Search results for: dynamic learning

Authors: G. Krishna Mohana Rao, P. Ravi Kumar

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Milling is the process of removing unwanted material with suitable tool. Even though the milling process is having wider application, the vibration of machine tool and work piece during the process produces chatter on the products. Various methods of preventing the chatter have been incorporated into machine tool systems. Damper is cut into equal number of parts. Each part is called as finger. Multiple fingers were inserted in the hollow portion of the shank to reduce tool vibrations. In the present work, nonlinear static and dynamic analysis of the damper inserted end milling cutter used to reduce the chatter was done. A comparison is made for the milling cutter with multiple dampers. Surface roughness was determined by machining with multiple finger inserted milling cutters.

Keywords: damping inserts, end milling, vibrations, nonlinear dynamic analysis, number of fingers

Procedia PDF Downloads 525

Authors: Eunice H. Li

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This e-Poster presents how the ‘digital backpack’ was introduced to primary school children in Ivory Coast. The idea of a ‘digital backpack’ was initiated by Mr. Thierry N’Doufou in 2012, who later designed and presented to the rest of the world in September 2014. The motivation behind the backpack was to relieve children of the heavy-weight they carry in their school backpacks. Another motivation was to promote Ivory Coast as a country where all children are brought into the digital era. Thierry N’Doufou regards education as the means by which his nation and the entire African Continent can be developed as a prosperous territory. The ‘digital backpack’ contains the entire curriculum for each class and favours a constructivist approach to learning. The children’s notes and exercises are also included in the pack. Additionally, teachers and parents are able to monitor remotely children’s activities while they are working with the ‘backpack’. Teachers are also able to issue homework, assess student’s progress and manage the student’s coursework. This means that teachers should always think the most appropriate pedagogies that can be used to help children to learn. Furthermore, teachers, parents and fellow students are able to have conversations and discussions by using web portals. It is also possible to access more apps if children would like to have additional learning activities. From the presentation in the e-Poster, it seems reasonable to conclude that the ‘digital backpack’ has potential to reach other-level of education. In this way, all will be able to benefit from this new invention.

Keywords: pedagogy, curriculum, constructivism, social constructivism, distance learning environment, ubiquitous learning environment

Procedia PDF Downloads 659

Authors: Shatha Ahmad Alharthi

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Prior research suggests that reduced social interaction can negatively affect well-being and impair mental health (e.g., depression and anxiety), resulting in lower academic performance. The COVID-19 pandemic has significantly limited social interaction among Saudi Arabian school children since the government closed schools and implemented lockdown restrictions to reduce the spread of the disease. These restrictions have resulted in prolonged remote learning for middle school students with unknown consequences for perceived academic performance, mental health, and well-being. This research project explores how middle school Saudi students’ current remote learning practices affect their mental health (e.g., depression and anxiety) and well-being during the lockdown. Furthermore, the study will examine the association between social interaction, mental health, and well-being pertaining to students’ perceptions of their academic achievement. Research findings could lead to a better understanding of the role of lockdown on depression, anxiety, well-being and perceived academic performance. Research findings may also inform policy-makers or practitioners (e.g., teachers and school leaders) about the importance of facilitating increased social interactions in remote learning situations and help to identify important factors to consider when seeking to re-integrate students into a face-to-face classroom setting. Potential implications for future educational research include exploring remote learning interventions targeted at bolstering students’ mental health and academic achievement during periods of remote learning.

Keywords: depression, anxiety, academic performance, social interaction

Procedia PDF Downloads 118

Authors: Yacoub Aljaffery

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As much as we think negatively about social media such as TV and smart phones, there are many positive benefits our society can get from it. Cartoons, for example, are made specifically for children. However, in this paper, we will prove how cartoon videos can have a positive impact on adults, especially college students. Since cartoons are meant to be a good learning tool for children, as well as adults, we will show our audience how they can use cartoon in teaching critical thinking and other language skills.

Keywords: social media, TV, teaching, learning, cartoon movies

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Rupam Saikia, Ashim Kanti Dey

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Sonapur is an intense hilly region on the border of Assam and Meghalaya lying in North-East India and is very near to a seismic fault named as Dauki besides which makes the region seismically active. Besides, these recently two earthquakes of magnitude 6.7 and 6.9 have struck North-East India in January and April 2016. Also, the slope concerned for this study is adjacent to NH 44 which for a long time has been a sole important connecting link to the states of Manipur and Mizoram along with some parts of Assam and so has been a cause of considerable loss to life and property since past decades as there has been several recorded incidents of landslide, road-blocks, etc. mostly during the rainy season which comes into news. Based on this issue this paper reports a static and dynamic slope stability analysis of Sonapur which has been carried out in MIDAS GTS NX. The slope being highly unreachable due to terrain and thick vegetation in-situ test was not feasible considering the current scope available so disturbed soil sample was collected from the site for the determination of strength parameters. The strength parameters were so determined for varying relative density with further variation in water content. The slopes were analyzed considering plane strain condition for three slope heights of 5 m, 10 m and 20 m which were then further categorized based on slope angles 30, 40, 50, 60, and 70 considering the possible extent of steepness. Initially static analysis under dry state was performed then considering the worst case that can develop during rainy season the slopes were analyzed for fully saturated condition along with partial degree of saturation with an increase in the waterfront. Furthermore, dynamic analysis was performed considering the El-Centro Earthquake which had a magnitude of 6.7 and peak ground acceleration of 0.3569g at 2.14 sec for the slope which were found to be safe during static analysis under both dry and fully saturated condition. Some of the conclusions were slopes with inclination above 40 onwards were found to be highly vulnerable for slopes of height 10 m and above even under dry static condition. Maximum horizontal displacement showed an exponential increase with an increase in inclination from 30 to 70. The vulnerability of the slopes was seen to be further increased during rainy season as even slopes of minimal steepness of 30 for height 20 m was seen to be on the verge of failure. Also, during dynamic analysis slopes safe during static analysis were found to be highly vulnerable. Lastly, as a part of the study a comparative study on Strength Reduction Method (SRM) versus Limit Equilibrium Method (LEM) was also carried out and some of the advantages and disadvantages were figured out.

Keywords: dynamic analysis, factor of safety, slope stability, strength reduction method

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Authors: Alvyra Galkienė, Ona Monkevičienė

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The full participation of all pupils in the overall educational process is defined by the concept of inclusive education, which is gradually evolving in education policy and practice. It includes the full participation of all pupils in a shared learning experience and educational practices that address barriers to learning. Inclusive education applying the principles of Universal Design for Learning (UDL), which includes promoting students' involvement in learning processes, guaranteeing a deep understanding of the analysed phenomena, initiating self-directed learning, and using e-tools to create a barrier-free environment, is a prerequisite for the personal success of each pupil. However, the sustainability of quality education is affected by the transformation of education systems. This was particularly evident during the period of the forced transition from contact to distance education in the COVID-19 pandemic. Research Problem: The transformation of the educational environment from real to virtual one and the loss of traditional forms of educational support highlighted the need for new research, revealing the individual profiles of teachers using UDL-based learning and the pathways of sustainable transfer of successful practices to non-conventional learning environments. Research Methods: In order to identify individual latent teacher profiles that encompass the essential components of UDL-based inclusive teaching and direct leadership of students' learning, the quantitative analysis software Mplius was used for latent profile analysis (LPA). In order to reveal proven, i.e., sustainable, pathways for the transit of the components of UDL-based inclusive learning to distance learning, latent profile transit analysis (LPTA) via Mplius was used. An online self-reported questionnaire was used for data collection. It consisted of blocks of questions designed to reveal the experiences of subject teachers in contact and distance learning settings. 1432 Lithuanian, Latvian, and Estonian subject teachers took part in the survey. Research Results: The LPA analysis revealed eight latent teacher profiles with different characteristics of UDL-based inclusive education or traditional teaching in contact teaching conditions. Only 4.1% of the subject teachers had a profile characterised by a sustained UDL approach to teaching: promoting pupils' self-directed learning; empowering pupils' engagement, understanding, independent action, and expression; promoting pupils' e-inclusion; and reducing the teacher's direct supervision of the students. Other teacher profiles were characterised by limited UDL-based inclusive education either due to the lack of one or more of its components or to the predominance of direct teacher guidance. The LPTA analysis allowed us to highlight the following transit paths of teacher profiles in the extreme conditions of the transition from contact to distance education: teachers staying in the same profile of UDL-based inclusive education (sustainable transit) or jumping to other profiles (unsustainable transit in case of barriers), and teachers from other profiles moving to this profile (ongoing transit taking advantage of the changed new possibilities in the teaching process).

Keywords: distance education, latent teacher profiles, sustainable transit, UDL

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Authors: Thomas Turner

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Due to the Covid-19 pandemic alternative ways of delivering dental education were required. As a result, many institutions moved teaching online. The impact of this is poorly understood. Is online problem-based learning (PBL) and case-based learning (CBL) effective and is it suitable in the post-pandemic era? PBL and CBL are both types of interactive, group-based learning which are growing in popularity within many dental schools. PBL was first introduced in the 1960’s and can be defined as learning which occurs from collaborative work to resolve a problem. Whereas CBL encourages learning from clinical cases, encourages application of knowledge and helps prepare learners for clinical practice. To evaluate the use of online PBL and CBL. A literature search was conducted using the CINAHL, Embase, PubMed and Web of Science databases. Literature was also identified from reference lists. Studies were only included from dental education. Seven suitable studies were identified. One of the studies found a high learner and facilitator satisfaction rate with online CBL. Interestingly one study found learners preferred CBL over PBL within an online format. A study also found, that within the context of distance learning, learners preferred a hybrid curriculum including PBL over a traditional approach. A further study pointed to the limitations of PBL within an online format, such as reduced interaction, potentially hindering the development of communication skills and the increased time and technology support required. An audience response system was also developed for use within CBL and had a high satisfaction rate. Interestingly one study found achievement of learning outcomes was correlated with the number of student and staff inputs within an online format. Whereas another study found the quantity of learner interactions were important to group performance, however the quantity of facilitator interactions was not. This review identified generally favourable evidence for the benefits of online PBL and CBL. However, there is limited high quality evidence evaluating these teaching methods within dental education and there appears to be limited evidence comparing online and faceto-face versions of these sessions. The importance of the quantity of learner interactions is evident, however the importance of the quantity of facilitator interactions appears to be questionable. An element to this may be down to the quality of interactions, rather than just quantity. Limitations of online learning regarding technological issues and time required for a session are also highlighted, however as learners and facilitators get familiar with online formats, these may become less of an issue. It is also important learners are encouraged to interact and communicate during these sessions, to allow for the development of communication skills. Interestingly CBL appeared to be preferred to PBL in an online format. This may reflect the simpler nature of CBL, however further research is required to explore this finding. Online CBL and PBL appear promising, however further research is required before online formats of these sessions are widely adopted in the post-pandemic era.

Keywords: case-based learning, online, problem-based learning, remote, virtual

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Authors: Fouad. Inel, Lakhdar. Khochemane

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This article presents a comparative response specification performance between two controllers of three and four cable based robots for various applications. The main objective of this work is: The first is to use the direct and inverse geometric model to study and simulate the end effector position of the robot with three and four cables. A graphical user interface has been implemented in order to visualizing the position of the robot. Secondly, we present the determination of static and dynamic tensions and lengths of cables required to flow different trajectories. At the end, we study the response of our systems in closed loop with a Proportional-Integrated Derivative (PID) and Proportional-Integrated (PD) controllers then this last are compared the results of the same examples using MATLAB/Simulink; we found that the PID method gives the better performance, such as rapidly speed response, settling time, compared to PD controller.

