Search results for: academic learning stress

Authors: Chaiwat Tantarangsee

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The purposes of this study are: 1) to study the frequent English writing errors of students registering the course: Reading and Writing English for Academic Purposes II, and 2) to find out the results of writing error correction by using coded indirect corrective feedback and writing error treatments. Samples include 28 2nd year English Major students, Faculty of Education, Suan Sunandha Rajabhat University. Tool for experimental study includes the lesson plan of the course; Reading and Writing English for Academic Purposes II, and tool for data collection includes 4 writing tests of short texts. The research findings disclose that frequent English writing errors found in this course comprise 7 types of grammatical errors, namely Fragment sentence, Subject-verb agreement, Wrong form of verb tense, Singular or plural noun endings, Run-ons sentence, Wrong form of verb pattern and Lack of parallel structure. Moreover, it is found that the results of writing error correction by using coded indirect corrective feedback and error treatment reveal the overall reduction of the frequent English writing errors and the increase of students’ achievement in the writing of short texts with the significance at .05.

Keywords: coded indirect corrective feedback, error correction, error treatment, frequent English writing errors

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Authors: Monika Benedek

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This paper intends to present a pilot study of blended ear-training course at a Finnish university. The course ran for ten weeks and included both traditional (offline) group lessons for 90 minutes each week and an online learning platform. Twelve students majored in musicology and music education participated in the course. The aims of pilot research were to develop a new blended ear-training course at university level, to determine the ideal amount of workload in each part of the blended instruction (offline and online) and to develop the course material. The course material was selected from the Classical period in order to develop students’ aural skills together with their stylistic knowledge. Students were asked to provide written feedback of the course content and learning approaches of face-to-face group lessons and online learning platform each week during the course. Therefore, the teaching material is continuously planned for each week. This qualitative data collection and weekly analysis of data are on progress. However, based on the teacher-researcher’s experiences and the students’ feedback already collected, it could be seen that the blended instruction would be an ideal teaching strategy for ear-trainging at the music programmes of universities to develop students’ aural skills and stylistic knowledge. It is also presumed that such blended instruction with less workload would already improve university students’ aural skills and related musicianship skills. The preliminary findings of research also indicated that students generally found those ear-training tasks the most useful to learn online that combined listening, singing, singing and playing an instrument. This paper intends to summarise the final results of the pilot study.

Keywords: blended-learning, ear-training, higher music education, online-learning, pilot study

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Authors: Tzu-Ping Lin, Shing-Ru Yang

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Climate change has a significant impact on human living environment, while the traditional settlement may suffer extreme thermal stress due to its specific building type and living behavior. This study selected Lutaoyang, which is the largest settlement in mountainous areas of Tainan County, for the investigation area. The microclimate parameters, such as air temperature, relative humidity, wind speed, and mean radiant temperature. The micro climate parameters were also simulated by the ENVI-met model. The results showed the banyan tree area providing good thermal comfort condition due to the shading. On the contrary, the courtyard (traditionally for the crops drying) surrounded by low rise building and consisted of artificial pavement contributing heat stress especially in summer noon. In the climate change simulations, the courtyard will become very hot and are not suitable for residents activities. These analytical results will shed light on the sustainability related to thermal environment in traditional settlements and develop adaptive measure towards sustainable development under the climate change challenges.

Keywords: thermal environment, traditional settlement, ENVI-met, Taiwan

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Yumeng He

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The purpose of this paper is to establish the “extenics” of children music education, the “extenics” thought and methods are introduced into the children music education field. Discussions are made from the perspective of children music education on how to generate new music cognitive from music cognitive, how to generate new music education from music education and how to generate music learning from music learning. The research methods including the extensibility of music art, extensibility of music education, extensibility of music capability and extensibility of music learning. Results of this study indicate that the thought and research methods of children’s extended music education not only have developed the “extenics” concept and ideological methods, meanwhile, the brand-new thought and innovative research perspective have been employed in discussing the children music education. As indicated in research, the children’s extended music education has extended the horizon of children music education, and has endowed the children music education field with a new thought and research method.

