Search results for: learning physical

Authors: Nisa Özge Önal, Kamil Karaçuha, Göksu Hazar Erdinç, Banu Bahar Karaçuha, Ertuğrul Karaçuha

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The study aims to use a mathematical approach with the fractional calculus which is developed to have the ability to continuously analyze the factors related to the children’s height development. Until now, tracking the development of the child is getting more important and meaningful. Knowing and determining the factors related to the physical development of the child any desired time would provide better, reliable and accurate results for childcare. In this frame, 7 groups for height percentile curve (3th, 10th, 25th, 50th, 75th, 90th, and 97th) of Turkey are used. By using discrete height data of 0-18 years old children and the least squares method, a continuous curve is developed valid for any time interval. By doing so, in any desired instant, it is possible to find the percentage and location of the child in Percentage Chart. Here, with the help of the fractional calculus theory, a mathematical model is developed. The outcomes of the proposed approach are quite promising compared to the linear and the polynomial method. The approach also yields to predict the expected values of children in the sense of height.

Keywords: children growth percentile, children physical development, fractional calculus, linear and polynomial model

Procedia PDF Downloads 154

Authors: L. J. de Jager

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In the South African English school curriculum, one of the aims is to achieve communicative competence, the knowledge of using language competently and appropriately in a speech community. Communicatively competent speakers should not only produce grammatically correct sentences but also produce contextually appropriate sentences for various purposes and in different situations. As most speakers of English are non-native speakers, achieving communicative competence remains a complex challenge. Moreover, the changing needs of society necessitate not merely language proficiency, but also technological proficiency. One of the burning issues in the South African educational landscape is the replacement of the standardised literacy model by the pedagogy of multiliteracies that incorporate, by default, the exploration of technological text forms that are part of learners’ everyday lives. It foresees learners as decoders, encoders, and manufacturers of their own futures by exploiting technological possibilities to constantly create and recreate meaning. As such, 21st century learners will feel comfortable working with multimodal texts that are intrinsically part of their lives and by doing so, become authors of their own learning experiences while teachers may become agents supporting learners to discover their capacity to acquire new digital skills for the century of multiliteracies. The aim is transformed practice where learners use their skills, ideas, and knowledge in new contexts. This paper reports on a research project on the integration of technology for language learning, based on the technological pedagogical content knowledge framework, conceptually founded in the theory of multiliteracies, and which aims to achieve communicative competence. The qualitative study uses the community of inquiry framework to answer the research question: How does the integration of technology transform language teaching of preservice teachers? Pre-service teachers in the Postgraduate Certificate of Education Programme with English as methodology were purposively selected to source and evaluate apps for teaching and learning English. The participants collaborated online in a dedicated Blackboard module, using discussion threads to sift through applicable apps and develop interactive lessons using the Apps. The selected apps were entered on to a predesigned Qualtrics form. Data from the online discussions, focus group interviews, and reflective journals were thematically and inductively analysed to determine the participants’ perceptions and experiences when integrating technology in lesson design and the extent to which communicative competence was achieved when using these apps. Findings indicate transformed practice among participants and research team members alike with a better than average technology acceptance and integration. Participants found value in online collaboration to develop and improve their own teaching practice by experiencing directly the benefits of integrating e-learning into the teaching of languages. It could not, however, be clearly determined whether communicative competence was improved. The findings of the project may potentially inform future e-learning activities, thus supporting student learning and development in follow-up cycles of the project.

Keywords: apps, communicative competence, English teaching, technology integration, technological pedagogical content knowledge

Procedia PDF Downloads 171

Authors: Iman Jabbar

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Within the scope of professionalism and in order to compete with the modern world, teachers, are expected to develop their teaching skills and activities in addition to their professional knowledge. At the college level, the teacher should be able to face classroom challenges through his engagement with the learning situation to understand the students and their needs. In our field of TESOL, the role of the English teacher is no longer restricted to teaching English texts, but rather he should endeavor to enhance the students’ skills such as communication and critical analysis. Within the literature of professionalism, there are certain strategies and tools that an English teacher should adopt to develop his competence and performance. Reflective practice, which is an exploratory process, is one of these strategies. Another strategy contributing to classroom development is motivation. It is crucial in students’ learning as it affects the quality of learning English in the classroom in addition to determining success or failure as well as language achievement. This is a qualitative study grounded on interpretive perspectives of teachers and students regarding the process of professional development. This study aims at (a) understanding how teachers at the college level conceptualize reflective practice and motivation inside EFL classroom, and (b) exploring the methods and strategies that they implement to practice reflection and motivation. This study and is based on two questions: 1. How do EFL teachers perceive and view reflection and motivation in relation to their teaching and professional development? 2. How can reflective practice and motivation be developed into practical strategies and actions in EFL teachers’ professional context? The study is organized into two parts, theoretical and practical. The theoretical part reviews the literature on the concept of reflective practice and motivation in relation to professional development through providing certain definitions, theoretical models, and strategies. The practical part draws on the theoretical one, however; it is the core of the study since it deals with two issues. It involves the research design, methodology, and methods of data collection, sampling, and data analysis. It ends up with an overall discussion of findings and the researcher's reflections on the investigated topic. In terms of significance, the study is intended to contribute to the field of TESOL at the academic level through the selection of the topic and investigating it from theoretical and practical perspectives. Professional development is the path that leads to enhancing the quality of teaching English as a foreign or second language in a way that suits the modern trends of globalization and advanced technology.

Keywords: professional development, motivation, reflection, learning

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

Procedia PDF Downloads 158

Authors: Brady, Imelda Katherine, Da Cunha Fanego, Iria

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Today's university students are considered digital natives and the use of Information Technologies (ITs) forms a large part of their study and learning. In the context of language studies, applications that help with revisions of grammar or vocabulary are particularly useful, especially if they are open access. There are studies that show the effectiveness of this type of application in the learning of English as a foreign language and that using IT can help learners become more autonomous in foreign language acquisition, given that these applications can enhance awareness of the learning process; this means that learners are less dependent on the teacher for corrective feedback. We also propose that the exploitation of these technologies also enhances the work of the language instructor wishing to incorporate IT into his/her practice. In this context, the aim of this paper is to present the creation of a repository of tools that provide support in the writing and correction of texts in English and the assessment of their usefulness on behalf of university students enrolled in the English Studies Degree. The project seeks to encourage the development of autonomous learning through the acquisition of skills linked to the self-correction of written work in English. To comply with the above, our methodology follows five phases. First of all, a selection of the main open-access online applications available for the correction of written texts in English is made: AutoCrit, Hemingway, Grammarly, LanguageTool, OutWrite, PaperRater, ProWritingAid, Reverso, Slick Write, Spell Check Plus and Virtual Writing Tutor. Secondly, the functionalities of each of these tools (spelling, grammar, style correction, etc.) are analyzed. Thirdly, explanatory materials (texts and video tutorials) are prepared on each tool. Fourth, these materials are uploaded into a repository of our university in the form of an institutional blog, which is made available to students and the general public. Finally, a survey was designed to collect students’ feedback. The survey aimed to analyse the usefulness of the blog and the quality of the explanatory materials as well as the degree of usefulness that students assigned to each of the tools offered. In this paper, we present the results of the analysis of data received from 33 students in the 1st semester of the 21-22 academic year. One result we highlight in our paper is that the students have rated this resource very highly, in addition to offering very valuable information on the perceived usefulness of the applications provided for them to review. Our work, carried out within the framework of a teaching innovation project funded by our university, emphasizes that teachers need to design methodological strategies that help their students improve the quality of their productions written in English and, by extension, to improve their linguistic competence.

Keywords: academic blog, open access tools, online self-correction, written production in English, university learning

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

Procedia PDF Downloads 131

Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

Procedia PDF Downloads 116

Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Mustafa Tufekci, Caner Guven

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In Automotive Industry, sliding door systems that are also used as body closures, are safety members. Extreme product tests are realized to prevent failures in a design process, but these tests realized experimentally result in high costs. Finite element analysis is an effective tool used for the design process. These analyses are used before production of a prototype for validation of design according to customer requirement. In result of this, the substantial amount of time and cost is saved. Finite element model is created for geometries that are designed in 3D CAD programs. Different element types as bar, shell and solid, can be used for creating mesh model. The cheaper model can be created by the selection of element type, but combination of element type that was used in model, number and geometry of element and degrees of freedom affects the analysis result. Sliding door system is a good example which used these methods for this study. Structural analysis was realized for sliding door mechanism by using FE models. As well, physical tests that have same boundary conditions with FE models were realized. Comparison study for these element types, were done regarding test and analyses results then the optimum combination was achieved.

Keywords: finite element analysis, sliding door mechanism, element type, structural analysis

Procedia PDF Downloads 332

Authors: Bruna Azzari Puga, Richard Roe, Andre Pagani de Souza

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abstract outlines a proposed study to examine how and to what extent interactive, experiential, learner centered methodology develops learning of basic civic and democratic competencies among young children. It stems from the Literacy and Law course taught at Georgetown University Law Center in Washington, DC, since 1998. Law students, trained in best literacy practices and legal cases affecting literacy development, read “law related” children’s books and engage in interactive and extension activities with emerging readers. The law students write a monthly journal describing their experiences and a final paper: a conventional paper or a children’s book illuminating some aspect of literacy and law. This proposal is based on the recent adaptation of Literacy and Law to Brazil at Mackenzie Presbyterian University in São Paulo in three forms: first, a course similar to the US model, often conducted jointly online with Brazilian and US law students; second, a similar course that combines readings of children’s literature with activity based learning, with law students from a satellite Mackenzie campus, for young children from a vulnerable community near the city; and third, a course taught by law students at the main Mackenzie campus for 4th grade students at the Mackenzie elementary school, that is wholly activity and discourse based. The workings and outcomes of these courses are well documented by photographs, reports, lesson plans, and law student journals. The authors, faculty who teach the above courses at Mackenzie and Georgetown, observe that literacy, broadly defined as cognitive and expressive development through reading and discourse-based activities, can be influential in developing democratic civic skills, identifiable by explicit civic competencies. For example, children experience justice in the classroom through cooperation, creativity, diversity, fairness, systemic thinking, and appreciation for rules and their purposes. Moreover, the learning of civic skills as well as the literacy skills is enhanced through interactive, learner centered practices in which the learners experience literacy and civic development. This study will develop rubrics for individual and classroom teaching and supervision by examining 1) the children’s books and students diaries of participating law students and 2) the collection of photos and videos of classroom activities, and 3) faculty and supervisor observations and reports. These rubrics, and the lesson plans and activities which are employed to advance the higher levels of performance outcomes, will be useful in training and supervision and in further replication and promotion of this form of teaching and learning. Examples of outcomes include helping, cooperating and participating; appreciation of viewpoint diversity; knowledge and utilization of democratic processes, including due process, advocacy, individual and shared decision making, consensus building, and voting; establishing and valuing appropriate rules and a reasoned approach to conflict resolution. In conclusion, further development and replication of the learner centered literacy and law practices outlined here can lead to improved qualities of democratic teaching and learning supporting mutual respect, positivity, deep learning, and the common good – foundation qualities of a sustainable world.

Keywords: democracy, law, learner-centered, literacy

Procedia PDF Downloads 128

Authors: T. J. B. van Niekerk, J. Viviers, E. J. Cilliers

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Substantial efforts were made in South Africa, stemming from a historic political change in 1994, to remedy the inequality and injustice, resulting from a dispensation where spatial patterns were largely based on racial segregation. Spatially distorted patterns predominantly originated from colonialism in the beginning of the twentieth century, ensuing a physical imprint on South African cities relating to architecture, urban layout and planning, frequently reflecting European norms and standards. As a consequence of physical and land use barriers, and well-established dual cities, attempts to address spatial injustices, apart from limited occurrences in metropolitan areas, gravely failed. Interception of incessant segregated growth, combined with urban sprawl is becoming increasingly evident. Intervention is a prerequisite to duly address the impact of colonial planning and its legacy still prevalent in most urban areas. During 1998, the National Department of Transport prepared the “Moving South Africa” strategy; presenting the Corridor Densification Option Model for the first time, as it was deemed more fitting to the existing South African urban tenure patterns than more familiar planning approaches. Urban planners are progressively contemplating the Corridor Densification Option Model and its attributes, besides its transportation emphasis, as an alternative approach to address spatial imbalances and to attain the physical integration of contemporary urban forms. In attaining a clearer understanding of the Corridor Densification Option Model, its rationale was analysed in greater detail. This research further investigated the provisional applications of the model in spatially segregated cities and illustrated that viable options are present to effectively employ it. Research revealed that the application of the model will, however, be dependent on the occurrence of specific characteristics in spatially segregated cities to warrant augmentation thereof.

