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The peer-to-peer (P2P) economy is an emerging economic model where people can buy and sell, trade and share products and services with strangers online. The social interactions and the economic transactions are facilitated by digital platforms. It is believed that this model may contribute to sustainable development by extending the lifespan of existing products. Previous research has examined the motivations for individuals to participate in the P2P economy, including environmental, social, and economic factors. However, there remains a lack of theoretical consensus and comprehensive conceptualisations of the P2P economy as a whole. In light of previous conceptualisations and our own empirical research, this paper aims to provide a framework for the P2P secondhand practice-as-entity, drawing upon Social Practice Theory (SPT). Based on our empirical inquiries, the developed framework highlights that the P2P second-hand practice-as-entity comprises two main sub-practices, each characterised by shared and non-shared materials, meanings, and competencies. The paper thus has mainly theoretical implications, but also provides a more structured and holistic understanding of how the P2P secondhand economy operates, which is valuable for policymakers and practitioners attempting to promote the economic model for sustainable consumption.

Background: Age-related neurodegenerative diseases are constantly increasing with prediction that in 2050 over 60 % of population will suffer from some level of cognitive impairment. A cure for the Alzheimer's disease (AD) does not exist, so early diagnosis is of a great importance. Machine learning techniques can help in early diagnosis with deep medical data processing, disease understanding, intervention analysis and knowledge dis-covery for achieving better medical decision making. Methods: In this paper, we analyze the dataset consisting of 90 individuals and 482 input features. We investigate the achieved AD prediction performances using seven classifiers and five feature selection algorithms. We pay special focus on analyzing performance by utilizing only a subset of best ranked attributes to establish the minimum amount of input features that ensure acceptable performance. We also investigate the significance of neuropsychological (NP) and neuroradiological (NR) attributes for the AD diagnosis. Results: The accuracy for the whole set of attributes ranged between 66.22 % and 81.00 %, and the weighted average AUROC was between 76.3 % and 95.0 %. The best results were achieved by the naive Bayes classifier and the Relief feature selection algorithm. Additionally, Support Vector Machines classifier shows the most stable results since it depends the least on the feature selection algorithm which is used. As the main result of this paper, we compare the performance of models trained with automatically selected features to models trained with hand-selected features performed by medical experts (NP and NR features). Conclusions: The results reveal that unlike the NR attributes, the NP attributes achieve a good performance that is comparable to the full set of attributes, which suggests that they possess a high predictive power for AD diagnosis.

The peer-to-peer (P2P) economy is an emerging economic model where people can buy and sell, trade and share products and services with strangers online. The social interactions and the economic transactions are facilitated by digital platforms. It is believed that this model may contribute to sustainable development by extending the lifespan of existing products. Previous research has examined the motivations for individuals to participate in the P2P economy, including environmental, social, and economic factors. However, there remains a lack of theoretical consensus and comprehensive conceptualisations of the P2P economy as a whole. In light of previous conceptualisations and our own empirical research, this paper aims to provide a framework for the P2P secondhand practice-as-entity, drawing upon Social Practice Theory (SPT). Based on our empirical inquiries, the developed framework highlights that the P2P second-hand practice-as-entity comprises two main sub-practices, each characterised by shared and non-shared materials, meanings, and competencies. The paper thus has mainly theoretical implications, but also provides a more structured and holistic understanding of how the P2P secondhand economy operates, which is valuable for policymakers and practitioners attempting to promote the economic model for sustainable consumption.

The peer-to-peer (P2P) economy is an emerging economic model where people can buy and sell, trade and share products and services with strangers online. The social interactions and the economic transactions are facilitated by digital platforms. It is believed that this model may contribute to sustainable development by extending the lifespan of existing products. Previous research has examined the motivations for individuals to participate in the P2P economy, including environmental, social, and economic factors. However, there remains a lack of theoretical consensus and comprehensive conceptualisations of the P2P economy as a whole. In light of previous conceptualisations and our own empirical research, this paper aims to provide a framework for the P2P secondhand practice-as-entity, drawing upon Social Practice Theory (SPT). Based on our empirical inquiries, the developed framework highlights that the P2P second-hand practice-as-entity comprises two main sub-practices, each characterised by shared and non-shared materials, meanings, and competencies. The paper thus has mainly theoretical implications, but also provides a more structured and holistic understanding of how the P2P secondhand economy operates, which is valuable for policymakers and practitioners attempting to promote the economic model for sustainable consumption.

