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Background: Resident competence in physical exam skills is lacking. Despite the advantages technology has afforded modern medicine, the reliance on laboratory and radiologic data has contributed to a decline in bedside clinical skills. Multiple articles report that bedside teaching has declined, from once making up about 75% of clinical teaching in the 1960s to only 16% by 1978. Over time, residents have become less familiar with physical signs, causing them to place less importance on this part of patient care. Physical exam teaching in medical school is variable and few …

Wireless communications networks are operating at breaking point during an era of relentless traffic growth. Network operators must utilize scarce and expensive wireless spectrum efficiently in order to satisfy demand. Spectrum on the links between cells and user equipments ('users': smartphones, tablets, etc.) frequently becomes congested. Capacity can be increased by transmitting data packets via multiple links. Packets can be routed through multiple Long Term Evolution (LTE) links in existing fourth generation (4G) networks. In future 5G deployments, users will be equipped to receive packets over LTE, WiFi, and millimetre wave links simultaneously. How can we allocate spectrum on links, so that all customers experience an acceptable quality of service? Building effective schedulers for link allocation requires considerable human expertise. We automate the design process through the novel application of evolutionary algorithms. Evolved schedulers boost downlink rates by over 150% for the worst-performing users, relative to a single-link baseline. The proposed techniques significantly outperform a benchmark algorithm from the literature. The experiments illustrate the promise of evolutionary algorithms as a paradigm for managing 5G software-defined wireless communications networks.

The Family Life Cycle (FLC) model is frequently used for understanding travel patterns and consumer behaviour. The aim of this research was to consider the relationship between FLC stages and travel engagement in Australia. Visiting Friends and Relatives (VFRs) and non-VFRs (n = 102 029) were found to be significantly different in their FLC composition. In addition, the prevalence of FLC stages changed between the groups, with families comprising 27.5% of domestic VFRs yet comprising 37.9% of domestic non-VFRs. This study has provided a valuable assessment of the relationship between FLC stages and domestic travel in Australia and is the first paper to integrate two common approaches used in tourism for market segmentation. Copyright © 2016 John Wiley & Sons, Ltd.

This article is about teacher education reform. At the time of writing, teacher education (which is predominately the domain of universities in Australia), has undergone numerous critical reviews with little change effect. The teaching profession's struggles to cope with a changing world has been documented and an increasing push from Australian governments, at both the state and federal level for improved school outcomes are regular pieces in the national Australian press. A cadre of teacher education commentators call for a rethink on teacher education. This article showcases a disruptive model in teacher education and answers some of the ponderings around what teacher education could be and how it could be organised differently for the different world in which teachers now have to operate. More specifically, the article examines the Bachelor of Learning Management (BLM) which was developed at CentralQueenslandUniversityin2000andisstillinoperationtoday. TheBLMwas the first major revision and redevelopment of teacher education in Australia in twenty five years: this fact alone makes the BLM an interesting case study.

Purpose– This paper argues that in a well-organised school with strong leadership and vision coupled with a concerted effort to improve the teaching performance of each teacher, student achievement can be enhanced. The purpose of this paper is to demonstrate that while macro-effect sizes such as “whole of school” metrics are useful for school leaders in their professional development roles, there are important micro-conditions that can be uncovered in a more detailed analysis of student achievement data.Design/methodology/approach– Evidence of student achievement in a variety of standardised and non-standardised assessment tasks was subjected to examination in apost hoc, case study design. The assessment tasks were the South Australian Spelling Test Waddington Reading Test, a school-wide diagnostic writing task, teacher running records and National Assessment Program for Literacy and Numeracy. Performance in selected classrooms was compared on these tests utilising a variety of parametric quantitative statistics.Findings– School-based reform initiatives require external criteria on which to base decision making. Without such criteria based on data and the capacity to interpret it, interactions in the school culture have unanticipated consequences that have the potential to neutralise school improvement strategies. Further, findings suggest that fewer but sharper and quicker data collection tools are more valuable in such teacher decision making, but these require expertise to produce and interpret them.Research limitations/implications– This paper provides insights from one school, but the reported data are embedded in a sustained five year school reform programme.Practical implications– This paper documents a whole school organisational reform model devised by a school head and leadership team to improve student academic performance. The paper offers a process for developing a data-based school reform strategy for professional development to enhance both student achievement and school outcomes.Social implications– The paper outlines a model for school reform that is focused on all students increasing their academic outcomes. By emphasising collaborative teacher work based on research-justified teaching approaches, the model shows that social inequalities can be reversed.Originality/value– The paper outlines a whole of school reform model focused through a combination of distributed leadership, data-driven decision making, within a context of a coaching, mentoring and feedback regime for teachers. Together this model is an application of theoretical ideas to school reform.