Keywords: parallel cable-based robots, geometric modeling, dynamic modeling, graphical user interface, open loop, PID/PD controllers

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Authors: Ernesto A. Lagarda-Leyva, Manuel A. Valenzuela L., José G. Oshima C., Arnulfo A. Naranjo-Flores

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From August to December of 2016, a diagnostic and strategic planning study was carried out on the supply chain of the company Agropecuaria GABO S.A. de C.V. The final product of the study was the development of the strategic plan and a project portfolio to meet the demands of the three links in the supply chain of the Roma tomato exported annually to the United States of America. In this project, the strategic objective of ensuring the proper handling of the product was selected and one of the goals associated with this was the employment of quantitative methods to support decision making. Considering the antecedents, the objective of this case study was to develop a model to analyze the behavioral dynamics in the distribution chain, from the logistics of storage and shipment of Roma tomato in 81-case pallets (11.5 kg per case), to the two pre-cooling rooms and eventual loading onto transports, seeking to reduce the bottleneck and the associated costs by means of a dynamic interface. The methodology used was that of system dynamics, considering four phases that were adapted to the purpose of the study: 1) the conceptualization phase; 2) the formulation phase; 3) the evaluation phase; and 4) the communication phase. The main practical conclusions lead to the possibility of reducing both the bottlenecks in the cooling rooms and the costs by simulating scenarios and modifying certain policies. Furthermore, the creation of the dynamic interface between the model and the stakeholders was achieved by generating interaction with buttons and simple instructions that allow making modifications and observing diverse behaviors.

Keywords: agrilogistics, distribution, scenarios, system dynamics

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Authors: Mohammad Shams Esfand Abadi, Mohammad Ranjbar, Reza Ebrahimpour

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This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results.

Keywords: selective partial update, affine projection, dynamic selection, diffusion, adaptive distributed networks

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Authors: Ammar Alali, Mahmoud Abughaban, William Contreras Otalvora

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Optimizing the drilling process for cost and efficiency requires the optimization of the rate of penetration (ROP). ROP is the measurement of the speed at which the wellbore is created, in units of feet per hour. It is the primary indicator of measuring drilling efficiency. Maximization of the ROP can indicate fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT) and higher net costs. The proposed ROP optimization solution is a hybrid, data-driven system that aims to improve the drilling process, maximize the ROP, and minimize NPT. The system consists of two phases: (1) utilizing existing geological and drilling data to train the model prior, and (2) real-time adjustments of the controllable dynamic drilling parameters [weight on bit (WOB), rotary speed (RPM), and pump flow rate (GPM)] that direct influence on the ROP. During the first phase of the system, geological and historical drilling data are aggregated. After, the top-rated wells, as a function of high instance ROP, are distinguished. Those wells are filtered based on NPT incidents, and a cross-plot is generated for the controllable dynamic drilling parameters per ROP value. Subsequently, the parameter values (WOB, GPM, RPM) are calculated as a conditioned mean based on physical distance, following Inverse Distance Weighting (IDW) interpolation methodology. The first phase is concluded by producing a model of drilling best practices from the offset wells, prioritizing the optimum ROP value. This phase is performed before the commencing of drilling. Starting with the model produced in phase one, the second phase runs an automated drill-off test, delivering live adjustments in real-time. Those adjustments are made by directing the driller to deviate two of the controllable parameters (WOB and RPM) by a small percentage (0-5%), following the Constrained Random Search (CRS) methodology. These minor incremental variations will reveal new drilling conditions, not explored before through offset wells. The data is then consolidated into a heat-map, as a function of ROP. A more optimum ROP performance is identified through the heat-map and amended in the model. The validation process involved the selection of a planned well in an onshore oil field with hundreds of offset wells. The first phase model was built by utilizing the data points from the top-performing historical wells (20 wells). The model allows drillers to enhance decision-making by leveraging existing data and blending it with live data in real-time. An empirical relationship between controllable dynamic parameters and ROP was derived using Artificial Neural Networks (ANN). The adjustments resulted in improved ROP efficiency by over 20%, translating to at least 10% saving in drilling costs. The novelty of the proposed system lays is its ability to integrate historical data, calibrate based geological formations, and run real-time global optimization through CRS. Those factors position the system to work for any newly drilled well in a developing field event.

