1,103 research outputs found
An Engagement Strategy for Teaching Computing Concepts
The research work in this paper investigates a new teaching strategy that uses active learning through play to increase studentsâ uptake of learning computing concepts. The strategy promotes student engagement through playing a customized Jenga game. The game consists of a set of blocks, one side of each block is covered with a piece of dry-erase tape to allow erasing and writing on the blocks. This allows instructors to reuse this editable Jenga for developing their own game-based learning activities. The editable Jenga can be used without writing if needed. Three sample activities with writing have been developed and conducted to test the strategy experimentally in addition to a fourth activity without writing any terms on the Jenga blocks. The test results showed that the strategy improves the class average and promotes the student engagement. A survey has been conducted to get studentsâ feedback on the strategy. The survey results demonstrated that students like the play-based strategy. The contribution of this paper is the development of a teaching strategy that improves studentsâ engagement which in turn helps students to learn important computing concepts
Exploring Identifiers of Research Articles Related to Food and Disease using Artificial Intelligence
The research project aims to understand how variation in writing styles and flexibility of text mining methods control their ability to extract useful information from articles about food and health. Those areas of study are significant because they incorporate features of text mining methods and food-health articles. The project will build a database and mining tools that would change the way we search and collect information from scientific publications and the way we analyze this information for further applications.
The strategy to achieve the projectâs goal is to engage several teams of undergraduate students in Applied Computing to develop a food-health portal. Some teams will develop text mining tools and others use these tools and existing data-mining tools to extract the portal contents from articles about food-health. The information extracted will create and inform a database of food/health relationships.
The project addresses several issues of central importance to the success of text mining techniques extracting useful food-health information for serving society now and in future. Those include: how writing style of an article is determined automatically, how main topic of an article/document is identified automatically, how useful information is extracted from an article/document to help national and international researchers in conducting further research, how available food articles can be quickly utilized to help the society, how undergraduate students gain skills required for extracting useful information from the huge amount of data available on the internet
On implementing dynamically reconfigurable architectures
Dynamically reconfigurable architectures have the ability to change their structure at each step of a computation. This dissertation studies various aspects of implementing dynamic reconfiguration, ranging from hardware building blocks and low-level architectures to modeling issues and high-level algorithm design. First we derive conditions under which classes of communication sets can be optimally scheduled on the circuit-switched tree (CST). Then we present a method to configure the CST to perform in constant time all communications scheduled for a step. This results in a constant time implementation of a step of a segmentable bus, a fundamental dynamically reconfigurable structure. We introduce a new bus delay measure (bends-cost) and define the bends-cost LR-Mesh; the LR-Mesh is a widely used reconfigurable model. Unlike the (idealized) LR-Mesh, which ignores bus delay, the bends-cost LR-Mesh uses the number of bends in a bus to estimate its delay. We present an implementation for which the bends-cost is an accurate estimate of the actual delay. We present algorithms to simulate various LR-Mesh configuration classes on the bends-cost LR-Mesh. For semimonotonic configurations, a Î(N)*Î(N) bends-cost LR-Mesh with bus delay at most D can simulate a step of the idealized N*N LR-Mesh in O((log N/(log D-log Î))2) time (where Î is the delay of an N-element segmentable bus), while employing about the same number of processors. For some special cases this time reduces to O(log N/(log D-log Î)). If D=NΔ, for an arbitrarily small constant Δ \u3e 0, then the running times of bends-cost LR-Mesh algorithms are within a constant of their idealized counterparts. We also prove that with a polynomial blowup in the number of processors and D=NΔ, the bends-cost LR-Mesh can simulate any step of an idealized LR-Mesh in constant time, thereby establishing that these models have the same power. We present an implementation (in VHDL) of the Enhanced Self Reconfigurable Gate Array (E-SRGA) architecture and perform a cost-benefit study for different dynamic reconfiguration features. This study shows our approach to be feasible
Practice and Refactoring Log: A Reflection Based Learning Strategy to Improve the Fluency of Computing Students in Writing Computer Programs
The relationship between reflection and learning is evident. Reflection plays a significant role in learning by encouraging insight and complex learning. However, most students consider their work experiences at school as isolated and unrelated events. This work aims to investigate how to improve the studentsâ fluency in writing computer programs through reflection. A new deliverable, the Practice and Refactoring Log (âPAR Logâ), is requested for each assignment in order to engage students in the process of making meaning from their experiences when completing their assignments. The PAR Log shows and justifies all changes through the assignment development cycle. Two student groups were required to develop and submit the PAR Log with all assignments, for credit. The performance of each group was compared to a similar group taught the same course by the same professor using the same assignments but without requesting the PAR Log. This paper presents the background, methodology, results, analysis and conclusion of this investigation
MOTIVES OF THE EGYPTIAN EDUCATION FUTURE FOR SUSTAINABLE DEVELOPMENT: A COMPARATIVE ANALYSIS BETWEEN 2020 AND 2030
Purpose of the study: The current study aims to identify the engines of the future of Egyptian education to achieve the fourth goal of the United Nations Convention on Sustainable Development.
Methodology: The research followed the analytical method in the comparative analysis of the future of Egyptian education between 2020 and 2030.
Main Findings: The results of the research, in addition to the conclusions of researchers from the literature and previous studies, stressed on the importance of the existence of ten motives for the future of education for sustainable development 2030.
Applications of this study: This research attempts to address this dilemma through the perspectives and engines of education for sustainability. Where teachers can meet the Education for Sustainable Development (ESD) approach to enhance knowledge and positive attitudes towards appropriate action for sustainability in relevant, meaningful, exciting, and creative ways, this research offers experiences that allow pupils to become more connected to nature, develop children's sense of practical orientation that embraces positive attitudes, change behaviour and hope for the child's future.
Novelty/Originality of this study: There is no research or studies that addressed the motives of the Egyptian education future for sustainable development
New photo-luminescent inorganic materials: high-tec application in chemical sensing and labeling
This thesis describes the potential of various kinds of luminescent nanoparticles with respect to chemical sensing and biosensing. First, fluorescent silica nanoparticles (SiNPs) were prepared by covalent attachment of fluorophores to the amino-modified surface of SiNPs with a typical diameter of 15 nm. The SiNPs were used in novel kinds of Förster resonance energy transfer (FRET)-based affinity assays at the interface between nanoparticle and sample solution. Various labels were employed to obtain a complete set of colored SiNPs, with excitation maxima ranging from 337 to 659 nm and emission maxima ranging from 436 nm to the near infrared (710 nm). The nanoparticles were characterized in terms of size and composition using transmission electron microscopy, thermogravimetry, elemental analysis, and dynamic light scattering. The surface of the fluorescent SiNPs was biotinylated, and binding of labeled avidin to the surface was studied via FRET in two model cases.
Secondly, the upconverting luminescent nanoparticles (UCLNPs) consist of hexagonal NaYF4 nanocrystals doped with trivalent rare earth ions were synthesized by both the oleic acid (solvothermal) method and the ethylenediaminetetraacetic acid (coprecipitation) method. The nanoparticles were codoped using Yb3+ as the sensitizer ion, Er3+, Tm3+, or Ho3+ respectively as the emitting activator ions. An affinity system was demonstrated based on the interaction of two types of nanoparticles. The first type consists of UCLNPs of the type NaYF4:Yb,Er absorbing light in the infrared and showing green luminescence at 521 and 543 nm and red luminescence at 657 nm. The second type consists of gold nanoparticles (Au-NPs) with a size of about 50 nm, which absorb the green luminescence of the UCLNPs, but do not influence their red luminescence. A model system for a self referenced affinity system were established by labeling the UCLNPs with avidin and the AuNPs with biotin. In the presence of avidin-modified UCLNPs, the biotinylated Au-NPs can be detected in the range from 12 to 250 ”gâąmL-1 by rationing the intensity of the red (analyte-independent) emission band to that of the green (analyte-dependent) emission band. All nanoparticles were characterized in terms of size and composition using transmission electron microscopy, thermo-gravimetry, and FTIR spectroscopy.
Thirdly, different types of nanoparticles (made from silica, polystyrene and UCLNPs) carrying longwave absorbing and emitting fluorescent labels were prepared by conjugating reactive dyes to the surface of amino-modified particles. The dyes have a reactive chloro group capable of reacting with amino groups and thereby undergoing a change in color, typically from green to blue (the so-called chameleon effect). The latter show the effect of upconversion in that near-infrared laser light is converted into visible luminescence. They also show the unusual property of displaying dual emission, depending on whether their luminescence is photoexcited with visible light or near-infrared light. The amino groups on the surface of nanoparticles were detected via the chameleon effect of the applied amino-reactive dyes.
