Search results for: architectural design learning
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 18772

Search results for: architectural design learning

14722 Effectively Improving Cognition, Behavior, and Attitude of Diabetes Inpatients through Nutritional Education

Authors: Han Chih Feng, Yi-Cheng Hou, Jing-Huei Wu

Abstract:

Diabetes is a chronic disease. Nutrition knowledge and skills enable individuals with type 2 diabetes to optimize metabolic self-management and quality of life. This research studies the effect of nutritional education on diabetes inpatients in terms of their cognition, behavior, and attitude. The participants are inpatients diagnosed with diabetes at Taipei Tzu Chi Hospital. A total of 103 participants, 58 male, and 45 females, enrolled in the research between January 2018 and July 2018. The research evaluates cognition, behavior, and attitude level before and after nutritional education conducted by dietitians. The result shows significant improvement in actual consumption (2.5 ± 1.4 vs 3.8 ± 0.7; p<.001), diet control motivation (2.7 ± 0.8 vs 3.4 ± 0.6; p<.001), correct nutrition concept (1.2± 0.4 vs 2.4 ± 0.5; p<.001), learning willingness (2.7± 0.9 vs 3.4 ± 0.6; p<.001), cognitive behaviors (1.4 ± 0.5 vs 2.9 ± 0.7; p<.001). AC sugar (278.5 ± 321.5 vs 152.2 ± 49.1; p<.001) and HbA1C (10.3 ± 2.6 vs 8.6 ± 1.9; p<.001) are significant improvement after nutritional education. After nutritional education, participants oral hypoglycemic agents increased from 16 (9.2%) to 33 (19.0%), insulin decreased from 75 (43.1%) to 68 (39.1%), and hypoglycemic drugs combined with insulin decreased from 83 (47.7%) to 73 (42.0%).Further analysis shows that female inpatients have significant improvement in diet control motivation (3.91 ± 0.85 vs 4.44 ± 0.59; p<0.000), correct nutrition concept (3.24± 0.48 vs 4.47± 0.51; p<0.000), learning willingness (3.89 ± 0.86 vs 4.44 ± 0.59; p<0.000) and cognitive behaviors (2.42 ± 0.58 vs 4.02 ± 0.69; p<0.000); male inpatients have significant improvement in actual food intake (4.41± 0.92 vs 3.97 ± 0.42; p<0.000), diet control motivation (3.62 ± 0.86 vs 4.29 ± 0.62; p<0.000), correct nutrition concept (3.26 ± 0.44 vs 4.36 ± 0.49; p<0.000), learning willingness (3.72± 0.93 vs 4.33± 0.63; p<0.000) and cognitive behaviors (2.45± 0.54 vs 4.03± 0.77; p<0.000). In conclusion, nutritional education proves effective, regardless of gender, in improving an inpatient’s cognition, behavior, and attitude toward diabetes self-management.

Keywords: diabetes, nutrition education, actual consumption, diet control motivation, nutrition concept, learning willingness, cognitive behaviors

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14721 Ecological Relationships Between Material, Colonizing Organisms, and Resulting Performances

Authors: Chris Thurlbourne

Abstract:

Due to the continual demand for material to build, and a limit of good environmental material credentials of 'normal' building materials, there is a need to look at new and reconditioned material types - both biogenic and non-biogenic - and a field of research that accompanies this. This research development focuses on biogenic and non-biogenic material engineering and the impact of our environment on new and reconditioned material types. In our building industry and all the industries involved in constructing our built environment, building material types can be broadly categorized into two types, biogenic and non-biogenic material properties. Both play significant roles in shaping our built environment. Regardless of their properties, all material types originate from our earth, whereas many are modified through processing to provide resistance to 'forces of nature', be it rain, wind, sun, gravity, or whatever the local environmental conditions throw at us. Modifications are succumbed to offer benefits in endurance, resistance, malleability in handling (building with), and ergonomic values - in all types of building material. We assume control of all building materials through rigorous quality control specifications and regulations to ensure materials perform under specific constraints. Yet materials confront an external environment that is not controlled with live forces undetermined, and of which materials naturally act and react through weathering, patination and discoloring, promoting natural chemical reactions such as rusting. The purpose of the paper is to present recent research that explores the after-life of specific new and reconditioned biogenic and non-biogenic material types and how the understanding of materials' natural processes of transformation when exposed to the external climate, can inform initial design decisions. With qualities to receive in a transient and contingent manner, ecological relationships between material, the colonizing organisms and resulting performances invite opportunities for new design explorations for the benefit of both the needs of human society and the needs of our natural environment. The research follows designing for the benefit of both and engaging in both biogenic and non-biogenic material engineering whilst embracing the continual demand for colonization - human and environment, and the aptitude of a material to be colonized by one or several groups of living organisms without necessarily undergoing any severe deterioration, but embracing weathering, patination and discoloring, and at the same time establishing new habitat. The research follows iterative prototyping processes where knowledge has been accumulated via explorations of specific material performances, from laboratory to construction mock-ups focusing on the architectural qualities embedded in control of production techniques and facilitating longer-term patinas of material surfaces to extend the aesthetic beyond common judgments. Experiments are therefore focused on how the inherent material qualities drive a design brief toward specific investigations to explore aesthetics induced through production, patinas and colonization obtained over time while exposed and interactions with external climate conditions.

