Search results for: teaching & learning

Authors: María González Alriols, Itziar Egües, María A. Andrés, Mirari Antxustegi

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Several collaborative learning proposals were prepared to be applied in the laboratory sessions of chemistry in the first course of engineering studies. The aim was to engage the students from the beginning and to avoid absenteeism as well as to reach a more homogeneous level in the class. The students, divided into small groups of four or five mates, were asked to do an exercise before having the practical session in the lab. Precisely, each one of the groups was asked to study the theoretical fundamentals and the practical aspects of one lab session and to prepare a didactical video with this content, including the materials, equipment and reactants required, and the detailed experimental procedure. Furthermore, they should include the performance of the experiment step by step, indicating the faced difficulties and the obtained results and conclusions. After watching the video of this precise activity, the other groups of students would go to the lab to put into practice the session following the commands explained in the video. The evaluation of the video activity that is worth the 50% of the total mark of the laboratory sessions, is done depending on the success that the other groups of students had while doing the practical session that was explained in the video. This means that the successful transmission of knowledge to the rest of the mates in the class through the video was compulsory to pass the practical sessions and the subject. The other 50% of the mark depended on the understanding of the other students’ explanations and the success in the corresponding practical sessions. The experience was found to be very positive, as the engagement level was considerably higher, the absenteeism lower and the attitude in the laboratory much more responsible. The materials, reactants and equipment were used carefully, and no incidents were registered. Furthermore, the fact of having peer experts was useful to encourage critical thinking in a more relaxed way, with the teacher figure in a secondary position. Finally, the academic achievements were satisfactory as well, with a high percentage of students over the level required for passing the subject.

Keywords: collaborative learning, engineering instruction, chemistry, laboratory sessions

Procedia PDF Downloads 152

Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

Procedia PDF Downloads 163

Authors: Rajendra Chetty

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This paper analyses the experiences of teachers of literacy in underprivileged schools in the Western Cape, South Africa. The purpose is to provide teachers in poorly resourced schools within economically deprived areas an opportunity to voice their experiences of teaching literacy. The paper is based on an empirical study using interviews and classroom observation. A descriptive account of the observation data was followed by an interpretive analysis. The content analysis of the interview data led to the development of themes and patterns for the discussion. The study reveals key factors for literacy underachievement that include lack of critical and emancipatory pedagogies, resources, parental support, lack of teacher knowledge, absence of cognitive activities, and the social complexity of poverty. The paper recommends that a new model of literacy that is underpinned by critical pedagogy challenge inequality and provides strategic and sustained teacher support in disadvantaged schools is crucial in a society emerging from oppression and racism.

Keywords: critical pedagogy, disadvantaged schools, literacy, poverty

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Authors: Mohammad Hassanzadeh

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As far as incidental vocabulary learning is concerned, the basic contention of the Involvement Load Hypothesis (ILH) is that retention of unfamiliar words is, generally, conditional upon the degree of involvement in processing them. This study examined input modality and incidental vocabulary uptake in a task-induced setting whereby three variously loaded task types (marginal glosses, fill-in-task, and sentence-writing) were alternately assigned to one group of students at Allameh Tabataba’i University (n=2l) during six classroom sessions. While one round of exposure was comprised of the audiovisual medium (TV talk shows), the second round consisted of textual materials with approximately similar subject matter (reading texts). In both conditions, however, the tasks were equivalent to one another. Taken together, the study pursued the dual objectives of establishing a litmus test for the ILH and its proposed values of ‘need’, ‘search’ and ‘evaluation’ in the first place. Secondly, it sought to bring to light the superiority issue of exposure to audiovisual input versus the written input as far as the incorporation of tasks is concerned. At the end of each treatment session, a vocabulary active recall test was administered to measure their incidental gains. Running a one-way analysis of variance revealed that the audiovisual intervention yielded higher gains than the written version even when differing tasks were included. Meanwhile, task 'three' (sentence-writing) turned out the most efficient in tapping learners' active recall of the target vocabulary items. In addition to shedding light on the superiority of audiovisual input over the written input when circumstances are relatively held constant, this study for the most part, did support the underlying tenets of ILH.

Keywords: Keywords— Evaluation, incidental vocabulary learning, input mode, Involvement Load Hypothesis, need, search.

Procedia PDF Downloads 263

Authors: Paulina Clara Appiah, Kofi Issah, Timothy Letsa, Kennedy Nartey, Amanua Chinbuah, Adoma Dwomo-Fokuo, Jacqeline G. Asibey

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Background: The Every Newborn Action Plan provides evidence–based interventions to end preventable deaths in high burden countries. Brong Ahafo Region is one of ten regions in Ghana with less than half of its district hospitals having sick newborn units. Facility-based neonatal care is not prioritized and under-funded, and there is also inadequate knowledge and competence to manage the sick. The aim of this intervention was to make available in–patient care for sick newborns in all 19 district hospitals through the strengthening of facility-based systems. Methods: With the development and dissemination of the National Newborn Strategy and Action Plan 2014-2018, the country was able to attract PATH which provided the region with basic resuscitation equipment, supported hospital providers’ capacity building in Helping Babies Breathe, Essential Care of Every Baby, Infection Prevention and Management and held a symposia on managing the sick newborn. Newborn advocacy was promoted through newborn champions at the facility and community levels. Hospital management was then able to mobilize resources from communities, corporate organizations and from internally generated funds; created or expanded sick newborn care units and provided essential medicines and equipment. Kangaroo Mother Care was initiated in 6 hospitals. Pediatric specialist outreach services initiated comprised telephone consultations, teaching ward rounds and participating in perinatal death audits meetings. Newborn data capture and management was improved through the provision and training on the use of standard registers provided from the national level. Results: From February 2015 to November 2017, hospitals with sick newborn units increased from 7 to 19 (37%-100%). 180 pieces each of newborn ventilation bags and masks size 0, 1 and penguin suction bulbs were distributed to the hospitals, in addition to 20 newborn mannequin sets and 90 small clinical reminder posters. 802 providers (96.9%) were trained in resuscitation, of which 96% were successfully followed up in 6 weeks, 91% in 6 months and 80% in 12 months post-training. 53 clinicians (65%) were trained and mentored to manage sick newborns. 56 specialist teaching ward rounds were conducted. Data completeness improved from 92.6% - 99.9%. Availability of essential medicines improved from 11% to 100%. Number of hospital cots increased from 116 to 248 (214%). Cot occupancy rate increased from 57.4% to 92.5%. Hospitals with phototherapy equipment increased from 0 to 12 (63%). Hospitals with incubators increased from 1 to 12 (5%-63%). Newborn deaths among admissions reduced from 6.3% to 5.4%. Conclusion: Access to in-patient care increased significantly. Newborn advocacy successfully mobilized resources required for strengthening facility –based systems.

Keywords: facility-based systems, Ghana, in-patient care, newborn advocacy

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Authors: Paul Lam, Kevin Wong, Chi Him Chan

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Constant practicing and accurate, immediate feedback are the keys to improving students’ speaking skills. However, traditional oral examination often fails to provide such opportunities to students. The traditional, face-to-face oral assessment is often time consuming – attending the oral needs of one student often leads to the negligence of others. Hence, teachers can only provide limited opportunities and feedback to students. Moreover, students’ incentive to practice is also reduced by their anxiety and shyness in speaking the new language. A mobile app was developed to use artificial intelligence (AI) to provide immediate feedback to students’ speaking performance as an attempt to solve the above-mentioned problems. Firstly, it was thought that online exercises would greatly increase the learning opportunities of students as they can now practice more without the needs of teachers’ presence. Secondly, the automatic feedback provided by the AI would enhance students’ motivation to practice as there is an instant evaluation of their performance. Lastly, students should feel less anxious and shy compared to directly practicing oral in front of teachers. Technically, the program made use of speech-to-text functions to generate feedback to students. To be specific, the software analyzes students’ oral input through certain speech-to-text AI engine and then cleans up the results further to the point that can be compared with the targeted text. The mobile app has invited English teachers for the pilot use and asked for their feedback. Preliminary trials indicated that the approach has limitations. Many of the users’ pronunciation were automatically corrected by the speech recognition function as wise guessing is already integrated into many of such systems. Nevertheless, teachers have confidence that the app can be further improved for accuracy. It has the potential to significantly improve oral drilling by giving students more chances to practice. Moreover, they believe that the success of this mobile app confirms the potential to extend the AI-assisted assessment to other language skills, such as writing, reading, and listening.

Keywords: artificial Intelligence, mobile learning, oral assessment, oral practice, speech-to-text function

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Authors: Boubacar Niang, Denis Raulin

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This paper presents the design of a dedicated board for learning and programming with ATMEL AVR new generation micro controller’s family. This board designed as a "shield" for the Arduino Uno allows us to focus on the design and programming of basic micro controller functionalities in high level language with a considerable time saving because of dealing with additional components is not required.

Keywords: Arduino, microcontroller, programming, language

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

Procedia PDF Downloads 143

Authors: Earl Joy H. Ausa, Lianna Rozl Brosas, Niema T. Floro, Jycyl Joice R. Medina, Nejie A. Millarada

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This study examines the demographic and personality profiles of pre-service teachers at Rizal Technological University, Boni Campus. The participants consisted of 212 pre-service teachers from the College of Education with majors in various subjects. The majority of respondents were female (74.88%), and the average age of participants was 22. Using the NEO PI-R framework, the study assessed the personality traits of pre-service teachers. The results showed that pre-service teachers displayed average levels of neuroticism, extraversion, openness, agreeableness, and conscientiousness. This suggests that they possess a balanced combination of traits that are conducive to effective teaching, such as moderate emotional stability, a balanced preference for social interactions, curiosity, good interpersonal skills, and a strong work ethic. The personality enhancement program could also benefit the pre-service teachers. In conclusion, this study provides valuable insights into the demographic and personality characteristics of pre-service teachers.

Keywords: personality traits, neo PI- R, pre-service teachers, five-factor theory

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Authors: Shenlun Chen, Leonard Wee

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Tumor grading is an essential reference for colorectal cancer (CRC) staging and survival prognostication. The widely used World Health Organization (WHO) grading system defines histological grade of CRC adenocarcinoma based on the density of glandular formation on whole slide images (WSI). Tumors are classified as well-, moderately-, poorly- or un-differentiated depending on the percentage of the tumor that is gland forming; >95%, 50-95%, 5-50% and <5%, respectively. However, manually grading WSIs is a time-consuming process and can cause observer error due to subjective judgment and unnoticed regions. Furthermore, pathologists’ grading is usually coarse while a finer and continuous differentiation grade may help to stratifying CRC patients better. In this study, a deep learning based automatic differentiation grading algorithm was developed and evaluated by survival analysis. Firstly, a gland segmentation model was developed for segmenting gland structures. Gland regions of WSIs were delineated and used for differentiation annotating. Tumor regions were annotated by experienced pathologists into high-, medium-, low-differentiation and normal tissue, which correspond to tumor with clear-, unclear-, no-gland structure and non-tumor, respectively. Then a differentiation prediction model was developed on these human annotations. Finally, all enrolled WSIs were processed by gland segmentation model and differentiation prediction model. The differentiation grade can be calculated by deep learning models’ prediction of tumor regions and tumor differentiation status according to WHO’s defines. If multiple WSIs were possessed by a patient, the highest differentiation grade was chosen. Additionally, the differentiation grade was normalized into scale between 0 to 1. The Cancer Genome Atlas, project COAD (TCGA-COAD) project was enrolled into this study. For the gland segmentation model, receiver operating characteristic (ROC) reached 0.981 and accuracy reached 0.932 in validation set. For the differentiation prediction model, ROC reached 0.983, 0.963, 0.963, 0.981 and accuracy reached 0.880, 0.923, 0.668, 0.881 for groups of low-, medium-, high-differentiation and normal tissue in validation set. Four hundred and one patients were selected after removing WSIs without gland regions and patients without follow up data. The concordance index reached to 0.609. Optimized cut off point of 51% was found by “Maxstat” method which was almost the same as WHO system’s cut off point of 50%. Both WHO system’s cut off point and optimized cut off point performed impressively in Kaplan-Meier curves and both p value of logrank test were below 0.005. In this study, gland structure of WSIs and differentiation status of tumor regions were proven to be predictable through deep leaning method. A finer and continuous differentiation grade can also be automatically calculated through above models. The differentiation grade was proven to stratify CAC patients well in survival analysis, whose optimized cut off point was almost the same as WHO tumor grading system. The tool of automatically calculating differentiation grade may show potential in field of therapy decision making and personalized treatment.

Keywords: colorectal cancer, differentiation, survival analysis, tumor grading

Procedia PDF Downloads 126

Authors: Sara T. K. Li, Belinda H. J. Chung, Jeffery C. N. Yip, Janet H. Hsiao

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Music notation and text reading both involve statistical learning of music or linguistic structures. However, it remains unclear how music reading expertise influences text reading behavior. The present study examined this issue through an eye-tracking study. Chinese-English bilingual musicians and non-musicians read English sentences, Chinese sentences, musical phrases, and sentences in Tibetan, a language novel to the participants, with their eye movement recorded. Each set of stimuli consisted of two conditions in terms of structural regularity: syntactically correct and syntactically incorrect musical phrases/sentences. They then completed a sentence comprehension (for syntactically correct sentences) or a musical segment/word recognition task afterwards to test their comprehension/recognition abilities. The results showed that in reading musical phrases, as compared with non-musicians, musicians had a higher accuracy in the recognition task, and had shorter reading time, fewer fixations, and shorter fixation duration when reading syntactically correct (i.e., in diatonic key) than incorrect (i.e., in non-diatonic key/atonal) musical phrases. This result reflects their expertise in music reading. Interestingly, in reading Tibetan sentences, which was novel to both participant groups, while non-musicians did not show any behavior differences between reading syntactically correct or incorrect Tibetan sentences, musicians showed a shorter reading time and had marginally fewer fixations when reading syntactically correct sentences than syntactically incorrect ones. However, none of the musicians reported discovering any structural regularities in the Tibetan stimuli after the experiment when being asked explicitly, suggesting that they may have implicitly acquired the structural regularities in Tibetan sentences. This group difference was not observed when they read English or Chinese sentences. This result suggests that music reading expertise facilities reading texts in a novel language (i.e., Tibetan), but not in languages that the readers are already familiar with (i.e., English and Chinese). This phenomenon may be due to the similarities between reading music notations and reading texts in a novel language, as in both cases the stimuli follow particular statistical structures but do not involve semantic or lexical processing. Thus, musicians may transfer their statistical learning skills stemmed from music notation reading experience to implicitly discover structures of sentences in a novel language. This speculation is consistent with a recent finding showing that music reading expertise modulates the processing of English nonwords (i.e., words that do not follow morphological or orthographic rules) but not pseudo- or real words. These results suggest that the modulation of music reading expertise on language processing depends on the similarities in the cognitive processes involved. It also has important implications for the benefits of music education on language and cognitive development.

