Search results for: hybrid learning

Authors: Xiaosheng Chen, Jingjing Chen, Phil Reed, Dan Zhang

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Conscious intention can be a promising point cut to grasp consciousness and orient voluntary action. The current study adopted a random ratio (RR), yoked random interval (RI) reinforcement learning schedule instead of the previous highly repeatable and single decision point paradigms, aimed to induce voluntary action with the conscious intention that evolves from the interaction between short-range-intention and long-range-intention. Readiness potential (RP) -like-EEG amplitude and inter-trial-EEG variability decreased significantly prior to voluntary action compared to cued action for inter-trial-EEG variability, mainly featured during the earlier stage of neural activities. Notably, (RP) -like-EEG amplitudes decreased significantly prior to higher RI-reward rates responses in which participants formed a higher plane of conscious intention. The present study suggests the possible contribution of conscious intention-based processes to the neural activities from the earlier stage prior to voluntary action on reinforcement leanring schedule.

Keywords: Reinforcement leaning schedule, voluntary action, EEG, conscious intention, readiness potential

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Authors: Safayat Ali Shaikh

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Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India.

Keywords: artificial neural network, large irrigation project, modified simplex algorithm, optimal cropping pattern

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Authors: Clinton Chidiebere Anyanwu

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Secondary education is a vital part of a virtuous circle of economic growth within the context of a globalised knowledge economy. The teaching of Business Education entails teaching learners the essentials, rudiments, assumptions, and methods of business. Hence, it was deemed necessary for the study to investigate technology integration in Business Education. Drawing from the theoretical frameworks of technological pedagogical content knowledge (TPACK), and unified theory of acceptance and use of technology (UTAUT), the study observes teachers’ level of technology use in Business Education classrooms. Using a mixed-methods sequential explanatory design, probability, and purposive sampling, the majority of participants were found to be not integrating technology to an acceptable level and a small percentage was. After an analysis of constructs from UTAUT, some of this could be attributed to the lack of facilitating conditions in the teaching and learning of Business Education. The implication of the study findings is that poor investment in technology integration in secondary schools in Nigeria affects pedagogical implementations and effective teaching and learning of Business Education subjects. The study concludes that if facilitating conditions and professional development are considered to address the shortfalls in terms of TPACK, technology integration will become a reality in secondary schools in Nigeria.

Keywords: business education, secondary education, technology integration, TPACK, UTAUT

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Authors: Masoud Safarishaal

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Detecting the location of pole-to-earth faults is essential for the safe operation of the electrical system of the railroad. This paper aims to use a combination of evolutionary algorithms and neural networks to increase the accuracy of single pole-to-earth fault detection and location on the Tehran railroad power supply system. As a result, the Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network to improve the accuracy and convergence of the learning process. Due to the system's nonlinearity, fault detection is an ideal application for the proposed method, where the 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer, and typical Tehran metro parameters that have developed the silicon rectifier. Required data for the network learning process has been gathered from simulation results. The 600Hz component value will change with the change of the location of a single pole to the earth's fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the proposed methods can accurately predict the fault location.

Keywords: single pole-to-pole fault, Tehran railway, ICA, PSO, artificial neural network

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Authors: Moti Zwilling, Srečko Natek

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This paper aims to present an innovative predictive analytics analysis in order to find the best combination between two consumers who strive to find their partner or in internet sites. The methodology shown in this paper is based on analysis of consumer preferences and involves data mining and machine learning search techniques. The study is composed of two parts: The first part examines by means of descriptive statistics the correlations between a set of parameters that are taken between man and women where they intent to meet each other through the social media, usually the internet. In this part several hypotheses were examined and statistical analysis were taken place. Results show that there is a strong correlation between the affiliated attributes of man and woman as long as concerned to how they present themselves in a social media such as "Facebook". One interesting issue is the strong desire to develop a serious relationship between most of the respondents. In the second part, the authors used common data mining algorithms to search and classify the most important and effective attributes that affect the response rate of the other side. Results exhibit that personal presentation and education background are found as most affective to achieve a positive attitude to one's profile from the other mate.