Keywords: drilling optimization, geological formations, machine learning, rate of penetration

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Authors: Ahmed A. D. Sarhan, S. R. Besharaty, Javad Akbaria, M. Hamdi

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The capability of CNC gantry milling machines in manufacturing long components has caused the expanded use of such machines. On the other hand, the machines’ gantry rigidity can reduce under severe loads or vibration during operation. Indeed, the quality of machining is dependent on the machine’s dynamic behavior throughout the operating process. For this reason, this type of machines has always been used prudently and are non efficient. Therefore, they can usually be employed for rough machining and may not produce adequate surface finishing. In this paper, a CNC gantry milling machine with the potential to produce good surface finish has been designed and analyzed. The lowest natural frequency of this machine is 202 Hz at all motion amplitudes with a full range of suitable frequency responses. Meanwhile, the maximum deformation under dead loads for the gantry machine is 0.565µm, indicating that this machine tool is capable of producing higher product quality.

Keywords: frequency response, finite element, gantry machine, gantry design, static and dynamic analysis

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Authors: Chaiwat Waree

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The purposes of the research were to develop online lessons to strengthen the learning process of Master's degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University; to achieve the efficiency criteria of 80/80; and to study the satisfaction of students who use online lessons to strengthen the learning process of Master’s degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University. The sample consisted of 40 University students studying in semester 1, academic year 2012. The sample was determined by Purposive Sampling. Selected students were from the class which the researcher was the homeroom tutor. The tutor was responsible for the teaching of learning process. Tools used in the study were online lessons, 60-point performance test, and evaluation test of satisfaction of students on online lessons. Data analysis yielded the following results; 83.66/88.29 efficiency of online lessons measured against the criteria; the comparison of performance before and after taking online lessons using t-test yielded 29.67. The statistical significance was at 0.05; the average satisfaction level of forty students on online lessons was 4.46 with standard deviation of 0.68.

Keywords: online, lessons, curriculum, instruction

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Authors: Nicolò Vaiana, Giorgio Serino

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In this paper, a one-dimensional (1d) Parallel Elasto- Plastic Model (PEPM), able to simulate the uniaxial dynamic behavior of seismic isolators having a continuously decreasing tangent stiffness with increasing displacement, is presented. The parallel modeling concept is applied to discretize the continuously decreasing tangent stiffness function, thus allowing to simulate the dynamic behavior of seismic isolation bearings by putting linear elastic and nonlinear elastic-perfectly plastic elements in parallel. The mathematical model has been validated by comparing the experimental force-displacement hysteresis loops, obtained testing a helical wire rope isolator and a recycled rubber-fiber reinforced bearing, with those predicted numerically. Good agreement between the simulated and experimental results shows that the proposed model can be an effective numerical tool to predict the forcedisplacement relationship of seismic isolators within relatively large displacements. Compared to the widely used Bouc-Wen model, the proposed one allows to avoid the numerical solution of a first order ordinary nonlinear differential equation for each time step of a nonlinear time history analysis, thus reducing the computation effort, and requires the evaluation of only three model parameters from experimental tests, namely the initial tangent stiffness, the asymptotic tangent stiffness, and a parameter defining the transition from the initial to the asymptotic tangent stiffness.

Keywords: base isolation, earthquake engineering, parallel elasto-plastic model, seismic isolators, softening hysteresis loops

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Authors: Dahiru Bawa Muhammad

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Mathematics is accorded a prime position in basic education curriculum because it is envisaged to be an important tool in preparing children for life after school as well as equipping them with skills needed for secondary and higher education. As a result of this, the subject is made compulsory from primary through secondary school and candidates are expected to offer it and pass before fulfilling the requirement for higher education. Against this backdrop, this paper overviewed the basic education programme, context of teaching and learning mathematics at basic education level in Katsina State of Nigeria, relevance of the subject to different fields of human endeavours, challenges threatening the utility of the subject as a tool for the achievement of the goals of basic education programme and concluded by recommending how teaching and learning of mathematics can be improved for even development of citizens within nation states and enhanced/mutual sustainable development of nations in the global village.

