Search results for: Learning materials

Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

Procedia PDF Downloads 59

Authors: А. V. Panko, Е. V. Ablets, I. G. Kovzun, М. А. Ilyashov

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Based upon generalized analysis of modern know-how in the sphere of processing, concentration and purification of iron-ore raw materials (IORM), in particular, the most widespread ferrioxide-silicate materials (FOSM), containing impurities of phosphorus and other elements compounds, noted special role of nano technological initiatives in improvement of such processes. Considered ideas of role of nano particles in processes of FOSM carbonization with subsequent direct reduction of ferric oxides contained in them to metal phase, as well as in processes of alkali treatment and separation of powered iron from phosphorus compounds. Using the obtained results the wasteless solid-phase processing, concentration and purification of IORM and FOSM from compounds of phosphorus, silicon and other impurities excelling known methods of direct iron reduction from iron ores and metallurgical slimes.

Keywords: iron ores, solid-phase reduction, nanoparticles in reduction and purification of iron from silicon and phosphorus, wasteless method of ores processing

Procedia PDF Downloads 467

Authors: Nora Gavira Duron, Cinthya Moreda Gonzalez-Ortega, Reyna Susana Garcia Ruiz

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This paper objective is to analyze the mathematics performance in the Learning Evaluation National Plan (PLANEA for its Spanish initials: Plan Nacional para la Evaluación de los Aprendizajes), applied to Mexican students who are enrolled in the last elementary-school year over the 2017-2018 academic year. Such test was conducted nationwide in 3,573 schools, using a sample of 108,083 students, whose average in mathematics, on a scale of 0 to 100, was 45.6 points. 75% of the sample analyzed did not reach the sufficiency level (60 points). It should be noted that only 2% got a 90 or higher score result. The performance is analyzed while considering whether there are differences in gender, marginalization level, public or private school enrollment, parents’ academic background, and living-with-parents situation. Likewise, this variable impact (among other variables) on school performance by gender is evaluated, considering multivariate logistic (Logit) regression analysis. The results show there are no significant differences in mathematics performance regarding gender in elementary school; nevertheless, the impact exerted by mothers who studied at least high school is of great relevance for students, particularly for girls. Other determining variables are students’ resilience, their parents’ economic status, and the fact they attend private schools, strengthened by the mother's education.

Keywords: multivariate regression analysis, academic performance, learning evaluation, mathematics result per gender

Procedia PDF Downloads 130

Authors: Shreeyam, Ranjan Kumar Sah, Shivangi

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Recognizing and solving handwritten mathematical expressions can be a challenging task, particularly when certain characters are segmented and classified. This project proposes a solution that uses Convolutional Neural Network (CNN) and image processing techniques to accurately solve various types of equations, including arithmetic, quadratic, and trigonometric equations, as well as logical operations like logical AND, OR, NOT, NAND, XOR, and NOR. The proposed solution also provides a graphical solution, allowing users to visualize equations and their solutions. In addition to equation solving, the platform, called CNNCalc, offers a comprehensive learning experience for students. It provides educational content, a quiz platform, and a coding platform for practicing programming skills in different languages like C, Python, and Java. This all-in-one solution makes the learning process engaging and enjoyable for students. The proposed methodology includes horizontal compact projection analysis and survey for segmentation and binarization, as well as connected component analysis and integrated connected component analysis for character classification. The compact projection algorithm compresses the horizontal projections to remove noise and obtain a clearer image, contributing to the accuracy of character segmentation. Experimental results demonstrate the effectiveness of the proposed solution in solving a wide range of mathematical equations. CNNCalc provides a powerful and user-friendly platform for solving equations, learning, and practicing programming skills. With its comprehensive features and accurate results, CNNCalc is poised to revolutionize the way students learn and solve mathematical equations. The platform utilizes a custom-designed Convolutional Neural Network (CNN) with image processing techniques to accurately recognize and classify symbols within handwritten equations. The compact projection algorithm effectively removes noise from horizontal projections, leading to clearer images and improved character segmentation. Experimental results demonstrate the accuracy and effectiveness of the proposed solution in solving a wide range of equations, including arithmetic, quadratic, trigonometric, and logical operations. CNNCalc features a user-friendly interface with a graphical representation of equations being solved, making it an interactive and engaging learning experience for users. The platform also includes tutorials, testing capabilities, and programming features in languages such as C, Python, and Java. Users can track their progress and work towards improving their skills. CNNCalc is poised to revolutionize the way students learn and solve mathematical equations with its comprehensive features and accurate results.

