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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.

SESSION TITLE: Clinical Prediction and Diagnosis of OSA

Police social workers are crucial components of police departments when individuals or communities experience crises. They perform essential tasks, including well-being checks, crisis intervention, de-escalation, mediation, safety planning, referrals to community services, and other preventative measures to stabilize clients in crisis. The chapter will define police social work and give the reader insight into the stabilization approaches used by police social workers to prepare clients for their next level of care. The chapter begins with a brief history of the evolution of police social workers within the context of public policy and their most recent call to action to address mental health crises. The authors utilize a multi-tier approach to highlight stabilization approaches used by police social workers with a focus on empowering individuals, families, and communities to collaborate on solutions. The chapter uses case scenarios drawn from the experiences of police social workers and interns to demonstrate stabilization approaches. A racial equity, culturally responsive, and trauma-informed lens informs the approach to stabilizing client systems in law enforcement settings. © 2023, IGI Global. All rights reserved.

Articles in this issue examine (1) the primary sources of variability in reading and language achievement among Spanish-speaking English learners (ELs) in the United States, (2) the extent to which poor performance at the end of grade 2 is identifiable in developmental trajectories beginning in kindergarten, (3) the relations among core reading constructs of phonological awareness and decoding in both English and Spanish and the factors that affect their relationship, (4) the performance of different approaches to identification and the factors that influence how well they work, as well as (5) the growing literature focused on intervention for reading problems in this population. This article examines the literature on language minority students and disability identification and analyzes a large-scale longitudinal dataset (>4,000 ELs; >15,000 observations) to systematically characterize and describe the oral language and reading development of Spanish-speaking children designated as ELs from kindergarten to second grade, considering a range of factors that may potentially contribute to that characterization and its relation to academic performance. This systematic characterization should facilitate the development of an empirical basis for a theoretically grounded framework of typical development in ELs in order to more precisely identify those children with language and learning disabilities.

This study investigated early indicators of Spanish-speaking English learners (ELs) at risk for reading difficulties at the end of Grade 2 by examining their early bilingual oral language development, taking into account language of academic instruction. Standardized measures of reading and narrative samples were collected in English and Spanish from kindergarten to Grade 2 from 1,243 ELs primarily instructed in English or Spanish. Conditional growth curve models yielded four primary findings of reading and oral language development. First, ELs with low reading achievement at the end of Grade 2 demonstrated early reading difficulties during kindergarten. Second, although ELs demonstrated overall higher reading achievement in their instructed language, this difference decreased over time. Third, ELs with low reading achievement at the end of Grade 2 demonstrated lower oral language skills in each language over time. Fourth, ELs demonstrated overall higher oral language skills in their instructed language, yet these differences varied over time. The study provided a detailed description of the longitudinal relations among the bilingual reading and oral language skills of Spanish-speaking ELs during the early school years. These findings help to inform the processes of early identification and intervention for Spanish-speaking ELs who are likely to demonstrate reading achievement difficulties.

Use of the first fish pheromone biopesticide, 3-keto petromyzonol sulfate (3kPZS) in sea lamprey (Petromyzon marinus) control requires an understanding of both how the amount 3kPZS applied to a trap relates to catch, and how that relationship varies among stream types. By conducting 3kPZS dose-response experiments over two years and across six varied trapping contexts, we conclude (1) that 3kPZS application is best standardized by how much is emitted from the trap instead of the fully mixed concentration achieved downstream, and (2) that 3kPZS is more effective in wide streams (>30 m). In wide streams, emission of 3kPZS at 50 mg hr.−1 from the trap increased capture rate by 10–15% as sea lamprey were 25–50% more likely to enter the trap after encounter. However, in narrow streams (< 15 m), 50 mg hr.−1 3kPZS generally reduced probabilities of upstream movement, trap encounter, and entrance. While 3kPZS significantly influenced upstream movement, encounter, and capture probabilities, these behaviors were also highly influenced by water temperature, stream width, sea lamprey length, and sex. This study highlights that a pheromone component in a stream environment does not ubiquitously increase trap catch in all contexts, but that where, how, and when the pheromone is applied has major impacts on whether it benefits or hinders trapping efforts.

