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Birch plywood exhibits outstanding mechanical properties with regards to tensile, compressive, and shear behaviors, making this engineered wood product promising in timber connection applications. However, the edgewise bending strength and stiffness, which are often critical for the design of gusset plates, have not been investigated thoroughly. Moreover, in engineering applications, the size and moisture content of the plywood plate are in general very different from those adopted in laboratory testing according to current standards. This paper aims to investigate the influence of face grain angle, size, and moisture content on the edgewise bending strength and stiffness of birch plywood. In total, 288 birch plywood specimens were tested in three-point bending at five different face grain angles to the beam longitudinal axis (from 0 degrees (parallel) to 90 degrees (perpendicular), with an angle step of 22.5 degrees), with four different sizes (with the nominal depth of 20 mm, 30 mm, 40 mm, and 50 mm) and three different moisture contents (7.2 %, 11.9 %, and 21.8 %). Analytical and numerical models, both taking non-linear elasto-plastic compressive behaviors into account, were developed for the prediction of the ultimate moment capacity based on different failure definitions. Lastly, the relationships between the bending strength and elastic modulus were analyzed., QC 20221104

This paper proposes a novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network. First, we provide a sufficient condition for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. We present a sufficient condition for the model to be locally observable. We propose a Luenberger observer for the system state estimation and show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out. Copyright, QC 20220930

The paper proposes a modular-based approach to constraint handling in process optimization and control. This is partly motivated by the recent interest in learning-based methods, e.g., within bioproduction, for which constraint handling under uncertainty is a challenge. The proposed constraint handler, called predictive filter, is combined with an adaptive constraint margin to minimize the cost of violating soft constraints due to uncertainty and disturbances. The module can be combined with any controller and is based on modifying the controller output, in a least squares sense, such that constraints are satisfied within the considered horizon. The proposed method is computationally efficient and suitable for real-time applications. The effectiveness of the method is illustrated by a simple heater example and a nonlinear and time-varying example in penicillin fed-batch production optimization. Copyright, QC 20220920

Current European parking policies do not seem to steer towards a future where urban transport meets the climate goals. Prominent in current housing and parking policies are the so-called minimum parking standards. Recent research has shown that they contribute to increased car use and consequently to higher CO2 emissions. This is because they contribute to urban sprawl, extensive land use, increased housing and infrastructure construction costs, and that they restrict the number of flats per urban land unit. Other recent research shows that the construction of underground garages causes considerable CO2 emissions. This paper is based on previous research on the development of the transport sector to be in line with climate targets (i.e., the Paris Agreement). It intends to fill a research gap regarding how parking management can be designed to be consistent with these targets. Through a future study approach with Stockholm as a case example, this paper illustrates a policy shift in parking policies considered to be in line with national climate targets. The article presents concrete indicators to quantify the scope of change needed (e.g., removing 60,000 residential parking spaces and providing vehicle sharing with 7,500 cars and at least 7,500 bikes). The focus shift goes from providing physical parking spaces to providing satisfactory mobility and accessibility. We outline a pathway towards a future scenario of parking and mobility in Stockholm, with a combination of mobility services, parking restrictions (e.g., cap on parking spaces, removal of minimum parking standards), and citizen participation. The pathway is also analysed regarding equity, feasibility, and acceptance., QC 20220707, Mistra Sams

The last years have witnessed a growing scholarly interest in innovation ecosystems, including how keystone actors establish and orchestrate collaborative innovation. In parallel, temporary collaborative innovation efforts are becoming increasingly important to address societal problems. Temporary structures are also used to create a sense of urgency to prompt change. Thus, innovation ecosystems often incorporate temporary structures. However, while keystone actor capabilities to orchestrate an ecosystem over its lifecycle have been studied, the needs for capabilities to coordinate multiple parallel processes with different temporal logics have not been addressed. In this paper, we contribute to the literature on innovation ecosystems in industrial marketing research by examining the challenges and opportunities associated with temporary structures in an innovation ecosystem. Furthermore, we theorize on the implications for the keystone actor orchestration capabilities. The paper is based on a longitudinal case study of a construction project that was used by a keystone actor to gain momentum in its innovation work. Fourteen innovation projects, each involving external partners, were con-nected to the construction project. We conclude that temporary structures are powerful to enhance innovation, but that preparation is essential to enable the seizing of opportunities in a collaborative and transitory context. Specific orchestration capabilities relate to integrating a linear, time-bound logic with an iterative innovation logic, and to matching ambitions in at the temporary systems with capabilities at the level of the permanent organizations. An important aspect is to find a balance between temporary external resources and long-term internal resources., QC 20220721

Predictive Maintenance (PdM) has continually attracted interest from the manufacturing community due to its significant potential in reducing unexpected machine downtime and related cost. Much attention to existing PdM research has been paid to perceiving the fault, while the identification and estimation processes are affected by many factors. Many existing approaches have not been able to manage the existing knowledge effectively for reasoning the causal relationship of fault. Meanwhile, complete correlation analysis of identified faults and the corresponding root causes is often missing. To address this problem, graph-based approaches (GbA) with cognitive intelligence are proposed, because the GbA are superior in semantic causal inference, heterogeneous association, and visualized explanation. In addition, GbA can achieve promising performance on PdM's perception tasks by revealing the dependency relationship among parts/components of the equipment. However, despite its advantages, few papers discuss cognitive inference in PdM, let alone GbA. Aiming to fill this gap, this paper concentrates on GbA, and carries out a comprehensive survey organized by the sequential stages in PdM, i. e., anomaly detection, diagnosis, prognosis, and maintenance decision-making. Firstly, GbA and their corresponding graph construction methods are introduced. Secondly, the implementation strategies and instances of GbA in PdM are presented. Finally, challenges and future works toward cognitive PdM are proposed. It is hoped that this work can provide a fundamental basis for researchers and industrial practitioners in adopting GbAbased PdM, and initiate several future research directions to achieve the cognitive PdM., QC 20220707