Keywords: dating sites, social networks, machine learning, decision trees, data mining

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Authors: K. Durairaj, I. N. Umar

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The asynchronous discussion forum is one of the most widely used activities in learning management system environment. Online forum allows participants to interact, construct knowledge, and can be used to complement face to face sessions in blended learning courses. However, to what extent do the students perceive the benefits or advantages of forum remain to be seen. Through content and social network analyses, instructors will be able to gauge the students’ engagement and knowledge construction level. Thus, this study aims to analyze the students’ level of knowledge construction and their participation level that occur through online discussion. It also attempts to investigate the relationship between the level of knowledge construction and their social interaction patterns. The sample involves 23 students undertaking a master course in one public university in Malaysia. The asynchronous discussion forum was conducted for three weeks as part of the course requirement. The finding indicates that the level of knowledge construction is quite low. Also, the density value of 0.11 indicating that the overall communication among the participants in the forum is low. This study reveals that strong and significant correlations between SNA measures (in-degree centrality, out-degree centrality) and level of knowledge construction. Thus, allocating these active students in a different groups aids the interactive discussion takes place. Finally, based upon the findings, some recommendations to increase students’ level of knowledge construction and also for further research are proposed.

Keywords: asynchronous discussion forums, content analysis, knowledge construction, social network analysis

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Authors: Akshay Dirisala

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Humans had developed a highly organized and efficient civilization to live in by improving the basic needs of humans like housing, transportation, and utilities. These developments have made a huge impact on major environmental factors. Air pollution is one prominent environmental factor that needs to be addressed to maintain a sustainable and healthier lifestyle. Textiles have always been at the forefront of helping humans shield from environmental conditions. With the growth in the field of electronic textiles, we now have the capability of monitoring the atmosphere in real time to understand and analyze the environment that a particular person is mostly spending their time at. Integrating textiles with the particulate matter sensors that measure air quality and pollutants that have a direct impact on human health will help to understand what type of air we are breathing. This research idea aims to develop a textile product and a process of collecting the pollutants through particulate matter sensors, which are equipped inside a smart textile product and store the data to develop a machine learning model to analyze the health conditions of the person wearing the garment and periodically notifying them not only will help to be cautious of airborne diseases but will help to regulate the diseases and could also help to take care of skin conditions.

Keywords: air pollution, e-textiles, particulate matter sensors, environment, machine learning models

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Authors: Chrysanthi S. Tiliakou, Katerina T. Frantzi

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Writing has always been considered a most demanding skill for English as a Foreign Language learners as well as for native speakers. Novice foreign language writers are asked to handle a limited range of vocabulary to produce writing tasks at lower levels. Adverbs are the parts of speech that are not used extensively in the early stages of English as a Foreign Language writing. An additional problem with learning new adverbs is that, next to learning their meanings, learners are expected to acquire the proper placement of adverbs in a sentence. The use of adverbs is important as they enhance “expressive richness to one’s message”. By exploring the patterns of use of adverbs, researchers and educators can identify types of adverbs, which appear more taxing for young learners or that puzzle novice English as a Foreign Language writers with their placement, and focus on their teaching. To this end, the study examines the use of adverbs on two written Corpora of young learners of English of A1 – A2 levels and determines the types of adverbs used, their frequencies, problems in their use, and whether there is any differentiation between levels. The Antconc concordancing tool was used for the Greek Learner Corpus, and the Corpuscle concordancing tool for the Norwegian Corpus. The research found a similarity in the normalized frequencies of the adverbs used in the A1-A2 level Greek Learner Corpus with the frequencies of the same adverbs in the Norwegian Learner Corpus.

Keywords: learner corpora, young learners, writing, use of adverbs

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Debesh R. Roy

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The fundamental issue in understanding the origin and growth mechanism of nanomaterials, from a fundamental unit is a big challenging problem to the scientists. Recently, an immense attention is generated to the researchers for prediction of exceptionally stable atomic cluster units as the building units for future smart materials. The present study is a systematic investigation on the stability and electronic properties of a series of bimetallic (semiconductor-alkaline earth) clusters, viz., BxMg3 (x=1-5) is performed, in search for exceptional and/ or unusual stable motifs. A very popular hybrid exchange-correlation functional, B3LYP as proposed by A. D. Becke along with a higher basis set, viz., 6-31+G[d,p] is employed for this purpose under the density functional formalism. The magic stability among the concerned clusters is explained using the jellium model. It is evident from the present study that the magic stability of B4Mg3 cluster arises due to the jellium shell closure.

Keywords: atomic clusters, density functional theory, jellium model, magic clusters, smart nanomaterials

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Authors: Alev Atak

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In this paper, we aim to create a financial sentiment index by investigating the company’s voluntary information disclosures. We retrieve structured content from BIST 100 companies’ financial reports for the period 1998-2018 and extract relevant financial information for sentiment analysis through Natural Language Processing. We measure strategy-related disclosures and their cross-sectional variation and classify report content into generic sections using synonym lists divided into four main categories according to their liquidity risk profile, risk positions, intra-annual information, and exposure to risk. We use Word Error Rate and Cosin Similarity for comparing and measuring text similarity and derivation in sets of texts. In addition to performing text extraction, we will provide a range of text analysis options, such as the readability metrics, word counts using pre-determined lists (e.g., forward-looking, uncertainty, tone, etc.), and comparison with reference corpus (word, parts of speech and semantic level). Therefore, we create an adequate analytical tool and a financial dictionary to depict the importance of granular financial disclosure for investors to identify correctly the risk-taking behavior and hence make the aggregated effects traceable.

Keywords: financial sentiment, machine learning, information disclosure, risk

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Authors: Neha Toppo

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Method and strategies of teaching instruction highly influence learning of students. In second language teaching, number of ways and methods has been suggested by different scholars and researchers through times. The present article deals with the role of teaching instruction in developing compositional ability of students in writing. It focuses on the secondary level students of rural areas, whose exposure to English language is limited and they face challenges even in simple compositions. The students till high school suffer with their disability in writing formal letter, application, essay, paragraph etc. They face problem in note making, writing answers in examination using their own words and depend fully on rote learning. It becomes difficult for them to give language to their own ideas. Teaching writing composition deserves special attention as writing is an integral part of language learning and students at this level are expected to have sound compositional ability for it is useful in numerous domains. Effective method of instruction could help students to learn expression of self, correct selection of vocabulary and grammar, contextual writing, composition of formal and informal writing. It is not limited to school but continues to be important in various other fields outside the school such as in newspaper and magazine, official work, legislative work, material writing, academic writing, personal writing, etc. The study is based on the experimental method, which hypothesize that guided instruction will be more effective in teaching writing compositions than usual instruction in which students are left to compose by their own without any help. In the test, students of one section are asked to write an essay on the given topic without guidance and another section are asked to write the same but with the assistance of guided instruction in which students have been provided with a few vocabulary and sentence structure. This process is repeated in few more schools to get generalize data. The study shows the difference on students’ performance using both the instructions; guided and unguided. The conclusion of the study is followed by the finding that writing skill of the students is quite poor but with the help of guided instruction they perform better. The students are in need of better teaching instruction to develop their writing skills.

Keywords: composition, essay, guided instruction, writing skill

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Authors: Mark Shiu-kee Shum, Dan Shi

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The paper aims to introduce the ‘Reading to Learn, Learning to Write’ (R2L) pedagogy and its application in teaching reading and writing to non-Chinese speaking (NCS) students in Hong Kong. Guided by the teaching and learning cycles accentuated in R2L pedagogy, sufficient scaffolding was provided for students with an explicit teaching method in literacy education. To understand the influence of using R2L pedagogy on students’ reading and writing abilities across different genres, quantitative data were collected by pre- and post-test of reading and writing tasks in the two different genres of narration and explanation. The pre-test and post-test were used to assess students’ writing performance based on the three textual components of context, discourse, and graphic features, while the reading abilities were assessed at the literal, inferred and interpretive levels of reading comprehension to measure the effectiveness of R2L pedagogy on their literacy improvement. The findings show the use of R2L pedagogy has been proven more effective in improving NCS students’ writing abilities than developing their reading capacity. It is hoped that the R2L-based pedagogic practices can serve as teaching references and pedagogic rationale for L1 language teachers and raise their metalinguistic awareness in teaching Chinese to non-Chinese speaking students in Hong Kong and beyond.

Keywords: pedagogical inclusiveness, literacy education, ethnic minority, reading and writing

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Authors: Chathurika Sewwandi Kannangara, Jerome Carson

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The objective of this research is to identify strengths among the individuals with dyslexia and design a positive psychology intervention to support such individuals. Dyslexia is a combination of abilities and difficulties that affect the learning process in areas as such reading, spelling and writing. It is a persistent condition. The research aims to adapt positive psychology techniques to support individuals with dyslexia. Population of the research will be undergraduate and college level students with dyslexia. First phase of the study will be conducted on a sample of undergraduate and college level students with dyslexia in Bolton, UK. The concept of treatment in positive psychology is not only to fix the component just what is wrong, instead it is also to develop and construct on what is right in the individual. The first phase of the research aims to identify the signature strengths among the individuals with dyslexia using Interviews, Descriptions on personal experiences on ‘My life with Dyslexia’, and Values in Action (VIA) strength survey. In order to conduct the survey for individuals with dyslexia, the VIA survey has been hosted in a website which is solely developed in the form of dyslexia friendly context. Dyslexia friendly website for surveys had designed and developed following the British Dyslexia Association guidelines. The findings of the first phase would be utilized for the second phase of the research to develop the positive psychology intervention.

Keywords: dyslexia, signature strengths, positive psychology, qualitative study, learning difficulties

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Authors: Caroline Labib

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This paper reports on a qualitative study exploring the approaches teachers used to integrate computers or smart tablets into their program planning. Their aim was to integrate ICT into children’s play, thereby supporting children’s learning and development. Data was collected in preschool settings in Melbourne in 2016. Interviews with teachers, observations of teacher interactions with children and copies of teachers’ planning and observation documents informed the study. The paper looks closely at findings from two early childhood settings and focuses on exploring the differing approaches two EC teachers have adopted when integrating iPad or computers into their settings. Data analysis revealed three key approaches which have been labelled: free digital play, guided digital play and teacher-led digital use. Importantly, teacher decisions were influenced by the interplay between the opportunities that the ICT tools offered, the teachers’ prior knowledge and experience about ICT and children’s learning needs and contexts. This paper is a snapshot of two early childhood settings, and further research will encompass data from six more early childhood settings in Victoria with the aim of exploring a wide range of motivating factors for early childhood teachers trying to integrate ICT into their programs.

Keywords: early childhood education (ECE), digital play, information and communication technologies (ICT), play, and teachers' interaction approaches

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Authors: David Nantes, George Gilbert

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There are a lot of risks that might crop up as a tunneling project develops, and it's crucial to be aware of them. Due to the unexpected nature of tunneling projects and the interconnectedness of risk occurrences, the risk assessment approach presents a significant challenge. The purpose of this study is to provide a hybrid FDEMATEL-ANP model to help prioritize risks during tunnel construction projects. The ambiguity in expert judgments and the relative severity of interdependencies across risk occurrences are both taken into consideration by this model, thanks to the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL). The Analytic Network Process (ANP) method is used to rank priorities and assess project risks. The authors provide a case study of a subway tunneling construction project to back up the validity of their methodology. The results showed that the proposed method successfully isolated key risk factors and elucidated their interplay in the case study. The proposed method has the potential to become a helpful resource for evaluating dangers associated with tunnel construction projects.

Keywords: risk, prioritization, FDEMATEL, ANP, tunneling construction projects

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Authors: Elisaveta Doncheva, Jelena Djokikj, Ognen Tuteski, Bojana Hadjieva

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In the past decade, additive manufacturing technology or 3D printing has been promoted as an efficient method for fabricating hybrid composite materials and structures with superior mechanical properties and complex shape and geometry. Fused deposition modeling (FDM) process is commonly used additive manufacturing technique for production of polymer products. Therefore, many studies and experiments are focused on investigating the possibilities for improving the obtained results on product properties as a key factor for expanding the spectrum of their application. This article provides an extensive review on recent research advances in FDM and reports on studies that cover the effects of process parameters, material, and design of the product properties. The paper conclusions provide a clear up-to date information for optimum efficiency and enhancement of the mechanical properties of 3D printed samples and recommends further research work and investigations.

