Search results for: artificial teacher

Authors: L. O'Sullivan, C. Murphy

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This study examined the impact, if any, of mobile technology on the achievement of United Nations Sustainable Development Goal 4: Quality Education for All. It focused specifically on teachers and their practice, in a school with large class sizes and limited teaching resources. Teachers in third grade in a large public school in Mozambique were provided with an iPad connected to a projector, powered by a mobile solar-panel. Teachers also participated in ten days of professional development workshops over thirteen months. Teacher discussions, micro-teaching sessions and classes in the school were video-recorded, and data was triangulated using surveys and additional documents including class plans, digital artifacts created by teachers, workshop notes and researcher field notes. The catalyst for teachers’ creativity development was to use the photographic capabilities of the iPad to capture the local context and make lessons relevant to the lived experience of the students. In the transition stage, teachers worked with lesson plans and support from the professional development workshops to make small incremental changes to their practice, which scaffolded their growing competence in the creative use of the technology as a tool for teaching and developing new teaching resources. Over the full period of the study, these small changes in practice resulted in a cultural shift in how teachers approached all lessons, even those in which they were not using the technology. They developed into working as a community of practice. The digital lessons created were re-used and further developed by other teachers, providing a relevant and valuable bank of content in a context lacking in books and other teaching resources. This study demonstrated that mobile technology proved to be a successful catalyst for impacting creative teaching practice in this context, and supports the Quality Education for All Sustainable Development Goal.

Keywords: mobile technology, creative teaching, sub-Saharan Africa, quality education for all

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Authors: Kashi Raj Pandey

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Transformative learning is a form of learning rooted in learners' life experiences and their inherent love for learning. It emphasizes the importance of incorporating students' everyday work through the use of learning diaries and reflective journals. It encourages learners to take a proactive role in their own improvement, fostering creativity and promoting informed discussions about the learning process. Reflecting on the personal experience with English language learning in a rural village in Nepal where rote memorization was the prevailing teaching method, this traditional approach hindered a deeper understanding of the language, prompting the author to recognize the need for more effective pedagogy. In this study, the author delved into the cultural contextualization of English language learning, taking into account learners' backgrounds. The study’s findings highlighted the importance of equity, inclusion, mutuality, and social justice in the classroom, emphasizing the significance of integrating students' lived experiences into the pedagogical approach. This, in turn, can encourage students to engage in profound and collaborative learning practices within the realm of English language education. Upon successfully implementing the research findings, including the eight key conditions of transformative learning, in multiple classrooms, the author collaborated with international educationists and government stakeholders in Nepal. The purpose was to disseminate the research findings, conduct teacher training workshops, and systematically enhance Nepali students’ English language learning. These methods have already demonstrated a significant improvement in student engagement within the same school where the author once learned English as a child. This study aims to explore teachers’ decision-making process regarding the transition from traditional teaching methods to interactive ones, which have gained national recognition within the ESL/EFL teaching community in Nepal. By sharing these experiences, it is expected that other teachers will also contemplate adopting transformative learning pedagogy in their own classrooms.

Keywords: reflection, student engagement, pedagogy, transformative learning

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Authors: Jui-Chien Hsieh

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Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

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Authors: Phusit Phukamchanoad, Chutchai Ditchareon, Suwaree Yordchim

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This research was to study on background and social and cultural context of Kamchanoad community for sustainable tourism management. All data was collected through in-depth interview with village headmen, community committees, teacher, monks, Kamchanoad forest field officers and respected senior citizen above 60 years old in the community who have lived there for more than 40 years. Altogether there were 30 participants for this research. After analyzing the data, content from interview and discussion, Kamchanoad has both high land and low land in the region as well as swamps that are very capable of freshwater animals’ conservation. Kamchanoad is also good for agriculture and animal farming. 80% of Kamchanoad’s land are forest, freshwater and rice farms. Kamchanoad was officially set up as community in 1994 as “Baan Nonmuang”. Inhabitants in Kamchanoad make a living by farming based on sufficiency economy. They have rice farm, eucalyptus farm, cassava farm and rubber tree farm. Local people in Kamchanoad still believe in the myth of Srisutto Naga. They are still religious and love to preserve their traditional way of life. In order to understand how to create successful tourism business in Kamchanoad, we have to study closely on local culture and traditions. Outstanding event in Kamchanoad is the worship of Grand Srisutto, which is on the full-moon day of 6th month or Visakhabucha Day. Other big events are also celebration at the end of Buddhist lent, Naga firework, New Year celebration, Boon Mahachart, Songkran, Buddhist Lent, Boon Katin and Loy Kratong. Buddhism is the main religion in Kamchanoad. The promotion of tourism in Kamchanoad is expected to help spreading more income for this region. More infrastructures will be provided for local people as well as funding for youth support and people activities.

