Uncertainty quantification and apportionment in air quality models using the polynomial chaos method

Current air quality models generate deterministic forecasts by assuming perfect model, perfectly known parameters, and exact input data. However, our knowledge of the physics is imperfect. It is of interest to extend the deterministic simulation results with “error bars” that quantify the degree of uncertainty, and analyze the impact of the uncertainty input on the simulation results. This added information provides a confidence level for the forecast results. Monte Carlo (MC) method is a popular approach for air quality model uncertainty analysis, but it converges slowly. This work discusses the polynomial chaos (PC) method that is more suitable for uncertainty quantification (UQ) in large-scale models. We propose a new approach for uncertainty apportionment (UA), i.e., we develop a PC approach to attribute the uncertainties in model results to different uncertainty inputs. The UQ and UA techniques are implemented in the Sulfur Transport Eulerian Model (STEM-III). A typical scenario of air pollution in the northeast region of the USA is considered. The UQ and UA results allow us to assess the combined effects of different input uncertainties on the forecast uncertainty. They also enable to quantify the contribution of input uncertainties to the uncertainty in the predicted ozone and PAN concentrations.     

A framework for implementing robotic process automation projects

Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.

     

Beyond loop bounds: comparing annotation languages for worst-case execution time analysis

Worst-case execution time (WCET) analysis is concerned with computing a precise-as-possible bound for the maximum time the execution of a program can take. This information is indispensable for developing safety-critical real-time systems, e. g., in the avionics and automotive fields. Starting with the initial works of Chen, Mok, Puschner, Shaw, and others in the mid and late 1980s, WCET analysis turned into a well-established and vibrant field of research and development in academia and industry. The increasing number and diversity of hardware and software platforms and the ongoing rapid technological advancement became drivers for the development of a wide array of distinct methods and tools for WCET analysis. The precision, generality, and efficiency of these methods and tools depend much on the expressiveness and usability of the annotation languages that are used to describe feasible and infeasible program paths. In this article we survey the annotation languages which we consider formative for the field. By investigating and comparing their individual strengths and limitations with respect to a set of pivotal criteria, we provide a coherent overview of the state of the art. Identifying open issues, we encourage further research. This way, our approach is orthogonal and complementary to a recent approach of Wilhelm et al. who provide a thorough survey of WCET analysis methods and tools that have been developed and used in academia and industry.     

Usability evaluation methods for the web: A systematic mapping study

Context

In recent years, many usability evaluation methods (UEMs) have been employed to evaluate Web applications. However, many of these applications still do not meet most customers’ usability expectations and many companies have folded as a result of not considering Web usability issues. No studies currently exist with regard to either the use of usability evaluation methods for the Web or the benefits they bring.

Objective

The objective of this paper is to summarize the current knowledge that is available as regards the usability evaluation methods (UEMs) that have been employed to evaluate Web applications over the last 14 years.

Method

A systematic mapping study was performed to assess the UEMs that have been used by researchers to evaluate Web applications and their relation to the Web development process. Systematic mapping studies are useful for categorizing and summarizing the existing information concerning a research question in an unbiased manner.

Results

The results show that around 39% of the papers reviewed reported the use of evaluation methods that had been specifically crafted for the Web. The results also show that the type of method most widely used was that of User Testing. The results identify several research gaps, such as the fact that around 90% of the studies applied evaluations during the implementation phase of the Web application development, which is the most costly phase in which to perform changes. A list of the UEMs that were found is also provided in order to guide novice usability practitioners.

Conclusions

From an initial set of 2703 papers, a total of 206 research papers were selected for the mapping study. The results obtained allowed us to reach conclusions concerning the state-of-the-art of UEMs for evaluating Web applications. This allowed us to identify several research gaps, which subsequently provided us with a framework in which new research activities can be more appropriately positioned, and from which useful information for novice usability practitioners can be extracted.     

Algorithms to Separate $\{0,\frac{1}{2}\}$ -Chvátal-Gomory Cuts

Chvátal-Gomory cuts are among the most well-known classes of cutting planes for general integer linear programs (ILPs). In case the constraint multipliers are either 0 or , such cuts are known as -cuts. It has been proven by Caprara and Fischetti (Math. Program. 74:221–235, 1996) that separation of -cuts is -hard. In this paper, we study ways to separate -cuts effectively in practice. We propose a range of preprocessing rules to reduce the size of the separation problem. The core of the preprocessing builds a Gaussian elimination-like procedure. To separate the most violated -cut, we formulate the (reduced) problem as integer linear program. Some simple heuristic separation routines complete the algorithmic framework. Computational experiments on benchmark instances show that the combination of preprocessing with exact and/or heuristic separation is a very vital idea to generate strong generic cutting planes for integer linear programs and to reduce the overall computation times of state-of-the-art ILP-solvers.     

