized using Bland-Altman plots.Results: No statistically significant differences in VASs were found comparing PER and paper forms for VAS Global, VAS Pain, and VAS Fatigue (p=0.295, 0.463, and 0.288, respectively). ICCs for VAS Global, Pain, and Fatigue ranged from 0.889 to 0.952, indicating excellent agreement. Bland-Altman plots for VAS did not show any proportional bias. The mean difference for DAS28 calculated by VASs from paper vs PER was -0.02 (n=65, p =0.660), and the mean difference for BASDAI was 0.04 (n=11, p =0.742). ICCs for DAS28 and BASDAI were 0.962 and 0.985, respectively. Of the participating patients, 60% preferred PER.Conclusion: Internet-based reporting for patient-reported outcomes in a clinical setting resulted in similar data for VASs and corresponding disease activity scores to clinic-based reporting on paper forms.

Objective: The Swedish Rheumatology Quality Register has implemented an internet-based method (PER) for registering patient-recorded outcome measures. The aim of this study was to compare the agreement between visual analogue scales (VASs) reported via PER and clinic-based reporting using paper forms.Methods: In a cross-sectional study (70 patients), the results of 79 registrations of VASs for global health, pain, and fatigue from PER were compared with corresponding clinic-based paper registrations. For patients with polyarthritis, 28-joint count Disease Activity Scores (DAS28) were computed. Patients with axial disease also completed Bath Ankylosing Spondylitis Disease Activity Index and Functional Index (BASDAI and BASFI) questionnaires. Mean differences and intraclass correlation coefficients (ICCs) were calculated. Agreement was visualized using Bland-Altman plots.Results: No statistically significant differences in VASs were found comparing PER and paper forms for VAS Global, VAS Pain, and VAS Fatigue (p=0.295, 0.463, and 0.288, respectively). ICCs for VAS Global, Pain, and Fatigue ranged from 0.889 to 0.952, indicating excellent agreement. Bland-Altman plots for VAS did not show any proportional bias. The mean difference for DAS28 calculated by VASs from paper vs PER was -0.02 (n=65, p =0.660), and the mean difference for BASDAI was 0.04 (n=11, p =0.742). ICCs for DAS28 and BASDAI were 0.962 and 0.985, respectively. Of the participating patients, 60% preferred PER.Conclusion: Internet-based reporting for patient-reported outcomes in a clinical setting resulted in similar data for VASs and corresponding disease activity scores to clinic-based reporting on paper forms.

Objective: The Swedish Rheumatology Quality Register has implemented an internet-based method (PER) for registering patient-recorded outcome measures. The aim of this study was to compare the agreement between visual analogue scales (VASs) reported via PER and clinic-based reporting using paper forms.Methods: In a cross-sectional study (70 patients), the results of 79 registrations of VASs for global health, pain, and fatigue from PER were compared with corresponding clinic-based paper registrations. For patients with polyarthritis, 28-joint count Disease Activity Scores (DAS28) were computed. Patients with axial disease also completed Bath Ankylosing Spondylitis Disease Activity Index and Functional Index (BASDAI and BASFI) questionnaires. Mean differences and intraclass correlation coefficients (ICCs) were calculated. Agreement was visualized using Bland-Altman plots.Results: No statistically significant differences in VASs were found comparing PER and paper forms for VAS Global, VAS Pain, and VAS Fatigue (p=0.295, 0.463, and 0.288, respectively). ICCs for VAS Global, Pain, and Fatigue ranged from 0.889 to 0.952, indicating excellent agreement. Bland-Altman plots for VAS did not show any proportional bias. The mean difference for DAS28 calculated by VASs from paper vs PER was -0.02 (n=65, p =0.660), and the mean difference for BASDAI was 0.04 (n=11, p =0.742). ICCs for DAS28 and BASDAI were 0.962 and 0.985, respectively. Of the participating patients, 60% preferred PER.Conclusion: Internet-based reporting for patient-reported outcomes in a clinical setting resulted in similar data for VASs and corresponding disease activity scores to clinic-based reporting on paper forms.