Keywords: social and culture area, tourism management, Kamchanoad Community, Udon Thani Province

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Authors: Shane Theunissen, Rob Grandy

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Historically and we would fairly conventionally within our formal schooling systems, we have convergent testing where all the students are expected to converge on the same answer, and that answer has been determined by an external authority that is reproducing knowledge of the hegemon. Many youths may not embody the cultural capital that's rewarded in formal schooling contexts as they aren't able to converge on the required answer that's being determined by the classroom teacher or the administrators. In this paper, we explore divergent processes that promote creative problem-solving. We embody this divergent process in our measurement of fundamental growth needs. To this end, we utilize the Mosaic Approach as a method for implementing the Outcomes That Matter framework. Outcomes That Matter is the name of the measurement tool built around the Circle of Courage framework, which is a way of identifying fundamental growth needs for young people. The Circle of Courage was developed by Martin-Broken-Leg and colleagues as a way to connect indigenous child-rearing philosophies with contemporary resilience and positive psychology research. The Outcomes that Matter framework puts forward four categories of growth needs for young people. These are: Belonging, which on a macro scale is acceptance into the greater community of practice, Mastery which includes a constellation of concepts including confidence, motivation, self-actualization, and self-determination, Independence refers to a sense of personal power into autonomy within a context where creativity and problem solving, and a personal voice can begin to emerge, and finally Generosity which includes interpersonal things like conflict resolution and teamwork. Outcomes of Matter puts these four domains into a measurement tool that facilitates collaborative assessment between the youth, teachers, and recreation therapists that allows for youth-led narratives pertaining to their fundamental growth outcomes. This application of the Outcomes That Matter framework is unique as it may be the first application of this framework in an educational boatbuilding context.

Keywords: collaboration, empowerment, outcomes that matter, mosaic approach, boat building

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Authors: Ahmed Chouari

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Although research methods has been an integral part of the curriculum in Moroccan higher education for decades, it seems that the research methods teaching pedagogy that teachers use suffers from a serious absence of a body of literature in the field. Also, the various challenges that both teachers and students of research methods face have received little interest by researchers in comparison to other fields such as applied linguistics. Therefore, the main aim of this study is to remedy to this situation by exploring one of the major issues in teaching research methods – that is, the phenomenon of students’ dissatisfaction with the research methods course in higher education in Morocco. The aim is also to understand students’ attitudes and perceptions on how to make the research methods course more effective in the future. Three qualitative research questions were used: (1) To what extent are graduate students satisfied with the pedagogies used by the teachers of the research methods course in Moroccan higher education? (2) To what extent are graduate students satisfied with the approach used in assessing research methods in Moroccan higher education? (3) What are students’ perceptions on how to make the research methods course more effective in Moroccan higher education? In this study, a qualitative content analysis was adopted to analyze students’ views and perspectives about the major factors behind their dissatisfaction with the course at the School of Arts and Humanities – University of Moulay Ismail. A semi-structured interview was used to collect data from 14 respondents from two different Master programs. The results show that there is a general consensus among the respondents about the major factors behind the ineffectiveness of the course. These factors include theory-practice gap, heavy reliance on theoretical knowledge at the expense of procedural knowledge, and ineffectiveness of some teachers. The findings also reveal that teaching research methods in Morocco requires more time, better equipment, and more competent teachers. Above all, the findings indicate that today there is an urgent need in Morocco to shift from teacher-centered approaches to learner-centered approaches in teaching the research methods course. These findings, thus, contribute to the existing literature by unraveling the factors that impede the learning process, and by suggesting a set of strategies that can make course more effective.

