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Plant operators and policy makers frequently use energy benchmarking to assess the potential for reducing energy consumption from industrial plants. As benchmarking studies require considerable resources and the cooperation of plant operators it is tempting to try to merge or compare data from different studies. This paper reviews published benchmarks and energy-saving estimates from the paper and pulp industries to explore how comparable data from independent studies are. A literature review was conducted which identified that benchmarks were either produced through a top-down process using annual production and fuel consumption data or through a bottom-up process from process-level data. It was concluded that top-down benchmarks are useful in measuring national trends but are of little value to individual plants. For common process such as Kraft pulp production it is possible to compare values from different studies but only if sufficient information is given in the original studies to confirm that their scope is identical. However, it is unlikely that improvement rates in energy use can be inferred from the difference between studies that use different sources, as the degree of disagreement between contemporary studies is of the same order as the identified potential energy savings. Benchmarking studies were found to provide good summaries of potential technological improvements although there is some inconsistency in estimations of potential impacts. [ABSTRACT FROM AUTHOR]

A new shell element has been proposed for geometrically non-linear, finite element analyses of axially moving paper web. Web instability problems of paper travelling in a printing system, such as wrinkling and fluttering, pose special challenges in the numerical analyses. For examples, these are finite bending stiffness (as opposed to membrane), orthotropy, and non-uniform distributions of mechanical and hygro-properties (in different length scales). The new formulation has accounted for paper transport velocity and paper bending stiffness based on a mixed Lagrangian–Eulerian description of the motion. A natural coordinate system has been employed in the analytical and finite element formulations. Benchmarking with different finite elements in various tests showed that the proposed element is indeed more stable and reliable for the chosen application than existing elements. The usability of the shell element has been successfully demonstrated by two example problems: wrinkling of stretched isotropic and orthotropic membranes, and the vibration of a web showing a speed-tensioning effect above a critical web transport velocity. [Copyright &y& Elsevier]

Highlights: [•] Investigates benchmarked performance of global and emerging knowledge cities. [•] Introduces a knowledge-based urban development performance assessment model. [•] Applies the assessment model into an international comparative study. [•] Reveals insights on scrutinizing the development perspectives of knowledge cities. [ABSTRACT FROM AUTHOR]

• Good documentation practice is the foundation for promoting medication safety in young population. • Study identified poor documentation practice through paper and electronic means of medical records. • Newly introduced electronic medical records did not improve the rates of metabolic monitoring nor the quality of documented monitoring. • Youth are at significant risk for antipsychotic induced metabolic syndrome the repercussion of which may impact years of productivity. • Potentially electronic health records could provide health workers with real-time information access, and develop accurate, relevant and structured information thereby adhering to clinical evidence. Since the development of digital records, claims have been made that they improve audits. Clinical audits play important role in evaluation of evidence-practice gaps. Antipsychotic medications are one of the commonly prescribed group of drugs in severe adverse mental conditions. Youth and young people are highly prone to develop drug induced metabolic syndrome. Present study evaluated the extent of data documentation on evidence for metabolic monitoring of antipsychotics and compared paper based to electronic records with good documentation standards. First phase of this study involved a retrospective clinical audit of paper-based documentation on the extent of documentation of weight (primary outcome); lipid and blood glucose (secondary outcomes) of youth prescribed atypical antipsychotics. This was undertaken in three public mental health clinics and a public/private developmental service in Australia based on paper-based documentation. The second phase included auditing electronic data capture from one community clinic. Evidence of documentation was compared with practice standards and published clinical audits (adherence rate benchmark: 40–60%). A total of 310 cases were assessed of which 51 and 37 cases met the eligibility criteria for paper-based and electronic based audit respectively as a component of clinical audit. Evidence of paper documentation of weight was 43% among participants and was comparable with other published clinical audits (p = 0.07) with poor monitoring rates for other blood tests. Findings revealed poor rate of documentation at 35.1% (13 cases), 5.4% (2 cases) and 8.1% (3 cases) for weight, lipid assessments and glucose monitoring, respectively based on electronic records. Present study demonstrate lack of good documentation practices on metabolic monitoring of youth prescribed antipsychotics. It appears transitioning from paper to electronic records did not impact the rate of increase in documentation of metabolic monitoring. This study recommends inclusion of e-monitoring icon with built in metabolic monitoring chart as a component of youth prescribed antipsychotic case records. Good documentation practice is a first step in determination of causality of antipsychotics induced metabolic syndrome. Appropriate strategies to a user-friendly electronic reminder system will be crucial to address on the mechanistic of documentation. [ABSTRACT FROM AUTHOR]

Objectives: To synthesize the findings of cognitive interview and usability studies performed to assess the measurement equivalence of patient-reported outcome (PRO) instruments migrated from paper to electronic formats (ePRO), and make recommendations regarding future migration validation requirements and ePRO design best practice.Methods: We synthesized findings from all cognitive interview and usability studies performed by a contract research organization between 2012 and 2015: 53 studies comprising 68 unique instruments and 101 instrument evaluations. We summarized study findings to make recommendations for best practice and future validation requirements.Results: Five studies (9%) identified minor findings during cognitive interview that may possibly affect instrument measurement properties. All findings could be addressed by application of ePRO best practice, such as eliminating scrolling, ensuring appropriate font size, ensuring suitable thickness of visual analogue scale lines, and providing suitable instructions. Similarly, regarding solution usability, 49 of the 53 studies (92%) recommended no changes in display clarity, navigation, operation, and completion without help. Reported usability findings could be eliminated by following good product design such as the size, location, and responsiveness of navigation buttons.Conclusions: With the benefit of accumulating evidence, it is possible to relax the need to routinely conduct cognitive interview and usability studies when implementing minor changes during instrument migration. Application of design best practice and selecting vendor solutions with good user interface and user experience properties that have been assessed in a representative group may enable many instrument migrations to be accepted without formal validation studies by instead conducting a structured expert screen review. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose lazy bagging (LB), which builds bootstrap replicate bags based on the characteristics of test instances. Upon receiving a test instance , LB trims bootstrap bags by taking into consideration ''s nearest neighbors in the training data. Our hypothesis is that an unlabeled instance''s nearest neighbors provide valuable information to enhance local learning and generate a classifier with refined decision boundaries emphasizing the test instance''s surrounding region. In particular, by taking full advantage of ''s nearest neighbors, classifiers are able to reduce classification bias and variance when classifying . As a result, LB, which is built on these classifiers, can significantly reduce classification error, compared with the traditional bagging (TB) approach. To investigate LB''s performance, we first use carefully designed synthetic data sets to gain insight into why LB works and under which conditions it can outperform TB. We then test LB against four rival algorithms on a large suite of 35 real-world benchmark data sets using a variety of statistical tests. Empirical results confirm that LB can statistically significantly outperform alternative methods in terms of reducing classification error. [Copyright &y& Elsevier]

