To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Variable OH/IR stars are Asymptotic Giant Branch (AGB) stars with an optically thick circumstellar envelope that emit strong OH 1612 MHz emission. They are commonly observed throughout the Galaxy but also in the LMC and SMC. Hence, the precise inference of the distances of these stars will ultimately result in better constraints on their mass range in different metallicity environments. Through a multi-year long-term monitoring program at the Nancay Radio telescope (NRT) and a complementary high-sensitivity mapping campaign at the eMERLIN and JVLA to measure precisely the angular diameter of the envelopes, we have been re-exploring distance determination through the phase-lag method for a sample of stars, in order to refine the poorly-constrained distances of some and infer the currently unknown distances of others. We present here an update of this project.
Objectives: The aim of this study was to demonstrate the utility of an evidence-based assessment (EBA) model to establish a multimodal set of tools for identifying students at risk for perceived post-injury academic problems. Methods: Participants included 142 students diagnosed with concussion (age: M=14.95; SD=1.80; 59% male), evaluated within 4 weeks of injury (median=16 days). Demographics, pre-injury history, self- and parent-report measures assessing symptom severity and executive functions, and cognitive test performance were examined as predictors of self-reported post-injury academic problems. Results: Latent class analysis categorized participants into “high” (44%) and “low” (56%) levels of self-reported academic problems. Receiver operating characteristic analyses revealed significant discriminative validity for self- and parent-reported symptom severity and executive dysfunction and self-reported exertional response for identifying students reporting low versus high academic problems. Parent-reported symptom ratings [area under the receiver operating characteristic curve (AUC)=.79] and executive dysfunction (AUC=.74), and self-reported ratings of executive dysfunction (AUC=.84), symptoms (AUC=.80), and exertional response (AUC=.70) each classified students significantly better than chance (ps<.001). Hierarchical logistic regression indicated that, of the above, self-reported symptoms and executive dysfunction accounted for the most variance in the prediction of self-reported academic problems. Conclusions: Post-concussion symptom severity and executive dysfunction significantly predict perceived post-injury academic problems. EBA modeling identified the strongest set of predictors of academic challenges, offering an important perspective in the management of concussion by applying traditional strengths of neuropsychological assessment to clinical decision making. (JINS, 2016, 22, 1038–1049)
Catherine Esnouf, Institut National de la Recherche Agronomique (INRA), Paris,Marie Russel, Institut National de la Recherche Agronomique (INRA), Paris,Nicolas Bricas, Centre de Co-opération Internationale en Recherche Agronomique pour le Développement (CIRAD), Paris
The duALIne project chose to examine the methods used to assess food sustainability in a chapter of its own, separate from the sectorial approaches presented previously, so that this examination could be as open as possible. This chapter focuses in particular on the specific issues posed by food vis-à-vis the methods currently used to measure sustainability. Under this approach, this chapter looks firstly at the complexity of food systems, then how the associated challenges of sustainability could be structured and finally presents some methods and indicators and the research questions they raise.
Measuring performance has become a widespread activity in modern societies. It is the benchmark by which political and economic choices are regularly backed and/or justified. Performance indicators, whatever their objective, have seen exponential development, as have the operators who construct them. Assessing the performance of food systems through the prism of sustainable development is still a recent concern that requires in-depth reflection, both in terms of its scope and of the issue(s) to be assessed on the one hand, and regarding the choices of the sustainable challenges targeted or the assessment methods to be used on the other.
When we speak, we configure the vocal tract which shapes the visible motions of the face and the patterning of the audible speech acoustics. Similarly, we use these visible and audible behaviors to perceive speech. This book showcases a broad range of research investigating how these two types of signals are used in spoken communication, how they interact, and how they can be used to enhance the realistic synthesis and recognition of audible and visible speech. The volume begins by addressing two important questions about human audiovisual performance: how auditory and visual signals combine to access the mental lexicon and where in the brain this and related processes take place. It then turns to the production and perception of multimodal speech and how structures are coordinated within and across the two modalities. Finally, the book presents overviews and recent developments in machine-based speech recognition and synthesis of AV speech.