Keywords: AL, ML, hand written equation solver, maths, computer, CNNCalc, convolutional neural networks

Procedia PDF Downloads 100

Authors: Priya Varshney, Soumya S. Mohapatra

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Fabrication of anti-corrosion and self-cleaning superhydrophobic coatings for metallic surfaces which are regenerable and durable in the aggressive conditions has shown tremendous interest in materials science. In this work, the superhydrophobic coatings on metallic surfaces (aluminum, steel, copper) were prepared by two-step and one-step chemical etching process. In two-step process, roughness on surface was created by chemical etching and then passivation of roughened surface with low surface energy materials whereas, in one-step process, roughness on surface by chemical etching and passivation of surface with low surface energy materials were done in a single step. Beside this, the effect of etchant concentration and etching time on wettability and morphology was also studied. Thermal, mechanical, ultra-violet stability of these coatings were also tested. Along with this, regeneration of coatings and self-cleaning, corrosion resistance and water repelling characteristics were also studied. The surface morphology shows the presence of a rough microstuctures on the treated surfaces and the contact angle measurements confirms the superhydrophobic nature. It is experimentally observed that the surface roughness and contact angle increases with increase in etching time as well as with concentration of etchant. Superhydrophobic surfaces show the excellent self-cleaning behaviour. Coatings are found to be stable and maintain their superhydrophobicity in acidic and alkaline solutions. Water jet impact, floatation on water surface, and low temperature condensation tests prove the water-repellent nature of the coatings. These coatings are found to be thermal, mechanical and ultra-violet stable. These durable superhydrophobic metallic surfaces have potential industrial applications.

Keywords: superhydrophobic, water-repellent, anti-corrosion, self-cleaning

Procedia PDF Downloads 264

Authors: Adriana Stefan, Cristina-Elisabeta Pelin, George Pelin, Maria Daniela Stelescu, Elena Manaila

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Elastomeric composites have been known for a long time, but, to our knowledge, space and the aeronautic community has been directing a special attention to them only in the last decade. The required properties of advanced elastomeric materials used in space applications (such as O-rings) are sealing, abrasion, low-temperature flexibility, the long-term compression set properties, impact resistance and low-temperature thermal stability in different environments, such as ionized radiations. Basically, the elastomeric nanocomposites are composed of a rubber matrix and a wide and varied range of nanofillers, added with the aim of improving the physico-mechanical and elasticity modulus properties of the materials as well as their stability in different environments. The paper presents a partial synthesis of the research regarding the use of silicon carbide in nanometric form and/or organophylized montmorillonite as fillers in butyl rubber matrix. The need of composite materials arose from the fact that stand-alone polymers are ineffective in providing all the superior properties required by different applications. These drawbacks can be diminished or even eliminated by incorporating a new range of additives into the organic matrix, fillers that have important roles in modifying properties of various polymers. A composite material can provide superior and unique mechanical and physical properties because it combines the most desirable properties of its constituents while suppressing their least desirable properties. The commercial importance of polymers and the continuous increase of their use results in the continuous demand for improvement in their properties to meet the necessary conditions. To study the performance of the elastomeric nanocomposites were mechanically tested, it will be tested the qualities of tensile at low temperatures and RT and the behavior at the compression at cryogenic to room temperatures and under different environments. The morphology of specimens will be investigated by optical and scanning electronic microscopy.

Keywords: elastomeric nanocomposites, O-rings, space applications, mechanical properties

Procedia PDF Downloads 271

Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

Procedia PDF Downloads 47

Authors: Lily Ranjbar, Haori Yang

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Online education has been around for several decades, but the importance of online education became evident after the COVID-19 pandemic. Eventhough the online delivery approach works well for knowledge building through delivering content and oversight processes, it has limitations in developing hands-on laboratory skills, especially in the STEM field. During the pandemic, many education institutions faced numerous challenges in delivering lab-based courses, especially in the STEM field. Also, many students worldwide were unable to practice working with lab equipment due to social distancing or the significant cost of highly specialized equipment. The laboratory plays a crucial role in nuclear science and engineering education. It can engage students and improve their learning outcomes. In addition, online education and virtual labs have gained substantial popularity in engineering and science education. Therefore, developing virtual labs is vital for institutions to deliver high-class education to their students, including their online students. The School of Nuclear Science and Engineering (NSE) at Oregon State University, in partnership with SpectralLabs company, has developed an Advanced Virtual Radiation Detection and Measurement Lab (AVR-DML) to offer a fully online Master of Health Physics program. It was essential for us to use a system that could simulate nuclear modules that accurately replicate the underlying physics, the nature of radiation and radiation transport, and the mechanics of the instrumentations used in the real radiation detection lab. It was all accomplished using a Realistic, Adaptive, Interactive Learning System (RAILS). RAILS is a comprehensive software simulation-based learning system for use in training. It is comprised of a web-based learning management system that is located on a central server, as well as a 3D-simulation package that is downloaded locally to user machines. Users will find that the graphics, animations, and sounds in RAILS create a realistic, immersive environment to practice detecting different radiation sources. These features allow students to coexist, interact and engage with a real STEM lab in all its dimensions. It enables them to feel like they are in a real lab environment and to see the same system they would in a lab. Unique interactive interfaces were designed and developed by integrating all the tools and equipment needed to run each lab. These interfaces provide students full functionality for data collection, changing the experimental setup, and live data collection with real-time updates for each experiment. Students can manually do all experimental setups and parameter changes in this lab. Experimental results can then be tracked and analyzed in an oscilloscope, a multi-channel analyzer, or a single-channel analyzer (SCA). The advanced virtual radiation detection and measurement laboratory developed in this study enabled the NSE school to offer a fully online MHP program. This flexibility of course modality helped us to attract more non-traditional students, including international students. It is a valuable educational tool as students can walk around the virtual lab, make mistakes, and learn from them. They have an unlimited amount of time to repeat and engage in experiments. This lab will also help us speed up training in nuclear science and engineering.