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

This paper proposes a cognitive digital twin framework for smart manufacturing, and especially for human-robot-collaboration cases. The proposed framework comprises three layers (field, edge, and cloud layers) based on the 5G communication network. In the field layer, the physical twin's data from the physical machine and human operators are transmitted through the edge layer and then to the cloud layer to virtualize the digital twin. The cloud layer generates inference model generation by deep learning training and updates the inference model in the edge layer to make the field's machine smart. Especially, human operators' models are built based on the multimodal fusion in the cloud layer for cognitive function. Also, edge-cloud collaborative computing is presented to implement the proposed framework. Finally, the study is validated with a human-robot-collaboration case involving 5G edge computing., QC 20220516

We consider the life-cycle optimal portfolio choice problem faced by an agent receiving labor income and allocating her wealth to risky assets and a riskless bond subject to a borrowing constraint. In this paper, to reflect a realistic economic setting, we propose a model where the dynamics of the labor income has two main features. First, labor income adjusts slowly to financial market shocks, a feature already considered in Biffis et al. (2015). Second, the labor income gamma i of an agent i is benchmarked against the labor incomes of a population gamma(n) := (gamma(1),gamma(2), ...,gamma(n)) of n agents with comparable tasks and/or ranks. This last feature has not been considered yet in the literature and is faced taking the limit when n -> +infinity so that the problem falls into the family of optimal control of infinite-dimensional McKean-Vlasov Dynamics, which is a completely new and challenging research field. We study the problem in a simplified case where, adding a suitable new variable, we are able to find explicitly the solution of the associated HJB equation and find the optimal feedback controls. The techniques are a careful and nontrivial extension of the ones introduced in the previous papers of Biffis et al. (2015, 0000). (C) 2021 Elsevier B.V. All rights reserved., QC 20220524

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

This paper presents the Storybox Methodology which combines a novel framework for structuring knowledge and conversations around a story (D-PAF), with a live chatroom-based training approach that builds the conversation knowledge base via live chatroom interactions. Chatbots have achieved success as intelligent interfaces in education, health, sales and support, but their move towards mainstream adoption has been hindered by the large amount of development resources required, in terms of data collection, preparation, user testing and technical knowledge. The complexity of the development task often necessitates both a system author and a domain expert working effectively together, adding further complexity and risk. Overcoming these barriers could increase feasibility of chatbots in a range of expert contexts. In education, there are groups of learners who do not enjoy reading and writing. Storytelling chatbots might be able to introduce these groups to enjoyable new ways to read and write, having a beneficial impact on their education and future prospects. This paper proposes the Storybox Methodology for the rapid development of storytelling chatbots. Storybox is evaluated by creating, training and testing ‘The Ghost’, a chatbot enacting Hamlet's Ghost character from William Shakespeare's dramatic tragedy. The results showed that after a period of live chatbot training of only 25 training conversations, The Ghost was able to conduct convincing conversations with participants.

In order to continuously develop the special airfoil for wind turbine and further improve the energy efficiency of wind turbine, based on the bionic airfoil Dol-Rot 24° that was proposed by the streamline contour of the Phocoenoides Dalli, another novel bionic airfoil is constructed combined with the lever movement shape of the dolphin skeleton, and the airfoil is named Dol-Rot 24°-2. For the cross section of NREL phase VI horizontal axis wind turbine blade (S809 airfoil), Dol-Rot 24°, and Dol-Rot 24°-2 are adopted and numerically simulated to quantitatively analyze from both aspects of aerodynamic performance and noise characteristics. Using the above two novel airfoils profiles, two 3D bionic blades named Dol-Blade-1 and Dol-Blade-2 suitable for NREL phase VI HAWT are established. The results show that: compared with S809 airfoil, Dol-Rot 24°-2 airfoil can greatly improve the lift coefficient, while Dol-Rot 24° airfoil can more effectively inhibit the flow separation on the suction surface. In terms of noise characteristics, compared with S809 airfoil, the noise of the two types of dolphin airfoils does not increase significantly. In addition, Dol-Blade-1 can comprehensively improve the low-speed shaft torque of the blade, while the load of the wind turbine hardly changes. The low-speed shaft torque of Dol-Blade-2 is even better than that of Dol-Blade-1. However, the blade root bending moment of Dol-Blade-2 increases obviously, which inevitably increases the load of the wind turbine. In this paper, the new bionic method is proposed to improve the wind energy utilization of wind turbine on the premise of strictly controlling the aerodynamic noise and load level of blade. The research results of this paper can provide a reference for the optimization of energy efficiency and noise research of NREL phase VI HAWT.

The slowdown in China’s economic growth in the past decade has forced many migrant workers to return or plan to return to their rural hometowns temporarily. Extant studies on intergenerational mobility pay less attention to temporary migrant households. This paper investigates how migrant father’s return intention influences children’s educational outcomes and permanent incomes using a high-quality nationwide dataset of China (CLDS2012-2018). It finds that though there is no significant correlation between fathers’ and children’s educational attainments, the higher the return probability of the father, the less educational investment he would make in his child. This paper contributes to the literature in two ways: theoretically, it provides a new perspective to observe the intergenerational mobility of migrant workers; and methodologically, it corrects life cycle bias by controlling for the ages of fathers and children and using multi-year mean incomes.

