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This paper reviews digital tools for supporting the Circular Economy (CE) in the built environment. The study provides a bibliometric analysis and focuses on computer-aided design (CAD), building information modeling (BIM), and computational plugins that can be used by practitioners. While Life Cycle Assessment (LCA) is the primary methodology for evaluating buildings' environmental performance, the study identifies tools beyond LCA, including computational methods and circularity indicators, that can support the evaluation of circular design strategies. Our review highlights limitations in tools’ functionalities, including a lack of representative data for LCA and underdeveloped circularity indicators. The paper calls for further development of these tools in terms of interoperability aspects, integration of more sources of data for LCA and circularity, and possibilities for a comprehensive evaluation of design choices. Computational plugins offer greater flexibility, while BIM-LCA integrations have the potential to replace dedicated LCA software and spreadsheets. Additionally, the study identifies opportunities for novel digital methods, such as algorithms for circular design with various types of reused building elements, and sharing of digital twins and material passports. This research can inform future studies and support architects and engineers in their efforts to create a sustainable built environment., QC 20231204, ReCreate project (Horizon 2020 grant no. 958200)

For traffic safety reasons, Sweden has lowered speed limits on major roads that lack directional separation of traffic. For some of these roads, regional authorities, municipalities, and other local stakeholders have sent appeals to the government opposing the implemented speed limit reductions. The appeals have mainly referred to negative effects on regional development and have suggested that the speed limit reductions be abandoned. This paper identifies the characteristics of roads where appeals against speed limit reductions have been filed and where local stakeholders claim that speed limit reductions are a threat to accessibility and regional development. The results of logistic regression modelling show that appeals are more likely for speed limit reductions on long road sections, on European roads, and in areas with a state university, and less likely in areas with high population density and areas defined as vulnerable by the Swedish Agency for Economic and Regional Growth. We investigated these policy conflicts using frame theory. In this paper, we identify two policy frames: speed limits for traffic safety and speed limits for regional development. These two policy frames are related to different views on how to best strive towards regional accessibility and safety goals in remote areas. The different views are likely not easily aligned by more information or facts since they concern deeper questions, such as what constitutes basic accessibility of good quality and how large risks are acceptable on the road network. This study thus uncovers not only a policy disagreement but a policy controversy in the Swedish context.

The water industries (WIN) are now approaching towards sustainability of resource use, recovery process, and water and energy management based on the circular economy (CRE) framework. Thus, the integration of electrodialysis (ED) technology in the WIN with a CRE paradigm should be recommended for ensuring the sustainability of ED desalination, resource, and energy recovery (EDDRER). According to the literature review, and to the best of our knowledge, there is no systematic study devoted to the sustainable EDDRER; hence a comprehensive and critical knowledge generation of EDDRER is essential for further technological advancements of ED. Consequently, this review paper investigated the plausible incorporation of ED in the WIN for a CRE of EDDRER. The recent progress of EDDRER has been described comprehensively and critically. Moreover, an all-inclusive techno-economics and environmental sustainability analysis of EDDRER from WIN for a CRE has been carried out. This paper marks the first instance in which energy recovery techniques employing ED have been reported and critically discussed. In addition, the latest case studies of EDDRER in the WIN have been discussed critically, and the significant scaling-up issues of EDDRER have been assessed based on the state-of-the-art recent scientific findings. Furthermore, the potential mitigation measures for the scaling-up issues have also been addressed. This study is the first comprehensive assessment of EDDRER from WIN for a closed-loop economy. The novel insights of this study could be essential for the development of a sustainable CRE-based EDDRER process for WIN to attain sustainable development goals (SDGs).

This paper reviews digital tools for supporting the Circular Economy (CE) in the built environment. The study provides a bibliometric analysis and focuses on computer-aided design (CAD), building information modeling (BIM), and computational plugins that can be used by practitioners. While Life Cycle Assessment (LCA) is the primary methodology for evaluating buildings' environmental performance, the study identifies tools beyond LCA, including computational methods and circularity indicators, that can support the evaluation of circular design strategies. Our review highlights limitations in tools’ functionalities, including a lack of representative data for LCA and underdeveloped circularity indicators. The paper calls for further development of these tools in terms of interoperability aspects, integration of more sources of data for LCA and circularity, and possibilities for a comprehensive evaluation of design choices. Computational plugins offer greater flexibility, while BIM-LCA integrations have the potential to replace dedicated LCA software and spreadsheets. Additionally, the study identifies opportunities for novel digital methods, such as algorithms for circular design with various types of reused building elements, and sharing of digital twins and material passports. This research can inform future studies and support architects and engineers in their efforts to create a sustainable built environment., QC 20231204, ReCreate project (Horizon 2020 grant no. 958200)

The aim of this paper is to analyse contemporary transitions in the aviation industry in Sweden. We take a durational perspective to consider narratives as coordinating mechanism in sustainability transitions. We find that industry actors are constructing narratives for alternative aircraft fuels and technologies as they seek to maintain aviation's societal function whilst mitigating its climate impact. By reconciling memories of the past with their expectations for the future, narratives act to coordinate actors’ transition activities in the present. In this way, narrative are more than an initiator of transitions, but constitute paths in-the-making, highlighting the agency of actors in enacting change in the present and shaping sustainability transitions of the future., QC 20231124

Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented., QC 20231127

In application domains such as routing, network analysis, scheduling, and planning, directed graphs are widely used as both formal models and core data structures for the development of efficient algorithmic solutions. In these areas, graphs are often evolving in time: for example, connection links may fail due to temporary technical issues, meaning that edges of the graph cannot be traversed for some time interval and alternative paths have to be followed. In classical computation graphs have been implemented both explicitly through adjacency matrices/lists and symbolically as ordered binary decision diagrams. Moreover, ad-hoc visit procedures have been developed to deal with dynamically evolving graphs. Quantum computation, exploiting interference and entanglement, has provided an exponential speed-up for specific problems, e.g., database search and integer factorization. In the quantum framework everything must be represented and manipulated using reversible operators. This poses a challenge when one has to deal with traversals of dynamically evolving directed graphs. Graph traversals are not intrinsically reversible because of converging paths. In the case of dynamically evolving graphs also the creation/destruction of paths comes into play against reversibility. In this paper we propose a novel high level graph representation in quantum computation supporting dynamic connectivity typical of real-world network applications. Our procedure allows to encode any multigraph into a unitary matrix. We devise algorithms for computing the encoding that are optimal in terms of time and space and we show the effectiveness of the proposal with some examples. We describe how to react to edge/node failures in constant time. Furthermore, we present two methods to perform quantum random walks taking advantage of this encoding: with and without projectors. We implement and test our encoding obtaining that the theoretical bounds for the running time are confirmed by the empirical

Palaeoenvironmental data are fundamental in determining the manifold impacts of climate change. This paper argues that sessile barnacles are an excellent palaeoenvironmental proxy: they are present in nearly all nearshore environments, and their shell consists of diagenetically stable low-magnesium calcite and grows fast enough to record short-term variations. To demonstrate their utility, specimens from several Western Mediterranean Pliocene and Pleistocene barnacle-rich deposits are analysed herein, using for the first time a combination of sedimentology, taphonomy, stable isotope geochemistry and detailed comparisons with modern counterparts. Within shelf carbonate systems, barnacle diversity is highest in the shallow, nearshore waters and decreases moving offshore, thus providing a good proxy for the reconstruction of water depth and distance from the coastline. Barnacle taphonomy is also informative. Well-preserved complete specimens are characteristic of protected areas, whereas less well-preserved specimens occur in high-energy areas. The presence/absence of opercular plates is also particularly relevant for evaluating hydrodynamic conditions. As regards the C and O stable isotope ratios, due to the porous and coarse-grained nature of the deposits in which barnacle remains are usually found, the shells are often exposed to meteoric water percolation during diagenesis. Among the analysed specimens, only those collected from fine-grained deposits display no evidence of alteration and have isotopic ratios in line with those of their modern counterparts. These fossils display intra-shell variations that in modern barnacles have been related to short-term environmental changes (e.g., seasonal cycles). These results demonstrate that barnacles can always be useful for detailed palaeoenvironmental reconstructions based on skeletal assemblages. Furthermore, with further research aimed at gathering more data and assessing potentially interfering signals, they could bec

The aim of this paper is to analyse contemporary transitions in the aviation industry in Sweden. We take a durational perspective to consider narratives as coordinating mechanism in sustainability transitions. We find that industry actors are constructing narratives for alternative aircraft fuels and technologies as they seek to maintain aviation's societal function whilst mitigating its climate impact. By reconciling memories of the past with their expectations for the future, narratives act to coordinate actors’ transition activities in the present. In this way, narrative are more than an initiator of transitions, but constitute paths in-the-making, highlighting the agency of actors in enacting change in the present and shaping sustainability transitions of the future., QC 20231124

Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented., QC 20231127

In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, have set a path to convert neural activities recorded by sensors from the human scalp via electroencephalography into valid commands for robot control and task execution. Thanks to the advancement of sensor technologies, non-invasive and invasive sensor headsets have been designed and developed to achieve stable recording of brainwave signals. However, robust and accurate extraction and interpretation of brain signals in brain robotics are critical to reliable task-oriented and opportunistic applications such as brainwave-controlled robotic interactions. In response to this need, pervasive technologies and advanced analytical approaches to translating and merging critical brain functions, behaviours, tasks, and environmental information have been a focus in brain-controlled robotic applications. These methods are composed of signal processing, feature extraction, representation of neural activities, command conversion and robot control. Artificial intelligence algorithms, especially deep learning, are used for the classification, recognition, and identification of patterns and intent underlying brainwaves as a form of electroencephalography. Within the context, this paper provides a comprehensive review of the past and the current status at the intersection of robotics, neuroscience, and artificial intelligence and highlights future research directions., QC 20230818

Tool breakage monitoring (TBM) during milling operations is crucial for ensuring workpiece quality and mini-mizing economic losses. Under the premise of sufficient training data with a balanced distribution, TBM methods based on statistical analysis and artificial intelligence enable accurate recognition of tool breakage conditions. However, considering the actual manufacturing safety, cutting tools usually work in normal wear conditions, and acquiring tool breakage signals is extremely difficult. The data imbalance problem seriously affects the recog-nition accuracy and robustness of the TBM model. This paper proposes a TBM method based on the auxiliary classier Wasserstein generative adversarial network with gradient penalty (ACWGAN-GP) from the perspective of data generation. By introducing Wasserstein distance and gradient penalty terms into the loss function of ACGAN, ACWGAN-GP can generate multi-class fault samples while improving the network's stability during adversarial training. A sample filter based on multiple statistical indicators is designed to ensure the quality and diversity of the generated data. Qualified samples after quality assessment are added to the original imbalanced dataset to improve the tool breakage classifier's performance. Artificially controlled face milling experiments for TBM are carried out on a five-axis CNC machine to verify the effectiveness of the proposed method. Experimental results reveal that the proposed method outperforms other popular imbalance fault diagnosis methods in terms of data generation quality and TBM accuracy, and can meet the real-time requirements of TBMs., QC 20230901