Keywords: advanced radiation detection and measurement, virtual laboratory, realistic adaptive interactive learning system (rails), online education in stem fields, student engagement, stem online education, stem laboratory, online engineering education

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

Procedia PDF Downloads 178

Authors: Maali-Amel Mersel, Lajos Fodor, Otto Horvath

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Photocatalytic H₂ production by H₂S decomposition is regarded to be an environmentally friendly process to produce carbon-free energy through direct solar energy conversion. For this purpose, sulphide-based materials, as photocatalysts, were widely used due to their excellent solar spectrum responses and high photocatalytic activity. The loading of proper co-catalysts that are based on cheap and earth-abundant materials on those semiconductors was shown to play an important role in the improvement of their efficiency. In this research, CdS-ZnS composite was studied because of its controllable band gap and excellent performance for H₂ evolution under visible light irradiation. The effects of the modification of this photocatalyst with different types of materials and the influence of the preparation parameters on its H₂ production activity were investigated. The CdS-ZnS composite with an enhanced photocatalytic activity for H₂ production was synthesized from ammine complexes. Two types of modification were used: compounds of Ni-group metals (NiS, PdS, and Pt) were applied as co-catalyst on the surface of CdS-ZnS semiconductor, while NiS, MnS, CoS, Ag₂S, and CuS were used as a dopant in the bulk of the catalyst. It was found that 0.1% of noble metals didn’t remarkably influence the photocatalytic activity, while the modification with 0.5% of NiS was shown to be more efficient in the bulk than on the surface. The modification with other types of metals results in a decrease of the rate of H₂ production, while the co-doping seems to be more promising. The preparation parameters (such as the amount of ammonia to form the ammine complexes, the order of the preparation steps together with the hydrothermal treatment) were also found to highly influence the rate of H₂ production. SEM, EDS and DRS analyses were made to reveal the structure of the most efficient photocatalysts. Moreover, the detection of the conduction band electron on the surface of the catalyst was also investigated. The excellent photoactivity of the CdS-ZnS catalysts with and without modification encourages further investigations to enhance the hydrogen generation by optimization of the reaction conditions.

Keywords: H₂S, photoactivity, photocatalytic H₂ production, CdS-ZnS

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Authors: X. Lu, T. Lu, S. Javadi

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A simple dynamic approach is presented for analyzing thermal and moisture dynamics of buildings, which is of particular relevance to understanding climate change impacts on buildings, including assessment of risks and applications of resilience strategies. With the goal to demonstrate the proposed modeling methodology, to verify the model, and to show that wooden materials provide a mechanism that can facilitate the reduction of moisture risks and be more resilient to global warming, a wooden church equipped with high precision measurement systems was taken as a test building for full-scale time-series measurements. Sensitivity analyses indicate a high degree of accuracy in the model prediction regarding the indoor environment. The model is then applied to a future projection of climate indoors aiming to identify significant environmental factors, the changing temperature and humidity, and effective response to the climate change impacts. The paper suggests that wooden building materials offer an effective and resilient response to anticipated future climate changes.

Keywords: dynamic model, forecast, climate change impact, wooden structure, buildings