The release of RNA-containing extracellular vesicles (EV) into the extracellular milieu has been demonstrated in a multitude of different in vitro cell systems and in a variety of body fluids. RNA-containing EV are in the limelight for their capacity to communicate genetically encoded messages to other cells, their suitability as candidate biomarkers for diseases, and their use as therapeutic agents. Although EV-RNA has attracted enormous interest from basic researchers, clinicians, and industry, we currently have limited knowledge on which mechanisms drive and regulate RNA incorporation into EV and on how RNA-encoded messages affect signalling processes in EV-targeted cells. Moreover, EV-RNA research faces various technical challenges, such as standardisation of EV isolation methods, optimisation of methodologies to isolate and characterise minute quantities of RNA found in EV, and development of approaches to demonstrate functional transfer of EV-RNA in vivo. These topics were discussed at the 2015 EV-RNA workshop of the International Society for Extracellular Vesicles. This position paper was written by the participants of the workshop not only to give an overview of the current state of knowledge in the field, but also to clarify that our incomplete knowledge - of the nature of EV(-RNA)s and of how to effectively and reliably study them - currently prohibits the implementation of gold standards in EV-RNA research. In addition, this paper creates awareness of possibilities and limitations of currently used strategies to investigate EV-RNA and calls for caution in interpretation of the obtained data.

Extracellular vesicles (EVs), such as exosomes and microvesicles, are released by different cell types and participate in physiological and pathophysiological processes. EVs mediate intercellular communication as cell-derived extracellular signalling organelles that transmit specific information from their cell of origin to their target cells. As a result of these properties, EVs of defined cell types may serve as novel tools for various therapeutic approaches, including (a) anti-tumour therapy, (b) pathogen vaccination, (c) immune-modulatory and regenerative therapies and (d) drug delivery. The translation of EVs into clinical therapies requires the categorization of EV-based therapeutics in compliance with existing regulatory frameworks. As the classification defines subsequent requirements for manufacturing, quality control and clinical investigation, it is of major importance to define whether EVs are considered the active drug components or primarily serve as drug delivery vehicles. For an effective and particularly safe translation of EV-based therapies into clinical practice, a high level of cooperation between researchers, clinicians and competent authorities is essential. In this position statement, basic and clinical scientists, as members of the International Society for Extracellular Vesicles (ISEV) and of the European Cooperation in Science and Technology (COST) program of the European Union, namely European Network on Microvesicles and Exosomes in Health and Disease (ME-HaD), summarize recent developments and the current knowledge of EV-based therapies. Aspects of safety and regulatory requirements that must be considered for pharmaceutical manufacturing and clinical application are highlighted. Production and quality control processes are discussed. Strategies to promote the therapeutic application of EVs in future clinical studies are addressed.

The Something Awful Let’s Play subforum is widely acknowledged as the birthplace of the Let’s Play (LP) media phenomenon. LPs typically involve people recording themselves playing games while providing commentary. LPs are an important media form in themselves as well as being an important antecedent to many contemporary and popular media forms such as live streaming, esports and speed-running. An examination of the Something Awful LP subforum can contribute to an understanding of the origins of LPs and the community that created them. In this paper, we report on a study of the Something Awful LP subforum and describe the kinds of engagement the community participates in the top threads, as well as looking to see if there are specific individuals responsible for guiding the subforum overall. We collected data from the thousands of public threads posted in the LP subforum, from its inception in 2007 to the end of 2020. The analysis of these postings presented in this paper draws on previous understandings of the behavioral roles, forms of engagement, and policing of practices that often occur on internet forums as part of the regulation and organization of associated online communities. Our results show that the LP subforum was not dominated by a small minority of users that dictated the community’s LP posting, recording and commentary practices, and that the content of the specific threads was much more important in determining what forms of LPs became popular.

