Vision, culture, and image: a systematic review of higher education online branding

Guided by The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the primary objective of this study was to gain insight into higher education online branding. An integrated mixed-method synthesis was used to summarize 76 qualitative, quantitative, and mixed-method, peer-reviewed empirical studies from 2011 to 2021. The Vision-Culture-Image Alignment Model and Twelve Categories of Determinants of Selective Reporting were recruited to limit and outline potential bias and conflicts. The results reflect insights from over 100 countries, 2,400 institutions, 13,000 participants, 800 websites, and seven social media platforms. This review indicated that institutional brands often align with history, geography, and employment industries. While institutions have scaffolded digital technologies to extend their ability to connect, they often rely on low-engagement activities rooted in broadcasting information. Students, in turn, seek out institutionally-mediated technology to gain personalized insights into technological capability and culture. They also connect online to form subcultures more readily, and enhance their educational experience

Parsing genetic models

Applications of computer vision have seen great success recently, yet there are few approaches dealing with visual illustrations. We propose a collection of computer vision applications for parsing genetic models. Genetic models are a visual illustration often used in the biological sciences literature. These are used to demonstrate how a discovery fits into what is already known about a biological system. A system that determines the interactions present in a genetic model can be valuable to researchers studying such interactions. The proposed system contains three parts. First, a triplet network is deployed to decide whether or not a figure is a genetic model. Second, a popular object detection network YOLOvS is trained to locate regions of interest within genetic models using various deep learning training techniques. Lastly, we propose an algorithm that can infer the relationships between the pairs of genes or textual features present in the genetic model

Linear synchronous motor based propulsion for the futuristic hyperloop transportation system

Current trends are leading towards an electrified future of transportation. Its popularity amongst the public is continuously increasing this trend. Today, the demands for an economical, carbon conscience and faster mode of transportation is imperative. Thus, the proposal for a new mode of transportation known as the Hyperloop has been popularized. Although, there are only some reports regarding its design. More specifically, reports regarding the propulsion system design methodology for the Hyperloop is lackluster. This thesis provides an initial steppingstone to a modeling and simulation study for the propulsion system in a Hyperloop. PSIM is used to develop the model and design the full power system and controller. To verify and validate the design, hardware in the loop technology is used. The controller implemented is tested through a Texas Instrument digital signal processor. The Typhoon real time verification shows matching results with the PSIM offline simulation model

Modeling and experimental investigation of renewable energy and ammonia-based systems for carbon capturing and useful outputs

This thesis work focuses on developing ammonia-based carbon capturing systems that produce useful chemical outputs to offset the energy penalty typically imposed by implementing a carbon capture retrofitting to a power plant. These systems have been investigated through models that are based on exergy and economics tools. The motivation, and the objectives of this work are mentioned. Next, a thorough literature review of the topic of ammonia-based carbon capture systems is provided here to identify the gaps in knowledge. This review concluded that there is a significant lack in experimental investigations of ammonia-based carbon capture systems that are powered by renewable energy sources. Also, the direction of future carbon capture systems is moving towards co-producing of useful and valuable chemicals to offset the costs of operating such systems. By knowing this, renewable energy and ammonia-based carbon capturing systems that produce ammonium bicarbonate are developed and described. Thermodynamic models of the present carbon capturing systems are established using the energy and exergy tools. After that, exergoeconomic models are explained for these systems. Results of the simulation work show that the use of an electrochemical ammonia synthesizer has 13.3% lower energy requirements compared to the use of a proton-exchange membrane electrolyzer and the Haber-Bosch process for ammonia synthesis. The cost of producing ammonium bicarbonate is almost 16% of the market price of this chemical commodity. This indicates that the developed carbon capturing system are financially feasible to produce monetary value

Functionalized carbon surfaces for clean electrochemical energy systems

The development and implementation of clean energy technologies is the way to overcome the global energy crisis and reduce pollution. Therefore, new energy solutions are rapidly needed. Fuel cells (FCs) may become one such solution. FCs are devices that utilize chemical reactions to directly produce electric energy. While these devices are ideal clean energy sources for the transportation industry, conventional FCs are based on expensive materials that implement platinum catalysts on a carbon support (Pt/C). The high cost and limited availability of platinum hinders the applicability of FCs. Nitrogen and metal-doped carbon supports have been investigated as a non-precious metal replacement for costly precious metal-based materials in various electrochemical energy applications. However, the design of non-precious metal materials (NPMMs) involves high temperature pyrolysis treatments, which leads to an almost random distribution of nitrogen atoms on the surface and therefore can limit efficiency. In this work, a method to graft only the most active nitrogenous groups and/or transition metals on the surface of carbon supports was developed. Specifically, diazonium coupling chemistry to covalently attach molecularly defined moieties bearing a terpyridine (tpy) group onto the surface of carbon supports, followed by the introduction of Fe (or other transition metal) centers into anchored tpy groups. Pyridinic nitrogenous groups, which are the basis of tpy, are believed to be required for high activity in the oxygen reduction reaction (ORR) in FC applications and are thought to increase capacitance in SC applications. Upon metal coordination into the tpy sites, the metal-N3/C catalyst shows promising activity for the ORR and SC applications and opens the door for molecularly controlled, inexpensive, and efficient materials. Importantly, upon an energy intensive heat-treatment, the material???s activity does not improve. Confirming that this system???s properties are dictated by the molecularly defined tpy-Fe units, new efficient NPMMs can be fabricated using energy-saving conditions. This design can be applied and optimized to create a family of NPMMs for various clean energy applications. Modifying carbon-based materials opens the way for new low-cost materials for clean energy systems such as FCs, SC, and water oxidation