Procedia PDF Downloads 134

Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

Procedia PDF Downloads 271

Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Tarmi A'Vard

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This paper discusses the transformative learning experience for students participating in the Inside-Out Prison Exchange Program (Inside-out) and explores the value this pedagogical approach may have in youth justice centers. Inside-Out is a semester-long university course which is unique as it takes 15 university students, with their textbook and theory-based knowledge, behind the walls to study alongside 15 incarcerated students, who have the lived experience of the criminal justice system. Inside-out is currently offered in three Victorian prisons, expanding to five in 2020. The Inside-out pedagogy which is based on transformative dialogic learning is reliant upon the participants sharing knowledge and experiences to develop an understanding and appreciation of the diversity and uniqueness of one another. Inside-out offers the class an opportunity to create its own guidelines for dialogue, which can lead to the student’s sense of equality, which is fundamental in the success of this program. Dialogue allows active participation by all parties in reconciling differences, collaborating ideas, critiquing and developing hypotheses and public policies, and encouraging self-reflection and exploration. The structure of the program incorporates the implementation of circular seating (where the students alternate between inside and outside), activities, individual reflective tasks, group work, and theory analysis. In this circle everyone is equal, this includes the educator, who serves as a facilitator more so than the traditional teacher role. A significant function of the circle is to develop a group consciousness, allowing the whole class to see itself as a collective, and no one person holds a superior role. This also encourages participants to be responsible and accountable for their behavior and contributions. Research indicates completing academic courses, like Inside-Out, contributes positively to reducing recidivism. Inside-Out’s benefits and success in many adult correctional institutions have been outlined in evaluation reports and scholarly articles. The key findings incorporate the learning experiences for the students in both an academic capability and professional practice and development. Furthermore, stereotypes and pre-determined ideas are challenged, and there is a promotion of critical thinking and evidence of self-discovery and growth. There is empirical data supporting positive outcomes of education in youth justice centers in reducing recidivism and increasing the likelihood of returning to education upon release. Hence, this research could provide the opportunity to increase young people’s engagement in education which is a known protective factor for assisting young people to move away from criminal behavior. In 2016, Tarmi completed the Inside-Out educator training in Philadelphia, Pennsylvania, and has developed an interest in exploring the pedagogy of Inside-Out, specifically targeting young offenders in a Youth Justice Centre.

Keywords: dialogic transformative learning, inside-out prison exchange program, prison education, youth justice

Procedia PDF Downloads 113

Authors: Reginald Umunakwe, Obinna Chibuzor Okoye, Uzoma Samuel Nwigwe, Damilare John Olaleye, Akinlabi Oyetunji

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The potential for the development of PPS-AlMMCs as light weight material for industrial applications was investigated. Periwinkle shells were milled and the density of the particles determined. Particulate periwinkle shell of particle size 75µm was used to reinforce aluminium 6063 alloy at 10wt% filler loading using two-step stir casting technique. The composite materials were stirred for five minutes in a semi-solid state and the stirring time varied as 3, 6 and 9 minutes at above the liquidus temperature. A specimen was also produced with pre-heated filler. The effect of variation in stirring time and reinforcement pre-heating on the porosity of the composite materials was investigated. The results of the analysis show that a composition of reinforcement pre-heating and stirring for 3 minutes produced a composite material with the lowest porosity of 1.05%.

Keywords: composites, periwinkle shell, two-step casting, porosity

Procedia PDF Downloads 336

Authors: Mihaela Beregoi, Horia Iovu, Cristina Busuioc, Alexandru Evanghelidis, Elena Matei, Monica Enculescu, Ionut Enculescu

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Nanomaterials field is very attractive for all researchers who are attempting to develop new devices with the same or improved properties than the micro-sized ones, while reducing the reagents and power consumptions. In this way, a wide range of nanomaterials were fabricated and integrated in applications for electronics, optoelectronics, solar cells, tissue reconstruction and drug delivery. Obviously, the most appealing ones are those dedicated to the medical domain. Different types of nano-sized materials, such as particles, fibers, films etc., can be synthesized by using physical, chemical or electrochemical methods. One of these techniques is electrospinning, which enable the production of fibers with nanometric dimensions by pumping a polymeric solution in a high electric field; due to the electrostatic charging and solvent evaporation, the precursor mixture is converted into nonwoven meshes with different fiber densities and mechanical properties. Moreover, polyaniline is a conducting polymer with interesting optical properties, suitable for displays and electrochromic windows. Otherwise, polyaniline is an electroactive polymer that can contract/expand by applying electric stimuli, due to the oxidation/reduction reactions which take place in the polymer chains. These two main properties can be exploited in order to synthesize smart materials that change their dimensions, exhibiting in the same time good electrochromic properties. In the context aforesaid, a poly(methyl metacrylate) solution was spun to get webs composed of fibers with diameter values between 500 nm and 1 µm. Further, the polymer meshes were covered with a gold layer in order to make them conductive and also appropriate as working electrode in an electrochemical cell. The gold shell was deposited by DC sputtering. Such metalized fibers can be transformed into smart materials by covering them with a thin layer of conductive polymer. Thus, the webs were coated with a polyaniline film by the electrochemical route, starting from and aqueous solution of aniline and sulfuric acid, where sulfuric acid acts as oxidant agent. For the polymerization of aniline, a saturated calomel electrode was employed as reference, a platinum plate as counter electrode and the gold covered webs as working electrode. Chronoamperometry was selected as deposition method for polyaniline, by modifying the deposition time. Metalized meshes with different fiber densities were used, the transmission ranging between 70 and 80 %. The morphological investigation showed that polyaniline layer has a granular structure for all deposition experiments. As well, some preliminary optical tests were done by using sulfuric acid as electrolyte, which revealed the modification of polyaniline colour from green to dark blue when applying a voltage. In conclusion, new multilayered materials were obtained by a simple approach: the merge of the electrospinning method benefits with polyaniline chemistry. This synthesis method allows the fabrication of structures with reproducible characteristics, suitable for display or tissue substituents.