Keywords: biogenic and non-biogenic, natural processes of transformation, colonization, patina

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14720 Cardiokey: A Binary and Multi-Class Machine Learning Approach to Identify Individuals Using Electrocardiographic Signals on Wearable Devices

Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

Abstract:

Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

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14719 Collaborative Team Work in Higher Education: A Case Study

Authors: Swapna Bhargavi Gantasala

Abstract:

If teamwork is the key to organizational learning, productivity, and growth, then, why do some teams succeed in achieving these, while others falter at different stages? Building teams in higher education institutions has been a challenge and an open-ended constructivist approach was considered on an experimental basis for this study to address this challenge. For this research, teams of students from the MBA program were chosen to study the effect of teamwork in learning, the motivation levels among student team members, and the effect of collaboration in achieving team goals. The teams were built on shared vision and goals, cohesion was ensured, positive induction in the form of faculty mentoring was provided for each participating team and the results have been presented with conclusions and suggestions.

Keywords: teamwork, leadership, motivation and reinforcement, collaboration

Procedia PDF Downloads 379
14718 End-to-End Spanish-English Sequence Learning Translation Model

Authors: Vidhu Mitha Goutham, Ruma Mukherjee

Abstract:

The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

Procedia PDF Downloads 180
14717 Learning Academic Skills through Movement: A Case Study in Evaluation

Authors: Y. Salfati, D. Sharef Bussel, J. Zamir

Abstract:

In this paper, we present an Evaluation Case Study implementing the eight principles of Collaborative Approaches to Evaluation (CAE) as designed by Brad Cousins in the past decade. The focus of this paper is sharing a rich experience in which we achieved two main goals. The first was the development of a valuable and meaningful new teacher training program, and the second was a successful implementation of the CAE principles. The innovative teacher training program is based on the idea of including physical movement during the process of teaching and learning academic themes. The program is called Learning through Movement. This program is a response to a call from the Ministry of Education, claiming that today children sit in front of screens and do not exercise any physical activity. In order to contribute to children’s health, physical, and cognitive development, the Ministry of Education promotes learning through physical activities. Research supports the idea that sports and physical exercise improve academic achievements. The Learning through Movement program is operated by Kaye Academic College. Students in the Elementary School Training Program, together with students in the Physical Education Training Program, implement the program in collaboration with two mentors from the College. The program combines academic learning with physical activity. The evaluation began at the beginning of the program. During the evaluation process, data was collected by means of qualitative tools, including interviews with mentors, observations during the students’ collaborative planning, class observations at school and focus groups with students, as well as the collection of documentation related to the teamwork and to the program itself. The data was analyzed using content analysis and triangulation. The preliminary results show outcomes relating to the Teacher Training Programs, the student teachers, the pupils in class, the role of Physical Education teachers, and the evaluation. The Teacher Training Programs developed a collaborative approach to lesson planning. The students' teachers demonstrated a change in their basic attitudes towards the idea of integrating physical activities during the lessons. The pupils indicated higher motivation through full participation in classes. These three outcomes are indicators of the success of the program. An additional significant outcome of the program relates to the status and role of the physical education teachers, changing their role from marginal to central in the school. Concerning evaluation, a deep sense of trust and confidence was achieved, between the evaluator and the whole team. The paper includes the perspectives and challenges of the heads and mentors of the two programs as well as the evaluator’s conclusions. The evaluation unveils challenges in conducting a CAE evaluation in such a complex setting.

Keywords: collaborative evaluation, training teachers, learning through movement

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14716 Improving Mathematics and Engineering Interest through Programming

Authors: Geoffrey A. Wright

Abstract:

In an attempt to address shortcomings revealed in international assessments and lamented in legislation, many schools are reducing or eliminating elective courses, applying the rationale that replacing "non-essential" subjects with core subjects, such as mathematics and language arts, will better position students in the global market. However, there is evidence that systematically pairing a core subject with another complementary subject may lead to greater overall learning in both subjects. In this paper, we outline the methods and preliminary findings from a study we conducted analyzing the influence learning programming has on student mathematical comprehension and ability. The purpose of this research is to demonstrate in what ways two subjects might complement each other, and to better understand the principles and conditions that encourage what we call lateral transfer, the synergistic effect that occurs when a learner studies two complementary subjects.