Keywords: comprehensive evaluations, extension thought, extension cognition music education, extensibility

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Matan Cohen, Maxim Shoshany

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Climatic, topographic and geological diversity, together with frequent disturbance and recovery cycles, produce highly complex spatial patterns of trees, shrubs, dwarf shrubs and bare ground patches. Assessment of spatial and temporal variations of these life-forms patterns under climate change is of high ecological priority. Here we report on one of the first attempts to discriminate between images of three Mediterranean life-forms patterns at three densities. The development of an extensive database of orthophoto images representing these 9 pattern categories was instrumental for training and testing pre-trained and newly-trained DL models utilizing DenseNet architecture. Both models demonstrated the advantages of using Deep Learning approaches over existing spectral and spatial (pattern or texture) algorithmic methods in differentiation 9 life-form spatial mixtures categories.

Keywords: texture classification, deep learning, desert fringe ecosystems, climate change

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: N. Ismail, O. Thammajinda, U. Thongpanya

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Games-based learning (GBL) has become increasingly important in teaching and learning. This paper explains the first two phases (analysis and design) of a GBL development project, ending up with a prototype design based on students’ and teachers’ perceptions. The two phases are part of a full cycle GBL project aiming to help secondary school students in Thailand in their study of Comprehensive Sex Education (CSE). In the course of the study, we invited 1,152 students to complete questionnaires and interviewed 12 secondary school teachers in focus groups. This paper found that GBL can serve students in their learning about CSE, enabling them to gain understanding of their sexuality, develop skills, including critical thinking skills and interact with others (peers, teachers, etc.) in a safe environment. The objectives of this paper are to outline the development of GBL variables from the research question(s) into the developers’ flow chart, to be responsive to the GBL beneficiaries’ preferences and expectations, and to help in answering the research questions. This paper details the steps applied to generate GBL variables that can feed into a game flow chart to develop a GBL prototype. In our approach, we detailed two models: (1) Game Elements Model (GEM) and (2) Game Object Model (GOM). There are three outcomes of this research – first, to achieve the objectives and benefits of GBL in learning, game design has to start with the research question(s) and the challenges to be resolved as research outcomes. Second, aligning the educational aims with engaging GBL end users (students) within the data collection phase to inform the game prototype with the game variables is essential to address the answer/solution to the research question(s). Third, for efficient GBL to bridge the gap between pedagogy and technology and in order to answer the research questions via technology (i.e. GBL) and to minimise the isolation between the pedagogists “P” and technologist “T”, several meetings and discussions need to take place within the team.

Keywords: games-based learning, engagement, pedagogy, preferences, prototype

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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Authors: Yasser F. Hassan

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In this paper, a proposed model of cellular automata is studied by means of fractional integral function. A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. The paper discusses how using fractional integral function for representing cellular automata memory or state. The architecture of computing and learning model will be given and the results of calibrating of approach are also given.

Keywords: fractional integral, cellular automata, memory, learning

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Authors: Bi Zhao

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Neural network technology has greatly improved the output of machine translation in terms of both fluency and accuracy, which greatly increases its appeal for young users. The present exploratory study aims to find out how the Chinese undergraduates perceive and use machine translation in their daily life. A survey is conducted to collect data from 100 undergraduate students from multiple Chinese universities and with varied academic backgrounds, including arts, business, science, engineering, and medicine. The survey questions inquire about their use (including frequency, scenarios, purposes, and preferences) of and attitudes (including trust, quality assessment, justifications, and ethics) toward machine translation. Interviews and tasks of evaluating machine translation output are also employed in combination with the survey on a sample of selected respondents. The results indicate that Chinese undergraduate students use machine translation on a daily basis for a wide range of purposes in academic, communicative, and entertainment scenarios. Most of them have preferred machine translation tools, but the availability of machine translation tools within a certain scenario, such as the embedded machine translation tool on the webpage, is also the determining factor in their choice. The results also reveal that despite the reportedly limited trust in the accuracy of machine translation output, most students lack the ability to critically analyze and evaluate such output. Furthermore, the evidence is revealed of the inadequate awareness of ethical responsibility as machine translation users among Chinese undergraduate students.