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to MEF. However, although many efforts have been made on developing MEF algorithms, the lack of benchmarking studies makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus hindering the development of this field significantly. In this paper, we fill this gap by proposing a benchmark of multi-exposure image fusion (MEFB), which consists of a test set of 100 image pairs, a code library of 21 algorithms, 20 evaluation metrics, 2100 fused images, and a software toolkit. To the best of our knowledge, this is the first benchmarking study in the field of MEF. This paper also gives a literature review on MEF methods with a focus on deep learning-based algorithms. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare the performance of MEF algorithms. • A literature review on multi-exposure image fusion is presented. • A multi-exposure image fusion benchmark is proposed. • The benchmark integrates a test set, algorithms, evaluation metrics, and a toolkit. • Comprehensive performance comparison is conducted and presented. [ABSTRACT FROM AUTHOR]

• A comprehensive benchmarking study on the performance of continuum models. • Critical discussions on the performance of the models regarding each of the benchmarking criteria. • Models that explicitly account for congestion wave speed in their governing equations perform robustly. • Issues in the calibration procedure are investigated. • The performance index designed for calibration purposes is not a suitable measure for evaluating the real-world performances. Numerous continuum models have been proposed for freeway traffic, but their performance for real-world traffic flows has not been rigorously evaluated and compared in the literature. This paper conducts a comprehensive benchmarking study on single-pipe continuum models by using traffic data from the German A5 autobahn. Model families are examined, and the best representative models are selected within the families. A set of benchmarking criteria is designed, ranging from the operational measures (e.g., delay and travel time) as well as the complex traffic phenomena (i.e., oscillations, capacity drop, and hysteresis phenomena). Suitable real-world traffic scenarios are carefully selected concerning the benchmarking criteria, and the selected models are comprehensively assessed for real-world scenarios. The paper comprehensively discusses the performances of each model regarding each aspect of the benchmarking criteria. [ABSTRACT FROM AUTHOR]

Background: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds.Methods: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. Daily ICU ward rounds were observed during two periods of 8 weeks. We compared paper checklists (control) with a dynamic (digital) clinical checklist (DCC, intervention). The primary outcome was compliance with best clinical practice, measured as the percentages of checked items and unchecked critical items. Secondary outcomes included ICU stay and the usability of digital checklists. Data are presented as median (interquartile range).Results: Clinical characteristics and severity of critical illness were similar during both control and intervention periods of study. A total of 36 clinicians visited 197 patients during 352 ward rounds using the paper checklist, compared with 211 patients during 366 ward rounds using the DCC. Per ICU round, a median of 100% of items (94.4-100.0) were completed by DCC, compared with 75.1% (66.7-86.4) by paper checklist (P=0.03). No critical items remained unchecked by the DCC, compared with 15.4% (8.3-27.3) by the paper checklist (P=0.01). The DCC was associated with reduced ICU stay (1 day [1-3]), compared with the paper checklist (2 days [1-4]; P=0.05). Usability of the DCC was judged by clinicians to require further improvement.Conclusions: A digital checklist improved compliance with best clinical practice, compared with a paper checklist, during ward rounds on a mixed ICU.Clinical Trial Registration: NCT03599856. [ABSTRACT FROM AUTHOR]

Containers offer an efficient solution allowing the application's isolation. Most of the papers dealing with the container's isolation focus on benchmarking container's solutions. However, in this study, we focus on a static comparison of different features proposed by container solutions. First, we will focus on the container's runtimes, then, we present the different solutions used in the study. The most common container solutions are compared, such as LXC, LXD, Singularity, Docker, Kata-containers, and gVisor. We consider container features as isolation, storage, network, and security capabilities. For each container feature, all container solutions will be compared to find the most efficient one. Finally, this paper compares the different default container configurations and attempts to find the most efficient container solution based on all compared features. [ABSTRACT FROM AUTHOR]

This research makes a focus on evolutionary computing environments i.e. Pervasive, Internet of Things, Artificially Intelligent (AI) and Big Data risks management. The contribution of paper lies in identifying, managing risks in advanced computing paradigms, mitigating security risks and benchmarking smart softwares. As the security and privacy of big volume data is heated discussion these days due to several vulnerabilities in data posed by its pervasive nature so the paper has implemented the security risks mitigation in big data projects. In addition, the benchmarking of pervasive software is done with the help of an intelligent tool for detecting application frameworks and security vulnerabilities. The application is scanned, and the intelligent tool quantifies the severity levels to provide the possible solutions. This tool also collects application metrices to benchmark it against technology, application business drivers and properties to improve the quantitative Performance of the software. This research also puts an analytical foundation for various risks management concepts in evolutionary environments i.e. Pervasive, AI, Internet of Things and Big Data. [ABSTRACT FROM AUTHOR]

Highlights • Review the existing data sources of US commercial building energy usage. • Compare energy results from survey and simulation data for US commercial buildings. • Discuss the different applications of existing data sources for US commercial building energy. • Provide the cases of applications to guide the users to use the data sources. Abstract U.S. commercial buildings consumed around 18% of total primary energy in 2017 and a 2.23 EJ increase is expected by 2050. Energy-related data for commercial buildings can be used for various applications, including benchmarking, building component analysis, market potential analysis, and policy making. Although there are plenty of data sources for energy usage in commercial buildings, they have not been thoroughly reviewed and summarized. As a result, users do not have comprehensive guidelines about selections of right data sources for specific application needs. To fill this gap, this paper conducts a comprehensive review to summarize data sources for energy usage in U.S. commercial buildings and discuss their usages for different applications. First, the paper summarizes the survey and simulation data sources for energy usage. The data sources are compared in terms of their data collection methods, released information, and relevant features. Second, this paper analyzes the applications for different survey and simulation data sources. This review categorizes the applications of data sources into five categories, including energy performance benchmarks, energy use forecasts and predictions, energy use contributions of building components, supports of building energy policies, and urban-scale energy use analysis. Moreover, the paper introduces several cases to demonstrate the usages of these data sources. [ABSTRACT FROM AUTHOR]

