Impact of introducing practical obstetric multi-professional training (PROMPT) into maternity units in Victoria, Australia

Objective: To assess the introduction of Practical Obstetric Multi-professional Training (PROMPT) into maternity units and evaluate effects on organisational culture and perinatal outcomes. Design: A retrospective cohort study. Setting: Maternity units in eight public hospitals in metropolitan and regional Victoria, Australia. Population: Staff in eight maternity units and a total of 43 408 babies born between July 2008 and December 2011. Methods: Representatives from eight Victorian hospitals underwent a single day of training (Train the Trainer), to conduct PROMPT. Organisational culture was compared before and after PROMPT. Clinical outcomes were evaluated before, during and after PROMPT. Main outcome measures: The number of courses run and the proportion of staff trained were determined. Organisational culture was measured using the Safety Attitude Questionnaire. Clinical measures included Apgar scores at 1 and 5 minutes (Apgar 1 and Apgar 5), cord lactate, blood loss and length of baby's stay in hospital. Results: Seven of the eight hospitals conducted PROMPT. Overall about 50% of staff were trained in each year of the study. Significant increases were found in Safety Attitude Questionnaire scores representing domains of teamwork (Hedges' g 0.27, 95% confidence interval [95% CI] 0.13-0.41), safety (Hedges' g 0.28, 95% CI 0.15-0.42) and perception of management (Hedges' g 0.17, 95% CI 0.04-0.31). There were significant improvements in Apgar 1 (OR 0.84, 95% CI 0.77-0.91), cord lactates (odds ratio 0.92, 95% CI 0.85-0.99) and average length of baby's stay in hospital (Hedges' g 0.03, 95% CI 0.01-0.05) during or after training, but no change in Apgar 5 scores or proportion of cases with high blood loss. Conclusion: PROMPT can be introduced using the Train the Trainer model. Improvements in organisational culture and some clinical measures were observed following PROMPT

A practical vision system for the detection of moving objects

The main goal of this thesis is to review and offer robust and efficient algorithms for the detection (or the segmentation) of foreground objects in indoor and outdoor scenes using colour image sequences captured by a stationary camera. For this purpose, the block diagram of a simple vision system is offered in Chapter 2. First this block diagram gives the idea of a precise order of blocks and their tasks, which should be performed to detect moving foreground objects. Second, a check mark () on the top right corner of a block indicates that this thesis contains a review of the most recent algorithms and/or some relevant research about it. In many computer vision applications, segmenting and extraction of moving objects in video sequences is an essential task. Background subtraction has been widely used for this purpose as the first step. In this work, a review of the efficiency of a number of important background subtraction and modelling algorithms, along with their major features, are presented. In addition, two background approaches are offered. The first approach is a Pixel-based technique whereas the second one works at object level. For each approach, three algorithms are presented. They are called Selective Update Using Non-Foreground Pixels of the Input Image , Selective Update Using Temporal Averaging and Selective Update Using Temporal Median , respectively in this thesis. The first approach has some deficiencies, which makes it incapable to produce a correct dynamic background. Three methods of the second approach use an invariant colour filter and a suitable motion tracking technique, which selectively exclude foreground objects (or blobs) from the background frames. The difference between the three algorithms of the second approach is in updating process of the background pixels. It is shown that the Selective Update Using Temporal Median method produces the correct background image for each input frame. Representing foreground regions using their boundaries is also an important task. Thus, an appropriate RLE contour tracing algorithm has been implemented for this purpose. However, after the thresholding process, the boundaries of foreground regions often have jagged appearances. Thus, foreground regions may not correctly be recognised reliably due to their corrupted boundaries. A very efficient boundary smoothing method based on the RLE data is proposed in Chapter 7. It just smoothes the external and internal boundaries of foreground objects and does not distort the silhouettes of foreground objects. As a result, it is very fast and does not blur the image. Finally, the goal of this thesis has been presenting simple, practical and efficient algorithms with little constraints which can run in real time

Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking

In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and traditional features. Then the results from these two approaches are compared with state-of-the-art trackers. The results are obtained by executing the methods on the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the second method caused up to 15.9%. The proposed methods can still be improved by extracting more distinguishable features

A PRACTICAL APPROACH TO REAL-TIME DYNAMIC BACKGROUND GENERATION BASED ON A TEMPORAL MEDIAN FILTER

In many computer vision applications, segmenting and extraction of moving objects in video sequences is an essential task. Background subtraction, by which each input image is subtracted from the reference image, has often been used for this purpose. In this paper, we offer a novel background-subtraction technique for real-time dynamic background generation using color images that are taken from a static camera. The new algorithm, which is based on ‘temporal median filter with exponentially weighted moving average (EWMA) filtering’, is presented that effectively implements a temporal mode operation. The proposed method has the advantage that the parameters of the algorithm are computed automatically. In addition, the new method could start its operation for a sequence of images in which moving objects are included. The efficiency and robustness of the new algorithm is confirmed by the results obtained on a number of outdoor image sequences

Avaliação do Padrão de Senha Utilizado pelos Alunos de Tecnologia da Informação em Saúde