Keywords: eye movement behavior, eye-tracking, music reading expertise, sentence reading, structural regularity, visual processing

Procedia PDF Downloads 367

Authors: Lik Hang Gary Wong

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Background: Practice learning is crucial for preparing the healthcare profession to meet the real challenge upon graduation. Students are required to demonstrate their competence in managing interpersonal challenges, such as teamwork with other professionals and communicating well with the service users, during the placement. Such competence precedes clinical practice, and it may eventually affect students' actual performance in a clinical context. Unfortunately, there were limited studies investigating how such competence affects students' performance in practice learning. Objectives: The aim of this study is to investigate how self-rated interpersonal competence affects students' actual performance during clinical placement. Methods: 40 occupational therapy students from Hong Kong were recruited in this study. Prior to the clinical placement (level two or above), they completed an online survey that included the Interpersonal Communication Competence Scale (ICCS) measuring self-perceived competence in interpersonal communication. Near the end of their placement, the clinical educator rated students’ performance with the Student Practice Evaluation Form - Revised edition (SPEF-R). The SPEF-R measures the eight core competency domains required for an entry-level occupational therapist. This study adopted the cross-sectional observational design. Pearson correlation and multiple regression are conducted to examine the relationship between students' interpersonal communication competence and their actual performance in clinical placement. Results: The ICCS total scores were significantly correlated with all the SPEF-R domains, with correlation coefficient r ranging from 0.39 to 0.51. The strongest association was found with the co-worker communication domain (r = 0.51, p < 0.01), followed by the information gathering domain (r = 0.50, p < 0.01). Regarding the ICCS total scores as the independent variable and the rating in various SPEF-R domains as the dependent variables in the multiple regression analyses, the interpersonal competence measures were identified as a significant predictor of the co-worker communication (R² = 0.33, β = 0.014, SE = 0.006, p = 0.026), information gathering (R² = 0.27, β = 0.018, SE = 0.007, p = 0.011), and service provision (R² = 0.17, β = 0.017, SE = 0.007, p = 0.020). Moreover, some specific communication skills appeared to be especially important to clinical practice. For example, immediacy, which means whether the students were readily approachable on all social occasions, correlated with all the SPEF-R domains, with r-values ranging from 0.45 to 0.33. Other sub-skills, such as empathy, interaction management, and supportiveness, were also found to be significantly correlated to most of the SPEF-R domains. Meanwhile, the ICCS scores correlated differently with the co-worker communication domain (r = 0.51, p < 0.01) and the communication with the service user domain (r = 0.39, p < 0.05). It suggested that different communication skill sets would be required for different interpersonal contexts within the workplace. Conclusion: Students' self-perceived interpersonal communication competence could predict their actual performance during clinical placement. Moreover, some specific communication skills were more important to the co-worker communication but not to the daily interaction with the service users. There were implications on how to better prepare the students to meet the future challenge upon graduation.

Keywords: interpersonal competence, clinical education, healthcare professional education, occupational therapy, occupational therapy students

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Authors: Obasa Joshua Ifeoluwa

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Phonetics is the foundation and most important part of language learning. This article, through an acoustic experiment as well as using Praat software, uses Yoruba students’ Chinese consonants, vowels, and tones pronunciation to carry out a visual comparison with that of native Chinese speakers. This article is aimed at Yoruba native speakers learning Chinese phonetics; therefore, Yoruba students are selected. The students surveyed are required to be at an elementary level and have learned Chinese for less than six months. The students selected are all undergraduates majoring in Chinese Studies at the University of Lagos. These students have already learned Chinese Pinyin and are all familiar with the pinyin used in the provided questionnaire. The Chinese students selected are those that have passed the level two Mandarin proficiency examination, which serves as an assurance that their pronunciation is standard. It is discovered in this work that in terms of Mandarin’s consonants pronunciation, Yoruba students cannot distinguish between the voiced and voiceless as well as the aspirated and non-aspirated phonetics features. For instance, while pronouncing [ph] it is clearly shown in the spectrogram that the Voice Onset Time (VOT) of a Chinese speaker is higher than that of a Yoruba native speaker, which means that the Yoruba speaker is pronouncing the unaspirated counterpart [p]. Another difficulty is to pronounce some affricates like [tʂ]、[tʂʰ]、[ʂ]、[ʐ]、 [tɕ]、[tɕʰ]、[ɕ]. This is because these sounds are not in the phonetic system of the Yoruba language. In terms of vowels, some students find it difficult to pronounce some allophonic high vowels such as [ɿ] and [ʅ], therefore pronouncing them as their phoneme [i]; another pronunciation error is pronouncing [y] as [u], also as shown in the spectrogram, a student pronounced [y] as [iu]. In terms of tone, it is most difficult for students to differentiate between the second (rising) and third (falling and rising) tones because these tones’ emphasis is on the rising pitch. This work concludes that the major error made by Yoruba students while pronouncing Chinese sounds is caused by the interference of their first language (LI) and sometimes by their lingua franca.

