Professional identity

This chapter discusses the concept of professional identity and how professional teachers develop their identity. Policy contexts as well as research contexts are discussed in terms of how these shape professional working conditions

Electrical noise reduction techniques contributing to improved data quality at the National Transonic Facility

In initial use, the high-speed digital data acquisition systems at Langley Research Center's National Transonic Facility produced data containing unacceptably high noise levels. Described is a process whereby the contributing noise sources were identified and eliminated. The effects of 60 Hz power, system grounding, EMI/RFI, and other problems are discussed and the corrective action taken is outlined. The overall effort resulted in an improvement of greater than 5:1 in system performance. Although the report describes a system specifically used for wind tunnel data acquisition, the corrective techniques employed are generally applicable to large scale high-speed data systems where signal resolution in the low microvolts range is important

What do students feel about PBL? Discussion of interim findings from a small scale study of medical and education students' experiences of problem-based learning in a Scottish university

This paper looks at the experiences of problem-based learning among groups of medical and education students in the University of Glasgow. PBL has been in use in the university in a number of different contexts for some time now, and this paper sets out to give those participating in professional education taught by this methodology a voice in assessing its effectiveness and in articulating their own experiences of it. Using qualitative methods, students were invited to respond to a range of issues. The results indicate acceptance for the methodology as an effective teaching medium, but indicate a range of differing perceptions both between the 2 groups and within the groups themselves

Export Performance in Small and Medium Enterprises in New South Wales: Sectoral and Regional Dimensions

This paper discusses the results from a survey of 146 value-adding exporters from regional New South Wales, Australia, the majority of whom were small and medium enterprises, using the Australian definition of having less than 200 employees. This study established that SME regional exporters were successful in gaining and maintaining sales in overseas markets in a variety of product areas. It thus raises the question of what factors lie behind this process. By identifying the causes of successful exporting in regional areas, policy-makers can design programs which best meet the needs of these firms and will encourage growth in their exports in the future.Export performance, small and medium-sized enterprises, sectoral and regional dimensions, Australia

Urban recreational fisheries in the Australian coastal zone:The sustainability challenge

Recreational fishing is an important wildlife harvesting activity in urban coastal areas, and recreational harvest in these areas can frequently exceed the commercial harvest. Recreational fishing is a key way that many members of the public experience the environment. The activity enhances social capital, promotes respect for nature, provides health benefits and can provide economic benefits to coastal communities. It is also an important driver of the science on aquatic animals and habitats, and an important tangible reason for many members of the public to conserve and protect aquatic resources. Overall, there has been little specific consideration of urban recreational fisheries management in Australia, despite the paramount importance of urban areas as a focus of recreational fishing activity. This paper identifies that in order to maximize individual and societal benefits from recreational fishing, there needs to be a refocussing of management with the aim of being more holistic. Historically, fisheries management in Australia has focused on maximum sustainable yield (MSY) or maximum economic yield (MEY) which is relevant for the commercial fishing sector, but neither of these is directly relevant to recreational fisheries. This paper identifies that Urban Fisheries Management Plans are required that recognize the specific issues associated with urban recreational fisheries. These plans need to coordinate within and between levels of government and have clear management objectives relevant to urban recreational fisheries. Enhanced opportunities for meaningful citizen science can be incorporated at multiple levels within these plans and this can engender public support for environmental stewardship, as well as fill a very important gap in the knowledge base necessary for managing the activity. As urban recreational fisheries are often occurring in highly modified or degraded habitats, a central element of these plans needs to be habitat restoration and this can have broader benefits for aquatic health. Other management tools include habitat creation (e.g., artificial reefs), optimization of coastal infrastructure as fisheries habitat, and stock enhancement. Overall, Urban Fisheries Management Plans represent a necessary evolution of fisheries management to better address the specific challenges of urban recreational fisheries management, and to best ensure that benefits are optimised

SME Information Sourcing for Innovation and Export Market Development: From Local or External Networks?

A survey analysis of innovation information and input sourcing of New South Wales regional exporting firms indicates that the majority of regional exporters were small to medium sized enterprises (SMEs). The analysis shows that these SMEs have been able to establish their own extensive information linkages into the international economy. Consequently, the need to assess and develop the benefits of linkages between small and large firms is not highly significant within the New South Wales regions. The analysis indicates that international networking by SMEs brings knowledge to the regions, which facilitates intra-firm learning. However, it suggests that SME’s local or regional linkages are relatively underdeveloped, as a source of new knowledge for innovation activity. This is in contrast to the main body of economic literature, which argues that small regional exporters utilize local networks as a major input into their success. This research identifies intensification in the usage of regional networks as one means of improving SME performance in more remote regions. The analysis also indicates that a two-way effect results by the diversity of regional SME export sector base. Firstly, it restricts the client-supplier relationships preventing closer industry specific collaborations but secondly, it can be advantageous in that it restricts competition between regional exporters. This creates conditions allowing some information sharing regarding the opportunities and ways of entering overseas markets, which do not affect the competitive position of the mentoring firm. In concluding, the paper argues that the basic requirements for regional learning development are in place but requires an increase in the interaction intensity between local SMEs in order to achieve a higher level of collaboration and knowledge sharing.New South Wales, SMEs, small and medium enterprises, regional development, innovation, international networking

Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde