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CONTEXT: Residents need their supervisors in the operating room to inform them on how to use their expertise in present and future occasions. A few studies hint at such explicit teaching behaviour, however without explaining its underlying mechanisms. Understanding and improving explicit teaching becomes more salient nowadays, as access of residents to relevant procedures is decreasing, while end-terms of training programs remain unchanged: high quality patient care.OBJECTIVES: A structured analysis of 1. The practices supervisors use for explicit teaching and 2. How supervisors introduce explicit teaching in real time during surgical procedures.METHODS: An observational qualitative collection study in which all actions of nine supervisor-resident dyads during a total hip replacement procedure were videotaped. Interactions in which supervisors explicitly or implicitly inform residents how to use their expertise now and in future occasions were included for further analysis, using the iterative inductive process of conversation analysis.RESULTS: 1. Supervisors used a basic template of if/then rules for explicit teaching, which they regularly customized by adding metaphors, motivations, and information about preference, prevalence and consequence. 2. If/then rules are introduced by supervisors to solve a (potential) problem in outcome for the present patient in reaction to local circumstances, e.g. what residents said, did or were about to do.CONCLUSIONS: If/then rules add the why to the what. Supervisors upgrade residents' insights in surgical procedures (professional vision) and teach the degree of individual freedom and variation of their expert standards for future occasions. These insights can be beneficial in improving supervisors' teaching skills.

This article presents a method for structured robust control design for systems with a mixture of parametric and dynamic uncertainty. The proposed method alternates between an analysis step and a synthesis step. Samples of the parametric uncertainty are computed during the analysis steps, thus yielding an array of uncertain systems containing only dynamic uncertainty. The controller is then synthesized on this array of uncertain models. This synthesis step itself involves an alternation between constructing a D-scale for each of the uncertain systems and tuning a single controller for the entire collection of scaled plants. The controller tuning is performed using structured control design techniques. The proposed method is utilized to design a flutter suppression controller for a flexible aircraft. The aircraft dynamics are described by both a high-fidelity and a reduced-order model. The design objectives for flutter suppression are to achieve robust stabilization in the presence of mixed uncertainty. The proposed structured design method yields a single, low-order, linear time-invariant (LTI) controller, which increases the flutter speed by 15%. Additional robustness analyses and high-fidelity simulations are provided to assess the controller performance.

Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection. Design/methodology/approach A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations. Findings The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results. Practical implications The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. Originality/value A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

Knowledge Discovery in Database (KDD) is a structured analysis process aimed at getting new and correct information, finding patterns from complex data, and being useful. Data mining is at the core of the KDD process. Clustering is a data mining method that is suitable for optimizing library services because it can cluster books effectively and efficiently, with the K-Means algorithm data can be clustered and information from each centroid value of each cluster. Library services can optimize the placement of books so that students can quickly find books according to their reading interest more effectively and can be attracted to other books because they are in one grouping. Meanwhile, the library can prioritize the procurement of the next book. Optimization of library services in the cluster using the K-Means method. Clustering interest in reading has the criteria for the number of books available, borrowed books, and the length of time the books are borrowed. The book data is clustered into 3, namely very interested, in demand, and less desirable. After doing the calculation process from 40 samples of book types, it resulted in 6 iterations, and the final results were 3 clustering, namely cluster 1 of 4 books that were of great interest, cluster 2 of 20 books that were of interest, and cluster 3 of 16 books that were less desirable. This research can be used as a recommendation reference for optimizing library services both for the layout and procurement of books by prioritizing the types of books that are of great interest.

Despite its tremendous potential, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Among other issues, there are hard limits to the number of qubits that can be integrated into a single chip. Multi-core architectures are a firm candidate for unlocking the scalability of quantum processors. Nonetheless, the vulnerability and complexity of quantum communications make this a challenging approach. A comprehensive design should imply consolidating the communications stack in the quantum computer architecture. In this paper, we explain how this vision, by entangling communications and computation in the core of the design, may help to solve the open challenges. We also summarize the first results of our application of structured design methodologies backing this vision. With our work, we hope to contribute with design guidelines that may help unleash the potential of quantum computing.

Good patient flow in an acute hospital is concerned with ensuring patients experience minimal delays throughout the hospital journey, from the emergency department to the wards, outpatients and to a suitable discharge destination. Good flow requires effective processes, staff buy-in and staff education. This study aimed to explore ways in which this topic is currently taught in an Irish acute hospital group. Participants were recruited to engage in semi-structured interviews about their experience of teaching patient flow. Following qualitative data analysis using a structured analysis guide, five main themes were identified: current methods, unstructured nature of teaching, frustration with frequency, dissemination of teaching/learning and opportunities for improvement. Recommendations from this study could be used to support a formalised approach to teaching this topic in the future. The use of the Teaching for Understanding framework and Universal Design for Learning principles are strongly advocated to support the development of a nationwide module, to structure the topics to be taught and provide guidance on how to effectively and efficiently teach this topic in Ireland.

Multi-label learning is a machine learning classification problem, in which an example belongs to more than one classes at the same time. Recently, multi-label learning has aroused a great deal of attention, and has achieved great success in the fields of text and image classification. In this paper, we propose a new method for multi-label learning, which is named as analysis discriminative dictionary learning for multi-label classification (ADML). We first incorporate analytical discrimination dictionary learning and sparse representation into multi-label classifier to obtain a unified model. The incoherence promoting term and reconstruction error for each label are minimized to obtain the dictionary. We then incorporate an analysis inconsistency promotion term into the model, which minimizes the reconstruction error of the dictionary with the corresponding label of the data. Further, we calculate a linear classifier by taking the label relationships into account. It is worth noting that we implicitly consider the label relationships in the analysis dictionary and linear classifier. Finally, we conduct experiments on 15 datasets to test the performance of the proposed ADML method and baselines. The results show that the proposed ADML method can deliver higher performance than previous multi-label methods.

if you want to find out that which is the best suitable analysis then you have to find outs all the merits and demerits of that both analysis methods. The aspect of this paper is to describe combine both the traditional (structured) and the object-oriented approach, certain methodology for information systems development. Despite the fact that objects-oriented paradigm is actually widely adopted for software analysis, design, and implementation, there are still a large number of companies that continue to utilize the structured approach to develop software analysis and design. The empirical study that we present considers both an Uncontrolled and a controlled experiment with Master students. with the existing structured approach of developing a system there is significant way to know how appropriate the OO topics. So, in recent time, Objects-orientation analysis is largely acceptable subject. This paper discourses some basics about these two design paradigms.

Crafting systemic engineering fundamentals into a structured design model that includes demand definition, systemic design, and efficient operations enables the railway design process to achieve viable solutions. The design model incorporates types of efficiencies that are fundamental to successfully operating new railway systems. A relationship between the geometrical layout of the new track and the technical efficiency of the dynamic hauling process and fuel consumption was determined. New results were obtained from simulation work done on the operating efficiency of the crossing of trains, and cast new light on the traditional formulae used for track capacity. The effect of total systemic efficiency on the resultant cycle time and rolling stock fleet sizes is demonstrated in this paper. The rail tariff and the internal rate of return (IRR) are interlinked, and the effect of inefficiencies on the performance and returns of the new railway project is illustrated.

The article describes the uses of multi-input/multi-output systems, with the aim of assessment of structural changes and stability in the Russian transport sector. Empirical input-output data for the period between the years 2000 and 2014 are used. A basic model and overview of the literature are given. The article summarizes the assessment of structural changes and stability. The author gives his own vision of a state of the Russian transport sector. The analysis is carried out for the transport sector as a whole as well as for the various transport modes. Analytical and quantitative results are presented. Assessments of the dynamics of several key economic indicators of the Russian transport sector based on the model calculations are discussed. The indicators were compared based on World Input-Output Database (WIOD) version 2016. It is shown that the share of intermediate goods and services in the transport sector’s gross output fell, while the share of final goods increased from 2000 to 2014. But the share of air transport in the transport sector’s gross output as a whole was stable in 2000-2014.