Keywords: competencies, learner-centered teaching, research methods, student autonomy, pedagogy

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: I. S. R. Butswat, D. Zahraddeen

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This study identified and described some skill-based and necessity-driven entrepreneurship in animal agriculture (AA). AA is an integral segment of the world food industry, and provides a good and rapid source of income. The contribution of AA to the Sub-Saharan economy is quite significant, and there are still large opportunities that remain untapped in the sector. However, it is imperative to understand, simplify and package the various components of AA in order to pave way for rapid wealth creation, poverty eradication and women empowerment programmes in sub-Saharan Africa and other developing countries. The entrepreneurial areas of AA highlighted were animal breeding, livestock fattening, dairy production, poultry farming, meat production (beef, mutton, chevon, etc.), rabbit farming, wool/leather production, animal traction, animal feed industry, commercial pasture management, fish farming, sport animals, micro livestock production, private ownership of abattoirs, slaughter slabs, animal parks and zoos, among others. This study concludes that reproductive biotechnology such as oestrous synchronization, super-/multiple ovulation, artificial insemination and embryo transfer can be employed as a tool for improvement of genetic make-up of low-yielding animals in terms of milk, meat, egg, wool, leather production and other economic traits that will necessitate sustainable job and wealth creations.

Keywords: animal, agriculture, entreprenurship, wealth

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Authors: Leonie Laskowitz

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A self-confident appearance is a basic prerequisite for success in the world of work 4.0. Within a few seconds, people convey a first impression that usually lasts. Artificial intelligence is making it increasingly important how our virtual selves appear and communicate (nonverbally) in digital worlds such as the metaverse. In addition to the modified creation of an avatar, the field of photogrammetry is developing fast, creating exact likenesses of ourselves in virtual environments. Given the importance of self-representation in virtual space for future collaborations, it is important to investigate the impact of phenotype in virtual worlds and how an avatar type can profitably be used situationally. We analyzed the effect of self-similar versus desirable self-presentation as an avatar on one's self-awareness, considering various theoretical constructs in the area of self-awareness and stress stimuli. The avatars were arbitrarily created on the one hand and scanned on the other hand with the help of a lidar sensor, the state-of-the-art photogrammetry method. All subjects were exposed to the established Trier Social Stress Test. The results showed that especially insecure people prefer to create rather than be scanned when confronted with a stressful work situation. (1) If they are in a casual work environment and a relaxed situation, they prefer a 3D photorealistic avatar that reflects them in detail. (2) Confident people will give their avatar their true appearance in any situation, while insecure people would only do so for honesty and authenticity. (3) Thus, the choice of avatar type has considerable impact on self-confidence in different situations.

Keywords: avatar, virtual identity, self-presentation, metaverse, virtual reality, self-awareness

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Londeka Ngubane

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The use of corporal punishment is not a new phenomenon in the South African education system as it was, for a long time, recognised as a fitting form of punishment for ill-disciplined and disobedient children. The growing recognition that corporal punishment is an act of violence against children has resulted in the abolishment of this form of punishment in society and particularly in schools. However, regardless of criminalising corporal punishment, it appears to be a disciplinary measure that is persistently used by some educators. Historically and currently, the intimate connection between corporal punishment and discipline has not merely been a convention of human thinking, as this practice is given recognition in various definitions in dictionaries. ‘To discipline’ is habitually stated to mean ‘to punish’. The notion of ‘disciplining children’ also comes from entrenched common conceptions about children and their relationship with adults. Corporal punishment has, for a long time, been associated with the rearing and education of children, and this practice thus pervades schooling across nations. In many societies, punishment is a term that is closely linked with the self-perception of teachers who feel that they must be ‘in control’ and have ‘the upper hand’ in order to be respected. This impression of control is evident in the widespread conception of education which is to ‘socialize’ children in ‘desirable ways’ of ‘sitting in a formal classroom’, ‘behaving’ in school, ‘following instructions’ from the teacher, talking only when asked to, and finishing tasks on time. It was against this backdrop that a comprehensive review of relevant literature was undertaken and that individual interviews were conducted with fifty learners from four schools (two junior secondary and two senior secondary schools) in a selected township area in KwaZulu-Natal Province. The main aim of the study was to explore and thus understand learners’ views on the administration of corporal punishment regardless of the fact that it was legally abolished. It was envisaged that the interviews with the learners would elicit rich data that would enhance the researcher’s insight into their perceptions of the persistent use of corporal punishment as a disciplinary measure in their schools. The study was thus premised on the assumption, which had been strengthened by anecdotal and media evidence, that corporal punishment was still administered in some schools in South Africa and in schools in the study area in particular.