Improving understanding of rape proclivity

The current dissertation included three studies that together aimed to improve understanding of rape proclivity as a potential construct related to sexual violence. In the first study, participants' understanding of the items on the Rape Proclivity Measure were assessed to gain insight into participants' perspectives about rape scenarios and to examine the content validity of the measure. Most participants (68.7% to 95.8%) did not view the wording of scenarios as ambiguous, and understood scenarios as incidents of sexual violence, indicating that the Rape Proclivity Measure is comprehensible and has good content validity. However, participants were more likely to label rape scenarios involving: (a) a stranger perpetrator, and (b) use of physical force, as incidents of rape, indicating that their definition of rape matches the rape scripts prevalent in North America. The second study examined the relationship between rape proclivity and various correlates of rape, namely deviant sexual interests, offence supportive cognition (both rape and antisocial), and history of past sexually aggressive behaviour. The aim was to identify the most relevant variables, and to evaluate whether rape proclivity forms a distinct construct or whether it overlaps substantially with one of these other constructs. There was a strong correlation between rape proclivity and correlates of rape. However, in a factor analyses, the various measures of rape proclivity did not form a distinct construct indicating that different measures of rape proclivity may not be assessing the same construct. Finally, the third study aimed to address the gap in the literature regarding the role of rape proclivity, assessed by rape proclivity measures, as a predictor of sexually aggressive behaviour. Results indicated that rape proclivity measured by Sexual Experience Survey-Tactics First Revised (SES-TFR) predicted future sexual violence, but the Rape Proclivity Measure did not. This means that rape proclivity may be a factor related to the perpetration of sexual offending, but care must be taken in the measures that are used. Once proclivity can be identified in a reliable and valid manner, it can be targeted in programs designed to prevent sexual violence

Design and development of a machine learning-based framework for phishing website detection

Phishing is a social engineering cyber attack to steal personal information from users. Attackers solicit individuals to click phishing links by sending them emails or social media text messages with deceptive content. With the development and applications of machine learning technology, solutions for detecting phishing links have emerged. Subsequently, performance optimization achieved by machine learning-based approaches were predominantly limited to the datasets used to train the model, such as few open source datasets, poorly characterized data points, and outdated datasets. This thesis introduces a framework based on multiple phishing detection strategies, which are whitelist, blacklist, heuristic rules, and machine learning models, to improve accuracy and flexibility. In the machine learning-based method, three traditional models and three deep learning models are trained and compared the performance of their test results, and concluded that the Gated Recurrent Units (GRU) model achieved the highest accuracy of 99.18%. Furthermore, in the expert-driven heuristic rule-based strategy, seven new HTML-based features are proposed. Finally, a prototype has been developed, with a browser extension to display detection results in real-time

Novel electrode materials and advanced electrochemical diagnostics for electrochromic devices

Electrochromic materials (ECMs) are a class of ???smart??? materials used in a variety of applications due to the ability to alter their optical properties with the application of a potential. For these applications, ECMs are fashioned into electrochromic devices (ECDs). However, commercialization of ECDs are limited due to challenges with cyclic stability and optical durability. Commonly, it is seen that ECM development is vastly investigated, while insight into the overall ECD architecture is limited. In this work, the effect of the counter electrode (CE) and operating potentials were investigated using an Fe (II) terpyridine based complex as the ECM. From this, it was found that by improving the charge-storage properties at the CE and limiting the potential window used, ECD degradation could be minimized. Furthermore, by employing these modifications it was found that the lifetime of these ECDs were able to undergo months of constant cycling, as opposed to days, with further improvement to their optical properties

A systematic review examining the effects of mHealth interventions on dietary adherence in patients with cardiovascular diseases

A systematic review was conducted to determine if diet-focused mHealth interventions are effective for supporting dietary adherence in patients with cardiovascular disease (CVD), a population where nonadherence is common. A comprehensive literature search identified thirteen studies which met inclusion criteria: adults with a CVD diagnosis, use of an mHealth intervention, and measures of dietary adherence. Studies were excluded if interventions involved open dialogue or were qualitative studies or systematic reviews. Eight studies supported using mHealth interventions for improving dietary adherence, four showed mixed results, and one showed no improvements. Eight studies evaluated text and/or app-based mHealth interventions and found that their interactive features improved dietary adherence more compared to solely information delivering interventions. Overall, most mHealth interventions improved dietary adherence, however, nine studies had high risk of bias due to the outcome measurement, thus caution is advised when applying these findings to clinical settings for patients with CVD

Design and development of a novel DC/DC bidirectional converter with dual solar/PV-based snow removal and EV charging functionality

Different factors affect solar photovoltaic (PV) systems by decreasing input energy and reducing the conversion efficiency of the system. One of these factors is the effect of snow cover on PV panels, a subject lacking sufficient academic research. This thesis reviews current research for snow removal in solar PV modules and power electronic circuit topologies used. Additionally, it presents the design, analysis and modelling of a smart heating system for solar PV electric vehicle (EV) charging applications. The system is based on a bidirectional buck-boost DC/DC converter that redirects the grid/battery power into heating of the PV modules thus removing snow cover, as well as providing the function of MPPT when required to charge the EV battery. A performance evaluation by simulating and testing the system under various climatic conditions is presented validating the usefulness of the proposed converter to be used in solar PV systems under extreme winter conditions