Analysis of the Energy Potential of Solar Light of the Western Region of Ukraine with the Account of Climatic Conditions

An experimental facility for measuring and recording the flux density of solar radiation is designed and installed. An electrical circuit is developed and a pyranometer model is developed to measure the level of solar radiation, and it is graduated with a Soler Power Meter DT-1307 solar radiation flux meter. The time distribution of the flux density of solar energy is analyzed and the surface energy density of solar radiation is calculated for Ternopil. The influence of climatic conditions on the energy of solar radiation is determined. Analytical dependencies are obtained on the basis of comparison of the measured values of the flux density of solar radiation and the cloud cover taken from meteorological services. The energy potential of solar radiation during 2012-2015 in the western region of Ukraine is calculated, as well as the average monthly and average annual energy density of solar radiation. It is determined that the annual average density of the solar energy flux is 1045.9 kW∙h/m2, and its deviation does not exceed 5%. It is shown that the most favorable months for the use of solar energy are from March to September of each year

Analysis of assessment tools of Engineering degrees

This work presents an analysis of the assessment tools used by professors at the Universitat Politécnica de Catalunya to assess the generic competencies introduced in the Bachelor’s Degrees in Engineering. In order to conduct this study, a survey was designed and administered anonymously to a sample of the professors most receptive to educational innovation at their own university. All total, 80 professors responded to this survey, of whom 26% turned out to be members of the university’s own evaluation innovation group (https://www.upc.edu/rima/grups/grapa), GRAPA. This percentage represents 47% of the total GRAPA membership, meaning that nearly half of the professors most concerned about evaluation at the university chose to participate. The analysis of the variables carried out using the statistical program SPSS v19 shows that for practically 49% of those surveyed, rubrics are the tools most commonly used to assess generic competencies integrated in more specific ones. Of those surveyed, 60% use them either frequently or always. The most frequently evaluated generic competencies were teamwork (28%), problem solving (26%), effective oral and written communication (24%) and autonomous learning (13%), all of which constitute commonly recognized competencies in the engineering profession. A two-dimensional crosstabs analysis with SPSS v19 shows a significant correlation (Asymp. Sig. 0.001) between the type of tool used and the competencies assessed. However, no significant correlation was found between the type of assessment tool used and the type of subject, type of evaluation (formative or summative), frequency of feedback given to the students or the degree of student satisfaction, and thus none of these variables can be considered to have an influence on the kind of assessment tool used. In addition, the results also indicate that there are no significant differences between the instructors belonging to GRAPA and the rest of those surveyed.Preprin

Teaching Software Engineering through Robotics

This paper presents a newly-developed robotics programming course and reports the initial results of software engineering education in robotics context. Robotics programming, as a multidisciplinary course, puts equal emphasis on software engineering and robotics. It teaches students proper software engineering -- in particular, modularity and documentation -- by having them implement four core robotics algorithms for an educational robot. To evaluate the effect of software engineering education in robotics context, we analyze pre- and post-class survey data and the four assignments our students completed for the course. The analysis suggests that the students acquired an understanding of software engineering techniques and principles

Learning style preference and critical thinking perception among engineering students

Engineering education plays a vital role towards modernization of world. Therefore, engineering students need to be nurture with multiple skills like learning preferences and critical thinking skills. This study has been conducted to identify the learning style preferences and critical thinking perception of the engineering students from three programs electrical engineering, mechanical engineering and civil engineering at Universiti Tun Hussein Onn Malaysia (UTHM), Johor. Survey research design was applied in this study. The quantitative data was collected by two questionnaires Index of Learning Styles (ILS) that is based on Felder-Silverman Learning Style Model (FSLSM) and Critical Thinking Skills (CTS) questionnaire which consists of analysis, evaluation, induction and deduction in terms of problem solving and decision making. A total of 315 final year engineering students were participated in this study. Data was analyzed in descriptive and inferential statistics involving tests Analysis of Variance (ANOVA), Pearson Correlation and linear regression. The study discovered that engineering students are preferred to be visual learners (83.80%). Visual learning style denotes FSLSM input dimension and visual learners learn best by diagrams, charts, maps and graphical presentations. This study also found that engineering students possess critical thinking perception in all dimensions. However, there is no statistical significant difference of learning style found among engineering programs as “p” value found 0.357. Whereas, there is statistical significant critical thinking difference found among engineering programs as “p” value found 0.006. Lastly, findings revealed that there is no significant relationship found between learning styles and critical thinking skills. The study findings suggested that providing preferred learning style (visual learning style) in classroom will enhance students’ academic achievement and increase their cognitive level. This study might serve as a guideline for educators to facilitate learners to enhance their learning and thinking for better outcomes in academia as well as in workplace

Extending an alginate drug delivery experiment to teach computational modeling and engineering analysis to 1st year biomedical engineering students

Engaging biomedical engineering (BME) students in the first year has been an important part of The University of Texas at Austin’s strategy to improve student motivation, retention, and self-efficacy. First year engineering curricula across the country have increasingly included an introduction to engineering or design course in addition to core math and science courses. At UT Austin, a first-year design course and drug-delivery design class module has been previously described[1]. This course has since been expanded from 1 credit hour to 3 credit hours and the drug-delivery design module has been enhanced to include computational design and analysis using 2 different tools (Microsoft Excel and MATLAB). Previously, students analyzed their experimental data using simple curve fitting to determine the diffusivity constant. This paper describes the instruction of a Fick’s law-based computational simulation implemented in both Excel and MATLAB in order to match students’ experimental data. Students were able to use their simulation to solve for the diffusion coefficient and to estimate the amount of drug (a dye was used as a surrogate for a drug) lost in the drug delivery device loading process. In addition, students learned how to use both Excel and MATLAB for engineering analysis so that they will be prepared for future engineering courses. General Excel and MATLAB competencies were tested using low-stakes in-class quizzes and students’ attitudes were measured from end-of-semester course and instructor surveys. Students showed functional Excel and MATLAB knowledge and responded positively on course and instructor surveys.Cockrell School of Engineerin

Multidisciplinary analysis for highway engineering purposes

There are no author-identified significant results in this report

Engineering failure analysis and design optimisation with HiP-HOPS

The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved