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Training and development serve as important mechanisms through which organizations can prepare employees for the future of work. This chapter outlines implications for training, including how extant research can inform future interventions and how these approaches might adapt to account for technological, demographic, and behavioral shifts in the workplace.
To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings.
A retrospective review was conducted of otoscopic images analysed using transfer learning with Google's Inception-V3 convolutional neural network architecture. The ‘gold standard’ ‘ground truth’ was defined by otolaryngologists. Perforation size was categorised as less than one-third (small), one-third to two-thirds (medium), or more than two-thirds (large) of the total tympanic membrane diameter.
A total of 233 tympanic membrane images were used (183 for training, 50 for testing). The algorithm correctly identified intact and perforated tympanic membranes (overall accuracy = 76.0 per cent, 95 per cent confidence interval = 62.1–86.0 per cent); the area under the curve was 0.867 (95 per cent confidence interval = 0.771–0.963).
A proof-of-concept image-classification artificial intelligence algorithm can be used to detect tympanic membrane perforations and, with further development, may prove to be a valuable tool for ear disease screening. Future endeavours are warranted to develop a point-of-care tool for healthcare workers in areas distant from otolaryngology.
This chapter delves into the problem of wireless-aware path planning for UAVs with a focus on cellular-connected UAV user equipment (UAV UE) that can communicate with ground cellular networks. To this end, we present a very focused study on interference-aware path planning for cellular-connected UAV UEs, in which each UAV aims at achieving a tradeoff between various quality-of-service and mission goals, such as minimizing wireless latency and interference caused on the ground network. To this end, we first motivate the need for wireless-aware path planning for UAV UE and, then, we introduce a rigorous system model for a wireless network with UAV UEs. We then formally pose the wireless-aware path planning problem for UAV UEs using the framework of game theory. We subsequently provide a reinforcement learning solution that can be used to design autonomous, self-organizing wireless-aware path planning mechanisms for UAV UEs while balancing the various wireless and mission objectives of the drones. We also show how some of the unique features of UAVs, such as their altitude and their ability to establish line-of-sight, will have significant impact on the way in which their trajectory is designed.
In this chapter, we focus on how wireless communication resources (spectral, temporal, and power) can be optimized and managed in wireless networks that support UAVsWestart by analyzing a very unique problem related to wireless networks supported by hovering UAV base stations: Cell association in hover time constraints. We show how the presence of hover time constraints for the UAVs will drastically change the way in which cell association is performed. Then, we generalize the problem of cell association to a fully fledged 3D cellular system that integrates both UAV base stations and UAV user equipment. Subsequently, we investigate the problem of spectrum and cache management in a wireless network supported by UAV base stations that are able to access both licensed and unlicensed spectrum resources.
This chapter studies the problem of UAV deployment for wireless communication purposes. In particular, we focus on the deployment of UAV base stations whose locations will strongly impact the performance that they can deliver. To this end, we start by providing a broad overview on the analytical tools that can be used to develop optimized deployment strategies for wireless networks with UAVs. Then, we investigate how UAV base stations can be deployed for optimizing the wireless coverage for a ground network of wireless devices that seek to communicate with UAV BSs in the downlink. We shed important light on how to deploy the UAV base stations, by determining their number and locations, in a way to maximize network performance, under various constraints, such as power. We then investigate the problem of optimally deploying UAV base stations for collecting data, in the uplink, from ground Internet of Things devices in an energy-efficient manner. We conclude our discussions by studying the deployment of UAV base stations that can leverage machine learning techniques to cache popular content and to track the mobility of ground users.
Research regarding the impact of in utero exposure to marijuana lags behind other areas of interest despite frequent use during pregnancy. The endocannabinoid system is intimately involved with guiding embryonic and fetal axon development and THC has been shown to disrupt microtubules in axonal growth cones, raising concern for potential long term consequences. While no gross birth defects have been associated with prenatal exposure to marijuana, some reduction in birth weight is common. Only three long term studies provide data on the consequences of prenatal exposure: OPPS (Canada), MHPCD (US), and Generation R (Netherlands). Immediate effects on newborns include increased tremors, exaggerated startle, a high-pitched cry and abnormal sleep cycling. Disturbances in memory and verbal development, sustained attention, increased impulsivity and hyperactivity have been documented at various ages from early childhood through the first two decades of life. fMRIs during executive function testing in 18-22-year-olds prenatally exposed to marijuana reveal compensatory recruitment of wider areas of cortex than in controls. Psychological and behavioral problems have also been reported as early as age 6. Both pediatricians (AAP) and obstetricians (ACOG) caution against marijuana use during pregnancy and lactation.
Youngmin Kim’s scholarly engagement with the history of Chinese political thought has led him to grapple with a problem that is central to the project of deparochializing political theory: how to delineate the boundaries of a thought tradition such that it is tractable as an object of study and deepened understanding. Rather than looking to geographic regions or broad intellectual traditions to provide the requisite boundaries for units of comparison, Kim turns to the self-identification of individuals as bearers of “a collective identity that they themselves construct.” In this chapter, Kim argues that the tradition of True Way Learning (TWL), a branch of Confucian tradition that became dominant in mid- to late imperial China, constitutes a sufficiently well-bounded community of thought and practice to serve as a useful comparator with similarly bounded traditions in other historical contexts. The chapter develops a comparison between TWL and the Kantian and Madisonian republican traditions in Euro-American thought and holds out the possibility that TWL might still serve as an ideational resource for twenty-first-century Chinese reformers, just as Enlightenment republicanism has inspired reimaginings of anti-despotic political order among American and European thinkers.
This chapter discusses mathematical models of learning in neural circuits with a focus on reinforcement learning. Formal models of learning provide insights into how we adapt to a complex, changing environment, and how this adaptation may break down in psychopathology. Computational clinical neuroscience is motivated to use mathematical models of decision processes to bridge between brain and behavior, with a particular focus on understanding individual differences in decision making. The chapter reviews the basics of model specification, model inversion (parameter estimation), and model-based approaches to understanding individual differences in health and disease. It illustrates how models can be specified based on theory and empirical observations, how they can be fitted to human behavior, and how model-predicted signals from neural recordings can be decoded. A functional MRI (fMRI) study of social cooperation is used to illustrate the application of reinforcement learning (RL) to test hypotheses about neural underpinnings of human social behavior.
A rapid growth in computational power and an increasing availability of large, publicly accessible, multimodal data sets present new opportunities for psychology and neuroscience researchers to ask novel questions, and to approach old questions in novel ways. Studies of the personal characteristics, situation-specific factors, and sociocultural contexts that result in the onset, development, maintenance, and remission of psychopathology, are particularly well suited to benefit from machine learning methods. However, introductory textbooks for machine learning rarely tailor their guidance to the needs of psychology and neuroscience researchers. Similarly, the traditional statistical training of clinical scientists often does not incorporate these approaches. This chapter acts as an introduction to machine learning for researchers in the fields of clinical psychology and clinical neuroscience. It discusses these methods, illustrated through real and hypothetical applications in the fields of clinical psychology and clinical neuroscience. It touches on study design, selecting appropriate techniques, how (and how not) to interpret results, and more, to aid researchers who are interested in applying machine learning methods to clinical science data.
Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.
Consecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.
The trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.
A trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.
Online learning has become an increasingly expected and popular component for education of the modern-day adult learner, including the medical provider. In light of the recent coronavirus pandemic, there has never been more urgency to establish opportunities for supplemental online learning. Heart University aims to be ‘the go-to online resource’ for e-learning in congenital heart disease and paediatric acquired heart disease. It is a carefully-curated open access library of pedagogical material for all providers of care to children and adults with congenital heart disease or children with acquired heart disease, whether a trainee or a practicing provider. In this manuscript, we review the aims, development, current offerings and standing, and future goals of Heart University.
This article presents the results of an action-research project. The project consisted of a reflection process involving a year-long collaboration between a teacher, a teaching assistant and a speech therapist in a special education school, together with two researchers acting as counsellors. The reflection process sought to promote changes in the participants’ approaches to working on communication and language in the classroom. This article sets out to identify and describe the processes of change in the three teaching professionals’ conceptions of communication and language teaching and learning, and about their teaching role. The collaborative counselling lasted 32 weeks and consisted of a total of nine group counselling (GC) sessions. All of the nine GC sessions were transcribed in order to analyse the changes in the teaching professionals’ discourse. The ATLAS.ti 7.0 program was then used to select speech quotations and to group them into thematic clusters based on content analysis. The results indicate that the teaching professionals’ conceptions subtly changed during the collaborative counselling process; specifically, their conceptions about how to develop communication and language, classroom interaction, the educator’s role, organisation of context and curriculum planning.
Competition in the US Congress has been characterised along a single, left-right ideological dimension. We challenge this characterisation by showing that the content of legislation has far more predictive power than alternative measures, most notably legislators’ ideological positions derived from scaling roll call votes. Using a machine learning approach, we identify a topic model for final passage votes in the 111th through the 113th House of Representatives and conduct out-of-sample tests to evaluate the predictive power of bill topics relative to other measures. We find that bill topics and congressional committees are important for predicting roll call votes but that other variables, including member ideology, lack predictive power. These findings raise serious doubts about the claim that congressional politics can be boiled down to competition along a single left-right continuum and shed new light on the debate about levels of polarisation in Congress.
Given the persistent challenges of governing natural resources sustainably, there is an urgent need for comprehensive sustainability assessments that enable holistic understanding of current and future development scenarios. To spur necessary action, these assessments must also foster joint learning among multiple stakeholders. In this chapter, we present the ‘Sustainability Wheel’, a mixed-method, dialogue-based approach for assessing the sustainability of resource governance systems. The approach combines the transparent identification of general sustainability principles, the regional contextualization of these as subprinciples/indicators, and the scoring of the indicators based on deliberative dialogue in interdisciplinary teams, drawing on shared knowledge and understandings. Using the approach to examine sustainable water governance in the Swiss Alps, we demonstrate its effectiveness in integrating multiple perspectives, including those deriving from qualitative and quantitative research, and in facilitating communication with stakeholders. Furthermore, we explore its application to urban systems.
An important component in the analysis of policy instruments is the modelling of human behaviour. In the previous chapters, humans are (usually) considered as rational and perfectly informed profit-maximisers. This assumption is gradually relaxed in the present chapter. The first section addresses the issues of decreasing marginal utility, multiple objectives and time preference. The following section adds the problem of risk and uncertainty and presents a standard economic model for decision making under risk. The assumption of selfish profit maximisation is relaxed subsequently by introducing a model of fairness and inequity aversion. The following sections present approaches for modelling situations of imperfect information and limited cognitive abilities, as well as learning and behavioural change. The concluding section discusses and provides some linkages to Chapter 7 on individual-based models and provides some references from the literature on agent-based modelling.
Recent research has begun to investigate implicit learning at the level of meaning. The general consensus is that implicitly linking a word with a meaning is constrained by existing linguistic knowledge. However, another factor to consider is the extent to which attention is drawn to the relevant meanings in implicit learning paradigms. We manipulated the presence of cue saliency during implicit rule learning for a grammatical form (i.e., articles) linked to meaning (i.e., animacy vs. varying notions of size). In a series of experiments, participants learned four novel words but did not know that article usage also depended on a hidden rule, creating an opportunity for implicit rule learning. We found implicit learning through the use of a highly salient meaning (Experiment 1) or if image size was made salient by being explicitly cued (Experiment 3), but not in a low salient paradigm for intrinsic object size (Experiment 2). The findings suggest that implicit learning of semantic information might not be as constrained as previously argued. Instead, implicit learning might be additionally influenced by feature-focusing cues that make the meaning contrasts more salient and thereby more readily available to learning.
Over 2.4 million children in the public school system are diagnosed with a learning disability, including dyslexia and developmental dyscalculia. Previous research has shown that some teachers are unaware of the importance of working memory in a student’s academic and social realm and what working memory deficits may look like in the classroom. The relationship between learning disabilities, working memory, and behaviour problems were examined with tailored recommendations for improvement to provide insight for classroom educators. Three children from the United Kingdom, all of whom were 8 years old and presented with symptoms of learning disorders and low working memory profiles, were selected for case studies. Measures of working memory, behaviour, and academic attainment were included. Results from their standardised assessments indicated that each child had below average working memory, as well as low scores in arithmetic, writing and spelling skills. Each child also exhibited some type of behavioural problem, such as inattention or hyperactivity. Implications of the impact of their working memory profile on their academic outcomes and behaviour are discussed. Recommendations, such as Response to Intervention (RTI), are included for classroom educators to bridge the gap between research and practice.
To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer’s disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).
dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for “10 after 11.” A digital pen and custom software recorded patient’s drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.
Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25–125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.
Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.
Twenty-six percent of children experience a traumatic event by the age of 4. Negative events during childhood have deleterious correlates later in life, including antisocial behavior. However, the mechanisms that play into this relation are unclear. We explored deficits in neurocognitive functioning, specifically problems in passive avoidance, a construct with elements of inhibitory control and learning as a potential acquired mediator for the pathway between cumulative early childhood adversity from birth to age 7 and later antisocial behavior through age 18, using prospective longitudinal data from 585 participants. Path analyses showed that cumulative early childhood adversity predicted impaired passive avoidance during adolescence and increased antisocial behavior during late adolescence. Furthermore, poor neurocognition, namely, passive avoidance, predicted later antisocial behavior and significantly mediated the relation between cumulative early childhood adversity and later antisocial behavior. This research has implications for understanding the development of later antisocial behavior and points to a potential target for neurocognitive intervention within the pathway from cumulative early childhood adversity to later antisocial behavior.
This paper sets out to discuss the monolingual problem within computer-assisted language learning (CALL) research and CALL product development, namely a lack of knowledge about how CALL products and projects can support learners in using all their linguistic resources to achieve language-learning- and language-using-related goals, and a lack of CALL products and projects that realize this potential, or that support specific plurilingual skill development. It uses an analysis of CALL-related papers to demonstrate how far CALL is impacted by a monolingual bias that it inherited from language learning pedagogy. An analysis of articles from four CALL journals across 10 years shows that although the words bilingual and multilingual appear in these journals fairly regularly, terms such as plurilingual, third language, tertiary language, L3, translanguaging, and translingual are extremely rare. Also, only eight articles could be identified that use any of these eight keywords in their title. Trends across those papers are identified. In a discussion of existing CALL products and projects that incorporate more than one language, it is argued that while commercial products often include more than one language, this is frequently in a behaviorist or grammar-translation tradition, while innovative plurilingual products and projects tend to be non-commercial and often EU/EC-funded initiatives. The article argues that CALL research and product development can not only avoid this monolingual bias, but also actively contribute to our knowledge of how all linguistic resources can be used for language learning. It makes suggestions for relevant future research areas related to multilingual computer-assisted language learning (MCALL).