Wastewater-based epidemiology (WBE) has proven to be a useful surveillance tool during the ongoing SARS-CoV-2 pandemic, and has driven research into evaluating the most reliable and cost-effective techniques for obtaining a representative sample of wastewater. When liquid samples cannot be taken efficiently, passive sampling approaches have been used, however, insufficient data exists on their usefulness for multi-virus capture and recovery. In this study, we compared the virus-binding capacity of two passive samplers (cotton-based tampons and ion exchange filter papers) in two different water types (deionised water and wastewater). Here we focused on the capture of wastewater-associated viruses including Influenza A and B (Flu-A & B), SARS-CoV-2, human adenovirus (AdV), norovirus GII (NoVGII), measles virus (MeV), pepper mild mottle virus (PMMoV), the faecal marker crAssphage and the process control virus Pseudomonas virus phi6. After deployment, we evaluated four different methods to recover viruses from the passive samplers namely, (i) phosphate buffered saline (PBS) elution followed by polyethylene glycol (PEG) precipitation, (ii) beef extract (BE) elution followed by PEG precipitation, (iii) no-elution into PEG precipitation, and (iv) direct extraction. We found that the tampon-based passive samplers had higher viral recoveries in comparison to the filter paper. Overall, the preferred viral recovery method from the tampon passive samplers was the no-elution/PEG precipitation method. Furthermore, we evidenced that non-enveloped viruses had higher percent recoveries from the passive samplers than enveloped viruses. This is the first study of its kind to assess passive sampler and viral recovery methods amongst a plethora of viruses commonly found in wastewater or used as a viral surrogate in wastewater studies.

The mining industry faces a number of challenges that include rapid depletion of high-grade deposits, high energy cost for metal production, and growing environmental concerns associated with mining waste disposal. Copper slag is one of the mining wastes that is raising growing concern due to the huge volumes being produced and dumped annually worldwide. These huge quantities of slag not only cause environmental pollution and space problems for disposal, but they also waste valuable copper and cobalt. This is increasingly important due to the growing demand for these metals in emerging technologies, potentially resulting in a supply deficit from conventional mining production. To address these challenges, recycling and repurposing of copper slag waste using innovative technologies should be considered, creating a resource for a circular economy. This paper presents a review of copper slag production and its potential as a world resource for cobalt and copper metals. An overview of copper and cobalt production from ore reserves, and of global demand and supply risks for these metals is presented. The potential benefits of reprocessing copper slag waste using the circular economy model are discussed. This paper demonstrates that applying the circular economy principles to the copper slag waste has the potential to create additional economic value, improve the energy efficiency of the metal production, increase the supply of critical metals, and reduce the impacts of metal production on the environment.

While we attempt to develop the balanced error rate (BER) minimization learning framework for randomized learning of feedforward neural networks to deal with imbalanced datasets, it remains unclear whether the BER minimization learning framework can be effectively extended into its semi-supervised version. This paper proposes a new concept of accuracy maximization for randomized learning methods on imbalanced datasets for the first time, and theoretically proves that it is equivalent to the minimization of the generalized BER for the use of the selected neural networks. In particular, accuracy maximization can be easily extended to semi-supervised scenarios as its semi-supervised version is proved to be linearly dependent on its original. In this paper, based on the proposed accuracy maximization concept, we propose an accuracy-maximization learning framework, and further develop a new accuracy-maximization extreme learning machine AMELM by taking Extreme Learning Machine (ELM) as a typical randomized learning method for feedforward neural networks so as to handle challenging data issues such as the class imbalance and label scarcity. It is worth noting that the proposed accuracy maximization based framework is not only suitable for ELM, but can be extended to different randomized learning methods, such as Random Vector Functional Link Network (RVFL), and Schmidt Neural Network (SNN) for supervised and semi-supervised imbalanced data. The efficacy of AMELM is tested on extensive benchmark datasets. Experimental results show that AMELM can achieve satisfactory performances on labeled or partially labeled imbalanced data. Also, AMELM obtains at least comparable classification performance to other baseline methods yet has fewer hyperparameters to tune, showing its potential for practical applications.

Value co-creation is a core focus area in both B2B marketing and strategy research, necessitating resource utilization within and across organizational boundaries. In the Industrial Marketing and Purchasing (IMP) group, scholars have focused on the interactions among resources as one important way to analyze central questions about resources in business relationships and networks. This has produced a breadth of investigations and concepts that are locally defined and utilized. This may hamper further theoretical development and inhibit analytical precision. The purpose of this paper is to develop a more general shared understanding of resource interaction by identifying and explicating the key concepts used, and to assess its status as an approach. The paper synthesizes 20 years of research to identify key concepts and the relationships across concepts. This provides both a platform for further conceptual and empirical research within IMP and potential for cross-fertilization with parallel B2B areas.

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc

Nature-based Solutions function (NBS) as an umbrella concept for ecosystem-based approaches that are an alternative to traditional engineering solutions for Disaster Risk Reduction. Their rising popularity is explained partly by their entailing additional benefits (so-called co-benefits) for the environment, society, and economy. The few existing frameworks for assessing co-benefits are lacking guidance on co-benefit pre-assessment that is required for the NBS selec-tion and permission process. Going beyond these, this paper develops a comprehensive guidance on quantitative pre-assessment of potential co-benefits and disbenefits of NBS tackling Disaster Risk Reduction. It builds on methods and frameworks from existing NBS literature and related disciplines. Furthermore, this paper discusses the evaluation of the quantified results of the pre-assessment. In particular, the evaluation focuses on the significance of change of the estimated co-benefits and disbenefits as well as the sustainability of the NBS. This paper will support decision-making in planning processes on suitability and sustainability of Nature-based Solutions and assist in the preparation of Environmental Impact Assessments of projects.

Social acceptability has become an important issue, influencing public and private decision making in many different areas of society, including the management of uses of common marine resources and space. As these uses intensify and social interactions become more complex, effective governance is a prerequisite for sustainable decision making. Participatory approaches are broadly recognised as a tool for involving stakeholders in the decision-making process to increase the acceptability of collective choices. However, despite the recommendations of new governance frameworks which promote inclusive and democratic bottom-up approaches, there is an increased complexity to apply such approach at local level. This paper aims to capture this complexity through the analysis of the local agency-led participative process and ad-hoc interviews with coastal users to understand whether and how perception and attitudes towards aquaculture evolve throughout the process. Here we present the result of a 3-year collaborative action science-policy with the agency responsible to manage aquaculture in Andalucía region (South of Spain). Even though the effectiveness of this participatory process in aquaculture planning should be assessed over the long-term, here we have identified a number of social, environmental and economic elements that can generate local opposition, especially by traditional fishers. The paper highlights the fact that the inclusion of stakeholders in the decision-making processes is not sufficient ensure the acceptance of aquaculture development. The effectiveness of the participatory processes is limited by the lack of institutional frameworks to accompany these processes, by the insufficient skills and expertise in engineering participation of the Administration staff and the poor integrated vision in policy making. As a result, the lack of coherence between political objectives constructed on a national or European scale and the complex social reality of the te