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Recently, researchers have reported that by the amalgamation of concepts of design for manufacturing and assembly (DfMA), construction practices and technologies can be further improved. As DfMA is based on the principles of optimization of materials and coordination. The optimization and coordination achieved through DfMA at the early design stage can significantly contribute best practices, reduce time and delays and improve safety, and thereby enhance the overall productivity of the prefabricated construction project. However, the literature on DfMA is limited in prefabricated construction. This paper presents an updated and comprehensive review of DfMA approaches and their applications in manufacturing and prefabrication. The review is based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines with a slight modification. Scopus database was used to obtain relevant articles from the literature, and three search attempts were taken to ensure that the identified publications thoroughly cover the focus of this review. Prefabrication and manufacturing processes are compared and discussed with examples. The findings of this paper show practical analogies between prefabrication and manufacturing. Finally, the review is concluded with recommendations for future works and the potential opportunities for the application of DfMA in construction.

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

This paper focuses on how the chain of command is construed in Swedish municipal adult education. With a qualitative research approach and by drawing on the theoretical and methodological framework ‘What’s the “problem” represented to be?’ (WPR) approach, the aim is directed at identifying, interrogating, and reconstructing problem representations. The empirical material consists of contemporary key adult education policy that focuses on the chain of command and was analyzed in NVivo. Ethical considerations as well as data credibility, validity and reliability reflections were made in the form of WPR-guided self-reflexivity. The results indicate that both heads of education and principals are held responsible for the ‘weaknesses’ of the chain of command. Different solutions are also construed to solve the ‘problem’ that calls for increased trust between the actors in the chain of command. However, this trust seems to be underpinned by a request for more follow-ups, rather than a trust in the principal’s pedagogical leadership. The way that these issues are construed as specific kinds of ‘problems’ and ‘solutions’ has effects on the individuals that are involved in adult education. Based on the analysis, the need for policymakers to work on understanding the unintentional consequences of policies is stressed.

For heat resistant alloys, long-term structural stability at high temperatures is a critical issue for alloy design and applications. In this paper, the long-term creep behaviours and structural stabilities of six heat resistant high Ni alloys and austenitic stainless steels have been studied. The longest creep rupture life is up to 359 283 hours. High Ni and Cr alloys show a good combination of high creep and oxidation resistances. Precipitation of nano MX particles with a very low growth rate improves long-term creep resistance at high temperatures. Long-term stable multiple nanoprecipitates of MX, Cu-rich, Laves and M23C6 phases can greatly contribute to the creep strength. Low Ni austenitic stainless steels show comparatively low oxidation and creep resistances. It was first found that at 800 & DEG;C, Cr2N could form in the low Ni steel with a long-term crept by the absorption of nitrogen from the air into the matrix.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

In this paper we address the problem of inconsistency in preference relations, pointing out the relevance of a meaningful representation in order to help decision maker to capture such inconsistencies. Dimension theory framework, despite its computational complexity, is considered here, pursuing in principle a decomposition of arbitrary preference relations in terms of linear orderings of alternatives. But we shall then stress that consistency should not be necessarily associated to a linear ordering. In this way, alternative decompositions of a preference relation can be proposed to decision maker, allowing an effective search for a useful representations of alternatives in terms of possible criteria. Such decompositions of our preference relations will then become the basis of a future decision aid model, always with the restricted aim of allowing the decision maker a better understanding of the problem. Inconsistencies may be not simply suppressed but understood, since they may contain relevant information., Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

In this paper, we discuss the ethical responsibilities of being a role model as an engineering teacher in higher education. We draw on virtue ethics, care ethics, ethics of freedom and role modeling theory, using Grande's framework for engineering education. We argue that the three ethical theories give different views on the ethics of role modeling as a teacher. Virtue ethics implies that the teacher should behave as virtuously as possible to encourage students to emulate character traits. Care ethics implies that the ethics of role modeling is fundamentally about care practices, emphazising relationships and values such as attentiveness and responsibility. Ethics of freedom urges the teacher to grant individuals opportunities to develop and be free from the control of others. By using role modeling and ethical theory, we aim to show how theory has an impact on both teaching practices and could underpin empirical research in engineering education.