As the Industry 4.0 takes shape, human operators experience an increased complexity of their daily tasks: they are required to be highly flexible and to demonstrate adaptive capabilities in a very dynamic working environment. It calls for tools and approaches that could be easily embedded into everyday practices and able to combine complex methodologies with high usability requirements. In this perspective, the proposed research work is focused on the design and development of a practical solution, called Sophos-MS, able to integrate augmented reality contents and intelligent tutoring systems with cutting-edge fruition technologies for operators' support in complex man-machine interactions. After establishing a reference methodological framework for the smart operator concept within the Industry 4.0 paradigm, the proposed solution is presented, along with its functional and non-function requirements. Such requirements are fulfilled through a structured design strategy whose main outcomes include a multi-layered modular solution, Sophos-MS, that relies on Augmented Reality contents and on an intelligent personal digital assistant with vocal interaction capabilities. The proposed approach has been deployed and its training potentials have been investigated with field experiments. The experimental campaign results have been firstly checked to ensure their statistical relevance and then analytically assessed in order to show that the proposed solution has a real impact on operators' learning curves and can make the difference between who uses it and who does not., 16 pages, 25 figures, 9 tables

Communication, discussion, and file sharing between divisions in a company is something every employee often does. The problem in this research is that not all users are connected smoothly so that it hinders communication and file transfers and in communication between divisions they have to install various applications on cellphones and PC users. This reduces the duration of time and efficiency in work. To solve this problem, a web-based chat application was designed. Because the development of these applications must use a good and structured design. Web-based chat application was created to solve this problem. This web-based application can be accessed via a web browser without having to install the application on a cellphone or laptop user. Based on the design of a web-based chat application that has been created, it is expected to be a solution for discussing, communicating and sharing files between employees in a company. In addition, it is useful to keep in touch and share files without having to install applications on cellphone or laptop users. The result of this research is that this web-based chat application can be a solution to keep communicating between employees who have difficulty accessing the internet.

Purpose: To analyze the strategic position of clinical pharmacy education in Saudi Arabia and ensure that it meets the needs of the current industry in the country.Methods: A comprehensive and structured analysis of strengths, weaknesses, opportunities, and threats (SWOT) was performed with more than 100 clinical pharmacists, academic staff, and students as participants. An internal factor evaluation (IFE), external factor evaluation (EFE), and SWOT matrix were conducted to compare the provisions of the current system with market needs.Results: The analysis involved more than 40 academic staff members, 35 clinical pharmacists, 50 students, and several administrative staff members. The EFE and IFE were calculated as 2.06 and 2.2, respectively, and were lower than the accepted international standard. The internal and external analyses involved 37 and 20 items, respectively. Finally, a SWOT matrix was constructed to help for proposing the strategies. Several strategies were recommended to address eventual shortcomings.Conclusion: The great paradigm shifts in the pharmaceutical market warrant an equal paradigm shift in clinical pharmacy education. More strategic data are needed to further analyze the market in the next five to ten years. Adopting the current educational modality to best match the needs of the pharmaceutical market is also essential. Keywords: Clinical pharmacy, Education, SWOT analysis, Strategic planning

The creative process of Sustainable Product + Service Systems (PSSs) that involve a variety of social, economic and environmental elements is a very complex process that can be characterized as a wicked problem. The following study highlights that, currently, the tools used in PSS assist in the system design process but often explore the design process intuitively based on the designer’s experience. When dealing with complex problems it is important to use a more structured design approach, which involves multidisciplinary teams to explore systematic design techniques. In this sense, a method recognized to assist in the process of creating complex systems is the heuristic. The use of heuristics is considered a cognitive strategy that not only accelerates the creative process, but also generates solutions, regardless of spontaneous creativity. In this article, an analysis of the creative potential of heuristics was carried out with the blueprint tool. The research method used included an unsystematic theoretical survey, followed by an exploratory case study. Therefore, it is intended to contribute to the detection of emphases and gaps in the literature and demonstrate through practical application, the interaction of designers with heuristic principles in the creation process of PSSs.

Design of new technology with children has been widely investigated, and lately several workshops have been organised with children for designing novel IoT or smart objects, e.g., for smart cities or parks. Gradually, the research focus has shifted, from an analysis of the technology itself, which children help create, towards an analysis of children’s possible gains in design workshops. However, in spite of several recent efforts, it is still unclear what happens when children participate in diverse parts of a design process, ranging from ideation to prototyping. This paper investigates it, focussing on children’s gains in relation to their knowledge of design. A structured design workshop can help obtain such gains and analyse their achievements. Therefore this paper presents a structured design workshop with 27 children, aged from 11 to 14 years old, ideating, programming and prototyping smart objects for their town park. The workshops was structured and centred around a board-game with cards, so as to playfully make children reflect about design, and promote their awareness of what they were doing. Data were gathered in relation to children’s reflections from different perspectives, during the workshop, and in relation to their awareness of design, after the workshop. The analysis of the gathered data suggests that the workshop positively affected children’s reflections on design and awareness of design, giving indications for future work concerning critical design with children.