Keywords: corridor densification option model, spatially segregated settlements, integration, urban restructuring

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Authors: M. A. Ferhat, M. N. Boukhatem, F. Chemat

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Eucalyptus essential oil is extracted from Eucalyptus globulus of the Myrtaceae family and is also known as Tasmanian blue gum or blue gum. Despite the reputation earned by aromatic and medicinal plants of Algeria. The objectives of this study were: (i) the extraction of the essential oil from the leaves of Eucalyptus globulus Labill., Myrtaceae grown in Algeria, and the quantification of the yield thereof, (ii) the identification and quantification of the compounds in the essential oil obtained, and (iii) the determination of physical and chemical properties of EGEO. The chemical constituents of Eucalyptus globulus essential oil (EGEO) of Blida origin has not previously been investigated. Thus, the present study has been conducted for the determination of chemical constituents and different physico-chemical properties of the EGEO. Chemical composition of the EGEO, grown in Algeria, was analysed by Gas Chromatography-Mass Spectrometry. The chemical components were identified on the basis of Retention Time and comparing with mass spectral database of standard compounds. Relative amounts of detected compounds were calculated on the basis of GC peak areas. Fresh leaves of E. globulus on steam distillation yielded 0.96% (v/w) of essential oil whereas the analysis resulted in the identification of a total of 11 constituents, 1.8 cineole (85.8%), α-pinene (7.2%), and β-myrcene (1.5%) being the main components. Other notable compounds identified in the oil were β-pinene, limonene, α-phellandrene, γ-terpinene, linalool, pinocarveol, terpinen-4-ol, and α-terpineol. The physical properties such as specific gravity, refractive index and optical rotation and the chemical properties such as saponification value, acid number and iodine number of the EGEO were examined. The oil extracted has been analyzed to have 1.4602-1.4623 refractive index value, 0.918-0.919 specific gravity (sp.gr.), +9 - +10 optical rotation that satisfy the standards stipulated by European Pharmacopeia. All the physical and chemical parameters were in the range indicated by the ISO standards. Our findings will help to access the quality of the Eucalyptus oil which is important in the production of high value essential oils that will help to improve the economic condition of the community as well as the nation.

Keywords: chemical composition, essential oil, eucalyptol, gas chromatography

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Authors: Sylvain Perrin, Thomas Saillour

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The Grande-Motte port and seafront development project on the French Mediterranean coastline entailed evaluating wave impact loads (pressures and forces) on the new beach seawall and comparing the resulting scour potential at the base of the existing and new seawall. A physical model was built at ARTELIA’s hydraulics laboratory in Grenoble (France) to provide insight into the evolution of scouring overtime at the front of the wall, quasi-static and impulsive wave force intensity and distribution on the wall, and water and sand overtopping discharges over the wall. The beach was constituted of fine sand and approximately 50 m wide above mean sea level (MSL). Seabed slopes were in the range of 0.5% offshore to 1.5% closer to the beach. A smooth concrete structure will replace the existing concrete seawall with an elevated curved crown wall. Prior the start of breaking (at -7 m MSL contour), storm-driven maximum spectral significant wave heights of 2.8 m and 3.2 m were estimated for the benchmark historical storm event dated of 1997 and the 50-year return period storms respectively, resulting in 1 m high waves at the beach. For the wave load assessment, a tensor scale measured wave forces and moments and five piezo / piezo-resistive pressure sensors were placed on the wall. Light-weight sediment physical model and pressure and force measurements were performed with scale 1:18. The polyvinyl chloride light-weight particles used to model the prototype silty sand had a density of approximately 1 400 kg/m3 and a median diameter (d50) of 0.3 mm. Quantitative assessments of the seabed evolution were made using a measuring rod and also a laser scan survey. Testing demonstrated the occurrence of numerous impulsive wave impacts on the reflector (22%), induced not by direct wave breaking but mostly by wave run-up slamming on the top curved part of the wall. Wave forces of up to 264 kilonewtons and impulsive pressure spikes of up to 127 kilonewtons were measured. Maximum scour of -0.9 m was measured for the new seawall versus -0.6 m for the existing seawall, which is imputable to increased wave reflection (coefficient was 25.7 - 30.4% vs 23.4 - 28.6%). This paper presents a methodology for the setup and operation of a physical model in order to assess the hydrodynamic and morphodynamic processes at a beach seawall during storms events. It discusses the pros and cons of such methodology versus others, notably regarding structures peculiarities and model effects.