Keywords: Chinese undergraduates, machine translation, trust, usage

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Authors: Bunthida Chunngam, Thanyanan Worasesthaphong

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This research had objective to develop of the website learning the local wisdom in Phra Nakhon Si Ayutthaya province and studied satisfaction of system user. This research sample was multistage sample for 100 questionnaires, analyzed data to calculated reliability value with Cronbach’s alpha coefficient method α=0.82. This system had 3 functions which were system using, system feather evaluation and system accuracy evaluation which the statistics used for data analysis was descriptive statistics to explain sample feature so these statistics were frequency, percentage, mean and standard deviation. This data analysis result found that the system using performance quality had good level satisfaction (4.44 mean), system feather function analysis had good level satisfaction (4.11 mean) and system accuracy had good level satisfaction (3.74 mean).

Keywords: website, learning, local wisdom, Phra Nakhon Si Ayutthaya province

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Authors: Jozef Jurcik, Miroslav Gutten, Milan Sebok, Daniel Korenciak, Jerzy Roj

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The paper presents the research results of electronic fuel injection system, which can be used for diagnostics of automotive systems. In the paper is described the construction and operation of a typical fuel injection system and analyzed its electronic part. It has also been proposed method for the detection of the injector malfunction, based on the analysis of differential current or voltage characteristics. In order to detect the fault state, it is needed to use self-learning process, by the use of an appropriate self-learning algorithm.

Keywords: electronic fuel injector, diagnostics, measurement, testing device

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Authors: Nongyao Intasaso, Atchara Rattanama, Navarat Pewnual

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This research is survey research purposed to study the factor affected to knowledge management efficiency of personnel in Rajamangala University of Technology Srivijaya, and study the problem of knowledge management affected to knowledge development of personnel in the university. The tool used in this study is structures questioner standardize rating scale in 5 levels. The sample selected by purposive sampling and there are 137 participation calculated in 25% of population. The result found that factor affected to knowledge management efficiency in the university included (1) result from the organization factor found that the university provided project or activity that according to strategy and mission of knowledge management affected to knowledge management efficiency in highest level (x̅ = 4.30) (2) result from personnel factor found that the personnel are eager for knowledge and active to learning to develop themselves and work (Personal Mastery) affected to knowledge management efficiency in high level (x̅ = 3.75) (3) result from technological factor found that the organization brought multimedia learning aid to facilitate learning process affected to knowledge management efficiency in high level (x̅ = 3.70) and (4) the result from learning factor found that the personnel communicated and sharing knowledge and opinion based on acceptance to each other affected to knowledge management efficiency in high level (x̅ = 3.78). The problem of knowledge management in the university included the personnel do not change their work behavior, insufficient of collaboration, lack of acceptance in knowledge and experience to each other, and limited budget. The solutions to solve these problems are the university should be support sufficient budget, the university should follow up and evaluate organization development based on knowledge using, the university should provide the activity emphasize to personnel development and assign the committee to process and report knowledge management procedure.

Keywords: knowledge management, efficiency, personnel, learning process

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Authors: Anas H. Aljemely, Jianping Xuan

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Rolling bearing fault diagnosis plays a pivotal issue in the rotating machinery of modern manufacturing. In this research, a raw vibration signal and improved deep learning method for bearing fault diagnosis are proposed. The multi-dimensional scales of raw vibration signals are selected for evaluation condition monitoring system, and the deep learning process has shown its effectiveness in fault diagnosis. In the proposed method, employing an Exponential linear unit (ELU) layer in a convolutional neural network (CNN) that conducts the identical function on positive data, an exponential nonlinearity on negative inputs, and a particular convolutional operation to extract valuable features. The identification results show the improved method has achieved the highest accuracy with a 100-dimensional scale and increase the training and testing speed.

Keywords: bearing fault prognostics, developed CNN model, multiple-scale evaluation, deep learning features

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Authors: Leilani Forby, Farid Pazhoohi, Alan Kingstone

