Reading comprehension intervention for students with autism spectrum disorder level 1 using the iPad graphic organizer app

Research has found that students with autism spectrum disorder (ASD) may face difficulties in reading comprehension. This study explored the functional relationship between the use of a graphic organizer (GO) app on the iPad and the number of correct independent responses to reading comprehension questions by one student with ASD Level 1. The study used an applied behavior analysis intervention that used an ABAB design. Aseven-question reading comprehension questionnaire was used to collect data Strong evidence was found of a functional relationship between the use of the graphic organizer (GO) on the iPad and an increase in correct independent responses by the student. Future research can develop to replicate the results and further explore how the use of a GO app on the iPad affects reading comprehension in students with ASD

Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids

High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources' participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory. © 2013 IEEE

Exploring the Dynamic Voltage Signature of Renewable Rich Weak Power System

Large-scale renewable energy-based power plants are becoming attractive technically and economically for generation mix around the world. Nevertheless, network operation has significantly changed due to the rapid integration of renewable energy in supply side. The integration of more renewable resources, especially inverter-based generation, deteriorates power system resilience to disturbances and substantially affects stable operations. The dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs) due to the limited capabilities of inverter-based resources (IBRs). A heavily loaded and stressed renewable rich grid is susceptible to fault-induced delayed voltage recovery. Hence, it is crucial to examine the system response upon disturbances, to understand the voltage signature, to determine the optimal location and sizing of grid-connected IBRs. Moreover, the IBRs fault contribution mechanism investigation is essential in adopting additional grid support devices, control coordination, and the selection of appropriate corrective control schemes. This article utilizes a comprehensive assessment framework to assess power systems' dynamic voltage signature with large-scale PV under different realistic operating conditions. Several indices quantifying load bus voltage recovery have been used to explore the system' s steady-state, transient response, and voltage trajectories. The recovery indices help extricate the signature and influence of IBRs. The proposed framework's applicability is carried out on the New England IEEE-39 bus test system using the DIgSILENT platform. © 2013 IEEE

Preparation of activated carbon from date palm trunks for removal of eosin dye

Date palm trees are abundant and cheap natural resources in Saudi Arabia. In this study, an activated carbon was prepared from palm trunks by chemical processes. The chemical activation was performed by impregnation of the raw materials after grinding with H3PO4 solution (63%), followed by placing of the sample solution on a muffle furnace at 400 ºC for 30 min, and then at 800 ºC for 10 min. The morphology of the fabricated material was checked using scanning electron microscopy that showed the rough surfaces on the carbon samples. The use of fabricated activated carbon for removal of eosin dye from aqueous solutions at different contact time, initial dye concentration, pH and adsorbent doses was investigated. The experimental results show that the adsorption process attains equilibrium within 20 min. The adsorption isotherm equilibrium was studied by means of the Langmuir and Freundlich isotherms, and it was found that the data fit the Langmuir isotherm equation with maximum monolayer adsorption capacity of 126.58 mg g-1. The results indicated that the home made activated carbon prepared from palm trunks has the ability to remove eosin dye from aqueous solution and it will be a promising adsorbent for the removal of harmful dyes from waste water

Developing an ESP-Based Language Learning Environment to Help Students Improve Critical Thinking Skills in Written Output

In recent years, as a result of both research discoveries in the fields of foreign language acquisition and learning, the concept of teaching and learning has undergone a significant transformation. English for Specific Purposes (ESP) is a learner-centered approach to teaching English as a foreign language that emphasizes developing communicative proficiency in niche industries such agronomy, commerce, academia, accountancy, education, engineering, and information technology. This concept of English-for-Teaching as a restricted form of ESP for the classroom builds on the knowledge that instructors already have about teaching while also introducing and validating specific classroom terminology. When students interact and cooperate with one another, ESP practice emerges naturally in a language learning setting. Two major aspects that ESP highlights are the growth of dialogical interaction and the establishment of ecologically complete learning environments. In this essay, we create an ESP to aid students in the development of their critical thinking (CT) abilities in written output. We combine the Synergy model, Brain-based learning, and the Flipped Classroom models to create an ESP environment. Students CT abilities and academic success served as the studys criteria. The Course Satisfaction Questionnaire and placement exams were used to obtain the statistical data. Using the Cronbach Alpha coefficient (CAC) and Spearman correlation coefficient, the test on CT data was interpreted, and the combined data was examined using SPSS (V 26.0). By immersing students in problem-solving- based learning (PBL), this paradigm helps students develop their CT skills. It also helps students achieve academically by elevating their sense of accountability for learning outcomes and promoting the use of a variety of learning strategies