Keywords: beach, impacts, scour, seawall, waves

Procedia PDF Downloads 157

Authors: Duangkamol Thitivesa

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This paper focuses on the use of project work as a pretext for applying the conventions of writing, or the correctness of mechanics, usage, and sentence formation, in a content-based class in a Rajabhat University. Its aim was to explore to what extent the student teachers’ academic achievement of the basic writing features against the 70% attainment target after the use of project is. The organization of work around an agreed theme in which the students reproduce language provided by texts and instructors is expected to enhance students’ correct writing conventions. The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of achievement test and student writing works. The scores in the summative achievement test were analyzed by mean score, standard deviation, and percentage. It was found that the student teachers do more achieve of practicing mechanics and usage, and less in sentence formation. The students benefited from the exposure to texts during conducting the project; however, their automaticity of how and when to form phrases and clauses into simple/complex sentences had room for improvement.

Keywords: project-based learning, project work, writing conventions, academic achievement

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Authors: Yan Zhang

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With the development of commercial movies, more and more instructors are drawn to adapt film pedagogy to teach history and culture. By challenging traditional standards of classroom culture, instruction through film represents an intersection of modernity and adaptability which is no longer optional but essential to maintaining educational accessibility. First, this presentation describes special features of the film that can be used in the classroom and help students acquire intercultural communicative competence (ICC) and achieve the learning goal. Second, the author brings forward the 5 A STAIRCASE model (Acknowledge-Adjust-Acculturate-Act-Assess) to explore how students acquire international communicative competence. Third, this article presents the intersections between new digital environments and classroom practice, such as how films can contribute to combining classical and contemporary Chinese cultures seamlessly and how film pedagogy can be an effective way to get students to engage in deeper critical thinking by exposing them to visuals, music, language, and styling which do not exist in traditional learning formats. Last, the student’s final video project will be exemplified at the end, demonstrating how to engage students in the analysis and experience of history and culture.

Keywords: intercultural education, curriculum, media, history

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Authors: N. Khodarahmi, L. Ford

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Decades of research on the impact of trauma in early childhood suggest severe risks to the mental health, emotional, social and physical development of a young child. Trauma-exposed students can pose a variety of different levels of challenges to schools and educators of young children and to date, few studies have addressed ECE teachers’ role in providing trauma support. The present study aims to contribute to this literature by exploring the beliefs of British Columbia’s (BC) early childhood education (ECE) teachers in their level of readiness and capability to work within a trauma-informed practice (TIP) framework to support their trauma-exposed students. Through a sequential, mix-methods approach, a self-report questionnaire and semi-structured interviews will be used to gauge BC ECE teachers’ knowledge of TIP, their preparedness, and their ability in using this framework to support their most vulnerable students. Teacher participants will be recruited through the ECEBC organization and various school districts in the Greater Vancouver Area. Questionnaire data will be primarily collected through an online survey tool whereas interviews will be taking place in-person and audio-recorded. Data analysis of survey responses will be largely descriptive, whereas interviews, once transcribed, will be employing thematic content analysis to generate themes from teacher responses. Ultimately, this study hopes to highlight the necessity of utilizing the TIP framework in BC ECE classrooms in order to support both trauma-exposed students and provide essential resources to compassionate educators of young children.

