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As the world continues to ponder issues of equity and diversity, U.S. public schools face an expanding demographic divide between teachers and students. While diverse groups of public school students show an increase in population, the teaching workforce in the U.S. remains overwhelmingly White. The purpose of this systematic review is to examine how preservice teachers (PST) are being prepared to be culturally responsive. A total of 26 studies published between 2006 and 2020 were reviewed. The results indicate that PSTs’ learning experiences are varied and tend to be stand-alone approaches focused on changing the attitudes and beliefs of PSTs. Findings also bring to light the vagueness of terminology used in the research to define cultural groups of students, the conspicuous absence of studies related to LGBTQ+ populations, and the lack of study replications. Implications for future research are discussed.

As emerging digital technologies have been used for disruptive innovation and business models, an essential component for hospitality researchers and practitioners is to determine the role of disruptive technologies and innovation in hospitality businesses. This study synthesizes prior research on disruptive innovation and identifies disruptive technologies in the hospitality context. A thematic analysis was performed through a computer-assisted qualitative data analysis approach using 23 publicly traded hospitality companies. Results reveal that disruptive technology and innovation are among the most critical strategic aspects in contemporary hospitality firms. This study provides contributions to hospitality researchers and practitioners to implement disruptive technologies for superior business performance. This study is among the first to introduce and synthesize disruptive technologies and innovation in the hospitality context.

This study provides a new perspective on the determinants of the spread of voluntary corporate social responsibility (CSR) adoption by incorporating the potential role of its adoption by industry competitors. We find supportive evidence that firms make CSR adoption decisions in response to competitive pressure as well as institutional mimetic pressures. Based on an event history analysis of longitudinal data from a sample of 711 Korean publicly traded firms over a 12-year period, our findings suggest that the CSR behavior of competitors is positively associated with a focal firm's earlier adoption of CSR, leading to the diffusion of CSR across firms. Specifically, this study shows that the pure rivalry-driven pressure from non-leader competitors has a stronger positive relationship with earlier CSR adoption. The results also indicate that a firm's CSR adoption decision is accelerated by competitive rivalry as well as social pressures arising from institutional mimetic isomorphism.

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.

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.

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

This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.

SESSION TITLE: Clinical Prediction and Diagnosis of OSA

AbstractThe autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.