The peer-to-peer (P2P) economy is an emerging economic model where people can buy and sell, trade and share products and services with strangers online. The social interactions and the economic transactions are facilitated by digital platforms. It is believed that this model may contribute to sustainable development by extending the lifespan of existing products. Previous research has examined the motivations for individuals to participate in the P2P economy, including environmental, social, and economic factors. However, there remains a lack of theoretical consensus and comprehensive conceptualisations of the P2P economy as a whole. In light of previous conceptualisations and our own empirical research, this paper aims to provide a framework for the P2P secondhand practice-as-entity, drawing upon Social Practice Theory (SPT). Based on our empirical inquiries, the developed framework highlights that the P2P second-hand practice-as-entity comprises two main sub-practices, each characterised by shared and non-shared materials, meanings, and competencies. The paper thus has mainly theoretical implications, but also provides a more structured and holistic understanding of how the P2P secondhand economy operates, which is valuable for policymakers and practitioners attempting to promote the economic model for sustainable consumption.

In a recent paper, the author demonstrated that, in contrast with the prevailing view of eventual gradual regional differentiation from a hypothetical Cretaceous pantropical mangrove belt around the Tethys Sea, the Caribbean mangroves originated de novo in the Eocene after the evolutionary appearance of the first mangrove-forming tree species known for the region, the ancestor of the extant Pelliciera. This paper represents a second step in the analysis of the evolution of Caribbean mangroves dealing with the most important change experienced by these communities, occurring across the Eocenesingle bondOligocene transition (EOT), which is termed here the Caribbean mangrove revolution. This shift consisted of the disappearance of the primeval Pelliciera mangroves and their replacement by mangrove communities dominated by Rhizophora, a newly emerged mangrove tree that still dominates extant Caribbean mangroves. This paper first reviews the available literature on the EOT global disruption (tectonic and paleogeographic reorganizations, ocean circulation, cooling, Antarctic glaciation, sea-level fall) and its regional manifestations in the study area, along with the corresponding biotic responses. This provides the paleoenvironmental framework with which to analyze the EOT mangrove revolution using the >80 pollen records available for the region. In the circum-Caribbean region, cooling of 3-6 °C and a sea-level fall of 67 m were recorded between 33.8 and 33.5 Ma, which led to significant shifts in dispersal pathways and barriers, as well as in marine paleocurrents. Late Eocene mangroves were dominated by the autochthonous Pelliciera (up to 60% of pollen assemblages), while Rhizophora, which likely arrived from the Indo-Pacific region by long-distance dispersal, was absent or very scarce. After the EOT, the situation was radically different, as the mangroves were widely dominated by Rhizophora, and Pelliciera, when present, was a subordinate mangrove element (<10%). At th

Introduction: Contact lens (CL) practice is an ever-changing field with clinical knowledge, techniques and equipment continuously evolving. These new developments are backed with clinical trials and research to ensure that practitioners feel confident that there is an evidence base to support these advances. Evidence-based practice is now a crucial part of CL practice, and its importance also filters down to CL education. For example, lectures are one of the most popular tools for an educator but, is standing at the front of a lecture theatre full of students a more effective way of teaching than providing the same material for students to read by themselves? What evidence exists specific to CL education? Method: An expert panel of educators completed a comprehensive literature review of current evidence of teaching methods in CL training, or if not available then what can be learnt from other health care professional training that could be potentially applicable to CL education. Results: Due to the amount of evidence available in the overall subject area relating to healthcare education, the initial plan of compiling evidence into one narrative review paper was discarded in favour of producing two linked papers. Here, the first paper details definitions of terminology, and also teaching methods. The second paper focuses on assessment and specific clinical training required to attain CL practice competency. In this first paper, no direct evidence of the spreading and benefit of new education strategies evidence such as flipped classrooms, spaced learning, test-enhanced learning, group work, CBL, PBL, TBL, and reflective practice in CL education was found. The only technique that was widely used in the CL field was case reports and the group discussion of them. Nevertheless, the authors found a consensus of opinion from other disciplines that are transferable to CL teaching and could help students meet the intended learning outcomes. Conclusion: There is a small amou