The field of social finance, which includes a range of investment practices that consider both the social and financial characteristics of investments, has grown tremendously over the past decade. As the Millennial generation acquires increasing amounts of wealth, the trend is likely to continue, and wealth managers will need to adapt. In this article, the authors propose a “post-modern” portfolio theory that incorporates metrics of social return into the portfolio construction process. The article first develops an investor utility function that accommodates each investor’s unique preferences for different types of social impact. Using the heterogeneous expectations version of the capital asset pricing model, the authors then expand the utility function into a model of general equilibrium. Estimation and forecasting problems are discussed, as are the implications of this model for both investors who care only about traditional financial utility, as well as those who value other forms of well-being in the context of their portfolio goals. Finally, the article offers wealth advisors a simple approach to implementation that is amenable to clients with varying degrees of interest in social return.

Evolutionary Computation is used to automatically evolve small cell schedulers on a realistic simulation of a 4G-LTE heterogeneous cellular network. Evolved schedulers are then further augmented by human design to improve robustness. Extensive analysis of evolved solutions and their performance across a wide range of metrics reveals evolution has uncovered a new human-competitive scheduling technique which generalises well across cells of varying sizes. Furthermore, evolved methods are shown to conform to accepted scheduling frameworks without the evolutionary process being explicitly told the form of the desired solution. Evolved solutions are shown to out-perform a human-engineered state-of-the-art benchmark by up to 50%. Finally, the approach is shown to be flexible in that tailored algorithms can be evolved for specific scenarios and corner cases, allowing network operators to create unique algorithms for different deployments, and to postpone the need for costly hardware upgrades. This work appears in full in Fenton et al., "Towards Automation & Augmentation of the Design of Schedulers for Cellular Communications Networks", Evolutionary Computation, 2018. DOI 10.1162/evco_a_00221.

Small Cells are being deployed alongside pre-existing Macro Cells in order to satisfy demand during the current era of exponential growth in mobile traffic. Heterogeneous networks are economical because both cell tiers share the same scarce and expensive spectrum. However, customers at cell edges experience severe cross-tier interference in channel sharing Het-Nets, resulting in poor service quality. Techniques for improving fairness globally have been developed in previous works. In this paper, a novel method for service differentiation at the level of individual customers is proposed. The proposed algorithm redistributes spectrum on a millisecond timescale, so that premium customers experience minimum downlink rates exceeding a target threshold. System level simulations indicate that downlink rate targets of at least 1 [Mbps] are always satisfied under the proposed scheme. By contrast, naive scheduling achieves the 1 [Mbps] target only 83% of the time. Quality of service can be improved for premium customers without significantly impacting global fairness metrics. Flexible service differentiation will be key to effectively monetizing the next generation of 5G wireless communications networks.

