Understanding British Values in Primary Schools: Policy and Practice

This book will explore the notion and values and British values, how school promote and engage with the education agenda linked to the fundamental British values of democracy, the rule of law, individual liberty, mutual respect and tolerance of those with different faiths and beliefs

Standardised training simulations: a case study of the water industry in Australia

This article is a case study which discusses how a realistic game-based simulation training platform may tackle the challenges raised by the requirements for a national competencies certification framework and the requirements of a new generation of trained water operators. The proposed platform aims to 1) enhance training experience with learn by doing and scenario- based learning experience, 2) increase training capacity for the water industry, 3) provide cost effective methods to deliver training in remote areas, and 4) attract young people to join the water industry workforce. The National Water Commission of Australia is currently developing a national competencies framework for certification of water treatment operators. This framework aims to ensure that the water operator role is carried out by qualified staff. In response to the introduction of this new competencies framework, there will be an increase in demand for skills training in the water industry to ensure that employees at water treatment facilities possess the required certifications. Currently, the water industry is facing several challenges to meet the future training demands required by the new competencies framework. The lack of standardised training manuals and assessment criteria make it very difficult to ensure that training is consistent between different water treatment facilities. Australia is an immense country with sparse distribution of population centres and large distances between towns, which create an insurmountable problem for delivery of training. In particular, for rural areas, there is a substantial lack of trainers available to deliver training. Travel expenses and loss of work days for travelling makes training a very expense exercise. Also, the water industry is facing an ageing workforce with an average age over 45. There are less than 5% of trained water operators under 25 years of age. With the ever increasing technological advancements being introduced into the water industry, a new generation of water operator who are technology savvy are required. In response to these problems, a realistic game-based simulation training platform is being developed for water operator training. The pilot training module focuses on the collection of water samples. This particular task was chosen as it is a statutory requirement for the industry to perform water testing at regular intervals and it is a critical factor in the quality assurance process. It is also important as thousands of dollars can be wasted on each contaminated water sample. During the development of this training application, several technical challenges were encountered. The challenges include rendering of realistic water, simulating physics for small objects, and using two handed controls for object manipulation. Future development of this training platform will also be discussed

Neonatal cause-of-death estimates for the early and late neonatal periods for 194 countries: 2000-2013.

OBJECTIVE: To estimate cause-of-death distributions in the early (0-6 days of age) and late (7-27 days of age) neonatal periods, for 194 countries between 2000 and 2013. METHODS: For 65 countries with high-quality vital registration, we used each country's observed early and late neonatal proportional cause distributions. For the remaining 129 countries, we used multinomial logistic models to estimate these distributions. For countries with low child mortality we used vital registration data as inputs and for countries with high child mortality we used neonatal cause-of-death distribution data from studies in similar settings. We applied cause-specific proportions to neonatal death estimates from the United Nations Inter-agency Group for Child Mortality Estimation, by country and year, to estimate cause-specific risks and numbers of deaths. FINDINGS: Over time, neonatal deaths decreased for most causes. Of the 2.8 million neonatal deaths in 2013, 0.99 million deaths (uncertainty range: 0.70-1.31) were estimated to be caused by preterm birth complications, 0.64 million (uncertainty range: 0.46-0.84) by intrapartum complications and 0.43 million (uncertainty range: 0.22-0.66) by sepsis and other severe infections. Preterm birth (40.8%) and intrapartum complications (27.0%) accounted for most early neonatal deaths while infections caused nearly half of late neonatal deaths. Preterm birth complications were the leading cause of death in all regions of the world. CONCLUSION: The neonatal cause-of-death distribution differs between the early and late periods and varies with neonatal mortality rate level. To reduce neonatal deaths, effective interventions to address these causes must be incorporated into policy decisions

A Multi-Population FA for Automatic Facial Emotion Recognition

Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models