Abstract Nuclear Decommissioning Projects and Programmes (NDPs) are characterized by high complexity and variety, and a schedule that can take decades. Moreover, NDPs estimates at completion can reach billions of Euro and (for many of these projects) keep increasing, while there is a limited understanding of why this happens. To address this knowledge gap, this paper describes how to statistically test the association between the NDP characteristics and the NDP cost performance. The implementation of statistics on a pool of European NDPs highlights the significance of several country-specific and site-specific characteristics (e.g. respectively, the governance system and the availability of facilities to deal with radioactive material on site). Hence, the original contribution of this paper consists in (i) the selection of statistical tests suitable for analysing small sample sizes (i.e. NDPs) and (ii) the presentation of the results from the implementation of these tests on a pool of 24 European NDPs with an illustrative purpose. Highlights • Nuclear decommissioning projects and programmes often suffer from cost overruns. • There is a limited understanding of what causes these cost overruns. • The association between project characteristics and performance needs investigation. • This paper presents a quantitative approach to statistically test this association. • Some country-specific and site-specific characteristics emerge as relevant results. [ABSTRACT FROM AUTHOR]

Abstract While project benchmarking based on key performance indicators is regarded as a crucial technique for mature project delivery in the construction industry, incorporating it into an organization's routine is a cumbersome and time-consuming endeavor as it entails considerable time and human efforts for collecting and providing project information, and validating the quality of collected data. To overcome this challenge, this paper introduces an approach that leverages Building Information Modeling (BIM), which allows for a more streamlined benchmarking process. The approach presented in this paper focuses on healthcare projects which have been benchmarked using a comprehensive set of cost, schedule, dimension, and planning performance metrics through a mature sector-specific benchmarking program at Construction Industry Institute (CII). As an initial step in the formulation of such a tool, this paper investigates the potential of leveraging BIM for benchmarking through close scrutiny of contents embedded in real-world models collected from six healthcare projects. Functional requirements were, then, established to realize a BIM-based benchmarking tool for healthcare projects by developing conceptual process flow, use cases, and data flow diagrams. The requirements are further illustrated in mock-ups of a prototype system. Highlights • A new project performance benchmarking approach that leverages BIM data is introduced. • The potential of leveraging BIM for benchmarking is investigated focusing on healthcare projects • Functional requirements are established to create a BIM-based benchmarking tool • Mock-ups of the prototype system are developed to demonstrate the vision of the tool [ABSTRACT FROM AUTHOR]

Abstract Multi-label classification has attracted great attention from different domains. RA k EL (Random k -label sets) is an effective high-order multi-label learning approach. However, this method exploits label correlations randomly, which cannot make full use of label correlations, causing poor performance of sub-classifiers. In the paper, a novel ensemble method is proposed for multi-label classification. Instead of randomly selecting small-size subsets, this paper constructs a set of k -labelset based on local positive and negative pairwise label correlations, which are captured by KNN and matrix similarity measure. Furthermore, considering the possible missing or noisy labels in predictions, a rectification mechanism based on local label correlations is established to lessen global errors, which enhances the accuracy of the classification procedure. The experimental results over eight benchmark datasets based on multiple evaluation metrics show that these two strategies can achieve relatively better performance compared with the RA k EL method and other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Abstract The paper reports an experimentally identified list of benchmark data sets that are hard for representative classification and feature selection methods. This was done after systematically evaluating a total of 48 combinations of methods, involving eight state-of-the-art classification algorithms and six commonly used feature selection methods, on 129 data sets from the UCI repository (some data sets with known high classification accuracy were excluded). In this paper, a data set for classification is called hard if none of the 48 combinations can achieve an AUC over 0.8 and none of them can achieve an F-Measure value over 0.8; it is called easy otherwise. A total of 15 out of the 129 data sets were found to be hard in that sense. This paper also compares the performance of different methods, and it produces rankings of classification methods, separately on the hard data sets and on the easy data sets. This paper is the first to rank methods separately for hard data sets and for easy data sets. It turns out that the classifier rankings resulting from our experiments are somehow different from those in the literature and hence they offer new insights on method selection. It should be noted that the Random Forest method remains to be the best in all groups of experiments. [ABSTRACT FROM AUTHOR]

Highlights • The paper addresses the topic of Compressed Air Systems (CAS) benchmarking. • CAS represent up to 10% of the industrial energy needs. • The research is a direct result of the European Directive 2012/27/EU. • A data analysis focused on CAS has been carried out in energy-intensive companies. • A benchmarking system has then been designed. Abstract Compressed Air Systems (CAS) are one of the most common and energy intensive utilities in industry, representing up to 10% of the industrial energy needs. Thus, they can be considered as one of the main target systems while planning energy efficiency actions implementation in industry. Nevertheless, most of the available benchmarks of energy consumption are based on nominal data, thus referring to the quality of the design, while benchmarks based on measurements under actual operating conditions are not yet available. In accordance with the Italian transposition of the European Directive 2012/27/EU (i.e. Legislative Decree 102/2014), large and energy-intensive business organizations must perform energy audits at least every four years starting in 2015 and the Italian Energy, New Technology and Environment Agency (ENEA) has the task of collecting and analysing them. In this context, a data collection focused on CAS has been carried out and the subsequent data analysis, focused on nine most promising industrial sectors, has allowed to assess the current state of the art of CAS’s energy efficiency in Italy and to design a possible benchmarking system. Such a system is composed of a set of indicators and a benchmarking visual tool in the form of a matrix. In particular, results related to the Manufacturing of basic metals will be presented for sake of brevity. In this paper we present the results obtained from both the data analysis and the first application of the proposed benchmarking system, together with next steps for the creation of reliable sectorial and cross-sectorial benchmarks. [ABSTRACT FROM AUTHOR]

Abstract This study aims to examine the greenhouse gas (GHG) performance of Korea from the perspective of local governments, based on the non-radial directional distance function (DDF). Since DDF is the methodology for the effective reduction of an undesirable variable, such as carbon or GHG emission, it is better to obtain more reliable empirical results for green growth performance in provinces of Korea. From the empirical results, we found that Seoul and Ulsan show the highest score in gas technical efficiency (GTE), implying that the performance of these two cities on GHG performance is worth of benchmarking. Secondly, we decomposed GTE into pure technical efficiency (PTE) and scale efficiency (SE), and found that Gwangju and Jeju showed unity in PTE scores, implying that these two cities are efficient in various returns to scale conditions. In addition, most of the local governments show high SE performance. Finally, we derived shadow price and found out the results for metropolitan cities belonging to the high shadow price group from the fact that they regulated GHG emissions more effectively. The results of this study offer very interesting and unique implications for policymakers among Korean central and local governments toward the sustainable governance and environmental policies. Highlights • We analyzed the environmental performance of 16 provinces in Korea. • By the decomposition of GTE, we examined pure technical efficiency and scale efficiency. • The paper examines the feasibility of the restructuring of the local economy with return to scale. • To find out the feasibility of the emission trading policy, the paper analyzes the shadow prices of the local governments. [ABSTRACT FROM AUTHOR]