Keywords: corporal punishment, ban, school learners, South Africa

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Authors: Yukiko Fujiwara

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As a language learning methodology, Content and Language Integrated Learning (CLIL) has become increasingly prevalent in Japan. Most CLIL classroom practice and its research are conducted in EFL fields. However, much less research has been done in the Japanese language learning setting. Therefore, there are still many issues to work out using CLIL in the Japanese language teaching (JLT) setting. it is expected that more research will be conducted on both authentically and academically. Under such circumstances, this is one of the few classroom-based CLIL researches experiments in JLT and aims to find an effective course design for a class with students at different proficiency levels. The class was called ‘Japanese culture A’. This class was offered as one of the elective classes for International exchange students at a Japanese university. The Japanese proficiency level of the class was above the Japanese Language Proficiency Test Level N3. Since the CLIL approach places importance on ‘authenticity’, the class was designed with materials and activities; such as books, magazines, a film and TV show and a field trip to Kyoto. On the field trip, students experienced making traditional Japanese desserts, by receiving guidance directly from a Japanese artisan. Through the course, designated task sheets were used so the teacher could get feedback from each student to grasp what the class proficiency gap was. After reading an article on Japanese culture, students were asked to write down the words they did not understand and what they thought they needed to learn. It helped both students and teachers to set learning goals and work together for it. Using questionnaires and interviews with students, this research examined whether the attempt was effective or not. Essays they wrote in class were also analyzed. The results from the students were positive. They were motivated by learning authentic, natural Japanese, and they thrived setting their own personal goals. Some students were motivated to learn Japanese by studying the language and others were motivated by studying the cultural context. Most of them said they learned better this way; by setting their own Japanese language and culture goals. These results will provide teachers with new insight towards designing class materials and activities that support students in a multilevel CLIL class.

Keywords: authenticity, CLIL, Japanese language and culture, multilevel class

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Habibi Said Mustafa, Hiroko Ono

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Since 2006, Afghanistan is dealing with one of the most dramatic trend of urban movement in its history, cities and towns are expanding in size and number. Kabul is the capital of Afghanistan and as well as the fast-growing city in the Asia. The influx of the returnees from neighbor countries and other provinces of Afghanistan caused high rate of artificial growth which slums increased. As an unwanted consequence of this growth, today informal settlements have covered a vast portion of the city. Land Readjustment (LR) has proved to be an important tool for developing informal settlements and reorganizing urban areas but its implementation always varies from country to country and region to region within the countries. Consequently, to successfully develop the informal settlements in Kabul, we need to define an Afghan model of LR specifically for Afghanistan which needs to incorporate all those factors related to the socio-economic condition of the country. For this purpose, a part of the old city of Kabul has selected as a study area which is located near the Central Business District (CBD). After the further analysis and incorporating all needed factors, the result shows a positive potential for the implementation of an adaptable Land Readjustment model for Kabul city which is more sustainable and socio-economically friendly. It will enhance quality of life and provide better urban services for the residents. Moreover, it will set a vision and criteria by which sustainable developments shall proceed in other similar informal settlements of Kabul.

Keywords: adaptation, informal settlements, Kabul, land readjustment, preservation

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Moschos Vogiatzis, K. Perakis

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Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process.

Keywords: classification, land use/land cover, mapping, random forest

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Authors: Yi-Hsiang Pan, Chen-Hui Huang, Wei-Ting Hsu

