Entrepreneurial orientation and international performance: the moderating effect of decision-making rationality

This research examines how entrepreneurial orientation (EO) influences international performance (IP) of the firm taking into account the moderating effect of decision-making rationality (DR) on the EO–IP association. Such an investigation is significant because it considers the interplay of strategic decision-making processes supported by the bounded rationality concept in the entrepreneurship field. Drawing from a study on activities of 216 firms in the United States and United Kingdom, the evidence suggests that DR positively moderates the EO–IP association. The findings suggest that managers can improve IP by combining EO with rational (analytical) processes in their strategic decisions

Pragmatic factors that determine main clause constituent order variation in Greek: a diachronic perspective

This article sets out to empirically assess the theoretical claim that there is a move towards syntacticization in Modern Greek.  This is conducted within Lambrecht’s (1994) theoretical framework of different information-structure types, which had to be specially adapted for Greek. In Classical Greek, it is not possible to identify a single word order pattern as the unmarked one. Furthermore, there is no direct mapping between syntactic constructions and pragmatic contexts. By contrast, in Modern Greek, SV(O) has been ‘promoted’ to the status of the unmarked word order type. There is also a more direct correlation between syntactic configurations and pragmatic functions

Modelling drivers’ braking behaviour and comfort under normal driving

The increasing growth of population and a rising number of vehicles, connected to an individual, demand new solutions to reduce traffic delays and enhance road safety. Autonomous Vehicles (AVs) have been considered as an optimal solution to overcome those problems. Despite the remarkable research and development progress in the area of (semi) AVs over the last decades, there is still concern that occupants may not feel safe and comfortable due to the robot-like driving behaviour of the current technology. In order to facilitate their rapid uptake and market penetration, ride comfort in AVs must be ensured.Braking behaviour has been identified to be a crucial factor in ride comfort. There is a dearth of research on which factors affect the braking behaviour and the comfort level while braking and which braking profiles make the occupants feel safe and comfortable. Therefore, the primary aim of this thesis is to model the deceleration events of drivers under normal driving conditions to guide comfortable braking design. The aim was achieved by exploiting naturalistic driving data from three projects: (1) the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project, (2) the Field Operational Test (FOT) conducted by Loughborough University and Original Equipment Manufacturer (OEM), and (3) the UDRIVE Naturalistic Driving Study.A total of about 35 million observations were examined from 86 different drivers and 644 different trips resulting in almost 10,000 deceleration events for the braking features analysis and 21,600 deceleration events for the comfort level analysis. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. The examined factors were kinematics, situational, driver and trip characteristics with the first two categories to affect the most the deceleration features. More specifically, the initial speed and the reason for braking play a significant role, whereas the driver’s characteristics, i.e. the age and gender do not affect the deceleration features, except for driver’s experience which significantly affects the deceleration duration.An algorithm was developed to calculate the braking profiles, indicating that the most used profile follows smooth braking at the beginning followed by a harder one. Moreover, comfort levels of drivers were analysed using the Mixed Multinomial Logit models to identify the effect of the explanatory factors on the comfort category of braking events. Kinematic factors and especially TTC and time headway (THW) were found to affect the most the comfort level. Particularly, when TTC or THW are increased by 1 second, the odds of the event to be “very comfortable” are respectively 1.03 and 4.5 times higher than being “very uncomfortable”. Moreover, the driver’s characteristic, i.e. age and gender affect significantly the comfort level of the deceleration event. Findings from this thesis can support vehicle manufacturers to ensure comfortable and safe braking operations of AVs.</div

Intention Detection of Gait Adaptation in Natural Settings

Gait adaptation is an important part of gait analysis and its neuronal origin and dynamics has been studied extensively. In neurorehabilitation, it is important as it perturbs neuronal dynamics and allows patients to restore some of their motor function. Exoskeletons and robotics of the lower limbs are increasingly used to facilitate rehabilitation as well as supporting daily function. Their efficiency and safety depends on how well can sense the human intention to move and adapt the gait accordingly. This paper presents a gait adaptation scheme in natural settings. It allows monitoring of subjects in more realistic environment without the requirement of specialized equipment such as treadmill and foot pressure sensors. We extract gait characteristics based on a single RBG camera whereas wireless EEG signals are monitored simultaneously. We demonstrate that the method can not only successfully detect adaptation steps but also detect efficiently whether the subject adjust their pace to higher or lower speed

Use of baked milk challenges and milk ladders in clinical practice: a worldwide survey of healthcare professionals

In previous years, the cornerstone of the management of Cow's Milk Allergy (CMA) was solely based on the strict avoidance of all cow's milk (CM) and foods containing CM from the patient's diet [1]. More recently, the importance of baked milk (BM) introduction into the diet of children with CMA has become well-recognised as a part of CMA management. Current research suggests that 75% of children become tolerant to baked/heated forms of CM such as muffin and waffles before they become tolerant to pure/uncooked forms of CM [2]

Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols

Traditional methods of training a Brain-Computer Interface (BCI) on motor imagery (MI) data generally involve multiple intensive sessions. The initial sessions produce simple prompts to users, while later sessions additionally provide realtime feedback to users, allowing for human adaptation to take place. However, this protocol only permits the BCI to update between sessions, with little real-time evaluation of how the classifier has improved. To solve this problem, we propose an adaptive BCI training framework which will update the classifier in real time to provide more accurate feedback to the user on 4-class motor imagery data. This framework will require only one session to fully train a BCI to a given subject. Three variations of an adaptive Riemannian BCI were implemented and compared on data from both our own recorded datasets and the commonly used BCI Competition IV Dataset 2a. Results indicate that the fastest and least computationally expensive adaptive BCI was able to correctly classify motor imagery data at a rate 5.8% higher than when using a standard protocol with limited data. In addition it was confirmed that the adaptive BCI automatically improved its performance as more data became available

Reconstruction of 3D deformation from 2D MR velocity mapping with incompressibility constraints

This paper presents a new method for calculating 3D myocardial deformation from multislice 2D magnetic resonance velocity mapping. The method first involves the rectification of in-plane velocity distribution with a variational vector restoration method. This restored 2D velocity is then used to estimate the through-plane velocity component by applying a local incompressibility constraint. A global optimization procedure was then used to derive the velocity distribution that conforms to the incompressibility constraint. The proposed method was validated by using a simulation phantom with different levels of noise. The derived velocity field permits a full 3D deformation analysis of the myocardium

Comparison of Brain Networks based on Predictive Models of Connectivity

In this study we adopt predictive modelling to identify simultaneously commonalities and differences in multi-modal brain networks acquired within subjects. Typically, predictive modelling of functional connectomes from structural connectomes explores commonalities across multimodal imaging data. However, direct application of multivariate approaches such as sparse Canonical Correlation Analysis (sCCA) applies on the vectorised elements of functional connectivity across subjects and it does not guarantee that the predicted models of functional connectivity are Symmetric Positive Matrices (SPD). We suggest an elegant solution based on the transportation of the connectivity matrices on a Riemannian manifold, which notably improves the prediction performance of the model. Randomised lasso is used to alleviate the dependency of the sCCA on the lasso parameters and control the false positive rate. Subsequently, the binomial distribution is exploited to set a threshold statistic that reflects whether a connection is selected or rejected by chance. Finally, we estimate the sCCA loadings based on a de-noising approach that improves the estimation of the coefficients. We validate our approach based on resting-state fMRI and diffusion weighted MRI data. Quantitative validation of the prediction performance shows superior performance, whereas qualitative results of the identification process are promising.Comment: 7 pages, 4 figure