This paper attempts to answer whether Islamic banks can have their own benchmark rate. In so doing, the paper investigates the nature of the relationship Islamic interbank benchmark rate (IIBR) and its comparable conventional counterpart, London interbank offer rate (LIBOR). The dynamics of the two series are investigated to examine the stability of the spread between IIBR and LIBOR, referred to as ‘Islamic premium’ or ‘piety premium’. The findings suggest that there are both long-term and short-term dynamic relationships between the two rates providing significant evidence of their convergence and co-movement. Our results also show that the existence of the IIBR-LIBOR spread is a reflection of the cost of funding and profit potential of the participating IIBR rate-setters. We find that, in addition to the determinants of the credit spreads, fundamental news of the panel banks are dominant factors driving the ‘piety premium’. We argue that the Islamic banking industry is operating in a global context, where it is highly improbable that its rates can decouple from the global benchmarks. Given that Islamic banking products and their risk return profile are similar to conventional products, arbitrage activities force Islamic rates to converge with the global benchmark rates. [ABSTRACT FROM AUTHOR]

This paper investigates 12 major airports in Pakistan for potential cost inefficiencies. We identify inefficiencies by benchmarking the productive performance of airports using Data Envelopment Analysis (DEA) and data for 2012. To improve the ability of DEA to differentiate performance levels, we impose restrictions on the possible weights of inputs and outputs in the DEA procedure. The definition of these weight restrictions is based on additional information on feasible production trade-offs and relative input prices. To the best of our knowledge, this paper provides the first application of predefined weight restrictions in a DEA analysis of airport efficiency. The results suggest that there are cost inefficiencies at several airports, which are mainly caused by overstaffing and overinvestment in capacity. Furthermore, we find that the operational scale of most airports is inefficiently small, so that increases in traffic will result in declining unit costs. [ABSTRACT FROM AUTHOR]

The degradation of the environment, caused in part by end of life and end of used products, necessitates the set-up of recovery networks. These networks may be owned by the manufacturer or they can be outsourced to third parties. Most manufacturers prefer outsourcing, so this study proposes a benchmarked recovery process for third party reverse logistics providers (3PRLP) by evaluating design criteria, implementation criteria, process control characteristics, and business orientation policies. Expertise efficiency of third party providers dictates their competitiveness and/or survival. In an effort to help third parties enhance value recovery from the process in the market, this paper seeks to establish a meaningful benchmark that will dictate best practices through quality function deployment (QFD). In particular, the paper offers a theoretical background to measure the relative importance among the various types of criteria. The proposed technique also helps to establish guidelines by which to prioritize the various characteristics. This study finds that the main driving factor regarding waste management centers on mandatory laws. It also concludes that when government relaxes its policies, environmentally oriented businesses are able to strengthen their systems. [ABSTRACT FROM AUTHOR]

Improved energy efficiency among industrial companies is recognized as a key effort to reduce emissions of greenhouse gases. In this context, benchmarking industrial energy efficiency plays an important part in increasing industrial companies' awareness of their energy efficiency potential. A method for calculating an energy efficiency index is proposed in this paper. The energy efficiency index is used to benchmark the energy performance of industrial small and medium-sized companies' support and production processes. This enables the possibility to compare the energy performance of single energy end-use processes. This paper's proposed energy efficiency index is applied to energy data from 11 sawmills that participated in the Swedish national energy audit program. The index values were compared with each sawmill's energy saving potential, as stated in the energy audits. One conclusion is that the energy efficiency index is suitable as an energy strategy tool in industrial energy management and could be used both by industrial SMEs and by governmental agencies with an auditing role. However, it does require a harmonized categorization of energy end-use processes as well as quality assured energy data. Given this, a national energy end-use database could be created to facilitate the calculation of an energy efficiency index. [ABSTRACT FROM AUTHOR]

Within the urban water cycle, energy issues have been mostly studied for wastewater treatment plants, ignoring that drinking water treatment plants (DWTPs) also consume a significant quantity of energy. Knowing the real energy consumption of DWTPs is the starting point for any energy-saving initiative. This paper benchmarks the energy efficiency measures of a sample of real DWTPs using the data envelopment analysis methodology. Subsequently, whether these facilities are affected by economies of scale is investigated. This issue is essential for planning new DWTPs that minimize energy consumption. In the second stage of analysis, some structural and managerial variables affecting energy efficiency are explored. The results showed that most of the DWTPs analysed have a suitable size but can greatly improve in terms of saving energy. It was found that the age of the plants and the water company operating them significantly affects its energy efficiency. The approach applied in this paper is of great interest for water regulators and company managers since it enables them to learn from the best practices for reducing energy consumption in DWTPs and contributes to improving the sustainability and efficiency of the urban water cycle. [ABSTRACT FROM AUTHOR]

Multi-objectivization means that helper objectives are added to an optimization problem with the purpose of altering the search space in a way that improves the progress of the optimization algorithm. In this paper, a new method for multi-objectivization is proposed that is based on a two-step process. In the first step, a helper objective that conflicts with the main objective is added, and in the second step a helper objective that is in harmony with, but subservient to, the main objective is added. In contrast to existing methods for multi-objectivization, the proposed method aims at obtaining improved results in real-world optimizations by focusing on three aspects: (a) adding as little extra complexity to the problem as possible, (b) achieving an optimal balance between exploration and exploitation in order to promote an efficient search, and (c) ensuring that the main objective, which is of main interest to the user, is always prioritized. Results from evaluating the proposed method on a complex real-world scheduling problem and a theoretical benchmark problem show that the method outperforms both a traditional single-objective approach and the prevailing method for multi-objectivization. Besides describing the proposed method, the paper also outlines interesting aspects of multi-objectivization to investigate in the future. [ABSTRACT FROM AUTHOR]

This paper interrogates four case studies against a resilience framework developed in the course of a research programme. A resilience framework provided the necessary structure to generalise across the case studies and begin to extract lessons from the research. By utilising the framework it is possible to benchmark resilience traits, measure progress or decline, articulate the need for diversity and the balancing of priorities, and provide a more holistic guide for policy-makers. The four case study projects are presented before a discussion links the main findings from the cases to the resilience framework. By applying a theoretical framework to diverse pieces of work, the paper demonstrates an approach to learning systematically from complex and multi-disciplinary research. In particular, the framework captures how the scope of projects expanded over time to incorporate additional dimensions of resilience. This finding highlights the need for flexibility in multi-disciplinary research projects to allow for the inclusion of dimensions that emerge as important in the course of the research. [ABSTRACT FROM AUTHOR]

