ProVoc: An app to train vocabulary depth in order to foster children's reading comprehension

Many studies have demonstrated the crucial role of vocabulary in predicting reading performance in general. More recent work has indicated that one particular facet of vocabulary (its depth) is more closely related to language comprehension, especially inferential comprehension. On this basis, we developed a training application to specifically improve vocabulary depth. The objective of this study was to test the effectiveness of a mobile application designed to improve vocabulary depth. The effectiveness of this training was examined on 3rd and 4th grade children's vocabulary (breadth and depth), decoding and comprehension performances. A randomized waiting-list control paradigm was used in which an experimental group first received the intervention during the first 4 weeks (between pretest and post-test1), thereafter, a waiting control group received the training for the next 4 weeks (between postest1 and posttest2). Results showed that the developed application led to significant improvements in terms of vocabulary depth performance, as well as a significant transfer effect to reading comprehension. However, we did not observe such a beneficial effect on either vocabulary breadth or written word identification. These results are discussed in terms of the links between vocabulary depth and comprehension, and the opportunities the app presents for remedying language comprehension deficits in children.     

Residual Stress Evolution in Repair Welds

Abstract: The aim of this paper is to study the influence of the repair procedure on the evolution of residual stress distribution in butt welds. C-Mn steel coupon plates of 11 mm thickness were prepared, subjected to stress relief heat treatment and welded with two beads, using the gas metal arc welding process. After the deposition of two weld beads, two successive repair welds were made on the middle length of the second bead. Longitudinal and transverse residual stresses were measured in the vicinity of the weld, after deposition of the two weld beads and after each repair weld. Residual stress evaluation was conducted using an X-ray diffraction method. The repair welds caused a decrease in magnitude of the initial longitudinal residual welding stresses, and an increase of the transverse residual stress magnitude, in tension at points within the repair length, and in compression at points outside the repair length.     

Structure discovery in medical databases: a conceptual clustering approach

Clustering is an important data analysis tool for discovering structure in data sets. Although research on conceptual clustering has produced algorithms showing significant advantages over earlier numerical ones, existing methods still present some limitations regarding applicability to biomedical domains. In this paper we describe ADAGIO, a conceptual clustering algorithm combining a low-cost preordering process with a breadth-first incremental control strategy that incorporates merging and splitting operators. Experimental evaluation indicated that the algorithm achieves a good balance between structure discovery performance and computational efficiency, and demonstrated the comparative effectiveness of its missing information handling process. ADAGIO is able to handle qualitative, quantitative and mixed-type data. An application example to a cancer domain is given, where the algorithm was able to suggest interesting epidemiological interpretations.     

The longitudinal impact of enterprise system users’ pre-adoption expectations and organizational support on post-adoption proficient usage

Although enterprise systems (ES) are ubiquitous, many firms report less than stellar payoffs from these costly investments, with underutilization often attributed to failures in the implementation process. Unfortunately, research has not provided sufficient insights into these failures, in part because it has focused on actual usage, as opposed to proficient usage, as the benchmark for successful implementation. Moreover, research has not generally examined how the adoption process unfolds over time, thus overlooking potential underlying mechanisms that may help explain how adopters achieve proficiency. To begin addressing these shortcomings, we study how adopters’ pre-adoption expectations, enacted over time, can influence their post-adoption proficiency, by shaping how and why they spend time using the system during the adoption period. We analyzed time-lagged survey data from 153 financial analysts, required to adopt new ES-based software, at a multinational bank. We found that adopters who hold pre-adoption expectations reflecting greater internal and external motives to adopt the system as well as systematically integrate it into their work routines are more apt to use the system in ways that enhance their cumulative knowledge of it, and subsequently develop higher levels of proficiency post-adoption. Moreover, greater organizational support enhanced the impact of adopters’ expectations on proficiency, except when their actual use is low in which case organizational support had an adverse effect.     

