The rise of the ‘E-resistor’: towards a new perspective on work-based New Communication Technology (NCT) non-use

No description supplie

Representing Mathematical Concepts Associated With Formulas Using Math Entity Cards

We introduce Math Entity Cards, a modified version of existing Entity Cards specifically tailored for Math Information Retrieval. Math Entity Cards help connect formulas to titles and description and make the navigation between formulas and text related to formulas, seamless. These cards are populated from a new knowledge base, created by extracting and combining formulas, titles and descriptions from three different sources, Wikidata, Wiktionary & ProofWiki. We demonstrate a novel approach of using entity cards for auto-complete by integrating our cards into a Math-Aware Search Interface: MathSeer. This helps create a new ecosystem for consuming information during formula editing and search. We design and conduct a human experiment, in a math information retrieval setting and find statistical evidence for the usefulness of individual card components

My Clinic web application

The purpose of this web application is to provide doctors and patients with one location where their data is stored and they can access through a secured environment. It will allow doctors to send perscriptions electronically to the pharmacy and allow their patients to view and edit medical appointments and their medical information online as well

Pathways to next-generation redox flow batteries

Redox flow batteries (RFBs) provide a promising pathway towards grid-scale energy storage but are inhibited from widespread implementation due to high costs. RFBs are divided into aqueous (Aq) and nonaqueous (NAq) redox flow batteries, both of which show distinct challenges to build low-cost RFBs with battery prices less than 100 per kWh-1. Overcoming these cost challenges requires a detailed electrolyte techno-economic (TE) model, which explicitly quantifies RFB redox active material, salt, and solvent costs. TE model results identify active species concentration and cell voltage as critical cost-constraining parameters for nonaqueous and aqueous RFBs respectively. Active species concentration targets for NAqRFBs are decreased by increasing cell voltage, and by decreasing area-specific resistance, redox active material molecular weight, and salt molecular weight and concentration. Similarly, cell voltage targets for AqRFBs are decreased by decreasing area-specific resistance and redox active material cost per unit mass and molecular weight. Alternative design pathways for nonaqueous and aqueous RFBs are proposed which decrease NAqRFB redox active material molality targets to 1.1 mol kg-1 and AqRFB cell voltage targets to 0.6 V, and which could potentially decrease RFB battery price to 90 per kWh-1. This TE model is used to analyze a group of experimentally tested nitrobenzene derivatives to find optimal redox active material potential, molecular weight, and salt molecular weight for next-generation nonaqueous RFBs. Nitrobenzene derivatives are found to have a battery price of 260 per kWh-1 when used with TBAPF6 salt, but on switching to TMABF4 salt with lower molecular weight, the battery price can be reduced further to 160 per kWh-1 albeit with higher active material molality targets. Finally, an analytical model of redox active species crossover in nonaqueous RFBs is developed and implemented in order to reduce coulombic inefficiencies in RFBs by selecting optimal operating parameters. The degree of crossover is found to be highly sensitive to current density and separator permeability, and can be decreased by an order of magnitude using thicker separators and higher current densities

Adapting feedback types according to students’ affective states

Affective states play a significant role in students’ learning behaviour. Positive affective states can enhance learning, while negative ones can inhibit it. This paper describes the development of an affective state reasoner that is able to adapt the feedback type according to students’ affective states in order to evoke positive affective states and as such improve their learning experience. The reasoner relies on a dynamic Bayesian network trained with data gathered in a series of ecologically valid Wizard-of-Oz studies, where the effect of feedback on students’ affective states was investigated

How does risk aversion shape investors’ intentions? Evidence from the Indian corporate bond market

Risk aversion plays a crucial role in understanding how individuals make financial decisions and allocate their resources. This study analyzes the influence of risk aversion on behavioral intentions and explores the mediating role of attitudes, subjective norms, and perceived behavioral control. Additionally, it investigates the moderating effect of gender and financial literacy on behavioral intentions of investors. A sample of 400 people was collected from Indian retail investors by administering a structured questionnaire through stock brokering firms, and data were analyzed using Partial least squares – Structural equation modelling in the Smart PLS 3.3.9 software. The research found that risk aversion, attitude, subjective norms, and perceived behavioral control significantly impact an investor’s intention. Among all the antecedents of behavioral intentions, perceived behavioral control (β 0.481*) was found as a significant predictor of the intention compared to attitude (β 0.154*), subjective norms (β 0.224*) and risk aversion (β 0.082*) factors. Further, mediation analysis found that attitude, subjective norms, and perceived behavioral control partially mediated the relationship between risk aversion and intention. Lastly, the multi-group analysis revealed that gender and financial literacy did not moderate the association between risk aversion and intention