Keywords: Chinese, Yoruba, error analysis, experimental phonetics, consonant, vowel, tone

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Authors: Folasade R. Sulaiman

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Pupils’ health and nutrition are basically important to their schooling. The preponderance of avoidable deaths among children in Africa (WHO, 2000) may not be unconnected with the nutritional and health education status of families and communities that have their children as school clients. This study adopted a descriptive survey design focusing on the assessment of the level of nutritional and health education of families and community members in the rural riverine areas of Ogun State. Two research questions were raised. The Nutritional and Health Education of Families and Communities Inventory (NHEFCI) was used to collect data from 250 rural child-bearing aged women, and 0.73 test-retest reliability coefficient was established to determine the strength of the instrument. Data collected were analysed using descriptive statistics of frequency counts, percentages and mean in accordance with research questions raised in the study. The findings revealed amongst others: that 65% of the respondents had low level of nutritional and health education among the families and community members; while 72% had low level of awareness of the possible influence of nutritional and health education on the learning outcomes of the children. Based on the findings, it was recommended among others that government should intensify efforts on sensitization, mass literacy campaign etc.; also improve upon the already existing School Feeding Programme in Nigerian primary schools to provide at least one balanced diet for children while in school; community health workers, social workers, Non-Governmental Organizations (NGO) should collaborate with international Organizations like UNICEF, UNESCO, WHO etc. to organize sensitization programmes for members of the rural riverine communities on the importance of meeting the health and nutritional needs of their children in order to attain their educational potentials.

Keywords: nutritional and health education, learning capacities, school-age children, universal basic education, rural riverine areas

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Rose Shayeghi, Pejman Hosseinioun

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The Multiple Intelligences theory characterizes human intelligence as a multifaceted entity that exists in all human beings with varying degrees. The most important contribution of this theory to the field of English Language Teaching (ELT) is its role in identifying individual differences and designing more learner-centered programs. The present study aims at investigating the relationship between different elements of multiple intelligence and grammar scores. To this end, 63 female Iranian EFL learner selected from among intermediate students participated in the study. The instruments employed were a Nelson English language test, Michigan Grammar Test, and Teele Inventory for Multiple Intelligences (TIMI). The results of Pearson Product-Moment Correlation revealed a significant positive correlation between grammatical accuracy and linguistic as well as interpersonal intelligence. The results of Stepwise Multiple Regression indicated that linguistic intelligence contributed to the prediction of grammatical accuracy.

Keywords: multiple intelligence, grammar, ELT, EFL, TIMI

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Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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Authors: Weixiao Wei, Chris Shei

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This paper describes Mandate of Heaven as a source of justification for the ruling regime from ancient China approximately three thousand years ago. Initially, the kings of Shang dynasty simply nominated themselves as the sons of Heaven sent to Earth to rule the common people. As the last generation of the kings became corrupted and ruled withbrutal force and crueltywhich directly caused their destruction, the successive kings of Zhou dynasty realised the importance of virtue and the provision of goods to the people. Legitimacy of the ruling regimes became rested not entirely on random allocation of the throne by an unknown supernatural force but on a foundation comprising morality and the ability to provide goods. The latter composite was picked up by the current ruling regime, the Chinese Communist Party, and became the cornerstone of its political legitimacy, also known as ‘performance legitimacy’ where economic development accounts for the satisfaction of the people in place of election and other democratic means of providing legal-rational legitimacy. Under this circumstance, it becomes important as well for the ruling party to use political rhetoric to convince people of the good performance of the government in the economy, morality, and foreign policy. Thus, we see a lot of propaganda materials in both government policy statements and international press conference announcements. The former consists mainly of important speeches made by prominent figures in Party conferences which are not only made publicly available on the government websites but also become obligatory reading materials for university entrance examinations. The later consists of announcements about foreign policies and strategies and actions taken by the government regarding foreign affairsmade in international conferences and offered in Chinese-English bilingual versions on official websites. This documentation strategy creates an impressive image of the Chinese Communist Party that is domestically competent and international strong, taking care of the people it governs in terms of economic needs and defending the country against any foreign interference and global adversities. This political discourse system comprising reading materials fully extractable from government websites also becomes excellent repertoire for teaching and researching in contemporary Chinese language, discourse and rhetoric, Chinese culture and tradition, Chinese political ideology, and Chinese-English translation. This paper aims to provide a detailed and comprehensive description of the current Chinese political discourse system, arguing about its lineage from the rhetorical convention of Mandate of Heaven in ancient China and its current concentration on serving the people in place of election, human rights, and freedom of speech. The paper will also provide guidelines as to how this discourse system and the manifestation of official documents created under this system can become excellent research and teaching materials in applied linguistics.

