For systems executing repetitive tasks, how to realize the perfect tracking objective is generally desirable, for which an effective method called “iterative learning control (ILC)” emerges thanks to the incorporation of the repetitive execution of systems into an ILC design framework. However, nonrepetitive (iteration-varying) uncertainties are often inevitable in practice and greatly degrade the tracking accuracy of ILC, which has not been treated well, regardless of considerable robust ILC results. This motivates this article to develop a new design method to improve the tracking accuracy of ILC by adopting a high-order extended state observer (ESO) to address ill effects of nonrepetitive uncertainties and uncertain system models. With the designed ESO-based ILC, the robust tracking of any desired trajectory can be achieved such that the tracking error can be decreased to vary in a small bound depending continuously on the bounds of high-order variations of nonrepetitive uncertainties with respect to the iteration. It makes the tracking accuracy of ILC possible to be regulated through the design of ESO, of which the validity is demonstrated by including a simulation example.Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes a teacher-student network without prior professional information for automated filtering of useful frame-level features by a gated mechanism and extracting high-level features by using knowledge distillation to capture the results of EEG emotion recognition from a teacher network and student networks. Then, the results from subnetworks are integrated by using the novel decision module, which, motivated by the voting mechanism, adjusts the composition of feature vectors and improves the weight of accurate prediction to optimize the integration effect. During training, an innovative data privacy protection mechanism is applied for avoiding data sharing, where each student network only inherits weights from all trained networks and does not inherit the training dataset. Here, the framework can be repeatedly optimized and improved by only training the next student subnetwork on new EEG signals. Experimental results show that our framework improves the accuracy of EEG emotion recognition by more than 5% and gets state-of-the-art performance for EEG emotion recognition in the subject-independent mode.In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.The visualization of results while the simulation is running is increasingly common in extreme scale computing environments. We present a novel approach for in situ generation of image databases to achieve cost savings on supercomputers. Our approach, a hybrid between traditional inline and in transit techniques, dynamically distributes visualization tasks between simulation nodes and visualization nodes, using probing as a basis to estimate rendering cost. Our hybrid design differs from previous works in that it creates opportunities to minimize idle time from four fundamental types of inefficiency variability, limited scalability, overhead, and rightsizing. We demonstrate our results by comparing our method against both inline and in transit methods for a variety of configurations, including two simulation codes and a scaling study that goes above 19K cores. Our findings show that our approach is superior in many configurations. As in situ visualization becomes increasingly ubiquitous, we believe our technique could lead to significant amounts of reclaimed cycles on supercomputers.Environmental sensors provide crucial data for understanding our surroundings. For example, air quality maps based on sensor readings help users make decisions to mitigate the effects of pollution on their health. Standard maps show readings from individual sensors or colored contours indicating estimated pollution levels. However, showing a single estimate may conceal uncertainty and lead to underestimation of risk, while showing sensor data yields varied interpretations. We present several visualizations of uncertainty in air quality maps, including a frequency-framing ‘`dotmap” and small multiples, and we compare them with standard contour and sensor-based maps. In a user study, we find that including uncertainty in maps has a significant effect on how much users would choose to reduce physical activity, and that people make more cautious decisions when using uncertainty-aware maps. Additionally, we analyze think-aloud transcriptions from the experiment to understand more about how the representation of uncertainty influences people’s decision-making. Our results suggest ways to design maps of sensor data that can encourage certain types of reasoning, yield more consistent responses, and convey risk better than standard maps.In this study, we explore how one can use cavity polariton formation and a non-Condon vibronic coupling mechanism to form a type of hybrid light-matter state we denote as Herzberg-Teller (HT) vibronic polaritons. We use simple models to define the basic characteristics of these hybrid light-matter excitations including their dispersive energies. Experimentally, we find evidence of HT polaritons in the light emission spectra from copper(II) tetraphenylporphyrin (CuTPP) molecules strongly coupled to both single and multimode Fabry-Perot resonator structures. For specific resonator designs, we find evidence of significant enhancement of light emission from a short-lived sing-doublet state of CuTPP, which couples to a higher energy singlet state via a non-Condon vibronic mechanism. The results of a two-state model support the conclusion that this enhancement and the temperature-dependent dispersion of the light emission peak energy stem from radiative relaxation into cavity photon states dressed by collective vibrations of the molecules participating in polariton formation. These results show how researchers can leverage the complex interplay of electronic and nuclear degrees of freedom in light absorbing molecules to form a vaster array of coherent light-matter states and potentially transform platforms in optoelectronic and photocatalytic technologies.A powerful and environmentally friendly electrochemical manganese-promoted free radical selenylation reaction between boronic acids and diselenide reagents was established. This electrochemical protocol provides a practically applicable way to a series of valuable organoselenium compounds with the use of easy available materials. Mechanistic experiments implied that the seleno-radical formed via direct or indirect electrochemical oxidation of diselenide may be involved as a key species in this transformation.The radiation dose sheet generated by the CT scanner is a form that displays important information about an examination. It functions as a road map for the examination, detailing what CT examinations were performed and what parameters were used to perform them. One essential element of the radiation dose sheet, the volume CT dose index (CTDIvol), is a commonly used radiation dose index that is displayed on most CT scanners. The CTDIvol is used for quality control and is helpful for comparing the radiation output among different protocols and different scanners. The dose-length product (DLP) is a radiation dose index that builds on the CTDIvol by incorporation of the scan length. The DLP is combined with a conversion coefficient and used to determine the effective dose from the CT examination. Determining the effective dose is a way to estimate the whole-body radiation dose, even if the CT examination is confined to a smaller part of the body. In addition to these values, other data about the study from the CT scanner manufacturer, including the tube voltage and tube current-time product, usually are displayed on CT scanners. These values are major determinants of the image quality and radiation dose. The radiation dose sheet is a useful tool for radiologists, technologists, and physicists, allowing them to comprehend the technical details of a CT examination. The authors describe the components of the radiation dose sheet, the relationships of these components with one another, and the contributions of these components to the radiation dose. ©RSNA, 2022.

The purpose of this study was to examine speech-language pathologists’ (SLPs) opinions on their scope of practice related to reading, self-reported background training, current caseloads, and confidence in their abilities to define, assess, and provide effective treatment for reading-related difficulties.

SLPs (

= 271) from across the United States completed an online survey assessing their opinions on scope of practice, education and training in reading, and confidence in defining, assessing, and treating reading-related difficulties.

A majority of respondents agreed that the identification, assessment, prevention, and intervention of reading disabilities are all within the scope of practice of SLPs. However, a majority also reported that literacy instruction is more heavily the responsibility of teachers than SLPs, and approximately half felt similarly regarding prevention, assessment, identification, and intervention of reading disabilities. Biocytin Many respondents did not feel that their training in reading was adequate and felt that more graduate coursework should be dedicated to literacy. There was a lot of variability in responses when asked how often respondents focus on reading skills with clients, ranging from almost daily to never; however, results indicate that SLPs rarely administer reading assessments. Overall, respondents were more confident in their ability to define versus assess or provide therapy for various reading subskills.

Despite SLPs agreeing that reading is within their scope of practice and feeling confident in some aspects of reading, graduate programs for speech-language pathology may need to provide greater training in literacy, especially related to reading assessment and diagnosis.

Despite SLPs agreeing that reading is within their scope of practice and feeling confident in some aspects of reading, graduate programs for speech-language pathology may need to provide greater training in literacy, especially related to reading assessment and diagnosis.