PURPOSE: Sepsis is a major global health problem with an estimated 49 million cases globally each year causing as many as 11 million deaths. The primary objective of this commentary is to describe the impacts of sepsis and critical illness on communication and swallowing function, and to discuss management strategies considering the Sustainable Development Goals (SDGs). RESULT: Communication and swallowing disabilities can occur with sepsis and critical illness. A holistic framework to optimise function, recovery, and future research priorities across the lifespan can be developed through the SDGs. CONCLUSION: Communication and swallowing disabilities following critical illness associated with sepsis have global impacts. Early multidisciplinary engagement is key to optimising individuals' function. Collaborative research, education, and public awareness is urgently needed to increase equity in health outcomes across populations. This commentary paper supports progress towards good health and well-being (SDG 3), quality education (SDG 4) and reduced inequalities (SDG 10).

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

This paper considers the problem of scheduling periodic maintenance items for oil and gas plants. Each maintenance item involves various maintenance tasks and may require temporary equipment shutdowns, which are costly and highly disruptive to production. The aim is to minimise equipment shutdowns by grouping maintenance items with similar shutdown requirements into short-term maintenance operations called campaigns. Real plants can involve tens of thousands of maintenance items and thus manually scheduling the campaigns is an extreme challenge. In this paper, we develop a mixed-integer linear programming model for optimally allocating maintenance items to campaigns so that total shutdown cost is minimised. The model incorporates constraints on maintenance deadlines, campaign times, maintenance item suppression and labour hours per campaign. We solve the model for realistic scenarios involving data for Karratha Gas Plant in Western Australia, which is the main processing plant for the massive North West Shelf oil and gas project.

While lean thinking may help tackle waste, rework remains an ongoing problem during the construction of infrastructure projects. Often too much emphasis is placed on applying lean tools rather than harnessing the human factor and establishing a culture to mitigate rework. Thus, this paper proposes the need for construction organisations to transition from the prevailing error prevention culture (i.e. Quality-I) that pervades practice to one based on error management (i.e. Quality-II) if rework is to be contained and reduced. Accordingly, this paper asks: What type of error culture is required to manage errors that result in rework and to support lean thinking during the construction of infrastructure projects? We draw on the case of a program alliance of 129 water infrastructure projects and make sense of how it enacted, in addition to lean thinking, a change initiative to transition from error prevention to an error management culture to address its rework problem. We observed that leadership, psychological safety and coaching were pivotal for cultivating a culture where there was an acceptance that ‘errors happen’ and effort was directed at mitigating their adverse consequences. The contributions of this paper are twofold as we provide: (1) a new theoretical underpinning to mitigate rework and support the use of lean thinking during the construction of infrastructure projects grounded in Quality-II; and (2) practical suggestions, based on actual experiences, which can be readily employed to monitor and anticipate rework at the coalface of construction.

PURPOSE: This review articulates current understanding of the aesthetic, artistic and creative contributions that Dance makes to Health and Wellbeing across the lifecourse within publications 2000-2019, an under-researched area. METHODS: Review Questions: What are the aesthetic, artistic and creative contributions that Dance makes to Health and Wellbeing across the lifecourse? And what methodologies are appropriate for investigating these contributions? A database keyword search identified 769 articles and 91 evaluations. 109 documents were identified for further in-depth analysis and rating, resulting in 24 papers (11 articles, 3 PhD studies, 10 evaluation reports), which were thematically analysed. RESULTS: Findings offer seven interrelated contributions that Dance makes to Health and Wellbeing: embodiment, identity, belonging, self-worth, aesthetics, affective responses and creativity. There was less insight regarding different methodologies, and discussions focused on quantitative data's limitations. There were insights into inclusion of embodied voices, subjective accounts, and lived experiences. CONCLUSION: Whilst acknowledging challenges, this paper illuminates the key contributions of dance to arts and health. It provides a future conceptual research agenda (prioritizing identity and creativity) and associated methodological developments. It recommends expanding geographical/lifecourse research, better defining terms, fuller epistemological critiques to open space for new methodologies, and continued attendance to appropriate rigour criteria.

Robots are increasingly appearing as a potential answer to the ‘care crisis’ facing a number of countries. Although it is anticipated that many positives will flow from the application of these technologies, they are also likely to generate unexpected consequences and risks. This paper explores the use of robots within disability and aged care settings in the Australian and New Zealand contexts. Informed by thirty-five semi-structured interviews with a range of stakeholders, the paper explores why this area is so difficult to govern examining areas identified as generating tensions around the use of robots in care settings. In each of these areas some respondents saw the introduction of robots to be relatively straightforward applications that do not require extensive structures of governance. Others, however, viewed these applications as having potentially greater implications and the need to govern for these over the longer term. The three areas of tension that we explore in this paper relate to independence and surveillance, the re-shaping of human interaction and who can care. These tensions illustrate some of the problems involved in governing robots in a care service context and some of the potentially difficult issues that governments will need to resolve if these technologies are to be effective. We conclude the paper arguing what is needed is a responsive regulation approach to help resolve some of the complexities and tensions in overseeing these technologies.

© 2020 Informa UK Limited, trading as Taylor & Francis Group. This paper investigates the weighted coordinated motion of coupled harmonic oscillators over weighted cooperative–competitive interactions. First, the traditional synchronisation and containment control concepts are extended to the weighted synchronisation and weighted containment control for the new cooperative–competitive networks. Then, weighted synchronisation problem and weighted containment tracking motion are respectively solved for the coupled harmonic oscillators. Compared with some existing results, the frequency parameters of coupled harmonic oscillators are unknown in this paper and some novel networked structures are introduced in order to make the harmonic oscillator followers converge to the convex hull spanned by leaders' weighted states. Particularly, if the topologies formed by every leader group and follower group are interactively balanced and every follower is weight-product balanced, or if some leader groups are not interactively balanced but satisfy some conditions, the coupled harmonic oscillators can all realise the weighted containment tracking motion. Simulation results are given to demonstrate the effectiveness of the proposed control schemes.

In recent years, a revolution named Industry 4.0 has arisen. Industry 4.0 is presented as the integration of new advances in areas such as Cyber-Physical Systems, the Internet of Things and Everything (IoE), Cloud computing, the Internet of Services, Big Data Analysis, Smart Factories, Augmented Reality, among others. Industry 4.0 is not only a new industrial revolution, but also a crucial integration challenge that involves several actors from the IoE, which are people, data, services, and things. This paper proposes an approach to analyze the integration challenges in the context of Industry 4.0 using five integration levels, which are connection, communication, coordination, cooperation, and collaboration (5 C). In that sense, this paper presents a state of the art of recent studies in Industry 4.0 from an integration perspective, categorized according to the 5 C integration levels versus the four actors of IoE. Specifically, this paper considers several works intended to solve problems of autonomic integration in Industry 4.0 at the highest levels of the 5 C integration stack (coordination, cooperation, and collaboration). Also, this paper presents a case study from an integration perspective, which contemplates autonomy, self-organizing, among other aspects, in order to turn a traditional industry into a smart factory regarding the Industry 4.0 concept. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

The term ‘impact’ is everywhere. Organizations and individuals want to fund projects for impact, measure impact, and showcase the impact of effort, expertise and financial investment, but clear definitions and understandings of what having an impact really means for people and institutions appear lacking or ad-hoc. This paper explores ‘impact’ in the areas of education and research into government practice. For governments, the impact agenda involves operating in increasingly tight fiscal environments with mounting pressure to articulate and demonstrate return on investment. For education providers, there are increasing calls to justify and prove why investment in education is an efficient and effective endeavor. For universities, this includes a shift from a traditional publication-focused research impact culture to a wider societal impact one that demonstrates direct and indirect benefits to society. This paper conceptualizes impact as a “puzzle” with many pieces, with education and research making up key pieces that can and need to fit together better. In doing so, the paper identifies four problem areas to help guide thinking toward clarity about what ‘impact’ entails. To aid collective progress in this space, we detail key issues facing the education and research sectors. Based on our analysis we arrive at a set of questions intended to help guide thinking and actions toward collectively increasing the ability to generate and demonstrate the impact of both into government practice and society at large.

