ABSTRACT
Rigorous Coupled Wave Analysis (RCWA) method is highly efficient for the simulation of diffraction efficiency and field distribution patterns in periodic structures and textured optoelectronic devices. GPU has been increasingly used in complex scientific problems such as climate simulation and the latest Covid-19 spread model. In this paper, we break down the RCWA simulation problem to key computational steps (eigensystem solution, matrix inversion/multiplication) and investigate speed performance provided by optimized linear algebra GPU libraries in comparison to multithreaded Intel MKL CPU library running on IRIDIS 5 supercomputer (1 NVIDIA v100 GPU and 40 Intel Xeon Gold 6138 cores CPU). Our work shows that GPU outperforms CPU significantly for all required steps. Eigensystem solution becomes 60% faster, Matrix inversion improves with size achieving 8x faster for large matrixes. Most significantly, matrix multiplication becomes 40x faster for small and 5x faster for large matrix sizes. © 2023 SPIE.
ABSTRACT
This report uses Spring 2022 data from nationally representative surveys of principals and math teachers in kindergarten through grade 12 (K-12) to explore students' opportunities to prepare for and take advanced math. The authors found that small high schools, high schools in rural areas, and high schools that predominantly serve students from historically marginalized communities tend to offer fewer advanced math courses (e.g., precalculus, Advanced Placement math courses) and that uneven access to advanced math begins in middle school. K-12 teachers who work in schools that predominately serve students living in poverty are more likely to report skipping standards-aligned content and replacing the skipped content with concepts from previous grade levels. Also, more than half of K-12 math teachers said they need additional support for delivering high-quality math instruction, especially teachers who work in schools that serve predominantly high-poverty students. In the wake of the disproportionate impacts of the COVID-19 pandemic on students living in poverty and students of color, these results highlight a critical need for resources to support teachers and to increase student access to advanced courses. [For technical information about the surveys and analysis in this report, see "Learn Together Surveys. 2022 Technical Documentation and Survey Results. Research Report. RR-A827-9" (ED626092).]
ABSTRACT
We investigated whether worked examples could be used to reduce cognitive load on mathematics learners who may have reduced available cognitive resources due to experiencing anxiety or excess stress. Across 2 days, 280 fifth-grade students learned from a difficult lesson on ratio, half of whom reviewed worked examples at key problem-solving opportunities during instruction. We also measured two sources of students' worry during learning: math anxiety and worries about learning during the pandemic. We explored the attentional and affective effects of worked examples and worries in addition to their effects on learning. Results suggest that math anxiety, but not pandemic learning worries, negatively predicted procedural and conceptual learning from the lesson. In line with previous research and cognitive load theory, math anxiety also predicted greater mind wandering during testing and lower situational interest during learning. Critically, reviewing worked examples during learning mitigated these effects on learning and engagement. Pandemic-related learning worries were unrelated to learning outcomes but did predict affective and motivational outcomes. Educational implications are discussed.
ABSTRACT
The changes forced by the COVID-19 pandemic, have made educational institutions adopt new practices in the use of VLEs platforms and, one of these is to homogenize virtual classrooms, for which this study aims to diagnose how effective are the digital resources for cloned courses, taking as a pilot the subject of Linear Algebra. The development of this research is longitudinal, empirical-analytical, and quantitative. The study is carried out in two periods, from October 2021 to September 2022, at the Salesian Polytechnic University in the city of Guayaquil, Ecuador, with a total of 944 first year students of engineering careers. As a result, fewer courses for academic risk monitoring were obtained, as well as a higher satisfaction among students and professors involved. It is concluded that the cloned classrooms are a factor of improvement in the learning results to be achieved. © 2023 IEEE.
ABSTRACT
This paper presents the development of a dynamical tropical algebra-based model of a vaccination center, which can be used to control and optimize the admission of users during center's operation. In addition, an analysis of closed-loop control methods designed to maximize the system performance in terms of service rate and minimize users' waiting time, while observing occupancy constraints due to social distancing protocols recommended by sanitary authorities due to Covid epidemic, is presented. © 2023, Brazilian Society for Automatics--SBA.