IsoGeometric analysis using T-splines on two-patch geometries

We develop optimal approximation estimates for T-splines in the case of geometries obtained by gluing two standard tensor product patches. We derive results both for the T-spline space in the parametric domain and the mapped T-NURBS in the physical one. A set of numerical tests in complete accordance with the theoretical developments is also presented.     

A hybrid linear-neural model for time series forecasting

This paper considers a linear model with time varying parameters controlled by a neural network to analyze and forecast nonlinear time series. We show that this formulation, called neural coefficient smooth transition autoregressive model, is in close relation to the threshold autoregressive model and the smooth transition autoregressive model with the advantage of naturally incorporating linear multivariate thresholds and smooth transitions between regimes. In our proposal, the neural-network output is used to induce a partition of the input space, with smooth and multivariate thresholds. This also allows the choice of good initial values for the training algorithm.     

Histology, histochemistry and stereology of the adipose fin of Prochilodus lineatus

The adipose fin is small, nonpared, and usually located medially between the dorsal and caudal fin. Its taxonomic occurrence is very restrict; thus, it represents an important trace for taxon distinction. As it does not play a known vital physiological roll and it is easily removed, it is commonly used in marking and recapture studies. The present study characterizes the adipose fin of Prochilodus lineatus, as it is poorly explored by the literature. The adipose fin consists basically of a loose connective core, covered by a stratified epithelium supported by collagen fibers. At the epithelium, pigmented cells and alarm substance cells are found. Despite the name, adipocytes or lipid droplets are not observed on the structure of the fin.     

Energy Efficient Cloud Service Provisioning: Keeping Data Center Granularity in Perspective

The cost of power and its associated delivery are becoming significant factors in the total expenditure of large-scale data centers. Numerous techniques have been proposed to address the energy efficiency issue in cloud systems. Recently, some efforts have been made to decentralize the cloud via distributing data centers in diverse geographical positions, at different scales. In this paper, we elaborate on the energy effectiveness of service provisioning on different cloud architectures, from a mega-data center to a nano data center, which provides the extreme decentralization in terms of cloud architecture, as well as P2P-clouds or community network clouds. We study the energy consumption through an analytical and simulation framework for video streaming and MapReduce applications.     

A Linear Stochastic Programming Model for Optimal Leveraged Portfolio Selection

The literature of portfolio optimization is extensive and covers several important aspects of the asset allocation problem. However, previous works consider simplified linear borrowing cost functions that leads to suboptimal allocations. This paper aims at efficiently solving the leveraged portfolio selection problem with a thorough borrowing cost representation comprising a number lenders with different rates and credit limits. We propose a two-stage stochastic programming model for asset and debt allocation considering a CVaR-based risk constraint and a convex piecewise-linear borrowing cost function. We compare our model to its counterpart with the fixed borrowing rate approximation used in literature. Numerical results show our model significantly improves performance in terms of risk-return trade-off.     

The ECHORD project proposals analysis – Research profiles,collaboration patterns and research topic trends

This paper investigates the research profiles, collaboration patterns and research topic trends which can be identified in the proposals submitted to the ECHORD (European Clearing House for Open Robotics Development) FP7 project. On a country level, clusters were identified and characterized by patterns of proposal production per inhabitant, score and international cooperation. Belgium and Sweden constitute a cluster characterized by high proposal production, with very high scores and extensive international collaboration. Belgium also excels from another cluster analysis, being as the only country where 100% of proposals involve industry–academia cooperation and obtain scores above 10. Other findings show that single partner proposals have significantly lower quality than multi-partner proposals but, on the other hand, the number of countries involved shows no influence on the quality of the proposals. Despite the high number of industrial participants present on the proposals, it is observed that they play secondary roles in the proposals, with a very low number projects leaded by companies. Also, it is observed that partnerships between research institutions (non-universities) are the most successful. Concerning topics of the proposals, the technology human–robot interface and the product vision robot for small-scale manufacturing are the most significant. Finally, the paper shows clusters of institutions extracted from the giant network of relations obtained from the ECHORD set of proposals.