N=2 Superstrings with (1,2m) Spacetime Signature

We show that the $N=2$ superstring in $d=2D\ge6$ real dimensions, with criticality achieved by including background charges in the two real time directions, exhibits a ``coordinate-freezing'' phenomenon, whereby the momentum in one of the two time directions is constrained to take a specific value for each physical state. This effectively removes this time direction as a physical coordinate, leaving the theory with $(1,d-2)$ real spacetime signature. Norm calculations for low-lying physical states suggest that the theory is ghost free.Comment: 8 page

Luttinger liquid versus charge density wave behaviour in the one-dimensional spinless fermion Holstein model

We discuss the nature of the different ground states of the half-filled Holstein model of spinless fermions in 1D. In the metallic regime we determine the renormalised effective coupling constant and the velocity of the charge excitations by a density-matrix renormalisation group (DMRG) finite-size scaling approach. At low (high) phonon frequencies the Luttinger liquid is characterised by an attractive (repulsive) effective interaction. In the charge-density wave Peierls-distorted state the charge structure factor scales to a finite value indicating long-range order.Comment: 2 pages, 3 figures, submitted to SCES'0

Spectrum of TeV Particles in Warped Supersymmetric Grand Unification

In warped supersymmetric grand unification, XY gauge particles appear near the TeV scale along with Kaluza-Klein towers of the standard model gauge fields. In spite of this exotic low-energy physics, MSSM gauge coupling unification is preserved and proton decay is naturally suppressed. In this paper we study in detail the low-lying mass spectrum of superparticles and GUT particles in this theory, taking supersymmetry breaking to be localized to the TeV brane. The masses of the MSSM particles, Kaluza-Klein modes, and XY states are all determined by two parameters, one which fixes the strength of the supersymmetry breaking and the other which sets the scale of the infrared brane. A particularly interesting result is that for relatively strong supersymmetry breaking, the XY gauginos and the lowest Kaluza-Klein excitations of the MSSM gauginos may both lie within reach of the LHC, providing the possibility that the underlying unified gauge symmetry and the enhanced N=2 supersymmetry of the theory will both be revealed.Comment: 29 pages, 5 figure

Review of EEG-based pattern classification frameworks for dyslexia

Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia

Cold Induction of EARLI1, a Putative Arabidopsis Lipid Transfer Protein, Is Light and Calcium Dependent

As sessile organisms, plants must adapt to their environment. One approach toward understanding this adaptation is to investigate environmental regulation of gene expression. Our focus is on the environmental regulation of EARLI1, which is activated by cold and long-day photoperiods. Cold activation of EARLI1 in short-day photoperiods is slow, requiring several hours at 4ºC to detect an increase in mRNA abundance. EARLI1 is not efficiently cold-activated in etiolated seedlings, suggesting that photomorphogenesis is necessary for its cold activation. Cold activation of EARLI1 is inhibited in the presence of the calcium channel blocker lanthanum chloride or the calcium chelator EGTA. Addition of the calcium ionophore Bay K8644 results in cold-independent activation of EARLI1. These data suggest that EARLI1 is not an immediate target of the cold response, and that calcium flux affects its expression. EARLI1 is a putative secreted protein and has motifs found in lipid transfer proteins. Over-expression of EARLI1 in transgenic plants results in reduced electrolyte leakage during freezing damage, suggesting that EARLI1 may affect membrane or cell wall stability in response to low temperature stress

The use of animal sensor data for predicting sheep metabolisable energy intake using machine learning

The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems. The work presented in this paper aimed to develop a model for predicting the metabolisable energy intake (MEI) of sheep by using temperature, pitch angle, roll angle, distance, speed, and grazing time data obtained directly from wearable sensors on the sheep. A Deep Belief Network (DBN) algorithm was used to predict MEI, which to our knowledge, has not been attempted previously. The results demonstrated that the DBN method could predict the MEI for sheep using sensor data alone. The mean square error (MSE) values of 4.46 and 20.65 have been achieved using the DBN model for training and testing datasets, respectively. We also evaluated the influential sensor data variables, i.e., distance and pitch angle, for predicting the MEI. Our study demonstrates that the application of machine learning techniques directly to on-animal sensor data presents a substantial opportunity to interpret biological interactions in grazing systems directly from sensor data. We expect that further development and refinement of this technology will catalyse a step-change in extensive livestock management, as wearable sensors become widely used by livestock producers

Anomaly Freedom and Realisations for Super-W3W_3 Strings

We construct new multi-field realisations of the $N=2$ super-$W_3$ algebra, which are important for building super-$W_3$ string theories. We derive the structure of the ghost vacuum for such theories, and use the result to calculate the intercepts. These results determine the conditions for physical states in the super-$W_3$ string theory.Comment: 22 page

Using a multi-level tailored design process to develop a customer satisfaction survey for university evaluation

A multi-level procedure is described in order to develop a total quality management survey tool in the field of engineering academia. As a first step a review of available evaluation tools for universities is conducted, resulting in over 150 items used for evaluation purposes. Secondly all dimensions of educational evaluation used in previous research are summarized, resulting in 15 dimensions. In a third step, items are assigned to the dimensions, overlapping items were combined or removed, and item content and dimensions were adjusted to the specific conditions of the target faculty. Fourthly, the resulting twelve dimensions were used in first, investigative interviews in the target population. Results indicate that eleven dimensions sufficiently mapped all aspects of evaluation. After revising the items to improve understanding in a fifth step cognitive pretests were conducted. The final revision resulted in 83 items assigned to eleven dimensions

Empirical competence-testing: A psychometric examination of the German version of the Emotional Competence Inventory

The “Emotional Competence Inventory“ (ECI 2.0) by Goleman and Boyatzis assesses emotional intelligence (EI) in organizational context by means of 72 items in 4 clusters (self-awareness, self- management, social awareness, social skills) which at large consist of 18 competencies. Our study examines the psychometric properties of the first German translation of this instrument in two different surveys (N = 236). If all items are included in reliability analysis the ECI is reliable (Cronbach’s Alpha = .90), whereas the reliability of the four sub dimensions is much smaller (Alpha = .62 - .81). For 43 items the corrected item-total correlation with its own scale is higher than correlations with the other three clusters. Convergent validity was examined by using another EI instrument (Wong & Law, 2002). We found a significant correlation between the two instruments (r = .41). The German version of the ECI seems to be quite useful, although the high reliability is achieved by a large number of items. Possibilities of improvement are discussed