Keywords: electrospinning, fibers, smart materials, polyaniline

Procedia PDF Downloads 278

Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

Procedia PDF Downloads 351

Authors: Aisha Iddrisu

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The proliferation of cyberviolence in the wave of increased social media consumption calls for immediate attention both at the local and global levels. With over 4.70 billion social media users worldwide and 8.8 social media users in Ghana, various forms of violence have become the order of the day in most countries and communities. Cyber violence is defined as producing, retrieving, and sharing of hurtful or dangerous online content to cause emotional, psychological, or physical harm. The urgency and severity of cyber violence have led to the enactment of laws in various countries though lots still need to be done, especially in Ghana. In Ghana, studies on cyber violence have not been extensively dealt with. Existing studies concentrate only on one form or the other form of cyber violence, thus cybercrime and cyber bullying. Also, most studies in Africa have not explored cyber violence forms using empirical theories and the few that existed were qualitatively researched, whereas others examine the effect of cyber violence rather than examining why those who involve in it behave the way they behave. It is against this backdrop that this study aims to examine various cyber violence behaviour among social media users in Ghana by applying the theory of Self-control and Social control theory. This study is important for the following reasons. The outcome of this research will help at both national and international level of policymaking by adding to the knowledge of understanding cyberviolence and why people engage in various forms of cyberviolence. It will also help expose other ways by which such behaviours are enforced thereby serving as a guide in the enactment of the rightful rules and laws to curb such behaviours. It will add to literature on consequences of new media. This study seeks to confirm or reject to the following research hypotheses. H1 Social media usage has direct significant effect of cyberviolence behaviours. H2 Ineffective parental management has direct significant positive relation to Low self-control. H3 Low self-control has direct significant positive effect on cyber violence behaviours among social, H4 Differential association has significant positive effect on cyberviolence behaviour among social media users in Ghana. H5 Definitions have a significant positive effect on cyberviolence behaviour among social media users in Ghana. H6 Imitation has a significant positive effect on cyberviolence behaviour among social media users in Ghana. H7 Differential reinforcement has a significant positive effect on cyberviolence behaviour among social media users in Ghana. H8 Differential association has a significant positive effect on definitions. H9 Differential association has a significant positive effect on imitation. H10 Differential association has a significant positive effect on differential reinforcement. H11 Differential association has significant indirect positive effects on cyberviolence through the learning process.

Keywords: cyberviolence, social media users, self-control theory, social learning theory

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Authors: Jeff Moussodji, Dominique Drouin

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The highly complex technology requirements of today’s integrated circuits (ICs), lead to the increased use of several materials types such as metal structures, brittle and porous low-k materials which are used in both front end of line (FEOL) and back end of line (BEOL) process for wafer manufacturing. In order to singulate chip from wafer, a critical laser-grooving process, prior to blade dicing, is used to remove these layers of materials out of the dicing street. The combination of laser-grooving and blade dicing allows to reduce the potential risk of induced mechanical defects such micro-cracks, chipping, on the wafer top surface where circuitry is located. It seems, therefore, essential to have a fundamental understanding of the physics involving laser-dicing in order to maximize control of these critical process and reduce their undesirable effects on process efficiency, quality, and reliability. In this paper, the study was based on the convergence of two approaches, numerical and experimental studies which allowed us to investigate the interaction of a nanosecond pulsed laser and BEOL wafer materials. To evaluate this interaction, several laser grooved samples were compared with finite element modeling, in which three different aspects; phase change, thermo-mechanical and optic sensitive parameters were considered. The mathematical model makes it possible to highlight a groove profile (depth, width, etc.) of a single pulse or multi-pulses on BEOL wafer material. Moreover, the heat affected zone, and thermo-mechanical stress can be also predicted as a function of laser operating parameters (power, frequency, spot size, defocus, speed, etc.). After modeling validation and calibration, a satisfying correlation between experiment and modeling, results have been observed in terms of groove depth, width and heat affected zone. The study proposed in this work is a first step toward implementing a quick assessment tool for design and debug of multiple laser grooving conditions with limited experiments on hardware in industrial application. More correlations and validation tests are in progress and will be included in the full paper.