Although locating a company in a tax haven is not illegal per se, it is likely to be part of a scheme purported to erode the tax base or to shift profits to less-taxed jurisdictions. For this reason, this type of location decision is usually targeted by anti-avoidance laws, that can take the form either of specific rules or general standards that, ex-post, sanction or limit the location decision. However, rules entail higher drafting costs and are easy to circumvent whereas standards entail more uncertainty costs. The goal of this paper is to illustrate that the risk of aggressive location decisions can be predicted ex-ante using publicly available data and that this prediction can be used by tax authorities. In the paper, we do two things. First, we use publicly available accounting data for the period 2015–2019 on 4031 group ultimate owners (GUO) of active listed companies resident in one of the 27 European Union countries to predict the probability that these companies would have at least a subsidiary in a tax haven, by spring 2021, as well as the intensity in the use of tax havens. Second, we discuss how this prediction can be used by tax authorities in the context of a new administrative preventive approach that complements the traditional legal approach. This approach can increase welfare by reducing uncertainty, thus increasing investments and economic growth.

The UK government is promoting the Modern Methods of Construction (MMC) by presumption in favour due to outperforming traditional methods in meeting its industry goals. Despite the public construction sector’s recognition of the benefits, MMC uptake across this influential purchasing power is yet far from satisfactory. Such low adoption rates have been linked to MMC firms’ business strategies contributing to public clients’ indecision. To investigate such arguments, the study utilises a questionnaire survey and gathers responses from seventy-four decision-makers of UK-established MMC businesses. Results are then discussed in relation to the contingency theory from a Business Model Canvas (BMC) perspective, revealing the divergence between organisations outperforming others in the public sector, i.e. achieving fit. To the authors’ knowledge, this is the first construction management study to empirically propose the relationships between the nine business model elements of the BMC in relation to a specific market. The findings of this paper suggest the critical role of the Target Customer element in reaching alignment with the external environment by acting as a receiver and provider of information, superseding the importance of solely focusing on communicating added values and extended capabilities. Our findings also suggest that MMC organisations outperforming others in the UK public sector are those who are understanding public clients’ needs the most, and thus, continuously changing their business strategies to sustain alignment with any change in those needs. Overall, the paper proposes a three-dimensional consideration to achieve fit, namely, 1) internal consistency of the elements (inside-out), 2), changing external environment (outside-in), and 3) relationships between the nine elements (inside-in). The study depicts the problematic nature of the internal interrelations responsible for reaching fit in the UK public sector and deepens the understanding of how t

Background: Supervised learning modelling and data-driven optimisation (SLDO) methods have only recently gathered interest in the monoclonal antibody (mAb) platform process development application, but have already demonstrated their advantages over traditional approaches in reducing development costs and accelerating research efforts. With potential usage in multiple unit operations, there is a need for mapping existing SLDO methodologies with the corresponding mAb applications. Methods: We performed a scoping review of mAb process development studies with at least one SLDO method published prior to April 26, 2022. A team of four independent reviewers conducted a search and synthesised characteristics of the eligible studies from four literature databases. Results: We identified 30 relevant studies from 1785 citations and 118 full-text papers. 70% were upstream studies (n = 21), and the majority of papers were published between 2010 and 2022 (n = 27, 90%). Multivariate data analysis (MVDA) techniques were identified as the most common SLDO methods (n = 11), and were typically used to model heterogeneous and high-dimensional bioprocess data. While the main usage of SLDO in process development was predictive modelling, a few studies also focused on data pre-processing, knowledge transfer, and optimisation. Conclusions: Despite the data challenges inherent to the mAb industry, SLDO has been demonstrated to be an efficient solution to some process development use cases such as knowledge transfer, process characterisation, optimisation, and predictive modelling. As biopharmaceutical companies are advancing their digital transformation, SLDO methods will need to be further developed and studied from a more integrative perspective to remain competitive against other platform development approaches.

Human-Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human -centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human-robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human-robot symbiotic relation is evolving with a 5C intelligence - from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self -organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others' operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human-robot flexible production., QC 20230227

In modern manufacturing, vision-based defect recognition is an essential technology to guarantee product quality, and it plays an important role in industrial intelligence. With the developments of industrial big data, defect images can be captured by ubiquitous sensors. And, how to realize accuracy recognition has become a research hotspot. In the past several years, many vision-based defect recognition methods have been proposed, and some newly-emerged techniques, such as deep learning, have become increasingly popular and have addressed many challenging problems effectively. Hence, a comprehensive review is urgently needed, and it can promote the development and bring some insights in this area. This paper surveys the recent advances in vision-based defect recognition and presents a systematical review from a feature perspective. This review divides the recent methods into designed-feature based methods and learned-feature based methods, and summarizes the advantages, disadvantages and application scenarios. Furthermore, this paper also summarizes the performance metrics for vision-based defect recognition methods. And some challenges and development trends are also discussed., QC 20220308