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The purposes of the study were to understand these topics as follows: 1. To explore the effect of merging teaching personal and social responsibility (TPSR) with sports education model on students' game performance and responsibility. 2. To explore the effect of sports education model on students' game performance and responsibility. 3. To compare the difference between "merging TPSR with sports education model" and "sports education model" on students' game performance and responsibility. The participants include three high school physical education teachers and six physical education classes. Every teacher teaches an experimental group and a control group. The participants had 121 students, including 65 students in the experimental group and 56 students in the control group. The research methods had game performance assessment, questionnaire investigation, interview, focus group meeting. The research instruments include personal and social responsibility questionnaire and game performance assessment instrument. Paired t-test test and MANCOVA were used to test the difference between "merging TPSR with sports education model" and "sports education model" on students' learning performance. 1) "Merging TPSR with sports education model" showed significant improvements in students' game performance, and responsibilities with self-direction, helping others, cooperation. 2) "Sports education model" also had significant improvements in students' game performance, and responsibilities with effort, self-direction, helping others. 3.) There was no significant difference in game performance and responsibilities between "merging TPSR with sports education model" and "sports education model". 4)."Merging TPSR with sports education model" significantly improve learning atmosphere and peer relationships, it may be developed in the physical education curriculum. The conclusions were as follows: Both "Merging TPSR with sports education model" and "sports education model" can help improve students' responsibility and game performance. However, "Merging TPSR with sports education model" can reduce the competitive atmosphere in highly intensive games between students. The curricular projects of hybrid TPSR-Sport Education model is a good approach for moral character education.

Keywords: curriculum and teaching model, sports self-efficacy, sport enthusiastic, character education

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Authors: Roya Ahmadi Ahangar, Hamid Madadyari

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The seeker optimization algorithm (SOA) is increasingly gaining popularity among the researchers society due to its effectiveness in solving some real-world optimization problems. This paper provides the load-frequency control method based on the SOA for removing oscillations in the power system. A three-zone power system includes a thermal zone, a hydraulic zone and a wind zone equipped with robust proportional-integral-differential (PID) controllers. The result of simulation indicates that load-frequency changes in the wind zone for the multi-zone system are damped in a short period of time. Meanwhile, in the oscillation period, the oscillations amplitude is not significant. The result of simulation emphasizes that the PID controller designed using the seeker optimization algorithm has a robust function and a better performance for oscillations damping compared to the traditional PID controller. The proposed controller’s performance has been compared to the performance of PID controller regulated with Particle Swarm Optimization (PSO) and. Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithms in order to show the superior capability of the proposed SOA in regulating the PID controller. The simulation results emphasize the better performance of the optimized PID controller based on SOA compared to the PID controller optimized with PSO, GA and ABC algorithms.

Keywords: load-frequency control, multi zone, robust PID controller, wind generation

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Authors: Nancy Ayman Kamal Mohamed Mehrz

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As the economies of other countries in the Mediterranean Basin, the tourism sector in our country has a high denominator in economics. Tourism businesses, which are building blocks of tourism, sector faces with a variety of problems during their activities. These problems faced make business efficiency and competition conditions of the businesses difficult. Most of the problems faced by the tourism businesses and the information of consumers about consumers’ rights were used in this study, which is conducted to determine the problems of tourism businesses in the Central Anatolia Region. It is aimed to contribute the awareness of staff and executives working at tourism sector and to attract attention of businesses active concurrently with tourism sector and legislators. E-tourism is among the issues that have recently been entered into the field of tourism. In order to achieve this type of tourism, Information and Communications Technology (or ICT) infrastructures as well as Co-governmental organizations and tourism resources are important. In this study, the opinions of managers and tourism officials about the e-tourism in Leman city were measured; it also surveyed the impact of level of digital literacy of managers and tourism officials on attracting tourists. This study was conducted. One of the environs of the Esfahan province. This study is a documentary – survey and the sources include library resources and also questionnaires. The results obtained indicate that if managers use ICT, it may help e-tourism to be developed in the region, and increasing managers’ beliefs on e-tourism and upgrading their level of digital literacy may affect e-tourism development.

Keywords: financial problems, the problems of tourism businesses, tourism businesses, internet, marketing, tourism, tourism management economic competitiveness, enhancing competitiveness

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: Basak Ozkendirci

Abstract:

The discovery of chemical dyes, the development of regenerated fibers, and warp knitting technology have enormous effects on the fashion world. The trends created by the information obtained in the context of various studies today shape the fashion world. Trend analysts must follow scientific developments as well as sociological events, political developments and artwork to obtain healthy data on trends. Digital printing technologies have changed the dynamics of textile printing production and also the style of printed designs. Fashion designers already have started design 3D printed accessories and garments. The research fields like the internet of things, artificial intelligence, hologram technologies, mechatronics, energy storage systems, nanotechnology are seen as the technologies that will change the social life and economy of the future. It is clear that research carried out in these areas will affect the textiles of the future and whereat the trends of fashion. The article aims to create a future vision for trend researchers and designers by giving clues about the changes to be experienced in the fashion world. In the first part of the article, information about the scientific studies that are thought to shape the future is given, and the forecasting about how the inventions that can be obtained from these studies can be adapted at the textile are presented. In the second part of the article, examples of how the new generation of innovative textiles will affect the daily life experience of the user are given.