Keywords: laser-dicing, nano-second pulsed laser, wafer multi-stack, multiphysics modeling

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Authors: Hidayati, Eni Rahmi, Deli Mona, Cytha Nilam Chairani, Aulina Refri Rahmi

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Background: Restoration materials could undergo changes in their clinical properties such as changes in roughness of the restoration's surface. An increase of surface roughness accelerates bacterial colonization and plaque maturation. It can be prevented by mechanically clean the tooth surface by brushing the teeth using toothpaste. Toothpaste may contain abrasives materials that usually found in whitening toothpaste. Those abrasive materials could increase the roughness of the restoration`s surface. Glass ionomer cement (GIC) is one of the restorative material widely used to this day. GC America has made an innovation called EQUIA to improve their wear resistance by coating the surface of GIC using G-Coat Plus. Objective: To determine the effect of teeth was brushing with whitening and non-whitening toothpaste to the surface roughness of coated and uncoated restoration (GIC). Methods: This research was a laboratory experimental with pretest-posttest group design. There were 28 samples which were divided into 2 groups. The first group was brushed with whitening toothpaste and the second group was brushed with non-whitening toothpaste. Each group was divided into group which coated by G-Coat Plus and group which left uncoated. The value of surface roughness was measured by using Roughness Tester. The data was analyzed by using independent t-test to determine differences between the surface roughness of coated samples and uncoated samples brushed with whitening and non-whitening toothpaste. Result: It was found that average roughness differences before and after being brushed by whitening toothpaste were smaller in coated samples than in uncoated samples (0.07 ± 0.09 < 0.12 ± 0.02). Similar results were also found in samples brushed by non-whitening toothpaste (0.02 ± 0.01 0.03 ± 0.01). The differences of average roughness in samples brushed with non-whitening toothpaste were smaller than samples brushed with whitening toothpaste. Conclusion: The data showed there were statistically significant differences between the surface roughness of coated samples and uncoated samples brushed with non-whitening toothpaste (p < 0.05) but the was no statistically significant to samples brushed with whitening toothpaste (p > 0.05).

Keywords: surface roughness, toothpaste, EQUIA, coating

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Authors: Jaana Sepp, Ulvi Kõrgemaa, Kristi Puusepp, Õie Tähtla

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COVID-19 was officially recognized as a pandemic in late 2019 by the WHO, and it has led to changes in the education sector. Educational institutions were closed, and most schools adopted distance learning. Estonia is known as a digitally well-developed country. Based on that, in the pandemic time, nursing education continued, and new technological solutions were implemented. To provide nursing education, special focus was paid on quality and flexibility. The aim of this paper is to present administrative, digital, and technological solutions which support Estonian nursing educators to continue the study process in the pandemic time and to develop a sustainable solution for nursing education for the future. This paper includes the authors’ analysis of the documents and decisions implemented in the institutions through the pandemic time. It is a case study of Estonian nursing educators. Results of the analysis show that the implementation of distance learning principles challenges the development of innovative strategies and technics for the assessment of student performance and educational outcomes and implement new strategies to encourage student engagement in the virtual classroom. Additionally, hospital internships were canceled, and the simulation approach was deeply implemented as a new opportunity to develop and assess students’ practical skills. There are many other technical and administrative changes that have also been carried out, such as students’ support and assessment systems, the designing and conducting of hybrid and blended studies, etc. All services were redesigned and made more available, individual, and flexible. Hence, the feedback system was changed, the information was collected in parallel with educational activities. Experiences of nursing education during the pandemic time are widely presented in scientific literature. However, to conclude our study, authors have found evidence that solutions implemented in Estonian nursing education allowed the students to graduate within the nominal study period without any decline in education quality. Operative information system and flexibility provided the minimum distance between the students, support, and academic staff, and likewise, the changes were implemented quickly and efficiently. Institution memberships were updated with the appropriate information, and it positively affected their satisfaction, motivation, and commitment. We recommend that the feedback process and the system should be permanently changed in the future to place all members in the same information area, redefine the hospital internship process, implement hybrid learning, as well as to improve the communication system between stakeholders inside and outside the organization. The main limitation of this study relates to the size of Estonia. Nursing education is provided by two institutions only, and similarly, the number of students is low. The result could be generated to the institutions with a similar size and administrative system. In the future, the relationship between nurses’ performance and organizational outcomes should be deeply investigated and influences of the pandemic time education analyzed at workplaces.

Keywords: hybrid learning, nursing education, nursing, COVID-19

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Authors: Alireza Pouladkhan, Mohammad Yavari Foroushani, Ali Mortazavi

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This paper presents a finite element model for a sandwich plate containing a piezoelectric core. A sandwich plate with a piezoelectric core is constructed using the shear mode of piezoelectric materials. The orientation of poling vector has a significant effect on deflection and stress induced in the piezo-actuated adaptive sandwich plate. In the present study, the influence of this factor for a clamped-clamped-free-free and simple-simple-free-free square sandwich plate is investigated using Finite Element Method. The study uses ABAQUS (v.6.7) software to derive the finite element model of the sandwich plate. By using this model, the study gives the influences of the poling vector angle on the response of the smart structure and determines the maximum transverse displacement and maximum stress induced.

