Winner-Take-All Contention of Innovation under Globalization: A Simulation Analysis and East Asia's Empirics

This paper sets out to examine how innovation enhances export competitiveness: The proposition that export volume becomes enhanced as more productivity-enhancing innovation is captured by the exporting economy is the focus of this study. From a Schumpeterian perspective, innovation can be characterized by continuous creation and subsequent diffusion of newer technologies on the basis of the exporters' existing capital stock. Then we highlight the theoretical possibility that concentration of innovative activities in a small group of "winner" economies would lead to larger shares of "winner" economies' exports of innovation-active commodities than those commodities for which technology involved is already mature. The world's export data corroborates this theoretical prediction overall, and a focus upon East Asia has revealed the region's increasing resort to technology-intensive commodity sectors, which has presumably been enabled through attracting technology-bearing inward foreign direct investment. Considering the overall gains from innovation, acceleration of full "cycle" of innovation and imitation might be a desirable option.

Neural bruise prediction models for fruit handling and machinery evaluation.

Some neural bruise prediction models have been implemented in the laboratory, for the most traded fruit species and varieties, allowing the prediction of the acceptability or rejectability for damages, with respect to the EC Standards. Different models have been built for both quasi-static (compression) and dynamic (impact) loads covering the whole commercial ripening period of fruits. A simulation process has been developed gathering the information on laboratory bruise models and load sensor calibrations for different electronic devices (IS-100 and DEA-1, for impact and compression loads respectively). Some evaluation methodology has been designed gathering the information on the mechanical properties of fruits and the loading records of electronic devices. The evaluation system allows to determine the current stage of fruit handling process and machinery.

Large vortex-like structure of dipole field in computer models of liquid water and dipole-bridge between biomolecules

We propose a framework to describe the cooperative orientational motions of water molecules in liquid water and around solute molecules in water solutions. From molecular dynamics (MD) simulation a new quantity “site-dipole field” is defined as the averaged orientation of water molecules that pass through each spatial position. In the site-dipole field of bulk water we found large vortex-like structures of more than 10 Å in size. Such coherent patterns persist more than 300 ps although the orientational memory of individual molecules is quickly lost. A 1-ns MD simulation of systems consisting of two amino acids shows that the fluctuations of site-dipole field of solvent are pinned around the amino acids, resulting in a stable dipole-bridge between side-chains of amino acids. The dipole-bridge is significantly formed even for the side-chain separation of 14 Å, which corresponds to five layers of water. The way that dipole-bridge forms sensitively depends on the side-chain orientations and thereby explains the specificity in the solvent-mediated interactions between biomolecules.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Improvement of mathematical models for simulation of vehicle handling. Volume 6: programmers' guide for the driver module. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Improvement of mathematical models for simulation of vehicle handling. Volume 1: user's guide for the five degree of freedom models. Final report.

Mode of access: Internet.

Improvement of mathematical models for simulation of vehicle handling. Volume 2: programmers' guide for the five degree of freedom models. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Improvement of mathematical models for simulation of vehicle handling. Volume 5: programmers' guide for the vehicle model. Part I: documentation through subroutine IOUT. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Improvement of mathematical models for simulation of vehicle handling. Volume 5: programmers' guide for the vehicle model. Part 2: documentation for subroutines PRTDIC through KNMTC. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Aboriginal self-determination in Australia - The effects of minority-majority frames and target universalism on majority collective guilt and compensation attitudes

In the context of Aboriginal-Anglo Australian relations, we tested the effect of framing (multiculturalism versus separatism) and majority group members' social values (universalism) on the persuasiveness of Aboriginal group rhetoric, majority collective guilt, attitudes toward compensation, and reparations for Aboriginals. As predicted, Anglo Australians who are low on universalism report more collective guilt when presented with a multiculturalist than a separatist Aboriginal frame, whereas those high on universalism report high levels of guilt independent of frame. The same pattern was predicted and found for the persuasiveness of the rhetoric and attitudes toward compensation. Our data suggest that (a) for individuals low in universalism, framing produces attitudes consonant with compensation because it produces collective guilt and (b) the reason that universalists are more in favor of compensation and reparation is because of high collective guilt. We discuss the strategic use of language to create power through the manipulation of collective guilt in political contexts.

Bad apples in bad barrels revisited: Cognitive moral development, just world beliefs, rewards, and ethical decision-making

In this study, we test the interactive effect on ethical decision-making of (1) personal characteristics, and (2) personal expectancies based on perceptions of organizational rewards and punishments. Personal characteristics studied were cognitive moral development and belief in a just world. Using an in-basket simulation, we found that exposure to reward system information influenced managers' outcome expectancies. Further, outcome expectancies and belief in a just world interacted with managers' cognitive moral development to influence managers' ethical decision-making. In particular, low-cognitive moral development managers who expected that their organization condoned unethical behavior made less ethical decisions while high cognitive moral development managers became more ethical in this environment. Low cognitive moral development managers also behaved less ethically when their belief in a just world was high.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Cognitive Approach to e-Learning in Sciences and Technologies

This article describes the approach adopted and the results obtained by the international team developing WBLST (Web Based Learning in Sciences and Technologies) a Web-based application for e-learning, developed for the students of “UVPL: Université Virtuelle des Pays de la Loire”. The developed e-learning system covers three levels of learning activities - content, exercises, and laboratory. The delivery model is designed to operate with domain concepts as relevant providers of semantic links. The aim is to facilitate the overview and to help the establishment of a mental map of the learning material. The implemented system is strongly based on the organization of the instruction in virtual classes. The obtained quality of the system is evaluated on the bases of feedback form students and professors.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.