The increasing fragmentation and the rising number of organizations in society create major challenges when implementing knowledge structures. In this article, we propose that the concept of metagovernance is useful when addressing these challenges. The enhancement of knowledge structures in the Swedish social services is our study object. Networks at national, regional and local levels have been organized and facilitated by national resources and agreements since 2003. The shifting of participants, switching assignments and ambiguity about the networks purposes created difficult conditions for successful implementation. In this paper, we argue that fragile top-down government and shifting local network organization provide limited opportunities to ensure that new knowledge structures will have an impact on social work practice. Research is based upon monitoring a national program that started in 2003, affecting five universities and their surrounding regions and municipalities. The article is also based upon observations and interviews among national and regional actors. Results show that national agencies are hands-off, but still in control by setting up legal and discursive frameworks for those networks. It is argued that this creates new challenges for participating organizations and professionals in social work.

This paper addresses an analytical gap in critical security studies related to the social construction, legitimation, and institutionalisation of referents objects, or the for whom of security. As it lays out, referent objects tend to be assessed based on pre-theoretical commitments that themselves fall outside of the scope of critical security analysis. This has important analytical and ethical consequences, which I heuristically illustrate in relation to the individual in both Copenhagen School securitisation theory and human-centred security. In one case, the individual is understood as an atomised Hobbesian figure at odds with the collective, and in the other, as a socially embedded figure representative of humanity. Incommensurate ontological baselines have on the one hand stymied fruitful dialogue between these two influential approaches. More importantly, however, fixed perspectives on the individual have also served to limit each approachs purview, even on their own terms. In highlighting the value of de-naturalising the individual, I lay out a broader argument for problematising referent objects more generally, as a more productive way of thinking about security that moves the conversation beyond practically endless articulations of potential danger. Overall, I argue that the intersubjective processes by which any referent object is constituted as a legitimate claimant to security, is as central to the critical study and ethical practice of security, as that of putative threats to it., Funding Agencies|Stiftelsen for Miljostrategisk Forskning (The Swedish Foundation for Strategic Environmental Research)

This paper addresses an analytical gap in critical security studies related to the social construction, legitimation, and institutionalisation of referents objects, or the for whom of security. As it lays out, referent objects tend to be assessed based on pre-theoretical commitments that themselves fall outside of the scope of critical security analysis. This has important analytical and ethical consequences, which I heuristically illustrate in relation to the individual in both Copenhagen School securitisation theory and human-centred security. In one case, the individual is understood as an atomised Hobbesian figure at odds with the collective, and in the other, as a socially embedded figure representative of humanity. Incommensurate ontological baselines have on the one hand stymied fruitful dialogue between these two influential approaches. More importantly, however, fixed perspectives on the individual have also served to limit each approachs purview, even on their own terms. In highlighting the value of de-naturalising the individual, I lay out a broader argument for problematising referent objects more generally, as a more productive way of thinking about security that moves the conversation beyond practically endless articulations of potential danger. Overall, I argue that the intersubjective processes by which any referent object is constituted as a legitimate claimant to security, is as central to the critical study and ethical practice of security, as that of putative threats to it., Funding Agencies|Stiftelsen for Miljostrategisk Forskning (The Swedish Foundation for Strategic Environmental Research)

Mycoplasma gallisepticum is the primary causative agent of chronic respiratory disease in poultry, and vaccination is the measure most commonly used for its control. Pathological changes caused by M. gallisepticum are mainly observed in the trachea and air sacs, but assessment of air sac lesions is subjective. Standardized parameters for evaluation of pathological changes, and their reproducibility and discrimination in uninfected and infected groups, are critical when assessing the efficacy of M. gallisepticum vaccination. This study reviewed and critically appraised the published literature on evaluation of vaccine efficacy against pathological changes caused by M. gallisepticum in poultry in the trachea and air sacs. A search of four electronic databases, with subsequent manual filtering, identified 23 eligible papers published since 1962 describing the assessment of histopathological changes in the trachea using tracheal lesion scores and/or measurement of tracheal mucosal thicknesses and assessment of gross air sac lesions using lesion scores. Measurement of tracheal lesions proved a more reliable and robust method of assessing disease induced by M. gallisepticum when compared to assessment of air sac lesions, highlighting the importance of including assessment of tracheal lesions as the primary outcome variable in vaccine efficacy studies. In addition, this study also identified the necessity for use of a standardized model for evaluation and reporting on M. gallisepticum vaccines to minimize variations between vaccine efficacy studies and to allow direct comparisons between them.RESEARCH HIGHLIGHTS Tracheal and air sac lesions have been used to assess M. gallisepticum vaccine efficacy.The specific parameters and statistical tests used to compare tracheal and air sac lesions vary greatly.Measures of tracheal lesions are more discriminatory than measures of air sac lesions.A standardized model is needed to evaluate vaccines against infection with M. galliseptic

International Retirement Migration (IRM) is a growing phenomenon in the Global South. Using the example of the city of Cotacachi in the Ecuadorian Andes, this paper analyzes the spatio-temporal growth of the properties acquired by foreign retirees. We have developed a multi-temporal map that visualizes the spatio-temporal patterns of foreign-owned real estate properties and explains them in the historical context of land tenure. As no official spatial data is available for foreign-owned properties in Cotacachi, the mapping was developed based on data triangulation from remote sensing, participatory mapping, document analysis (e.g. a cadastral database), and semi-structured interviews with key stakeholders. With this origin approach, the map reveals a significant growth in the number of properties and the size of the land acquired by foreigners particularly since the 2008 United States' housing crisis. Most of the foreign-owned properties are located at the urban fringe and have been built on former colonial hacienda lands in direct proximity to existing indigenous communities.

Geohazard risk is high in Arab countries due to ineffective disaster preparedness measures, mismanagement, lack of public awareness, inadequate funding and lack of stakeholder support. One such country is Egypt, which is hit by floods every year that cost lives and bring the economy to a standstill. Moreover, not much has been done to map flood-prone areas. In this paper, flood susceptibility modelling was evaluated in the Ras Gharib region of Egypt using two effective techniques machine learning technique-MLT (Support Vector Machine (SVM)) and deep learning method-DL (Convolutional Neural Networks (CNN)). Thirteen flood related factors and flood inventory layer were prepared to construct these models. Validation was performed with 30% of the flood locations where receiver operating characteristic (ROC) curves showed that the deep learning technique (CNN) gave a prediction accuracy of 86.5% (high performance), while the MLTs (SVM) gave 71.6% (medium performance). The results show that CNN provides 17% better than SVM which indicates a powerful and accurate model in flood susceptibility mapping. Results were confirmed using the Astro Digital images shortly after the 2016 flood, in which the CNN model provides a good agreement.

Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although manual interpretation is costly and labor-intensive, it is preferred over automatic and semi-automatic building damage mapping frameworks such as machine learning and deep learning because of its reliability. Therefore, this review paper explores various automatic and semi-automatic building damage mapping techniques with a quest to understand the pros and cons of different methodologies to narrow the gap between research and practice. Further, the research gaps and opportunities are identified for the future development of real-world scenarios earthquake-induced building damage mapping. This review can serve as a guideline for researchers, decision-makers, and practitioners in the emergency management service domain.

This paper presents the results of field and laboratory studies of slurry tracks along the South Coast rail line in NSW, Australia. Site investigations on fouled tracks were followed by a series of laboratory tests to determine the properties of mud fines, and how they can reduce track performance. This study reveals two distinctly different ways of forming slurry tracks, i.e., non-subgrade and subgrade mud pumping, resulting in different characteristics of degraded tracks. More cohesive the fouling materials are, the greater the reduction in hydraulic conductivity (kb) and shear strength (Sb) of the contaminated ballast. When the fouling index FI > 30%, kb drops severely, causing insufficient drainage capacity of the track while the loss of Sb can exceed 22%. Different types of fouling index are also discussed with reference to the field and laboratory data, followed by proposed empirical equations to estimate the values of kb and Sb.