International audience; In dataflow representations for signal processing systems, applications are represented as directed graphs in which vertices represent computations and edges correspond to buffers that store data as it passes among computations. The edges in the dataflow graph are single-input, single-output components that manage data transmission in a first-in, first-out (FIFO) fashion. In this paper, we formulate the vertices and edges into concepts called "active blocks" and "passive blocks", respectively in the graph representation. Computation in the dataflow graph is represented as "active blocks", while the concept of dataflow buffers is represented as "passive blocks". Like dataflow edges, passive blocks are used to store data during the intervals between its production and consumption by actors. However, passive blocks can have multiple inputs and multiple outputs, and can incorporate operations on and rearrangements of the stored data subject to certain constraints. We define a form of flowgraph representation that is based on replacing dataflow edges with the proposed concept of passive blocks. We present a structured design methodology for utilizing this new form of signal processing flowgraph, and demonstrate its application to improving memory management efficiency, and execution time performance.

In the light of the current coronavirus crisis, business-to-business firms face a variety of challenges in a complex and fast-changing environment. In order to provide structured analysis and to guide strategic decision-making, we present a novel, five-step approach for analyzing the impact of a crisis on a firm's business model. We applied the approach with eight business-to-business firms and find support for its usefulness. The evidence suggests very different impacts of the coronavirus crisis on business-to-business firms, and that understanding these differences is important for strategizing during the crisis but also to navigating successfully into the future. We also describe six different types of crisis impacts on business models. We conclude by developing managerial implications and questions for future research., Highlights • We present a novel, five-step approach for analyzing the impact of a crisis on a business model. • We define six different types of crisis impacts on business models. • We develop managerial implications for managing through a crisis by analyzing business model alignment.

Purpose This paper aims to present a new model of marketing analysis that is capable of using the embedded knowledge that sits untapped in the history of marketing thought to solve contemporary marketing problems – the conceptual-historical analytical research model (CHARM). Design/methodology/approach This paper outlines the evolution of historical analysis methods (HAM), along with critiques and enhancements of the prior processes offered by Savitt (1980), Nevett (1991) and Golder (2000). From these foundations, the paper outlines the components of the model of historical analysis, detailing the development of the analytical template design. It also details the four-step process of engaging structured revisits of past knowledge for contemporary problem-solving. Findings The CHARM for problem-solving in marketing is a knowledge-gathering system that informs marketing decisions addressing contemporary problems. This is achieved through the use of embedded knowledge from a corpus of historical texts. Research limitations/implications This paper provides a method for future researchers to apply for replicable examination of historical texts and to assist intercoder reliability for multi-author history projects through the application of structured templates. Practical implications The CHARM for problem-solving in marketing is a knowledge-gathering system that informs marketing decisions addressing contemporary problems. This is achieved through the use of embedded knowledge from a corpus of historical texts. Originality/value The CHARM process applies a systematic protocol for engaging qualitative sources for historical analysis through preset data collection templates, structured analysis frameworks and definitional understanding templates for improved replicability. This paper presents a new model of approaching historical analysis through a problem-solving lens, whereby historical sources become the foundations for the solution to a problem, rather than just the literature review that identifies the presence of gap.

PurposeThe purpose of this paper is to systematically analyse how building information modelling (BIM) transforms complex infrastructure settings (i.e. airports) around digital technologies by enhancing connectivity and collaboration between major stakeholders and construction technology solutions. The objectives include understanding each project party's perspective for BIM implementation to align their motivations and connectivity along project's supply chain network and how BIM drives construction technology ecosystem uses for a more integrative and collaborative project delivery.Design/methodology/approachThe study adopts qualitative data collection and analysis methods comprising semi-structured interviews, thematic analysis and an explanatory case study of a large-hub airport project.FindingsThe study findings show that enabling BIM implementation leads to streamlining construction technology ecosystem uses that increase connectivity within project parties' processes. Airports – as hosting high-value assets – can fast realize value of synergistic activities throughout project delivery by harnessing significant amount of siloed data created by each major party.Originality/valueThe study provides a structured analysis of how complex project settings leverage construction technology uses through their core BIM processes by highlighting multi-party approaches from an ecosystem perspective. This study also contributes to the body of knowledge and practice by presenting a transferrable and scalable approach for leveraging connected construction technology in a large-scale project involving fragmented processes managed by a large number of stakeholders.

Engineer-To-Order (ETO) companies develop complex one-of-a-kind products based on specific customer demands. Given the product uniqueness, the commissioning plays an important role in the product development process. However, the project variety and low data availability hinder the analysis of the commissioning processes. This paper proposes a framework for the structured analysis of commissioning processes in ETO companies by analysing the impacts from product requirements and design on the commissioning performance. A case study presents the practical application of the developed framework.

Celem artykułu jest zastosowanie koncepcji świadka moralnego sformułowanej przez Avishaia Margalita w książce The Ethics of Memory do analizy notatek sporządzonych przez Załmena Gradowskiego podczas jego pracy w Sonderkommando w Auschwitz-Birkenau. Aplikacja tej dość ogólnej, ale wyraźnie zarysowanej, koncepcji do konkretnego a jednocześnie trudnego świadectwa pozwoli z jednej strony doprecyzować istotne aspekty kontekstu, jaki kształtuje świadectwo moralne, z drugiej zaś wydzielić i przeanalizować w systematyczny sposób świadectwo moralne Z. Gradowskiego. Artykuł będzie również okazją do sformułowania uwag i pytań dotyczących koncepcji świadka moralnego, których zaadresowanie pozwoliłoby dookreślić paradygmatycznego świadka moralnego na bardziej szczegółowym poziomie, a tym samym zaproponować ustrukturyzowaną analizę niezwykle subtelnego fenomenu, jakim jest każde świadectwo moralne.

We reexamine the literature comparing contingent valuation (CV) and choice experiments (CEs) in terms of stated preference design features, and empirically investigate an understudied dimension in these comparisons, namely, presentation effects. Structured analysis of the literature suggests that the comparisons of CV and CEs as broad descriptive terms may mask the many design and methodological differences seen in implementations of the approaches. In the empirical component, we find preference disclosure to be unaffected by text and table presentation formats in elicitation tasks, except when only the first task is considered. Implications of our findings for stated preference research are discussed.

Purpose The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters. Design/methodology/approach Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM. Findings It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric. Research limitations/implications This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery. Practical implications The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads. Originality/value This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.

This article reflects on the investigations that have driven research projects, student designs and collective endeavours in Advanced Landscape and Urbanism research group at the University of Greenwich. The article sets out three frames: Firstly, an emphasis on landscapes being socially constructed through interrelations between individuals, people and the worlds they produce. Secondly, an embrace of diversity of landscape approaches, resulting in projects created from unique combinations of experiences, methods, skills and ambitions – but differentiated through focused research projects and structured design studios. Third, an embrace of tensions within and between landscapes, working with the complexities and contradictions that landscapes entail, as much as with the potential agency of landscape relations.

Life-cycle costing (LCC) is gaining increased attention in the architecture, engineering, construction, and operations industry as a vital methodology for assessing the economic sustainability throughout the lifetime of building projects. LCC has recently became part of procurement policies, certification schemes, and national regulations. Despite the increased attention, the concept faces limited adoption in the design practices due to various challenges of implementation. The Industrial PhD research aims to understand and explain the current practices regarding LCC and provide new insights into the challenges and opportunities of integrating LCC in building design.This is an article-based PhD thesis written by Maria Saridaki. The PhD research was conducted in an Industrial PhD program that is partially funded by the Danish architectural company Vandkunsten Architects (www.vandkunsten.com) and the Innovation Fund Denmark.