Keywords: finite element method, sandwich plate, poling vector, piezoelectric materials, smart structure, electric enthalpy

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Authors: Jawad Ul Haq, Evan Mazur, Ahmed Qureshi, Mohamed Al-Hussein

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The uses of wood in furniture, building, bridges and aluminum in transportation and construction, make aluminum and forest economy a prominent matter in North America. Machines available to date to cut the aforementioned materials are mostly industry oriented with complex structure and operations which require special training and skill. Furthermore, requirements such as pneumatics, 3-phase supply are associated with cost, maintenance, and safety hazards. Power saws are very useful tools used to cut and shape materials; however, they can cause serious hand injuries. Operator’s hands in table saw are vulnerable as they are used to guide pieces into the saw. Apart from hands, saw operator is also prone to material being kicked back out of the saw or sustain eye or respiratory injuries due to rapidly flying sawdust and other debris. In this paper, design of an automatic saw cutting machine has been proposed to ensure safety, portability, usage at domestic level and capability to cut both aluminum and wood. This paper demonstrates detailed Mechanical design in SOLIDWORKS and Control Systems using Programmable Logic Controller (PLC), based on the aforementioned design objectives.

Keywords: programmable logic controller, saw cutting, control, automation

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Authors: Andres Bautista, Alicia Porras, Juan P. Casas, Maribel Silva

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The Colombian aeronautical industry has stimulated research in the mechanical behavior of materials under different loading conditions aircrafts are generally exposed during its operation. The Calima T-90 is the first military aircraft built in the country, used for primary flight training of Colombian Air Force Pilots, therefore, it may be exposed to adverse operating situations such as hard landings which cause impact loads on the aircraft that might produce the impact fatigue phenomenon. The Calima T-90 structure is mainly manufactured by composites materials generating assemblies and subassemblies of different components of it. The main method of bonding these components is by using adhesive joints. Each type of adhesive bond must be studied on its own since its performance depends on the conditions of the manufacturing process and operating characteristics. This study aims to characterize the typical adhesive joints of the aircraft under usual loads. To this purpose, the evaluation of the effect of adhesive thickness on the mechanical performance of the joint under quasi-static loading conditions, constant amplitude fatigue and cyclic impact fatigue using single lap-joint specimens will be performed. Additionally, using a double cantilever beam specimen, the influence of the thickness of the adhesive on the crack growth rate for mode I delamination failure, as a function of the critical energy release rate will be determined. Finally, an analysis of the fracture surface of the test specimens considering the mechanical interaction between the substrate (composite) and the adhesive, provide insights into the magnitude of the damage, the type of failure mechanism that occurs and its correlation with the way crack propagates under the proposed loading conditions.

Keywords: adhesive, composites, crack propagation, fatigue

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Authors: Chin-Yu Lin, Kun-Chi Wang, Shih-Hsu Wang, Wei-Chih Wang

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A construction superintendent needs to know not only the amount of quantities of cost items or materials completed to develop a daily report or calculate the daily progress (earned value) in each day, but also the amount of quantities of materials (e.g., reinforced steel and concrete) to be ordered (or moved into the jobsite) for performing the in-progress or ready-to-start construction activities (e.g., erection of reinforced steel and concrete pouring). These daily construction management tasks require great effort in extracting accurate quantities in a short time (usually must be completed right before getting off work every day). As a result, most superintendents can only provide these quantity data based on either what they see on the site (high inaccuracy) or the extraction of quantities from two-dimension (2D) construction drawings (high time consumption). Hence, the current practice of providing the amount of quantity data completed in each day needs improvement in terms of more accuracy and efficiency. Recently, a three-dimension (3D)-based building information model (BIM) technique has been widely applied to support construction quantity takeoffs (QTO) process. The capability of virtual reality (VR) allows to view a building from the first person's viewpoint. Thus, this study proposes an innovative system by integrating VR (using 'Unity') and BIM (using 'Revit') to extract quantities to support the above daily construction management tasks. The use of VR allows a system user to be present in a virtual building to more objectively assess the construction progress in the office. This VR- and BIM-based system is also facilitated by an integrated database (consisting of the information and data associated with the BIM model, QTO, and costs). In each day, a superintendent can work through a BIM-based virtual building to quickly identify (via a developed VR shooting function) the building components (or objects) that are in-progress or finished in the jobsite. And he then specifies a percentage (e.g., 20%, 50% or 100%) of completion of each identified building object based on his observation on the jobsite. Next, the system will generate the completed quantities that day by multiplying the specified percentage by the full quantities of the cost items (or materials) associated with the identified object. A building construction project located in northern Taiwan is used as a case study to test the benefits (i.e., accuracy and efficiency) of the proposed system in quantity extraction for supporting the development of daily reports and the orders of construction materials.

