Search results for: Learning materials

Authors: Hyuk-Hoon Kim, Young Gi Min

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Background: Acute poisoning is the common cause of morbidity and mortality. Characteristics of acute poisoning differ between countries. While other countries operate the database system for poisoning, Korea has not collected the database for acute poisoning. Distribution of incidence of acute poisoning depending on the types of materials have also not studied in Korea. Our aims are to evaluate the etiologic and demographic characteristics of acute poisoning cases and to obtain up-to-date information on acute poisonings. Method: We retrospectively recorded cases of acute poisoning from eight emergency departments of second level or university hospitals from different cities in Gyeonggi province in Korea from April 2006 and March 2015. The distributions of incidence of acute poisoning depending on the types of materials are mapped by geographic information system. Result: A total of 3,449 poisoned cases were analyzed. Mean estimated age of patients was 39.56 ± 22.40 years. Mean male to female ratio of patients was 1:1.4. Mean proportion of intentional poisoning was 57.9%. Common materials are benzodiazepine (16.6%), carbon monoxide (10.5%), pesticide (8.1%) and zolpidem (7.1%) Common route of exposure is ingestion (79.5%) and followed by inhalation (16.5%). Common treatment methods are gastric lavage (20%) and activated charcoal (30%). Most cases had uneventful recovery; 61.4% were treated as outpatients and 0.1% of the poisoning resulted in death in ER. Conclusion: Even though the cases enrolled in our study is not the overall cases of acute poisoning in Korea, our study could be the basis of countermeasures for analysis and prevention of acute poisoning in Korea.

Keywords: acute poisoning, emergency department, epidemiology, Korea

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Radek Novotny, Michal Novak, Daniela Marusakova, Monika Sipova, Hugo Fuentes, Peter Borst

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This research has been conducted within the European HORIZON 2020 project ECC-SMART. The main objective is to assess whether it is feasible to design and develop a small modular reactor (SMR) that would be cooled by supercritical water (SCW). One of the main objectives for material research concerns the corrosion of the candidate cladding materials. The experimental part has been conducted in support of the qualification procedure of the future SCW-SMR constructional materials. The last objective was to identify the gaps in current norms and guidelines. Apart from corrosion, resistance testing of candidate materials stresses corrosion cracking susceptibility tests have been performed in supercritical water. This paper describes part of these tests, in particular, those slow strain rate tensile loading applied for tangential ring shape specimens of two candidate materials, Alloy 800H and 310S stainless steel. These ring tensile tests are one the methods used for tensile testing of nuclear cladding. Here full circular heads with dimensions roughly equal to the inner diameter of the sample and the gage sections are placed in the parallel direction to the applied load. Slow strain rate tensile tests have been conducted in 380 or 500oC supercritical water applying two different elongation rates, 1x10-6 and 1x10-7 s-1. The effect of temperature and dissolved oxygen content on the SCC susceptibility of Alloy 800H and 310S stainless steel was investigated when two different temperatures and concentrations of dissolved oxygen were applied in supercritical water. The post-fracture analysis includes fractographic analysis of the fracture surfaces using SEM as well as cross-sectional analysis on the occurrence of secondary cracks. Assessment of the effect of environment and dissolved oxygen content was by comparing to the results of the reference tests performed in air and N2 gas overpressure. The effect of high temperature on creep and its role in the initiation of SCC was assessed as well. It has been concluded that the applied test method could be very useful for the investigation of stress corrosion cracking susceptibility of candidate cladding materials in supercritical water.

Keywords: stress corrosion cracking, ring tensile tests, super-critical water, alloy 800H, 310S stainless steel

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Authors: Jennifer Gallup, Joel Bocanegra, Greg Callan, Abigail Vaughn

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Students with disabilities (SWD) engaged in a distance summer program delivered over multiple virtual mediums that used gaming principles to teach and practice self-regulated learning (SRL) through the process of exploring possible jobs. Gaming quests were developed to explore jobs and teach transition skills. Students completed specially designed quests that taught and reinforced SRL and problem-solving through individual, group, and teacher-led experiences. SRL skills learned were reinforced through guided job explorations over the context of MinecraftEDU, zoom with experts in the career, collaborations with a team over Marco Polo, and Zoom. The quests were developed and laid out on an accessible web page, with active learning opportunities and feedback conducted within multiple virtual mediums including MinecraftEDU. Gaming mediums actively engage players in role-playing, problem-solving, critical thinking, and collaboration. Gaming has been used as a medium for education since the inception of formal education. Games, and specifically board games, are pre-historic, meaning we had board games before we had written language. Today, games are widely used in education, often as a reinforcer for behavior or for rewards for work completion. Games are not often used as a direct method of instruction and assessment; however, the inclusion of games as an assessment tool and as a form of instruction increases student engagement and participation. Games naturally include collaboration, problem-solving, and communication. Therefore, our summer program was developed using gaming principles and MinecraftEDU. This manuscript describes a virtual learning summer program called Virtual Academy New and Exciting Transitions (VAN) that was redesigned from a face-to-face setting to a completely online setting with a focus on SWD aged 14-21. The focus of VAN was to address transition planning needs such as problem-solving skills, self-regulation, interviewing, job exploration, and communication for transition-aged youth diagnosed with various disabilities (e.g., learning disabilities, attention-deficit hyperactivity disorder, intellectual disability, down syndrome, autism spectrum disorder).

Keywords: autism, disabilities, transition, summer program, gaming, simulations

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Authors: Djamel Djeghader, Bachir Redjel

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The composite materials of glass fibers can be used as a repair material for damage elements under repeated stresses, and in various environments. A cyclic bending characterization of a glass/polyester composite material was carried out with consideration of the period of immersion in water. These tests describe the behavior of materials and identify the mechanical fatigue characteristics using the Wohler Curve for different immersion time: 0, 90, 180 and 270 days in water. These curves are characterized by a dispersion in the lifetimes were modeled by straight whose intercepts are very similar and comparable to the static strength. This material deteriorates fatigue at a constant rate, which increases with increasing immersion time in water at a constant speed. The endurance limit seems to be independent of the immersion time in the water.

Keywords: fatigue, composite, glass, polyester, immersion, wohler

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Authors: Nagore Guerra Bilbao, Clemente Lobato Fraile

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This paper outlines the design and development of the MENDEPRO questionnaire, designed to analyze mentoring performance within a professional development process carried out with professors at the University of the Basque Country, Spain. The study took into account the international research carried out over the past two decades into teachers' professional development, and was also based on a thorough review of the most common instruments used to identify and analyze mentoring styles, many of which fail to provide sufficient psychometric guarantees. The present study aimed to gather empirical data in order to verify the metric quality of the questionnaire developed. To this end, the process followed to validate the theoretical construct was as follows: The formulation of the items and indicators in accordance with the study variables; the analysis of the validity and reliability of the initial questionnaire; the review of the second version of the questionnaire and the definitive measurement instrument. Content was validated through the formal agreement and consensus of 12 university professor training experts. A reduced sample of professors who had participated in a lifelong learning program was then selected for a trial evaluation of the instrument developed. After the trial, 18 items were removed from the initial questionnaire. The final version of the instrument, comprising 33 items, was then administered to a sample group of 99 participants. The results revealed a five-dimensional structure matching theoretical expectations. Also, the reliability data for both the instrument as a whole (.98) and its various dimensions (between .91 and .97) were very high. The questionnaire was thus found to have satisfactory psychometric properties and can therefore be considered apt for studying the performance of mentoring in both induction programs for young professors and lifelong learning programs for senior faculty members.

Keywords: higher education, mentoring, professional development, university teaching

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Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

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In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: John E. Boberg, Steven J. Bourgeois

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The decline in student engagement and motivation through the middle years is well documented and clearly associated with a decline in mathematics achievement that persists through high school. To combat this trend and, very often, to meet high-stakes accountability standards, a growing number of parents, teachers, and schools have implemented various methods to incentivize learning. However, according to Self-Determination Theory, forms of incentivized learning such as public praise, tangible rewards, or threats of punishment tend to undermine intrinsic motivation and learning. By focusing on external forms of motivation that thwart autonomy in children, adults also potentially threaten relatedness measures such as trust and emotional engagement. Furthermore, these controlling motivational techniques tend to promote shallow forms of cognitive engagement at the expense of more effective deep processing strategies. Therefore, any short-term gains in apparent engagement or test scores are overshadowed by long-term diminished motivation, resulting in inauthentic approaches to learning and lower achievement. The current study focuses on the relationships between student trust, engagement, and motivation during these crucial years as students transition from elementary to middle school. In order to test the effects of controlled motivational techniques on achievement in mathematics, this quantitative study was conducted on a convenience sample of 22 elementary and middle schools from a single public charter school district in the south-central United States. The study employed multi-source data from students (N = 1,054), parents (N = 7,166), and teachers (N = 356), along with student achievement data and contextual campus variables. Cross-sectional questionnaires were used to measure the students’ self-regulated learning, emotional and cognitive engagement, and trust in teachers. Parents responded to a single item on incentivizing the academic performance of their child, and teachers responded to a series of questions about their acceptance of various incentive strategies. Structural equation modeling (SEM) was used to evaluate model fit and analyze the direct and indirect effects of the predictor variables on achievement. Although a student’s trust in teacher positively predicted both emotional and cognitive engagement, none of these three predictors accounted for any variance in achievement in mathematics. The parents’ use of incentives, on the other hand, predicted a student’s perception of his or her controlled motivation, and these two variables had significant negative effects on achievement. While controlled motivation had the greatest effects on achievement, parental incentives demonstrated both direct and indirect effects on achievement through the students’ self-reported controlled motivation. Comparing upper elementary student data with middle-school student data revealed that controlling forms of motivation may be taking their toll on student trust and engagement over time. While parental incentives positively predicted both cognitive and emotional engagement in the younger sub-group, such forms of controlling motivation negatively predicted both trust in teachers and emotional engagement in the middle-school sub-group. These findings support the claims, posited by Self-Determination Theory, about the dangers of incentivizing learning. Short-term gains belie the underlying damage to motivational processes that lead to decreased intrinsic motivation and achievement. Such practices also appear to thwart basic human needs such as relatedness.

Keywords: controlled motivation, student engagement, incentivized learning, mathematics achievement, self-determination theory, student trust

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Authors: A. Evcin, N. Çiçek Bezir, R. Duman, N. Duman

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Nowadays, a great concern is placed on the harmfulness of ultraviolet radiation (UVR) which attacks human bodies. Nanocarbon materials, such as carbon nanotubes (CNTs), carbon nanofibers (CNFs) and graphene, have been considered promising alternatives to shielding materials because of their excellent electrical conductivities, very high surface areas and low densities. In the present work, carbon nanofibers have been synthesized from solutions of Polyacrylonitrile (PAN)/ N,N-dimethylformamide (DMF) by electrospinning method. The carbon nanofibers have been stabilized by oxidation at 250 °C for 2 h in air and carbonized at 750 °C for 1 h in H2/N2. We present the fabrication and characterization of transparent and ultraviolet (UV) shielding CNF/polymer composites. The content of CNF filler has been varied from 0.2% to 0.6 % by weight. UV Spectroscopy has been performed to study the effect of composition on the transmittance of polymer composites.

Keywords: electrospinning, carbon nanofiber, characterization, composites, nanofiber, ultraviolet radiation

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Authors: Zehra Sultan

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The study is based on the task-based language teaching approach which is found to be very effective in the EFL/ESL classroom. This approach engages learners to acquire the usage of authentic language skills by interacting with the real world through sequence of pedagogical tasks. The use of technology enhances the effectiveness of this approach. This study throws light on the historical background of TBLT and its efficacy in the EFL/ESL classroom. In addition, this study precisely talks about the implementation of this approach in the General Foundation Programme of Muscat College, Oman. It furnishes the list of the pedagogical tasks embedded in the language curriculum of General Foundation Programme (GFP) which are skillfully allied to the College Graduate Attributes. Moreover, the study also discusses the challenges pertaining to this approach from the point of view of teachers, students, and its classroom application. Additionally, the operational success of this methodology is gauged through formative assessments of the GFP, which is apparent in the students’ progress.

Keywords: task-based language teaching, authentic language, communicative approach, real world activities, ESL/EFL activities

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Authors: Mrityunjay Kumar Sinha, Mayank Srivastava

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The position of interface and temperature variation of phase change thermal energy storage system under constant heat injection and radiative heat injection is analysed during charging/discharging process by Heat balance integral method. The charging/discharging process is solely governed by conduction. Phase change material is kept inside a rectangular cavity. Time-dependent fixed temperature and radiative boundary condition applied on one wall, all other walls are thermally insulated. Interface location and temperature variation are analysed by using MATLAB.

Keywords: conduction, melting/solidification, phase change materials, Stefan’s number

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Authors: Helena Costa Oliveira, Carmem Oliveira, Rita Moutinho

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The Balanced Scorecard (BSC) is a continuous strategic monitoring model focused not only on financial issues but also on internal processes, patients/users, and learning and growth. Initially dedicated to business management, it currently serves organizations of other natures - such as hospitals. This paper presents a BSC designed for a Portuguese Acute Pain Unit (APU). This study is qualitative and based on the experience of collaborators at the APU. The management of APU is based on four perspectives – users, internal processes, learning and growth, and financial and legal. For each perspective, there were identified strategic objectives, critical factors, lead indicators and initiatives. The strategic map of the APU outlining sustained strategic relations among strategic objectives. This study contributes to the development of research in the health management area as it explores how organizational insufficiencies and inconsistencies in this particular case can be addressed, through the identification of critical factors, to clearly establish core outcomes and initiatives to set up.

Keywords: acute pain unit, balanced scorecard, hospital management, organizational performance, Portugal

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Authors: Ramon Erasmo Verdin Rodriguez

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Many has been the ways that historically the managers and gurus has been trying to get closer to the perfect supply chain, but since this topic is so vast and very complex the bigger the companies are, the duty has not been certainly easy. On this research, you are going to see thru the entrails of the logistics that happens at a glass manufacturing company with the number one raw material of the process that is the silica sand. After a very quick passage thru the supply chain, this document is going to focus on the way that raw materials flow thru the system, so after that, an analysis and research can take place to improve the logistics. Thru Operations Research techniques, it will be analyzed the current scheme of distribution and inventories of raw materials at a glass company’s plants, so after a mathematical conceptualization process, the supply chain could be optimized with the purpose of reducing the uncertainty of supply and obtaining an economic benefit at the very end of this research.

Keywords: inventory management, operations research, optimization, supply chain

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Authors: Nicole Nicholson, Anne Spillane

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The purpose of this quantitative, descriptive analysis was to determine the success of a post-graduate new teacher education program, designed to teach educators the knowledge and skills necessary to use technology in the classroom, improve the ability to communicate with stakeholders, and implement EBPs and UDL principles into instruction for students with ASD (Autism Spectrum Disorders ). The success of candidates (n=20) in the program provided evidence as to how candidates were effectively able to use technology to create meaningful learning opportunities and implement EBPs for individuals with ASD. ≥90% of participants achieved the following competencies: podcast creation; technology used to share information about assistive technology; and created a resource website on ASD (including information on EBPs, local and national support groups, ASD characteristics, and the latest research on ASD). 59% of students successfully created animation. Results of the analysis indicated that the teacher education program was successful in teaching candidates desired competencies during its first year of implementation.

Keywords: autism spectrum disorders, ASD, evidence based practices, EBP, universal design for learning, UDL

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Authors: Merey M. Sarsengeldin, Alexandr S. Kolokhmatov, Galiya Seidaliyeva, Alexandr Ozerov, Sanim T. Imatayeva

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This series of interdisciplinary studies is an attempt to investigate and develop a scalable model of fair socioeconomic relations on the base of blockchain using positive psychology techniques and Machine Learning algorithms for data analytics. In this particular study, we use hyperinteraction approach and intuition to investigate their influence on 'wisdom of crowds' via created mobile application which was created for the purpose of this research. Along with the public blockchain and private Decentralized Autonomous Organization (DAO) which were elaborated by us on the base of Ethereum blockchain, a model of fair financial relations of members of DAO was developed. We developed a smart contract, so-called, Fair Price Protocol and use it for implementation of model. The data obtained from mobile application was analyzed by ML algorithms. A model was tested on football matches.

Keywords: blockchain, Naïve Bayes algorithm, hyperinteraction, intuition, wisdom of crowd, decentralized autonomous organization

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Authors: Bensacia Nabila, Hadj-Ziane Amel, Sefah Karima

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Carbon nanocomposites have received more attention in the last years in view of their special properties such as low density, high specific surface area, and thermal and mechanical stability. Taking into account the importance of these materials, many studies aimed at improving the synthesis process have been conducted. However, the presence of impurities could affect significantly the properties of these materials, and the characterization of these compounds is an important challenge to assure the quality of the new carbon nanocomposites. The present study aims to develop a new recyclable decontaminating material for dyes removal. This new material consists of an active element based on carbon nanotubes wrapped in a microcapsule of iron oxide. The adsorbent is characterized by Transmission electron microscopy, X-ray diffraction and the surface area was measured by the BET method.

Keywords: carbon nanocomposite, chitozen, elimination, dyes

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Authors: Lam Pham, Peter Hubber, Russell Tytler

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Students’ conceptual understandings of chemistry concepts/phenomena involve capability to coordinate across the three levels of Johnston’s triangle model. This triplet model is based on reasoning about chemical phenomena across macro, sub-micro and symbolic levels. In chemistry education, there is a need for further examining inquiry-based approaches that enhance students’ conceptual learning and problem solving skills. This research adopted a directed inquiry pedagogy based on students constructing and coordinating representations, to investigate senior school students’ capabilities to flexibly move across Johnston’ levels when learning dilution and molar concentration concepts. The participants comprise 50 grade 11 and 20 grade 10 students and 4 chemistry teachers who were selected from 4 secondary schools located in metropolitan Melbourne, Victoria. This research into classroom practices used ethnographic methodology, involved teachers working collaboratively with the research team to develop representational activities and lesson sequences in the instruction of a unit on solution chemistry. The representational activities included challenges (Representational Challenges-RCs) that used ‘representational tools’ to assist students to move across Johnson’s three levels for dilution phenomena. In this report, the ‘representational tool’ called ‘cross and portion’ model was developed and used in teaching and learning the molar concentration concept. Students’ conceptual understanding and problem solving skills when learning with this model are analysed through group case studies of year 10 and 11 chemistry students. In learning dilution concepts, students in both group case studies actively conducted a practical experiment, used their own language and visualisation skills to represent dilution phenomena at macroscopic level (RC1). At the sub-microscopic level, students generated and negotiated representations of the chemical interactions between solute and solvent underpinning the dilution process. At the symbolic level, students demonstrated their understandings about dilution concepts by drawing chemical structures and performing mathematical calculations. When learning molar concentration with a ‘cross and portion’ model (RC2), students coordinated across visual and symbolic representational forms and Johnson’s levels to construct representations. The analysis showed that in RC1, Year 10 students needed more ‘scaffolding’ in inducing to representations to explicit the form and function of sub-microscopic representations. In RC2, Year 11 students showed clarity in using visual representations (drawings) to link to mathematics to solve representational challenges about molar concentration. In contrast, year 10 students struggled to get match up the two systems, symbolic system of mole per litre (‘cross and portion’) and visual representation (drawing). These conceptual problems do not lie in the students’ mathematical calculation capability but rather in students’ capability to align visual representations with the symbolic mathematical formulations. This research also found that students in both group case studies were able to coordinate representations when probed about the use of ‘cross and portion’ model (in RC2) to demonstrate molar concentration of diluted solutions (in RC1). Students mostly succeeded in constructing ‘cross and portion’ models to represent the reduction of molar concentration of the concentration gradients. In conclusion, this research demonstrated how the strategic introduction and coordination of chemical representations across modes and across the macro, sub-micro and symbolic levels, supported student reasoning and problem solving in chemistry.

Keywords: cross and portion, dilution, Johnston's triangle, molar concentration, representations

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Authors: Marcus Goh Tian Xi, Alicia Chua Si Wen, Eunice Gan Ghee Wu, Helen Bound, Lee Liang Ying, Albert Lee

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The Map of Dialogical Inquiry provides a conceptual look at the underlying nature of future-oriented skills. According to the Map, learning is learner-oriented, with conversational time shifted from teachers to learners, who play a strong role in deciding what and how they learn. For example, in courses operating on the principles of Dialogical Inquiry, learners were able to leave the classroom with a deeper understanding of the topic, broader exposure to differing perspectives, and stronger critical thinking capabilities, compared to traditional approaches to teaching. Despite its contributions to learning, the Map is grounded in a qualitative approach both in its development and its application for providing feedback to learners and educators. Studies hinge on openended responses by Map users, which can be time consuming and resource intensive. The present research is motivated by this gap in practicality by aiming to develop and validate a quantitative measure of the Map. In addition, a quantifiable measure may also strengthen applicability by making learning experiences trackable and comparable. The Map outlines eight learning aspects that learners should holistically engage. This research focuses on the Analysing aspect of learning. According to the Map, Analysing has four key components: liking or engaging in logic, using interpretative lenses, seeking patterns, and critiquing and deconstructing. Existing scales of constructs (e.g., critical thinking, rationality) related to these components were identified so that the current scale could adapt items from. Specifically, items were phrased beginning with an “I”, followed by an action phrase, to fulfil the purpose of assessing learners' engagement with Analysing either in general or in classroom contexts. Paralleling standard scale development procedure, the 26-item Analysing scale was administered to 330 participants alongside existing scales with varying levels of association to Analysing, to establish construct validity. Subsequently, the scale was refined and its dimensionality, reliability, and validity were determined. Confirmatory factor analysis (CFA) revealed if scale items loaded onto the four factors corresponding to the components of Analysing. To refine the scale, items were systematically removed via an iterative procedure, according to their factor loadings and results of likelihood ratio tests at each step. Eight items were removed this way. The Analysing scale is better conceptualised as unidimensional, rather than comprising the four components identified by the Map, for three reasons: 1) the covariance matrix of the model specified for the CFA was not positive definite, 2) correlations among the four factors were high, and 3) exploratory factor analyses did not yield an easily interpretable factor structure of Analysing. Regarding validity, since the Analysing scale had higher correlations with conceptually similar scales than conceptually distinct scales, with minor exceptions, construct validity was largely established. Overall, satisfactory reliability and validity of the scale suggest that the current procedure can result in a valid and easy-touse measure for each aspect of the Map.

Keywords: analytical thinking, dialogical inquiry, education, lifelong learning, pedagogy, scale development

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Authors: Linda Weinberg, Miriam Symon

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While few institutions of higher education in Israel offer international programs taught entirely in English, many Israeli students today can study at least one content course taught in English during their degree program. In particular, with the growth of international partnerships and opportunities for student mobility, English medium instruction is a growing phenomenon. There are however no official guidelines in Israel for how to develop and implement content courses in English and no training to help lecturers prepare for teaching their materials in a foreign language. Furthermore, the implications for the students and the nature of the courses themselves have not been sufficiently considered. In addition, the institution must have lecturers who are able to teach these courses effectively in English. An international project funded by the European Union addresses these issues and a set of guidelines which provide guidance for lecturers in adapting their courses for delivery in English have been developed. A train-the-trainer approach is adopted in order to cascade knowledge and experience in English medium instruction from experts to language teachers and on to content teachers thus maximizing the scope of professional development. To accompany training, a model English medium course has been created which serves the dual purpose of highlighting alternatives to the frontal lecture while integrating language learning objectives with content goals. This course can also be used as a standalone content course. The development of the guidelines and of the course utilized backwards, forwards and central design in an iterative process. The goals for combined language and content outcomes were identified first after which a suitable framework for achieving these goals was constructed. The assessment procedures evolved through collaboration between content and language specialists and subsequently were put into action during a piloting phase. Feedback from the piloting teachers and from the students highlight the need for clear channels of communication to encourage frank and honest discussion of expectations versus reality. While much of what goes on in the English medium classroom requires no better teaching skills than are required in any classroom, the understanding of students' abilities in achieving reasonable learning outcomes in a foreign language must be rationalized and accommodated within the course design. Concomitantly, preparatory language classes for students must be able to adapt to prepare students for specific language and cognitive skills and activities that courses conducted in English require. This paper presents findings from the implementation of a purpose-designed English medium instruction course arrived at through an iterative backwards, forwards and central design process utilizing feedback from students and lecturers alike leading to suggested guidelines for English medium instruction in higher education.

Keywords: English medium instruction, higher education, iterative design process, train-the-trainer

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Joy Chadwick

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This Study of Teaching and Learning (SoTL) research focused on the experiences of teacher candidates involved in an inclusive education methods course within a four-year direct entry Bachelor of Education program. The placement of this course within the final fourteen-week practicum semester is designed to facilitate deeper theory-practice connections between effective inclusive pedagogical knowledge and the real life of classroom teaching. The course focuses on supporting teacher candidates to understand that effective instruction within an inclusive classroom context must be intentional, responsive, and relational. Diversity is situated not as exceptional but rather as expected. This interpretive qualitative study involved the analysis of twenty-nine teacher candidate reflective journals and six individual teacher candidate semi-structured interviews. The journal entries were completed at the start of the semester and at the end of the semester with the intent of having teacher candidates reflect on their beliefs of what it means to be an effective inclusive educator and how the course and practicum experiences impacted their understanding and approaches to teaching in inclusive classrooms. The semi-structured interviews provided further depth and context to the journal data. The journals and interview transcripts were coded and themed using NVivo software. The findings suggest that instructional frameworks such as universal design for learning (UDL), differentiated instruction (DI), response to intervention (RTI), social emotional learning (SEL), and self-regulation supported teacher candidate’s abilities to meet the needs of their students more effectively. Course content that focused on specific exceptionalities also supported teacher candidates to be proactive rather than reactive when responding to student learning challenges. Teacher candidates also articulated the importance of reframing their perspective about students in challenging moments and that seeing the individual worth of each child was integral to their approach to teaching. A persisting question for teacher educators exists as to what pedagogical knowledge and understanding is most relevant in supporting future teachers to be effective at planning for and embracing the diversity of student needs within classrooms today. This research directs us to consider the critical importance of addressing personal attributes and mindsets of teacher candidates regarding children as well as considering instructional frameworks when designing coursework. Further, the alignment of an inclusive education course during a teaching practicum allows for an iterative approach to learning. The practical application of course concepts while teaching in a practicum allows for a deeper understanding of instructional frameworks, thus enhancing the confidence of teacher candidates. Research findings have implications for teacher education programs as connected to inclusive education methods courses, practicum experiences, and overall teacher education program design.

Keywords: inclusion, inclusive education, pre-service teacher education, practicum experiences, teacher education

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Authors: Newton Muhury, Armando A. Apan, Tek Maraseni

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This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: El Hadi Bouguerra, Nouredine Retiel

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To avoid or at least to attenuate the global warming, it is essential to reduce the energy consumption of the buildings where the biggest potential of savings exists. The impending danger can come from the increase in the needs of air conditioning not only because of the climate warming but also the fast equipping of emerging or developing countries. Passive solutions exist and others are in promising development and therefore, must be applied wherever it is possible. Even if they do not always avoid the resort to an active cooling (mechanical), they allow lowering the load at an acceptable level which can be possibly taken in relay by the renewable energies. These solutions have the advantage to be relatively less expensive and especially adaptable to the existing housing. However, it is the internal convection resistance that controls the heat exchange between the phase change materials (PCM) and the indoor temperature because of the very low heat coefficients of natural convection. Therefore, it is reasonable to link the switch temperature Tm to the temperature of the substrate (walls and ceiling) because conduction heat transfer is dominant. In this case, external conditions (heat sources such as solar irradiation and ambient temperatures) and conductivities of envelope constituents are the most important factors. The walls are not at the same temperature year round; therefore, it is difficult to set a unique switch temperature for the whole season, making the average values a key parameter. With this work, the authors’ aim is to see which parameters influence the optimum switch temperature of a PCM and additionally, if a better selection of PCMs relating to their optimum temperature can enhance their energetic performances.

Keywords: low energy building, energy conservation, phase change materials, PCM

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Authors: L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead

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Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.