The fuzzy front end of engineering design can present a difficult challenge, and as such, recent engineering design research has focused on guiding and influencing the way a designer ideates. Early ideation can be especially difficult when attempting to integrate specific design objectives in product design, called Design for X (DfX). This paper presents two experiments exploring the efficacy of a structured Design for the Environment (DfE) design method called the Guidelines and Regulations for Early design for the Environment (GREEn) Quiz that provides designers with sustainable design knowledge during the conceptual design phase. The GREEn Quiz operates on a web-based platform and queries the designer about their design concepts; an end-of-quiz report provides abstract DfE knowledge to designers. While this abstract knowledge was able to be applied by designers in a former study, we hypothesize that providing targeted, specific design strategies during conceptual design will enable novice designers to better integrate DfE. In this study, we created these DfE strategies, integrated these into the GREEn Quiz, and studied the efficacy of these strategies when presented to designers at both the expert and novice levels. Results suggest that respondents with access to the strategy-based GREEn Quiz produced concepts with evidence of a broader range of sustainable design decisions and higher solution quality scores. This work shows the promise of supplemental DfE methods for concept generation to enable the design of more environmentally sustainable products.

The Internet of Things (IoT) is the new paradigm of our times, where smart devices and sensors from across the globe are interconnected in a global grid, and distributed applications and services impact every area of human activity. With its huge economic impact and its pervasive influence over our lives, IoT is an attractive target for criminals, and cybersecurity becomes a top priority for the IoT ecosystem. Although cybersecurity has been the subject of research for decades, the large-scale IoT architecture and the emergence of novel threats render old strategies largely inefficient. Deep learning may provide cutting edge solutions for IoT intrusion detection, with its data-driven, anomaly-based approach and ability to detect emerging, unknown attacks. This survey offers a detailed review of deep learning models that have been proposed for IoT intrusion detection. Solutions have been classified by model in a comprehensive, structured analysis of how deep learning has been applied for IoT cybersecurity and their unique contributions to the development of effective IoT intrusion detection solutions.

Worldwide, research community has studied the benefits of green and blue spaces implementation in urban areas, generating a great amount of literature regarding this topic. Since these solutions are of interest to face climate change impacts in cities, the European Commission (EC) has funded several projects to make an extensive review of the available literature. Three of these projects were especially studied here, namely EKLIPSE, Mapping Assessment of Ecosystem and their Services- Urban Ecosystem (MAES: Urban Ecosystems), and NATure-based URban innoVATION (NATURVATION). They all aim to identify the physical and social impacts, benefits and trade-offs of Nature-Based Solutions (NbS).To objectively compare findings presented in the deliverable reports, a text-mining approach was carried out. This methodology coupled with a data visual representation allowed to convert the EC projects reports (corpus) into a meaningful structured analysis. As a result, a graphical representation was created, making possible to recognize concepts, patterns and attributes addressed by each text, as well as stakeholders and their position with respect to the topic. The text mining analysis was implemented through Gargantex Blue Jasmine Version (an open source software developed by ISC-PIF). Gargantex results permitted to recover a list of key-terms from each corpus based in their co-occurrence in the whole text. These terms were used to elaborate a visual representation or network, placing the words strongly related close to each other and characterizing the obtained clusters by a similar color.This approach underlined the specific focus of each project: the conciliation between urbanisation and urban ecosystems (MAES), or the economic valuation and monetisation of NbS (NATURVATION) for instance. Moreover, it demonstrated that despite the different literature review methodologies of each report/project, there are some common trends exhibited by the obtained graphical networks and their statistical attributes. For instance, the need to assess the NbS performance with some adapted indicators; and the important EC supporting role in the implementation of NbS. Similarly, some regulating (e.g. water quality or temperature reduction) and cultural (e.g. recreation or health benefits) services are more addressed. This analysis can be applied to all kind of corpus, which makes it easy to understand different and similar concepts and approaches of a set of text data. A text-mining analysis can be conducted over the direct references of NbS benefits, on a collection of publications of a research database like Scopus or Science Direct.

