Hamiltonian, Path Integral and BRST Formulations of Large N Scalar QCD2QCD_{2} on the Light-Front and Spontaneous Symmetry Breaking

Recently Grinstein, Jora, and Polosa have studied a theory of large-$N$ scalar quantum chromodynamics in one-space one-time dimension. This theory admits a Bethe-Salpeter equation describing the discrete spectrum of quark-antiquark bound states. They consider gauge fields in the adjoint representation of $SU(N)$ and scalar fields in the fundamental representation. The theory is asymptotically free and linearly confining. The theory could possibly provide a good field theoretic framework for the description of a large class of diquark-antidiquark (tetra-quark) states. Recently we have studied the light-front quantization of this theory without a Higgs potential. In the present work, we study the light-front Hamiltonian, path integral and BRST formulations of the theory in the presence of a Higgs potential. The light-front theory is seen to be gauge-invariant, possessing a set of first-class constraints. The explicit occurrence of spontaneous symmetry breaking in the theory is shown in unitary gauge as well as in the light-front 't Hooft gauge.Comment: Accepted for publication in Eur. Phys. J.

Distributed leadership: A model for student leadership engagement

Developing student leadership has never been more demanded of higher education institutions than within a 21st century VUCA (volatile, uncertain, complex and ambiguous) world. Globally, there is an increasing preference for organisations to become more devolved and operate more collaboratively (Hayward 2015). Rapid socio-economic changes and enhanced access to higher education, have seen 21st century institutions around the world facing major challenges to their governance systems, curriculum, external relations, research and financing (Jones and Harvey 2017; Black 2015; Jones 2014; Shin and Harman 2009). South African higher education institutions have not escaped these global challenges but the issues have been further exacerbated by the legacy of apartheid and demands for political redress, transformation and equal access (Habib 2016; South African Civil Society Information Services 2013). Such issues are unique to the South African higher education landscape and are demonstrated in the frustrations of the country’s youth, which are regularly being played out on our streets and university campuses. This study sought student leaders’ opinion on their role in participative institutional governance using distributed leadership as the underpinning model

The relationship between demographic groups and perception of inclusion in a South African organisation

The extreme demographic-role misrepresentation within organisations is a key business and societal issue in post-Apartheid South Africa. This research relates to deepening the understanding about the perception of inclusion with respect to demographic groups such as race/ethnicity, gender, age, tenure, religion, sexual orientation, disability, position/grade, department, as well as site location. Secondly, it seeks to understand which groups perceive inclusion less positively than other groups, when we consider the occurrence of all the groups simultaneously using structural equation modelling (SEM)

The relationship between demographic groups and perception of inclusion in a South African organisation

The extreme demographic-role misrepresentation within organisations is a key business and societal issue in post-Apartheid South Africa. This research relates to deepening the understanding about the perception of inclusion with respect to demographic groups such as race/ethnicity, gender, age, tenure, religion, sexual orientation, disability, position/grade, department, as well as site location. Secondly, it seeks to understand which groups perceive inclusion less positively than other groups, when we consider the occurrence of all the groups simultaneously using structural equation modelling (SEM)

Applying Decision Tree Algorithm and Neural Networks to Predict Forest Fires in Lebanon

Fires have been threatening green forestry all over the world. In Lebanon, green areas declined dramatically during the last decades, what imposes an urgent intervention with strict governmental policies and support of non-governmental organizations. The orientation is towards techniques that predict high fire risks, allowing for precautions to preclude fire occurrences or at least limit their consequences. Two data mining techniques are used for the purpose of prediction and decision-making: Decision trees and back propagation forward neural networks. Four meteorological attributes are utilized: temperature, relative humidity, wind speed and daily precipitation. The obtained tree drawn from applying the first algorithm could classify these attributes from the most significant to the least significant and better foretell fire incidences. Adopting neural networks with different training algorithms shows that networks with 2 inputs only (temperature and relative humidity) retrieve better results than 4-inputs networks with less mean squared error. Feed forward and Cascade forward networks are under scope, with the use of different training algorithms

Forest Fires Prediction: A Proposal for a new hybrid index

All over the world, statistics show that forest fires rate has been increasing in the recent decades although many studies and various indices were proposed to predict fire occurrence and then take in advance the necessary actions. These indices use only weather data to make their decision of prediction. In this paper, a new proposal for a fire detection index is presented that combines between meteorological and topographic parameters. The reason is to reduce errors due to inaccuracies in weather prediction. The parameters of slope, aspect and elevation are introduced, and a comparison is held between the proposed index and other existing indices that reveals the distinction of the new combination over the present models: Angstrom, Nesterov, KBDI and Canadian Fire Weather Index. The implementation of the proposed hybrid index using data from Lebanon demonstrated its ability to accurately predict the hazard of fire occurrence

Implementation of Intelligent Monitoring System for year Elderly

One of three adults 65 or older falls every year. Injuries sustained among the elderly because of falls are a major problem worldwide. Demographic predictions of population aged 65 and over suggest the need for telemedicine applications in the eldercare domain. In this paper, we propose an intelligent surveillance system that monitors human activities with a particular interest of the problem of fall detection. To make the motion detection and object tracking fully automatic and robust under different illumination conditions, combination of best-fit approximated ellipse around the face and temporal changes of head position, would provide a useful cue for detection of different behaviors. The system identifies the face, collects data suchas speed of movement, and triggers an alarm on the determination of fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system

Automated Monitoring System for Fall Detection in the Elderly

Falls are a major problem for the elderly people living independently. Accordingto the World Health Organization, falls and sustained injuries are the third cause of chronic disability. In the last years there have been many commercial solutions aimed at automatic and non automatic detection of falls like the social alarm (wrist watch with a button that is activated by the subject in case of a fall event), and the wearable fall detectors that are based on combinations of accelerometers and tilt sensors. Critical problems are associated with those solutions like button is often unreachable after the fall, wearable devices produce many false alarms and old people tend to forget wearing them frequently. To solve these problems, we propose an automated monitoring that will detects the face of the person, extract features such as speed and determines if a human fall has occurred. An alarm is triggered immediately upon detection of a fall

Real Time Recognition of Elderly Daily Activity using Fuzzy Logic through Fusion of Motion and Location Data

One of the major problems that may encounter old people at home is falling. Approximately, one of three adults of the age of 65 or older falls every year. The World Health Organization reports that injuries due to falls are the third most common cause of chronic disability. In this paper, we proposed an approach to indoor human daily activity recognition, which combines motion and location data by using a webcam system, with a particular interest to the problem of fall detection. The proposed system identifies the face and the body in a given area, collects motion data such as face and body speeds and location data such as center of mass and aspect ratio; then the extracted parameters will be fed to a Fuzzy logic classifier that classify the fall event in two classes: fall and not fall