In a military context, the objective is to automatically detect tactical vehicles and dismounts at beyond visual range such as those in Figure 3.1, while eliminating all the other uninteresting movers. It is easy to find movers; what is hard is to find only the movers of tactical interest. There are numerous moving targets detectable in any significant FOR. They include people, insects, projectiles, missiles, animals, cars, trucks, trains, fences, power lines and suspended cables, leaves on trees and grass, ventilators, fans, clouds, rain, helicopters, UAVs, etc. What distinguishes many of these is that, although they have short-term movements and RCS that are similar to targets of interest, their ground coordinates do not change much. Usually a ground target must be observed for many seconds before its ground coordinates have changed enough for unequivocal discrimination. Even aircraft that have high ground speeds require seconds of observation for discrimination and trajectory determination. Trajectory discrimination over tens of seconds allows the sorting of projectiles from insects and birds, fixed wing aircraft from helicopters, wheeled vehicles from tracked vehicles, etc. These multiple observations if statistically independent are often called “Looks".

Everything from your house electricity to cosmic rays involves electromagnetic energy. There is electromagnetic radiation bathing us all the time from your electric blanket to TV to cellphones to radioactive decay of the bricks in your house to cosmic rays. Each has its own characteristic oscillation frequency and corresponding wavelength related by the velocity of light. Figure 2.1 shows much of the electromagnetic spectrum including the general location of frequency and source of emissions in that band. In addition the energy per photon increases as the wavelength gets shorter. So even though it is easy to feel those big fat photons coming out of an open fire or oven, the much shorter wavelengths have far more energy to penetrate most materials. Nonetheless, all of these wavelengths can carry significant energy. Radars are limited in the same way as your eye but, with significant signal processing and high signal to noise and interference ratios (SNIR), dramatic improvements in accuracy and acuity can be achieved. This is enabled by use of coherent processing not available to the naked eye.

Noise of all kinds limits ultimate detection performance for all electromagnetic sensor systems whether cosmic, UV, visible, IR, microwave or RF. Thermal noise is often thought of as the ultimate limitation on detection and tracking but often it is self-noise in a sensor system, which limits performance. Low-frequency systems with seemingly very low noise figures (NF) are often limited by sky noise from the residual of the “Big Bang,”from auroras and from what used to be called “Monkey Talk.”Great strides have been made in signal processing in the last 50 years. This is the result of dramatic improvements in signal processing hardware as well as a better theoretical understanding. In spite of this, many systems provide disappointing performance. The two main reasons for shortfalls are sensor signal processor self-noise and the physical world's continuing refusal to be normal (Gaussian). Although we have undreamed of device performance and number smashing today, we are inevitably forced by economics to limit hardware complexity that gives rise to significant self-noise. Noise and interference in the real world are “bursty,”“lumpy,”discrete, not stationary, anomalous and even malevolent! This chapter is an introduction to analog and digital signal processing self-noise analysis and heuristic means to control ill-behaved internal and external noise.

Ground or surface moving target detection (often called ground moving target indication or GMTI) consists of radar or EO detection of humans or tactical vehicles moving on or near the earth's surface. Animals and humans have little difficulty detecting movement and sorting unimportant movements from an important movement at short range. This is the result of hundreds of millions of years of selective evolution. For example, the frog's eye is especially adapted to detect and react to a fly-sized object moving through its field of vision. It is so good that its tongue is on the move to where the fly will be (not where it is) in less than 100 ms. Sadly, radars and EO sensors are not nearly that good over their ranges of operation (much longer than a frog's tongue!). In the following sections, the fundamental characteristics for discrimination of ground mover RCS, Doppler signature and clutter will be discussed.

Obviously the applications mentioned in this chapter are just a sampling of radar based persistent surveillance. Also, the descriptions have left out many details necessary for understanding, analysis and design. Some of the basics will be discussed in the chapters that follow. Hopefully, the reader will have a good intuitive feel for all the important aspects of Intercept, GMTI and SAR by the end of this introductory book. The references and appendices will provide more detail on many of the topics in this book.