Keywords: mandate of heaven, Chinese communist party, performance legitimacy, serving the people, political discourse

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Authors: Łukasz Bis

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The author is going to discuss virtual reality and its importance for new didactic methods. It has been known for years that experience-based education gives much better results in terms of long-term memory than theoretical study. However, practice is expensive - virtual reality allows the use of an empirical approach to learning, with minimized production costs. The author defines what makes a given VR experience appropriate (adequate) for the didactic and cognitive process. The article is a kind of a list of guidelines and their importance for the VR experience under development.

Keywords: virtual reality, education, universal design, guideline

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Authors: Vangmayee V. Subban, Suzan Deelan. Pinto, Somashekara Haralakatta Shivananjappa, Shwetha Prabhu, Jayashree S. Bhat

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Introduction and Need: During early years of schooling, the instruction in the schools mainly focus on children’s word decoding abilities. However, the skilled readers should master all the components of reading such as word decoding, reading fluency and comprehension. Nevertheless, the relationship between each component during the process of learning to read is less clear. The studies conducted in alphabetical languages have mixed opinion on relative contribution of word decoding and reading fluency on reading comprehension. However, the scenarios in alphasyllabary languages are unexplored. Aim and Objectives: The aim of the study was to explore the role of word decoding, reading fluency on reading comprehension abilities in children learning to read Kannada between the age ranges of 5.6 to 8.6 years. Method: In this cross sectional study, a total of 60 typically developing children, 20 each from Grade I, Grade II, Grade III maintaining equal gender ratio between the age range of 5.6 to 6.6 years, 6.7 to 7.6 years and 7.7 to 8.6 years respectively were selected from Kannada medium schools. The reading fluency and reading comprehension abilities of the children were assessed using Grade level passages selected from the Kannada text book of children core curriculum. All the passages consist of five questions to assess reading comprehension. The pseudoword decoding skills were assessed using 40 pseudowords with varying syllable length and their Akshara composition. Pseudowords are formed by interchanging the syllables within the meaningful word while maintaining the phonotactic constraints of Kannada language. The assessment material was subjected to content validation and reliability measures before collecting the data on the study samples. The data were collected individually, and reading fluency was assessed for words correctly read per minute. Pseudoword decoding was scored for the accuracy of reading. Results: The descriptive statistics indicated that the mean pseudoword reading, reading comprehension, words accurately read per minute increased with the Grades. The performance of Grade III children found to be higher, Grade I lower and Grade II remained intermediate of Grade III and Grade I. The trend indicated that reading skills gradually improve with the Grades. Pearson’s correlation co-efficient showed moderate and highly significant (p=0.00) positive co-relation between the variables, indicating the interdependency of all the three components required for reading. The hierarchical regression analysis revealed 37% variance in reading comprehension was explained by pseudoword decoding and was highly significant. Subsequent entry of reading fluency measure, there was no significant change in R-square and was only change 3%. Therefore, pseudoword-decoding evolved as a single most significant predictor of reading comprehension during early Grades of reading acquisition. Conclusion: The present study concludes that the pseudoword decoding skills contribute significantly to reading comprehension than reading fluency during initial years of schooling in children learning to read Kannada language.

Keywords: alphasyllabary, pseudo-word decoding, reading comprehension, reading fluency

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Authors: Khaled Ibrahim, Mathew Parackal, Damien Mather, Paul Hansen

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The integration between marketing and technology has given brands unprecedented opportunities to reach accurate customer data and competence to change customers' behaviour. Technology has generated a transformation within brands from traditional branding to algorithmic branding. However, brands have utilised customer data in non-cognitive programmatic targeting. This algorithmic persuasion may be effective in reaching the targeted audience. But it may encounter a moral conflict simultaneously, as it might not consider our social principles. Moral branding is a critical topic; particularly, with the increasing interest in commercial settings to teaching machines human morals, e.g., autonomous vehicles and chatbots; however, it is understudied in the marketing literature. Therefore, this paper aims to investigate the recent moral branding literature. Furthermore, applying human-like mind theory as initial framing to this paper explores a more comprehensive concept involving human morals, machine behaviour, and branding.