Keywords: biotextiles, fashion trends, nanotextiles, new materials, smart textiles, techno textiles

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Khalidou Ba, Abdelkrim Chahine, Mohamed Ebn Touhami

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A strong industrial interest is focused on the development of coatings for anticorrosion protection. In this context, phosphate composite materials are expanding strongly due to their chemical characteristics and their interesting physicochemical properties. Sol-gel coatings offer high homogeneity and purity that may lead to obtain coating presenting good adhesion to metal surface. The goal behind this work is to develop efficient coatings for corrosion protection of steel to extend its life. In this context, a sol gel process allowing to obtain thin film coatings on carbon steel with high resistance to corrosion has been developed. The optimization of several experimental parameters such as the hydrolysis time, the temperature, the coating technique, the molar ratio between precursors, the number of layers and the drying mode has been realized in order to obtain a coating showing the best anti-corrosion properties. The effect of these parameters on the microstructure and anticorrosion performance of the films sol gel coating has been investigated using different characterization methods (FTIR, XRD, Raman, XPS, SEM, Profilometer, Salt Spray Test, etc.). An optimized coating presenting good adhesion and very stable anticorrosion properties in salt spray test, which consists of a corrosive attack accelerated by an artificial salt spray consisting of a solution of 5% NaCl, pH neutral, under precise conditions of temperature (35 °C) and pressure has been obtained.

Keywords: sol gel, coating, corrosion, XPS

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Authors: Natacha Jesus-Silva, Carla S. Pereira, Natercia Durao, Maria Das Dores Formosinho, Cristina Costa-Lobo

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Any assessment process, by its very nature, raises a wide range of doubts, uncertainties, and insecurities of all kinds. The evaluation process should be ethically irreproachable, treating each and every one of the evaluated according to a conduct that ensures that the process is fair, contributing to all recognize and feel well with the processes and results of the evaluation. This is a very important starting point and implies that positive and constructive conceptions and attitudes are developed regarding the evaluation of teaching performance, where students' responsibility is desired. It is not uncommon to find teachers feeling threatened at various levels, in particular as regards their autonomy and their professional dignity. Evaluation must be useful in that it should enable decisions to be taken to improve teacher performance, the quality of teaching or the learning climate of the school. This study is part of a research project whose main objective is to identify, select, evaluate and synthesize the available evidence on Quality Indicators in Higher Education. In this work, the 01 parameters resulting from pedagogical surveys in a Portuguese higher education institution in the north of the country will be presented, surveys for the 2015/2016 school year, presented to 1751 students, in a total of 11 degrees and 18 master's degrees. It has analyzed the evaluation made by students with respect to the performance of a group of 68 teachers working full time. This paper presents the lessons learned in the last three academic years, allowing for the identification of the effects on the following areas: teaching strategies and methodologies, capacity of systematization, learning climate, creation of conditions for active student participation. This paper describes the procedures resulting from the descriptive analysis (frequency analysis, descriptive measures and association measures) and inferential analysis (ANOVA one-way, MANOVA one-way, MANOVA two-way and correlation analysis).

Keywords: teaching performance, higher education, students responsibility, indicators of teaching management

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Authors: Simon Arunga, Teddy Kwaga, Rita Kageni, Michael Gichangi, Nyawira Mwangi, Fred Kagwa, Rogers Mwavu, Amos Baryashaba, Luis F. Nakayama, Katharine Morley, Michael Morley, Leo A. Celi, Jessica Haberer, Celestino Obua

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Purpose: The main aim for creating the Multimodal Database of Retinal Images for Africa (MoDRIA) was to provide a publicly available repository of retinal images for responsible researchers to conduct algorithm development in a bid to curb the challenges of ophthalmic artificial intelligence (AI) in Africa. Methods: Data and retina images were ethically sourced from sites in Uganda and Kenya. Data on medical history, visual acuity, ocular examination, blood pressure, and blood sugar were collected. Retina images were captured using fundus cameras (Foru3-nethra and Canon CR-Mark-1). Images were stored on a secure online database. Results: The database consists of 7,859 retinal images in portable network graphics format from 1,988 participants. Images from patients with human immunodeficiency virus were 18.9%, 18.2% of images were from hypertensive patients, 12.8% from diabetic patients, and the rest from normal’ participants. Conclusion: Publicly available data repositories are a valuable asset in the development of AI technology. Therefore, is a need for the expansion of MoDRIA so as to provide larger datasets that are more representative of Sub-Saharan data.

