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This article examines racial capitalism from a semiotic perspective, arguing that economic value, like language and race, can be described in situated and indexical terms. I attempt to show how raciolinguistic bias in and around the workplace is linked to a larger labor market in which minoritized labor is reproduced in a systemic way, and to explore hegemonic formations of racialization in the workplace and beyond. The jumping-off point for much of my argument is the work of the historian and political theorist Cedric Robinson.

This study reports results on the ex ante predictability of stock returns using real-time stock market data in Vietnam, a frontier market, from June 2008 to June 2021. Countries classified as a frontier market are often known for currency manipulation, financial market illiquidity, and political instability. Despite the enormous risk usually posed by these inefficiencies, potential profits are large and achievable for many investors. This study provides evidence on existing a strategy to form out-of-sample long portfolios that generate statistically significant and positive mean monthly returns even in the presence of transaction costs. I also justify the magnitude of these returns by showing that they exceed those of VnIndex and MSCI Vietnam Index. The results reject the hypothesis that the stock prices in Vietnamese market follow random walks, thus oppose the stock market efficiency hypothesis. Evidence found in this study provides a better understanding of informational efficiency in a frontier equity market setting. Specifically, there are several implications on portfolio selection strategies, stock price patterns, and trading behavior bias related to Vietnamese stock market can be drawn from this study.

"This poetry anthology, with poems from poets throughout New England and from other states - is a result of Peterborough Poetry Project's second poetry contest. We invited poets, writers, and observers to submit up to three poems about New Hampshire - past, present, future, or fantasy. Forty-eight poems from the contest form this book. The poems are in three different sections by themes: People, Places, and The Wild, but readers may find that several poems have more than one theme. A poem may appear to be about nature, but also our reactions to it. Another poem may appear to be true, but might be pure fantasy. Such is the nature of poetry: read it for the obvious, then read it again to see if more reveals itself"--Back cover

This entry explores the question of how to conceptualize literacy as a deictic concept, one that continually changes as new technologies for literacy and learning emerge. It suggests a dual-level conceptualization of theory: a New Literacies theory as an overarching theory that encompasses perspectives and findings from the many studies of literacy, which are referred to as new literacies theories, using lower case. It then focuses special attention on an important lower-case theory, the new literacies of online research and comprehension. This new literacies theory frames online reading as a process of problem-based inquiry involving the new skills, strategies, dispositions, and social practices that take place as we use the Internet to solve problems and answer questions. Current understanding of online reading to learn from a New Literacies perspective is informed by recent research using assessments that measure students' ability to conduct online research in science and comprehend what they read in a virtual online world. Findings suggest that online reading requires different skills than reading paper materials; that differences across modes of reading are important for school learning; and that the Internet is best conceived as a literacy issue rather than a technology issue.

Literacy has become deictic (Leu, 2000); the meaning of literacy is rapidly changing as new technologies for literacy continually appear and new social practices of literacy quickly emerge.

Although a much older concept, it is only within the decade of 2010–2019 that this term “social justice” came into prominence in school psychology. This chapter provides a combination of research analysis and a push to personalize and apply the concept of social justice. The chapter begins by defining social justice and describing why this concept is so critical for school psychologists. As part of this overview of social justice, the authors share some of their own stories as examples of how professionals might draw upon their own value systems and experiences to develop as agents of social justice. Readers are also provided with reflection questions to personalize the concept. The bulk of the chapter focuses on what it might look like to “fight” for social justice in school psychology, highlighting concepts such as developing critical self-awareness (with coverage of implicit bias and cultural humility) and using one’s leadership skills to work in an inclusive, participatory manner. A personal action plan template is provided. The chapter closes with a call to action, framing social justice as a mechanism for maximizing both the professional impact and personal satisfaction of one’s work as a school psychologist.

Forecasting the daily flows of rivers is a challenging task that have a significant impact on the environment, agriculture, and people life. This paper investigates the river flow forecasting problem using two types of Deep Neural Networks (DNN) structures, Long Short-Term Memory (LSTM) and Layered Recurrent Neural Networks (L-RNN) for two rivers in the USA, Black and Gila rivers. The data sets collected for a period of seven years for Black river (six years for training and one year for testing) and four years for Gila river (three years for training and one year for testing) were used for our experiments. An order selection method based partial auto-correlation sequence was employed to determine the appropriate order for the proposed models in both cases. Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF) were used to evaluate to developed models. The obtained results show that the proposed LSTM is able to produce an excellent model in each case study.

Design of the Proportional-Integral-Derivative (PID) controller for an industrial process represents a challenge due to process complexity and non-linearity. Traditional methods such as Ziegler-Nichols (ZN) for PID controller tuning do not provide an optimal gain; thus, might leave the system with potential instability condition and cause significant losses and damages to the system. This paper investigates the merits of evolutionary and swarm-based optimization algorithms in fine-tuning the parameters of a PID controller. Here, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithm were utilized to optimize the PID controller for a DC motor system. Various fitness functions were provided for the presented algorithms to compute the performance of the controller. A new fitness function was proposed to achieve an outstanding control response for the DC motor system. Results demonstrate the efficacy of the proposed methods in improving closed loop system response.

Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms.

Sleep is an essential part of health and longevity persons. As people grow older, the quality of their sleep becomes vital. Poor sleep quality can make negative physiological, psychological, and social impacts on the elderly population, causing a range of health problems including coronary heart disease, depression, anxiety, and loneliness. Early detection, proper diagnosis, and treatments for sleep disorders can be achieved by identifying sleep patterns through long-term sleep monitoring. Although many studies developed sleep monitoring systems by using non-invasive measures such as body temperature, pressure, or body movement signal, research is still limited to detect sleep position changes by using a depth camera. The present study is intended (1) to identify concerns on the existing sleep monitoring system based on the literature review and (2) propose to developing a non-invasive sleep monitoring system using an infrared depth camera. For the literature review, various journal/conference papers have been reviewed to understand the characteristics, tools, and algorithms of the existing sleep monitoring systems. For the system development and validation, we collected data for the sleep positions from two subjects (35 years old man and 84 years old women) during the four-hour sleep. Kinect II depth sensor was used for data collection. We found that the averaged depth data is useful measure to notify the participants’ positional changes during the sleep.

SESSION TITLE: Clinical Prediction and Diagnosis of OSA

Robotic systems have been evolving since decades and touching almost all aspects of life, either for leisure or critical applications. Most of traditional robotic systems operate in well-defined environments utilizing pre-configured on-board processing units. However, modern and foreseen robotic applications ask for complex processing requirements that exceed the limits of on-board computing power. Cloud computing and the related technologies have high potential to overcome on-board hardware restrictions and can improve the performance efficiency. This research highlights the advancements in robotic systems with focus on cloud robotics as an emerging trend. There exists an extensive amount of effort to leverage the potentials of robotic systems and to handle arising shortcomings. Moreover, there are promising insights for future breed of intelligent, flexible, and autonomous robotic systems in the Internet of Things era.