Search results for: teaching and learning effectiveness

Authors: Elaid Bouchetob, Bouchra Nadji

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This research paper compares the efficiency and reliability (R(t)) of SiC-based and Si-based DC-DC buck converters in thin layer PV systems with an AI-based MPPT controller. Using Simplorer/Simulink simulations, the study assesses their performance under varying conditions. Results show that the SiC-based converter outperforms the Si-based one in efficiency and cost-effectiveness, especially in high temperature and low irradiance conditions. It also exhibits superior reliability, particularly at high temperature and voltage. Reliability calculation (R(t)) is analyzed to assess system performance over time. The SiC-based converter demonstrates better reliability, considering factors like component failure rates and system lifetime. The research focuses on the buck converter's role in charging a Lithium battery within the PV system. By combining the SiC-based converter and AI-based MPPT controller, higher charging efficiency, improved reliability, and cost-effectiveness are achieved. The SiC-based converter proves superior under challenging conditions, emphasizing its potential for optimizing PV system charging. These findings contribute insights into the efficiency, reliability, and reliability calculation of SiC-based and Si-based converters in PV systems. SiC technology's advantages, coupled with advanced control strategies, promote efficient and sustainable energy storage using Lithium batteries. The research supports PV system design and optimization for reliable renewable energy utilization.

Keywords: efficiency, reliability, artificial intelligence, sic device, thin layer, buck converter

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Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

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Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

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Authors: Cissy Li

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Mentorship is a commonly used strategy for career development that has obvious benefits for students in undergraduate pre-service teacher training programs. In contrast to teaching practicum, which generally involves pedagogical supervision and performance evaluation by teachers, mentorship is more focused on sharing experiences, supporting challenges, and nurturing skills in order to promote personal and professional growth. To empower pre-service teachers and prepare them for potential challenges in the context of local English language teaching (ELT), an alumni mentoring program was established in the framework of communities of practice (CoP), with the mentors being in-service graduates working in local schools and mentees being students on the teacher-training programme in a Hong Kong university. By triangulating audio transcripts of mentoring sessions delivered by three top mentors with data from questionnaire responses and mentor logs, this paper examines the mentoring styles of the three best mentors from the metadiscursive perspective. It was found that, in a community of practice, mentors who may seem to enjoy a relative more dominant position, in fact, had to strategically and pragmatically employ metadiscursive resources to manage relationships with the mentees and organize talks in the mentoring process. Other attributing factors for a successful mentoring session include mentor personality and prior mentorship experiences, nature of the activities in the session, and group dynamics. This paper concludes that it is the combination of all the factors that constitute a particular mentoring style. The findings have implications for mentoring programs in teacher preparation.

Keywords: mentoring, teacher education, mentoring style, metadiscourse

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Authors: S. K. Ashiquer Rahman

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The innovative knowledge threshold significantly shifted education from traditional to an online version which was an emergent state of arts for academic programs of any higher education institutions; the substantive situation thus raises the importance of deliberative integration of education, Knowledge, technology and sustainability as well as knowledge platforms, e.g., ePLANETe. In fact, the concept of 'ePLANETe' an innovative knowledge platform and its functionalities as an experimental digitized platform for contributing sustainable assessment of academic programs of higher education institution(HEI). Besides, this paper assessed and define the common sustainable development challenges of higher education(HE) and identified effective approach and tools of 'ePLANETe’ that is enable to practices sustainability assessment of academic programs through the deliberation methodologies. To investigate the effectiveness of knowledge tools and approach of 'ePLANETe’, I have studied sustainable challenges digitized pedagogical content as well as evaluation of academic programs of two public universities in France through the 'ePLANETe’ evaluation space. The investigation indicated that the effectiveness of 'ePLANETe’s tools and approach perfectly fit for the quality assessment of academic programs, implementation of sustainable challenges, and dynamic balance of ecosystem within the university communities and academic programs through 'ePLANETe’ evaluation process and space. The study suggests to the relevant higher educational institution’s authorities and policymakers could use this approach and tools for assessing sustainability and enhancing the sustainability competencies of academic programs for quality education

