The use of process simulation methods in support of organisational learning in availability contracting

Traditional business models in the aerospace industry are based on a conventional supplier to customer relationship based on the design, manufacture and subsequent delivery of the physical product. Service provision, from the manufacturer's perspective, is typically limited to the supply of procedural documentation and the provision of spare parts to the end user as the product passes through the latter stages of its intended lifecycle. Challenging economic and political conditions have resulted in end users re-structuring their core business activities, particularly in the defence sector. This has resulted in the need for original equipment manufacturers (OEMs) to integrate and manage support service activities in partnership with the customer to deliver platform availability. This improves the probability of commercial sustainability for the OEM through shared operational risks while reducing the cost of platform ownership for the customer. The need for OEMs to evolve their design, manufacture and supply strategies by focusing on customer requirements has revealed a need for reconstruction of traditional internal behaviours and design methodologies. Application of organisational learning is now a well recognised principle for innovative companies to achieve long term growth and sustained technical development, and hence, greater market command. It focuses on the process by which the organisation's knowledge and value base changes, leading to improved problem solving ability and capacity for action. From the perspective of availability contracting, knowledge and the processes by which it is generated, used and retained, become primary assets within the learning organisation. This paper will introduce the application of digital methods to asset management by demonstrating how the process of learning can benefit from a digital approach, how product and process design can be integrated within a virtual framework and finally how the approach can be applied in a service context.

A study to evaluate the introduction of simulation as a teaching strategy for the mental health and learning disability fields of nursing, in an undergraduate nursing curriculum within one higher education institution

High Fidelity Simulation or Human Patient Simulation is an educational strategy embedded within nursing curricula throughout many healthcare educational institutions. This paper reports on an evaluative study that investigated the views of a group of Year 2 undergraduate nursing students from the mental health and the learning disability fields of nursing (n = 75) in relation to simulation as a teaching pedagogy. The study took place in the simulation suite within a School of Nursing and Midwifery in the UK. Two patient scenarios were used for the session and participants completed a 22-item questionnaire consisting of three biographical information questions and a 19-item Likert scale. Descriptive statistics were employed to illustrate the data and non-parametric testing (Mann-Whitney U test) was employed to test a number of hypotheses. Overall students were positive about the introduction of patient scenarios using the human patient simulator into the undergraduate nursing curriculum. This study used a small, convenience sample in one institution and therefore the results obtained cannot be generalised to nursing education before further research can be conducted with larger samples and a mixed-method research approach. However these results provide encouraging evidence to support the use of simulation within the mental health and the learning disability fields of nursing, and the development and implementation of further simulations to complement the students’ practicum.

Simulation using novel equipment designed to explain spirometric abnormalities in respiratory disease enhances learning in higher cognitive domains

Simulation of disorders of respiratory mechanics shown by spirometry provides insight into the pathophysiology of disease but some clinically important disorders have not been simulated and none have been formally evaluated for education. We have designed simple mechanical devices which, along with existing simulators, enable all the main dysfunctions which have diagnostic value in spirometry to be simulated and clearly explained with visual and haptic feedback. We modelled the airways as Starling resistors by a clearly visible mechanical action to simulate intra- and extra-thoracic obstruction. A narrow tube was used to simulate fixed large airway obstruction and inelastic bands to simulate restriction. We hypothesized that using simulators whose action explains disease promotes learning especially in higher domain educational objectives. The main features of obstruction and restriction were correctly simulated. Simulation of variable extra-thoracic obstruction caused blunting and plateauing of inspiratory flow, and simulation of intra-thoracic obstruction caused limitation of expiratory flow with marked dynamic compression. Multiple choice tests were created with questions allocated to lower (remember and understand) or higher cognitive domains (apply, analyse and evaluate). In a cross-over design, overall mean scores increased after 1½ h simulation spirometry (43-68 %, effect size 1.06, P < 0.0001). In higher cognitive domains the mean score was lower before and increased further than lower domains (Δ 30 vs 20 %, higher vs lower effect size 0.22, P < 0.05). In conclusion, the devices successfully simulate various patterns of obstruction and restriction. Using these devices medical students achieved marked enhancement of learning especially in higher cognitive domains.

A Proposed Framework for Simulation Based Learning of Inheritance

Different types of serious games have been used in elucidating computer science areas such as computer games, mobile games, Lego-based games, virtual worlds and webbased games. Different evaluation techniques have been conducted like questionnaires, interviews, discussions and tests. Simulation have been widely used in computer science as a motivational and interactive learning tool. This paper aims to evaluate the possibility of successful implementation of simulation in computer programming modules. A framework is proposed to measure the impact of serious games on enhancing students understanding of key computer science concepts. Experiments will be held on the EEECS of Queen’s University Belfast students to test the framework and attain results.

Learning to manage complexity through simulation: students' challenges and possible strategies

Many have called for medical students to learn how to manage complexity in healthcare. This study examines the nuances of students' challenges in coping with a complex simulation learning activity, using concepts from complexity theory, and suggests strategies to help them better understand and manage complexity.Wearing video glasses, participants took part in a simulation ward-based exercise that incorporated characteristics of complexity. Video footage was used to elicit interviews, which were transcribed. Using complexity theory as a theoretical lens, an iterative approach was taken to identify the challenges that participants faced and possible coping strategies using both interview transcripts and video footage.Students' challenges in coping with clinical complexity included being: a) unprepared for 'diving in', b) caught in an escalating system, c) captured by the patient, and d) unable to assert boundaries of acceptable practice.Many characteristics of complexity can be recreated in a ward-based simulation learning activity, affording learners an embodied and immersive experience of these complexity challenges. Possible strategies for managing complexity themes include: a) taking time to size up the system, b) attuning to what emerges, c) reducing complexity, d) boundary practices, and e) working with uncertainty. This study signals pedagogical opportunities for recognizing and dealing with complexity.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

Special issue on design and programming of signal processors for multimedia communication:Special issue on design and programming of signal processors for multimedia communication

The technical challenges in the design and programming of signal processors for multimedia communication are discussed. The development of terminal equipment to meet such demand presents a significant technical challenge, considering that it is highly desirable that the equipment be cost effective, power efficient, versatile, and extensible for future upgrades. The main challenges in the design and programming of signal processors for multimedia communication are, general-purpose signal processor design, application-specific signal processor design, operating systems and programming support and application programming. The size of FFT is programmable so that it can be used for various OFDM-based communication systems, such as digital audio broadcasting (DAB), digital video broadcasting-terrestrial (DVB-T) and digital video broadcasting-handheld (DVB-H). The clustered architecture design and distributed ping-pong register files in the PAC DSP raise new challenges of code generation.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

Teaching and learning technologies in Higher Education: Applied behaviour analysis and autism.

Professionals on both international and national levels who work with children with autism are expressing the need for graduate-level training in applied behaviour analysis. The implementation of effective instruction in higher education for professionals working with children with autism and their families is a complex undertaking: the learner needs to acquire an understanding of the principles and procedures of applied behaviour analysis and also adapt this knowledge to the learning prerequisites of individuals with autism. In this paper we outline some current thinking about adult education and blended learning technologies and then describe and illustrate with examples emerging possibilities of multimedia technology in the development of teaching materials. We conclude that synergies between graduate-level curriculum requirements, knowledge of adult learning, and communication technology are necessary to establish comprehensive learning environments for professionals who specialize in autism intervention.