With the increase in the number of avenues for production of data everywhere including logistics, using this data has become an obvious next step. In this paper, we look at the major logistics problems in details as discussed in a seminal work in highlighting the problems in logistics by X. Li in 2014. Together with discussing the problems, we also look at the trends in solving these problems using machine learning. We look at the recent work done in the particular fields using machine learning (ML) and develop the correlation of the trends of using supervised, unsupervised and reinforcement learning in solving these problems. This correlation is developed using a table and a diagram showing ML techniques and the trends in the fields of the problems are discussed. COVID-19 has greatly accelerated these trends. This paper serves as a gentle introduction to ML techniques in the field of logistics for researchers who are new to the field., QC 20211221

The development towards a fourth industrial revolution requires a transformation of industries and integration of technologies. Industry 4.0 was developed to investigate the implications of new technologies in manufacture and production corporations. Construction 4.0 is the corresponding initiative for the architecture, engineering, construction and operation industry. The purpose of this study was to compare papers that address the two concepts to find possible areas of research collaborations and opportunities for knowledge transfer. A literature review was performed of journal papers from 2020 to investigate the current application of Industry and Construction 4.0 in research. The results show several similarities between Industry and Construction 4.0 regarding data and information handling and the need to transform industries. Industry 4.0 had a focus on management and integration of technologies that can create value through entire processes. Construction 4.0 was oriented towards discovering solutions to bridge between different actors participating in a sequential process. The results identify possibilities for interdisciplinary collaborations, knowledge exchange and areas for joint explorations between the two research disciplines., QC 20220112

Fibre collection on screens is here used as a collective name for any preferred or non-preferred deposition of fibres, appearing in e.g. paper manufacturing, recycling of elongated particles or equipment clogging. The fibre collection on screens is typically discussed on basis of the application and may distinguish between fibre collection being a friend or foe of the process of interest. We report an extensive experimental investigation of fibre collection and provide a systematic discussion based on two parameters describing the screen geometry: the fibre length to the screen opening size (L-Fibre/D-Open), and fibre length to the distance between the openings (L-Fibre/S-Open). The first parameter, L-Fibre/D-Open, discriminates between the two fibre collection modes: (i) fibre retention (L-Fibre/D-Open, for example paper forming) and (ii) fibre stapling (small L-Fibre/D-Open, for example deposition on pins and edges). The second parameter L-Fibre/S-Open controls the fibre collection rate for both modes (with higher collection rates for higher values), but through different physics. In fibre retention, the successful collection is probabilistic and large for small screen openings and a fibre-orientation parallel to the screen. Since a decrease of L-Fibre/S-Open results in a smaller open area and hence to higher acceleration of the suspension upstream of the screen, the fibre orientation is skewed towards a screen normal orientation and fibres tend to pass through the holes. In the case of fibre stapling, the successful collection comes from an immobilization of the fibre when fibre-solid friction force exceeds the hydrodynamic drag force for fibres deposited close to the edge of the holes. For L-Fibre/S-Open above 1 a fibre bends over the solid hole spacing and is fixated on two support points. For L-Fibre/S(Open )below 1 restrains a successful fibre fixation on two support points why fibre collection on the solid screen is hindered and prevented. The impact of the, QC 20210203

We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-agent (student), but cannot directly transfer its knowledge to the student. For example, the teacher's knowledge of the system might be abstract or the teacher and student might be employing different model classes, which renders the teacher's parameters uninformative to the student. In this paper, we propose correctional learning as an approach to the above problem: suppose that in order to assist the student, the teacher can intercept the observations collected from the system and modify them to maximize the amount of information the student receives about the system. We formulate a general solution as an optimization problem, which for a multinomial system instantiates itself as an integer program. Furthermore, we obtain finite-sample results on the improvement that the assistance from the teacher results in (as measured by the reduction in the variance of the estimator) for a binomial system. In numerical experiments, we illustrate the proposed algorithms and verify the theoretical results that have been derived in the paper., QC 20211022