The suite of microwave and RF apertures in a fighter, surveillance aircraft or spacecraft might be as many as 20 apertures distributed throughout the vehicle performing radar, data link, navigation, missile warning, direction finding, jamming or other functions over a frequency range covering several decades. There are apertures distributed over the platform that point forward and aft, right and left, as well as up and down. Some apertures will be shared for communications, radio navigation and identification (CNI) as well as identification, friend or foe (IFF) due to compatible frequencies and geometries. Data links such as JTIDS/Link 16 and Link 22 can share apertures with GPS and L-band satellite communications (L-SATCOM). There also may be dedicated data link apertures. EW apertures must be broadband by nature and can be shared with radar warning receivers (RWR), radar auxiliaries and some types of CNI.

Another way to detect slow-moving tactical targets is by means of HRM coupled with some form of change detection between multiple images. The change detection methods are coherent change detection (CCD) and shadow detection, noncoherent change detection (NCD), video SAR, inverse SAR (ISAR) and synthetic monopulse. Each of these will be discussed but first an understanding of conventional SAR is required. Often, fast(er) moving targets and slow-moving targets and SAR maps are acquired by the same waveform as described at the end of Chapter 6. The separate target types are usually separated by filtering.

Grammar-Guided Genetic Programming has shown its capability to evolve beyond human-competitive transmission schedulers for the benefit of large and heterogeneous communications networks. Despite this performance, a large margin of improvement is demonstrated to still exist. We have recently proposed a multi-level grammar approach which evolves structurally interesting individuals using a small grammar, before introducing a thorough grammar to probe a larger search space and evolve better-performing individuals. We investigate the advantage of using a hierarchical approach with multiple small grammars at the lower level instead of a unique one, in conjunction with a full grammar at the upper level. While we confirm in our experiment that the multi-level approach outperforms the use of a unique grammar, we demonstrate that two hierarchical grammar configurations achieve significantly better results than the multi-level approach. We also show the existence of an ideal number of small grammars that could be used in the lower level of the hierarchical approach to achieve the best performance.

Co-ordination of Inter-Cell Interference through scheduling enables telecommunication companies to better exploit their Heterogeneous Networks. However, it requires from these entities to implement an effective scheduling algorithm. The state-of-the-art for the scheduling in Heterogeneous Networks is a Grammar-Guided Genetic Programming algorithm which evolves, from a given grammar, an expression that maps to the scheduling of transmissions. We evaluate in our work the possibility of improving the results obtained by the state-of-the-art using a layered grammar approach. We show that starting with a small restricted grammar and introducing the full functionality after 10 generations outperforms the state-of-the-art, even when varying the algorithm used to generate the initial population and the maximum initial tree depth.

Operators deploy Small Cells in high traffic regions to boost the capacity of their wireless networks. However, User Equipments (UEs) at Small Cell edges experience severe interference from neighbouring high-powered Macro Cells. A fair trade-off between cell-edge and cell-centre performance can be realised by intelligently scheduling Small Cell attached UEs. Grammar-based Genetic Programming is employed to learn models that map measurement reports to schedules on a millisecond timescale. The evolved schedulers are then aggregated into ensembles. The proposed system significantly outperforms a state of the art benchmark algorithm and is within 10% of the estimated optimum.

Wireless communications networks are a global trillion dollar industry, where small improvements can scale to provide significant cost savings to networks operators. In a field full of NP-hard optimisation problems, heuristic optimisation techniques such as Evolutionary Computation offer a means to provide bespoke, scalable solutions. Grammatical Genetic Programming is applied to optimise three aspects of an LTE Heterogeneous Network: setting optimal Small Cell powers and biases, Macro Cell ABS patterns, and Small Cell scheduling. The evolved heuristics yield minimum downlink rates three times greater than a baseline technique, and twice that of a state-of-the-art industry standard benchmark. This work appears in full in Fenton et al., "Multilayer Optimization of Heterogeneous Networks using Grammatical Genetic Programming", IEEE Transactions on Cybernetics, 2017. DOI: 10.1109/TCYB.2017.2688280.

