E-RUN version 1.1: Observational gridded runoff estimates for Europe, link to data in NetCDF format (69 MB)

River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first assemble an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 12) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950 - December 2015) on a 0.5° x 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

E-RUN version 1.0: Observational gridded runoff estimates for Europe, link to data in NetCDF format (68 MB)

River runoff is an essential climate variable as it is directly linked to the terrestrial water balance and controls a wide range of climatological and ecological processes. Despite its scientific and societal importance, there are to date no pan-European observation-based runoff estimates available. Here we employ a recently developed methodology to estimate monthly runoff rates on regular spatial grid in Europe. For this we first collect an unprecedented collection of river flow observations, combining information from three distinct data bases. Observed monthly runoff rates are first tested for homogeneity and then related to gridded atmospheric variables (E-OBS version 11) using machine learning. The resulting statistical model is then used to estimate monthly runoff rates (December 1950-December 2014) on a 0.5° × 0.5° grid. The performance of the newly derived runoff estimates is assessed in terms of cross validation. The paper closes with example applications, illustrating the potential of the new runoff estimates for climatological assessments and drought monitoring.

Predicting Academic Performance Based on Students' Blog and Microblog Posts

This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in the context of a project-based learning scenario run on our eMUSE platform. Data is collected from six student cohorts, from six consecutive installments of the Web Applications Design course, comprising of 343 students. A significant model was obtained by relying on the textual complexity and longitudinal analysis indices, applied on the English contributions of 148 students that were actively involved in the undertaken projects.

Enhancing the numerical performance of fire field models (CMS Paper)

This paper describes two new techniques designed to enhance the performance of fire field modelling software. The two techniques are "group solvers" and automated dynamic control of the solution process, both of which are currently under development within the SMARTFIRE Computational Fluid Dynamics environment. The "group solver" is a derivation of common solver techniques used to obtain numerical solutions to the algebraic equations associated with fire field modelling. The purpose of "group solvers" is to reduce the computational overheads associated with traditional numerical solvers typically used in fire field modelling applications. In an example, discussed in this paper, the group solver is shown to provide a 37% saving in computational time compared with a traditional solver. The second technique is the automated dynamic control of the solution process, which is achieved through the use of artificial intelligence techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxation using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate the potential for enhanced solution reliability due to obtaining acceptable convergence within each time step, unlike some of the comparison simulations.

Online MBPeT dashboard : enabling performance testing as a service in the cloud

In this thesis, tool support is addressed for the combined disciplines of Model-based testing and performance testing. Model-based testing (MBT) utilizes abstract behavioral models to automate test generation, thus decreasing time and cost of test creation. MBT is a functional testing technique, thereby focusing on output, behavior, and functionality. Performance testing, however, is non-functional and is concerned with responsiveness and stability under various load conditions. MBPeT (Model-Based Performance evaluation Tool) is one such tool which utilizes probabilistic models, representing dynamic real-world user behavior patterns, to generate synthetic workload against a System Under Test and in turn carry out performance analysis based on key performance indicators (KPI). Developed at Åbo Akademi University, the MBPeT tool is currently comprised of a downloadable command-line based tool as well as a graphical user interface. The goal of this thesis project is two-fold: 1) to extend the existing MBPeT tool by deploying it as a web-based application, thereby removing the requirement of local installation, and 2) to design a user interface for this web application which will add new user interaction paradigms to the existing feature set of the tool. All phases of the MBPeT process will be realized via this single web deployment location including probabilistic model creation, test configurations, test session execution against a SUT with real-time monitoring of user configurable metric, and final test report generation and display. This web application (MBPeT Dashboard) is implemented with the Java programming language on top of the Vaadin framework for rich internet application development. The Vaadin framework handles the complicated web communications processes and front-end technologies, freeing developers to implement the business logic as well as the user interface in pure Java. A number of experiments are run in a case study environment to validate the functionality of the newly developed Dashboard application as well as the scalability of the solution implemented in handling multiple concurrent users. The results support a successful solution with regards to the functional and performance criteria defined, while improvements and optimizations are suggested to increase both of these factors.