This paper addresses the issue of data injection attacks on actuators in control systems. Considering attacks that aim at maximizing impact while remaining undetected, the paper revisits the recently proposed output-to-output gain, which is compared to classical sensitivity metrics such as H-infinity and H_. In its original formulation, the output-to-output gain is unbounded for strictly proper systems. This limitation is further investigated and addressed by modifying the performance output of the system and ensuring that the system from attack signal to performance output is also strictly proper. With this system description, and by using the theory of dissipative systems, a Bi-linear Matrix Inequality (BMI) is formulated for system design. Using this BMI, a design algorithm is proposed based on the heuristic of alternating minimization. Through numerical simulations of the proposed algorithm, it is found that the output-to-output gain presents advantages over the other metrics: the effect of the attack is reduced in the performance output and increased in the detection output in a relatively large spectrum of frequencies.

This paper addresses the issue of data injection attacks on actuators in control systems. Considering attacks that aim at maximizing impact while remaining undetected, the paper revisits the recently proposed output-to-output gain, which is compared to classical sensitivity metrics such as H-infinity and H_. In its original formulation, the output-to-output gain is unbounded for strictly proper systems. This limitation is further investigated and addressed by modifying the performance output of the system and ensuring that the system from attack signal to performance output is also strictly proper. With this system description, and by using the theory of dissipative systems, a Bi-linear Matrix Inequality (BMI) is formulated for system design. Using this BMI, a design algorithm is proposed based on the heuristic of alternating minimization. Through numerical simulations of the proposed algorithm, it is found that the output-to-output gain presents advantages over the other metrics: the effect of the attack is reduced in the performance output and increased in the detection output in a relatively large spectrum of frequencies.

This paper addresses the issue of data injection attacks on actuators in control systems. Considering attacks that aim at maximizing impact while remaining undetected, the paper revisits the recently proposed output-to-output gain, which is compared to classical sensitivity metrics such as H-infinity and H_. In its original formulation, the output-to-output gain is unbounded for strictly proper systems. This limitation is further investigated and addressed by modifying the performance output of the system and ensuring that the system from attack signal to performance output is also strictly proper. With this system description, and by using the theory of dissipative systems, a Bi-linear Matrix Inequality (BMI) is formulated for system design. Using this BMI, a design algorithm is proposed based on the heuristic of alternating minimization. Through numerical simulations of the proposed algorithm, it is found that the output-to-output gain presents advantages over the other metrics: the effect of the attack is reduced in the performance output and increased in the detection output in a relatively large spectrum of frequencies.

This paper addresses the issue of data injection attacks on actuators in control systems. Considering attacks that aim at maximizing impact while remaining undetected, the paper revisits the recently proposed output-to-output gain, which is compared to classical sensitivity metrics such as H-infinity and H_. In its original formulation, the output-to-output gain is unbounded for strictly proper systems. This limitation is further investigated and addressed by modifying the performance output of the system and ensuring that the system from attack signal to performance output is also strictly proper. With this system description, and by using the theory of dissipative systems, a Bi-linear Matrix Inequality (BMI) is formulated for system design. Using this BMI, a design algorithm is proposed based on the heuristic of alternating minimization. Through numerical simulations of the proposed algorithm, it is found that the output-to-output gain presents advantages over the other metrics: the effect of the attack is reduced in the performance output and increased in the detection output in a relatively large spectrum of frequencies.

This paper addresses the issue of data injection attacks on actuators in control systems. Considering attacks that aim at maximizing impact while remaining undetected, the paper revisits the recently proposed output-to-output gain, which is compared to classical sensitivity metrics such as H-infinity and H_. In its original formulation, the output-to-output gain is unbounded for strictly proper systems. This limitation is further investigated and addressed by modifying the performance output of the system and ensuring that the system from attack signal to performance output is also strictly proper. With this system description, and by using the theory of dissipative systems, a Bi-linear Matrix Inequality (BMI) is formulated for system design. Using this BMI, a design algorithm is proposed based on the heuristic of alternating minimization. Through numerical simulations of the proposed algorithm, it is found that the output-to-output gain presents advantages over the other metrics: the effect of the attack is reduced in the performance output and increased in the detection output in a relatively large spectrum of frequencies.

