Search results for: embedded learning support

Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

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The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

Procedia PDF Downloads 105

Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

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Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Wang Yanping Karen, Mohd Rafeah Siti, Park MI Kyoung

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Formalin-fixed paraffin embedded (FFPE) tissue is important in the area of oncological diagnostics. This method of preserving tissues enabling them to be stored easily at ambient temperature for a long time. This decreases the risk of losing the DNA quantity and quality after extraction, reducing sample wastage, and making FFPE more cost effective. However, extracting DNA from FFPE tissue is a challenge as DNA purified is often highly cross-linked, fragmented, and degraded. In addition, this causes problems for many downstream processes. In this study, there will be a comparison of DNA extraction efficiency between One BioMed’s Xceler8 automated platform with commercial available extraction kits (Qiagen and Roche). The FFPE tissue slices were subjected to deparaffinization process, pretreatment and then DNA extraction using the three mentioned platforms. The DNA quantity were determined with real-time PCR (BioRad CFX ) and gel electrophoresis. The amount of DNA extracted with the One BioMed’s X8 platform was found to be comparable with the other two manual extraction kits.

Keywords: DNA extraction, FFPE tissue, qiagen, roche, one biomed X8

Procedia PDF Downloads 91

Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Julius Steinkopf, Eric Rost, Aike Hessel, Sonia Lippke

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Objective: This study examined possible determinants of return to work (RTW) in individuals who participated in a medical rehabilitation program longitudinally over a time period of six months. Design/methodology/approach: N=1,044 rehabilitants were included in the baseline measurement in terms of completing a questionnaire during their medical rehabilitation. About 30% (n=350) have remained in the study in terms of participating in computer-assisted telephone interviewing (CATI) six months later. Frequency analyses and Regression analyses were run. Findings: About 70% of the rehabilitants returned to work six months after rehabilitation. Regression analyses revealed that the RTW rates were significantly predicted by gender (OR=0.12, men were more likely to return), perceived social support (OR=3.01) and current physical functioning (OR=1.25). Furthermore RTW motives, like expected monetary rewards (OR=25.2) and feelings of being needed (OR=0.18) same as motives for not returning to work (nRTW), like the wish to stop working in order to spend time with the spouse (OR=0.13) or a lack of enjoyment of work (OR=3.81), significantly predicted return to work rates. Life satisfaction, self-efficacy beliefs, mental health, current income, educational background or age did not significantly increase explained variance (all ps > .05). Practical implications: Taking theses predictors into account provides options to increase the effectiveness of interventions aiming at increasing RTW: Medical rehabilitations should not only aim at improving the physical functioning but also to enhance beneficial motives and social support as well as support women specifically in order to improve the effectiveness of medical rehabilitation and public health interventions. Originality/value: Illness-caused work absences are related to high financial costs and individual burden. Despite of the public health and societal implications, this is one of the very few studies investigating systematically fitness and health for the return to work.

Keywords: gender, fitness, health, physical functioning

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Authors: R. R. Govind

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In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

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Authors: Fathi Mohamed Abdrabbo, Khaled Esayed Gaaver, Musab Musa Eldooma

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This paper presents an attempt made to investigate the behavior of a single vertical steel hollow pile embedded in sand subjected to compressive inclined load at various inclination angles α through FEM package MIDAS GTS/NX 2019. The effect of the inclination angle and slenderness ratio on the performance of the pile was investigated. Inclined load caring capacity and pile stiffness, as well as lateral deformation profiles along with the pile, were presented. The global, vertical, and horizontal load displacements of pile head, as well as the deformation profiles along the pile and the pile stiffness, are significantly affected by α. It was observed that the P-Y curves of the pile-soil system are independent of α. Also, the slenderness ratios are markedly affecting the behavior of the pile. In addition, there was a noticeable effect of the horizontal load component of the applied load on the vertical behavior of the pile, whereas there was no influence of the presence of vertical load on the horizontal behavior of the pile.

Keywords: deep foundation, piles, inclined load, pile deformations

Procedia PDF Downloads 134

Authors: Ebrahim Panah, Muhammad Yasir Babar

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Thanks to the development of online technology and social networks, people are able to communicate as well as learn. WhatsApp is a popular social network which is growingly gaining popularity. This app can be used for communication as well as education. It can be used for instructor-learner, learner-learner, and learner-content interactions; however, very little knowledge is available on these potentials of WhatsApp. The current study was undertaken to investigate university students’ perceptions of WhatsApp used as a tool for instructor-learner dialogue, learner-content dialogue, and learner-learner dialogue. The study adopted a survey approach and distributed the questionnaire developed by Google Forms to 54 (11 males and 43 females) university students. The obtained data were analyzed using SPSS version 20. The result of data analysis indicates that students have positive attitudes towards WhatsApp as a tool for Instructor-Learner Dialogue: it easy to reach the lecturer (4.07), the instructor gives me valuable feedback on my assignment (4.02), the instructor is supportive during course discussion and offers continuous support with the class (4.00). Learner-Content Dialogue: WhatsApp allows me to academically engage with lecturers anytime, anywhere (4.00), it helps to send graphics such as pictures or charts directly to the students (3.98), it also provides out of class, extra learning materials and homework (3.96), and Learner-Learner Dialogue: WhatsApp is a good tool for sharing knowledge with others (4.09), WhatsApp allows me to academically engage with peers anytime, anywhere (4.07), and we can interact with others through the use of group discussion (4.02). It was also found that there are significant positive correlations between students’ perceptions of Instructor-Learner Dialogue (ILD), Learner-Content Dialogue (LCD), Learner-Learner Dialogue (LLD) and WhatsApp Application in classroom. The findings of the study have implications for lectures, policy makers and curriculum developers.

Keywords: instructor-learner dialogue, learners-contents dialogue, learner-learner dialogue, whatsapp application

Procedia PDF Downloads 146

Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

Procedia PDF Downloads 86

Authors: Rie Adachi

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In Japan, it is required to improve students’ English communicative proficiency and the Education Ministry will start English education for the third grade and upper from year 2020 on. Considering the problems with the educational system, Content and Language Integrated Learning (CLIL) is more appropriate to be employed in elementary schools rather than just introducing English lessons. Effective CLIL takes place in the 4Cs Framework, and different strategies are used in various activities, such as arts and crafts, bodily expression, singing, playing roles, etc. After a CLIL workshop for local teachers focused on the 4Cs, the writer conducted a survey of the 36 participants using a questionnaire and found that they did not know the word CLIL, but seemed to have an interest after attending the workshop. The writer concluded that researchers and practitioners need to spread awareness of the 4Cs framework, to apply CLIL into Japanese educational context, to provide CLIL teacher training program and so on, in order to practice CLIL in Japanese elementary schools and nurture students with a global mindset.

Keywords: CLIL, 4Cs, homeroom teachers, intercultural understanding

Procedia PDF Downloads 157

Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: Joy Aworookoroh

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Writing is one of the active skills in language learning. Students of English as a foreign language are expected to write efficiently and proficiently in the language; however, there are usually challenges to optimal performance and competence in writing. Errors, on the other hand, in a foreign language learning situation are more positive than negative as they provide the basis for solving the limitations of the students. This paper investigates the situation in the Department of English, Taraba State University Jalingo. Students are administered a descriptive writing test across different levels of study. The target students are multilingual with an L1 of either Kuteb, Hausa or Junkun languages. The essays are accessed to identify the different kinds of errors in them alongside the classification of the order. Errors of correctness, clarity, engagement, and delivery were identified. However, the study identified that the degree of errors reduces alongside the experience and exposure of the students to an EFL classroom.

Keywords: errors, writings, descriptive essay, multilingual

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Lana Whiskeyjack, Kyle Napier

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The Spirit of the language embodies the motivation for indigenous people to connect with the indigenous language of their lineage. While the concept of the spirit of the language is often woven into the discussion by indigenous language revitalizationists, particularly those who are indigenous, there are few tangible terms in academic research conceptually actualizing the term. Through collaborative work with indigenous language speakers, elders, and learners, this research sets out to identify the spirit of the language, the catalysts of disconnection from the spirit of the language, and the sources of reconnection to the spirit of the language. This work fundamentally addresses the terms of engagement around collaboration with indigenous communities, itself inviting a decolonial approach to community outreach and individual relationships. As indigenous researchers, this means beginning, maintain, and closing this work in the ceremony while being transparent with community members in this work and related publishing throughout the project’s duration. Decolonizing this approach also requires maintaining explicit ongoing consent by the elders, knowledge keepers, and community members when handling their ancestral and indigenous knowledge. The handling of this knowledge is regarded in this work as stewardship, both in the handling of digital materials and the handling of ancestral Indigenous knowledge. This work observes recorded conversations in both nêhiyawêwin and English, resulting from 10 semi-structured interviews with fluent nêhiyawêwin speakers as well as three structured dialogue circles with fluent and emerging speakers. The words were transcribed by a speaker fluent in both nêhiyawêwin and English. The results of those interviews were categorized thematically to conceptually actualize the spirit of the language, catalysts of disconnection to thespirit of the language, and community voices methods of reconnection to the spirit of the language. Results of these interviews vastly determine that the spirit of the language is drawn from the land. Although nêhiyawêwin is the focus of this work, Indigenous languages are by nature inherently related to the land. This is further reaffirmed by the Indigenous language learners and speakers who expressed having ancestries and lineages from multiple Indigenous communities. Several other key differences embody this spirit of the language, which include ceremony and spirituality, as well as the semantic worldviews tied to polysynthetic verb-oriented morphophonemics most often found in indigenous languages — and of focus, nêhiyawêwin. The catalysts of disconnection to the spirit of the language are those whose histories have severed connections between Indigenous Peoples and the spirit of their languages or those that have affected relationships with the land, ceremony, and ways of thinking. Results of this research and its literature review have determined the three most ubiquitously damaging interdependent factors, which are catalysts of disconnection from the spirit of the language as colonization, capitalism, and Christianity. As voiced by the Indigenous language learners, this work necessitates addressing means to reconnect to the spirit of the language. Interviewees mentioned that the process of reconnection involves a whole relationship with the land, the practice of reciprocal-relational methodologies for language learning, and indigenous-protected and -governed learning. This work concludes in support of those reconnection methodologies.

Keywords: indigenous language acquisition, indigenous language reclamation, indigenous language revitalization, nêhiyawêwin, spirit of the language

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Authors: Tai-Ping Chang

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In the present study, the effects on chaotic motion of single-walled carbon nanotube (SWCNT) due to the linear and nonlinear damping are investigated. By using the Hamilton’s principle, the nonlinear governing equation of the single-walled carbon nanotube embedded in a matrix is derived. The Galerkin’s method is adopted to simplify the integro-partial differential equation into a nonlinear dimensionless governing equation for the SWCNT, which turns out to be a forced Duffing equation. The variations of the Lyapunov exponents of the SWCNT with damping and harmonic forcing amplitudes are investigated. Based on the computations of the top Lyapunov exponent, it is concluded that the chaotic motion of the SWCNT occurs when the amplitude of the periodic excitation exceeds certain value, besides, the chaotic motion of the SWCNT occurs with small linear damping and tiny nonlinear damping.

Keywords: chaotic motion, damping, Lyapunov exponents, single-walled carbon nanotube

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Authors: Waleed Maqableh, Ahmad Al-Hamad, Manjit Sidhu

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Augmented reality (AR) technology has the capability to provide many benefits in the field of education as a modern technology which aided learning and improved the learning experience. This paper evaluates AR based application with multiple markers interaction technique (touch-to-print) which is designed for analyzing the kinematics of 4BL mechanism in mechanical engineering. The application is termed as AR-4BL-MAST and it allows the users to touch the symbols on a paper in natural way of interaction. The evaluation of this application was performed with mechanical engineering students and human–computer interaction (HCI) experts to test its effectiveness as a tangible user interface application where the statistical results show its ability as an interaction technique, and it gives the users more freedom in interaction with the virtual mechanical objects.

Keywords: augmented reality, multimedia, user interface, engineering, education technology

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Authors: Omer Oral, Y. Emre Yilmaz

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Topology optimization is an approach that optimizes material distribution within a given design space for a certain load and boundary conditions by providing performance goals. It uses various restrictions such as boundary conditions, set of loads, and constraints to maximize the performance of the system. It is different than size and shape optimization methods, but it reserves some features of both methods. In this study, interior structures of the parts were optimized by using SIMP (Solid Isotropic Material with Penalization) method. The volume of the part was preassigned parameter and minimum deflection was the objective function. The basic idea behind the theory was considered, and different methods were discussed. Rhinoceros 3D design tool was used with Grasshopper and TopOpt plugins to create and optimize parts. A Grasshopper algorithm was designed and tested for different beams, set of arbitrary located forces and support types such as pinned, fixed, etc. Finally, 2.5D shapes were obtained and verified by observing the changes in density function.

Keywords: Grasshopper, lattice structure, microstructures, Rhinoceros, solid isotropic material with penalization method, TopOpt, topology optimization

Procedia PDF Downloads 115

Authors: Taha Ahmed Husain

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The dynamic characteristics such as speed and torque as well as voltages and currents of pwm brushless DC motor inverter are analyzed with a MATLAB model. The contribution of external load torque and friction torque is monitored. The switching function technique is adopted for the current control of the embedded three phase inverter that drives the brushless DC motor.In switching functions the power conversions circuits can be modeled according to their functions rather than circuit topologies. Therefore, it can achieve simplification of the overall power conversion functions. The trapezoidal type (back emf) is used in the model as ithas lower switching loss compared with sinusoidal type (back emf). Results show reliable time analysis for speed, torque, phase and line voltages and currents and the effect of current commutation is clearly observed.

Keywords: BLDC motor, brushless dc motors, pwm inverter, DC motor control, trapezoidal back emf, ripple torque in brushless DC motor

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Authors: Ahmed Amrani, Oussama Allali, Amira Ben Hamida, Felix Defrance, Stephanie Morland, Eva Pineau, Thomas Lacroix

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The energy planning process is a very complex task that involves several stakeholders and requires the consideration of several local and global factors and constraints. In order to optimize and simplify this process, we propose a tool-based iterative approach applied to district heating planning. We build our tool with the collaboration of a French territory using actual district data and implementing the European incentives. We set up an iterative process including data visualization and analysis, identification and extraction of information related to the area concerned by the operation, design of sustainable planning scenarios leveraging local renewable and recoverable energy sources, and finally, the evaluation of scenarios. The last step is performed by a dynamic digital twin replica of the city. Territory’s energy experts confirm that the tool provides them with valuable support towards sustainable energy planning.

Keywords: climate change, data management, decision support, digital twin, district heating, energy planning, renewables, smart city

Procedia PDF Downloads 157

Authors: Priyanka Patil, Logan Manikam

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Background: The first 1000 days of life are a critical window and can result in adverse health consequences due to inadequate nutrition. South-Asian (SA) communities face significant health disparities, particularly in maternal and child health. Community-based interventions, often employing Participatory-Learning and Action (PLA) approaches, have effectively addressed health inequalities in lower-income nations. The aim of this study was to assess the feasibility of implementing a PLA intervention to improve infant feeding and care practices in SA communities living in London. Methods: Comprehensive analyses were conducted to assess the feasibility/fidelity of this pilot randomized controlled trial. Summary statistics were computed to compare key metrics, including participant consent rates, attendance, retention, intervention support, and perceived effectiveness, against predefined progression rules guiding toward a definitive trial. Secondary outcomes were analyzed, drawing insights from multiple sources, such as The Children’s-Eating-Behaviour Questionnaire (CEBQ), Parental-Feeding-Style Questionnaires (PFSQ), Food-diary, and the Equality-Impact-Assessment (EIA) tool. A video analysis of children's mealtime behavior trends was conducted. Feedback interviews were collected from study participants. Results: Process-outcome measures met predefined progression rules for a definitive trial, which deemed the intervention as feasible and acceptable. The secondary outcomes analysis revealed no significant changes in children's BMI z-scores. This could be attributed to the abbreviated follow-up period of 6 months, reduced from 12 months, due to COVID-19-related delays. CEBQ analysis showed increased food responsiveness, along with decreased emotional over/undereating. A similar trend was observed in PFSQ. The EIA tool found no potential discrimination areas, and video analysis revealed a decrease in force-feeding practices. Participant feedback revealed improved awareness and knowledge sharing. Conclusion: This study demonstrates that a co-adapted PLA intervention is feasible and well-received in optimizing infant-care practices among South-Asian community members in a high-income country. These findings highlight the potential of community-based interventions to enhance health outcomes, promoting health equity.

Keywords: child health, childhood obesity, community-based, infant nutrition

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

Procedia PDF Downloads 117

Authors: B. Bricklin Zeff

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This research explores the development and application of a specialized chatbot tailored for a nursing English course, with a primary objective of augmenting student engagement through situational content and responsiveness to key expressions and vocabulary. Introducing the chatbot, elucidating its purpose, and outlining its functionality are crucial initial steps in the research study, as they provide a comprehensive foundation for understanding the design and objectives of the specialized chatbot developed for the nursing English course. These elements establish the context for subsequent evaluations and analyses, enabling a nuanced exploration of the chatbot's impact on student engagement and language learning within the nursing education domain. The subsequent exploration of the intricate language model development process underscores the fusion of scientific methodologies and artistic considerations in this application of artificial intelligence (AI). Tailored for educators and curriculum developers in nursing, practical principles extending beyond AI and education are considered. Some insights into leveraging technology for enhanced language learning in specialized fields are addressed, with potential applications of similar chatbots in other professional English courses. The overarching vision is to illuminate how AI can transform language learning, rendering it more interactive and contextually relevant. The presented chatbot is a tangible example, equipping educators with a practical tool to enhance their teaching practices. Methodologies employed in this research encompass surveys and discussions to gather feedback on the chatbot's usability, effectiveness, and potential improvements. The chatbot system was integrated into a nursing English course, facilitating the collection of valuable feedback from participants. Significant findings from the study underscore the chatbot's effectiveness in encouraging more verbal practice of target expressions and vocabulary necessary for performance in role-play assessment strategies. This outcome emphasizes the practical implications of integrating AI into language education in specialized fields. This research holds significance for educators and curriculum developers in the nursing field, offering insights into integrating technology for enhanced English language learning. The study's major findings contribute valuable perspectives on the practical impact of the chatbot on student interaction and verbal practice. Ultimately, the research sheds light on the transformative potential of AI in making language learning more interactive and contextually relevant, particularly within specialized domains like nursing.

Keywords: chatbot, nursing, pragmatics, role-play, AI

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Authors: Jiao Li

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Language teachers have been expected to play diversified new roles in times of educational changes. Considering the critical role that materials play in teaching and learning, language teachers have been increasingly involved in developing materials. Using identity as an analytic lens, this study aims to explore language teachers’ experiences as materials developers in China, focusing on the challenges they face and responses to them. It will adopt a multimethod approach. At the first stage, about 12 language teachers who have developed or are developing materials will be interviewed to have a broad view of their experiences. At the second stage, three language teachers who are developing materials will be studied by collecting interview data, policy documents, and data obtained from online observation of their group meetings so as to gain a deeper understanding of their experiences in materials development. It is expected that this study would have implications for teacher development, materials development, and curriculum development as well.

Keywords: educational changes, teacher development, teacher identity, teacher learning, materials development

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Authors: Wiriranai Brilliant Masara

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The African Peace and Security Architecture is widely celebrated and revered as a paragon of the will to address peace and security challenges in Africa. However, like any other institution, it is embedded with operational and institutional challenges that prevent it from effectively carrying out its mandate and turning goals into achieved results. The article examines the fundamental flaws and weaknesses of the African Peace and Security Architecture by focusing on its institutions, norms, instruments, and its relationship to Africa’s Regional Economic Communities. Therefore, the article reviews the flaws of the five elements of the African Peace and Security Architecture which are the Peace and Security Council, Panel of the Wise, Continental Early Warning System, African Standby Force, and Peace Fund.

