Joint attention in the first year: The coordination of gaze and affect between 7 and 10 months of age

We used a multilevel growth model to describe the developmental trajectories of infant’s coordinated attention between people and objects between 7 and 10 months of age. Additionally, we assed whether the coordinated attention looks were accompanied by smiles as infants interacted social partners. These results confirm the emergence of visual joint attention skills before the end of the first year. These results will be useful in the construction of robotic systems that engage in joint attention

Calculation of sub-surface-initiated fatigue fractures in gears

Power-transmitting gears are typically heat-treated, most often case-hardened, to improve the fatigue strength and therefore to ensure higher fatigue life. The heat treatment causes higher hardness in the surface area as well as compressive residual stresses in the hardened layer. The near-surface compressive residual stresses are compensated by tensile stresses in higher depths of the gear volume. Pitting and tooth root breakage are the most common failure modes of gears, which are well researched and are also addressed in ISO 6336 [14]. The assessment of these failure modes provides the basis for the dimensioning of gears in the design phase. However, subsurfaceinitiated failures, like tooth flank fracture (TFF), can also appear at loads below the allowable level of loading for pitting and tooth root bending. TFF is a fatigue damage with crack initiation in the region of the transition between compressive and tensile residual stresses and usually leads to a total loss of drive. The existing calculation models for fatigue strength of gears with regard to TFF consider residual stresses differently. The base of the investigated calculation models is a local comparison of the occurring stresses and the strength value in the gear volume. The outcome of the calculation model from Oster [26] is highly influenced by the residual stress state. However, the material-physical model by Hertter [10] is more tolerant to slightly varying residual stresses. Further approaches such as Weber [34] and Konowalcyk [18] are based on the ideas of Oster and Hertter. The verification of the models is complicated due to the lack of residual stress measurements in larger depths under the gear flank surface. For example, residual stress measurement by Xray diffraction is only possible up to depths of approximately one millimeter. Therefore, tensile residual stresses in the inner tooth volume are considered zero in the common residual stresses calculation of Lang [19] and are not considered in the current calculation approach of ISO/DTS 6336-4 [15]. The paper describes local calculation approaches for the fatigue strength of gears with different consideration of residual stresses. Furthermore, the crack initiation point, which is mandatory for the validation of an approach, is examined. The failure mode TFF is hereby the key

Large scale continuous integration and delivery:Making great software better and faster

Since the inception of continuous integration, and later continuous delivery, the methods of producing software in the industry have changed dramatically over the last two decades. Automated, rapid and frequent compilation, integration, testing, analysis, packaging and delivery of new software versions have become commonplace. This change has had significant impact not only on software engineering practice, but on the way we as consumers and indeed as a society relate to software. Moreover, as we live in an increasingly software-intensive and software-dependent world, the quality and reliability of the systems we use to build, test and deliver that software is a crucial concern. At the same time, it is repeatedly shown that the successful and effective implementation of continuous engineering practices is far from trivial, particularly in a large scale context. This thesis approaches the software engineering practices of continuous integration and delivery from multiple points of view, and is split into three parts, accordingly. Part I focuses on understanding the nature of continuous integration and differences in its interpretation and implementation. In order to address this divergence and provide practitioners and researchers alike with better and less ambiguous methods for describing and designing continuous integration and delivery systems, Part II applies the paradigm of system modeling to continuous integration and delivery. Meanwhile, Part III addresses the problem of traceability. Unique challenges to traceability in the context of continuous practices are highlighted, and possible solutions are presented and evaluated

Literature and Propaganda: The Structure of Conversion in Schenzinger\u27s Hitlerjunge Quex

Propaganda literature as a genre can profitably be analyzed by means of a structuralist approach, as Susan R. Suleiman has shown in her study of the French ideological novel. Extending her discussion of the structure of confrontation and the structure of apprenticeship, this study postulates the structure of conversion as a fundamental form of propaganda literature. Through loss of self to a greater entity, the central character in fiction exemplifying this form finds a new identity in self-submergence. A once-popular novel by the German pro-fascist author Karl Aloys Schenzinger, Hitlerjunge Quex ( 1932), serves as a model for investigation into the structure of conversion. Religious and psychological dimensions of the central character\u27s experience merge in a representation of conversion that is all the more powerfully ideological for disguising its political and racial assumptions. Eros and Thanatos meet in the mythic heightening of self-sacrifice, culminating in martyrdom. A consideration often ignored by structuralist critics, the use of stylistic means to reinforce implied messages, is shown to be a significant element in Hitlerjunge Quex. The value of a structuralist approach to propaganda lies in its elucidation of hidden assumptions, exposing them to critical judgment

Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort

The rise of Alzheimers Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimers Disease risk

A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease

This paper explores deterioration in Alzheimer’s Disease using Machine Learning. Subjects were split into two datasets based on baseline diagnosis (Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration at final visit (a binomial essentially yes/no categorisation) using data from the Alzheimer’s Disease Neuroimaging Initiative (demographics, genetics, CSF, imaging, and neuropsychological testing etc). Six machine learning models, including gradient boosting, were built, and evaluated on these datasets using a nested cross-validation procedure, with the best performing models being put through repeated nested cross-validation at 100 iterations. We were able to demonstrate good predictive ability using CART predicting which of those in the cognitively normal group deteriorated and received a worse diagnosis (AUC = 0.88). For the mild cognitive impairment group, we were able to achieve good predictive ability for deterioration with Elastic Net (AUC = 0.76)

Feasibility randomised controlled trial of a one-day CBT workshop (“DISCOVER”) for 15-18 year olds with anxiety and/or depression in clinic settings

Background: “DISCOVER” one-day cognitive behavioural therapy (CBT) workshops have been developed to provide accessible, developmentally-sensitive psychological support for older adolescents experiencing emotional difficulties. Previous school-based evaluations of the DISCOVER model have shown positive outcomes. Aims: The current study aimed to test the model for clinically-referred adolescents, in real-world settings. Method: A randomised controlled trial (RCT) assessed feasibility, acceptability and preliminary outcomes of the DISCOVER intervention, in comparison with usual care, for 15-18-year-olds with emotional difficulties. Participants were recruited from outpatient clinic waiting lists in UK child and adolescent mental health services (CAMHS). Research feasibility indicators included rates of recruitment, randomisation, intervention participation (group workshops and individualised follow-up telephone calls), and data collection (at baseline and 8-week follow-up). Intervention acceptability was assessed using a structured service satisfaction questionnaire and semi-structured qualitative interviews with intervention participants. Preliminary clinical outcomes were explored using adolescent-reported validated measures of depression, anxiety and well-being. Results: N=24 participants were randomised to intervention and usual care groups. Workshop attendance was good and high levels of treatment satisfaction were reported, although feasibility challenges emerged in recruitment and randomisation. Trends were found towards potential improvements in anxiety and well-being for the intervention group, but the effect estimate for depression was imprecise; interpretability was also limited due to the small sample size. Conclusions: DISCOVER appears to be a feasible and acceptable intervention model for clinically-referred 15-18-year-olds with emotional difficulties. A full-scale RCT is warranted to evaluate effectiveness; protocol modifications may be necessary to ensure feasible recruitment and randomisation procedures