Adaptive model-driven user interface development systems

Adaptive user interfaces (UIs) were introduced to address some of the usability problems that plague many software applications. Model-driven engineering formed the basis for most of the systems targeting the development of such UIs. An overview of these systems is presented and a set of criteria is established to evaluate the strengths and shortcomings of the state-of-the-art, which is categorized under architectures, techniques, and tools. A summary of the evaluation is presented in tables that visually illustrate the fulfillment of each criterion by each system. The evaluation identified several gaps in the existing art and highlighted the areas of promising improvement

An Analysis of Adaptation as a Response to Climate Change

Climate change is likely to have relevant effects on our future socio-economic systems. It is therefore important to identify how markets and policy jointly react to expected climate change to protect our societies and well-being. This study addresses this issue by carrying out an integrated analysis of both optimal mitigation and adaptation at the global and regional level. Adaptation responses are disentangled into three different modes: reactive adaptation, proactive (or anticipatory) adaptation, and investments in innovation for adaptation purposes. The size, the timing, the relative contribution to total climate-related damage reduction, and the benefit-cost ratios of each of these strategies are assessed for the world as a whole, and for developed and developing countries in both a cooperative and a non-cooperative setting. The study also takes into account the role of price signals and markets in inducing and diffusing adaptation. This leads to two scenarios: A pessimistic one, in which policy-driven adaptation bears the burden, together with mitigation, of reducing climate damage; and an optimistic one, in which markets also autonomously contribute to reducing some damages by modifying sectoral structure, international trade flows, capital distribution and land allocation. For all scenarios, the costs and benefits of adaptation are assessed using WITCH, an integrated assessment, intertemporal optimization, forward-looking model. Extensive sensitivity analysis with respect to the size of climate damages and of the discount rate has also been carried out.Climate change impacts, mitigation, adaptation, integrated assessment model

Engineering Adaptive Model-Driven User Interfaces for Enterprise Applications

Enterprise applications such as enterprise resource planning systems have numerous complex user interfaces (UIs). Usability problems plague these UIs because they are offered as a generic off-the-shelf solution to end-users with diverse needs in terms of their required features and layout preferences. Adaptive UIs can help in improving usability by tailoring the features and layout based on the context-of-use. The model-driven UI development approach offers the possibility of applying different types of adaptations on the various UI levels of abstraction. This approach forms the basis for many works researching the development of adaptive UIs. Yet, several gaps were identified in the state-of-the-art adaptive model-driven UI development systems. To fill these gaps, this thesis presents an approach that offers the following novel contributions: - The Cedar Architecture serves as a reference for developing adaptive model-driven enterprise application user interfaces. - Role-Based User Interface Simplification (RBUIS) is a mechanism for improving usability through adaptive behavior, by providing end-users with a minimal feature-set and an optimal layout based on the context-of-use. - Cedar Studio is an integrated development environment, which provides tool support for building adaptive model-driven enterprise application UIs using RBUIS based on the Cedar Architecture. The contributions were evaluated from the technical and human perspectives. Several metrics were established and applied to measure the technical characteristics of the proposed approach after integrating it into an open-source enterprise application. Additional insights about the approach were obtained through the opinions of industry experts and data from real-life projects. Usability studies showed the approach’s ability to significantly improve usability in terms of end-user efficiency, effectiveness and satisfaction

Model-Based Systems Engineering Approach to Distributed and Hybrid Simulation Systems

INCOSE defines Model-Based Systems Engineering (MBSE) as the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases. One very important development is the utilization of MBSE to develop distributed and hybrid (discrete-continuous) simulation modeling systems. MBSE can help to describe the systems to be modeled and help make the right decisions and partitions to tame complexity. The ability to embrace conceptual modeling and interoperability techniques during systems specification and design presents a great advantage in distributed and hybrid simulation systems development efforts. Our research is aimed at the definition of a methodological framework that uses MBSE languages, methods and tools for the development of these simulation systems. A model-based composition approach is defined at the initial steps to identify distributed systems interoperability requirements and hybrid simulation systems characteristics. Guidelines are developed to adopt simulation interoperability standards and conceptual modeling techniques using MBSE methods and tools. Domain specific system complexity and behavior can be captured with model-based approaches during the system architecture and functional design requirements definition. MBSE can allow simulation engineers to formally model different aspects of a problem ranging from architectures to corresponding behavioral analysis, to functional decompositions and user requirements (Jobe, 2008)

Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints

A multi-agent knowledge model for SMEs mechatronic supply chains.

19International audienceThe main concern of this research work is to analyse and model supply chains (SCs) in a particular context which is that of small and medium enterprises (SMEs) in the field of mechatronic. The study is based on the analysis of the organisational features, the actors' behaviour, and performance considerations. The development of the model relies on an iterative framework that progressively integrates different aspects into the model. This framework is the ArchMDE process, which is based on MDE (Model Driven Engineering). A major feature of this work lies in its contribution to two different areas of research. The first contribution of the work is to propose a generic metamodel for SCs. Based on a literature review, an incremental framework is proposed for the modelling of SCs in terms of concepts, structure and relationships. The application of the framework to the studied context is described and its result is a domain-metamodel for SCs. The second contribution of this work lies in the formalisation of the dynamic behaviour of the concepts in the metamodel. This formalisation is based on the multi-agent approach. An agentification of the metamodel is thus drawn, thanks to the natural links between multiagent theory and SC reality. This step leads to an agentified-domain-metamodel which also includes the monitoring of the SC and synchronisation protocols. By adding relationships and dynamic behavior aspects, we obtain a metamodel of the domain that can be implemented, with its static and dynamic aspects. To validate this model, an industrial case study is detailed and has been instantiated and encoded in JAVA

Structural Estimation with a Randomized Trial of a Principal Agent Model of Medical Insurance with Moral Hazard

Despite the importance of principal-agent models in the development of modern economic theory, there are few estimations of these models. We contribute to fill this gap in a field where moral hazard has traditionally been considered important: the utilization of health care services. This paper presents a model where the individual decides to have treatment or not when she suffers an illness spell. The decision is taken on the basis of comparing benefits and out-of-pocket monetary costs of treatment. In the paper, we recover the estimates of the corresponding principal agent model and obtain an approximation to the optimal contract.

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly