Search results for: learning and teaching

Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: neural networks, radial basis functions, metamodelling, python machine learning libraries

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Authors: Ardalan Tofighi Soleimandarabi

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Artificial intelligence has revolutionized the concrete construction industry and improved processes by increasing efficiency, accuracy, and sustainability. This article examines the applications of artificial intelligence in predicting the compressive strength of concrete, optimizing mixing plans, and improving structural health monitoring systems. Artificial intelligence-based models, such as artificial neural networks (ANN) and combined machine learning techniques, have shown better performance than traditional methods in predicting concrete properties. In addition, artificial intelligence systems have made it possible to improve quality control and real-time monitoring of structures, which helps in preventive maintenance and increases the life of infrastructure. Also, the use of artificial intelligence plays an effective role in sustainable construction by optimizing material consumption and reducing waste. Although the implementation of artificial intelligence is associated with challenges such as high initial costs and the need for specialized training, it will create a smarter, more sustainable, and more affordable future for concrete structures.

Keywords: artificial intelligence, concrete construction, compressive strength prediction, structural health monitoring, stability

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Authors: Hadeel Al Obaidy, Amani Al Heela

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The main purpose of this paper is to examine the concept of semantic web and the role that ontology and semantic annotation plays in the development of semantic web services. The paper focuses on semantic web infrastructure illustrating how ontology and annotation work to provide the learning capabilities for building content semantically. To improve productivity and quality of software, the paper applies approaches, notations and techniques offered by software engineering. It proposes a conceptual model to develop semantic web services for the infrastructure of web information retrieval system of digital libraries. The developed system uses ontology and annotation to build a knowledge based system to define and link the meaning of a web content to retrieve information for users’ queries. The results are more relevant through keywords and ontology rule expansion that will be more accurate to satisfy the requested information. The level of results accuracy would be enhanced since the query semantically analyzed work with the conceptual architecture of the proposed system.

Keywords: semantic web services, software engineering, semantic library, knowledge representation, ontology

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Authors: Chenchen Liu, Jacques Audran

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Using tablet apps with a certain educational purpose to promote young children’s cognitive development, is quite common now. Developing an educational app on an Ipad like tablet, especially for a young child (age 3-5) requires an optimal level of challenge to continuously attract children’s attention and obtain an educational effect. Adaptive difficulty adjustment, which could dynamically set the difficulty in the challenge according to children’s performance, seems to be a good solution. Since space concept plays an important role in young children’s cognitive development, we made an experimental comparison in a French kindergarten between one group of 23 children using an educational app ‘Debout Ludo’ with adaptive difficulty settings and another group of 20 children using the previous version of ‘Debout Ludo’ with a classic incremental difficulty adjustment. The experiment results of spatial problem solving indicated that a significantly higher learning outcome was acquired by the young children who used the adaptive version of the app.

Keywords: adaptive difficulty, spatial problem solving, tactile tablet, young children

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Authors: Rachel Barker

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The exponential velocity in the truly knowledge-intensive world today has increasingly bombarded organizations with unfathomable challenges. Hence organizations are introduced to strange lexicons of descriptors belonging to a new paradigm of who, what and how knowledge at individual and organizational levels should be managed. Although organizational knowledge has been recognized as a valuable intangible resource that holds the key to competitive advantage, little progress has been made in understanding how knowledge sharing at individual level could benefit knowledge use at collective level to ensure added value. The research problem is that a lack of research exists to measure knowledge sharing through a multi-layered structure of ideas with at its foundation, philosophical assumptions to support presuppositions and commitment which requires actual findings from measured variables to confirm observed and expected events. The purpose of this paper is to address this problem by presenting a theoretical approach to measure knowledge sharing in emerging knowledge organizations. The research question is that despite the competitive necessity of becoming a knowledge-based organization, leaders have found it difficult to transform their organizations due to a lack of knowledge on who, what and how it should be done. The main premise of this research is based on the challenge for knowledge leaders to develop an organizational culture conducive to the sharing of knowledge and where learning becomes the norm. The theoretical constructs were derived and based on the three components of the knowledge management theory, namely technical, communication and human components where it is suggested that this knowledge infrastructure could ensure effective management. While it is realised that it might be a little problematic to implement and measure all relevant concepts, this paper presents effect of eight critical success factors (CSFs) namely: organizational strategy, organizational culture, systems and infrastructure, intellectual capital, knowledge integration, organizational learning, motivation/performance measures and innovation. These CSFs have been identified based on a comprehensive literature review of existing research and tested in a new framework adapted from four perspectives of the balanced score card (BSC). Based on these CSFs and their items, an instrument was designed and tested among managers and employees of a purposefully selected engineering company in South Africa who relies on knowledge sharing to ensure their competitive advantage. Rigorous pretesting through personal interviews with executives and a number of academics took place to validate the instrument and to improve the quality of items and correct wording of issues. Through analysis of surveys collected, this research empirically models and uncovers key aspects of these dimensions based on the CSFs. Reliability of the instrument was calculated by Cronbach’s a for the two sections of the instrument on organizational and individual levels.The construct validity was confirmed by using factor analysis. The impact of the results was tested using structural equation modelling and proved to be a basis for implementing and understanding the competitive predisposition of the organization as it enters the process of knowledge management. In addition, they realised the importance to consolidate their knowledge assets to create value that is sustainable over time.

Keywords: innovation, intellectual capital, knowledge sharing, performance measures

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Authors: Emilia A. Campayo-Munoz, Alberto Cabedo-Mas

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Music students have to face the challenges of musical practice -such as discipline in study, competitiveness, or performance anxiety- that require good emotional management to enable successful performance. However, few rigorous implementations focused on studying the influence of emotional skills in student's musical performance. Responding to this gap in the literature, this study aims to explore the relationship between emotional skills and musical performance in the context of elementary music education in conservatoires. Given the individual nature of the instrumental studies and the difficult availability of teachers to be trained in emotional education, it was decided to conduct a multiple case study in a Spanish music conservatoire. Author 1 carried out the implementation of the research with three 10-year-old students who were selected from her piano class. All of them attended the third year of their piano studies. The research processes consisted of the implementation of a set of specific and cross-sectional activities designed 'ad hoc' to be articulated in the subjects of individual instrument -piano- and ensemble in parallel to the contents of musical nature. The CE-360º questionnaire was used to measure different aspects of the students' emotional skills from a multi-angle perspective, each of the questionnaires being responded by oneself, three teachers and three peers, before and after the implementation. The data from the questionnaire were compared with the grades that the students obtained during the first and last quarter of the school year in the attended subjects. Acknowledging the complexity of emotional development, the results indicate possible relations between emotional skills and musical performance in music education in conservatoires. The results show that for the cases explored; there exists a relationship between emotional skills and musical performance. Although generalizations cannot be made, this study reinforces the need to further explore emotional development in instrumental teaching and suggest the importance of inviting teachers to reflect on the pedagogical practices extended in the conservatoires and to develop and implement those that promote the work of the students' emotions.

Keywords: conservatoires, emotional skills, music education, musical performance

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Authors: S. J. Yune, K. H. Park

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The purpose of this study was to examine the validity of the research process for enhancing the humanities competence of the medical students. The research process was developed to be operated as a core subject course of 3 semesters. Among them, the research process for enhancing humanities capacity consisted of humanities and societies (6 teams) and education-psychology (2teams). The subjects of this study were 88-second grade students and 22 professors who participated in the research process. Among them, 13 professors participated in the study of humanities and 37 students. In the validity test, the professors were more likely to have more validity in the research process than the students in all areas of logic (p = .001), influence (p = .037), process (p = .001). The validity of the professor was higher than that of the students. The professors highly evaluated the students' learning outcomes and showed the most frequency to the prize group. As a result of analyzing the agreement between the students and the professors through the Kappa coefficient, the agreement degree of communication and cooperation competence was moderate to .430. Problem-solving ability was .340, which showed a fair degree of agreement. However, other factors showed only a slight degree of agreement of less than .20.

Keywords: research process, medical school, humanities competence, validity verification

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Authors: Avanika Gupta

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Education plays a significant and remedial role in balancing the socio-economic fabric of a country. Inclusive education is considered as the most appropriate mode of teaching students with hearing impairment (SwHI) by various national and international legislations. But inclusive education is still an evolving concept among the disability studies scholars and policy makers in India. The study aimed to examine accessibility of SwHI in mainstream schools if there are special provisions for SwHI. The study also intended to identify if the provisions are same for deaf and hard-of-hearing students. Using stratified random sampling technique, a school was selected from each of the six administrative zones of Nagpur city. All the selected schools had primary and secondary level education and were co-educational in nature. Interview with principals of these schools and focused-group- observation method showcased lack of accessibility for SwHI in attending schools. Not even a single school had a hearing impaired student, either deaf or hard-of-hearing depicting the double marginalization of SwHI. This is despite the fact that the right to education is a fundamental right in India, and national legislation on disability has special provisions for ensuring educational opportunities to SwHI. None of the schools even had an Indian Sign Language (ISL) instructor. Both observations seemed cause and effect of one another. One of the principals informed that they have seats for all students with disabilities but they usually lie vacant due to lack of awareness among the parents. One school had 2 students with locomotive impairment while another had a student with visual impairment. Principals of two special schools were also interviewed to understand the reason behind the low enrollment rate of SwHI in mainstream schools. Guardian preference, homogeneity, relatable faculty, familiar environment were some of the chief reasons mentioned. Few suggestions for the policymakers, teachers, guardians and the students are also recommended so that Indian education system could become inclusive in true sense.

Keywords: deaf, hard-of-hearing, inclusive education, India, Nagpur, students with hearing impairment

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Authors: Adebowale Olukayode Efunnowo

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This study was carried out to assess the awareness and utilization of Social Network Tools (SNTs) among agricultural science students in Colleges of Education in Ogun State, Nigeria. Simple random sampling techniques were used to select 280 respondents from the study area. Descriptive statistics was used to describe the objectives while Pearson Product Moment Correlation was used to test the hypothesis. The result showed that the majority (71.8%) of the respondents were single, with a mean age of 20 years. Almost all (95.7%) the respondents were aware of Facebook and 2go as a Social Network Tools (SNTs) while 85.0% of the respondents were not aware of Blackplanet, LinkedIn, MyHeritage and Bebo. Many (41.1%) of the respondents had views that using SNTs can enhance extensive literature survey, increase internet browsing potential, promote teaching proficiency, and update on outcomes of researches. However, 51.4% of the respondents perceived that SNTs usage as what is meant for the lecturers/adults only while 16.1% considered it as mainly used by internet fraudsters. Findings revealed that about 50.0% of the respondents browsed Facebook and 2go daily while more than 80% of the respondents used Blackplanet, MyHeritage, Skyrock, Bebo, LinkedIn and My YearBook as the need arise. Major constraints to the awareness and utilization of SNTs were high cost and poor quality of ICTs facilities (77.1%), epileptic power supply (75.0%), inadequate telecommunication infrastructure (71.1%), low technical know-how (62.9%) and inadequate computer knowledge (61.1%). The result of PPMC analysis showed that there was an inverse relationship between constraints and utilization of SNTs at p < 0.05. It can be concluded that constraints affect efficient and effective utilization of SNTs in the study area. It is hereby recommended that management of colleges of education and agricultural institutes should provide good internet connectivity, computer facilities, and alternative power supply in order to increase the awareness and utilization of SNTs among students.

Keywords: awareness, utilization, social network tools, constraints, students

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Yosra Mefteh Rekik

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A computational agent-based model of financial markets stresses interactions and dynamics among a very diverse set of traders. The growing body of research in this area relies heavily on computational tools which by-pass the restrictions of an analytical method. The main goal of this research is to understand how the stock market operates and behaves how to invest in the stock market and to study traders’ behavior within the context of the artificial stock markets populated by heterogeneous agents. All agents are characterized by adaptive learning behavior represented by the Artificial Neuron Networks. By using agent-based simulations on artificial market, we show that the existence of heterogeneous agents can explain the price dynamics in the financial market. We investigate the relation between market diversity and market efficiency. Our empirical findings demonstrate that greater market heterogeneity play key roles in market efficiency.

Keywords: agent-based modeling, artificial stock market, heterogeneous expectations, financial stylized facts, computational finance

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Authors: Kevin. S. Badni

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Due to the rapid advances in the use of information technology and students’ familiarity with technology, learning styles in higher education are being reshaped. One of the technology developments that has gained considerable attention in recent years is Augmented Reality (AR), where technology is used to combine overlays of digital data on physical real-world settings. While AR is being heavily promoted for entertainment by mobile phone manufacturers, it has had little adoption in higher education due to the required upfront investment that an instructor needs to undertake in creating relevant AR applications. This paper discusses a case study that uses a low upfront development approach and examines the impact on generation-Z students’ motivation whilst studying design history over a four-semester period. Even though the upfront investment in creating the AR support was minimal, the results showed a noticeable increase in student motivation. The approach used in this paper can be easily transferred to other disciplines and other areas of design education.

Keywords: augmented reality, history, motivation, technology

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Authors: Lillian M. Seo, Curtis L. Petersen, Ryan J. Halter, David Kotz, John A. Batsis

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Background: Resistance-based exercises effectively enhance muscle strength, which is especially important in older populations as it reduces the risk of disability. Our group developed a Bluetooth-enabled handle for resistance exercise bands that wirelessly transmits relative force data through low-energy Bluetooth to a local smartphone or similar device. The system has the potential to measure home-based exercise interventions, allowing health professionals to monitor compliance. Its feasibility has already been demonstrated in both clinical and field-based settings, but it remained unclear whether the system’s usability persisted upon repeated use. The current study sought to assess the usability of this system and its users’ satisfaction with repeated use by deploying the device among younger adults to gather formative information that can ultimately improve the device’s design for older adults. Methods: A usability study was conducted in which 32 participants used the above system. Participants executed 10 repetitions of four commonly performed exercises: bicep flexion, shoulder abduction, elbow extension, and triceps extension. Each completed three exercise sessions, separated by at least 24 hours to minimize muscle fatigue. At its conclusion, subjects completed an adapted version of the usefulness, satisfaction, and ease (USE) questionnaire – assessing the system across four domains: usability, satisfaction, ease of use, and ease of learning. The 20-item questionnaire examined how strongly a participant agrees with positive statements about the device on a seven-point Likert scale, with one representing ‘strongly disagree’ and seven representing ‘strongly agree.’ Participants’ data were aggregated to calculate mean response values for each question and domain, effectively assessing the device’s performance across different facets of the user experience. Summary force data were visualized using a custom web application. Finally, an optional prompt at the end of the questionnaire allowed for written comments and feedback from participants to elicit qualitative indicators of usability. Results: Of the n=32 participants, 13 (41%) were female; their mean age was 32.4 ± 11.8 years, and no participants had a physical impairment. No usability questions received a mean score < 5 of seven. The four domains’ mean scores were: usefulness 5.66 ± 0.35; satisfaction 6.23 ± 0.06; ease of use 6.25 ± 0.43; and ease of learning 6.50 ± 0.19. Representative quotes of the open-ended feedback include: ‘A non-rigid strap-style handle might be useful for some exercises,’ and, ‘Would need different bands for each exercise as they use different muscle groups with different strength levels.’ General impressions were favorable, supporting the expectation that the device would be a useful tool in exercise interventions. Conclusions: A simple usability assessment of a Bluetooth-enabled resistance exercise band supports a consistent and positive user experience among young adults. This study provides adequate formative data, assuring the next steps can be taken to continue testing and development for the target population of older adults.

Keywords: Bluetooth, exercise, mobile health, mHealth, usability

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: N. Schneider, M. Bruchhage, M. Hartweg, G. Mutungi, J. O Regan, S. Deoni

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Observational neuroimaging studies suggest differences between breast-fed and formula-fed infants in developmental myelination, a key brain process for learning and cognitive development. However, the possible effects of a nutrient formulation on myelin development in healthy term infants in an intervention study have not been investigated. Objective was, therefore, to investigate the efficacy of a nutrient formulation with higher levels of myelin-relevant nutrients as compared to a control formulation with lower levels of the same nutrients on brain myelination and cognitive development in the first 6 months of life. The study is an ongoing randomized, controlled, double-blind, two-center, parallel-group clinical trial with a nonrandomized, non-blinded arm of exclusively breastfed infants. The current findings result from a staged statistical analysis at 6 months; the recruitment and intervention period has been completed for all participants. Follow-up visits at 12, 18 and 24 months are still ongoing. N= 81 enrolled full term, neurotypical infants of both sexes were randomized into either the investigational (N= 42) or the control group (N= 39), and N= 108 children in the breast-fed arm served as a natural reference group. The effect of a blend of docosahexaenoic acid, arachidonic acid, iron, vitamin B12, folic acid as well as sphingomyelin from a uniquely proceed whey protein concentrate enriched in alpha-lactalbumin and phospholipids in an infant nutrition product matrix was investigated. The main outcomes for the staged statistical analyses at 6 months included brain myelination measures derived from MRI. Additional outcomes were brain volume, cognitive development and safety. The full analyses set at 6 months comprised N= 66 infants. Higher levels of myelin-relevant nutrients compared to lower levels resulted in significant differences in myelin structure, volume, and rate of myelination as early as 3 and 6 months of life. The cross-sectional change of means between groups for whole-brain myelin volume was 8.4% for investigational versus control formulation (3.5% versus the breastfeeding reference) group at 3 months and increased to 36.4% for investigational versus control formulation (14.1% versus breastfeeding reference) at 6 months. No statistically significant differences were detected for early cognition scores. Safety findings were largely similar across groups. This is the first pediatric nutritional neuroimaging study demonstrating the efficacy of a myelin nutrient blend on developmental myelination in well-nourished term infants. Myelination is a critical process in learning and development. The effects were demonstrated across the brain, particularly in temporal and parietal regions, known to be functionally involved in sensory, motor and language skills. These first results add to the field of nutritional neuroscience by demonstrating early life nutrition benefits for brain architecture which may be foundational for later cognitive and behavioral outcomes. ClinicalTrials.gov Identifier: NCT03111927 (Infant Nutrition and Brain Development - Full-Text View - ClinicalTrials.gov).

Keywords: brain development, infant nutrition, MRI, myelination

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: Nattamon Srithammee, Ratchanikorn Chonchaiya

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This study explores the impact of Augmented Reality (AR) technology on mathematics education, focusing on Area and Volume concepts at Phra Dabos School in Thailand. We developed a mobile augmented reality application to present these mathematical concepts innovatively. Using a mixed-methods approach, we assessed the knowledge of 79 students before and after using the application. The results showed a significant improvement in students' understanding of Area and Volume, with average test scores increasing from 3.70 to 9.04 (p < 0.001, Cohen's d = 2.05). Students also reported increased engagement and satisfaction. Our findings suggest that augmented reality technology can be a valuable tool in mathematics education, particularly for enhancing the understanding of abstract concepts like Area and Volume. This study contributes to research on educational technology in STEM education and provides insights for educators and educational technology developers.

Keywords: augmented reality, mathematics education, area and volume, educational technology, STEM education

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Authors: Preethy Mary Donald, Renjith George

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Oral tori are localized non-neoplastic protuberances of maxilla and mandible. Torus palatinus (TP) is found on the midline of the roof of mouth existing as single growth or in clusters. Torus mandibularis(TM) is located on the lingual aspect of the mandible commonly between canine and premolar region. Etiology of their presence was not clear and was found to be multifactorial. Their variations in relation to age, gender, ethnicity and also the characteristics of TP and TM have become the interest of multiple studies. The objectives of this study were to determine the prevalence of torus palatinus (TP) and torus mandibularis (TM) among patients who have visited outpatient department, Faculty of Dentistry, Melaka Manipal Medical College. 108 patients were examined for the presence of oral tori at the outpatient department, Faculty of Dentistry, Melaka-Manipal Medical College. Factors such as age, gender, ethnicity of the patients and size, shape, location of the oral tori were studied. For TP, Malays (62.96%) have been found to have the highest prevalence than Chinese (43.3%) and Indians (35.71%). For TM, Chinese (7.46%) had predominated compared to Malays (7.41%) and Indians (0%). There is no significant association between occurrence of TP and TM with age, gender and ethnicity. For Torus palatinus, the most common size was Grade 1(1-3mm), most common location was molar region, and the most common shape was spindle. For Torus mandibularis, the most frequent location was canine premolar region and exists in unilateral single or bilateral single fashion. The overall prevalence rates were 47.2% for TP and 6.48% for TM. However, there is no significant association between occurrence of TP and TM with age, gender and ethnicity. The results showed variations in clinical characteristics and support the findings that occurrence of tori is a dynamic phenomenon which is multifactorial owing to the environmental factors such as stress from occlusion and dietary habits. It could be due to the genetic make-up of the individual.

Keywords: torus palatinus, torus mandibularis, age, gender

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Authors: Tolga Barisik, Ali Fuat Guneri, K. Dastan

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Major industrial facilities have a high potential for fire risk. In particular, the indices used for the detection of hidden fire are used very effectively in order to prevent the fire from becoming dangerous in the initial stage. These indices provide the opportunity to prevent or intervene early by determining the stage of the fire, the potential for hazard, and the type of the combustion agent with the percentage values of the ambient air components. In this system, artificial neural network will be modeled with the input data determined using the Levenberg-Marquardt algorithm, which is a multi-layer sensor (CAA) (teacher-learning) type, before modeling the modeling methods in the literature. The actual values produced by the indices will be compared with the outputs produced by the network. Using the neural network and the curves to be created from the resulting values, the feasibility of performance determination will be investigated.

Keywords: artifical neural networks, fire, Graham Index, levenberg-marquardt algoritm, oxygen decrease percentage index, risk assessment, Trickett Index

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Authors: Sandra So, Mohd Roslan Ismail, Safurah Jaafar

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With the rapid proliferation of data in healthcare has provided an opportune platform creation of Artificial Intelligence (AI). AI has brought a paradigm shift for healthcare professionals, promising improvement in delivery and quality. This study aims to determine the perception of healthcare personnel on perceived ease of use, perceived usefulness, and subjective norm toward attitude for artificial intelligence acceptance. A cross-sectional single institutional study of employees’ perception of adopting AI in the hospital was conducted. The survey was conducted using a questionnaire adapted from Technology Acceptance Model and a four-point Likert scale was used. There were 96 or 75.5% of the total population responded. This study has shown the significant relationship and the importance of ease of use, perceived usefulness, and subjective norm to the acceptance of AI. In the study results, it concluded that the determining factor to the strong acceptance of AI in their practices is mostly those respondents with the most interaction with the patients and clinical management.

Keywords: artificial intelligence, machine learning, perceived ease of use, perceived usefulness, subjective norm

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Authors: S. Kawther, C. Marshall

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Educational neuroscience has the intention of transforming research findings of the underpinning neural processes of learning to educational practices. A main criticism of the field, hitherto, is that less focus has been put on studying the in-progress practical application of these findings. Therefore, this study aims to gain a better understanding of teachers’ perceptions of the practical application and utilization of brain knowledge. This was approached by investigating the answer to 'What are the facilitators and barriers for bringing research from neuroscience to bear on education?'. Following a qualitative design, semi-structured interviews were conducted with 12 teachers who had a proficient course in educational neuroscience. Thematic analysis was performed on the transcribed data applying Braun & Clark’s steps. Findings emerged with four main themes: time, knowledge, teacher’s involvement, and system. These themes revealed that some effective brain-based practices are being engaged in by the teachers. However, the lack of guidance and challenges regarding this implementation were also found. This study discusses findings in light of the development of educational neuroscience implementation.

Keywords: brain-based, educational neuroscience, neuroeducation, neuroscience-informed

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Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

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Authors: Hanan Eltayeb, Reem Al Refaie

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To be able to connect and relate to shows spoken in a foreign language, advanced learners must understand not only linguistics inferences but also cultural, metacognitive, and pragmatic connotations in colloquial Egyptian TV series. These connotations are needed to both understand the different facets of the dramas put before them, and they’re also consistently grown and formulated through watching these shows. The inferences have become a staple in the Egyptian colloquial culture over the years, making their way into day-to-day conversations as Egyptians use them to speak, relate, joke, and connect with each other, without having known one another from previous times. As for advanced learners, they need to understand these inferences not only to watch these shows, but also to be able to converse with Egyptians on a level that surpasses the formal, or standard. When faced with some of the somewhat recent shows on the Egyptian screens, learners faced challenges in understanding pragmatics, cultural, and religious background of the target language and consequently not able to interact effectively with a native speaker in real-life situations. This study aims to enhance the linguistic and cultural proficiency of learners through studying two genres of TV Colloquial Egyptian series. Study samples derived from two recent comedian and social Egyptian series ('The Seventh Neighbor' سابع جار, and 'Nelly and Sherihan' نيللي و شريهان). When learners watch such series, they are usually faced with a problem understanding inferences that have to do with social, religious, and political events that are addressed in the series. Using discourse analysis of the sematic, semantic, pragmatic, cultural, and linguistic characteristics of the target language, some major deductions were highlighted and repeated, showing a pattern in both. The research paper concludes that there are many sets of lingual and para-lingual phrases, idioms, and proverbs to be acquired and used effectively by teaching these series. The strategies adopted in the study can be applied to different types of media, like movies, TV shows, and even cartoons, to enhance student proficiency.

Keywords: Egyptian series, culture, linguistic competence, pragmatics, semantics, social

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

Abstract:

The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Emmanuel Abayomi Rotimi

Abstract:

Context: Rabbits production plays important role in increasing animal protein supply in Nigeria. Rabbit production provides a cheap, affordable, and healthy source of meat. The growth of animals involves an increase in body weight, which can change the conformation of various parts of the body. Live weight and linear measurements are indicators of growth rate in rabbits and other farm animals. Aims: This study aimed to define the body dimensions of New Zealand rabbits and also to investigate the morphometric traits variables that contribute to body conformation by the use of principal component analysis (PCA). Methods: Data were obtained from 80 New Zealand rabbits (40 bucks and 40 does) raised in Livestock Teaching and Research Farm, Federal University Dutsinma. Data were taken on body weight (BWT), body length (BL), ear length (EL), tail length (TL), heart girth (HG) and abdominal circumference (AC). Data collected were subjected to multivariate analysis using SPSS 20.0 statistical package. Key results: The descriptive statistics showed that the mean BWT, BL, EL, TL, HG, and AC were 0.91kg, 27.34cm, 10.24cm, 8.35cm, 19.55cm and 21.30cm respectively. Sex showed significant (P<0.05) effect on all the variables examined, with higher values recorded for does. The phenotypic correlation coefficient values (r) between the morphometric traits were all positive and ranged from r = 0.406 (between EL and BL) to r = 0.909 (between AC and HG). HG is the most correlated with BWT (r = 0.786). The principal component analysis with variance maximizing orthogonal rotation was used to extract the components. Two principal components (PCs) from the factor analysis of morphometric traits explained about 80.42% of the total variance. PC1 accounted for 64.46% while PC2 accounted for 15.97% of the total variances. Three variables, representing body conformation, loaded highest in PC1. PC1 had the highest contribution (64.46%) to the total variance, and it is regarded as body conformation traits. Conclusions: This component could be used as selection criteria for improving body weight of rabbits.

Keywords: conformation, multicollinearity, multivariate, rabbits and principal component analysis

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Authors: Hossein Ghaedamini, Salman Farahbakhsh, Alireza Amirbeigi, Zahra Saghafi, Salman Daneshi, Alireza Ghaedamini

Abstract:

Background: Assessing the challenges of clinical education of medical students is one of the most important and sensitive parts of medical education. The aim of this study was to investigate the views and experiences of Kerman medical students on the factors that facilitate and inhibit the quality of clinical education. Materials and Methods: This research was qualitative and used a phenomenological approach. The study population included medical interns of Kerman University of Medical Sciences in 1400. The method of data collection was in-depth interviews with participants. Data were encoded and analyzed by Claizey stepwise model. Results: First, about 540 primary codes were extracted in the form of two main themes (facilitators and inhibitors) and 10 sub-themes including providing motivational models and creating interest in interns, high scientific level of professors and the appropriate quality of their teaching, the use of technology in the clinical education process, delegating authority and freedom of action and more responsibilities to interns, inappropriate treatment of some officials, professors, assistants and department staff with their interns, inadequate educational programming, lack of necessary cooperation and providing inappropriate treatment by clinical training experts for interns, inadequate evaluation method in clinical training for interns, poor quality mornings, the unefficiency of grand rounds, the inappropriate way of evaluating clinical training for interns, the lack of suitable facilities and conditions with the position of a medical intern, and the hardwork of some departments were categorized. Conclusion: Clinical education is always mixed with special principles and subtleties, and special attention to facilitators and inhibitors in this process has an important role in improving its quality.

Keywords: clinical education, medical students, qualitative study, education

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Authors: Attahiru Sifawa Ahmad

Abstract:

The media; electronic, print, and social, is playing very significant roles towards promoting political awareness and stability of any nation. However, for the media to play its role effectively, a clear and sound grasp of the language of communication is necessary. Otherwise, there is the tendency of the media spreading wrong and, or, misinterpreted information to the public, capable of generating rancour and political instability. One such clear misinterpretation or misrepresentation of information was the Hausa metaphorical expression, Kare Jinni Biri Jinni quoted from the statement made by Rtd. General Muhammadu Buhari, sometimes in April, 2013, while addressing his supporters from Niger State. In the political presentation of the term Kare - Jini Biri – Jini, quoted and translated by many print media in Nigeria, it was interpreted to mean; ‘The Dog and the Baboon will be soaked in blood’, denoting bloodshed and declaration of war. However, the term Kare - jini Biri - Jini, literally; the Dog with blood and the Baboon with blood, or, the Dog is bleeding the Baboon is bleeding, or, both the Dog and the Baboon sustained injuries. It is a metaphorical expression denoting a hot competition, and serious struggle, between two competing parties that are closer in strength and stamina. The expression got its origin among the hunting communities in traditional Hausa Societies. From experience, it was always not easy to wrestle and hunt Baboon by the Hunter’s Dog. In many instances, it ended a futile exercise, and even at instances whereby the latter hunted the former, it would be after a serious struggle with both two sustaining injuries. This paper seeks to highlight the poverty of vocabulary, and poor grasp of Nigerian languages among Journalists and young citizens in the country. The paper, therefore, advocated for the retention and effective teaching of the indigenous languages in primary and secondary school’s curriculums in Nigeria. The paper equally analysed the political origin of the print media in Nigeria, how since its first appearance, the print Media is being assigned very important political role by political elites in the country.

Keywords: Baboon, dog, media, politics

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Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

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Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

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Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

Abstract:

Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose.

Keywords: convolutional autoencoder, anomaly detection, web application, FLASK

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