Keywords: basic education, junior secondary school education, mathematical centre

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Yang Chen

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The property-led regeneration has played an important role in the process of rapid urbanization during the past twenty years in China, but it is also been criticized unsustainable as it always focuses on the economic aspect and overlooks the social issues, especially it has exacerbated the residential segregation in the inner city. Based on author’s studying the area around Nanjing railway station, this paper demonstrates that residential segregation indeed exists in the inner city through synthetic analysis on patterns of residents’ living, consumption and welfare, and to some extent, the segregation distribution characteristics represent in a concentric ring model. According to author’s further investigation on the property right and age of the dwelling buildings, the housing-commercialization-led regeneration is defined as the mainspring of the segregation. To solve these problems, the system of sustainable community should be established in both policy and practice, above all, well-designed public facilities including green infrastructure will be appropriate to promote the residential integration and sustainable development in contemporary China.

Keywords: China, dynamic mechanism, residential segregation, sustainable regeneration

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Sonia Sassi, Mostapha Tarfaoui, Hamza Benyahia

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Composite materials are increasingly being used as a substitute for metallic materials in many technological applications like aeronautics, aerospace, marine and civil engineering applications. For composite materials, the thermomechanical response evolves with the strain rate. The energy balance equation for anisotropic, elastic materials includes heat source terms that govern the conversion of some of the kinetic work into heat. The remainder contributes to the stored energy creating the damage process in the composite material. In this paper, we investigate the bulk thermomechanical behavior of adhesively-bonded composite assemblies to quantitatively asses the temperature rise which accompanies adiabatic deformations. In particular, adhesively bonded joints in glass/vinylester composite material are subjected to in-plane dynamic loads under a range of strain rates. Dynamic thermomechanical behavior of this material is investigated using compression Split Hopkinson Pressure Bars (SHPB) coupled with a high speed infrared camera and a high speed camera to measure in real time the dynamic behavior, the damage kinetic and the temperature variation in the material. The interest of using high speed IR camera is in order to view in real time the evolution of heat dissipation in the material when damage occurs. But, this technique does not produce thermal values in correlation with the stress-strain curves of composite material because of its high time response in comparison with the dynamic test time. For this reason, the authors revisit the application of specific thermocouples placed on the surface of the material to ensure the real thermal measurements under dynamic loading using small thermocouples. Experiments with dynamically loaded material show that the thermocouples record temperatures values with a short typical rise time as a result of the conversion of kinetic work into heat during compression test. This results show that small thermocouples can be used to provide an important complement to other noncontact techniques such as the high speed infrared camera. Significant temperature rise was observed in in-plane compression tests especially under high strain rates. During the tests, it has been noticed that sudden temperature rise occur when macroscopic damage occur. This rise in temperature is linked to the rate of damage. The more serve the damage is, a higher localized temperature is detected. This shows the strong relationship between the occurrence of damage and induced heat dissipation. For the case of the in plane tests, the damage takes place more abruptly as the strain rate is increased. The difference observed in the obtained thermomechanical response in plane compression is explained only by the difference in the damage process being active during the compression tests. In this study, we highlighted the dependence of the thermomechanical response on the strain rate of bonded specimens. The effect of heat dissipation of this material cannot hence be ignored and should be taken into account when defining damage models during impact loading.

Keywords: adhesively-bonded composite joints, damage, dynamic compression tests, energy balance, heat dissipation, SHPB, thermomechanical behavior

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Authors: Carla Van De Sande, Jana Vandenberg

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Just like physical skills, cognitive skills grow rusty over time unless they are regularly used and practiced, so academic breaks can have negative consequences on student learning and success. The Keeping in School Shape (KiSS) program is an engaging, accessible, and cost-effective intervention that harnesses the benefits of retrieval practice by using technology to help students maintain proficiency over breaks from school by delivering a daily review problem via text message or email. A growth mindset is promoted through feedback messages encouraging students to try again if they get a problem wrong and to take on a challenging problem if they get a problem correct. This paper reports on the relationship between confidence, accuracy, and decision-making during the implementation of the KiSS Program at a large university during winter break for students enrolled in an engineering introductory Calculus course sequence.

