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Organic–inorganic‐halide‐perovskite (OIHP) based light‐emitting diodes (LEDs) are becoming more and more well‐described as having multiple potential uses in lighting and display technology, owing to their broad color gamut and high color purity. Recent research reveals that hole‐transport layers (HTLs) perform crucial roles in realizing high‐performance and highly stable perovskite light‐emitting diodes (PeLEDs). However, the exploration of organic HTLs for obtaining efficient PeLEDs has lagged behind compared to electron‐transport layers. In the past few years, some innovative HTLs have been developed for PeLEDs, which have demonstrated excellent performances in the devices. It has been established that HTL/OIHP interface has a significant impact on the crystallization behaviors and electrical properties of OIHP film. Herein, different types of organic HTLs (polymer/small molecule) used for PeLEDs are discussed. Also, the mechanism of action of those HTLs in perovskite devices is also presented. Moreover, the recent progress of organic HTLs in blue, green, and red PeLED devices is highlighted. Furthermore, the strategies to improve the performance of existing standard HTLs are also reviewed. Additionally, the challenges present in the PeLED technology are briefly summarized. Lastly, viewpoints regarding the ongoing obstacles and forthcoming prospects are emphasized. [ABSTRACT FROM AUTHOR]

Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast‐independent model trained using synthetic images generated from the ground truth labels of very few subjects. We base our experiments on the dataset released by the developmental Human Connectome Project, for which high‐quality images are available for more than 700 babies aged between 26 and 45 weeks postconception. First, we confirm the impressive performance of a standard UNet trained on a few volumes, but also confirm that such models learn intensity‐related features specific to the training domain. We then confirm the robustness of the synthetic learning approach to variations in image contrast. However, we observe a clear influence of the age of the baby on the predictions. We improve the performance of this model by enriching the synthetic training set with realistic motion artifacts and over‐segmentation of the white matter. Based on extensive visual assessment, we argue that the better performance of the model trained on real T2w data may be due to systematic errors in the ground truth. We propose an original experiment allowing us to show that learning from real data will reproduce any systematic bias affecting the training set, while synthetic models can avoid this limitation. Overall, our experiments confirm that synthetic learning is an effective solution for segmenting neonatal brain MRI. Our adapted synthetic learning approach combines key features that will be instrumental for large multisite studies and clinical applications. [ABSTRACT FROM AUTHOR]

There appears to be a mismatch between apparent incompetence in the world and the amount of sociological research it attracts. The aim of this article is to outline a sociology of incompetence and justify its value. I begin by defining incompetence as unsatisfactory performance relative to standards. Incompetence is thus intrinsically sociological in being negotiated and socially (re)constituted. The next section foregrounds how widespread and serious incompetence is. This renders effective sociological understanding crucial to welfare. The article then systematically analyses uses of the term in the British Journal of Sociology (a good quality general journal) to assess the current state of research. This analysis fully confirms the neglect of incompetence as a research topic. The next section proposes suitable methods for preliminary incompetence research addressing distinctive challenges like the stigma of being incompetent. These sections then allow incompetence to be better contextualised by other contributing concepts like power, bureaucracy and meritocracy. The final section justifies suggestions about directions for future research. [ABSTRACT FROM AUTHOR]

Although mouse‐tracking has been taken as a real‐time window on different aspects of human decision‐making processes, whether purely semantic information affects response conflict at the level of motor output as measured through mouse movements is still unknown. Here, across two experiments, we investigated the effects of semantic knowledge by predicting participants' performance in a standard keyboard task and in a mouse‐tracking task through distributional semantics, a usage‐based modeling approach to meaning. In Experiment 1, participants were shown word pairs and were required to perform a two‐alternative forced choice task selecting either the more abstract or the more concrete word, using standard keyboard presses. In Experiment 2, participants performed the same task, yet this time response selection was achieved by moving the computer mouse. Results showed that the involvement of semantic components in the task at hand is observable using both standard reaction times (Experiment 1) as well as using indexes extracted from mouse trajectories (Experiment 2). In particular, mouse trajectories reflected the response conflict and its temporal evolution, with a larger deviation for increasing word semantic relatedness. These findings support the validity of mouse‐tracking as a method to detect deep and implicit decision‐making features. Additionally, by demonstrating that a usage‐based model of meaning can account for the different degrees of cognitive conflict associated with task achievement, these findings testify the impact of the human semantic memory on decision‐making processes. [ABSTRACT FROM AUTHOR]

The use of brief dyslexia rating scales is increasing given current dyslexia legislation efforts across the United States. The purpose of this article is to provide an overview of the historical context of the use of brief dyslexia rating scales, strengths, and limitations of using these measures, criteria for selecting these measures, and a review of three commonly used brief dyslexia rating scales in the United States. A discussion of how brief dyslexia rating scales can be used in conjunction with other forms of assessment is also provided. Implications for practice on using brief dyslexia rating scales are addressed in the context of both assessment and intervention. Practitioner points: Dyslexia screeners are intended to identify students who are at risk of not meeting reading performance standards and consist of both performance‐based measures and brief dyslexia rating scales.Caution should be taken when using brief dyslexia rating scale screeners due to low sample sizes for certain populations, limited numbers of students with dyslexia in the standardization sample of some measures, and variable specificity and sensitivity levels.Practitioners are encouraged to utilize performance‐based screeners if scores on a brief dyslexia rating scale screener fall below a cut score. [ABSTRACT FROM AUTHOR]