Keywords: African Union, African Peace and Security Architecture, peace and security council, continental early warning system, African Standby Force, Panel of the Wise, Peace Fund

Procedia PDF Downloads 122

Authors: Vasilis C. Hristidis, Ricardo Caceda, Ji Soo Kim, Brian Bronson, Emily A. Hill

Abstract:

Objective: Leave passes to provide authorized leave for hospitalized patients from a psychiatric inpatient unit. Though providing day passes was once a relatively common practice, there is relatively little data describing their safety and efficacy. Methods: This descriptive study examines the use of leave passes in an adult inpatient unit at a university hospital between 2017 and 2021, with attention to reasons for granting the day pass, duration, and outcome of the pass. Results: During the study period, ten patients with primary psychotic or mood disorders received 12 passes for either housing coordination, COVID-19 vaccination, or major family events. There were no fatalities or elopements. One patient experienced severe agitation and engaged in non-suicidal self-injurious behavior. A second patient showed mild, redirectable psychomotor agitation upon return to the unit. The remaining 10 passes were uneventful. Conclusions: Our findings support the view that patients with diverse diagnoses can safely be provided leave from an inpatient setting with adequate planning and support, yielding a low incidence of adverse events.

Keywords: passes, inpatient, psychiatry, inpatient leave, outcome

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Authors: Danzhao Cheng, Eugene Ch'ng

Abstract:

Virtual Reality technologies can reconstruct cultural heritage objects and sites to a level of realism. Concentrating mostly on documenting authentic data and accurate representations of tangible contents, current virtual heritage is limited to accumulating visually presented objects. Such constructions, however, are fragmentary and may not convey the inherent significance of heritage in a meaningful way. In order to contextualise fragmentary historical contents where history can be told, a strategy is to create a guided narrative via role-playing. Such an approach can strengthen the logical connections of cultural elements and facilitate creative synthesis within the virtual world. This project successfully reconstructed the Ningbo Sanjiangkou VR site in Yuan Dynasty combining VR technology and role-play game approach. The results with 80 pairs of participants suggest that VR role-playing can be beneficial in a number of ways. Firstly, it creates thematic interactivity which encourages users to explore the virtual heritage in a more entertaining way with task-oriented goals. Secondly, the experience becomes highly engaging since users can interpret a historical context through the perspective of specific roles that exist in past societies. Thirdly, personalisation allows open-ended sequences of the expedition, reinforcing user’s acquisition of procedural knowledge relative to the cultural domain. To sum up, role-playing in VR poses great potential for experiential learning as it allows users to interpret a historical context in a more entertaining way.

Keywords: experiential learning, maritime silk road, role-playing, virtual heritage, virtual reality

Procedia PDF Downloads 151

Authors: Kunyu Yan

Abstract:

Early childhood music education plays a significant role in an individual’s growth. Music can help children understand themselves and relate to others, and make connections between family, school, and society. In recent years, with the development of early childhood education in China, an increasing number of kindergartens have been established, and many of them pay more attention to music education. This research has two main aims. One is to discover how and why music is used in both public and private kindergartens. The second aim is to make recommendations for widening the use of music in kindergartens. In order to achieve these aims, the research uses two main methods. Firstly, it considers the historical background and cultural context of early childhood education in China; and secondly, it uses an approach that compares public and private kindergartens. In this research, six kindergartens were chosen from Qingdao city in Shandong Province as case studies, including 3 public kindergartens and 3 private kindergartens. This research was based on using three types of data collection methods: observation, semi-structured interviews with teachers, and questionnaires with parents. Participant and non-participant observational methods were used and included in daily routines at the kindergartens in order to experience the situation of music education first-hand. Interviews were associated with teachers’ views of teaching and learning music, the perceptions of the music context, and their strategies of using music. Lastly, the questionnaire was designed to obtain the views of current music education from the children’s parents in the respective kindergartens. The results are shown with three main themes: (1) distinct characteristics of public kindergartens (e.g., similar equipment, low tuition fee, qualified teachers, etc); (2) distinct characteristics of private kindergartens (e.g., various tuition fees, own teaching system, trained teachers, etc); and (3) differences between public and private kindergartens (e.g., funding, requirements for teachers, parents’ demands, etc). According to the results, we can see that the main purpose of using music in China is to develop the musical ability of children, and teachers focus on musical learning, such as singing in tune and playing instruments. However, as revealed in this research, there are many other uses and functions of music in these educational settings, including music used for non-musical learning (e.g., counting, learning language, etc.) or in supporting social routines.