ABSTRACT
In this paper, a new definition of intuitionistic fuzzy multisets (IFMS) has been introduced. Algebraic operations on these intuitionistic fuzzy multisets are defined, and their properties under these algebraic operations are studied. The author has also introduced a new notion of complement for an IFMS in which the complement of the original set is also an IFMS. The notion of distance and similarity between two IFMSs has been defined, and their properties have also been studied here. An application of IFMS in solving a medical diagnosis problem has been provided at the end. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
ABSTRACT
As community colleges emerge from the COVID-19 pandemic there may be a tendency to rely on technology to facilitate more online coursework. Online education has been a fixture of higher education since the mid-1990s, but there's always been a question as to whether it is effective as traditional, face-to-face coursework. This is especially important in College Algebra, already viewed as a barrier course for many students. If more students take College Algebra online, will the results be as good as students taking the course in a classroom? The purpose of this quantitative causal-comparative study is to identify the relationship between course modality and final grade percentage, after accounting for instructor and curriculum effects for college algebra courses taught both online and face-to-face.Previous research studied this question, but a consensus about the efficacy of online education was mixed. Some studies found that online students perform worse than face-to-face students in college algebra (Amro, 2014;Amro et al., 2015;Driscoll, 2012). Other studies found no difference between the modalities (Araeipour, 2013;Harrington et al., 2016;Huang, 2016). Research by Burch and Kuo (2010) and Graham and Lazari (2018) discovered online students perform better than face-to-face students. This study considered the question through the lens of Moore's Theory of Transactional Distance, which examines the distance between the learner and instructor, course content, interface, and other learners as a psychological distance rather than a spatial distance. Using one instructor teaching both online and face-to-face courses using the same materials was an attempt to keep transactional distance as a constant, mitigating instructor and curriculum effects that could impact a study comparing modalities. Previous research that accounted for the instructor and course materials found no significant difference in outcomes based on modality. This study looked at final grade percentages in College Algebra courses taught by one instructor with both online and face-to-face sections over the course of the 2017-2018 school year. Data were supplied by a two-year institution located in rural Arkansas. In addition to looking for the relationship between modality and final grade percentages, the study looked for relationships between gender and final grades, a student's age and final grades, as well as an interaction between online students and their age or gender on final grade percentages. Findings indicated there was no significant relationship between the course modality and final grade percentages. Additionally, there was no relationship between gender or age and final grades based on modality. However, one significant relationship the study found was that when women took online algebra, they scored over 15 points lower than men taking online algebra. There was no interaction between a student's age and taking an online college algebra course. Further research should expand on the notion of accounting for Transactional Distance while looking at the relationship between course modality and final grade percentages and expand the study to disciplines outside of college algebra. Finally, research should investigate whether the relationship changed after the COVID-19 pandemic altered perceptions and implementation of online courses. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
Earlier discovery of COVID-19 through precise diagnosis, particularly in instances with no evident symptoms, may reduce the mortality rate of patients. Chest X-ray images are the primary diagnostic tool for this condition. Patients exhibiting COVID-19 symptoms are causing hospitals to become overcrowded, which is becoming a big concern. The contribution that machine learning has made to big data medical research has been very helpful, opening up new ways to diagnose diseases. This study has developed a machine vision method to identify COVID-19 using X-ray images. The preprocessing stage has been applied to resize images and enhance the quality of X-ray images. The Gray-level co-occurrence matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) are then used to extract features from the X-ray images, and these features are combined to develop the performance classification via training by Support Vector Machine (SVM). The testing phase evaluated the model's performance using generalized data. This developed feature combination utilizing the GLCM and GLRLM algorithms assured a satisfactory evaluation performance based on COVID-19 detection compared to the immediate, single feature with a testing accuracy of 96.65%, a specificity of 99.54%, and a sensitivity of 97.98%. © 2022 IEEE.