The paper presents the problem of distributing potential games over communication graphs. Suppose a potential game can be designed for a group of agents (players) where each has access to all others' actions (strategies). The paper shows how to design a corresponding potential game for these agents if the full information assumption is replaced with communication over a network depicted by undirected graphs with certain properties. A state-based formulation for potential games is utilized. This provides degrees of freedom to handle the previous information limitation. Notions of Nash's equilibria for the developed game (called here distributed potential game) are presented, and relations between these equilibria and those of the full information game are studied. In part II of the paper learning Nash equilibria for the newly developed game is studied. The development focuses on providing a way to utilize available algorithms of the full information game. The motivation for the results comes from a platoon matching problem for heavy duty vehicles. Utilizing the newly developed distributed game, recent results based on potential games can be extended, providing a basis for an on-the-go strategy where platoon matching on road networks can be solved locally., QC 20210802

This study deals with security issues in dynamical networked control systems. The goal is to establish a unified framework of the attack detection stage, which includes the four processes of monitoring the system state, making a decision based on the monitored signal, disconnecting the corrupted subsystem, and operating the remaining system during restoration. This paper, in particular, considers a disconnection-aware attack detector design problem. Traditionally, observer-based attack detectors are designed based on the system model with a fixed network topology and cannot cope with a change of the topology caused by disconnection. The disconnection-aware design problem is mathematically formulated and a solution is proposed in this paper. A numerical example demonstrates the effectiveness of the proposed detector through an inverter-based voltage control system in a benchmark model., QC 20210629

This paper presents methods for experimental design, control, and optimization of perfusion bioreactors as well as the vision for their integration. After describing a generic model of perfusion bioreactors, the paper proposes a control scheme via rate estimation and feedback linearization with useful properties with respect to steady-state error, stability, and performance. The paper also introduces data-driven and hybrid procedures for experimental design that are tailored to the intended use of the model for steady-state optimization. Lastly, the methods are illustrated via a simulation example of a perfusion bioreactor., QC 20210720

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

Containerised freight is a key component of global and urban trade. Empty containers are a necessary outcome of such trade and have operational costs close to that of full containers yet are not a source of revenue as the latter is. A literature review revealed regional repositioning to be a complex problem due to a multitude of interacting parts and distributed decision making among stakeholders which have conflicting objectives. The goal is to optimise the activities related to urban goods movements and reduce the total social cost of these activities. The purpose of this study is to develop a detailed description and understanding of the empty container movements in Melbourne using a modelling approach designed to represent complex systems. This paper explores the cause and effects of the land-side empty container repositioning. Then the key characteristics of the system were captured into a conceptual model that documents and articulates the logistics processes, interactions, power-relations, problems and potential solutions of the freight system. The conceptual model was translated into a simulation model in the form of an agent-based model. The agent-based model was a proof-of-concept which provided an opportunity to graphically imitate the logistics processes and interactions of the system in a natural, flexible and dynamic way. Empty containers represent a sizeable proportion of logistics activity, however previous studies on container freight operations in Melbourne have tended to focus on commercially valuable, loaded containers - not empty containers. This paper closes the gap in understanding of empty container management in Melbourne as represented in a concept model and agent-based model.

In the context of implementing the Nagoya Protocol regarding access to genetic resources, diffusing updated BioTrade Principles by UNCTAD, and largely spreading reflections on global value chains (GVC), cosmetic companies involved in corporate social responsibility (CSR) strategies are eager to build sustainable and responsible supply chains for their raw materials. In southern countries, the “green gold” assertion brings hope to bioresource valorizations as development opportunities, but also suspicion regarding foreign interest. Building a value chain there requires a clear understanding of local situations. This paper contributes to the field of value-chain analyses through a feedback study of the failed implementation of a plant supply chain in an overseas French territory. Located in South America, French Guiana is a European Union outermost enclave in Amazonia. It has rich biodiversity associated with a wide range of traditional knowledge. Ten years ago, a project to build a sustainable supply chain with Piper marginatum was implemented there. Based on recent interviews with the actors involved in the project, this paper retraces the project chronology, describes the obstacles it met, and analyses its limitations and its perception by those participating in it. This value-chain project was built under the principles of BioTrade, but it still failed. Lack of proper monitoring, quality control, local support and long-term commitment of all partners resulted in its rapid abandonment. This paper outlines recommendations for future value chains in general and more specifically on French Guiana biodiversity.