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc

Value co-creation is a core focus area in both B2B marketing and strategy research, necessitating resource utilization within and across organizational boundaries. In the Industrial Marketing and Purchasing (IMP) group, scholars have focused on the interactions among resources as one important way to analyze central questions about resources in business relationships and networks. This has produced a breadth of investigations and concepts that are locally defined and utilized. This may hamper further theoretical development and inhibit analytical precision. The purpose of this paper is to develop a more general shared understanding of resource interaction by identifying and explicating the key concepts used, and to assess its status as an approach. The paper synthesizes 20 years of research to identify key concepts and the relationships across concepts. This provides both a platform for further conceptual and empirical research within IMP and potential for cross-fertilization with parallel B2B areas.

Nature-based Solutions function (NBS) as an umbrella concept for ecosystem-based approaches that are an alternative to traditional engineering solutions for Disaster Risk Reduction. Their rising popularity is explained partly by their entailing additional benefits (so-called co-benefits) for the environment, society, and economy. The few existing frameworks for assessing co-benefits are lacking guidance on co-benefit pre-assessment that is required for the NBS selec-tion and permission process. Going beyond these, this paper develops a comprehensive guidance on quantitative pre-assessment of potential co-benefits and disbenefits of NBS tackling Disaster Risk Reduction. It builds on methods and frameworks from existing NBS literature and related disciplines. Furthermore, this paper discusses the evaluation of the quantified results of the pre-assessment. In particular, the evaluation focuses on the significance of change of the estimated co-benefits and disbenefits as well as the sustainability of the NBS. This paper will support decision-making in planning processes on suitability and sustainability of Nature-based Solutions and assist in the preparation of Environmental Impact Assessments of projects.

Whenever there is a need for manufacturing products with higher deposition rates (usually more than 10 kg per hour) with different alloys of primary metal materials such as titanium, nickel, aluminum, steel and other superalloys, wire arc additive manufacturing is often considered the best AM (additive manufacturing) technology from the state of art technologies around practice. This practice usually incorporates usage of wire as a feedstock material with standard tools used for arc welding. The perks associated with higher deposition rates of material, low wastage and higher equipment costs combined with structure intactness makes it an irresistible process to override traditional billeting and forging methods ranging from low to medium complex manufacturing products. Large applications can be seen in existence with the help of this technology such as manufacturing assembly line for automobile sectors, wing ribs in aerospace industry, wind tunnels in hydraulic sector. This paper discusses the usual complexities faced with managing residual stress in manufactured product, also with prime focus on improving mechanical properties as compared to traditional methods while eliminating defects such as porosity and distortions are widely discussed in detail. Finally, the prospects of post machining operations carried over product such as non-destructive testing, in-situ process and monitoring are assessed.

The last years have witnessed a growing scholarly interest in innovation ecosystems, including how keystone actors establish and orchestrate collaborative innovation. In parallel, temporary collaborative innovation efforts are becoming increasingly important to address societal problems. Temporary structures are also used to create a sense of urgency to prompt change. Thus, innovation ecosystems often incorporate temporary structures. However, while keystone actor capabilities to orchestrate an ecosystem over its lifecycle have been studied, the needs for capabilities to coordinate multiple parallel processes with different temporal logics have not been addressed. In this paper, we contribute to the literature on innovation ecosystems in industrial marketing research by examining the challenges and opportunities associated with temporary structures in an innovation ecosystem. Furthermore, we theorize on the implications for the keystone actor orchestration capabilities. The paper is based on a longitudinal case study of a construction project that was used by a keystone actor to gain momentum in its innovation work. Fourteen innovation projects, each involving external partners, were con- nected to the construction project. We conclude that temporary structures are powerful to enhance innovation, but that preparation is essential to enable the seizing of opportunities in a collaborative and transitory context. Specific orchestration capabilities relate to integrating a linear, time-bound logic with an iterative innovation logic, and to matching ambitions in at the temporary systems with capabilities at the level of the permanent organizations. An important aspect is to find a balance between temporary external resources and long-term internal resources., All four authors share first authorship.

Objective: To perform a scoping review of how patients with COVID-19 are affected by acute limb ischaemia (ALI) and evaluate the recommendations of the 2020 ESVS ALI Guidelines for these patients. Methods: Research questions were defined, and a systematic literature search was performed following the PRISMA guidelines. Abstracts and unpublished literature were not included. The definition of ALI in this review is in accordance with the ESVS guidelines. Results: Most identified papers were case reports or case series, although population based data and data from randomised controlled trials were also identified. In total, 114 unique and relevant papers were retrieved. Data were conflicting concerning whether the incidence of ALI increased, or remained unchanged, during the pandemic. Case reports and series reported ALI in patients who were younger and healthier than usual, with a greater proportion affecting the upper limb. Whether or not this is coincidental remains uncertain. The proportion of men/women affected seems unchanged. Most reported cases were in hospitalised patients with severe COVID-19. Patients with ALI as their first manifestation of COVID-19 were reported. Patients with ALI have a worse outcome if they have a simultaneous COVID-19 infection. High levels of D-dimer may predict the occurrence of arterial thromboembolic events in patients with COVID-19. Heparin resistance was observed. Anticoagulation should be given to hospitalised COVID-19 patients in prophylactic dosage. Most of the treatment recommendations from the ESVS Guidelines remained relevant, but the following were modified regarding patients with COVID-19 and ALI: 1) CTA imaging before revascularisation should include the entire aorta and iliac arteries; 2) there should be a high index of suspicion, early testing for COVID-19 infection and protective measures are advised; and 3) there should be preferential use of local or locoregional anaesthesia during revascularisation. Conclusion: Alth