Keywords: brand machines, conceptual framework, moral branding, moral machines

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Authors: Nuur Insan Tangkelangi

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Needs on academic writing competence as the primary focus in higher education encourage the university institutions around the world to provide academic writing courses to support their students dealing with their tasks pertaining to this competence. However, a pilot study conducted previously in one of the universities in Palopo, a city in South Sulawesi, revealed that even though the institution has provided academic writing courses, supported by some workshops related to academic writing and some supporting facilities at campus, the students still face difficulties in completing their assignments related to academic writing, particularly in writing their theses. The present study focuses on investigating the specific needs of the students in the same institution in terms of competences required in academic writing. It is also carried out to examine whether the syllabus exists and accommodates the students’ needs or not. Questionnaire and interview were used to collect data from sixty students of sixth semester and two lecturers of the academic courses. The results reveal that the students need to learn all aspects of linguistic competence (language features, lexical phrases, academic language and vocabulary, and proper language) and some aspects in discourse competence (how to write introduction, search for appropriate literature, design research method, write coherent paragraphs, refer to sources, summarize and display data, and link sentences smoothly). Regarding the syllabus, it is found that the academic writing courses provided in the institution, where this study takes place, do not have syllabus. This condition is different from other institutions which provide syllabi for all courses. However, at the commencement of the course, the students and the lecturers have negotiated their learning goals, topics discussed, learning activities, and assessment criteria for the course. Therefore, even though the syllabus does not exist, but the elements of the syllabus are there. The negotiation between the students and the lecturers contributes to the students’ attitude toward the courses. The students are contented with the course and they feel that their needs in academic writing have been accommodated. However, some suggestions for the next academic writing courses are stated by the students. Considering the results of this study, a syllabus is then proposed which is expected to accommodate the specific needs of students in that institution.

Keywords: Students' needs, academic writing, syllabus design for instruction, case study

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: Vassilis Argyropoulos, Magda Nikolaraizi, Maria Papazafiri

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This study describes a series of learning workshops, which took place within CUIDAR project. The workshops aimed to empower children to share their experiences and views in relation to natural hazards and disasters. The participants in the workshops were ten primary school students who had severe visual impairments or multiple disabilities and visual impairments (MDVI). The main objectives of the workshops were: a) to promote access of the children through the use of appropriate educational material such as texts in braille, enlarged text, tactile maps and the implementation of differentiated instruction, b) to make children aware regarding their rights to have access to information and to participate in planning and decision-making especially in relation to disaster education programs, and c) to encourage children to have an active role during the workshops through child-led and experiential learning activities. The children expressed their views regarding the meaning of hazards and disasters. Following, they discussed their experiences and emotions regarding natural hazards and disasters, and they chose to place the emphasis on a hazard, which was more pertinent to them, their community and their region, namely fires. Therefore, they recalled fires that have caused major disasters, and they discussed about the impact that these fires had on their community or on their country. Furthermore, they were encouraged to become aware regarding their own role and responsibility to prevent a fire or get prepared and know how to behave if a fire occurs. They realized that prevention and preparation are a matter of personal responsibility. They also felt the responsibility to inform their own families. Finally, they met important people involved in fire protection such as rescuers and firefighters and had the opportunity to carry dialogues. In conclusion, through child led workshops, experiential and accessible activities, the students had the opportunity to share their own experiences, to express their views and their questions, to broaden their knowledge and to realize their personal responsibility in disaster risk reduction, specifically in relation to fires.

Keywords: accessibility, children, disasters, visual impairment

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Stanley Fore, Sharon Chipeperekwa

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The start of the 2011 academic year in South Africa saw a number of Institutions of Higher Learning introducing online registration for their students. The efficiency and effectiveness of Information Systems are increasingly becoming a necessity and not an option for many organizations. An information system should be able to allow end users to access information easily and navigate with ease. The selected University of Technology (UoT) in this research is one of the largest public institution of higher learning in the Western Cape Province and boasts of an enrolment of more than 30000 students per academic year. An observation was made that, during registration students’ stand in long queues waiting to register or for assistance to register. The system tends to ‘freeze’ whilst students are registering and students are in most cases unfamiliar with the system interface. They constantly have to enquire what to do next when going through online registration process. A mixed method approach will be adopted which comprises of quantitative and qualitative approaches. The study uses constructs of the updated DeLone and McLean IS success model (2003) to analyse and explain the student’s perceptions of the online registration system. The research was undertaken to establish the student’s perceptions of the online registration system. This research seeks to identify and analyse the challenges and success factors of introducing an online registration system whilst highlighting the extent to which this system has been able to solve the numerous problems associated with the manual era. The study will assist management and those responsible for managing the current system to determine how well the system is working or not working to achieve user satisfaction. It will also assist them going forward on what to consider before, during and after implementation of an information system. Respondents will be informed of the objectives of the research, and their consent to participate will be sought. Ethical considerations that will be applied to this study include; informed consent and protection from harm, right to privacy and involvement of the research.