This paper examines if eco-rating schemes improve environmental outcomes in the context of the international shipping industry. Shipping faces global environmental challenges and has recently witnessed the introduction of several eco-rating schemes aiming to improve the environmental performance of ships. Extending the private environmental governance literature into a mature service industry with global operations, the paper shows that concerns about eco-rating schemes’ effectiveness also have relevance here. Shipping eco-rating schemes fall short of best practices for design and governance, and this hampers improvement efforts. The study has policy implications for the achievement of improved environmental outcomes in the shipping industry. [ABSTRACT FROM AUTHOR]

The variable returns to scale data envelopment analysis (VRS-DEA) benchmarking models are developed by assuming convexity on technology. However, this assumption may be not consistent with the classical microeconomic theory where marginal product initially increases but diminishing returns eventually set in. Accordingly, the selected benchmarks may aggregate best practices, which are less attainable in performance improvement. This paper thus proposes a VRS-DEA benchmarking model with quasiconcave production function, consistent with classical microeconomic theory. A novel approach for determining the returns to scale of Decision-Making Units (DMUs) is developed. The hyperplane of the quasiconcave production function is characterized. Benchmarks are selected by minimizing the weighted distance between the evaluated DMU and the hyperplane. An empirical application of China's tourism sector is used to validate the approach. The empirical results show that Mergers and Acquisitions are beneficial for smaller hotels as the production of most regions in China's tourism sector is subject to increasing returns to scale. [ABSTRACT FROM AUTHOR]

This paper proposes a novel emergency medical service (EMS) location model that considers spatial accessibility (SA), equity, cost, random demand, and random travel time of EMS system. We first construct a utility function that incorporates overall SA and equity. Subsequently, under the constraint of a cost budget, we propose a distributionally robust optimization model with the objective of maximizing the utility. Building upon the Wasserstein ambiguity set, we reformulate the original model as a mixed integer p-order cone programming. To handle the computational challenges posed by norms and data size, we propose a utility cut and lift-polyhedron approximation cut generation algorithm. In the numerical experiments section, algorithm comparison, sensitivity analysis, and model comparison demonstrate the significant advantages of the proposed algorithm and model over different benchmarks and corresponding management insights are provided. • Propose a novel location model that considers spatial accessibility and equity. • Reformulate the original model as a mixed integer p-order cone programming. • Design a utility cut and lift-polyhedron approximation cut generation algorithm. • Demonstrate the superiority of our model through numerical experiments. [ABSTRACT FROM AUTHOR]

[Display omitted] • 33 worldwide open datasets of city-level building energy consumption are summarized. • Representative applications of 198 papers using the collected datasets are analyzed and compared. • Policy implications are presented for using the open datasets to support policymaking. • Useful non-energy datasets about weather, building features, urban forms, and occupant-related factors are recommended. • Solutions to privacy issues are explored, including anonymizing methods and proper data aggregation. Data related to building energy use fuels the research and applications on building energy efficiency, which is an essential measure to address global energy and environmental challenges. However, in most cities, there is a lack of comprehensive and publicly accessible building energy use datasets with necessary temporal and spatial granularities to support urban building energy modeling, regional energy planning, city-level building performance benchmarking, and policymaking on building energy efficiency. Data owners and governments are facing challenges in determining which and how building energy use data should be disclosed at the city level and how to protect data privacy. This review paper provides insights to answer these questions based on a comprehensive and critical review of worldwide open datasets and their applications in the built environment context. Detailed information about the collected 33 building energy datasets is summarized and categorized. Studies identified into 11 subdomains using these open datasets are critically analyzed and compared. Potential policy implications based on the studies are also proposed. Moreover, non-energy datasets that are frequently used in research relevant to urban building energy use are also introduced. Solutions to privacy issues are discussed to address concerns from data publishers. Finally, significant conclusions are made to support the proper disclosure of city-level building energy data. This review study is valuable to urban building energy data disclosure, urban building energy modeling, and data-driven energy policymaking. [ABSTRACT FROM AUTHOR]

The adoption of Building Information Modelling (BIM) across markets is a pertinent topic for academic discourse and industry attention. This is evidenced by the unrelenting release of national BIM initiatives; new BIM protocols; and candidate international standards. This paper is the second part of an ongoing Macro BIM Adoption study: the first paper “Macro BIM Adoption: Conceptual Structures” (Succar and Kassem, 2015) introduced five conceptual models for assessing macro BIM adoption across markets and informing the development of BIM adoption policies. This second paper clarifies how these models are validated through capturing the input of 99 experts from 21 countries using a survey tool; highlights the commonalities and differences between sample countries with respect to BIM adoption; and introduces sample tools and templates for either developing or calibrating BIM adoption policies. Survey data collected indicate that all five conceptual models demonstrate high levels of ‘clarity’, ‘accuracy’ and ‘usefulness’, the three metrics measured. They also indicate (1) varying rates of BIM diffusion across countries with BIM capability near the lower-end of the spectrum; (2) varying levels of BIM maturity with - the mean of - most macro BIM components falling below the medium level; (3) varying diffusion dynamics across countries with the prevalence of the middle-out diffusion dynamic; (4) varying policy actions across countries with a predominance of the passive policy approach; and (5) varying distribution of diffusion responsibilities among player groups with no detectable dominant pattern across countries. The two papers provide an opportunity to improve our understanding of BIM adoption dynamics across countries. Future research can build upon the models and tools introduced to enable (a) an expansion of benchmarking data through surveying additional countries; (b) identifying BIM adoption changes in surveyed countries over time; (c) correlating changes in adoption rates/patterns with policy interventions; (d) identifying BIM policy variations within the same country; (e) establishing statistical correlations between the conceptual models; and (f) developing new tools to facilitate BIM policy development and encouraging BIM adoption. [ABSTRACT FROM AUTHOR]