Keywords: additive manufacturing, critical parameters, filament, print orientation, 3D printing

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

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Authors: Ana Breda, Catarina Cruz

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Daily, we face unpredictable events, often attributed to chance, as there is no justification for such an occurrence. Chance, understood as a source of uncertainty, is present in several aspects of human life, such as weather forecasts, dice rolling, and lottery. Surprisingly, humans and some animals can quickly adjust their behavior to handle efficiently doubly stochastic processes (random events with two layers of randomness, like unpredictable weather affecting dice rolling). This adjustment ability suggests that the human brain has built-in mechanisms for perceiving, understanding, and responding to simple probabilities. It also explains why current trends in mathematics education include probability concepts in official curriculum programs, starting from the third year of primary education onwards. In the first years of schooling, children learn to use a certain type of (specific) vocabulary, such as never, always, rarely, perhaps, likely, and unlikely, to help them to perceive and understand the probability of some events. These are keywords of crucial importance for their perception and understanding of probabilities. The development of the probabilistic concepts comes from facts and cause-effect sequences resulting from the subject's actions, as well as the notion of chance and intuitive estimates based on everyday experiences. As part of a junior summer school program, which took place at a Portuguese university, a non-formal learning experiment was carried out with 18 children in the 5th and 6th grades. This experience was designed to be implemented in a dynamic of a serious ice-breaking game, to assess their levels of probabilistic, critical, and creative thinking in understanding impossible, certain, equally probable, likely, and unlikely events, and also to gain insight into how the non-formal learning context influenced their achievements. The criteria used to evaluate probabilistic thinking included the creative ability to conceive events classified in the specified categories, the ability to properly justify the categorization, the ability to critically assess the events classified by other children, and the ability to make predictions based on a given probability. The data analysis employs a qualitative, descriptive, and interpretative-methods approach based on students' written productions, audio recordings, and researchers' field notes. This methodology allowed us to conclude that such an approach is an appropriate and helpful formative assessment tool. The promising results of this initial exploratory study require a future research study with children from these levels of education, from different regions, attending public or private schools, to validate and expand our findings.

Keywords: critical and creative thinking, non-formal mathematics learning, probabilistic thinking, serious game

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Authors: Seulki Lee, Seoung Bum Kim

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Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

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Authors: Stephanie Al Otaiba, Kyle Roberts

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Background and Significance Globally far too many students read below grade level; thus improving literacy outcomes is vital. Research suggests that non-cognitive factors, including Social and Emotional Learning (SEL) are linked to success in literacy outcomes. Converging evidence exists that early interventions are more effective than later remediation; therefore teachers need strategies to support early literacy while developing students’ SEL and their vocabulary, or language, for learning. This presentation describe findings from a US federally-funded project that trained teachers to provide an evidence-based read-aloud program for young children, using commercially available books with multicultural characters and themes to help their students “GROW”. The five GROW SEL themes include: “I can name my feelings”, “I can learn from my mistakes”, “I can persist”, “I can be kind to myself and others”, and “I can work toward and achieve goals”. Examples of GROW vocabulary (from over 100 words taught across the 5 units) include: emotions, improve, resilient, cooperate, accomplish, responsible, compassion, adapt, achieve, analyze. Methodology This study used a mixed methods research design, with qualitative methods to describe data from teacher feedback surveys (regarding satisfaction, feasibility), observations of fidelity of implementation, and with quantitative methods to assess the effect sizes for student vocabulary growth. GROW Intervention and Teacher Training Procedures Researchers trained classroom teachers to implement GROW. Each thematic unit included four books, vocabulary cards with images of the vocabulary words, and scripted lessons. Teacher training included online and in-person training; researchers incorporated virtual reality videos of instructors with child avatars to model lessons. Classroom teachers provided 2-3 20 min lessons per week ranging from short-term (8 weeks) to longer-term trials for up to 16 weeks. Setting and Participants The setting for the study included two large urban charter schools in the South. Data was collected across two years; during the first year, participants included 7 kindergarten teachers and 108 and the second year involved an additional set of 5 kindergarten and first grade teachers and 65 students. Initial Findings The initial qualitative findings indicate teachers reported the lessons to be feasible to implement and they reported that students enjoyed the books. Teachers found the vocabulary words to be challenging and important. They were able to implement lessons with fidelity. Quantitative analyses of growth for each taught word suggest that students’ growth on taught words ranged from large (ES = .75) to small (<.20). Researchers will contrast the effects for more and less successful books within the GROW units. Discussion and Conclusion It is feasible for teachers of young students to effectively teach SEL vocabulary and themes during shared book reading. Teachers and students enjoyed the books and students demonstrated growth on taught vocabulary. Researchers will discuss implications of the study and about the GROW program for researchers in learning sciences, will describe some limitations about research designs that are inherent in school-based research partnerships, and will provide some suggested directions for future research and practice.

