Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures

An operational hydrological ensemble forecasting system based on a meteorological ensemble prediction system (M-EPS) coupled with a hydrological model searches to capture the uncertainties associated with the meteorological prediction to better predict river flows. However, the structure of the hydrological model is also an important source of uncertainty that has to be taken into account. This study aims at evaluating and comparing the performance and the reliability of different types of hydrological ensemble prediction systems (H-EPS), when ensemble weather forecasts are combined with a multi-model approach. The study is based on 29 catchments in France and 16 lumped hydrological model structures, driven by the weather forecasts from the European centre for medium-range weather forecasts (ECMWF). Results show that the ensemble predictions produced by a combination of several hydrological model structures and meteorological ensembles have higher skill and reliability than ensemble predictions given either by one single hydrological model fed by weather ensemble predictions or by several hydrological models and a deterministic meteorological forecast

Executable Model Development from Architectural Description with Application to the Time Sensitive Target Problem

As the Department of Defense (DoD) moves to a capabilities-based approach for requirements definition and systems development, it has become necessary to conceptualize and evaluate our needs at the System of Systems (SoS) level. Desired capabilities are often achievable only through seamless integration of many different systems. As the classical systems engineering approaches are not suited to effectively handle the complexity of SoS level concepts, an architectures-driven approach has emerged as a way of defining and evaluating these new concepts. While the use of architectures for documenting and tracking interfaces and interoperability concerns is generally understood, architectural analysis and the use of executable models for evaluation of architectures remain an open area of research. With this purpose in mind, this thesis will apply architectural-based analysis to the proposed Time Sensitive Effect Operation (TSEO2012) scenario. This scenario will become the baseline for architectural analysis, and an excursion to this baseline will add a Weapon Born Battle Damage Assessment (WBBDA) capability. By creating an executable model, the two architectural concepts can be compared against each other. The addition of a WBBDA capability to the TSEO architecture improves the efficiency of the time sensitive target operations by shortening the decision cycle for target re-strike. While this effort was successful in obtaining an executable model directly from the architecture description, it highlighted the importance of having sufficient and correct information contained in the architecture products

Process-oriented evaluation system for the use of robotic process automation

The administrative order processing is confronted with a variety of structural, procedural and organizational changes driven by the increasing demand for shorter delivery times and higher product variances. Thus, business processes become more complex and less transparent having a negative impact on administrative order processing. Studies estimate the waste in indirect areas at around 30 percent. The cause of this waste is, for example, missing information in the process step or interface losses during the transfer to another area of responsibility. This results in queries and coordination efforts that delay the order process. Among other things, robotic process automation (RPA) can be used to reduce waste. This enables the monitoring of administrative processes and the automation of sub-processes (activities). Identifying these automation potentials can be seen as a major challenge in administrative order processing due to the existing complexity. One way to discover automation potential is the use of data-driven tools such as process mining (PM). Using algorithms (e.g. a-miner), a process model is created based on data from central information systems (e.g. enterprise resource systems) allowing a systematical analyzation of causalities. Furthermore, PM can help to identify the relevant metrics for the RPA selection in a data-driven way, in order to support the selection process decisively. In the current state of research, most paper focuses on quantifiable key figures for evaluating RPA capabilities. Qualitative criteria for RPA use are rarely considered. This paper focuses on a qualitative criteria-based and quantitative indicator-based evaluation system for the use of RPA in administrative order processing The approach is validated in process mining software using a data set related to administrative order processing

Considerations about quality in model-driven engineering

The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.F.G, would like to thank COLCIENCIAS (Colombia) for funding this work through the Colciencias Grant call 512-2010. This work has been supported by the Gene-ralitat Valenciana Project IDEO (PROMETEOII/2014/039), the European Commission FP7 Project CaaS (611351), and ERDF structural funds.Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. https://doi.org/10.1007/s11219-016-9350-6S166(1985). Iso information processing—documentation symbols and conventions for data, program and system flowcharts, program network charts and system resources charts. ISO 5807:1985(E) (pp. 1–25).(2011). Iso/iec/ieee systems and software engineering – architecture description. 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A systematic literature review of studies on business process modeling quality. Information and Software Technology, 58, 187–205.DenHaan, J. (2009). 8 reasons why model driven development is dangerous @ONLINE.DenHaan, J. (2010). Model driven engineering vs the commando pattern @ONLINE.DenHaan, J. (2011a). Why aren’t we all doing model driven development yet @ONLINE.DenHaan, J. (2011b). Why there is no future model driven development @ONLINE.Di Ruscio, D., Iovino, L., & Pierantonio, A. (2013). Managing the coupled evolution of metamodels and textual concrete syntax specifications. cited By (since 1996)0.Dijkman, R.M., Dumas, M., & Ouyang, C. (2008). Semantics and analysis of business process models in {BPMN}. Information and Software Technology, 50(12), 1281–1294.Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., Ramos, I., & Fernández, L. (2011). A framework for the quality evaluation of mdwe methodologies and information technology infrastructures. 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Manage risk of sustainable product–service systems: a case-based operations research approach

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Sustainable product–service systems (SusPSSs) offer an innovation-driven approach to production based on providing results or functions with minimal material use and emissions. Networks of SusPSSs partners are central to the decision-making of sustainability policies. Evaluations and assessments of network-oriented risks sources are therefore crucial to informing an industrial firm’s reorientation towards SusPSS. Traditionally, these risks beleaguer production and continue to grow in significance with complex production and innovation processes. This article presents a novel operations research application for evaluating network-oriented risks of industrial firms in pursuing SusPSSs. The model conceptualises a framework for network risk metrics and applies a fuzzy-based multi-criteria decision-making technique to evaluate levels of risk associated with reorientations to SusPSS approaches. It takes explicit account of multiple risk sources in aiding decision-making and assists in indicating strategies for improving business sustainability. In addition, it compares and ranks alternative SusPSSs as a system and on an indicator basis, which is a practical and effective decision support tool. A case study of an industrial firm is conducted to verify the effectiveness and applicability of the proposed approach in supporting firms’ decision on SusPSSs

Digital Participatory Model as Part of a Data-Driven Decision Support System for Urban Vibrancy

Digital participation relies on computational systems as the instruments for expert engagement, data-driven insight, and informed decision-making. This study aims to increase expert engagement with the Bayesian-based decision support model in evaluating urban vibrancy decisions. In this study, urban vibrancy parameters are defined using "economic, use, and image value" measures. This article focuses on the visual aspect of vibrancy, defined as the image value of place. The image value is evaluated through likability and likability features. The case study area is the Eminönü Central Business District in the Istanbul Historic Peninsula due to its distinctive urban dynamics derived from the duality of being a cultural and cosmopolitan city center. This research presents a method as a decision support system (DSS) model based on the Bayesian belief network (BBN) and spatial BBN for supporting urban vibrancy decisions. The spatial BBNs monitor spatial outcomes of variables' dependencies that form through the BBN relationship network. Spatial BBN tools monitors the spatial impact of decisions for informed urban interventions. The results demonstrate that urban greening, pedestrianization, and human-scaled streetscapes should be prioritized to make streets more likable. The most significant intervention areas are Tahtakale for signboard regulation, Sultanahmet and Vefa for cultural landscape improvement, and Vefa and Mahmutpaşa for planning building enclosures. The participation is achieved by evaluating urban vibrancy with what-if scenarios using BBN. The developed DSS model addresses which parameters should be prioritized, and what are their spatial consequences. The use of spatial BBN tools presents certain limitations in terms of interoperability and user interaction. Overall, this research contributes to participatory urban planning by incorporating both conditional and spatial dependencies. This unique approach not only promotes a more holistic understanding of urban vibrancy but also contributes to the advancement of digital participation in urban planning decisions

Information Systems Development through an Integrated Framework

Information systems are essential entities for several organizations who strive to successfully run their business operations. One of the major problems faced by the organizations is that many of these information systems fail, and thus the organizations do not achieve their required targets in time. Many of the reasons for the information system failures documented in the literature are related to development methodologies or frameworks that are unable to handle both ‘hard’ and ‘soft’ system aspects. In general, the hard issues of the system are considered more significant than the soft issues, however, all the methodologies must be able to deal with all the system and business aspects. This thesis investigates the possibility of developing and evaluating a multimethodology framework that can be used for information systems development in an academic and business environment. The research explores the applicability of such a framework that comprehends both ‘soft’ and ‘hard’ system aspects in order to eliminate information system failures. Different software development approaches are investigated, including the dominant ‘domain-driven design’ (DDD) approach. A new multimethodological framework entitled ‘Systemic Soft Domain Driven Design’ (SSDDDF) has been developed by combining ‘soft system methodology’ as a guiding methodology, ‘unified modelling language’ as a business domain modelling approach, and a domain-driven design implementation pattern. This framework is intended as an improvement of the DDD approach. Soft and hard techniques are integrated through mapping from the ‘consensus primary task model’ of the soft approach to the ‘use cases’ of the hard approach. In addition, ‘soft language’ is introduced as a complement to DDD’s ‘ubiquitous language’, for facilitating the communication between the different stakeholders of a project. The implementation pattern (e.g., Naked Objects) is included for generating code from domain models. The framework has been evaluated as an information systems development approach through different undergraduate and postgraduate projects. Feedback from the developers has been positive and encouraging for further improvements in the future. The SSDDD framework has also been compared to different ISD methodologies and frameworks among of these DDD as an approach to ISD. The results of this comparison show that SSDDDF has advantages over DDD and significant improvements to DDD have been achieved. Finally, the research suggests an agenda for further improvements of the framework, while suggesting the development of different pattern languages

Text readability and summarisation for non-native reading comprehension

This thesis focuses on two important aspects of non-native reading comprehension: text readability assessment, which estimates the reading difficulty of a given text for L2 learners, and learner summarisation assessment, which evaluates the quality of learner summaries to assess their reading comprehension. We approach both tasks as supervised machine learning problems and present automated assessment systems that achieve state-of-the-art performance. We first address the task of text readability assessment for L2 learners. One of the major challenges for a data-driven approach to text readability assessment is the lack of significantly-sized level-annotated data aimed at L2 learners. We present a dataset of CEFR-graded texts tailored for L2 learners and look into a range of linguistic features affecting text readability. We compare the text readability measures for native and L2 learners and explore methods that make use of the more plentiful data aimed at native readers to help improve L2 readability assessment. We then present a summarisation task for evaluating non-native reading comprehension and demonstrate an automated summarisation assessment system aimed at evaluating the quality of learner summaries. We propose three novel machine learning approaches to assessing learner summaries. In the first approach, we examine using several NLP techniques to extract features to measure the content similarity between the reading passage and the summary. In the second approach, we calculate a similarity matrix and apply a convolutional neural network (CNN) model to assess the summary quality using the similarity matrix. In the third approach, we build an end-to-end summarisation assessment model using recurrent neural networks (RNNs). Further, we combine the three approaches to a single system using a parallel ensemble modelling technique. We show that our models outperform traditional approaches that rely on exact word match on the task and that our best model produces quality assessments close to professional examiners

Performance of hard handoff in 1xev-do rev. a systems

1x Evolution-Data Optimized Revision A (1xEV-DO Rev. A) is a cellular communications standard that introduces key enhancements to the high data rate packet switched 1xEV-DO Release 0 standard. The enhancements are driven by the increasing demand on some applications that are delay sensitive and require symmetric data rates on the uplink and the downlink. Some examples of such applications being video telephony and voice over internet protocol (VoIP). The handoff operation is critical for delay sensitive applications because the mobile station (MS) is not supposed to lose service for long periods of time. Therefore seamless server selection is used in Rev. A systems. This research analyzes the performance of this handoff technique. A theoretical approach is presented to calculate the slot error probability (SEP). The approach enables evaluating the effects of filtering, hysteresis as well as the system introduced delay to handoff execution. Unlike previous works, the model presented in this thesis considers multiple base stations (BS) and accounts for correlation of shadow fading affecting different signal powers received from different BSs. The theoretical results are then verified over ranges of parameters of practical interest using simulations, which are also used to evaluate the packet error rate (PER) and the number of handoffs per second. Results show that the SEP gives a good indication about the PER. Results also show that when considering practical handoff delays, moderately large filter constants are more efficient than smaller ones

OPERATIONAL RELIABILITY AND RISK EVALUATION FRAMEWORKS FOR SUSTAINABLE ELECTRIC POWER SYSTEMS

Driven by a confluence of multiple environmental, social, technical, and economic factors, traditional electric power systems are undergoing a momentous transition toward sustainable electric power systems. One of the important facets of this transformation is the inclusion of high penetration of variable renewable energy sources, the chief among them being wind power. The new source of uncertainty that stems from imperfect wind power forecasts, coupled with the traditional uncertainties in electric power systems, such as unplanned component outages, introduces new challenges for power system operators. In particular, the short-term or operational reliability of sustainable electric power systems could be at increased risk as limited remedial resources are available to the operators to handle uncertainties and outages during system operation. Furthermore, as sustainable electric power systems and natural gas networks become increasingly coupled, the impacts of outages in one network can quickly propagate into the other, thereby reducing the operational reliability of integrated electric power-gas networks (IEPGNs). In light of the above discussion, a successful transition to sustainable electric power systems necessitates a new set of tools to assist the power system operators to make risk-informed decisions amid multiple sources of uncertainties. Such tools should be able to realistically evaluate the hour- and day-ahead operational reliability and risk indices of sustainable electric power systems in a computationally efficient manner while giving full attention to the uncertainties of wind power and IEGPNs. To this end, the research is conducted on five related topics. First, a simulation-based framework is proposed to evaluate the operational reliability indices of generating systems using the fixed-effort generalized splitting approach. Simulations show improvement in computational performance when compared to the traditional Monte-Carlo simulation (MCS). Second, a hybrid analytical-simulation framework is proposed for the short-term risk assessment of wind-integrated power systems. The area risk method – an analytical technique, is combined with the importance sampling (IS)-based MCS to integrate the proposed reliability models of wind speed and calculate the risk indices with a low computational burden. Case studies validate the efficacy of the proposed framework. Third, the importance sampling-based MCS framework is extended to include the proposed data-driven probabilistic models of wind power to avoid the drawbacks of wind speed models. Fourth, a comprehensive framework for the operational reliability evaluation of IEPGNs is developed. This framework includes new reliability models for natural gas pipelines and natural gas-fired generators with dual fuel capabilities. Simulations show the importance of considering the coupling between the two networks while evaluating operational reliability indices. Finally, a new chance-constrained optimization model to consider the operational reliability constraints while determining the optimal operational schedule for microgrids is proposed. Case studies show the tradeoff between the reliability and the operating costs when scheduling the microgrids