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There is abundant research on the nature and content of mind wandering (MW) in neurotypical (NT) adults, however, there is little to no research in these areas on autistic individuals. The objective of the current study was to uncover any differences between low and high autistic trait individuals in their MW. In particular, we examined their attitudes toward, and the themes and temporal dimensions (past, present, future) of, their MW episodes. For our online study, we recruited 518 students (394 women and 124 men), between the ages of 18 and 51 years (M = 20.93, SD = 3.40) from the undergraduate Human Subject Pool at the University of British Columbia. Participants completed the Short Imaginal Processes Inventory (SIPI), which includes the three subscales Positive-Constructive Daydreaming (SIPI-PC), Guilt and Fear of Failure Daydreaming (SIPI-GFF), and Poor Attentional Control (SIPI-PAC). Participants also completed the Past (IPI-past) and Present (IPI-present) subscales of the Imaginal Processes Inventory (IPI), the Deliberate (MW-D) and Spontaneous (MW-S) Mind Wandering scales, the Short Form Perceived Stress Scale (PSS-4), and the 10-item Autism Quotient (AQ-10). Results showed that overall, participant AQ-10 scores were significantly correlated with MW-S, SIPI-GFF, and PSS-4 scores, such that as the number of autistic traits endorsed by participants increased, so did their reports of spontaneous mind wandering, guilt and fear of failure themed day dreaming, and stress levels. This same pattern held for female participants, however, AQ-10 scores were positively correlated with only PSS-4 scores for males. These results suggest that compared to males with autistic traits, MW in females with autistic traits is more similar to individuals with low autistic traits in terms of content and intentionality. Results are discussed in terms of clinical implications, their limitations, and suggested directions for future research.

Keywords: autism, deliberate, mind wandering, spontaneous, perceived stress

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: B. L. Avanthi Isaka, P. G. Ranjith

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The development of technically-sound enhanced geothermal systems (EGSs) is identified as a viable solution for world growing energy demand with immense potential, low carbon dioxide emission and importantly, as an environmentally friendly option for renewable energy production. The use of supercritical carbon dioxide (ScCO₂) as the working fluid in EGSs by replacing traditional water-based method is promising due to multiple advantages prevail in ScCO₂-injection for underground reservoir stimulation. The evolution of reservoir stimulation using ScCO₂ and the understanding of the flow behavior of a ScCO₂-stimulated geothermal reservoir is vital in applying ScCO₂-EGSs as a replacement for water-based EGSs. The study is therefore aimed to investigate the flow behavior of a ScCO₂-fractured rock medium at in-situ stress and temperature conditions. A series of permeability tests were conducted for ScCO₂ fractured Harcourt granite rock specimens at 90ºC, under varying confining pressures from 5–60 MPa using the high-pressure and high-temperature tri-axial set up which can simulate deep geological conditions. The permeability of the ScCO₂-fractured rock specimens was compared with that of water-fractured rock specimens. The results show that the permeability of the ScCO₂-fractured rock specimens is one order higher than that of water-fractured rock specimens and the permeability exhibits a non-linear reduction with increasing confining pressure due to the stress-induced fracture closure. Further, the enhanced permeability of the ScCO₂-induced fracture with multiple secondary branches was explained by exploring the CT images of the rock specimens. However, a single plain fracture was induced under water-based fracturing.

Keywords: supercritical carbon dioxide, fracture permeability, granite, enhanced geothermal systems

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Authors: Ibtisam Mohammed Salim Al Quraini

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This paper examines how introducing Omani literature in foreign language classrooms can influence the students' motivation in learning the language. The data was collected through the questionnaire which was administered to two samples (A and B) of the participants. Sample A was comprised of 30 female students from English department who are specialist in English literature in college of Arts and Social Science. Sample B in contrast was comprised of 10 female students who their major is English from college of Education. Results show that each genre in literature has different influence on the students' motivation in learning the language which proves that literacy texts are powerful. Generally, Omani English teachers tend to avoid teaching literature because they think that it is a difficult method to use in teaching field. However, the advantages and the influences of teaching poetries, short stories, and plays are discussed. Recommendations for current research and further research are also discussed at the end.

Keywords: education, plays, short stories, poems

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Authors: Xabier Olaz Moratinos

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Drone navigation is critical, particularly during the initial phases, such as the initial ascension, where pilots may fail due to strong external interferences that could potentially lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters at speeds with external disturbances pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

Keywords: machine learning, DQN, gazebo, navigation

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Authors: Moharram Sharifi

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Despite a growing interest in the study of the introduction section of Research Articles (RA), there have been few studies to investigate how academic writers engage with other voices and alternative positions in this academic genre. Therefore, the purpose of this study was to show how Native Speaker (NS) and (Non-Native Speaker (NNS) writers take positions and stances in research article introductions. For this purpose, Engagement resources based on the appraisal framework were investigated in sixty articles written by English NS and Iranian NNS published in applied linguistics journals. It was found that the mean occurrences of heteroglossic items in both corpora were larger than those of monoglossic items, but comparing the means of monoglossic engagements between the two corpora, it was revealed that NS writers’ corpus had larger mean occurrences of monoglossic engagements than NNS writers’ corpus implying the native’s stronger authorial stance in the texts. The results also revealed that there was no significant difference in the use of contractive and expansive engagements by NS writers (t (29) = -0.995, p>0.05), indicating a balanced use between the two options. However, the higher mean occurrences of expansive options compared with contractive options in the NNS corpus may suggest that NN writers open up more dialogic room for alternative positions in the RA introductions. The findings of this study may help writers to better perceive the creation of a strong authorial position using appropriate engagement resources in RA introductions.

Keywords: engagement, heteroglossic, monoglossic, introduction

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Authors: Mohamed Eldessouki, Ebraheem Shady, Yasser Gowayed

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Carbon nanotubes (CNTs) are known for having high elastic properties with high surface area that promote them as good candidates for reinforcing polymeric matrices. In composite materials, CNTs lack chemical bonding with the surrounding matrix which decreases the possibility of better stress transfer between the components. In this work, a chemical treatment for activating the surface of the multi-wall carbon nanotubes (MWCNT) was applied and the effect of this functionalization on the elastic properties of the epoxy nanocomposites was studied. Functional amino-groups were added to the surface of the CNTs and it was evaluated to be about 34% of the total weight of the CNTs. Elastic modulus was found to increase by about 40% of the neat epoxy resin at CNTs’ weight fraction of 0.5%. The elastic modulus was found to decrease after reaching a certain concentration of CNTs which was found to be 1% wt. The scanning electron microscopic pictures showed the effect of the CNTs on the crack propagation through the sample by forming stress concentrated spots at the nanocomposite samples.

Keywords: carbon nanotubes functionalization, crack propagation, elastic modulus, epoxy nanocomposites

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Authors: P. L. Cheng, I. N. Umar

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Online discussion forum is a popular e-learning technique that allows participants to interact and construct knowledge. This study aims to examine the levels of participation, categories of participants and the structure of their interactions in a forum. A convenience sampling of one course coordinator and 23 graduate students was selected in this study. The forums’ log file and the Social Network Analysis software were used in this study. The analysis reveals 610 activities (including viewing forum’s topic, viewing discussion thread, posting a new thread, replying to other participants’ post, updating an existing thread and deleting a post) performed by them in this forum, with an average of 3.83 threads posted. Also, this forum consists of five at-risk participants, six bridging participants, four isolated participants and five leaders of information. In addition, the network density value is 0.15 and there exist five reciprocal interactions in this forum. The closeness value varied between 28 and 68 while the eigen vector centrality value varied between 0.008 and 0.39. The finding indicates that the participants tend to listen more rather than express their opinions in the forum. It was also revealed that those who actively provide supports in the discussion forum were not the same people who received the most responses from their peers. This study found that cliques do not exist in the forum and the participants are not selective to whom they response to, rather, it was based on the content of the posts made by their peers. Based upon the findings, further analysis with different method and population, larger sample size and a longer time frame are recommended.

Keywords: e-learning, learning management system, online forum, social network analysis

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Authors: Julie Berame

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The study probed that out of eight (8) lessons in Science Six have been validated, lessons 1-3 got the descriptive rating of very satisfactory and lessons 4-8 got the descriptive rating of outstanding based on the content analysis of the prepared CBL lesson plans. The evaluation of the lesson plans focused on the three main features such as statements of the lesson objectives, lesson content, and organization and effectiveness. The study used developmental research procedure that contained three phases, namely: Development phase consists of determining the learning unit, lesson plans, creation of the table of specifications, exercises/quizzes, and revision of the materials; Evaluation phase consists of the development of experts’ assessment checklist, presentation of checklist to the adviser, comments and suggestions, and final validation of the materials; and try-out phase consists of identification of the subject, try-out of the materials using CBL strategy, administering science attitude questionnaire, and statistical analysis to obtain the data. The findings of the study revealed that the relevance and usability of CBL lessons 1 and 2 in terms of lesson objective, lesson content, and organization and effectiveness got the rating of very satisfactory (4.4) and lessons 3-8 got the rating of outstanding (4.7). The lessons 1-8 got the grand rating of outstanding (4.6). Additionally, results showed that CBL strategy helped foster positive attitude among students and achieved effectiveness in psychomotor learning objectives.