Keywords: building information model, construction management, quantity takeoffs, virtual reality

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Mahmood Dehqan, Peyman Peyvasteh

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In recent years there has been a growing interest in issues concerning the impact of sociocultural theory (SCT) of learning on different aspects of second/foreign language learning. This study aimed to find the possible effects of sociocultural teaching techniques on reading strategy of EFL learners. Indeed, the present research compared the impact of peer and teacher scaffolding on EFL learners’ reading strategy use across two proficiency levels. To this end, a pre-test post-test quasi-experimental research design was used and two instruments were utilized to collect the data: Nelson English language test and reading strategy questionnaire. Ninety five university students participated in this study were divided into two groups of teacher and peer scaffolding. Teacher scaffolding group received scaffolded help from the teacher based on three mechanisms of effective help within ZPD: graduated, contingent, dialogic. In contrast, learners of peer scaffolding group were unleashed from the teacher-fronted classroom as they were asked to carry out the reading comprehension tasks with the feedback they provided for each other. Results obtained from ANOVA revealed that teacher scaffolding group outperformed the peer scaffolding group in terms of reading strategy use. It means teacher’s scaffolded help provided within the learners’ ZPD led to better reading strategy improvement compared with the peer scaffolded help. However, the interaction effect between proficiency factor and teaching technique was non-significant, leading to the conclusion that strategy use of the learners was not affected by their proficiency level in either teacher or peer scaffolding groups.

Keywords: peer scaffolding, proficiency level, reading strategy, sociocultural theory, teacher scaffolding

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Authors: Kourosh Nazarian

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Many of the engineering materials, such as behavioral metals, have at least a certain level of linear behavior. It means that if the stresses are doubled, the deformations would be also doubled. In fact, these materials have linear elastic properties. Soils do not follow this law, for example, when compressed, soils become gradually tighter. On the surface of the ground, the sand can be easily deformed with a finger, but in high compressive stresses, they gain considerable hardness and strength. This is mainly due to the increase in the forces among the separate particles. Creeps also deform the soils under a constant load over time. Clay and peat soils have creep behavior. As a result of this phenomenon, structures constructed on such soils will continue their collapse over time. In this paper, the researchers analyzed and modeled the stresses and creeps in the southern plains of Kerman province in Iran through library-documentary, quantitative and software techniques, and field survey. The results of the modeling showed that these plains experienced severe stresses and had a collapse of about 26 cm in the last 15 years and also creep evidence was discovered in an area with a gradient of 3-6 degrees.

Keywords: Stress, creep, faryab, surface runoff

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Authors: Shimaa Nagro, Russell Campion

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Mobile devices are becoming ever more widely available, with growing functionality, and they are increasingly used as enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material's user interfaces for mobile devices is beset by many unresolved research problems such as those arising from constraints associated with mobile devices or from issues linked to effective learning. The proposed research aims to produce: (i) a method framework for the design and evaluation of educational material’s interfaces to be delivered on mobile devices, in multimedia form based on Human Computer Interaction strategies; and (ii) a software tool implemented as a fast-track alternative to use the method framework in full. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the method framework. The method framework is a framework to enable an educational designer to effectively and efficiently create educational multimedia interfaces to be used on mobile devices by following a particular methodology that contains practical and usable tools and techniques. It is a method framework that accepts any educational material in its final lesson plan and deals with this plan as a static element, it will not suggest any changes in any information given in the lesson plan but it will help the instructor to design his final lesson plan in a multimedia format to be presented in mobile devices.

Keywords: mobile learning, M-Learn, HCI, educational multimedia, interface design

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Authors: Behailu Bezabih Ayele

Abstract:

Illustrative expressions in art education and in overall learning are being given increasing attention in the transmission of knowledge. The objective of this paper, therefore, is to present an analysis of graffiti on school desks-a way of smuggling knowledge on the edge of classroom education and learning. The methodological approach focuses on the systematic collection and selection of desk graffiti. Four schools are chosen to reflect socioeconomic status and gender composition. The analysis focused on the categorization of graffiti by genre. This was followed by an analysis of the style, intensity as well as content of the messages in terms of overall social impacts. The paper grounds the analysis by reviewing the literature on modern education and art education in the Ethiopian context, as well as the place of desk graffiti. The findings generally show that the school desks and the school environment, by and large, have managed to serve as vessels through which formal and informal knowledge is acquired, transmitted, engrained into the students and transformed into messages by the students. The desks have also apparently served as a springboard to maximize the interfaces between several ideas and disciplines and communications. However, the very fact that the desks serve as massive channels of expression and knowledge transmission also points to a lack of breadth availability of channels of expression, perhaps confounding the ability of classrooms as means of outlet of expression and documentation for the students. This points to the need for efforts in education policy and funding of artistic endeavors for young students.

Keywords: artistic expression, desk graffiti, education, school children, Ethiopia

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