ABSTRACT
In view of the fact that the existing propagation models ignore the influence of different fields and different virus variants on individual infection, and the classical propagation models only describe the macroscopic situation of virus transmission, which cannot be specific to individual cases, this paper proposes 67ya microscopic virus propagation model based on hypergraph (HC-SIRS). Firstly, the concept of hypergraph is used to divide different fields of individuals into corresponding hyperedges. Based on different contact probabilities of each hyperedge, the contact probability matrix is formed to relate the contact between individuals. The individual infection probability of micro-virus propagation model based on hypergraph is deduced, and the corresponding differential equation is established. Secondly, the basic regeneration number and its characteristics of the model are derived. The upper bound of the basic regeneration number of the model is less than or equal to that of the classical SIRS model, indicating that the virus is more difficult to spread in this model. In fact, the different fields people live in and the different personal constitutions have a certain impact on the spread of the virus. The model is more comprehensive, so it is more suitable for simulating the spread of the virus in theory. Finally, the COVID-19 data of Diamond Princess and two cities in China are used for simulation experiments, and the mean absolute error(MAE) is used as the evaluation standard. The results showed that HC-SIRS could well simulate the spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The base reactivity of the mRNA sequence has a significant impact on the effectiveness of the mRNA vaccine in fighting against the pandemic of COVID-19. The annotation of mRNA sequence reactivity value is a time-consuming and labor-intensive work, which belongs to the private digital assets of each medical institution. It is not practical to train a predictive model by pooling private data from various parties. Fortunately, federated learning techniques can serve to collaboratively train a predictive model among medical institutions while preserving respective digital assets. However, due to the scarcity of data from each participant, conventional sequential prediction mod-els often fail to perform well. To overcome such a challenge, we propose a reactivity value prediction model based on both the self-attention and the convolutional attention mechanisms only requiring a small dataset of labeled samples. Inspired by BERT, we first train a self-attention feature extraction model through self-supervision using both labeled and unlabeled mRNA samples. In this way, the information of mRNA in the semantic space is deeply mined. Then, a convolutional attention block follows the self-attention block, to extract the attention matrix from the base-pair probability matrix and adjacency matrix. By doing so, the attention matrix can compensate for the insensitivity of the self-attention mechanism to the spatial information of mRNA. By using the Open Vaccine RNA database, experiments show that our prediction model for unseen mRNA has a better performance than other state-of-the-art deep learning models that are used to process gene sequences. Further ablation experiments demonstrate that the existence of the dual attention mechanism reduces the risk of overfitting, resulting in an excellent generalization capability of our model. © 2022 IEEE.
ABSTRACT
The current study investigated the effectiveness of three distinct educational technologies-two game-based applications (From Here to There and DragonBox 12+) and two modes of online problem sets in ASSISTments (an Immediate Feedback condition and an Active Control condition with no immediate feedback) on Grade 7 students' algebraic knowledge. More than 3,600 Grade 7 students across nine in-person and one virtual schools within the same district were randomly assigned to one of the four conditions. Students received nine 30-minute intervention sessions from September 2020 to March 2021. Hierarchical linear modeling analyses of the final analytic sample (N = 1,850) showed significantly higher posttest scores for students who used From Here to There and DragonBox 12+ compared to the Active Control condition. No significant difference was found for the Immediate Feedback condition. The findings have implications for understanding how game-based applications can affect algebraic understanding, even within pandemic pressures on learning.
ABSTRACT
The data age information is considerably more significant in open life, since individuals' well-being information just concluded regardless of whether COVID-19 impacted, and furthermore connected with all medical problems information. These information used to examine and anticipate the medical problems information by Machine Learning Algorithm, and afterward anticipated information need greater security. In this way, we applied the current strategy ChaCha technique and that strategy zeroed in as it were "encryption execution” so security is less. In this paper, to apply the new ES-BR22-001 strategy, this technique has 7 stages. The 1st stage is finding the K value. The 2nd stage is applying the K value in Eq. (1). The 3rd stage is finding the Sk values by using Eq. (1). The 4th stage is applying the Sk values in the sparse matrix. The 5th stage is sparse matrix values are converted into single line. The 6th stage is pairing all the values. The final stage is all paired values will be applied in the matrix. The new ES-BR22-001 method provides security and performance is good while compared to ChaCha method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method is based on the assumption that similar drugs share similar targets and vice versa. However, one major disadvantage is that only one similarity metric is used in MF models, which is not enough to represent the similarity between drugs or targets. In this work, we develop a similarity fusion enhanced MF model to incorporate different types of similarity for novel drug-target link prediction. We apply the proposed model on a drug-virus association dataset for anti-COVID drug prioritization, and compare the performance with other existing MF models developed for COVID. The results show that the similarity fusion method can provide more useful information for drug-drug and virus-virus similarity and hence improve the performance of MF models. The top 10 drugs as prioritized by our model are provided, together with supporting evidence from literature. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Measurement of e-commerce usability based on static quantities variable is state-of-the-art because of the adoption of sequential tracing of the next phase in the categorical data. An offline static model is trained. A static model is trained offline. In other words, we train the model once and then use it for a set period of time. The global COVID-19 outbreak has completely disrupted society and drastically altered daily life. The concept refers to an electronic commerce network that appears with thorough, understandable conviction, demand, and rapid confirmation as a replacement for the economic market’s "brick-and-mortar" model, which replaces how we do everything, including business strategy, and provides a better understanding with the interpretation of e-commerce features. This study was supervised to analyses usability assessments using statistical methods, as well as security assessments using online e-commerce security scanner tools, in order to investigate e-business standards that take into account the caliber of e-services in e-commerce websites across Asian nations. The method was developed to optimize complex systems based on multiple criteria. The initial (supplied) weights are used to determine the compromise ranking list and compromise solution. This paper examines the usability of e-commerce in rural areas using a new data set from the Jharkhand region. On the e-commerce websites of Jharkhand, India, usability is commonly considered in conjunction with learnability, memorability, effectiveness, engagement, efficiency, and completeness. Using a user-oriented questionnaire testing method, this survey attempts to close the gaps mentioned above. Then, across each column, divide each value by the column-wise sum that is created using their corresponding value, whichever produces a new matrix B. Finally, determine the row-wise sum of matrix B that represents the (3 X 1) matrix. Using model trees and bagging, this study addresses classification-related issues. This regression technique is useful for problems involving classification. The model is trained using secondary data from the MBTI 16 personality factors affecting personality category. Author
ABSTRACT
Mathematics is a collection of mental practices, attitudes and tools that humans have developed in our quest to understand the world (Singh & Brownwell, 2019). Educators have identified math and reading as the two core subjects that are essential for academic success. Achievement in math is considered to be the one of the most important predictors of economic success (Chazan, 2008). Educational leaders have become increasingly interested in finding the ideal placement for students to gain access to benchmark math curriculum that opens doors for advancement. In a competitive global market, educational and political leaders in the United States have continuously analyzed curriculum and sequencing practices in order to leverage their citizens in a position to be at the leading edge of achievement and contribution to the world's economy. Acceleration is one way, which schools have attempted to gain an advantage (Spielhagen, 2006). Accelerating curriculum often involves compacting concepts and moving through curriculum at a faster pace than previous practices. Acceleration combines elements of tracking (and detracking) and equity into the conversation. Systems thinking is an essential component for school district leaders as they consider the critical initiative of detracking and accelerating all students that must be well planned with a reasonable timeline (Burris, 2008). In an effort to identify effective methods for preparing math students to be future-ready, this study measures the impact of acceleration in 8th grade Algebra 1. Quantitative methods are used to study the impact of acceleration on student test scores and the number of advanced math courses students enroll in prior to graduating from high school. The study also investigates the impact of acceleration on the diversity that exists in school's advanced math courses. Finally, the study will determine how the COVID 19 pandemic altered progress for schools that accelerate all students compared to schools that do not accelerate. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
This paper presents and evaluates a hypothetical learning trajectory by which students bridge the transition from elementary to university-level instruction regarding the concept of vector. The trajectory consists of an instructional sequence of five tasks and begins with a problem in context. Each task is carried out with the support of a Virtual Interactive Didactic Scenario, accompanied by exploration and guided learning sheets, in which the problem is introduced through the simulation of the movement of a robotic arm. This proposal was implemented at the beginning of the SARS-CoV-2 pandemic using various digital media. Two teaching experiments were carried out with engineering students at a Mexican public university. We present the hypothetical learning trajectory that should be followed toward solving the task, and contrast it in each case with the students' actual learning trajectory. The results show that more than 70 % of the students successfully transitioned from the geometrical vector representation of elementary physics to the algebraic one. © 2023 Elsevier Inc.