Keywords: ePLANETe, deliberation, evaluation, competencies

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Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: Kruger Uys

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Poetry analysis plays a vital role in promoting critical thinking, literary appreciation, and language skills among learners. This paper proposes an innovative multimodal teaching approach that combines traditional textual analysis of poems with multimodal educational semiotic analysis of animated poetry films. The aim is to present a methodological framework through which poetry concepts and elements, along with the visual and auditory components in animated poetry films, can be comprehensively illuminated. Traditional textual analysis involves close reading, linguistic examination, and thematic exploration to identify, discuss, and apply poetry concepts. When combined with a multimodal edusemiotic analysis of the semiotic signs and codes present in animated poetry films, new perspectives emerge that enrich the interpretation of poetry. Furthermore, the proposed integrated approach, as prescribed by CAPS, enhances a holistic understanding of poetry terminology and elements, as well as complex linguistic and visual patterns that promote visual literacy, refined data interpretation skills, and learner engagement in the poetry classroom. To illustrate this phenomenon, the poem My mamma is bossies (My mom’s bonkers) by Jeanne Goosen (prescribed for Grade 10 Afrikaans Home Language learners in the CAPS curriculum) will be discussed. This study aims to contribute to the existing Afrikaans poetry curriculum but also equip all language educators to cultivate poetry appreciation, critical thinking, and creativity among learners in the ever-evolving landscape of education.

Keywords: edusemiotics, multimodality, poetry education, animated poetry films

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Authors: Vrajesh Chokshi

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In last three decades, the global economy is substantially changed. Today, we are living in highly inter-connected world. The global markets are more open and consumers are well informed about products and services. The information technology revolution has broken all barriers in global business. The E-commerce has given opportunities of global trades to corporate. The IT is extensively used in almost all industries. After liberalization in 1992, the Indian economy is also significantly changed. The IT (information technology) and ITES (IT enable services) are extensively used in supply chain management. In India, previously car rental service business was dominated by local organization and operated through local contact. This industry is very lucrative and to catch this opportunity, many new corporate have ventured into e-commerce car rental service business in India. As the market is very competitive, branding is also very important part of marketing strategy. Now, the E-commerce portals those are in car rental business in India have realized the importance of the same and have started usage of all types of communication channel to promote their brand in different Indian markets. At consumer side, the awareness is also being considerably increased due to marketing communication campaign run by these companies. This paper aims to understand effectiveness of branding strategies in car rental business in India and also tries to identify unique promotional strategies to consolidate brand image of this business in different Indian markets.

Keywords: branding strategies, car rental business, CRM (customer relationship management), ITES (information technology enabled services)

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Authors: Chaya B. M., C. Dhanush, Inti Sai Srikar, Akula Pavan Parvatalu, Chirag Gowda R

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Recently, there has been a lot of interest in µ-LED technology because of its exceptional qualities, including auto emission, high visibility, low consumption of power, rapid response and longevity. Light-emitting diodes (LED) using III-nitride, such as lighting sources, visible light communication (VLC) devices, and high-power devices, are finding increasing use as miniaturization technology advances. The use of micro-LED displays in place of traditional display technologies like liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) is one of the most prominent recent advances, which may even represent the next generation of displays. The development of fully integrated, multifunctional devices and the incorporation of extra capabilities into micro-LED displays, such as sensing, light detection, and solar cells, are the pillars of advanced technology. Due to the wide range of applications for micro-LED technology, the effectiveness and dependability of these devices in numerous harsh conditions are becoming increasingly important. Enough research has been conducted to overcome the under-effectiveness of micro-LED devices. In this paper, different Micro LED design structures are proposed in order to achieve optimized optical properties. In order to attain improved external quantum efficiency (EQE), devices' light extraction efficiency (LEE) has also been boosted.