Increasing evidence has shown that gene-gene interactions have important effects in biological processes of human diseases. Due to the high dimensionality of genetic measurements, interaction analysis usually suffers from a lack of sufficient information and has unsatisfactory results. Biological network information has been massively accumulated, allowing researchers to identify biomarkers while taking a system perspective, conducting network selection (of functionally related biomarkers), and accommodating network structures. In main-effect-only analysis, network information has been incorporated. However, effort has been limited in interaction analysis. Recently, link networks that describe the relationships between genetic interactions have been demonstrated as effective for revealing multi-scale hierarchical organisations in networks and providing interesting findings beyond node networks. In this study, we develop a novel structured Bayesian interaction analysis approach to effectively incorporate network information. This study is among the first to identify gene-gene interactions with the assistance of network selection, while simultaneously accommodating the underlying network structures of both main effects and interactions. It innovatively respects multiple hierarchies among main effects, interactions, and networks. The Bayesian technique is adopted, which may be more informative for estimation and prediction over some other techniques. An efficient variational Bayesian expectation-maximization algorithm is developed to explore the posterior distribution. Extensive simulation studies demonstrate the practical superiority of the proposed approach. The analysis of TCGA data on melanoma and lung cancer leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

With the advent of commercially available Mixed-Reality(MR)-headsets in recent years MR-assisted learning started to play a vital role in educational research, especially related to STEM (science, technology, engineering and mathematics) education. Along with these developments it seems viable to further frameworks and structured design processes for MR-based learning environments. Instead of a widely applicable framework for designing educational MR applications, we here consider the case of virtually enhancing physical hands-on experiments in STEM, where students are given a certain problem to solve, and how to design these. For this focused realm, we suggest an empirically driven problemand user-centred design process for MR applications to get novices to act more like experts and exemplify it for a specific experiment and problem set containing a non-trivial electric circuit with capacitors and coils., Comment: 15th International Technology, Education and Development Conference

Machine Learning (ML)-enabled software systems have been incorporated in many public demonstrations for automated driving (AD) systems. Such solutions have also been considered as a crucial approach to aim at SAE Level 5 systems, where the passengers in such vehicles do not have to interact with the system at all anymore. Already in 2016, Nvidia demonstrated a complete end-to-end approach for training the complete software stack covering perception, planning and decision making, and the actual vehicle control. While such approaches show the great potential of such ML-enabled systems, there have also been demonstrations where already changes to single pixels in a video frame can potentially lead to completely different decisions with dangerous consequences in the worst case. In this paper, a structured analysis has been conducted to explore video degradation effects on the performance of an ML-enabled pedestrian detector. Firstly, a baseline of applying “You only look once” (YOLO) to 1,026 frames with pedestrian annotations in the KITTI Vision Benchmark Suite has been established. Next, video degradation candidates for each of these frames were generated using the leading video compression codecs libx264, libx265, Nvidia HEVC, and AV1: 52 frames for the various compression presets for color frames, and 52 frames for gray-scale frames resulting in 104 degradation candidates per original KITTI frame and in 426,816 images in total. YOLO was applied to each image to compute the intersection-over-union (IoU) metric to compare the performance with the original baseline. While aggressively lossy compression settings result in significant performance drops as expected, it was also observed that some configurations actually result in slightly better IoU results compared to the baseline. Hence, while related work in literature demonstrated the potentially negative consequences of even simple modifications to video data when using ML-enabled systems, the findings from this work show that carefully chosen lossy video configurations preserve a decent performance of particular ML-enabled systems while allowing for substantial savings when storing or transmitting data. Such aspects are of crucial importance when, for example, video data needs to be collected from multiple vehicles wirelessly, where lossy video codecs are required to cope with bandwidth limitations for example.

Maintaining the functional integrity of ecosystems as climate pressures exceed natural rates of adaptation requires new knowledge and new approaches to governance and management. However, research into management interventions to assist regional ecosystem adaptation has generated both scientific and ethical debate. This paper reviews experience to date in order to identify the challenges and opportunities for assisted regional ecosystem adaptation and reflect on the implications for ongoing adaptation research. The review was informed by a database and structured analysis of some 450 reports, peer-reviewed manuscripts and books on participation theory and experience with novel technology development and assisted ecosystem adaptation. We identified five classes of challenges to adaptation research: 1) scientific conflicts and debates over the “facts”, 2) social challenges, 3) governance challenges, 4) epistemic challenges, and 5) ontological conflicts. We argue that engagement strategies linked to the multiple objectives of adaptation research provide opportunities for ecosystem adaptation.