Types of embedded ties in buyer-supplier relationships and their combined effects on innovation performance

[EN] Purpose of the paper - This research analyzes the impact of three types of embedded ties, namely, specialized complementary resources, idiosyncratic investments, and knowledge sharing, on the innovation capacity of the firms. We also study the particularities of the Machine-Tool industry. Theoretical background – Our evaluation of the embedded buyer-supplier ties is based on the potential sources of relational rents proposed by Dyer and Sing (1998). We also draw on Uzzi and Lancaster (2003), Noordhoff et al. (2011), among others, to discuss the positive and negative aspects of embedded ties. Design/Methodology/Approach ‐ Using data from a survey of 202 European machine-tool firms acting as buyers and sellers, we propose and evaluate a Structural Equation model. Findings ‐ Only knowledge-sharing routines exert a significant positive effect on product innovation performance. Neither an increase in the idiosyncratic investments nor in complementary resources and capabilities enhance innovation performance. Also, knowledge-sharing routines mediate in the effect from idiosyncratic investments on innovation performance. Research Limitations. ‐ The machine tool industry has unique characteristics that make this generalization difficult. Also, there is considerable difficulty associated with testing more deeply the interrelations among these embedded ties in the long run. It is plausible to understand that these interrelations operate within a gradual process. Originality/Value/Contribution of Paper ‐ This research contributes to a better understanding of the role of embedded ties on innovativeness. To the best of our knowledge, there is no previous international empirical research analyzing the mediation effects among specialized complementary resources, idiosyncratic investments and knowledge sharing, and their effects on the innovation capacity of firms.

Easy PRAM-based High-performance Parallel Programming with ICE

A poster of this paper will be presented at the 25th International Conference on Parallel Architecture and Compilation Technology (PACT ’16), September 11-15, 2016, Haifa, Israel.

A Hardware Implementation of a Run-Time Scheduler for Reconfigurable Systems

New generation embedded systems demand high performance, efficiency and flexibility. Reconfigurable hardware can provide all these features. However the costly reconfiguration process and the lack of management support have prevented a broader use of these resources. To solve these issues we have developed a scheduler that deals with task-graphs at run-time, steering its execution in the reconfigurable resources while carrying out both prefetch and replacement techniques that cooperate to hide most of the reconfiguration delays. In our scheduling environment task-graphs are analyzed at design-time to extract useful information. This information is used at run-time to obtain near-optimal schedules, escaping from local-optimum decisions, while only carrying out simple computations. Moreover, we have developed a hardware implementation of the scheduler that applies all the optimization techniques while introducing a delay of only a few clock cycles. In the experiments our scheduler clearly outperforms conventional run-time schedulers based on As-Soon-As-Possible techniques. In addition, our replacement policy, specially designed for reconfigurable systems, achieves almost optimal results both regarding reuse and performance.

Variability and predictability of elite competitive slalom canoe-kayak performance

Little is known about the race performance characteristics of elite-level slalom canoeists or the magnitude of improvement necessary to enhance medal-winning prospects. Final placing in this sport is determined by the aggregate of semi-final and final run times inclusive of penalty times. We therefore used mixed linear modelling to analyse these times for finalists ranked in the top and bottom half in the men's canoe, men's kayak, and women's kayak boat classes at World Cups, World Championships, and Olympic Games from 2000 to 2007. The run-to-run variability for top-ranked athletes at different courses ranged from 0.8% to 3.2% (90% confidence limits ×/÷1.11-1.31), reflecting differences in how challenging these courses were. The race-to-race variability of aggregate run time was 1.2-2.1% (×/÷~1.09); 0.3 of this variability yields the smallest worthwhile enhancement of 0.4-0.6%. The variabilities of bottom-ranked finalists were approximately double those of top-ranked finalists. The home advantage was small (0.3-0.8%), and incurring a penalty had a marginal effect on reducing actual run time (0.2-0.7%). Correlation coefficients for performance predictability within competitions (0.06-0.35), within years (0.12-0.47), and between years (0.12-0.43) were poor. In conclusion, the variability of performance and smallest worthwhile enhancements in slalom canoe-kayaking are larger than those of comparable sports, and race outcomes are largely unpredictable.

Determination of long-run and short-run dynamics in EC-VARMA models via canonical correlations

This article studies a simple, coherent approach for identifying and estimating error-correcting vector autoregressive moving average (EC-VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short-run VARMA dynamics, using the scalar component methodology. Finite-sample performance is evaluated via Monte Carlo simulations and the approach is applied to modelling and forecasting US interest rates. The results reveal that EC-VARMA models generate significantly more accurate out-of-sample forecasts than vector error correction models (VECMs), especially for short horizons.

Cross-sectional determinants of post-IPO stock performance: evidence from China

This paper examines the cross-sectional determinants of post-IPO long-term stock returns in China. We document that the aftermarket P/E ratio has the most robust negative association with post-IPO stock returns. The negative relation indicates that the market corrects the aftermarket overvaluation of IPO firms in the long run. Underwriter reputation has a positive effect on post-IPO stock returns, while board size has a negative impact, consistent with the views that reputable underwriters mitigate the information asymmetry in IPO pricing and over-sized boards reduce the effectiveness of corporate governance. However, we find little evidence indicating that the equity ownership structure is significantly associated with post-IPO stock returns.