Keywords: early literacy, learning science, language and vocabulary, social and emotional learning, multi-cultural

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Authors: Niall J. English, Sylvia Leatham, Maria Isabel Meza Silva, Denis P. Dowling

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The training and education of under- and post-graduate students can be promoted by more active learning especially in engineering, overcoming more passive and vicarious experiences and approaches in their documented effectiveness. However, the possibility of outreach to young pupils and school-children in primary and secondary schools is a lesser explored area in terms of Education and Public Engagement (EPE) efforts – as relates to feedback and influence on shaping 3rd-level engineering training and education. Therefore, the outreach and school-visit agenda constitutes an interesting avenue to observe how active learning, careers stimulus and EPE efforts for young children and teenagers can teach the university sector, to improve future engineering-teaching standards and enhance both quality and capabilities of practice. This intervention involved careers-outreach efforts to lead to statistical determinations of motivations towards engineering, manufacturing and training. The aim was to gauge to what extent this intervention would lead to an increased careers awareness in engineering, using the method of the schools-visits programme as the means for so doing. It was found that this led to an increase in engagement by school pupils with engineering as a career option and a greater awareness of the importance of manufacturing.

Keywords: outreach, education and public engagement, careers, peer interactions

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Authors: Zaki Ahmad Khan, Jamshed Siddiqui, Abdus Samad

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The Scheduling and mapping of tasks on a set of processors is considered as a critical problem in parallel and distributed computing system. This paper deals with the problem of dynamic scheduling on a special type of multiprocessor architecture known as Linear Crossed Cube (LCQ) network. This proposed multiprocessor is a hybrid network which combines the features of both linear type of architectures as well as cube based architectures. Two standard dynamic scheduling schemes namely Minimum Distance Scheduling (MDS) and Two Round Scheduling (TRS) schemes are implemented on the LCQ network. Parallel tasks are mapped and the imbalance of load is evaluated on different set of processors in LCQ network. The simulations results are evaluated and effort is made by means of through analysis of the results to obtain the best solution for the given network in term of load imbalance left and execution time. The other performance matrices like speedup and efficiency are also evaluated with the given dynamic algorithms.

Keywords: dynamic algorithm, load imbalance, mapping, task scheduling

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Authors: Golak Bihari Mahanta, Bibhuti Bhusan Biswal, B. B. V. L. Deepak, Amruta Rout, Gunji Balamurali

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Robots are widely used in the manufacturing industry for rapid production with higher accuracy and precision. With the help of End-of-Arm Tools (EOATs), robots are interacting with the environment. Robotic grippers are such EOATs which help to grasp the object in an automation system for improving the efficiency. As the robotic gripper directly influence the quality of the product due to the contact between the gripper surface and the object to be grasped, it is necessary to design and optimize the gripper mechanism configuration. In this study, geometric and kinematic modeling of the parallel gripper is proposed. Grey wolf optimizer algorithm is introduced for solving the proposed multiobjective gripper optimization problem. Two objective functions developed from the geometric and kinematic modeling along with several nonlinear constraints of the proposed gripper mechanism is used to optimize the design variables of the systems. Finally, the proposed methodology compared with a previously proposed method such as Teaching Learning Based Optimization (TLBO) algorithm, NSGA II, MODE and it was seen that the proposed method is more efficient compared to the earlier proposed methodology.

Keywords: gripper optimization, metaheuristics, , teaching learning based algorithm, multi-objective optimization, optimal gripper design

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Authors: Daniel Scherman, Daniel Madani, Shanu Gambhir, Marcus Ling Zhixing, Yingda Li

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Objective: This study aims to identify clinical factors that may predict failed endoscopic lumbar spine surgery to guide surgeons with patient selection during the initial learning curve. Methods: This is an Australasian prospective analysis of the first 105 patients to undergo lumbar endoscopic spine decompression by 3 surgeons. Modified MacNab outcomes, Oswestry Disability Index (ODI) and Visual Analogue Score (VAS) scores were utilized to evaluate clinical outcomes at 6 months postoperatively. Descriptive statistics and Anova t-tests were performed to measure statistically significant (p<0.05) associations between variables using GraphPad Prism v10. Results: Patients undergoing endoscopic lumbar surgery via an interlaminar or transforaminal approach have overall good/excellent modified MacNab outcomes and a significant reduction in post-operative VAS and ODI scores. Regardless of the anatomical location of disc herniations, good/excellent modified MacNab outcomes and significant reductions in VAS and ODI were reported post-operatively; however, not in patients with calcified disc herniations. Patients with central and foraminal stenosis overall reported poor/fair modified MacNab outcomes. However, there were significant reductions in VAS and ODI scores post-operatively. Patients with subarticular stenosis or an associated spondylolisthesis reported good/excellent modified MacNab outcomes and significant reductions in VAS and ODI scores post-operatively. Patients with disc herniation and concurrent degenerative stenosis had generally poor/fair modified MacNab outcomes. Conclusion: The outcomes of endoscopic spine surgery are encouraging, with a low complication and reoperation rate. However, patients with calcified disc herniations, central canal stenosis or a disc herniation with concurrent degenerative stenosis present challenges during the initial learning curve and may benefit from traditional open or other minimally invasive techniques.

