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Purpose: The purpose of this project was to examine the perspectives, practices, and confidence of speech-language pathologists (SLPs) regarding service provision for emergent bilinguals who use augmentative and alternative communication (AAC) through a nationwide survey. Method: Licensed SLPs (N = 179) completed an online survey, which included Likert-type and multiple-choice questions. Results: Findings from the survey revealed a discrepancy between SLPs’ perceptions and practices regarding service provision for emergent bilinguals who use AAC. Furthermore, most SLPs surveyed reported varying levels of confidence in serving this population and indicated that they frequently lacked training and resources to serve bilingual clients who use AAC. Conclusion: This research highlighted the need for increased resources, research, and education to support service provision for emergent bilinguals who use AAC. [ABSTRACT FROM AUTHOR]

Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 and 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1–6 h of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available. [ABSTRACT FROM AUTHOR]

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models' hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the "simplest" network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction. [ABSTRACT FROM AUTHOR]

Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization. [ABSTRACT FROM AUTHOR]

This paper examines how Latino immigrant families in California use information technology (IT) tools for their pre-existing personal, educational, and work-related activities inside their homes. In doing so, the paper re-examines the concept of access beyond the acquisition of these technologies, highlighting instead the importance of the local social and economical contexts of users and the implications for the design, implementation, and use of these technologies among immigrant communities. Results indicate that as these new comers appropriate IT tools to develop their personal, educational, and work-related needs and aspirations, they begin to develop important digital and language literacies, as well as new ways of interacting with formal institutions in the USA. In spite of new developments in technologies and social media since the study took place (2002), there is still little or no research that relies on direct observations to examine IT utilization inside the homes of immigrants and in relation to their pre-existing educational or labor-related activities. Results suggest a need for a more detailed study of these practices inside homes and their possible implications for educational and labor-related immigrant reform initiatives. [ABSTRACT FROM AUTHOR]

Political actors typically use language with the goal of persuading an audience. But what shapes the use of language in political settings? Is it differences between ideologues — liberals and conservatives — that change language use? Or is it support or opposition to the issue? Using techniques adapted from cognitive psychology we examine arguments used in ballot proposition elections and show them to exhibit systematic patterns in line with the theoretical arguments of Riker (1996). The actor’s choice of issue position — for or against — can be seen to imply that the arguments they advance in support of their position are constrained. More specifically, we show that arguments in support of propositions are consistently similar to each other and consistently dissimilar from arguments against a proposal in language use. These patterns of similarity and dissimilarity persist across a wide range of issues and actors. Identification of these patterns helps explain a persistent empirical regularity within ballot proposition politics: the advantage held by “NO” campaigns. [ABSTRACT FROM AUTHOR]

The article discusses how the implementation of a literacy framework in the school can improve academic achievement among students in urban elementary schools. It profiles some underperforming schools that are successful in improving academic achievement by adopting a literacy framework. One example is the Rosa Parks Community School in San Diego, California. The school educates approximately 1,450 to 1,500 students yearly. The school is situated in a community that is poor, has a high crime rate and one that needs health and social services. The school's progress was exceptional and noteworthy. To achieve this progress, teachers participated in discussions about their core beliefs about literacy.

The article discusses the Berkeley Parser, a software program for parsing sentences developed by Professor Daniel Klein and colleagues at the University of California, Berkeley. Given a sentence, the Parser attempts to identify all parts of speech, establish relations between them, and produce a tree graph showing the most probable structure of the sentence. The Berkeley algorithm is a syntactic rather than lexical parser, and can generate parses for German, Chinese, and English. A demonstration version of the software is available online.