Today, technology is used as an important and foremost tool to facilitate human life. Computers, tablets, smart phones and massive social networks have affected the health and medical professions. Keeping security in such environments is one of the important and critical issue. Choose of a proper password for user accounts is a challenging matter for security in the digital environment. This study was conducted to consider the authentication level for the academic users. The sample size was found using the Gregis Morgan formula. Therefore, the patterns of passwords used in cyber environments by 200 students of IT Sciences were investigated and considered. Descriptive statistics have been used to analyze the gathered data in this study. The results showed that the passwords selected by the students were poor and imaginable for breaking.Hoy en día, la tecnología se utiliza como una herramienta importante y principal para facilitar la vida humana. Las computadoras, tabletas, teléfonos inteligentes y redes sociales masivas han afectado a la salud y las profesiones médicas. Mantener la seguridad en tales entornos es uno de los temas importantes y críticos. La elección de una contraseña adecuada para las cuentas de usuario es un tema difícil para la seguridad en el entorno digital. Este estudio se realizó para considerar el nivel de autenticación para los usuarios académicos. El tamaño de la muestra se encontró utilizando la fórmula de Gregis Morgan. Por lo tanto, se investigaron y consideraron los patrones de contraseñas utilizadas en los entornos cibernéticos por 200 estudiantes de ciencias de TI. Se han utilizado estadísticas descriptivas para analizar los datos recopilados en este estudio. Los resultados mostraron que las contraseñas seleccionadas por los estudiantes eran deficientes e imaginables para romperlas.Hoje, a tecnologia é usada como uma ferramenta importante e importante para facilitar a vida humana. Computadores, tablets, smartphones e grandes redes sociais afetaram as profissões médicas e de saúde. Manter a segurança nesses ambientes é uma questão importante e crítica. A escolha de uma senha adequada para contas de usuário é um assunto desafiador para a segurança no ambiente digital. Este estudo foi realizado para considerar o nível de autenticação para os usuários acadêmicos. O tamanho da amostra foi encontrado usando a fórmula de Gregis Morgan. Portanto, os padrões de senhas utilizados em ambientes cibernéticos por 200 estudantes de Ciências da TI foram investigados e considerados. Estatísticas descritivas foram utilizadas para analisar os dados coletados neste estudo. Os resultados mostraram que as senhas selecionadas pelos alunos eram ruins e imagináveis para quebrar

THISTLE:trial of hands-on Interprofessional simulation training for local emergencies: a research protocol for a stepped-wedge clustered randomised controlled trial

Peer reviewedPublisher PD

Graph Neural Networks and Reinforcement Learning: A Survey

Graph neural network (GNN) is an emerging field of research that tries to generalize deep learning architectures to work with non-Euclidean data. Nowadays, combining deep reinforcement learning (DRL) with GNN for graph-structured problems, especially in multi-agent environments, is a powerful technique in modern deep learning. From the computational point of view, multi-agent environments are inherently complex, because future rewards depend on the joint actions of multiple agents. This chapter tries to examine different types of applying GNN and DRL techniques in the most common representations of multi-agent problems and their challenges. In general, the fusion of GNN and DRL can be addressed from two different points of view. First, GNN is used to influence the DRL performance and improve its formulation. Here, GNN is applied in relational DRL structures such as multi-agent and multi-task DRL. Second, DRL is used to improve the application of GNN. From this viewpoint, DRL can be used for a variety of purposes including neural architecture search and improving the explanatory power of GNN predictions

Cortical Processing Related to Intensity of a Modulated Noise Stimulus-a Functional Near-Infrared Study.

Sound intensity is a key feature of auditory signals. A profound understanding of cortical processing of this feature is therefore highly desirable. This study investigates whether cortical functional near-infrared spectroscopy (fNIRS) signals reflect sound intensity changes and where on the brain cortex maximal intensity-dependent activations are located. The fNIRS technique is particularly suitable for this kind of hearing study, as it runs silently. Twenty-three normal hearing subjects were included and actively participated in a counterbalanced block design task. Four intensity levels of a modulated noise stimulus with long-term spectrum and modulation characteristics similar to speech were applied, evenly spaced from 15 to 90 dB SPL. Signals from auditory processing cortical fields were derived from a montage of 16 optodes on each side of the head. Results showed that fNIRS responses originating from auditory processing areas are highly dependent on sound intensity level: higher stimulation levels led to higher concentration changes. Caudal and rostral channels showed different waveform morphologies, reflecting specific cortical signal processing of the stimulus. Channels overlying the supramarginal and caudal superior temporal gyrus evoked a phasic response, whereas channels over Broca's area showed a broad tonic pattern. This data set can serve as a foundation for future auditory fNIRS research to develop the technique as a hearing assessment tool in the normal hearing and hearing-impaired populations

The cost of local, multi-professional obstetric emergencies training

INTRODUCTION: We aim to outline the annual cost of setting up and running a standard, local, multi-professional obstetric emergencies training course, PROMPT (PRactical Obstetric Multi-Professional Training), at Southmead Hospital, Bristol, UK - a unit caring for approximately 6500 births per year. MATERIAL AND METHODS: A retrospective, micro-costing analysis was performed. Start-up costs included purchasing training mannequins and teaching props, printing of training materials and assembly of emergency boxes (real and training). The variable costs included administration time, room hire, additional printing and the cost of releasing all maternity staff in the unit, either as attendees or trainers. Potential, extra start-up costs for maternity units without established training were also included. RESULTS: The start-up costs were €5574 and the variable costs for 1 year were €143 232. The total cost of establishing and running training at Southmead for 1 year was €148 806. Releasing staff as attendees or trainers accounted for 89% of the total first year costs, and 92% of the variable costs. The cost of running training in a maternity unit with around 6500 births per year was approximately €23 000 per 1000 births for the first year and around €22 000 per 1000 births in subsequent years. CONCLUSIONS: The cost of local, multi-professional obstetric emergencies training is not cheap, with staff costs potentially representing over 90% of the total expenditure. It is therefore vital that organizations consider the clinical effectiveness of local training packages before implementing them, to ensure the optimal allocation of finite healthcare budgets