The heritability of chronic obstructive pulmonary disease (COPD) cannot be fully explained by recognized genetic risk factors identified as achieving genome-wide significance. In addition, the combined contribution of genetic variation to COPD risk has not been fully explored. We sought to determine: (1) whether studies of variants from previous studies of COPD or lung function in a larger sample could identify additional associated variants, particularly for severe COPD; and (2) the impact of genetic risk scores on COPD. We genotyped 3,346 single-nucleotide polymorphisms (SNPs) in 2,588 cases (1,803 severe COPD) and 1,782 control subjects from four cohorts, and performed association testing with COPD, combining these results with existing genotyping data from 6,633 cases (3,497 severe COPD) and 5,704 control subjects. In addition, we developed genetic risk scores from SNPs associated with lung function and COPD and tested their discriminatory power for COPD-related measures. We identified significant associations between SNPs near PPIC (P = 1.28 X 10(-8)) and PPP4R4/SERPINA1 (P = 1.01 X 10(-8)) and severe COPD; the latter association may be driven by recognized variants in SERPINA1. Genetic risk scores based on SNPs previously associated with COPD and lung function had a modest ability to discriminate COPD (area under the curve, similar to 0.6), and accounted for a mean 0.9-1.9% lower forced expiratory volume in 1 second percent predicted for each additional risk allele. In a large genetic association analysis, we identified associations with severe COPD near PPIC and SERPINA1. A risk score based on combining genetic variants had modest, but significant, effects on risk of COPD and lung function.

Wireless traffic is surging due to the prevalence of smart devices, rising demand for multimedia content and the advent of the “Internet of Things”. Network operators are deploying Small Cells alongside existing Macro Cells in order to satisfy demand during this era of exponential growth. Such Heterogeneous Networks (HetNets) are highly spectrally efficient because both cell tiers transmit using the same scarce and expensive bandwidth. However, load balancing and cross-tier interference issues constrain cell-edge rates in co-channel operation. Capacity can be increased by intelligently configuring Small Cell powers and biases, and the muting cycles of Macro Cells. This paper presents a customised Genetic Algorithm (GA) for reconfiguring HetNets. The GA converges within minutes so tailored settings can be pushed to cells in real time. The proposed GA lifts cell-edge (2.5th percentile) rates by 32% over a non-adaptive baseline that is used in practice. HetNets are highly dynamic environments. However, customers tend to cluster in hotspots which arise at predictable locations over the course of a typical day. An explicit memory of previously evolved solutions is maintained and used to seed fresh runs. System level simulations show that the 2.5th percentile rates are boosted to 36% over baseline when prior knowledge is utilised.

How might schools harness technological innovation for classroom effects? In this book the authors seek to answer this question by introducing and investigating the concept of Blended Learning through a review of current research literature. In this book, the authors consolidate the current state of Blended Learning research, by defining what is meant by Blended Learning before discussing specific technologies used in Blended Learning, the professional development required of teachers and how to implement whole of school Blended Learning regimes in schools. The book includes descriptions of popular Blended Learning models with real-world examples of their implementation, addressing both student and teacher perspectives. This book will serve as a guide to hastening the progress of Blended Learning towards the improvement of student outcomes in a world of continuous technological innovation and social change.

There is increasing pressure for society to move towards more sustainable use of its resources, and calls in the literature have been made to reassess marketing’s role in achieving such goals. This research examines how key behavioural factors influence household water use, in the context of a social marketing programme to reduce household water consumption. A model of the key drivers of household water consumption is developed and tested using a sample of 909 households in a regional city in Australia. The findings from this study support the model developed and show that in the absence of price as a rationing mechanism, the social marketing programme significantly reduces household water consumption. \ud \ud Statement of contribution: This is the first study to develop a comprehensive and empirically tested model of the non-price drivers of household water consumption, within the context of a social marketing intervention. The findings make a contribution to the field of consumer behaviour and social marketing by illustrating key behavioural drivers of water consumption. Consequently the study also shows how marketers can assist in preserving essential goods and services such as water.

