Designing Infographics to support teaching complex science subject: A comparison between static and animated Infographics

This thesis explores the proper principles and rules for creating excellent infographics that communicate information successfully and effectively. Not only does this thesis examine the creation of Infographics, it also tries to answer which format, Static or Animated Infographics, is the most effective when used as a teaching-aid framework for complex science subjects, and if compelling Infographics in the preferred format facilitate the learning experience. The methodology includes the creation of infographic using two formats (Static and Animated) of a fairly complex science subject (Phases Of The Moon), which were then tested for their efficacy as a whole, and the two formats were compared in terms of information comprehension and retention. My hypothesis predicts that the creation of an infographic using the animated format would be more effective in communicating a complex science subject (Phases Of The Moon), specifically when using 3D computer animation to visualize the topic. This would also help different types of learners to easily comprehend science subjects. Most of the animated infographics produced nowadays are created for marketing and business purposes and do not implement the analytical design principles required for creating excellent information design. I believe that science learners are still in need of more variety in their methods of learning information, and that infographics can be of great assistance. The results of this thesis study suggests that using properly designed infographics would be of great help in teaching complex science subjects that involve spatial and temporal data. This could facilitate learning science subjects and consequently impact the interest of young learners in STEM

Effectiveness of a Brief Parent Training In Pivotal Response Treatment For Young Children With Autism Spectrum Disorder

This study used a single-case research design across subjects. The purpose of this research was to investigate the effectiveness of a brief 6-hour training program in Pivotal Response Treatment (PRT) for parents of young children with Autism Spectrum Disorder (ASD) on the increased use of social functional utterances (SFU) by their children during play sessions. Baseline data were collected before the parent training. Training commenced once baseline trends showed stability, at which point the parents – all of whom were three fathers – received instruction in PRT motivational techniques for use in the home setting during play sessions. During the training sessions, the fathers were educated regarding how PRT motivational techniques and strategies are used and how to apply them in playtime with their children. After receiving the training, the parents then applied the PRT techniques during interactive play sessions over 8 weeks to develop the language use and social communication skills of their children with ASD. In this study, culture and language were factors considered as the parents and children were from Libya and spoke Arabic. This study is the first time these techniques have been implemented with this population of individuals. The cultural parenting interactions played a part in examining the results. The present research study demonstrated that following the brief training, the fathers were able to consistently utilize the PRT motivational techniques with their children with ASD during the intervention phase and that, once the intervention began, each of the three subject children with ASD showed an increase in mean frequency of social functional utterances. These exhibitions of increased SFU were a marked improvement, making the development of the brief training in PRT for parents worthwhile and cost-effective, in terms of personnel and time commitment. The significant increase in the mean frequencies of the PRT motivational techniques indicates that all the participating fathers successfully implemented the techniques with fidelity throughout the intervention phase of the research study. The visual inspection of the percentages of non-overlapping data values demonstrated that the intervention used in this research study was highly effective

An Empirical Study Towards an Automatic Phishing Attack Detection Using Ensemble Stacking Model

Phishing attacks have become one of the most attacks facing internet users, especially after the COVID-19 pandemic, as most organizations have transferred part or most of their work and communication to become online using well-known tools, like email, Zoom, WebEx, etc. Therefore, cyber phishing attacks have become progressively recent, directly and frankly reflecting the designated website, allowing the attacker to observe everything while the victim is exploring Webpages. Hence, utilizing Artificial Intelligence (AI) techniques has become a necessary approach that could be used to detect such attacks automatically. In this paper, we introduce an empirical analysis for automatic phishing detection using several well-known machine learning classification algorithms compared with an ensemble learning model for detecting phishing sites based on the uniform resource locator (URL) using two preprocessed datasets. In this empirical study, we concluded that the ensemble model grants accuracy 97.49% for dataset 1 and 98.69% for dataset 2, which gives higher accuracy than using a single machine learning classification algorithm such as Naive Bayes (NB), Decision Trees (DTs), Random Forest (RF), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). We also compared the proposed ensemble model with one of the most recent similar model

High Performance WLAN Using Smart Antenna

The need for higher data rates in WLANs boosts drastically because tremendous consumer interest in emerging multimedia applications, such as HDTV, has been increased. Currently, the IEEE 802.11a/b/g WLANs provide a limited data rate for the current user application requirements. In order to overcome substantial limitations of the existing WLANs, the next generation of WLANs, IEEE 802.11n, is in the course of development and expected to support higher throughput, larger coverage area and better QoS. The high performance IEEE 802.11n WLAN can improve data rate significantly by using smart antenna systems in the physical layer to take advantage of multi-path fading of wireless channels. In this thesis, an analytical model is developed to study the MAC performance and the underlying smart antenna technologies used in multi-path fading channels. Multiple antennas employed in the AP arise two popular approaches to provide a significant performance improvement, diversity and multiplexing. Considering the diversity gain of multiple antennas at the AP in which the AP with multiple antennas serves one user at a time, the capacity and throughput can be obtained. In addition, the AP is possible to serve multiple users in the downlink, by exploiting the multiplexing gain of the wireless channel. We investigate the maximum network throughput when the traffic intensity of the AP approaches to one. Unlike most of previous research which focus on either the physical or the MAC layer performance, our analytical model jointly considers the MAC protocol and the smart antenna technology

The Impact of Training Programs for Faculty Members' Skills Development: A Field Study of Najran University

This research aimed to assess the impact of training programs at the Deanship of Development and Quality-Najran University (DDQ-NU) on the development of faculty members' skills from their perspective according to variables gender, faculty, academic rank, and years of experience. Researchers used a descriptive method and designed a questionnaire composed of 42 items distributed on four domains: (job performance, group performance, participant satisfaction, and participant knowledge gained). The sample of the study consisted of 175 faculty members at Najran University who responded to the questionnaire. The most important results that there is a positive impact of training programs in DDQ-NU on improving faculty members' skills. Keywords: Evaluation, Faculty members, Human development, Saudi universities, Training Program

The development of a comprehensive framework for measuring business performance in construction

Business performance measurement has been the subject of considerable research over the past fifteen years. Most of this research has been triggered by the inadequacy of financial indicators and the increasing use of non-financial indicators. Consequently, companies have to choose from many available frameworks / methods to monitor their business performance such as the Balanced Scorecard, Excellence Models and industry key performance indicators (KPI). The choice of one framework / method over the others might omit important performance information, and the use of more than one simultaneously can cause confusion and the use of valuable time and resources. This paper describes the PhD research underway to develop a comprehensive business performance measurement framework for construction organizations. The research adopts a hypothetico-deductive approach that comprises two main stages. First, the framework is formulated from existing well-established frameworks in literature. The second stage is the empirical testing of the framework that uses triangulated methods for collecting and analysing data. The paper further discusses the scope of the research within the industry, and finally the use of the framework in measuring business performance and its interface with the construction KPI

Theoretical formulation of a framework for measuring business performance in construction

Business performance measurement, across industries, has significantly changed over the past two decades, integrating non-financial with financial measures. Moreover, the Egan and Latham reports have advocated performance improvement in the construction industry, with performance measurement being a key element. The purpose of this paper is to theoretically formulate a framework for measuring business performance in construction. The framework builds upon the well-established principles of the Balanced Scorecard and Business Excellence models. Formulation is based on integrating the criteria / perspectives of the founding frameworks into performance factors, and integrating the underlying logic. The formulation process is evaluated by comparing the proposed framework against the Balanced Scorecard, Excellence models, Total Quality Management frameworks in literature, and to the Performance Prism. The proposed framework is further adapted for construction companies and is shown to include the Construction Best Practice Programme - Key Performance Indicators that are based on Egan's industry report

Dynamic scheduling model for the construction industry

Purpose:Basic project control through traditional methods is not sufficient to manage the majority of realtime events in most construction projects. This paper proposes a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cashflow, throughout project construction.Design/methodology/approach:Upon reviewing the topic of Dynamic Scheduling, a worldwide Internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.Findings:The need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use, and software integration with contemporary project management practices.Practical/Social implications:Optimized real-time scheduling can provide better resources management including labour utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Originality/value:The paper illustrates the importance of DS in construction, identifies the user needs, and overviews the development, verification and validation of a model that supports the generation of high quality schedules beneficial to large scale projects.</div

A robust CNN Model for Diagnosis of COVID-19 based on CT scan images and DL techniques

The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world.  Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.