Keywords: finite difference time domain, light out coupling efficiency, far field intensity, power density, quantum efficiency, flat panel displays

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Authors: Rita A. Sunday-Kanu

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This article reports on an evaluation of lunch hour concert and its effectiveness in improving undergraduate performance ability. In particular, it examines the aptitude of students in classroom applied music and their reaction/responses to true life concert situations. It further investigated factors affecting students’ confidence during performances, the relationship between stage fright and confidence building in regular concert participation. The Department of Music, University of Port Harcourt runs monthly lunch our concerts which are coordinated by undergraduates for the university community. Forty music students who have participated in or coordinated lunch hour concerts were chosen for this survey. Eight music lecturers who have supervised the monthly lunch hour concert were also chosen for this study. The attitude and view on the effectiveness of lunch hour concert in enhancing students’ performance skills were gotten through questionnaires survey, in-depth interview and participant observation to determine if classroom based applied music alone is as successful in grooming performance genius as the lunch hour concert. Result indicated that students’ participation in lunch hour concert did indeed broaden and strengthened their performance experiences. This observation led to a recommendation that regular community based concerts be considered as a standard for performance practices in the university curriculum since it serves as a preparatory platform for acquiring professional performance skills before graduation.

Keywords: lunch hour concert, performance, performing skill, community concert

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Authors: King Kumire, Amos Kubeka

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The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: machine-learning, habitability, exoplanets, supercomputing

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Authors: King Kumire, Amos Kubeka

Abstract:

The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far, has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: exoplanets, habitability, machine-learning, supercomputing

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Authors: Jared Beard, Ali Baheri

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As autonomous systems become more prominent in society, ensuring their safe application becomes increasingly important. This is clearly demonstrated with autonomous cars traveling through a crowded city or robots traversing a warehouse with heavy equipment. Human environments can be complex, having high dimensional state and action spaces. This gives rise to two problems. One being that analytic solutions may not be possible. The other is that in simulation based approaches, searching the entirety of the problem space could be computationally intractable, ruling out formal methods. To overcome this, approximate solutions may seek to find failures or estimate their likelihood of occurrence. One such approach is adaptive stress testing (AST) which uses reinforcement learning to induce failures in the system. The premise of which is that a learned model can be used to help find new failure scenarios, making better use of simulations. In spite of these failures AST fails to find particularly sparse failures and can be inclined to find similar solutions to those found previously. To help overcome this, multi-fidelity learning can be used to alleviate this overuse of information. That is, information in lower fidelity can simulations can be used to build up samples less expensively, and more effectively cover the solution space to find a broader set of failures. Recent work in multi-fidelity learning has passed information bidirectionally using “knows what it knows” (KWIK) reinforcement learners to minimize the number of samples in high fidelity simulators (thereby reducing computation time and load). The contribution of this work, then, is development of the bidirectional multi-fidelity AST framework. Such an algorithm, uses multi-fidelity KWIK learners in an adversarial context to find failure modes. Thus far, a KWIK learner has been used to train an adversary in a grid world to prevent an agent from reaching its goal; thus demonstrating the utility of KWIK learners in an AST framework. The next step is implementation of the bidirectional multi-fidelity AST framework described. Testing will be conducted in a grid world containing an agent attempting to reach a goal position and adversary tasked with intercepting the agent as demonstrated previously. Fidelities will be modified by adjusting the size of a time-step, with higher-fidelity effectively allowing for more responsive closed loop feedback. Results will compare the single KWIK AST learner with the multi-fidelity algorithm with respect to number of samples, distinct failure modes found, and relative effect of learning after a number of trials.