Water shortages are an increasingly significant social and economic issue in many countries. Increasing the supply of water is one solution (e.g. desalination plants, new dams), but such measures are expensive. Using price to manage household water demand may be viewed as socially unequitable and politically contentious. Social marketing campaigns, where voluntary behaviour change is the goal, provide the potential to foster sustainable consumption of an increasingly scarce yet essential resource. This paper details a case study of successful water demand management in a drought affected region of South-Eastern Australia. In this region, water consumption was reduced to more sustainable levels through a targeted and successful social marketing campaign. This case is of significant relevance to the field of Social Marketing where there are increasing calls for research into environmental issues in general and water consumption in particular (Kotler, 2011). The extant research literature and this case study are integrated to form several propositions about household water consumption behaviour. Consequently, this paper contributes to the literature by providing a conceptualisation of how residents respond to water conservation related social marketing campaigns. Key issues include the potential for reciprocal behaviour by consumers when a water authority is perceived to manage the water problem effectively, and linking behaviour change through structural approaches (e.g. subsidies and restrictions) and voluntarist approaches (e.g. attitudinal change). Copyright © 2013 John Wiley & Sons, Ltd.

BackgroundTuberous sclerosis complex is a genetic disorder affecting every organ system, but disease manifestations vary significantly among affected individuals. The diverse and varied presentations and progression can be life-threatening with significant impact on cost and quality of life. Current surveillance and management practices are highly variable among region and country, reflective of the fact that last consensus recommendations occurred in 1998 and an updated, comprehensive standard is lacking that incorporates the latest scientific evidence and current best clinical practices.MethodsThe 2012 International Tuberous Sclerosis Complex Consensus Group, comprising 79 specialists from 14 countries, was organized into 12 separate subcommittees, each led by a clinician with advanced expertise in tuberous sclerosis complex and the relevant medical subspecialty. Each subcommittee focused on a specific disease area with important clinical management implications and was charged with formulating key clinical questions to address within its focus area, reviewing relevant literature, evaluating the strength of data, and providing a recommendation accordingly.ResultsThe updated consensus recommendations for clinical surveillance and management in tuberous sclerosis complex are summarized here. The recommendations are relevant to the entire lifespan of the patient, from infancy to adulthood, including both individuals where the diagnosis is newly made as well as individuals where the diagnosis already is established.ConclusionsThe 2012 International Tuberous Sclerosis Complex Consensus Recommendations provide an evidence-based, standardized approach for optimal clinical care provided for individuals with tuberous sclerosis complex.

BACKGROUND: Tuberous sclerosis complex is highly variable in clinical presentation and findings. Disease manifestations continue to develop over the lifetime of an affected individual. Accurate diagnosis is fundamental to implementation of appropriate medical surveillance and treatment. Although significant advances have been made in the past 15 years in the understanding and treatment of tuberous sclerosis complex, current clinical diagnostic criteria have not been critically evaluated or updated since the last clinical consensus conference in 1998. METHODS: The 2012 International Tuberous Sclerosis Complex Consensus Group, comprising 79 specialists from 14 countries, was organized into 12 subcommittees, each led by a clinician with advanced expertise in tuberous sclerosis complex and the relevant medical subspecialty. Each subcommittee focused on a specific disease area with important diagnostic implications and was charged with reviewing prevalence and specificity of diseaseassociated clinical findings and their impact on suspecting and confirming the diagnosis of tuberous sclerosis complex. RESULTS: Clinical features of tuberous sclerosis complex continue to be a principal means of diagnosis. Key changes compared with 1998 criteria are the new inclusion of genetic testing results and reducing diagnostic classes from three (possible, probable, and definite) to two (possible, definite). Additional minor changes to specific criterion were made for additional clarification and simplification. CONCLUSIONS: The 2012 International Tuberous Sclerosis Complex Diagnostic Criteria provide current, updated means using best available evidence to establish diagnosis of tuberous sclerosis complex in affected individuals.

Aortic dissection is a rare cause of death in young adults, which may be caused by acquired or congenital factors. We present the case of a 21-year-old man who died as a result of spontaneous aortic dissection and cardiac tamponade. At autopsy, signs of Marfan syndrome were evident both grossly and microscopically. Hodgkin lymphoma was also discovered, though was noncontributory to death. We review the causes of aortic dissection in young individuals, with a focus on the key differentiating features of predisposing inheritable connective tissue disorders. Given the implications for surviving family members, it is the obligation of the diligent forensic pathologist to be aware of these conditions, such that families may be alerted to the need for genetic counseling.

Massive increases in IP (Internet Protocol) traffic have led to rapid deployment of IP-based networks in metropolitan (metro) areas. In order to facilitate this deployment, computer-based design tools are needed. One of the most difficult decisions that engineers face in designing an IP network is choosing the IP link topology (i.e., the set of router-to-router connections). This is especially complicated when it is also necessary to route these IP links over an underlying physical network of optical fibers. In this paper, the authors describe a new heuristic for simultaneously designing a backbone IP link topology for a metro area network, and routing these IP links over a given physical network. The IP network must be designed for survivability in the event of a network failure (i.e., the loss of a physical link, router or IP link). Initially, they employ a Construction Heuristic that explicitly considers the number of router-to-router connections that would be carried over each physical link. In this way it seeks to minimize the impact of any single physical link failure. An optional Local Search routine then attempts to improve on the solution by a sequence of topology changes. IP link routings are adjusted at each topology change. This heuristic is readily able to be incorporated into an interactive design tool. Some computational experience is described.

Network operators are struggling to cope with exponentially increasing demand. Capacity can be increased by densifying existing Macro Cell deployments with Small Cells. The resulting two-tiered architecture is known as a Heterogeneous Network or 'HetNet'. Significant inter-tier interference in channel sharing HetNets is managed by resource interleaving in the time domain. A key task in this regard is scheduling User Equipment to receive data at Small Cells. Grammar-based Genetic Programming (GBGP) is employed to evolve models that map measurement reports to schedules on a millisecond timescale. Two different fitness functions based on evaluative and instructive feedback are compared. The former expresses an industry standard utility of downlink rates. Instructive feedback is obtained by computing highly optimised schedules offline using a Genetic Algorithm, which then act as target semantics for evolving models. This paper also compares two schemes for mapping the GBGP parse trees to Boolean schedules. Simulations show that the proposed system outperforms a state of the art benchmark and is within 17% of the estimated theoretical optimum. The impressive performance of GBGP illustrates an opportunity for the further use of evolutionary techniques in software-defined wireless communications networks.

Campylobacter concisus is a member of the oral microbiota that has been associated with the development of inflammatory bowel diseases. However, the role of the bacterium in disease aetiology remains poorly understood. Here, we examine optimal conditions for the growth of C. concisus, and the pathogenic potential of this bacterium in human gastrointestinal cells from the upper tract. Further, the presence of C. concisus in the lower tract of Crohn's disease (CD) patients undergoing therapy is observed, and the associations of C. concisus with the abundance of other microbial taxa and compounds they produce are evaluated. C. concisus strains had the ability to tolerate moderate levels of acidity, adhere to and invade esophageal and gastric cells; however, these properties did not correlate with their pathogenic potential in intestinal cells. The presence of the bacterium in the lower gut of CD patients was associated with an increased relative abundance of Faecalibacterium and Lachnospiraceae incertae sedis. Short chain fatty acids that can be produced by these microbial species did not appear to be responsible for this association. However, we identified genetic similarity between C. concisus and Firmicutes, specifically within aspartate and glutamate racemases. The potential pathogenesis of C. concisus in the upper gastrointestinal tract, and the responsiveness of the bacterium to therapy in a subset of CD patients warrant further investigation into whether this bacterium has a causal role in disease or its presence is incidental.