A flexible,object-centric approach for business process modelling

Mainstream business process modelling techniques often promote a design paradigm wherein the activities that may be performed within a case, together with their usual execution order, form the backbone on top of which other aspects are anchored. This Fordist paradigm, while effective in standardised and production-oriented domains, breaks when confronted with processes in which case-by-case variations and exceptions are the norm. We contend that the effective design of flexible processes calls for a substantially different modelling paradigm. Motivated by requirements from the human services domain, we explore the hypothesis that a framework consisting of a small set of coordination concepts, combined with established object-oriented modelling principles, provides a suitable foundation for designing highly flexible processes. Several human service delivery processes have been designed using this framework, and the resulting models have been used to realise a system to support these processes in a pilot environment.     

Multirate Explicit Adams Methods for Time Integration of Conservation Laws

This paper constructs multirate linear multistep time discretizations based on Adams-Bashforth methods. These methods are aimed at solving conservation laws and allow different timesteps to be used in different parts of the spatial domain. The proposed family of discretizations is second order accurate in time and has conservation and linear and nonlinear stability properties under local CFL conditions. Multirate timestepping avoids the necessity to take small global timesteps—restricted by the largest value of the Courant number on the grid—and therefore results in more efficient computations. Numerical results obtained for the advection and Burgers’ equations confirm the theoretical findings. This work was supported by the National Science Foundation through award NSF CCF-0515170.     

Reduced Aggregate Scattering Operators for Path Tracing

Aggregate scattering operators (ASOs) describe the overall scattering behavior of an asset (i.e., an object or volume, or collection thereof) accounting for all orders of its internal scattering. We propose a practical way to precompute and compactly store ASOs and demonstrate their ability to accelerate path tracing. Our approach is modular avoiding costly and inflexible scene‐dependent precomputation. This is achieved by decoupling light transport within and outside of each asset, and precomputing on a per‐asset level. We store the internal transport in a reduced‐dimensional subspace tailored to the structure of the asset geometry, its scattering behavior, and typical illumination conditions, allowing the ASOs to maintain good accuracy with modest memory requirements. The precomputed ASO can be reused across all instances of the asset and across multiple scenes. We augment ASOs with functionality enabling multi‐bounce importance sampling, fast short‐circuiting of complex light paths, and compact caching, while retaining rapid progressive preview rendering. We demonstrate the benefits of our ASOs by efficiently path tracing scenes containing many instances of objects with complex inter‐reflections or multiple scattering.     

Expedited simulation‐driven design optimization of UWB antennas by means of response features

In this work, a method for fast design optimization of broadband antennas is considered. The approach is based on a feature‐based optimization (FBO) concept where reflection characteristics of the structure at hand are formulated in terms of suitably defined feature points. Redefinition of the design problem allows for reducing the design optimization cost, because the dependence of feature point coordinates on antenna dimensions is less nonlinear than for the original frequency characteristics (here, S‐parameters). This results in faster convergence of the optimization algorithm. The cost of the design process is further reduced using variable‐fidelity electromagnetic (EM) simulation models. In case of UWB antennas, the feature points are defined, among others, as the levels of the reflection characteristic at its local in‐band maxima, as well as location of the frequency point which corresponds to acceptable reflection around the lower corner frequency within the UWB band. Also, the number of characteristic points depends on antenna topology and its dimensions. Performance of FBO‐based design optimization is demonstrated using two examples of planar UWB antennas. Moreover, the computational cost of the approach is compared with conventional optimization driven by a pattern search algorithm. Experimental validation of the numerical results is also provided.     

Maximal controllability of input constrained unstable systems by the addition of implicit constraints

The null controllable set of a system is the largest set of states that can be controlled to the origin. Control systems that have a region of attraction equal to the null controllable set are said to be maximally controllable closed-loop systems. In the case of open-loop unstable plants with amplitude constrained control it is well known that the null controllable set does not cover the entire state-space. Further the combination of input constraints and unstable system dynamics results in a set of state constraints which we call implicit constraints. It is shown that the simple inclusion of implicit constraints in a controller formulation results in a controller that achieves maximal controllability for a class of open-loop unstable systems.