Current European parking policies do not seem to steer towards a future where urban transport meets the climate goals. Prominent in current housing and parking policies are the so-called minimum parking standards. Recent research has shown that they contribute to increased car use and consequently to higher CO2 emissions. This is because they contribute to urban sprawl, extensive land use, increased housing and infrastructure construction costs, and that they restrict the number of flats per urban land unit. Other recent research shows that the construction of underground garages causes considerable CO2 emissions. This paper is based on previous research on the development of the transport sector to be in line with climate targets (i.e., the Paris Agreement). It intends to fill a research gap regarding how parking management can be designed to be consistent with these targets. Through a future study approach with Stockholm as a case example, this paper illustrates a policy shift in parking policies considered to be in line with national climate targets. The article presents concrete indicators to quantify the scope of change needed (e.g., removing 60,000 residential parking spaces and providing vehicle sharing with 7,500 cars and at least 7,500 bikes). The focus shift goes from providing physical parking spaces to providing satisfactory mobility and accessibility. We outline a pathway towards a future scenario of parking and mobility in Stockholm, with a combination of mobility services, parking restrictions (e.g., cap on parking spaces, removal of minimum parking standards), and citizen participation. The pathway is also analysed regarding equity, feasibility, and acceptance., QC 20220707, Mistra Sams

Additive manufacturing (AM) is characterised by several unique advantages, such as (freedom of) design, capability of fusing dissimilar materials, near-net-shape, and achieving a more sustainable production. While the increased precision of metal AM in recent years reduced the needed amount of post-processing to meet dimensional tolerance, the requirements for functional surfaces necessitate a well-understood post-processing, ranging from heat treatment to machining and finishing. The inherently rough initial (as-built) surface topography next to complex material microstructure affects the capability of post-processing/finishing operations to smooth the surface texture and obtain a favourable surface integrity. In this respect, a more fundamental understanding of the effects of material properties on post-processing/finishing is needed. Therefore, this review paper aims to establish the relationship between the characteristics of different AM technologies, microstructural properties of materials in as-built and heat-treated conditions, and the physical properties influencing the response of additively manufactured materials during post-processing/finishing operations. In particular, emphasis is placed on the physics-based understanding of how the microstructural characteristics of 316L, Ti6Al4V and Alloy 718 produced using the two principal technologies, Powder Bed Fusion (PBF) and Direct Energy Deposition (DED), influence their mechanical properties like tensile strengths, hardness and ductility. These properties are among the key factors influencing the response of material during post-processing/finishing operations involving material removal by shear deformation. This review paper also discusses the role of post-processing/finishing on fatigue performance, tribological behaviour and corrosion resistance of investigated AM materials. The paper summarises the state-of the art of post-processing/finishing operations and future research trends are highlighted., QC 20221206

Current European parking policies do not seem to steer towards a future where urban transport meets the climate goals. Prominent in current housing and parking policies are the so-called minimum parking standards. Recent research has shown that they contribute to increased car use and consequently to higher CO2 emissions. This is because they contribute to urban sprawl, extensive land use, increased housing and infrastructure construction costs, and that they restrict the number of flats per urban land unit. Other recent research shows that the construction of underground garages causes considerable CO2 emissions. This paper is based on previous research on the development of the transport sector to be in line with climate targets (i.e., the Paris Agreement). It intends to fill a research gap regarding how parking management can be designed to be consistent with these targets. Through a future study approach with Stockholm as a case example, this paper illustrates a policy shift in parking policies considered to be in line with national climate targets. The article presents concrete indicators to quantify the scope of change needed (e.g., removing 60,000 residential parking spaces and providing vehicle sharing with 7,500 cars and at least 7,500 bikes). The focus shift goes from providing physical parking spaces to providing satisfactory mobility and accessibility. We outline a pathway towards a future scenario of parking and mobility in Stockholm, with a combination of mobility services, parking restrictions (e.g., cap on parking spaces, removal of minimum parking standards), and citizen participation. The pathway is also analysed regarding equity, feasibility, and acceptance., QC 20220707, Mistra Sams

On the basis of literature sources analysis, the paper provides the rationale for the necessity of considering the limited digital devices capabilities when designing closed digital control systems for the complex electrical power grids. The problem of design is decomposed into two subproblems: design of current state observation vector digital transmission systems and current controlled process state estimation; design of digital systems for optimal control vector calculation, transmission and control actions realization. The paper presents consideration of the former problem, i.e. design of current state observation vector digital transmission systems and current controlled process state estimation: the mathematical model of digital system of information transmission and state estimation considering speed and reliability of technical means of implementation is presented; the functional structure of simulation complex is developed; the paper provides the formulation of the problem of estimating the optimal amount of information about the control object state, resulting in a solution of computational experiments simulating complex. © 2021 The Author(s).

Over the past decades, power companies have been implementing load forecasting to determine trends in the electric power system (EPS); therefore, load forecasting is applied to solve the problems of management and development of power systems. This paper considers the issue of building a model of medium-term forecasting of load graphs for EPS with specific properties, based on the use of ensemble machine learning methods. This paper implements the approach of identification of the most significant features to apply machine learning models for medium-term load forecasting in an isolated power system. A comparative study of the following models was carried out: linear regression, support vector regression (SVR), decision tree regression, random forest (Random Forest), gradient boosting over decision trees (XGBoost), adaptive boosting over decision trees (AdaBoost), AdaBoost over linear regression. Isolation of features from a time series allows for the implementation of simpler and more overfitting-resistant models. All the above makes it possible to increase the reliability of forecasts and expand the use of information technologies in the planning, management, and operation of isolated EPSs. Calculations of the total forecast error have proved that the characteristics of the proposed models are high quality and accurate, and thus they can be used to forecast the real load of a power system. © 2021 The Author(s).