This book is about designing the effective classroom curriculum. The authors argue that an effective classroom curriculum should be the goal of every teacher in every classroom around the world: effective that is for every student, not just those who find school easy! But how does one go about designing a classroom curriculum that is effective? What are the essential ingredients and how should these ingredients be organised for teaching effect? What role does Technology play in such classroom plans? In this book Lynch, Smith and Howarth provide an insight into these questions by providing a text that focuses on classroom teaching diagnostic and design strategies. Their intent in writing such a book is to enable the classroom teacher to develop, teach and assess a classroom curriculum where learning success for all students is the central goal.This text is compulsive reading for the teacher who wants to make a difference in their classrooms.

Ken Sell, David Lynch and Tina Doe, three accomplished and published experts in the field of education, bring together leading education researchers and school leaders to create a collection of chapters which focus on key aspects of effective school leadership. The book explores a model for whole of school improvement and examines key concepts such as; readiness for change, approaches to leadership, how to use data, parental engagement, as well as providing insights into aspects of schooling and teaching into the future

Heterogeneous Cellular Networks are multi-tiered cellular networks comprised of Macro Cells and Small Cells in which all cells occupy the same bandwidth. User Equipments greedily attach to whichever cell provides the best signal strength. While Macro Cells are invariant, the power and selection bias for each Small Cell can be increased or decreased (subject to pre-defined limits) such that more or fewer UEs attach to that cell. Setting optimal power and selection bias levels for Small Cells is key for good network performance. The application of Genetic Programming techniques has been proven to produce good results in the control of Heterogenous Networks. Expanding on previous works, this paper uses grammatical GP to evolve distributed control functions for Small Cells in order to vary their power and bias settings. The objective of these control functions is to evolve control functions that maximise a proportional fair utility of UE throughputs.

This paper discusses the thermal conversion of non-homogeneous residual biomass from urban wastes as well as from forest and agricultural operations into an ultrapure syngas used for the thermo-catalytic synthesis of methanol. The latter is a commodity/building block for subsequent synthesis of fuels and chemicals. This paper focuses on the feedstock choices made by Enerkem and on its conversion technology which embraces staged gasification, syngas conditioning and conversion of syngas into methanol and of the latter into ethanol. The fundamental concepts that have led to the development of the technology are the result of a joint R&D experimental effort between the Universite de Sherbrooke, Enerkem and the Edmonton Waste Management Center of Excellence. The technology has been scaled up, from 2009 to 2011, and production of biomethanol using 1.5 tonnes/h of feed (dry basis) is ongoing at Enerkem's demonstration plant in Westbury (Quebec) since June 2011. The conversion of the produced biomethanol into bioethanol has been proven at bench scale level, and we foresee the latter to be piloted in the first half of 2012. Based on the results of the demonstration, Enerkem is erecting a 100,000-tonne/year commercial plant in Edmonton. It will use secondary recovered fuel from the mechanical biological treatment process already implemented at the Edmonton Waste Management Center. Production of methanol is initially targeted for 2013.

We describe two applications ofmachine learning in the context of internet protocol (IP)/Optical networks. The first one allows agilemanagement of resources in a core IP/Optical network by using machine learning for shorttermand long-term prediction of traffic flows. It also allows joint global optimization of IP and optical layers using colorless/ directionless (CD) reconfigurable optical add-drop multiplexers (ROADMs). Multilayer coordination allows for significant cost savings, flexible new services to meet dynamic capacity needs, and improved robustness by being able to proactively adapt to new traffic patterns and network conditions. The second application is important as we migrate our networks to Open ROADM networks to allow physical routing without the need for detailed knowledge of optical parameters. We discuss a proof-of-concept study, where detailed performance data for established wavelengths in an existing ROADM network is used for machine learning to predict the optical performance of each wavelength. Both applications can be efficiently implemented by using a software-defined network controller.