Keywords: differences between private and public school, early childhood education, music education, uses and functions of music

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Authors: Nguyen Quynh Trang, Nguyễn Thị Hông Nhung

Abstract:

The study aimed to identify the effectiveness of using the mind map technique to enhance students’ medical vocabulary retention among a group of students at a higher education institution - Thai Nguyen University of Medicine and Pharmacy during the first semester of the school year 2022-2023. The research employed a quasi-experimental method, exploring primary sources such as questionnaires and the analyzed results of pre-and-post tests. Almost teachers and students showed high preferences for the implementation of the mind map technique in language teaching and learning. Furthermore, results from the pre-and-post tests between the experimental group and control one pointed out that this technique brought back positive academic performance in teaching and learning English. The research findings revealed that there should be more supportive policies to evoke the use of the mind map technique in a pedagogical context. Aim of the Study: The purpose of this research was to investigate whether using mind mapping can help students to enhance nursing students’ medical vocabulary retention and to assess the students’ attitudes toward using mind mapping as a tool to improve their vocabulary. The methodology of the study: The research employed a quasi-experimental method, exploring primary sources such as questionnaires and the analyzed results of pre-and-post tests. The contribution of the study: The research contributed to the innovation of teaching vocabulary methods for English teachers at a higher education institution. Moreover, the research helped the English teachers and the administrators at a university evoke and maintain the motivation of students not only in English classes but also in other subjects. The findings of this research were beneficial to teachers, students, and researchers interested in using mind mapping to teach and learn English vocabulary. The research explored and proved the effectiveness of applying mind mapping in teaching and learning English vocabulary. Therefore, teaching and learning activities were conducted more and more effectively and helped students overcome challenges in remembering vocabulary and creating motivation to learn English vocabulary.

Keywords: medical vocabulary retention, mind map technique, nursing students, medical vocabulary

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Authors: Naci Büyükkaracığan, Yıldırım Akyol

Abstract:

Information technology is called the technology which provides transmission of information elsewhere regardless of time, location, distance. Today, information technology is providing the occurrence of ground breaking changes in all areas of our daily lives. Information can be reached quickly to millions of people with help of information technology. In this Study, effects of information technology on students for educations and their learning preferences were demonstrated with using data obtained from questionnaires administered to students of 2015-2016 academic year at Selcuk University Kadınhanı Faik İçil Vocational School Construction Department. The data was obtained by questionnaire consisting of 30 questions that was prepared by the researchers. SPSS 21.00 package programme was used for statistical analysis of data. Chi-square tests, Mann-Whitney U test, Kruskal-Wallis and Kolmogorov-Smirnov tests were used in the data analysis for Descriptiving statistics. In a study conducted with the participation of 61 students, 93.4% of students' reputation of their own information communication device (computer, smart phone, etc.) That have been shown to be at the same rate and to the internet. These are just a computer of itself, then 45.90% of the students. The main reasons for the students' use of the Internet, social networking sites are 85.24%, 13.11% following the news of the site, as seen. All student assignments in information technology, have stated that they use in the preparation of the project. When students acquire scientific knowledge in the profession regarding their preferred sources evaluated were seen exactly when their preferred internet. Male students showed that daily use of information technology while compared to female students was statistically significantly less. Construction Package program where students are eager to learn about the reputation of 72.13% and 91.80% identified in the well which they agreed that an indispensable element in the professional advancement of information technology.

Keywords: information technologies, computer, construction, internet, learning systems

Procedia PDF Downloads 285

Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

Abstract:

Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

Procedia PDF Downloads 337