Fourth, the quenching effect of heavy metal ions and halide ions on the luminescence of UCLNPs in aqueous solution was studied. The effect was investigated for the ions Cu(II), Hg(II), Pb(II), Cd(II), Co(II), Ag(I), Fe(III), Zn(II), bromide and iodide, and was found to be particularly strong for Hg(II). Stern-Volmer plots were virtually linear up to 10 â 25 mM concentrations of the quencher, but deviate from linearity at higher quencher concentrations where static quenching caused an additional effect. The UCLNPs display two main emission bands (blue, green, red or near-infrared), and the quenching efficiencies for these found to be different. The effect seems to be generally associated with UCLNPs because it was observed for all particles doped with trivalent lanthanide ions including Yb(III), Er(III), Ho(III), and Tm(III)
Intent Detection through Text Mining and Analysis
The article is about the work investigated using n-grams, parts-Of-Speech and Support Vector machines for detecting the customer intents in the user generated contents. The work demonstrated a system of categorization of customer intents that is concise and useful for business purposes. We examined possible sources of text posts to be analyzed using three text mining algorithms. We presented the three algorithms and the results of testing them in detecting different six intents. This work established that intent detection can be performed on text posts with approximately 61% accuracy
A quality assurance technique for the static multileaf collimator mode based on intrinsic base lines
AbstractThe inspection of the static leaf positions of Multileaf Collimator (MLC) devices is essential for safe radiotherapy deliveries in both static and dynamic modes. The purpose of this study was to develop a robust, accurate and generic algorithm to measure the individual static MLC leaf positions. This was performed by extracting leaf tip locations from the radiographic film image and measuring their relative distance from a reference line on the film. The reference line was created with a selected set of MLC leaf sides. The film scaling was created and verified using the physical leaf width. The average measured distance corresponds to a leaf width of 10 mm was 9.95 ± 0.09 mm. The estimated reproducibility of the leaf tips location was ±0.26 mm. The code accuracy was checked by intentionally positioning set of leaves with small errors (1 mm), and the detected deviations from the expected positions ranged from â0.25 mm and +0.32 mm. The algorithm includes two self testing functions in order to detect failures of leaf positioning due to poor film quality and to avoid the potential systematic errors attributable to the improper collimator setting. The code is promising to be more efficient with Gafchromic and Electronic Portal Imaging Device (EPID)
Tensile and Impact Behaviour of Shape Memory Alloy Fibre Reinforced Engineered Cementitious Composites
Extreme loading events such as impact, blast, and earthquakes have often led to partial or total collapse of structures, associated with economic and human life loss. Therefore, civil engineers have been seeking innovative materials and systems that would allow designing resilient and smart structures which can withstand such catastrophic events. Recently, engineered cementitious composites (ECC) and shape memory alloys (SMA) have emerged as strong contenders in the production of smart and resilient structural systems.
The aims of this study are to explore the possible synergy between ECC and SMA for developing a novel hybrid fibre-reinforced ECC incorporating randomly dispersed SMA and polyvinyl-alcohol short fibres (HECC-SMAF) with possible strain recovery and superior impact resistance. The mechanical properties of the composite, including uniaxial tensile and strain recovery performance, were examined. Moreover, the behaviour of the composite under impact loading was explored using a drop weight impact test. Test specimens were also heat-treated to investigate possible pre-stressing effects of SMA fibres on the impact resistance of the ECC. A two-parameter Weibull distribution was used to analyze variations in experimental results in terms of reliability function. Furthermore, numerical simulation was developed to predict the behaviour of the composite under impact loading.
Results indicate that SMA fibres significantly enhanced the performance of the composite both under static and dynamic loading. Adding fibres beyond a certain dosage led to fibre clustering, thus, no further gain in tensile and impact performance was measured. The impact resistance of HECC-SMAF specimens was further improved after exposure to heat treatment. This highlights the significant contribution imparted by the local pre-stressing effect of SMA fibres to the impact resistance of the composite. The Weibull distribution was adequate to predict the impact failure strength of the new composite, allowing to avert additional costly experiments. Also, numerical simulation predictions of the impact behaviour of the hybrid composite were in good agreement with experimental findings, thus offering a suitable predictive tool and allowing to preclude costly and time-consuming experiments.
This research underscores the potential to engineer new cementitious composites with superior tensile properties and impact resistance for the protection of critical infrastructure in the event of explosive or impact loading
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