Keywords: multi-fidelity reinforcement learning, multi-fidelity simulation, safety validation, falsification

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Authors: Robert H. Bradley, Robert F. Corwyn

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There is abundant research showing that socio-economic status is implicated in parenting. However, additional factors such as family context, parent personality, parenting history and child behavior also help determine how parents enact the role of caregiver. Each of these factors not only helps determine how a parent will act in a given situation, but each can serve to moderate the influence of the other factors. Personality has long been studied as a factor that influences parental behavior, but it has almost never been considered as a moderator of family contextual factors. For this study, relations between three maternal personality characteristics (agreeableness, extraversion, neuroticism) and four aspects of parenting (harshness, sensitivity, stimulation, learning materials) were examined when children were 6 months, 36 months, and 54 months old and again at 5th grade. Relations between these three aspects of personality and the overall home environment were also examined. A key concern was whether maternal personality characteristics moderated relations between household income and the four aspects of parenting and between household income and the overall home environment. The data for this study were taken from the NICHD Study of Early Child Care and Youth Development (NICHD SECCYD). The total sample consisted of 1364 families living in ten different sites in the United States. However, the samples analyzed included only those with complete data on all four parenting outcomes (i.e., sensitivity, harshness, stimulation, and provision of learning materials), income, maternal education and all three measures of personality (i.e., agreeableness, neuroticism, extraversion) at each age examined. Results from hierarchical regression analysis showed that mothers high in agreeableness were more likely to demonstrate sensitivity and stimulation as well as provide more learning materials to their children but were less likely to manifest harshness. Maternal agreeableness also consistently moderated the effects of low income on parental behavior. Mothers high in extraversion were more likely to provide stimulation and learning materials, with extraversion serving as a moderator of low income on both. By contrast, mothers high in neuroticism were less likely to demonstrate positive aspects of parenting and more likely to manifest negative aspects (e.g., harshness). Neuroticism also served to moderate the influence of low income on parenting, especially for stimulation and learning materials. The most consistent effects of parent personality were on the overall home environment, with significant main and interaction effects observed in 11 of the 12 models tested. These findings suggest that it may behoove professional who work with parents living in adverse circumstances to consider parental personality in helping to better target prevention or intervention efforts aimed at supporting parental efforts to act in ways that benefit children.

Keywords: home environment, household income, learning materials, personality, sensitivity, stimulation

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Authors: Ruangdech Sirikit

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The purposes of this research were to study the variables affecting the quality assessment of mathematics subject in Thailand by using value-added analysis on TIMSS 2011. The data used in this research is the secondary data from the 2011 Trends in International Mathematics and Science Study (TIMSS), collected from 6,124 students in 172 schools from Thailand, studying only mathematics subjects. The data were based on 14 assessment tests of knowledge in mathematics. There were 3 steps of data analysis: 1) To analyze descriptive statistics 2) To estimate competency of students from the assessment of their mathematics proficiency by using MULTILOG program; 3) analyze value added in the model of quality assessment using Value-Added Model with Hierarchical Linear Modeling (HLM) and 2 levels of analysis. The research results were as follows: 1. Student level variables that had significant effects on the competency of students at .01 levels were Parental care, Resources at home, Enjoyment of learning mathematics and Extrinsic motivation in learning mathematics. Variable that had significant effects on the competency of students at .05 levels were Education of parents and self-confident in learning mathematics. 2. School level variable that had significant effects on competency of students at .01 levels was Extra large school. Variable that had significant effects on competency of students at .05 levels was medium school.

Keywords: quality assessment, value-added model, TIMSS, mathematics, Thailand

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Authors: Helena Baffoe

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The US has developed numerous programs over the past 50 years to enhance the lives of those who suffer from mental health illnesses and substance abuse, as well as the effectiveness of their treatments. Despite these advances over the past 50 years, there hasn't been a corresponding improvement in American public attitudes and beliefs about mental health disorders and substance abuse. Highly publicized acts of violence frequently elicit comments that blame the perpetrator's perceived mental health disorder since such people are thought to be substance abusers. Despite these strong public beliefs and perception about mental disorder and substance abuse, concreate empirical evidence that entail this perception is lacking, and evidence of their effectiveness has not been integrated. A rich data was collected from Substance Abuse and Mental Health Services Administration (SAMHSA) with a hypothesis that people who are diagnosed with a mental health disorder are likely to be diagnosed with substance abuse using logit regression analysis and Instrumental Variable. It was found that depressive, anxiety, and trauma/stressor mental disorders constitute the most common mental disorder in the United States, and the study could not find statistically significant evidence that being diagnosed with these leading mental health disorders in the United States does necessarily imply that such a patient is diagnosed with substances abuse. Thus, the public has a misconception of mental health and substance abuse issues, and social workers' responsibilities are outlined in order to assist ameliorate this attitude and perception.

Keywords: mental health disorder, substance use, empirical evidence, logistic regression

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Authors: Anuradha Chug, Sunali Gandhi

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Regression Testing is a very expensive and time-consuming process carried out to ensure the validity of modified software. Due to the availability of insufficient resources to re-execute all the test cases in time constrained environment, efforts are going on to generate test data automatically without human efforts. Many search based techniques have been proposed to generate efficient, effective as well as optimized test data, so that the overall cost of the software testing can be minimized. The generated test data should be able to uncover all potential lapses that exist in the software or product. Inspired from the natural behavior of bat for searching her food sources, current study employed a meta-heuristic, search-based bat algorithm for optimizing the test data on the basis certain parameters without compromising their effectiveness. Mathematical functions are also applied that can effectively filter out the redundant test data. As many as 50 Java programs are used to check the effectiveness of proposed test data generation and it has been found that 86% saving in testing efforts can be achieved using bat algorithm while covering 100% of the software code for testing. Bat algorithm was found to be more efficient in terms of simplicity and flexibility when the results were compared with another nature inspired algorithms such as Firefly Algorithm (FA), Hill Climbing Algorithm (HC) and Ant Colony Optimization (ACO). The output of this study would be useful to testers as they can achieve 100% path coverage for testing with minimum number of test cases.

Keywords: regression testing, test case selection, test case prioritization, genetic algorithm, bat algorithm

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

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Authors: Margot Whitfield, Andrea Ducent, Marie Catherine Rombaut, Katia Iassinovskaia, Deborah Fels

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Learning how to create inclusive media, such as closed captioning (CC) and audio description (AD), in North America is restricted to the private sector, proprietary company-based training. We are delivering (through synchronous and asynchronous online learning) the first Canadian post-secondary, practice-based continuing education course package in inclusive media for broadcast production and processes. Despite the prevalence of CC and AD taught within the field of translation studies in Europe, North America has no comparable field of study. This novel approach to audio visual translation (AVT) education develops evidence-based methodology innovations, stemming from user study research with blind/low vision and Deaf/hard of hearing audiences for television and theatre, undertaken at Ryerson University. Knowledge outcomes from the courses include a) Understanding how CC/AD fit within disability/regulatory frameworks in Canada. b) Knowledge of how CC/AD could be employed in the initial stages of production development within broadcasting. c) Writing and/or speaking techniques designed for media. d) Hands-on practice in captioning re-speaking techniques and open source technologies, or in AD techniques. e) Understanding of audio production technologies and editing techniques. The case study of the curriculum development and deployment, involving first-time online course delivery from academic and practitioner-based instructors in introductory Captioning and Audio Description courses (CDIM 101 and 102), will compare two different instructors' approaches to learning design, including the ratio of synchronous and asynchronous classroom time and technological engagement tools on meeting software platform such as breakout rooms and polling. Student reception of these two different approaches will be analysed using qualitative thematic and quantitative survey analysis. Thus far, anecdotal conversations with students suggests that they prefer synchronous compared with asynchronous learning within our hands-on online course delivery method.

Keywords: inclusive media theory, broadcasting practices, AVT post secondary education, respeaking, audio description, learning design, virtual education

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Authors: Jay Gohel, Anant Parghi

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A tuned liquid column damper (TLCD) is a modified passive system of tuned mass damper, where a liquid is used in place of mass in the structure. A TLCD consists of U-shaped tube with an orifice that produces damping against the liquid motion in the tube. This paper provides a state-of-the-art review on the vibration control of wind and earthquake excited structures using liquid dampers. Further, the paper will also discuss the theoretical background of TCLD, history of liquid dampers and existing literature on experimental, numerical, and analytical study. The review will also include different configuration of TLCD viz single TLCD, multi tuned liquid column damper (MTLCD), TLCD-Interior (TLCDI), tuned liquid column ball damper (TLCBD), tuned liquid column ball gas damper (TLCBGD), and pendulum liquid column damper (PLCD). The dynamic characteristics of the different configurate TLCD system and their effectiveness in reducing the vibration of structure will be discussed. The effectiveness of semi-active TLCD will be also discussed with reference to experimental and analytical results. In addition, the review will also provide the numerous examples of implemented TLCD to control the vibration in real structures. Based on the comprehensive review of literature, some important conclusions will be made and the need for future research will be identified for vibration control of structures using TLCD.