Background It is widely understood that statistical significance should not be equated with clinical significance, but the topic of clinical significance has not received much attention in the nursing literature. By contrast, interest in conceptualizing and operationalizing clinical significance has been a “hot topic” in other health care fields for several decades. Objectives The major purpose of this paper is to briefly describe recent advances in defining and quantifying clinical significance. The overview covers both group-level indicators of clinical significance (e.g., effect size indexes), and individual-level benchmarks (e.g., the minimal important change index). A secondary purpose is to describe the extent to which developments in clinical significance have penetrated the nursing literature. Methods A descriptive analysis of a sample of primary research articles published in three high-impact nursing research journals in 2016 was undertaken. A total of 362 articles were electronically searched for terms relating to statistical and clinical significance. Results Of the 362 articles, 261 were reports of quantitative studies, the vast majority of which (93%) included a formal evaluation of the statistical significance of the results. By contrast, the term “clinical significance” or related surrogate terms were found in only 33 papers, and most often the term was used informally, without explicit definition or assessment. Conclusions Raising consciousness about clinical significance should be an important priority among nurse researchers. Several recommendations are offered to improve the visibility and salience of clinical significance in nursing science. [ABSTRACT FROM AUTHOR]

Nuclear Decommissioning Projects and Programmes (NDPs) are jeopardized by several risks, long schedule and cost estimates that lay in the range of hundreds of billions of pounds. Moreover, in some countries, these estimates keep increasing and key stakeholders have a limited understanding of the determinants that engender this phenomena. Benchmarking refers to the process of comparing projects in order to identify best practices and generate ideas for improvement. However, even if it is the envisaged approach to tackle the decommissioning challenges (and due to the NDPs' uniqueness), until now, benchmarking has been only partially used. This paper proposes an innovative methodology to benchmark decommissioning projects, both from the nuclear and non-nuclear industry, within the UK and worldwide. From this cross-sectorial and cross-country analysis, it is possible to gather a list of key NDPs' characteristic and statistically test their correlation with the project performance. The ultimate aim of the research underpinning this paper is to investigate the possible causation between the NDPs' characteristics and the NDPs' performance and to develop guidelines to improve the selection, planning and delivery of future NDPs. [ABSTRACT FROM AUTHOR]

In this paper, a newly proposed Ant-Lion Optimizer (ALO) is applied to solve Optimal Reactive Power Dispatch (ORPD) problem of power system. The ORPD is a VAr planning problem and is a highly non linear, non convex, challenging optimization problem; usually devised as constrained Optimal Power Flow (OPF). This paper also proposes the improvement in the search capability of ALO. A novel weighted elitism concept is introduced in the elitism phase of the original ALO to improve the exploration property of the algorithm. The proposed Modified ALO (MALO) intelligently balances both exploration and exploitation, which enhances the hunting capability of ALO. Both ALO and MALO is used to determine the optimal settings of generator voltages, tap positions of tap changer transformers and VAr output of shunt capacitors to optimize objectives: like, the active power loss, total voltage deviation and voltage stability index. The proposed algorithm is programmed and simulated on MATLB and tested on IEEE-30 and 57 bus systems. The results are compared with ALO and other methods. The effectiveness of MALO is further verified by solving few benchmark mathematical functions. The numerical results reveal that, MALO gives better optimum solutions for the benchmark functions compare to original ALO and outperforms several other methods used in the literature to solve ORPD problems. The t-Test and parametric analysis confirms the robustness and consistency of the MALO over ALO and other similar methods. [ABSTRACT FROM AUTHOR]

Inertia, a tendency to resist initiating and adopting new changes, is a primary issue in established organizations. This paper explores how a principal can optimally address this issue when an agent has reputation concerns. It shows that the principal can motivate the agent to initiate a new change by damaging the agent's reputation when he just sits on the status quo. In doing so, compared to the benchmark case where inertia is not an issue, the principal extends monitoring that assesses the value of the new change, and reduces intervention in the agent's implementation of it. Thus the paper suggests that active monitoring and passive intervention can motivate the agent to initiate a new change. [ABSTRACT FROM AUTHOR]

Aquatic centres are major community facilities that require a large amount of water and energy to operate. Aquatic centres are very complex buildings which have made it difficult to obtain clear and verifiable indicators to measure and compare their energy and water consumptions. This paper clarifies and defines what constitutes an aquatic centre by investigating those operating within Victoria, Australia using the Internet. Information from 110 aquatic centres was collected and used to establish various categories of aquatic centre based on the types and number of amenities that they provide. This study defined an aquatic centre as a community or public venue that provides at least an indoor swimming pool and three different types of amenities (e.g. gymnasium, sauna/spa, café and crèche). This paper also discusses how the lack of clear definition of aquatic centres can create confusion when researching and comparing their energy and water usages. This is achieved by reviewing academic and industry literature and also comparing existing energy and water benchmarks of aquatic centres. This investigation also confirmed that there are no universal guidelines for benchmarking aquatic centres. Therefore, guidelines for defining aquatic centres and also for benchmarking energy and water use of aquatic centres are proposed. [ABSTRACT FROM AUTHOR]

Isogeometric Analysis (IGA) introduced in 2005 by Hughes et al. (2005) [1] exploits one mathematical basis representation for computer aided design (CAD), geometry and analysis during the entire engineering process. In this paper we extend this concept also for visualization. The presented post-processing and visualization techniques thereby strengthen the relation between geometry, analysis and visualization. This is achieved by facilitating the same mathematical function space used for geometry and analysis also for post-processing and visualization purposes. During non-linear analysis derivatives are incrementally computed and stored with different basis function representations. We introduce and investigate projection methods to be able to use the same function space for both displacements and stresses without loss of accuracy. To obtain a common representation for structured and unstructured meshes like hierarchical spline, locally refined B-spline (LR B-spline) and T-spline techniques we exploit Bézier decomposition in a post-processing step resulting in a Bézier element representation and constitute it as generalized representation. The typically used unrelated (fictitious) finite element mesh representation for visualization purposes are easily replaced without changing the underlying geometry as well as the algorithmic data structure. One further benefit of the used Bézier decomposition lies in the fact that it facilitates a natural parallel implementation on Graphics Processor Units (GPUs) exploiting shader programming. In this paper we have developed and investigated an accurate, efficient and practical post-processing pipeline for visualization of isogeometric analysis results . The proposed IGA visualization pipeline consists of three steps: (1) Projection , (2) Bézier decomposition and (3) Pixel-accurate rendering . We have tested four different projection methods. A description on how to perform Bézier decomposition of LR B-splines are given (whereas for hierarchical and T-splines this has been done before). Furthermore, the use of GPU shader programming to enable efficient and pixel-accurate visualization is detailed. The performance of the four different projection techniques has been tested on manufactured problems as well as on realistic benchmark problems. Furthermore, the IGA visualization pipeline has been demonstrated on a number of real-world applications. [ABSTRACT FROM AUTHOR]