Keywords: growth mindset, learning loss, on-the-go learning, retrieval practice

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: Felicia K. Oluwalola

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This paper examines the relevance of technology on education. It identified the concept of technology on education, bringing real-world learning to the classroom situation, examples of where technology can be used. This study established the fact that technology facilitates students learning compared with traditional method of teaching. It was recommended that the teachers should use technology to supplement, not replace, other instructional modes. It should be used in conjunction with hands-on labs and activities that also address the concepts targeted by the technology. Also, technology should be students centered and not teachers centered.

Keywords: computer, simulation, classroom teaching, education

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Authors: Xiao-Yan Li, Takashi Hashimoto, Guanhong Li, Shuo Yang

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In Japanese, onomatopoeia is an important element in the lively expression of feelings and experiences. It is very difficult for students of Japanese to acquire onomatopoeia, especially, its nuances. In this paper, based on traditional L2 learning theories, we propose a new method to improve the efficiency of teaching the nuances – both explicit and tacit - to non-native speakers of Japanese. The method for teaching the tacit nuances of onomatopoeia consists of three elements. First is to teach the formal rules representing the explicit nuances of onomatopoeic words. Second is to have the students create new onomatopoeic words by utilizing those formal rules. The last element is to provide feedback by evaluating the onomatopoeias created. Our previous study used five-grade relative estimation. However students were confused about the five-grade system, because they could not understand the evaluation criteria only based on a figure. In this new system, then, we built an evaluation database through native speakers’ narrative interpretation. We asked Japanese native speakers to describe their awareness of the nuances of onomatopoeia in writing. Then they voted on site and defined priorities for showing to learners on the system. To verify the effectiveness of the proposed method and the learning system, we conducted a preliminary experiment involving two groups of subjects. While Group A got feedback about the appropriateness of their onomatopoeic constructions from the native speakers’ narrative interpretation, Group B got feedback just in the form of the five-grade relative estimation. A questionnaire survey administered to all of the learners clarified our learning system availability and also identified areas that should be improved. Repetitive learning of word-formation rules, creating new onomatopoeias and gaining new awareness from narrative interpretation is the total process used to teach the explicit and tacit nuances of onomatopoeia.

Keywords: onomatopoeia, tacit nuance, narrative interpretation, e-learning system, second language teaching

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Authors: Hui-Yi Wang, Shu-Mei Chen, Mei-Fang Liu

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Background and purpose: Providing clinical experience in medical education is an important discipline method where students can gradually apply their academic knowledge to clinical situations. The purpose of this study was to establish self-assessment questionnaires for students to assess their learning outcomes for two fields of physical therapy, orthopedic physical therapy, and pediatric physical therapy, in a clinical fieldwork course. Methods: The questionnaires were developed based on the core competence dimensions of the course. The content validity of the questionnaires was evaluated and established by expert meetings. Among the third-year undergraduate students who took the clinical fieldwork course, there were 49 students participated in this study. Teachers arranged for the students to study two professional fields, and each professional field conducted a three-week clinical lesson. The students filled out the self-assessment questionnaires before and after each three-week lesson. Results: The self-assessment questionnaires were established by expert meetings that there were six core competency dimensions in each of the two fields, with 20 and 21 item-questions, respectively. After each three-week clinical fieldwork, the self-rating scores in each core competency dimension were higher when compared to those before the course, indicating having better clinical abilities after the lessons. The best self-rating scores were the dimension of attitude and humanistic literacy, and the two lower scores were the dimensions of professional knowledge and skills and problem-solving critical thinking. Conclusions: This study developed questionnaires for clinical fieldwork courses to reflect students' learning outcomes, including the performance of professional knowledge, practice skills, and professional attitudes. The use of self-assessment of learning performance can help students build up their reflective competencies. Teachers can guide students to pay attention to the performance of abilities in each core dimension to enhance the effectiveness of learning through self-reflection and improvement.