Keywords: earthquake, wind, tuned liquid column damper, passive response control, structures

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Authors: Micah J. Dobson

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After-school programs of physical education provide children with opportunities to engage in physical activities while developing healthy habits. To ensure that these programs are inclusive, diverse, and engaging, however, schools must consider various factors when designing and implementing them. This study sought to bring out efficient strategies for structuring after-school programs of physical education. The literature review was conducted using various databases and search engines. Some databases that index the journals include ERIC, Google Scholar, Scopus, Web of Science, and EBSCOhost. The search terms were combinations of keywords such as “after-school,” “physical education,” “inclusion,” “diversity,” “engagement,” “program design,” “program implementation,” “program effectiveness,” and “best practices.” The findings of this study suggest that schools that desire inclusivity must consider four key factors when designing and implementing after-school physical education programs. First, the programs must be designed with variety and fun by incorporating activities such as dance, sports, and games that appeal to all students. Second, instructors must be trained to create supportive and positive environments that foster student engagement while promoting physical literacy. Third, schools must collaborate with community stakeholders and organizations to ensure that programs are culturally inclusive and responsive. Fourth, schools can incorporate technology into their programs to enhance engagement and provide additional growth and learning opportunities.In conclusion, this study provides valuable insights into efficient strategies for structuring after-school programs of physical education that are inclusive, diverse, and engaging for all students. By considering these factors when designing and implementing their programs, schools can promote physical activity while supporting students’ overall well-being and health.

Keywords: after-school programs of physical education, community partnership, inclusivity, instructor training, technology

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Authors: Jin-Hee Kim

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The way that globalization distinguishes itself from the previous changes is scope and intensity of changes, which together affect many parts of a nation’s system. In this way, globalization has its relation with the concept of ‘internationalization’ in that a nation state formulates a set of strategies in many areas of its governance to actively react to it. In short, globalization is a ‘catalyst,’ and internationalization is a ‘response’. In this regard, the field of higher education is one of the representative cases that globalization has several consequences that change the terrain of national policy-making. Started and been dominated mainly by the Western world, it has now been expanded to the ‘late movers,’ such as Asia-Pacific countries. The case of internationalization of Korean higher education is, therefore, located in a unique place in this arena. Yet Korea still is one of the major countries of sending its students to the so-called, ‘first world.’ On the other hand, it has started its effort to recruit international students from the world to its higher education system. After new Millennium, particularly, internationalization of higher education has been launched in its full-scale and gradually been one of the important global policy agenda, striving in both ways by opening its turf to foreign educational service providers and recruiting prospective students from other countries. Particularly the latter, recruiting international students, has been highlighted under the government project named ‘Study Korea,’ launched in 2004. Not only global, but also local issues and motivations were based to launch this nationwide project. Bringing international students means various desirable economic outcomes such as reducing educational deficit as well as utilizing them in Korean industry after the completion of their study, to name a few. In addition, in a similar vein, Korea's higher education institutes have started to have a new comers of adult learners. When it comes to the questions regarding the quality and access of this new learning agency, the answer is quite tricky. This study will investigate the different dimension of education provision and learning process to empower diverse group regardless of nationality, race, class and gender in Korea. Listening to the voices of international students and adult learning as non-traditional participants in a changing Korean higher educational space not only benefit students themselves, but Korean stakeholders who should try to accommodate more comprehensive and fair educational provisions for more and more diversifying groups of learners.