Keywords: development, validation, center-based learning, science

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Authors: Çiğdem Bakır, Mecit Yüzkat

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With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: decision tree, water quality, water pollution, machine learning

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Authors: Derya Duran, Christine Jacknick

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This study explores post-task reflections in an English as a Medium of Instruction (EMI) setting, and it specifically focuses on how a teacher performs pedagogical practices such as reformulating, extending and evaluating following students’ spontaneous experience tellings in EMI classrooms. The data consist of 30 hours of video recordings from two EMI content classes, which were recorded for an academic term at a university in Turkey. The course, Guidance, is offered to fourth year undergraduate students as a compulsory course in the Department of Educational Sciences. The participants (n=78) study at the Faculty of Education, majoring in different educational departments (i.e., Computer Education and Instructional Technology, Elementary Education, Foreign Language Education). Using conversation analysis, we demonstrate that the teacher employs a variety of interactional resources to elicit (i.e., asking specific questions) and also provides (i.e., giving scientific information) as much content as possible, which also sheds light on the institutional fingerprints of the current research context. The study contributes to the existing research by unpacking articulation of personal experiences and cultivation of collaborativeness in classroom interaction. Moreover, describing the dialogic nature of these specific occasions, the study demonstrates how teacher and students address learning tasks together (collectivity), how they orient to each other turns interactionally (reciprocity), and how they keep the pedagogical focus in mind (purposefulness).

Keywords: conversation analysis, English as a medium of instruction, higher education, post-task reflections

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

Abstract:

In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

Procedia PDF Downloads 64

Authors: Yang Zhong, Meijie Xue

Abstract:

The purpose of this paper is to investigate the stresses and excess pore fluid pressure induced by the moving wheel pressure on saturated asphalt pavements, which is one of the reasons for a damage phenomenon in flexible pavement denoted stripping. The saturated asphalt pavement is modeled as multilayered poroelastic half space exerted by a wheel pressure, which is moving at a constant velocity along the surface of the pavement. The governing equations for the proposed analysis are based on the Biot’s theory of dynamics in saturated poroelastic medium. The governing partial differential equations are solved by using Laplace and Hankel integral transforms. The solutions for the stresses and excess pore pressure are expressed in the forms of numerical inversion Laplace and Hankel integral transforms. The numerical simulation results clearly demonstrate the induced deformation and water flow in the asphalt pavement.

Keywords: saturated asphalt pavements, moving loads, excess pore fluid pressure, stress of pavement, biot theory, stress and strain of pavement

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Authors: Ghulam Hasan Abbasi, Moazzam Jamil, M. Anwar-Ul-Haq

Abstract:

Heavy metals are significant pollutants in environment and their toxicity is a problem for survival of living things while Silicon (Si) is one of the most ubiquitous macroelements, performing an essential function in healing plants in response to environmental stresses. A hydroponic experiment was conducted to investigate the role of exogenous application of silicon under cadmium stress in six different maize hybrids with five treatments comprising of control, 7.5 µM Cd + 5 mM Si, 7.5 µM Cd + 10 mM Si, 15 µM Cd + 5 mM Si and 15 µM Cd + 10 mM Si. Results revealed that treatments of plants with 10mM Si application under both 7.5µM Cd and 15 µM Cd stress resulted in maximum improvement in plant morphological attributes (root and shoot length, root and shoot fresh and dry weight, leaf area and relative water contents) and antioxidant enzymes (POD and CAT) relative to 5 mM Si application in all maize hybrids. Results regarding Cd concentrations showed that Cd was more retained in roots followed by shoots and then leaves and maximum reduction in Cd uptake was observed at 10mM Si application. Maize hybrid 6525 showed maximum growth and least concentration of Cd whereas maize hybrid 1543 showed the minimum growth and maximum Cd concentration among all maize hybrids.

Keywords: antioxidant, cadmium, maize, silicon

Procedia PDF Downloads 504