Keywords: classification, falls, health risk factors, machine learning, older adults

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Authors: Nataliya G. Olkhovik

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Nowadays in the system of Russian higher medical education there have been undertaken initiatives that resulted in focusing on the resources of e-learning in teaching foreign languages. Obviously, the face-to-face communication in foreign languages bears much more advantages in terms of effectiveness in comparison with the potential of e-learning. Thus, we’ve faced the necessity of strengthening the capacity of e-learning via integration of active methods into the process of teaching foreign languages, such as project activity of students. Successful project activity of students should involve the following components: monitoring, control, methods of organizing the student’s activity in foreign languages, stimulating their interest in the chosen project, approaches to self-assessment and methods of raising their self-esteem. The contemporary methodology assumes the project as a specific method, which activates potential of a student’s cognitive function, emotional reaction, ability to work in the team, commitment, skills of cooperation and, consequently, their readiness to verbalize ideas, thoughts and attitudes. Verbal activity in the foreign language is a complex conception that consolidates both cognitive (involving speech) capacity and individual traits and attitudes such as initiative, empathy, devotion, responsibility etc. Once we organize the project activity by the means of e-learning within the ‘Foreign language’ discipline we have to take into consideration all mentioned above characteristics and work out an effective way to implement it into the teaching practice to boost its educational potential. We have integrated into the e-platform Moodle the module of project activity consisting of the following blocks of tasks that lead students to research, cooperate, strive to leadership, chase the goal and finally verbalize their intentions. Firstly, we introduce the project through activating self-activity of students by the tasks of the phase ‘Preparation of the project’: choose the topic and justify it; find out the problematic situation and its components; set the goals; create your team, choose the leader, distribute the roles in your team; make a written report on grounding the validity of your choices. Secondly, in the ‘Planning the project’ phase we ask students to represent the analysis of the problem in terms of reasons, ways and methods of solution and define the structure of their project (here students may choose oral or written presentation by drawing up the claim in the e-platform about their wish, whereas the teacher decides what form of presentation to prefer). Thirdly, the students have to design the visual aids, speech samples (functional phrases, introductory words, keywords, synonyms, opposites, attributive constructions) and then after checking, discussing and correcting with a teacher via the means of Moodle present it in front of the audience. And finally, we introduce the phase of self-reflection that aims to awake the inner desire of students to improve their verbal activity in a foreign language. As a result, by implementing the project activity into the e-platform and project activity, we try to widen the frameworks of a traditional lesson of foreign languages through tapping the potential of personal traits and attitudes of students.

Keywords: active methods, e-learning, improving verbal activity in foreign languages, personal traits and attitudes

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

Procedia PDF Downloads 315

Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

Procedia PDF Downloads 92

Authors: Zineb Kassab, Nassima El Miri, A. Aboulkas, Abdellatif Barakat, Mounir El Achaby

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Recently, more attention has been given to biopolymers with a focus on sustainable development and environmental preservation. Following this tendency, the attempt has been made to replace polymers derived from petroleum with superior biodegradable polymers (biopolymers). In this context, biopolymers are considered potential replacements for conventional plastic materials. However, some of their properties must be improved for better competitiveness, especially regarding their mechanical, thermal and barrier properties. Bio-nanocomposite technology using nanofillers has already been proven as an effective way to produce new materials with specific properties and high performances. With the emergence of nanostructured bio-composite materials, incorporating elongated rod-like cellulose nanocrystals (CNC) has attracted more and more attention in the field of nanotechnology. This study is aimed to develop bio-composite films of biopolymer matrices [Carboxymethyle cellulose (CMC), Starch (ST), Chitosan (CS) and Polyvinyl alcohol (PVA)] reinforced with cellulose nanocrystals (CNC) using the solution casting method. The CNC were extracted at a nanometric scale from lignocellulosic fibers via sulfuric acid hydrolysis and then characterized using X-ray diffraction (XRD), thermogravimetric analysis (TGA), confocal microscopy, infrared spectroscopy (IR), atomic force and transmission electron microscopies (AFM and TEM) techniques. The as extracted CNC were used as a reinforcing phase to produce a variety of bio-composite films at different CNC loading (0.5-10 wt %) with specific properties. The rheological properties of film-forming solutions (FFS) of bio-composites were studied, and their relation to the casting process was evaluated. Then, the structural, optical transparency, water vapor permeability, thermal stability and mechanical properties of all prepared bio-composite films were evaluated and studied in this report. The high performances of these bio-composite films are expected to have potential in biomaterials or packaging applications.

Keywords: biopolymer composites, cellulose nanocrystals, food packaging, lignocellulosic fibers

Procedia PDF Downloads 229