Keywords: online registration, information systems, University of Technology, end-users

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Authors: Anishur Rahman, Maria Areias, Diogo Simões, Ana Figeuiredo, Filipa Figueiredo, João Nunes

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The circular economy (CE) has gained increased awareness among academics, businesses, and decision-makers as it stimulates resource circularity in the production and consumption systems. A large epistemological study has explored the principles of CE, but scant attention eagerly focused on analysing how CE is evaluated, consented to, and enforced using economic metabolism data and business intelligent framework. Economic metabolism involves the ongoing exchange of materials and energy within and across socio-economic systems and requires the assessment of vast amounts of data to provide quantitative analysis related to effective resource management. Limited concern, the present work has focused on the regional flows pilot region from Portugal. By addressing this gap, this study aims to promote eco-innovation and sustainability in the regions of Intermunicipal Communities Região de Coimbra, Viseu Dão Lafões and Beiras e Serra da Estrela, using this data to find precise synergies in terms of material flows and give companies a competitive advantage in form of valuable waste destinations, access to new resources and new markets, cost reduction and risk sharing benefits. In our work, emphasis on applying artificial intelligence (AI) and, more specifically, on implementing state-of-the-art deep learning algorithms is placed, contributing to construction a business intelligent approach. With the emergence of new approaches generally highlighted under the sub-heading of AI and machine learning (ML), the methods for statistical analysis of complex and uncertain production systems are facing significant changes. Therefore, various definitions of AI and its differences from traditional statistics are presented, and furthermore, ML is introduced to identify its place in data science and the differences in topics such as big data analytics and in production problems that using AI and ML are identified. A lifecycle-based approach is then taken to analyse the use of different methods in each phase to identify the most useful technologies and unifying attributes of AI in manufacturing. Most of macroeconomic metabolisms models are mainly direct to contexts of large metropolis, neglecting rural territories, so within this project, a dynamic decision support model coupled with artificial intelligence tools and information platforms will be developed, focused on the reality of these transition zones between the rural and urban. Thus, a real decision support tool is under development, which will surpass the scientific developments carried out to date and will allow to overcome imitations related to the availability and reliability of data.

Keywords: circular economy, artificial intelligence, economic metabolisms, machine learning

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Authors: A. Raj Kumar, A. Okunseinde, P. Raghavan, V. Kapila

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Stroke is the leading cause of adult disability worldwide. With recent advances in immediate post-stroke care, there is an increasing number of young stroke survivors, under the age of 65 years. While most stroke survivors will regain the ability to walk, they often experience long-term arm and hand motor impairments. Long term upper limb rehabilitation is needed to restore movement and function, and prevent deterioration from complications such as learned non-use and learned bad-use. We have developed a novel virtual coach, a wearable instrumented rehabilitation jacket, to motivate individuals to participate in long-term skill re-learning, that can be personalized to their impairment profile. The jacket can estimate the movements of an individual’s arms using embedded off-the-shelf sensors (e.g., 9-DOF IMU for inertial measurements, flex-sensors for measuring angular orientation of fingers) and a Bluetooth Low Energy (BLE) powered microcontroller (e.g., RFduino) to non-intrusively extract data. The 9-DOF IMU sensors contain 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer to compute the quaternions, which are transmitted to a computer to compute the Euler angles and estimate the angular orientation of the arms. The data are used in a gaming environment to provide visual, and/or haptic feedback for goal-based, augmented-reality training to facilitate re-learning in a cost-effective, evidence-based manner. The full paper will elaborate the technical aspects of communication, interactive gaming environment, and physical aspects of electronics necessary to achieve our stated goal. Moreover, the paper will suggest methods to utilize the proposed system as a cheaper, portable, and versatile system vis-à-vis existing instrumentation to facilitate post-stroke personalized arm rehabilitation.

Keywords: feedback, gaming, Euler angles, rehabilitation, augmented reality

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Authors: Maya Saade, Rafic Younes, Pascal Lafon

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This paper explores the transformative impact of smartphones on pedagogy and presents a smartphone application developed specifically for engineering problem-solving and educational purposes. The widespread availability and advanced capabilities of smartphones have revolutionized the way we interact with technology, including in education. The ubiquity of smartphones allows learners to access educational resources anytime and anywhere, promoting personalized and self-directed learning. The first part of this paper discusses the overall influence of smartphones on pedagogy, emphasizing their potential to improve learning experiences through mobile technology. In the context of engineering education, this paper focuses on the development of a dedicated smartphone application that serves as a powerful tool for both engineering problem-solving and education. The application features an intuitive and user-friendly interface, allowing engineering students and professionals to perform complex calculations and analyses on their smartphones. The smartphone application primarily focuses on beam calculations and serves as a comprehensive beam calculator tailored to engineering education. It caters to various engineering disciplines by offering interactive modules that allow students to learn key concepts through hands-on activities and simulations. With a primary emphasis on beam analysis, this application empowers users to perform calculations for statically determinate beams, statically indeterminate beams, and beam buckling phenomena. Furthermore, the app includes a comprehensive library of engineering formulas and reference materials, facilitating a deeper understanding and practical application of the fundamental principles in beam analysis. By offering a wide range of features specifically tailored for beam calculation, this application provides an invaluable tool for engineering students and professionals looking to enhance their understanding and proficiency in this crucial aspect of a structural engineer.

Keywords: mobile devices in education, solving engineering problems, smartphone application, engineering education

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