The paper introduces a new taxonomy of ill-posed parametric inverse problems, formulated as global optimization ones. It systematizes irremediable problems, which appear quite often in the real life but cannot be solved using the regularization method. The paper also shows a new way of solving irremediable inverse problems by a complex memetic approach including: genetic computation with adaptive accuracy, random sample clustering and a sophisticated local approximation of misfit plateau regions. Finally, we use a benchmark function featuring cross-shaped plateau to discuss some factors that influence the quality of plateau shape approximation. [ABSTRACT FROM AUTHOR]

This paper aims to propose a new modified SPH method with novel treatments at the boundaries. Although SPH methods decrease contradictions due to grid distortions compared to traditional mesh-based methods, the penetration of particles through boundaries and the consistency problem make the simulation of problems with definite boundaries a concern. The use of ghost boundary particles and the insertion of artificial forces at the boundaries are the most popular boundary consistency treatments proposed thus far. The use of artificial forces causes the mixing of molecular and finite theories, which can violate the conservation of momentum. This paper shows how the use of ghost boundary particles can violate the continuity equation in problems with non-zero velocity divergence. This study proposes a novel ghost particle removed SPH (GR-SPH) method that discards all ghost particles and artificial forces at the boundaries. Liner layers and liner particles have been defined inside the domain instead of ghost boundary particles in such a way that the so-called violations can partially be remedied. Based on the continuity equation and kernel function unity specification, a novel truncation correction factor has been defined for density renormalization to override the consistency problem at the boundaries. In addition, a new method is proposed to detect the particles near complex wall boundaries and evaluate the normal distance from boundaries. Finally, some benchmark problems have been solved to show the capabilities of the new modified SPH method for the prediction of both particle location and pressure distribution with acceptable accuracy. The GR-SPH method facilitates programming, with fewer particles contributing to the computations. Comparison of its outcomes with published results shows that the new treatments executed at the boundaries are effective. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for the design of gain-scheduled output feedback controllers in the presence of hidden coupling terms, which naturally arise when endogenous signals are used as scheduling parameters. In this case, the controller gains vary with respect to system variables, leading to inner loops between the system and the linearized gain-scheduled controller dynamics. Such effects, also known as parasitic feedbacks, are inherently difficult to handle in classic gain-scheduling control design and are often omitted, which might result in performance degradations or even destabilization of the closed-loop system. This paper shows how such a pitfall can be avoided through the application of self-scheduling and eigenstructure assignment techniques. In this context, the main contribution of the present work is the extension of eigenstructure assignment-based self-scheduling techniques for explicit hidden coupling terms handling. The viability of the proposed method is illustrated through a pitch-axis missile autopilot benchmark problem. [ABSTRACT FROM AUTHOR]

This paper presents an optimization algorithm based on a meta-heuristic search inspired from the behavior of monkey. The main steps of the optimization search algorithm comprise climb process , watch-jump process and somersault process . The proposed method is a completion of the Monkey algorithm to the original steps; we have added two more: one-component perturbation and all-components perturbation. This way the monkeys have the possibility of accelerating the local convergence and also of searching better values globally. The modified algorithm for these processes have been designed and presented in the paper. The performance of the modified algorithm is tested on some benchmark functions. These functions are evaluated by considering the cases in which the problem is set as 30, 50 or even 100 dimensions . The computational results have demonstrated that the algorithm performs much better for solving a set of global optimization problems. [ABSTRACT FROM AUTHOR]

Quality Management is considered to be suitable as support for the integration of sustainability considerations in areas such as product development. The purpose of this paper is to review research in which Quality Management methods, tools or practices have been used in conjunction with sustainable development initiatives. We have identified four themes that synthesize the research on Quality Management and its support to approaches for sustainable development: (I) supporting sustainability through integration of management systems, (II) Quality Management as support to the implementation of Environmental Management Systems and to the management of sustainability, (III) supporting integration of sustainability considerations in daily work, and (IV) supporting stakeholder management and customer focus. By far the most research has been conducted within the first two themes. This paper also contributes with proposals for future research, such as the need to move beyond existing standards and management systems to enable more radical improvements, and the need for empirical evidence of the effect of integrated management systems on environmental performance. We also highlight the point that Quality Management practices and tools must be developed and adapted in order to support sustainability considerations. [ABSTRACT FROM AUTHOR]

This paper presents a novel stereo matching algorithm that synergistically exploits in an original way the Adaptive Census Transform and the Support Local Binary Pattern approaches. For the first time, the Support Local Binary Pattern technique is applied to efficiently process large windows of pixels without excessive computational requirements, and thus allowing easy design of specific integrated circuits. Results obtained for conventional benchmark image sets demonstrate that, despite the simplifications adopted to make the novel algorithm hardware-friendly, the method proposed here can reach qualities higher than its competitors. Several hardware implementations have been carried out and characterized on the Xilinx Virtex FPGA chips. For 640 × 480 stereo images and a disparity range equal to 60, the proposed architecture guarantees an average error in computing the map as low as 9% with a throughput rate up to 68 frames per second. The cheapest version of a system designed as reported in this paper occupies less than 49,000 slices, 112 DSPs and 32 BRAMs. [ABSTRACT FROM AUTHOR]

The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in an inaccurate representation of the source cell-phone related features, which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning. In the feature extraction part, based on the analysis of the time-series representation of recording source signals, we extract sequential Gaussian mean matrix with long-term and short-term representation ability by using the sensitivity of Gaussian mixture model to data distribution. In the model construction part, we design a structured spatio-temporal representation learning network C3D-BiLSTM to fully characterize the spatio-temporal information, combine 3D convolutional network and bidirectional long short-term memory network for short-term spectral information and long-time fluctuation information representation learning, and achieve accurate recognition of cell-phones by fusing spatio-temporal feature information of recording source signals. The method achieves an average accuracy of 99.03% for the closed-set recognition of 45 cell-phones under the CCNU_Mobile dataset and 98.18% in small sample size experiments, with recognition performance better than the existing state-of-the-art methods. The experimental results show that the method exhibits excellent recognition performance in multi-class cell-phones recognition. Our codes are available at https://github.com/CCNUZFW/STRLESCR-SGMM. [Display omitted] • The proposed method can model the long-term feature characterization of cell phones. • The proposed SGMM takes advantage of time-frequency and probability distribution. • Our method capitalizes on the synergies of 3D convolutional networks and BiLSTM. • Our method enhances the performance by at least 2% over six benchmark methods. [ABSTRACT FROM AUTHOR]