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

Several studies have shown that the school type at release can significantly influence the amplitude of the movements of a tagged fish. In particular, it has been shown that seamounts can have a retention effect on tuna, suggesting that their migration patterns may differ significantly depending on the school type at release. However, many tuna releases near well-recognised mid-Atlantic seamounts have not been well coded in the ICCAT-AOTTP database. In addition, the 'Free School' type modality was often assigned to individuals tagged or recaptured near a seamount. This paper explored different criteria for identifying seamounts in the tagging database with the aim to correct the questionable coding of certain school types. To do this, we first investigated how to identify "attractive" seamounts based on their morphology and the distribution of catch data from the European purse seine fleet. This conceptual approach led to several problems discussed in this paper and from which we proposed a simple recoding method using the literature results. The correction criteria made it possible to correct the school type of 4.9% of several records of tagged tuna during the ICCAT AOTTP programme.

This is the third in a series of papers that provides an historical record in this journal of contributions made by the most influential figures in the field of fluency disorders. The paper reflects on the long and productive career of Barry Guitar, documenting his outstanding achievements. The paper is based on interviews with him during 2022. Like no one else in our field, Barry Guitar has an understanding of the experience of stuttering and how to cope with it, and, throughout his career, he has used that understanding to inspire others to cope with it.

Synthetic gene circuit behaviour can be qualitatively different from the intended one due to effects from unintended interactions with the host cell. Such unintended interactions are essentially inevitable, since the circuits rely on the cellular resources in the host cell. The paper considers the feasibility of reconstructing such unintended interactions from time-series perturbation experiments. Identifying the specific interactions with the host cell is a prerequisite to mitigate their effects on the circuit behaviour. Previously proposed methods either cannot distinguish such unintended interactions from the intended direct interactions between circuit genes or require perturbing all the circuit genes by unique perturbations in separate experiments, which typically imply very costly experiments. We show herein that it in principle is feasible to identify all unintended interactions with the host cell from one single perturbation experiment. We also discuss fundamental limitations of steady-state data. The results are illustrated using a recently implemented three-gene cascade synthetic circuit in E. coli., QC 20230425

A challenge to apply deep learning-based computer vision technologies for assembly quality inspection lies in the diverse assembly approaches and the restricted annotated training data. This paper describes a method for overcoming the challenge by training an unsupervised domain adaptive object detection model on annotated synthetic images generated from CAD models and unannotated images captured from cameras. On a case study of pedal car front-wheel assembly, the model achieves promising results compared to other state-of-the-art object detection methods. Besides, the method is efficient to implement in production as it does not require manually annotated data., QC 20230525

Flexible and safe human-robot collaboration depends on accurately capturing the three-dimensional motion of humans and robots in the field of smart manufacturing. In this paper, a novel approach to developing a human-robot collaborative assembly system is proposed and applied to the field of digital twins. Within the context, a deep learning-based model is explored to develop a depth camera-based human recognition system for accurate prediction of key points for human skeletons model and high-precision human localisation in a human-robot collaborative setting. After the functional mapping of robot calibration, a collision warning module leverages coordinates of key human-robot points to facilitate efficient and safe human-robot collaborative assembly., QC 20230328

The paper considers continuous-time networked susceptible-infected-susceptible (SIS) diseases spreading over a population. Each agent represents a sub-population and has its own healing rate and infection rate; the state of the agent at a time instant denotes what fraction of the said sub-population is infected with the disease at the said time instant. By taking account of the changes in behaviors of the agents in response to the infection rates in real-time, our goal is to devise a feedback strategy such that the infection level for each agent strictly stays below a pre-specified value. Furthermore, we are also interested in ensuring that the closed-loop system converges either to the disease-free equilibrium or, when it exists, to the endemic equilibrium. The upshot of devising such a strategy is that it allows health administration officials to ensure that there is sufficient capacity in the healthcare system to treat the most severe cases. We demonstrate the effectiveness of our controller via numerical examples. Copyright, QC 20230309

In this paper, we analyze the problem of optimally allocating resources in a distributed and privacy-preserving manner. We propose a novel distributed optimal resource allocation algorithm with privacy-preserving guarantees, which operates over a directed communication network. Our algorithm converges in finite time and allows each node to process and transmit quantized messages. Our algorithm utilizes a distributed quantized average consensus strategy combined with a privacy-preserving mechanism. We show that the algorithm converges in finite-time, and we prove that, under specific conditions on the network topology, nodes are able to preserve the privacy of their initial state. Finally, to illustrate the results, we consider an example where test kits need to be optimally allocated proportionally to the number of infections in a region. It is shown that the proposed privacy-preserving resource allocation algorithm performs well with an appropriate convergence rate under privacy guarantees. Copyright, QC 20230309