Keywords: early childhood education, early learning classrooms, refugee students, trauma-exposed students, trauma-informed practice

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Hedyeh Gamini, Hadi Abdus

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Tsunami, as a natural disaster, is composed of waves that are usually caused by severe movements at the sea floor. Although tsunami and its consequences cannot be prevented in any way, by examining past tsunamis and extracting key points on how to deal with this incident and learning from it, a positive step can be taken to reduce the vulnerability of human settlements and reduce the risk of this phenomenon in architecture and urbanism. The method is reviewing and has examined the documents written and valid internet sites related to managing and reducing the vulnerability of human settlements in face of tsunami. This paper has explored the tsunamis in Indonesia (2004), Sri Lanka (2004) and Japan (2011), and of the study objectives has been understanding how they dealt with tsunami and extracting key points, and the lessons from them in terms of reduction of vulnerability of human settlements in dealing with the tsunami. Finally, strategies to prevent and reduce the vulnerability of communities at risk of tsunamis have been offered in terms of architecture and urban planning. According to what is obtained from the study of the recent tsunamis, the authorities' quality of dealing with them, how to manage the crisis and the manner of their construction, it can be concluded that to reduce the vulnerability of human settlements against tsunami, there are generally four ways that are: 1-Construction of tall buildings with opening on the first floor so that water can flow easily under and the direction of the building should be in a way that water passes easily from the side. 2- The construction of multi-purpose centers, which could be used as vertical evacuation during accidents. 3- Constructing buildings in core forms with diagonal orientation of the coastline, 4- Building physical barriers (natural and synthetic) such as water dams, mounds of earth, sea walls and creating forests

Keywords: tsunami, architecture, reducing vulnerability, human settlements, urbanism

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Authors: Mavis Yaa Konadu Agyemang

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Despite provisions to uphold and safeguard the rights of persons with disability in Ghana, there is evidence that they still encounter several challenges which limit their full and effective involvement in mainstream society, including the gold mining sector. The study sought to explore how persons with physical disability (PWPDs) experience gold mining in the Obuasi Municipal Area. A qualitative research design was used to discover and understand the experiences of PWPDs regarding mining. The purposive sampling technique was used to select five key informants for the study with the age range of (24-52 years) while snowball sampling aided the selection of 16 persons with various forms of physical disability with the age range of (24-60 years). In-depth interviews were used to gather data. The interviews lasted from forty-five minutes to an hour. In relation to the setting, the interviews of thirteen (13) of the participants with disability were done in their houses, two (2) were done on the phone, and one (1) was done in the office. Whereas the interviews of the five (5) key informants were all done in their offices. Data were analyzed using Creswell’s (2009) concept of thematic analysis. The findings suggest that even though land degradation affected everyone in the area, persons with mobility and visual impairment experienced many difficulties trekking the undulating land for long distances in search of arable land. Also, although mining activities are mostly labour-intensive, PWPDs were not employed even in areas where they could work. Further, the cost of items, in general, was high, affecting PWPDs more due to their economic immobility and paying for other sources of water due to land degradation and water pollution. The study also discovered that the peculiar conditions of PWPDs were not factored into compensation payments, and neither were females with physical disability engaged in compensation negotiations. Also, although some of the infrastructure provided by the gold mining companies in the area was physically accessible to some extent, it was not accessible in terms of information delivery. There is a need to educate the public on the effects of mining on PWPDs, their needs as well as disability issues in general. The Minerals and Mining Act (703) should be amended to include provisions that would consider the peculiar needs of PWPDs in compensation payment.

Keywords: mining, resettlement, compensation, environmental, social, disability

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Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

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Authors: Mouad Ouafi, Lahoucine Atourki, Larbi Laanab, Erika Vega, Miguel Mollar, Bernabe Marib, Boujemaa Jaber

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Despite the astonishing emergence of the methylammonium lead triiodide perovskite as a promising light harvester for solar cells, their physical properties in solution-processed MAPbI₃ are still crucial and need to be improved. The objective of this work is to investigate the hot airflow effect during the growth of MAPbI₃ films using the spin-coating process on their structural, optical and morphological proprieties. The experimental results show that many physical proprieties of the perovskite strongly depend on the air flow temperature and the optimization which has a beneficial effect on the perovskite quality. In fact, a clear improvement of the crystallinity and the crystallite size of MAPbI₃ perovskite is demonstrated by the XRD analyses, when the airflow temperature is increased up to 100°C. Alternatively, as far as the surface morphology is concerned, SEM micrographs show that significant homogenous nucleation, uniform surface distribution and pin holes free with highest surface coverture of 98% are achieved when the airflow temperature reaches 100°C. At this temperature, the improvement is also observed when considering the optical properties of the films. By contrast, a remarkable degradation of the MAPbI₃ perovskites associated to the PbI₂ phase formation is noticed, when the hot airflow temperature is higher than 100°C, especially 300°C.