ABSTRACT
The t-distributed stochastic neighbor embedding (t-SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-dimensional). It seeks to retain the structure of the data. An important component of the t-SNE algorithm is the initialization procedure, which begins with the random initialization of an LD vector. Points in this initial vector are then updated to minimize the loss function (the KL divergence) iteratively using gradient descent. This leads comparable points to attract one another while pushing dissimilar points apart. We believe that, by default, these algorithms should employ some form of informative initialization. Another essential component of the t-SNE is using a kernel matrix, a similarity matrix comprising the pairwise distances among the sequences. For t-SNE-based visualization, the Gaussian kernel is employed by default in the literature. However, we show that kernel selection can also play a crucial role in the performance of t-SNE.In this work, we assess the performance of t-SNE with various alternative initialization methods and kernels, using four different sets, out of which three are biological sequences (nucleotide, protein, etc.) datasets obtained from various sources, such as the well-known GISAID database for sequences of the SARS-CoV-2 virus. We perform subjective and objective assessments of these alternatives. We use the resulting t-SNE plots and k-ary neighborhood agreement (k-ANA) to evaluate and compare the proposed methods with the baselines. We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better. Moreover, we show that t-SNE also takes fewer iterations to converge faster with more intelligent initialization. © 2022 IEEE.
ABSTRACT
Prior research has shown that game-based learning tools, such as DragonBox 12+, support algebraic understanding and that students' in-game progress positively predicts their later performance. Using data from 253 seventh-graders (12–13 years old) who played DragonBox as a part of technology intervention, we examined (a) the relations between students' progress within DragonBox and their algebraic knowledge and general mathematics achievement, (b) the moderating effects of students' prior performance on these relations and (c) the potential factors associated with students' in-game progress. Among students with higher prior algebraic knowledge, higher in-game progress was related to higher algebraic knowledge after the intervention. Higher in-game progress was also associated with higher end-of-year mathematics achievement, and this association was stronger among students with lower prior mathematics achievement. Students' demographic characteristics, prior knowledge and prior achievement did not significantly predict in-game progress beyond the number of intervention sessions students completed. These findings advance research on how, for whom and in what contexts game-based interventions, such as DragonBox, support mathematical learning and have implications for practice using game-based technologies to supplement instruction. Practitioner notes What is already known about this topic DragonBox 12+ may support students' understanding of algebra but the findings are mixed. Students who solve more problems within math games tend to show higher performance after gameplay. Students' engagement with mathematics is often related to their prior math performance. What this paper adds For students with higher prior algebraic knowledge, solving more problems in DragonBox 12+ is related to higher algebraic performance after gameplay. Students who make more in-game progress also have higher mathematics achievement, especially for students with lower prior achievement. Students who spend more time playing DragonBox 12+ make more in-game progress;their demographic, prior knowledge and prior achievement are not related to in-game progress. Implications for practice and/or policy DragonBox 12+ can be beneficial as a supplement to algebra instruction for students with some understanding of algebra. DragonBox 12+ can engage students with mathematics across achievement levels. Dedicating time and encouraging students to play DragonBox 12+ may help them make more in-game progress, and in turn, support math learning. © 2023 The Authors. British Journal of Educational Technology published by John Wiley & Sons Ltd on behalf of British Educational Research Association.
ABSTRACT
The current generation data is most valuable in people's life, because data only decided people's health affected in COVID'19 or not, and not only COVID'19 all related to health issues data. To analyze and predict the health issue data by using Machine Learning Algorithm. This prediction issues data has most confidential data and need more security. So, applying the previous method is ChaCha method. This method focusing only performance not fully security. The new method is BR22-01. This method has five stages. The 1st stage is finding the secret key x & y value. The 2nd stage is applying key in Eq. (1). The 3rd stage is merge all values into single row then pair from left and swap the values in the HS matrix. The 4th stage is applying key in Eq. (2). The 5th stage is merge all values into single line then pair from left and swap the values in the HC matrix but reverse. The new method has provide good security as well as performance while compared to ChaCha method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In this paper I discuss running an online examination with STACK for a year 1 "Introduction to Linear Algebra” course. The COVID pandemic has continued to disrupt teaching during 2021–22, and were are still not able to return to the examination hall for a traditional exam. Instead, our examinations were still "take home”. Under these circumstances we wrote a fully automatically marked final test for the course. With over 800 students on the course in 2021–22, the paper-based examination was estimated to cost about 50 person-days to mark. Clearly, reducing this cost is an attractive prospect. However, important questions remain. To what extent can we write questions which cover the learning objectives of the course? How did the students do with the test? What recommendations can we offer for similar courses, and future years? In particular, do we need to return to the exam hall? © 2023,International Journal of Emerging Technologies in Learning.All Rights Reserved.