Keywords: complications, lumbar disc herniation, lumbar endoscopic spine surgery, predictors of failed endoscopic spine surgery

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Authors: Catarina Cruz, Ana Breda

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In the early years, until the children’s entrance at the elementary school, they are stimulated by their educators, through rich and attractive contexts, to explore and develop skills in different domains, from the socio-emotional to the cognitive. Many of these contexts trigger real or imaginary situations, familiar or not, through resources or pedagogical practices that incite children's curiosity, questioning, expression of ideas or emotions, social interaction, among others. Later, when children enter at the elementary school, their activity at school becomes more focused on developing skills in the cognitive domain, namely acquiring learning from different subject areas, such as Mathematics, Natural Sciences, History, among others. That is, to ensure that children develop the standardized learning recommended in the guiding curriculum documents, they spend part of their time applying formulas, memorizing information, following instructions, and so on, and in this way not much time is left to listen children, to learn about their interests and likes, as well as their perspective and questions about the surround world. In Elementary School, especially in the 1st Cycle, children are naturally curious, however, sometimes this skill is subtly conditioned by adults. Curious children learn more, since they have an intrinsic desire to know more, especially about what is unknown. When children think on subjects or themes that they are interested in or curious about, they attribute more meaning to this learning and retain it for longer. Therefore, it is important to approach subjects in the classroom that seduce or captivate children's attention, trigger them curiosity, and allow to hear their ideas. There are several resources, strategies and pedagogical practices to awaken children's curiosity, to explore their knowledge, to understand their perspectives and their way of thinking, to know a little more about their personality and to provide space for dialogue. The storytelling, its narrative’s exploration and interpretation is one of those pedagogical practices. Children’s literature, about real or imaginary subjects, stimulate children’s insights supported into their experiences, emotions, learnings and personality, and promote opportunities for children express freely their feelings and thoughts. This work focuses on a session developed with children in the 3rd year of schooling, from a Portuguese 1st Cycle Basic School, in which the story "From the Outside In and From the Inside Out" was presented. The story’s presentation was mainly centred on children’s activity, who read excerpts and interpreted/explored them through a dialogue led by one of the authors. The study presented here intends to show an example of how an exploration of a children's story can trigger ideas, thoughts, emotions or attitudes in children in the 3rd year of elementary school. To answer the research question, this work aimed to: identify ideas, thoughts, emotions or attitudes that emerged from the exploration of story; analyse aspects of the story and the orchestration/conduction of dialogue with/between children that facilitated or inhibited the emergence of ideas, thoughts, emotions or attitudes by children,

Keywords: storytelling, children’s perspectives, soft skills, non-formal learning contexts, orchestration

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Authors: Celeste Labuschagne, Sam Ramaila, Thasmai Dhurumraj

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Although formative assessment is acknowledged as crucial for teachers to gauge students’ understanding of subject content, applying formative assessment in an online context is more challenging than in a traditional Physical Sciences classroom. This study examines challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment. The empirical investigation adopted a generic qualitative design and involved three purposively selected Grade 10 Physical Sciences teachers from three different schools and quintiles within the Tshwane North District in South Africa. Data were collected through individual and focus group interviews. Technological, pedagogical, and content knowledge (TPACK) was utilised as a theoretical framework underpinning the study. The study identified a myriad of challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment. These challenges include the utilisation of Annual Teaching Plans, lack of technological knowledge, and internet connectivity. The Department of Basic Education faces the key imperative to provide continuous teacher professional development and concomitant online learning materials that can facilitate meaningful enactment of online formative assessment in various educational settings.