Keywords: physical therapy, clinical fieldwork course, learning outcomes assessment, medical education, self-reflection ability

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

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Authors: Johanna Schumacher, Toribio F. Otero, Victor H. Pascual

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Bending dual sensing-actuators based on electroactive polymers are faradaic motors meaning the consumed charge determines the actuator’s tip position. During actuation, consumed charges during oxidation and reduction result in different tip positions showing dynamic hysteresis effects with errors up to 25%. For a precise position control of these actuators, the characterization of the hysteresis effect due to irreversible reactions is crucial. Here, the investigation and modelling of dynamic hysteresis effects of polypyrrole-dodezylbenzenesulfonate (PPyDBS) actuators under ambient working conditions are presented. The hysteresis effect is studied for charge consumption at different frequencies and a rate-dependent hysteresis model is derived. The hysteresis model is implemented as closed loop system and is verified experimentally.

Keywords: dual sensing-actuator, electroactive polymers, hysteresis, position control

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Authors: Nilgun Karatas, Bihter Sakin

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In this study, the relationships between the use of language learning strategies and English language exit exam success were investigated in the university EFL learners’ context. The study was conducted at Fatih University Prep School. To collect data 3 classes from the A1 module in English language classes completed a questionnaire known as the English Language Learning Strategy Inventory or ELLSI. The data for the present study were collected from the preparatory class students who are studying English as a second language at the School of Foreign Languages. The students were placed into four different levels of English, namely A1, A2, B1, and B2 level of English competency according to European Union Language Proficiency Standard, by means of their English placement test results. The Placement test was conveyed at the beginning of the spring semester in 2014-2015.The ELLSI consists of 30 strategy items which students are asked to rate from 1 (low frequency) to 5 (high frequency) according to how often they use them. The questionnaire and exit exam results were entered onto SPSS and analyzed for mean frequencies and statistical differences. Spearman and Pearson correlation were used in a detailed way. There were no statistically significant results between the frequency of strategy use and exit exam results. However, most questions correlate at a significant level with some of the questions.

Keywords: individual differences, language learning strategies, Fatih University, English language

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Authors: Shamie Kumar

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Background: Radiologists and radiographers (RR) need to educate themselves and their colleagues to ensure that AI is integrated safely, useful, and in a meaningful way with the direction it always benefits the patients. AI education and training are fundamental to the way RR work and interact with it, such that they feel confident using it as part of their clinical practice in a way they understand it. Methodology: This exploratory research will outline the current educational and training gaps for radiographers and radiologists in AI radiology diagnostics. It will review the status, skills, challenges of educating and teaching. Understanding the use of artificial intelligence within daily clinical practice, why it is fundamental, and justification on why learning about AI is essential for wider adoption. Results: The current knowledge among RR is very sparse, country dependent, and with radiologists being the majority of the end-users for AI, their targeted training and learning AI opportunities surpass the ones available to radiographers. There are many papers that suggest there is a lack of knowledge, understanding, and training of AI in radiology amongst RR, and because of this, they are unable to comprehend exactly how AI works, integrates, benefits of using it, and its limitations. There is an indication they wish to receive specific training; however, both professions need to actively engage in learning about it and develop the skills that enable them to effectively use it. There is expected variability amongst the profession on their degree of commitment to AI as most don’t understand its value; this only adds to the need to train and educate RR. Currently, there is little AI teaching in either undergraduate or postgraduate study programs, and it is not readily available. In addition to this, there are other training programs, courses, workshops, and seminars available; most of these are short and one session rather than a continuation of learning which cover a basic understanding of AI and peripheral topics such as ethics, legal, and potential of AI. There appears to be an obvious gap between the content of what the training program offers and what the RR needs and wants to learn. Due to this, there is a risk of ineffective learning outcomes and attendees feeling a lack of clarity and depth of understanding of the practicality of using AI in a clinical environment. Conclusion: Education, training, and courses need to have defined learning outcomes with relevant concepts, ensuring theory and practice are taught as a continuation of the learning process based on use cases specific to a clinical working environment. Undergraduate and postgraduate courses should be developed robustly, ensuring the delivery of it is with expertise within that field; in addition, training and other programs should be delivered as a way of continued professional development and aligned with accredited institutions for a degree of quality assurance.

Keywords: artificial intelligence, training, radiology, education, learning

Procedia PDF Downloads 85