The paper considers the development of the Northern Sea Route (NSR) in modern Russia and China. The analysis of cargo turnover and the advantages of the NSR is carried out. The importance of the nuclear icebreaker fleet in the development of the NSR is determined and specified. The route is currently considered as transcontinental and it is planned to become part of the “Silk Road”. It belongs to the number of Russian national projects. As an international transit highway, the Northern Sea Route has a huge potential for international cooperation and development of the North and East of Russia. The cluster-modular approach in the organization of NSR is considered. © 2022 The Authors. Published by ELSEVIER B.V.

The study focuses on sustainability transitions in the oil and gas industry, particularly its strategies and perceptions towards mitigating climate change. The paper offers a quantitative analysis of survey data collected from 116 questionnaire responses involving representatives of oil and gas companies, academia and young professionals, supplemented by a qualitative analysis of selected companies' reports and outlooks. The empirical results show that the oil and gas industry is attentive towards climate change and has the capacity to transit towards sustainability. There are significant differences in how the industry perceives climate change mitigation policies and treats public debate on climate change, which can be explained by geography and the related socio-political context. We point to the importance of stability of macroeconomic factors, such as oil prices, for oil and gas companies to diversify and transit, as well as underline the need to gain a deeper understanding of the effect of external pressures. We argue for clearer and more inclusive regulation, coordination and dissemination for climate policies, and wider engagement of oil and gas companies in sustainable energy transition. This paper contributes with a meso-level assessment of sustainability transitions, and suggests a more diverse picture of incumbents by highlighting the need to revise their role in sustainability transition processes. We offer additional perspectives on transition pathways for policymakers.

Building construction deserves many attentions due to its huge energy consumption, while Phase Change Materials (PCMs) provide positive solutions for improving energy efficiency and enhancing the thermal properties of construction materials. However, PCMs also present some negative impacts, such as weakening mechanical properties and increasing costs, chemical instability and so on. In this paper, the main characteristics of PCMs, design and incorporating methods, effects on energy consumption and construction reliability are comprehensively reviewed and discussed. Although many materials have the capacity of phase change, some organic PCMs are more suitable due to the higher latent heat and favourable phase change point in buildings, when eutectic PCMs present greater potential to become the optimal one but much effort is required for investigations. Current design methods and application in construction materials can meet the essential requirements, but the effectiveness is inadequate, including low efficiency of phase changing, leading to low energy storage. Subsequently, some promising research direction and critical areas for optimization are also proposed accordingly in this paper. Future development of PCMs, including novel PCM and efficient incorporation, real applications and functions in buildings are proposed. Additionally, multifunctional construction materials combining PCM deserve much attention and possess promising prospect for energy saving in sustainable and energy efficient building construction.

Early-age thermal cracking and delayed ettringite formation (DEF) are short-term and long-term major issues to the durability of mass concrete and require a high level of consideration in the development of a suitable concrete mix design in placement and curing strategies, particularly in mixes of high cement content. Where DEF risk proves to be significant, contractors are required to adopt risk mitigation strategies including lowering concrete's temperature during placement by adding cool water or ice, for example, to the mix, embedding cooling pipes in the cast element and/or sequential placement of the concrete. These strategies can be implemented at different levels, as needed, to minimise the risk of cracking and DEF. Determining the most suitable and cost-efficient risk mitigation strategy, however, together with its implementation, is a complex task that requires consideration of each complex strategy under project-specific conditions. While a significant research effort has been invested to develop an improved understanding of the effectiveness of different early age thermal cracking and DEF risk mitigation strategies, the lack of reliable models capable of simulating and comparing the effectiveness of all scenarios remains elusive and considerably limits the ability of the practitioners in identifying the optimal approach. This paper presents a Multiphysics model developed to predict the effect of early-age thermal cracking and DEF risk mitigation scenarios. The model is validated using four real-life project Case Studies and is demonstrated to provide reliable results. In addition, the paper studies the comparative effectiveness of key risk mitigation strategies and the important parameters influencing their effectiveness.

Large scale vaccination of population is widely accepted to be the key to recovery from the devastating economic and public health impacts of the COVID-19 pandemic. However, low uptake of vaccine has challenged vaccination efforts in many parts of the world. The paper explores the determinants of demand for COVID-19 vaccination – specifically, the prevalence dependence hypothesis – that identifies infection prevalence and mortality as the key drivers of individual preventive behavior against infectious diseases. Using daily disease tracking and vaccination data from 47 European countries the paper finds strong evidence that COVID-19 infection rate and mortality rate drive future vaccination uptake. Specifically, results from fixed effects models suggest that while lagged infection prevalence induce vaccination uptake by 0.18 to 0.24 percent, while the effect of lagged mortality is significantly larger, ranging between 1.10 to 1.53 percent. The results highlight the critical role of behavioral response to epidemiological outcomes and are of critical significance for COVID-19 mitigation policies, especially as they relate to achieving vaccine-induced herd immunity and economic reopening.

Advanced engineering applications necessitate developing a new class of surface coatings that protect the surface components in these applications while also meeting the conflicting requirements for the necessary properties to improve the surface performance of many parts in biomedical, aerospace, electronics, automotive, marine, oil and gas pipelines, as well as electronics fields. However, surface coatings on many constituents have remained a difficult task for researchers due to the synchronous requirement for opposing characteristics in the same coating. In recent years, functionally graded coatings (FGCs) have been widely used to protect surfaces through achieving specific characteristics from one location to another to be suitable for different operating conditions. Deposition of FGCs with gradient behavior and high accuracy on the surface of substrate material is a versatile and successful approach for enhancing characteristics of the substrate material, primarily to increase its functionality and life span. Therefore, this review paper presents a detailed analysis of the extensively used production techniques to develop FGCs based on the process state during deposition. Also, the microstructure features, mechanical properties, wear resistance, and corrosion behavior of the produced FGCs have been explored in detail. Furthermore, the current review paper focuses on understanding FGC classifications and applications, as well as research gaps and future directions for these coatings. The findings of this paper are expected to give researchers and designers in this field a variety of options for permanent surfaces protection via following a well-organized review methodology.

