We love to read — a collaborative endeavor to build the foundation for lifelong readers

This article presents a model of a reading motivation project for a group of fourth grade students. The project incorporates strategies shown to promote engagement in literacy: opportunities for choice, reflection and social interaction. It features the use of metacognitive activities where students set weekly goals and reflect upon how they are growing as readers

System Model Bias Processing Approach for Regional Coordinated States Information Involved Filtering

In the Kalman filtering applications, the conventional dynamic model which connects the states information of two consecutive epochs by state transition matrix is usually predefined and assumed to be invariant. Aiming to improve the adaptability and accuracy of dynamic model, we propose multiple historical states involved filtering algorithm. An autoregressive model is used as the dynamic model which is subsequently combined with observation model for deriving the optimal window-recursive filter formulae in the sense of minimum mean square error principle. The corresponding test statistics characteristics of system residuals are discussed in details. The test statistics of regional predicted residuals are then constructed in a time-window for model bias testing with two hypotheses, that is, the null and alternative hypotheses. Based on the innovations test statistics, we develop a model bias processing procedure including bias detection, location identification, and state correction. Finally, the minimum detectable bias and bias-tonoise ratio are both computed for evaluating the internal and external reliability of overall system, respectively

YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network's feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets

The Influence of Irrelevant Visual Distractors on Eye Movement Control in Chinese Children with Autism Spectrum Disorder: Evidence from the Remote Distractor Paradigm

The current study examined eye movement control in autistic (ASD) children. Simple targets were presented in isolation, or with central, parafoveal, or peripheral distractors synchronously. Sixteen children with ASD (47-81 months) and nineteen age and IQ matched typically developing children (TD) were instructed to look to the target as accurately and quickly as possible. Both groups showed high proportions (40%) of saccadic errors towards parafoveal and peripheral distractors. For correctly executed eye movements to the targets, centrally presented distractors produced the longest latencies (time taken to initiate eye movements), followed by parafoveal and peripheral distractor conditions. Central distractors had a greater effect in the ASD group, indicating evidence for potential atypical voluntary attentional control in ASD children