Keywords: hot air flow, crystallinity, surface coverage, perovskite morphology

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Authors: Ayşegül Çabuk, Mine Işıksal

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Mathematics is a sense-making activity so that it requires meaningful learning. Hence based on this idea, meaningful mathematical connections are necessary to learn mathematics. At that point, the major question has become that which educational methods can provide opportunities to provide mathematical connections and to understand mathematics. The amalgam of reasoning and proof can be the one of the methods that creates opportunities to learn mathematics in a meaningful way. However, even if reasoning and proof should be included from prekindergarten to grade 12, studies in literature generally include secondary school students and pre-service mathematics teachers. With the light of the idea that the amalgam of reasoning and proof has significant effect on middle school students' mathematical learning, this study aims to investigate middle grade students' tendencies while reasoning the correctness of statements related to numbers. The sample included 272 middle grade students, specifically 69 of them were sixth grade students (25.4%), 101 of them were seventh grade students (37.1%) and 102 of them were eighth grade students (37.5%). Data was gathered through an achievement test including 2 essay types of problems about algebra. The answers of two items were analyzed both quantitatively and qualitatively in terms of students' solutions strategies while reasoning the correctness of the statements. Similar on the findings in the literature, most of the students, in all grade levels, used numerical examples to judge the statements. Moreover the results also showed that the majority of these students appear to believe that providing one or more selected examples is sufficient to show the correctness of the statement. Hence based on the findings of the study, even students in earlier ages have proving and reasoning abilities their reasoning's generally based on the empirical evidences. Therefore, it is suggested that examples and example-based reasoning can be a fundamental role on to generate systematical reasoning and proof insight in earlier ages.

Keywords: reasoning, mathematics learning, middle grade students

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Authors: Samantha Rix

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The purpose of this research paper is to investigate the cognitive processing of learners who receive input but produce very little or no output, and who, when they do produce output, exhibit a similar language proficiency as do those learners who produced output more regularly in the language classroom. Previous studies have investigated the benefits of output (with somewhat differing results); therefore, the presentation will begin with an investigation of what may underlie gains in proficiency without output. Consequently, a pilot study was designed and conducted to gain insight into the cognitive processing of low-output language learners looking, for example, at quantity and quality of noticing. This will be carried out within the paradigm of action classroom research, observing and interviewing low-output language learners in an intensive English program at a small Midwest university. The results of the pilot study indicated that autonomy in language learning, specifically utilizing strategies such self-monitoring, self-talk, and thinking 'out-loud', were crucial in the development of language proficiency for academic-level performance. The presentation concludes with an examination of pedagogical implication for classroom use in order to aide students in their language development.

Keywords: cognitive processing, language learners, language proficiency, learning strategies

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Authors: A. Eiguren-Munitis, N. Berasategi, J. M. Correa

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Changes in family structures, immigration, economic crisis, among others, hinder the connection between different generations. This situation gives rise to a greater lack of social protection of the groups in vulnerable situations, such as the elderly and children. There is a growing need to search for shared spaces where different generations manage to break negative stereotypes and interact with each other. The school environment provides a favourable context in which the approach of different generations can be worked on. The intergenerational experiences that take place within the school context help to introduce the educational ideology for a lifetime. This induces bilateral learning, which encourages citizen participation. For this reason, the general objective of this research is to deepen the impact that intergenerational experiences have on participating students. The research is carried out based on mixed methods. The qualitative and quantitative evaluation included pre-test and post-test questionnaires (n=148) and group interviews (n=43). The results indicate that the intergenerational experiences influence different levels, on the one hand, help to promote school motivation and on the other hand, help to reduce negative stereotypes towards older people thus contributing to greater social cohesion.