The COVID-19 outbreak has brought the indoor airborne transmission issue to the forefront. Although ventilation systems provide clean air and dilute indoor contaminated air, there is strong evidence that airborne transmission is the main route for contamination spread. This review paper aims to critically investigate ventilation impacts on particle spread and identify efficient ventilation strategies in controlling aerosol distribution in clinical and non-clinical environments. This article also examines influential ventilation design features (i.e., exhaust location) affecting ventilation performance in preventing aerosols spread. This paper shortlisted published documents for a review based on identification (keywords), pre-processing, screening, and eligibility of these articles. The literature review emphasizes the importance of ventilation systems’ design and demonstrates all strategies (i.e., mechanical ventilation) could efficiently remove particles if appropriately designed. The study highlights the need for occupant-based ventilation systems, such as personalized ventilation instead of central systems, to reduce cross-infections. The literature underlines critical impacts of design features like ventilation rates and the number and location of exhausts and suggests designing systems considering airborne transmission. This review underpins that a higher ventilation rate should not be regarded as a sole indicator for designing ventilation systems because it cannot guarantee reducing risks. Using filtration and decontamination devices based on building functionalities and particle sizes can also increase ventilation performance. This paper suggests future research on optimizing ventilation systems, particularly in high infection risk spaces such as multi-storey hotel quarantine facilities. This review contributes to adjusting ventilation facilities to control indoor aerosol transmission.

Big data from smartphone applications are enabling travel behavior studies at an unprecedented scale. In this paper, we examine pedestrian route choice preferences in San Francisco, California using a large, anonymized dataset of walking trajectories collected from an activity-based smartphone application. We study the impact of various street attributes known to affect pedestrian route choice from prior literature. Unlike most studies, where data has been constrained to a particular destination type (e.g. walking to transit stations) or limited in volume, a large number of actual trajectories presented here include a wide diversity of destinations and geographies, allowing us to describing typical pedestrians’ preferences in San Francisco as a whole. Other innovations presented in the paper include using a novel technique for generating alternative paths for route choice estimation and gathering previously hard-to-get route attribute information by computationally processing a large set of Google Street View images. We also demonstrate how the estimated coefficients can be operationalized for policy and planning to describe pedestrian accessibility to BART stations in San Francisco using ‘perceived distance’ as opposed to traversed distance.

© 2018 Elsevier B.V. DAG models are statistical models satisfying a collection of conditional independence relations encoded by the nonedges of a directed acyclic graph (DAG) G. Such models are used to model complex cause–effect systems across a variety of research fields. From observational data alone, a DAG model G is only recoverable up to Markov equivalence. Combinatorially, two DAGs are Markov equivalent if and only if they have the same underlying undirected graph (i.e., skeleton) and the same set of the induced subDAGs i→j←k, known as immoralities. Hence it is of interest to study the number and size of Markov equivalence classes (MECs). In a recent paper, we introduced a pair of generating functions that enumerate the number of MECs on a fixed skeleton by number of immoralities and by class size, and we studied the complexity of computing these functions. In this paper, we lay the foundation for studying these generating functions by analyzing their structure for trees and other closely related graphs. We describe these polynomials for some well-studied families of graphs including paths, stars, cycles, spider graphs, caterpillars, and balanced binary trees. In doing so, we recover connections to independence polynomials, and extend some classical identities that hold for Fibonacci numbers. We also provide tight lower and upper bounds for the number and size of MECs on any tree. Finally, we use computational methods to show that the number and distribution of high degree nodes in a triangle-free graph dictate the number and size of MECs.

© 2018 Elsevier Ltd Three decades of research studies in ground delay program (GDP) decision-making, and air traffic flow management in general, have produced several analytical models and decision support tools to design GDPs with minimum delay costs. Most of these models are centralized, i.e., the central authority almost completely decides the GDP design by optimizing certain centralized objectives. In this paper, we assess the benefits of an airline-driven decentralized approach for designing GDPs. The motivation for an airline-driven approach is the ability to incorporate the inherent differences between airlines when prioritizing, and responding to, different GDP designs. Such differences arise from the airlines’ diverse business objectives and operational characteristics. We develop an integrated platform for simulating flight operations during GDPs, an airline recovery module for mimicking the recovery actions of each individual airline under a GDP, and an algorithm for fast solution of the recovery problems to optimality. While some of the individual analytical components of our framework, model and algorithm share certain similarities with those used by previous researchers, to the best of our knowledge, this paper presents the first comprehensive platform for simulating and optimizing airline operations under a GDP and is the most important technological contribution of this paper. Using this framework, we conduct detailed computational experiments based on actual schedule data at three of the busiest airports in the United States. We choose the recently developed Majority Judgment voting and grading method as our airline-driven decentralized approach for GDP design because of the superior theoretical and practical benefits afforded by this approach as shown by multiple recent studies. The results of our evaluation suggest that adopting this airline-driven approach in designing the GDPs consistently and significantly reduces airport-wide delay costs compa

© 2019 High skilled workers gain from face to face interactions. If the skilled can move at higher speeds, then knowledge diffusion and idea spillovers are more likely to reach greater distances. This paper measures the knowledge creation consequences associated with the construction of China's high speed rail (HSR) network that connects mega cities, that feature the nation's best universities, to secondary cities. Since bullet trains reduce cross-city commute times, they reduce the cost of face-to-face interactions between skilled workers who work in different cities. Using a database listing research paper publication and citations, we document a complementarity effect between knowledge production and the transportation network. When connected by the HSR, co-author productivity rises, new co-author pairs emerge and more highly productive scientists migrate to the HSR cities.

© 2018 Elsevier B.V. This paper introduces a novel optimization problem resulting from the combination of two major existing problems arising at storage yards in container terminals. The Yard Crane Scheduling Problem is typically concerned with routing the crane given a sequence of storage and retrieval requests to perform, while the Container Relocation Problem tackles the minimization of relocations when retrieving containers in a simpler setting. This paper is the first to consider a model that integrates these two problems by scheduling storage, retrieval and relocations requests and deciding on storage and relocation positions. We formulate this problem as an integer program that jointly optimizes current crane travel time and future relocations. Based on the structure of the proposed formulation and the linear programming relaxation of subproblems, we propose a heuristic local search scheme. Finally, we show the value of our solutions on both simulated instances as well as real data from a port terminal.