Keywords: COVID-19, challenges, online formative assessment, physical sciences, TPACK

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Authors: Manish Saha, Manish Kumar Niranjan, Saket Asthana

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The K₀.₅Na₀.₅NbO₃ (KNN) system has emerged as one of the most promising lead-free piezoelectric over the years. In this work, we perform a comprehensive investigation of electronic structure, lattice dynamics and dielectric/ferroelectric properties of the room temperature phase of KNN by combining ab-initio DFT-based theoretical analysis and experimental characterization. We assign the symmetry labels to KNN vibrational modes and obtain ab-initio polarized Raman spectra, Infrared (IR) reflectivity, Born-effective charge tensors, oscillator strengths etc. The computed Raman spectrum is found to agree well with the experimental spectrum. In particular, the results suggest that the mode in the range ~840-870 cm-¹ reported in the experimental studies is longitudinal optical (LO) with A_1 symmetry. The Raman mode intensities are calculated for different light polarization set-ups, which suggests the observation of different symmetry modes in different polarization set-ups. The electronic structure of KNN is investigated, and an optical absorption spectrum is obtained. Further, the performances of DFT semi-local, metal-GGA and hybrid exchange-correlations (XC) functionals, in the estimation of KNN band gaps are investigated. The KNN bandgap computed using GGA-1/2 and HSE06 hybrid functional schemes are found to be in excellant agreement with the experimental value. The COHP, electron localization function and Bader charge analysis is also performed to deduce the nature of chemical bonding in the KNN. The solid-state reaction and hydrothermal methods are used to prepare the KNN ceramics, and the effects of grain size on the physical characteristics these ceramics are examined. A comprehensive study on the impact of different synthesis techniques on the structural, electrical, and photocatalytic properties of ferroelectric ceramics KNN. The KNN-S prepared by solid-state method have significantly larger grain size as compared to that for KNN-H prepared by hydrothermal method. Furthermore, the KNN-S is found to exhibit higher dielectric, piezoelectric and ferroelectric properties as compared to KNN-H. On the other hand, the increased photocatalytic activity is observed in KNN-H as compared to KNN-S. As compared to the hydrothermal synthesis, the solid-state synthesis causes an increase in the relative dielectric permittivity (ε^') from 2394 to 3286, remnant polarization (P_r) from 15.38 to 20.41 μC/cm^², planer electromechanical coupling factor (k_p) from 0.19 to 0.28 and piezoelectric coefficient (d_33) from 88 to 125 pC/N. The KNN-S ceramics are also found to have a lower leakage current density, and higher grain resistance than KNN-H ceramic. The enhanced photocatalytic activity of KNN-H is attributed to relatively smaller particle sizes. The KNN-S and KNN-H samples are found to have degradation efficiencies of RhB solution of 20% and 65%, respectively. The experimental study highlights the importance of synthesis methods and how these can be exploited to tailor the dielectric, piezoelectric and photocatalytic properties of KNN. Overall, our study provides several bench-mark important results on KNN that have not been reported so far.

Keywords: lead-free piezoelectric, Raman intensity spectrum, electronic structure, first-principles calculations, solid state synthesis, photocatalysis, hydrothermal synthesis

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Authors: Khalida Douibi, Rodrigo Balp, Solène Le Bars

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In the medical field, applications related to human experiments are frequently linked to reduced samples size, which makes the training of machine learning models quite sensitive and therefore not very robust nor generalizable. This is notably the case in Brain-Computer Interface (BCI) studies, where the sample size rarely exceeds 20 subjects or a few number of trials. To address this problem, several resampling approaches are often used during the data preparation phase, which is an overly critical step in a data science analysis process. One of the naive approaches that is usually applied by data scientists consists in the transformation of the entire database before the resampling phase. However, this can cause model’ s performance to be incorrectly estimated when making predictions on unseen data. In this paper, we explored the effect of data leakage observed during our BCI experiments for device control through the real-time classification of SSVEPs (Steady State Visually Evoked Potentials). We also studied potential ways to ensure optimal validation of the classifiers during the calibration phase to avoid overfitting. The results show that the scaling step is crucial for some algorithms, and it should be applied after the resampling phase to avoid data leackage and improve results.

Keywords: data leackage, data science, machine learning, SSVEP, BCI, overfitting

Procedia PDF Downloads 149