Solutions of Some Difference Equations Systems and Periodicity

In this article, analysis and investigation have been conducted on the periodic nature as well as the type of the solutions of the subsequent schemes of rational difference equations with a nonzero real numbers initial conditions

Practical performance analysis of real-time multiprocessor scheduling algorithms

This paper presents a practical performance analysing of two real-time multiprocessor scheduling algorithms, namely, Largest Remaining Execution-Time and Local Time Domain (LRE-TL) and Unfair Semi-Greedy (USG). The analysis is intended to reflect the behindthe- scene time overhead incurred by optimal real-time algorithms such as LRE-TL. The overhead is known to be capable of dismissing the actual optimality of such algorithms in practical applications. Here, the time overhead is measured in terms of the number of scheduler invocations and the time required by the scheduling event handlers. In the implementation of the proposed analysis method, the CPU profiler of Oracle JavaTM VisualVM was used to monitor the executions of LRE-TL and USG. The profiler measured the number of invocations of the scheduling event handlers for each algorithm and the total time required for all the invocations. The results revealed that USG outperformed LRE-TL on both measures, indicating that optimal algorithms may prove to be non-optimal in practical applications.Keywords: Real-time; Multiprocessor; Schedulin

Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation

This is the final version of the article. Available from ISBA via the DOI in this record.Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to a longitudinal epidemic data set, where calculating the marginal likelihood is made more challenging by the presence of large amounts of missing data. In this context, our importance sampling approach is shown to outperform existing methods for computing the marginal likelihood.PT was supported by a University of Warwick PhD scholarship. NA was supported by a PhD scholarship from the Saudi Arabian Government

Model selection for time series of count data

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordSelecting between competing statistical models is a challenging problem especially when the competing models are non-nested. An effective algorithm is developed in a Bayesian framework for selecting between a parameter-driven autoregressive Poisson regression model and an observationdriven integer valued autoregressive model when modeling time series count data. In order to achieve this a particle MCMC algorithm for the autoregressive Poisson regression model is introduced. The particle filter underpinning the particle MCMC algorithm plays a key role in estimating the marginal likelihood of the autoregressive Poisson regression model via importance sampling and is also utilised to estimate the DIC. The performance of the model selection algorithms are assessed via a simulation study. Two real-life data sets, monthly US polio cases (1970-1983) and monthly benefit claims from the logging industry to the British Columbia Workers Compensation Board (1985-1994) are successfully analysed

An Innovative Approach Based on Machine Learning to Evaluate the Risk Factors Importance in Diagnosing Keratoconus

Background and objective: Keratoconus is a non-inflammatory corneal condition affecting both eyes and is present in one out of every 2,000 people worldwide. The cornea deforms into a conical shape and thins, resulting in high-order aberrations and gradual vision loss. Risk factor analysis in the degradation of keratoconus is under-researched. Methods: This research work investigates and uses effective machine learning models to gain insight into how much the risk factors of a patient contribute towards the progressive stages of keratoconus, as well as how significant these factors are in the creation of an accurate prediction model. This research demonstrates the value of machine learning approaches on a clinical dataset. This research paper employs several machine learning algorithms to classify the patients' stage of keratoconus using clinical information, such as measurements of the cornea's topography, elevation, and pachymetry taken using pentacam equipment at Sydney's Vision Eye Institute Chatswood. Results: Eight different machine learning techniques were investigated over three variations of a dataset and achieved an average accuracy of 68, 80, and 90% for the risk factor, pentacam, and cumulative datasets, respectively. The results show a significant increase in accuracy and a 97% increase in AUC upon addition of risk factor data compared to the models trained on pentacam data alone. The machine learning methods shown in this paper outperform those in current research. Conclusions: This research highlights the importance of machine learning methods and risk factor data in the diagnosis of keratoconus and highlights the patient's primary optical aid as the strongest risk factor. The goal of this research is to support the work of the ophthalmologists in diagnosing keratoconus and provide better care for the patient