There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements effectively. Taking the 2021 TAAC competition as an opportunity, we developed a multimodal system to improve the ability of structured analysis of advertising video content. In our framework, we break down the video structuring analysis problem into two tasks, i.e., scene segmentation and multi-modal tagging. In scene segmentation, we build upon a temporal convolution module for temporal modeling to predict whether adjacent frames belong to the same scene. In multi-modal tagging, we first compute clip-level visual features by aggregating frame-level features with NeXt-SoftDBoF. The visual features are further complemented with textual features that are derived using a global-local attention mechanism to extract useful information from OCR (Optical Character Recognition) and ASR (Audio Speech Recognition) outputs. Our solution achieved a score of 0.2470 measured in consideration of localization and prediction accuracy, ranking fourth in the 2021 TAAC final leaderboard., 4 pages; 2 figures; ACM MM 2021 workshop; Tencent Advertising Algorithm Competition ACM Multimedia 2021 Grand Challenge

This paper presents a structured analysis in the area of measurement while drilling (MWD) data processing and verification methods, as well as describes the main nuances and certain specifics of “clean” data selection in order to build a “parent” training database for subsequent use in machine learning algorithms. The main purpose of the authors is to create a trainable machine learning algorithm, which, based on the available “clean” input data associated with specific conditions, could correlate, process and select parameters obtained from the drilling rig and use them for further estimation of various rock characteristics, prediction of optimal drilling and blasting parameters, and blasting results. The paper is a continuation of a series of publications devoted to the prospects of using MWD technology for the quality management of drilling and blasting operations at mining enterprises.

With the increasing number of electric vehicles (EV), the smart transportation system is becoming more closely related to the smart energy system in a smart city. However, because of the high complexity, dynamics, and large-scale of these two systems, it is challenging to study their operation problems systematically, especially in severe conditions, e.g., the 2021 winter Texas power traffic crisis. With artificial intelligence, federated learning, edge computing, and automatic control, a digital twin based smart city is proposed in this paper and it focuses on two applications: smart transportation and smart energy grid. Both systems are presented in a structured design manner with several components in the digital twin system, which also contains many real-implementations of different scenarios. The proposed digital twin of a smart city provides a potential clue for the problems mentioned above.

Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain. However, rich relational information between biological elements is retained in the non-Euclidean biomedical graphs, which is not learning friendly to classic machine learning methods. Graph representation learning aims to embed graph into a low-dimensional space while preserving graph topology and node properties. It bridges biomedical graphs and modern machine learning methods and has recently raised widespread interest in both machine learning and bioinformatics communities. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. To provide a comprehensive and structured analysis and perspective, we first categorize and analyze both graph embedding methods (homogeneous graph embedding, heterogeneous graph embedding, attribute graph embedding) and graph neural networks. Furthermore, we summarize their representative applications from molecular level to genomics, pharmaceutical and healthcare systems level. Moreover, we provide open resource platforms and libraries for implementing these graph representation learning methods and discuss the challenges and opportunities of graph representation learning in bioinformatics. This work provides a comprehensive survey of emerging graph representation learning algorithms and their applications in bioinformatics. It is anticipated that it could bring valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.

Purpose The purpose of this paper is to propose a structured procedure for the capture and evaluation of innovative ideas in the early stages of product development. The procedure is designed to take account of internal and external factors affecting the value of innovative ideas. Design/methodology/approach The proposed procedure is based on divergent and convergent approaches to innovation. Ideas are generated and captured using targeted questions and subsequently evaluated using modifications of the Kano model and failure mode and effect analysis (FMEA). Idea evaluation considers four criteria – delight, originality, market share potential and simplicity – which are used to compute an “idea priority number”. Findings A case study looking at innovations in swimming goggles verified the validity of the proposed procedure. The procedure also supports fact-based decision making and applies a structured perspective to the innovation process that makes it easier to manage. Research limitations/implications The case study made use of a pilot survey in which 32 customers participated. Although the case study was only intended as a demonstration of the method’s use, such a sample size could lead to unreliable results in certain cases. Originality/value Applications of standard or expanded FMEA do not add value to a product because the main purpose of the approach is to prevent failure. The proposed inversion of FMEA logic combined with customer view via the Kano model offers a method for the structured analysis of product innovations. No similar approach to the evaluation of innovations has so far appeared in the literature.