Keywords: intergenerational learning, school, stereotypes, social cohesion

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Authors: Muhammad Ramzan

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STEM Education and Critical Thinking Skills are important 21st-century skills. STEM Education is necessary to promote secondary school students’ critical thinking skills. These skills are critical for teachers to respond to students. Pakistan is in the preliminary stages of integrating STEM Education in institutions like other developing countries. Unfortunately, most secondary school students in Pakistan are unaware of STEM Education and teachers are not applying critical thinking skills in classrooms. The study's objectives mainly deal with; to identify the importance of STEM Education in the teaching-learning process; to find out the factors affecting critical thinking skills that can develop interest in students in STEM Education and suggestions on how to improve critical thinking skills in students regarding STEM Education. This study was descriptive. The population of the study was secondary school students. Data was collected from 200 secondary school students through a questionnaire. The research results show that critical thinking skills develop interest in students towards STEM Education.

Keywords: STEM education, teachers, students, critical thinking skills, teaching and learning process

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Authors: Ahmed Abdelrahman, Ahmed Abdelraheem

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There are many essential factors affecting the integration of information and communication technology (ICT) into teaching and learning, including technology infrastructure, institutional support, professional development, and faculty members’ beliefs regarding ICT integration. The present research project investigated the current status of integrating ICT into teaching and learning at Sultan Qaboos University (SQU). A sample of 220 faculty members from six different colleges and four administrators from the Center of Educational Technology (CET) and the Center for Information Systems (CIS) at SQU in Oman were chosen, and quantitative, qualitative design using a semi-structured questionnaire, interviews and checklists were employed. The findings show that SQU had a high availability of ICT infrastructure in terms of hardware, software, and support services, as well as adequate computer labs for educational purposes. However, the results also indicated that, although SQU provided a series of professional development workshops related to using ICT in teaching, few faculty members were interested. Furthermore, the finding indicated that the degree of ICT integration into teaching at SQU was at a medium level.

Keywords: information and communication technology, integration, professional development, teaching

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Brenda Ravenscroft, Ulemu Luhanga, Bev King

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A comprehensive evaluation of online learning needs to include a blend of educational design, technology use, and online instructional practices that integrate technology appropriately for developing and delivering quality online courses. Research shows that classroom-based evaluation tools do not adequately capture the dynamic relationships between content, pedagogy, and technology in online courses. Furthermore, studies suggest that using classroom evaluations for online courses yields lower than normal scores for instructors, and may affect faculty negatively in terms of administrative decisions. In 2014, the Faculty of Arts and Science at Queen’s University responded to this evidence by seeking an alternative to the university-mandated evaluation tool, which is designed for classroom learning. The Faculty is deeply engaged in e-learning, offering large variety of online courses and programs in the sciences, social sciences, humanities and arts. This paper describes the process by which a new student survey instrument for online courses was developed and piloted, the methods used to analyze the data, and the ways in which the instrument was subsequently adapted based on the results. It concludes with a critical reflection on the challenges of evaluating e-learning. The Student Evaluation of Online Teaching Effectiveness (SEOTE), developed by Arthur W. Bangert in 2004 to assess constructivist-compatible online teaching practices, provided the starting point. Modifications were made in order to allow the instrument to serve the two functions required by the university: student survey results provide the instructor with feedback to enhance their teaching, and also provide the institution with evidence of teaching quality in personnel processes. Changes were therefore made to the SEOTE to distinguish more clearly between evaluation of the instructor’s teaching and evaluation of the course design, since, in the online environment, the instructor is not necessarily the course designer. After the first pilot phase, involving 35 courses, the results were analyzed using Stobart's validity framework as a guide. This process included statistical analyses of the data to test for reliability and validity, student and instructor focus groups to ascertain the tool’s usefulness in terms of the feedback it provided, and an assessment of the utility of the results by the Faculty’s e-learning unit responsible for supporting online course design. A set of recommendations led to further modifications to the survey instrument prior to a second pilot phase involving 19 courses. Following the second pilot, statistical analyses were repeated, and more focus groups were used, this time involving deans and other decision makers to determine the usefulness of the survey results in personnel processes. As a result of this inclusive process and robust analysis, the modified SEOTE instrument is currently being considered for adoption as the standard evaluation tool for all online courses at the university. Audience members at this presentation will be stimulated to consider factors that differentiate effective evaluation of online courses from classroom-based teaching. They will gain insight into strategies for introducing a new evaluation tool in a unionized institutional environment, and methodologies for evaluating the tool itself.

Keywords: evaluation, online courses, student survey, teaching effectiveness

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