Predator encounters during early life can elicit behavioral and physiological responses that have fitness consequences during subsequent prey life stages. In threatened lake sturgeon (Acipenser fulvescens) and other lithophilic-spawning fishes, newly hatched larvae (free embryos) are exposed to abundant predators including aquatic insect larvae that co-occupy stream substrates. We investigated stress effects on lake sturgeon larvae after encounters with common aquatic insect predators by quantifying mortality, body size, cortisol levels, and swimming behavior. Free embryos were exposed to either Perlidae (stonefly obligate predators) or Isonychiidae (mayfly filterers and facultative predators) or to no predators (controls). Free embryos that encountered perlids experienced high mortality, elevated cortisol levels, and exhibited cortisol reactivity when subsequently exposed to an acute stressor. Free embryos that encountered isonychiids exhibited elevated mortality, and elevated cortisol and cortisol reactivity relative to controls. Findings indicate that lake sturgeon free embryos are stressed by exposure to members of benthic stream communities during early life stages (predation of nearby conspecifics), and that metrics of stress exhibited threat sensitivity. Data are consistent with predictions that individuals modulate antipredator behavior in response to the intensity of perceived predation threat in the environment. We determined that behavioral outcomes associated with encounters with aquatic insects altered future behavioral trajectories, potentially as an adaptive response that can affect predation rates in subsequent life stages. Results contribute to a broader understanding of how interspecies interactions among co-occurring predator and prey communities may impact individual fitness and fish population recruitment.

Water scarcity is increasingly a global cause of concern mainly due to widespread changes in climate conditions and increased consumptive water use driven by the exponential increase in population growth. In addition, increased pollution of fresh water sources due to rising production and consumption of pharmaceuticals and organic chemicals will further exacerbate this concern. Although surface water contamination by individual chemicals is often at very low concentration, pharmaceuticals for instance are designed to be efficacious at low concentrations, creating genuine concern for their presence in freshwater sources. Furthermore, the additive impact of multiple compounds may result in toxic or other biological effects that otherwise will not be induced by individual chemicals. Globally, different legislative frameworks have led to pre-emptive efforts which aim to ensure good water ecological status. Reports detailing the use and types of effect-based measures covering specific bioassay batteries that can identify specific mode of actions of chemical pollutants in the aquatic ecosystem to evaluate the real threat of pollutants to aquatic lives and ultimately human lives have recently emerged from monitoring networks such as the NORMAN network. In this review, we critically evaluate some studies within the last decade that have implemented effect-based monitoring of pharmaceuticals and organic chemicals in aquatic fauna, evaluating the occurrence of different chemical pollutants and the impact of these pollutants on aquatic fauna with special focus on pollutants that are contaminants of emerging concern (CEC) in urban wastewater. A critical discussion on studies that have used effect-based measures to assess biological impact of pharmaceutical/organic compound in the aquatic ecosystem and the endpoints measurements employed is presented. The application of effect-based monitoring of chemicals other than assessment of water quality status is also discussed.

Poly- and perfluoroalkyl substances (PFASs) are a group of synthetic organic surfactants that have become a global concern because of their toxicity and widespread presence in the aquatic environment and organisms globally. In this study, a new analytical method has been developed and validated for the analysis of 15 perfluorinated compounds in different water matrices: river water, drinking water and seawater. Water extraction was performed in anion exchange solid phase extraction cartridges, and extracts were analysed by liquid chromatography in tandem with mass spectrometry. Recoveries for target analytes were between 35 and 120%, depending on the water matrix. Method detection limits were in the range of 0.5–17 ng L−1. The validated method was applied to the determination of perfluorinated compounds in water samples around Ireland. Eight compounds out of fifteen were detected at least in one sample. Measured concentrations were higher in river water than seawater, and drinking water had the lowest levels, although still detectable for a considerable amount of compounds. The most prevalent compounds were PFPeA, PFOA and PFHxA, present in all types of water, and they had the highest concentrations.