Network Intrusion Detection Systems (NIDSs) are an increasingly important tool for the prevention and mitigation of cyber attacks. Over the past years, a lot of research efforts have aimed at leveraging the increasingly powerful models of Machine Learning (ML) for this purpose. A number of labelled synthetic datasets have been generated and made publicly available by researchers, and they have become the benchmarks via which new ML-based NIDS classifiers are being evaluated. Recently published results show excellent classification performance with these datasets, increasingly approaching 100 percent performance across key evaluation metrics such as Accuracy, F1 score, AUC, etc. Unfortunately, we have not yet seen these excellent academic research results translated into practical NIDS systems with such near-perfect performance. This motivated our research presented in this paper, where we analyse the statistical properties of the benign traffic in three of the more recent and relevant NIDS datasets, (CIC_IDS, UNSW_NB15, TON_IOT), by converting them into a common flow format. As a comparison, we consider two datasets obtained from real-world production networks, one from a university network and one from a medium size Internet Service Provider (ISP). Our results show that the two real-world datasets are quite similar among themselves in regards to most of the considered statistical features. Equally, the three synthetic datasets are also relatively similar within their group. However, and most importantly, our results show a distinct difference of most of the considered statistical features between the three synthetic datasets and the two real-world datasets. Since ML relies on the basic assumption of training and test datasets being sampled from the same distribution, this raises the question of how well the performance results of ML-classifiers trained on the considered synthetic datasets can translate and generalise to real-world networks. We believe this is an interesting and relevant question which provides motivation for further research in this space. [ABSTRACT FROM AUTHOR]

• A multi-compartment containment thermal–hydraulic code (named PARIRODHAN) is developed. • The code is generic and provides the flexibility to the user to decide the number of compartments, junctions and associated wall structures. • The code uses a pressure implicit scheme to solve conservation equations to obtain a stable and computationally efficient solution. • Other explicitly coupled models include Flashing model, critical flow model, pool evaporation model and vent thermal hydraulic model. • The code has been verified by comparing the code calculation with numerical (RELAP5/MOD3.2) and experimental (Containment Studies Facility (CSF) Experiment and Marviken Full Scale Containment Experiment-Blowdown 18) results. • Overall the results predicted by the code are in good agreement with the reference results. The development of a multi-compartment containment thermal–hydraulic code (named PARIRODHAN) is presented in this paper. It provides the capability to predict the containment thermal hydraulics along with steam and non-condensable transport inside a containment building that follows after the release of mass and energy from the heat transport system during postulated accident conditions. The code is generic and provides the flexibility to the user to decide the number of compartments, junctions and associated wall structures. The code uses a pressure implicit scheme to obtain a stable and computationally efficient solution. It has been verified by comparing the code calculation with numerical and experimental results. Some benchmarking studies largely affecting the containment thermal-hydraulics are also presented in the paper. The benchmarking results show the capability of the code in simulating containment behaviour under blowdown conditions. [ABSTRACT FROM AUTHOR]

Background: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data.Objective: The aim of this study is to review the use of ML with ECG data using a time series approach.Methods: Papers that address the subject of ML and the ECG were identified by systematically searching databases that archive papers from January 1995 to October 2019. Time series analysis was used to study the changing popularity of the different types of ML algorithms that have been used with ECG data over the past two decades. Finally, a meta-analysis of how various ML techniques performed for various diagnostic classifications was also undertaken.Results: A total of 757 papers was identified. Based on results, the use of ML with ECG data started to increase sharply (p < 0.001) from 2012. Healthcare applications, especially in heart abnormality classification, were the most common application of ML when using ECG data (p < 0.001). However, many new emerging applications include using ML and the ECG for biometrics and driver drowsiness. The support vector machine was the technique of choice for a decade. However, since 2018, deep learning has been trending upwards and is likely to be the leading technique in the coming few years. Despite the accuracy paradox, accuracy was the most frequently used metric in the studies reviewed, followed by sensitivity, specificity, F1 score and then AUC.Conclusion: Applying ML using ECG data has shown promise. Data scientists and physicians should collaborate to ensure that clinical knowledge is being applied appropriately and is informing the design of ML algorithms. Data scientists also need to consider knowledge guided feature engineering and the explicability of the ML algorithm as well as being transparent in the algorithm's performance to appropriately calibrate human-AI trust. Future work is required to enhance ML performance in ECG classification. [ABSTRACT FROM AUTHOR]

This paper develops and applies a Sustainability Ranking of Airports Index to benchmark the performance of airports across multiple factors. The index is a composite indicator with 5 dimensions and 25 indicators. The dimensions are airport services and quality, energy consumption and generation, carbon dioxide emissions and mitigation planning, environmental management and biodiversity, and atmosphere and low emission transport. The index is applied to a sample of 9 airports that take place among the busiest and best airports in the world based on passenger traffic and passenger satisfaction. Data is mined from Corporate Sustainability Reports and related sources. Amsterdam Schiphol, Frankfurt, Munich, Istanbul Atatürk, and London Heathrow airports are the top 5 airports in the sample. The linear regression between the results of the index and annual passenger traffic is found to be 0.1942. The results can be used to benchmark progress in decoupling airport operations from greater environmental impact. The paper provides recommendations for the 5 dimensions of the index based on best practices from the airports in the sample. The index is useful to allow airport managers to coordinate strategies for the sustainable development of energy, water, and environment systems in airports and lift-off towards more sustainable airport practices. [ABSTRACT FROM AUTHOR]

Today Information and Communications Technology (ICT) networks are a dominating component of our daily life. Centralized logging allows keeping track of events occurring in ICT networks. Therefore a central log store is essential for timely detection of problems such as service quality degradations, performance issues or especially security-relevant cyber attacks. There exist various software tools such as security information and event management (SIEM) systems, log analysis tools and anomaly detection systems, which exploit log data to achieve this. While there are many products on the market, based on different approaches, the identification of the most efficient solution for a specific infrastructure, and the optimal configuration is still an unsolved problem. Today׳s general test environments do not sufficiently account for the specific properties of individual infrastructure setups. Thus, tests in these environments are usually not representative. However, testing on the real running productive systems exposes the network infrastructure to dangerous or unstable situations. The solution to this dilemma is the design and implementation of a highly realistic test environment, i.e. sandbox solution, that follows a different – novel – approach. The idea is to generate realistic network event sequence (NES) data that reflects the actual system behavior and which is then used to challenge network analysis software tools with varying configurations safely and realistically offline. In this paper we define a model, based on log line clustering and Markov chain simulation to create this synthetic log data. The presented model requires only a small set of real network data as an input to understand the complex real system behavior. Based on the input׳s characteristics highly realistic customer specified NES data is generated. To prove the applicability of the concept developed in this work, we conclude the paper with an illustrative example of evaluation and test of an existing anomaly detection system by using generated NES data. [ABSTRACT FROM AUTHOR]