Keywords: education equity, access, globalisation, international students, adult learning, learning support

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Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

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Authors: A. T. Zaki

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The flipped classroom approach as a mode of blended learning was formally introduced to students of the English language modules at the British University in Egypt (BUE) at the start of the academic year 2015/2016. This paper aims to study the impact of the flipped classroom approach after three semesters of implementation. It will restrict itself to the examination of students’ achievement rates, student satisfaction, and how different student cohorts have benefited differently from the flipped practice. The paper concludes with recommendations of how the experience can be further developed.

Keywords: achievement rates, developmental experience, Egypt, flipped classroom, higher education, student cohorts, student satisfaction

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Authors: Amogh Katti, J. Sai Asritha, D. Nivedh, M. Kalyan Srinivas, B. Somnath Chakravarthi

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Today's modern education system uses Learning Management System (LMS) portals for the scoring and grading of student performances, to maintain student records, and teachers are instructed to accept assignments through online submissions of .pdf,.doc,.ppt, etc. There is a risk of data tampering in the traditional portals; we will apply the Blockchain model instead of this traditional model to avoid data tampering and also provide a decentralized mechanism for overall fairness. Blockchain technology is a better and also recommended model because of the following features: consensus mechanism, decentralized system, cryptographic encryption, smart contracts, Ethereum blockchain. The proposed system ensures data integrity and tamper-proof assignment submission and grading, which will be helpful for both students and also educators.

Keywords: education technology, learning management system, decentralized applications, blockchain

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Viktorya Sargsyan, Arax Hovhannesyan, Karine Abelyan

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Introduction: During the last decade, scientific studies have proven the importance of Early Childhood Development (ECD) interventions. These interventions are shown to create strong foundations for children’s intellectual, emotional and physical well-being, as well as the impact they have on learning and economic outcomes for children as they mature into adulthood. Many children in rural Armenia fail to reach their full development potential due to lack of early brain stimulation (playing, singing, reading, etc.) from their parents, and lack of community tools and services to follow-up children’s neurocognitive development. This is exacerbated by high rates of stunting and anemia among children under 3(CU3). This research study tested the effectiveness of an integrated ECD and Maternal, Newborn and Childhood Health (MNCH) model, called “Go Baby, Go!” (GBG), against the traditional (MNCH) strategy which focuses solely on preventive health and nutrition interventions. The hypothesis of this quasi-experimental study was: Children exposed to GBG will have better neurocognitive and nutrition outcomes compared to those receiving only the MNCH intervention. The secondary objective was to assess the effect of GBG on parental child care and nutrition practices. Methodology: The 14 month long study, targeted all 1,300 children aged 0 to 23 months, living in 43 study communities the in Gavar and Vardenis regions (Gegharkunik province, Armenia). Twenty-three intervention communities, 680 children, received GBG, and 20 control communities, 630 children, received MCHN interventions only. Baseline and evaluation data on child development, nutrition status and parental child care and nutrition practices were collected (caregiver interview, direct child assessment). In the intervention sites, in addition to MNCH (maternity schools, supportive supervision for Health Care Providers (HCP), the trained GBG facilitators conducted six interactive group sessions for mothers (key messages, information, group discussions, role playing, video-watching, toys/books preparation, according to GBG curriculum), and two sessions (condensed GBG) for adult family members (husbands, grandmothers). The trained HCPs received quality supervision for ECD counseling and screening. Findings: The GBG model proved to be effective in improving ECD outcomes. Children in the intervention sites had 83% higher odd of total ECD composite score (cognitive, language, motor) compared to children in the control sites (aOR 1.83; 95 percent CI: 1.08-3.09; p=0.025). Caregivers also demonstrated better child care and nutrition practices (minimum dietary diversity in intervention site is 55 percent higher compared to control (aOR=1.55, 95 percent CI 1.10-2.19, p =0.013); support for learning and disciplining practices (aOR=2.22, 95 percent CI 1.19-4.16, p=0.012)). However, there was no evidence of stunting reduction in either study arm. he effect of the integrated model was more prominent in Vardenis, a community which is characterised by high food insecurity and limited knowledge of positive parenting skills. Conclusion: The GBG model is effective and could be applied in target areas with the greatest economic disadvantages and parenting challenges to improve ECD, care practices and developmental outcomes. Longitudinal studies are needed to view the long-term effects of GBG on learning and school readiness.