This study investigates Italian blending from a synchronic perspective, based on data extracted from a dictionary of neologisms attested over the past two decades. The first part of the paper addresses the boundaries between blends and other morphological units. Neologisms in which at least one of the two constituents has undergone a shortening have been classified into three categories (i.e., compounds with combining forms, secreted affixation, and blends), and described according to a set of semantic and formal parameters. The second part of the paper provides an updated description of Italian blending by analyzing a sample of 200 blends. The analysis reveals that Italian blends exhibit a wide variety of structures, according to the parameters of linearization, shortening, and overlap. Overlap, along with the phonological resemblance, proved to be a factor that favors blend formation. Moreover, it is shown that although blending in Italian is an irregular mechanism overall, in rare cases (such as that of -iota from idiota ‘idiot’), a blend's part may acquire regularity and affix-like properties.

Whenever there is a need for manufacturing products with higher deposition rates (usually more than 10 kg per hour) with different alloys of primary metal materials such as titanium, nickel, aluminum, steel and other superalloys, wire arc additive manufacturing is often considered the best AM (additive manufacturing) technology from the state of art technologies around practice. This practice usually incorporates usage of wire as a feedstock material with standard tools used for arc welding. The perks associated with higher deposition rates of material, low wastage and higher equipment costs combined with structure intactness makes it an irresistible process to override traditional billeting and forging methods ranging from low to medium complex manufacturing products. Large applications can be seen in existence with the help of this technology such as manufacturing assembly line for automobile sectors, wing ribs in aerospace industry, wind tunnels in hydraulic sector. This paper discusses the usual complexities faced with managing residual stress in manufactured product, also with prime focus on improving mechanical properties as compared to traditional methods while eliminating defects such as porosity and distortions are widely discussed in detail. Finally, the prospects of post machining operations carried over product such as non-destructive testing, in-situ process and monitoring are assessed.

The last years have witnessed a growing scholarly interest in innovation ecosystems, including how keystone actors establish and orchestrate collaborative innovation. In parallel, temporary collaborative innovation efforts are becoming increasingly important to address societal problems. Temporary structures are also used to create a sense of urgency to prompt change. Thus, innovation ecosystems often incorporate temporary structures. However, while keystone actor capabilities to orchestrate an ecosystem over its lifecycle have been studied, the needs for capabilities to coordinate multiple parallel processes with different temporal logics have not been addressed. In this paper, we contribute to the literature on innovation ecosystems in industrial marketing research by examining the challenges and opportunities associated with temporary structures in an innovation ecosystem. Furthermore, we theorize on the implications for the keystone actor orchestration capabilities. The paper is based on a longitudinal case study of a construction project that was used by a keystone actor to gain momentum in its innovation work. Fourteen innovation projects, each involving external partners, were con- nected to the construction project. We conclude that temporary structures are powerful to enhance innovation, but that preparation is essential to enable the seizing of opportunities in a collaborative and transitory context. Specific orchestration capabilities relate to integrating a linear, time-bound logic with an iterative innovation logic, and to matching ambitions in at the temporary systems with capabilities at the level of the permanent organizations. An important aspect is to find a balance between temporary external resources and long-term internal resources., All four authors share first authorship.

Objective: To perform a scoping review of how patients with COVID-19 are affected by acute limb ischaemia (ALI) and evaluate the recommendations of the 2020 ESVS ALI Guidelines for these patients. Methods: Research questions were defined, and a systematic literature search was performed following the PRISMA guidelines. Abstracts and unpublished literature were not included. The definition of ALI in this review is in accordance with the ESVS guidelines. Results: Most identified papers were case reports or case series, although population based data and data from randomised controlled trials were also identified. In total, 114 unique and relevant papers were retrieved. Data were conflicting concerning whether the incidence of ALI increased, or remained unchanged, during the pandemic. Case reports and series reported ALI in patients who were younger and healthier than usual, with a greater proportion affecting the upper limb. Whether or not this is coincidental remains uncertain. The proportion of men/women affected seems unchanged. Most reported cases were in hospitalised patients with severe COVID-19. Patients with ALI as their first manifestation of COVID-19 were reported. Patients with ALI have a worse outcome if they have a simultaneous COVID-19 infection. High levels of D-dimer may predict the occurrence of arterial thromboembolic events in patients with COVID-19. Heparin resistance was observed. Anticoagulation should be given to hospitalised COVID-19 patients in prophylactic dosage. Most of the treatment recommendations from the ESVS Guidelines remained relevant, but the following were modified regarding patients with COVID-19 and ALI: 1) CTA imaging before revascularisation should include the entire aorta and iliac arteries; 2) there should be a high index of suspicion, early testing for COVID-19 infection and protective measures are advised; and 3) there should be preferential use of local or locoregional anaesthesia during revascularisation. Conclusion: Alth

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc

A methodological debate within social pharmacy is ongoing regarding how to apply a qualitative approach. This paper emanates from a workshop at the Nordic Social Pharmacy Conference in 2019, named 'How do we know it's good? A workshop on quality criteria in qualitative social and clinical pharmacy research', that addressed this debate. The aim of this paper is twofold (1) to present the main key points raised during the workshop and (2) based on these inputs to contribute to the ongoing discussion on qualitative methodology within social pharmacy research. This paper starts with what was discussed at the workshop and further elaborated are some of the challenges with conducting qualitative research within social pharmacy. These include methodological and disciplinary competence and insecurity, reflections on the consequences of that many social pharmacy researchers come from a natural science background and how this (possibly) shapes the practice of qualitative research within the field. For example, how concepts like transparency and saturation, together with checklists and quality criteria are understood and used. Finally, we make suggestions for the next step for qualitative research in social pharmacy.