Keywords: retina images, MoDRIA, image repository, African database

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Authors: Madelaine F. Gatchalian, Mary Jane D. Tepora

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This study was designed to determine the relationship between learning environment and motivation of CNHS, SY 2012-2013. There were 376 respondents taken randomly. Frequency distribution, percentage, mean, standard deviation, Mann Whitney Test, Kruskall Wallis One-way ANOVA and Spearman Rank Correlational Coefficient were used in analyzing the data. As to age, most of the respondents were 13 years old while female students outnumbered the male students. Majority of parents’ educational attainment of CNHS students were high school/vocational graduates. Most fathers worked in the private sector, while majority of the mothers were unemployed whose family income range from Php 5,000.00 to Php 14,999.00. Most of the respondents were first child composed of five family members. Findings showed no significant differences in perceived learning environment when respondents were grouped in terms of age, sex, parents’ educational attainment, parents’ occupation, sibling order and number of family members. Only monthly family income showed significant differences in perceived learning environment. There are no significant differences in perceived learning motivation when respondents were grouped in terms of age, sex, parents’ educational attainment (father), parents’ occupation (father), sibling order, and number of family members. Parents’ educational attainment (mother), parents’ occupation (mother) and monthly family income showed significant differences in perceived learning motivation. There is significant relationship between the six subscales of perceived learning environment, namely: student cohesiveness, teacher support, involvement, task orientation, cooperation and equity and perceived learning motivation of CNHS students, SY, 2012-2013. The results of this study indicated that learning environment including student cohesiveness, teachers support, involvement, task orientation, cooperation and equity is significantly related to students’ learning motivation.

Keywords: learning environment, motivation, demographic profile, secondary students

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Authors: Jaturong Matidtor, Krisna R. Torrissen, Saengtong Pongjareankit, Sudaporn Tongsiri, Jiraporn Rojtinnakorn

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Low survival rate has being particular problem in nursery of marble goby juvenile. The aim of this study was to investigate effect of garlic extract on protein digestive pancreatic enzymes, trypsin (T) and chymotrypsin (C). The marble goby were reared with commercial feed mixed garlic extract at concentration of 0 (control), 0.3, 0.5, 1.0, 3.0 and 5.0% (w/w) for 6 weeks. Analysis of the digestive enzymes at 2 and 6 weeks was performed. Growth parameters; weight gain (WG), specific growth rate (SGR) and feed efficiency (FE), were identified. For T, C and T/C at 2 weeks, values of T and T/C ratio of 0.3% (w/w) group showed significant difference (p < 0.05) with the highest values of 17685.64± 11981.77 U/mg protein and of 51.64 ± 27.46 U/mg protein, respectively. For C at 2 weeks, 0% (w/w) group showed the highest values of 16191.76± 2225.56 U/mg protein. Whereas value of T, C and T/C ratio at 6 weeks, there was no significant difference (p > 0.05). For growth performance, it significantly increased in all garlic extract fed groups (0.3-5.0%, w/w), both at 2 and 6 weeks. At 2 weeks, values of WG and SGR of 0.5% (w/w) group showed the highest values of 71.51 ± 1.60%, and 3.85 ± 0.07%, respectively. For FE, 0.3% (w/w) group showed the highest value of 60.21 ± 6.51%. At 6 weeks, it illustrated that all growth parameters of 5.0% (w/w) group were the highest values; WG = 35.06 ± 5.66%, SGR = 2.14 ± 0.30%, and FE = 5.86 ± 0.68%. We suggested that garlic extract could be available for protein digestive enzyme and growth enhancement in marble goby nursery with artificial feed. This result will be high benefit for commercial aquaculture of marble goby.

Keywords: marble goby, nursery, garlic extract, digestive enzyme, growth

Procedia PDF Downloads 315