Keywords: early childhood development, integrated interventions, parental practices, quasi-experimental study

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Authors: Jurijs Salijevs, Katrina Bolocko

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The integration of artificial intelligence and neural networks has significantly changed full-body imaging and high-resolution 3D mapping systems, and this paper reviews research in these areas. With an emphasis on their use in the early identification of melanoma and other disorders, the goal is to give a wide perspective on the current status and potential future of these medical imaging technologies. Authors also examine methodologies such as machine learning and deep learning, seeking to identify efficient procedures that enhance diagnostic capabilities through the analysis of 3D body scans. This work aims to encourage further research and technological development to harness the full potential of AI in disease diagnosis.

Keywords: artificial intelligence, neural networks, 3D scan, body scan, 3D mapping system, healthcare

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Authors: Sepideh Nikounejad, Mostafa Ghaderi, Nematollah Azizi, Per-Olof Thang, Mohamad Reza Neyestani

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From an educational perspective, an effective and efficient relationship between university and industry can be considered as an important means by which not only both sides are improved but also it brings many advantages and benefits for both parties. It means more specifically, mutual collaboration between universities and industry can not only reduce youth unemployment, but it can improve the quality of teaching and learning in higher education settings while providing more qualified people to industrial enterprises. Indeed the lack of effective interaction between Iranian universities and industry has confronted the country and created many challenges include in increasing number of unskillful and unemployed graduates. However, in order to suggest appropriate practical strategies, it is very important to see how this issue has been tackled by Swedish universities, which have had a good background in this collaboration and how they are connected to the industry in particular and labour market in general. The research aims to study and compare the mechanisms, processes, and policies of the current model in the relationships between university and industry in Iran and Sweden. As a qualitative study, grounded theory was applied. Data were collected via semi-structured interviews. Participants were selected purposefully and by the snowball sampling method. The findings indicate that despite reported needs from both sides for close collaborations between universities and industries in Iran, current policies and practices, including internship, laboratory, and financial support, need to be revised critically. However, in light of our findings on the Swedish policies and practices for linking universities and industries, some practical strategies will be suggested for the Iranian higher education context. In conclusion, cooperation models between universities and industries in Iran and Sweden will be described.

Keywords: cooperation, higher education, industry, comparative

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Authors: Dike Felix Okechukwu

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Studies and reports from authoritative sources such as the Intergovernmental Panel on Climate Change (IPCC) have stated that to effectively solve environmental sustainability challenges such as pollution, inappropriate waste disposal, and unsustainable consumption, there is a need for more research to seek solutions towards environmentally sustainable behavior. However, literature thus far reports only sporadic developments of TL in Environmental Sustainability because there are scarce reports showing the reliable process(es) to produce TL - for sustainability projects or otherwise. Nonetheless, a recently published article demonstrates how TL can be used to help young adults gain transformed mindsets and habits toward environmental sustainability behaviors and practices. This study, however, does not demonstrate, on a repeated basis, the dependability of the method or reliability of the procedures in using its proposed methodology to help young adults achieve transformed habits towards environmental sustainability behaviors, especially in diverse contexts. In this study, it is demonstrated, through repeated measures, a reliable process that can be used to achieve transformations in habits and mindsets toward environmental sustainability behaviors. To achieve this, the design adopted is multiple case studies and a thematic analysis techniques. Five cases in diverse contexts were used to analyze pieces of evidence of Transformative Learning Outcomes toward environmentally sustainable behaviors. Results from the study offer fresh perspectives on a reliable methodology that can be adopted to achieve Transformations in Habits and mindsets toward environmental sustainability behaviors.

Keywords: environmental sustainability, transformative learning, behaviour, learning, education

Procedia PDF Downloads 84