A challenge to apply deep learning-based computer vision technologies for assembly quality inspection lies in the diverse assembly approaches and the restricted annotated training data. This paper describes a method for overcoming the challenge by training an unsupervised domain adaptive object detection model on annotated synthetic images generated from CAD models and unannotated images captured from cameras. On a case study of pedal car front-wheel assembly, the model achieves promising results compared to other state-of-the-art object detection methods. Besides, the method is efficient to implement in production as it does not require manually annotated data., QC 20230525

A challenge to apply deep learning-based computer vision technologies for assembly quality inspection lies in the diverse assembly approaches and the restricted annotated training data. This paper describes a method for overcoming the challenge by training an unsupervised domain adaptive object detection model on annotated synthetic images generated from CAD models and unannotated images captured from cameras. On a case study of pedal car front-wheel assembly, the model achieves promising results compared to other state-of-the-art object detection methods. Besides, the method is efficient to implement in production as it does not require manually annotated data., QC 20230525

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.

A challenge to apply deep learning-based computer vision technologies for assembly quality inspection lies in the diverse assembly approaches and the restricted annotated training data. This paper describes a method for overcoming the challenge by training an unsupervised domain adaptive object detection model on annotated synthetic images generated from CAD models and unannotated images captured from cameras. On a case study of pedal car front-wheel assembly, the model achieves promising results compared to other state-of-the-art object detection methods. Besides, the method is efficient to implement in production as it does not require manually annotated data., QC 20230525

A challenge to apply deep learning-based computer vision technologies for assembly quality inspection lies in the diverse assembly approaches and the restricted annotated training data. This paper describes a method for overcoming the challenge by training an unsupervised domain adaptive object detection model on annotated synthetic images generated from CAD models and unannotated images captured from cameras. On a case study of pedal car front-wheel assembly, the model achieves promising results compared to other state-of-the-art object detection methods. Besides, the method is efficient to implement in production as it does not require manually annotated data., QC 20230525

Digital forensic examiners and stakeholders face increasing challenges during the investigation of Internet of Things (IoT) environments due to the heterogeneous nature of the IoT infrastructure. These challenges include guaranteeing the integrity of forensic evidence collected and stored during the investigation process. Similarly, they also encounter challenges in ensuring the transparency of the investigation process which includes the chain-of-custody and evidence chain. In recent years, some blockchain-based secure evidence models have been proposed especially for IoT forensic investigations. These proof-of-concept models apply the inherent properties of blockchain to secure the evidence chain of custody, maintain privacy, integrity, provenance, traceability, and verification of evidence collected and stored during the investigation process. Although there have been few prototypes to demonstrate the practical implementation of some of these proposed models, there is a lack of descriptive review of these blockchain-based IoT forensic models. In this paper, we report a comprehensive Systematic Literature Review (SLR) of the latest blockchain-based IoT forensic investigation process models. Particularly, we systematically review how blockchain is being used to securely improve the forensic investigation process and discuss the efficiency of these proposed models. Finally, the paper highlights challenges, open issues, and future research directions of blockchain technology in the field of IoT forensic investigations.

The case for CBDC does not just rest on its benefits or attractions, but also on its scope to avert potential problems associated with stablecoin. In this brief paper I critique the ‘illustrative scenario’ at the heart of the Bank of England’s recent New Forms of Digital Money discussion paper. I suggest that it does not deal realistically with the motives and practices of issuers. The argument speaks to contradictory trends both in the urgency of regulation of stablecoin and the underlying case for CBDC.

Several studies have shown that the school type at release can significantly influence the amplitude of the movements of a tagged fish. In particular, it has been shown that seamounts can have a retention effect on tuna, suggesting that their migration patterns may differ significantly depending on the school type at release. However, many tuna releases near well-recognised mid-Atlantic seamounts have not been well coded in the ICCAT-AOTTP database. In addition, the 'Free School' type modality was often assigned to individuals tagged or recaptured near a seamount. This paper explored different criteria for identifying seamounts in the tagging database with the aim to correct the questionable coding of certain school types. To do this, we first investigated how to identify "attractive" seamounts based on their morphology and the distribution of catch data from the European purse seine fleet. This conceptual approach led to several problems discussed in this paper and from which we proposed a simple recoding method using the literature results. The correction criteria made it possible to correct the school type of 4.9% of several records of tagged tuna during the ICCAT AOTTP programme.