Keywords: programming, engineering, technology, complementary subjects

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14715 A Reinforcement Learning Based Method for Heating, Ventilation, and Air Conditioning Demand Response Optimization Considering Few-Shot Personalized Thermal Comfort

Authors: Xiaohua Zou, Yongxin Su

Abstract:

The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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14714 Implementation of Quality Function Development to Incorporate Customer’s Value in the Conceptual Design Stage of a Construction Projects

Authors: Ayedh Alqahtani

Abstract:

Many construction firms in Saudi Arabia dedicated to building projects agree that the most important factor in the real estate market is the value that they can give to their customer. These firms understand the value of their client in different ways. Value can be defined as the size of the building project in relationship to the cost or the design quality of the materials utilized in finish work or any other features of building rooms such as the bathroom. Value can also be understood as something suitable for the money the client is investing for the new property. A quality tool is required to support companies to achieve a solution for the building project and to understand and manage the customer’s needs. Quality Function Development (QFD) method will be able to play this role since the main difference between QFD and other conventional quality management tools is QFD a valuable and very flexible tool for design and taking into the account the VOC. Currently, organizations and agencies are seeking suitable models able to deal better with uncertainty, and that is flexible and easy to use. The primary aim of this research project is to incorporate customer’s requirements in the conceptual design of construction projects. Towards this goal, QFD is selected due to its capability to integrate the design requirements to meet the customer’s needs. To develop QFD, this research focused upon the contribution of the different (significantly weighted) input factors that represent the main variables influencing QFD and subsequent analysis of the techniques used to measure them. First of all, this research will review the literature to determine the current practice of QFD in construction projects. Then, the researcher will review the literature to define the current customers of residential projects and gather information on customers’ requirements for the design of the residential building. After that, qualitative survey research will be conducted to rank customer’s needs and provide the views of stakeholder practitioners about how these needs can affect their satisfy. Moreover, a qualitative focus group with the members of the design team will be conducted to determine the improvements level and technical details for the design of residential buildings. Finally, the QFD will be developed to establish the degree of significance of the design’s solution.

Keywords: quality function development, construction projects, Saudi Arabia, quality tools

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14713 Mental Health Diagnosis through Machine Learning Approaches

Authors: Md Rafiqul Islam, Ashir Ahmed, Anwaar Ulhaq, Abu Raihan M. Kamal, Yuan Miao, Hua Wang

Abstract:

Mental health of people is equally important as of their physical health. Mental health and well-being are influenced not only by individual attributes but also by the social circumstances in which people find themselves and the environment in which they live. Like physical health, there is a number of internal and external factors such as biological, social and occupational factors that could influence the mental health of people. People living in poverty, suffering from chronic health conditions, minority groups, and those who exposed to/or displaced by war or conflict are generally more likely to develop mental health conditions. However, to authors’ best knowledge, there is dearth of knowledge on the impact of workplace (especially the highly stressed IT/Tech workplace) on the mental health of its workers. This study attempts to examine the factors influencing the mental health of tech workers. A publicly available dataset containing more than 65,000 cells and 100 attributes is examined for this purpose. Number of machine learning techniques such as ‘Decision Tree’, ‘K nearest neighbor’ ‘Support Vector Machine’ and ‘Ensemble’, are then applied to the selected dataset to draw the findings. It is anticipated that the analysis reported in this study would contribute in presenting useful insights on the attributes contributing in the mental health of tech workers using relevant machine learning techniques.

Keywords: mental disorder, diagnosis, occupational stress, IT workplace

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14712 An Optimization Tool-Based Design Strategy Applied to Divide-by-2 Circuits with Unbalanced Loads

Authors: Agord M. Pinto Jr., Yuzo Iano, Leandro T. Manera, Raphael R. N. Souza

Abstract:

This paper describes an optimization tool-based design strategy for a Current Mode Logic CML divide-by-2 circuit. Representing a building block for output frequency generation in a RFID protocol based-frequency synthesizer, the circuit was designed to minimize the power consumption for driving of multiple loads with unbalancing (at transceiver level). Implemented with XFAB XC08 180 nm technology, the circuit was optimized through MunEDA WiCkeD tool at Cadence Virtuoso Analog Design Environment ADE.

Keywords: divide-by-2 circuit, CMOS technology, PLL phase locked-loop, optimization tool, CML current mode logic, RF transceiver

Procedia PDF Downloads 466
14711 Comparison of Automated Zone Design Census Output Areas with Existing Output Areas in South Africa

Authors: T. Mokhele, O. Mutanga, F. Ahmed

Abstract:

South Africa is one of the few countries that have stopped using the same Enumeration Areas (EAs) for census enumeration and dissemination. The advantage of this change is that confidentiality issue could be addressed for census dissemination as the design of geographic unit for collection is mainly to ensure that this unit is covered by one enumerator. The objective of this paper was to evaluate the performance of automated zone design output areas against non-zone design developed geographies using the 2001 census data, and 2011 census to some extent, as the main input. The comparison of the Automated Zone-design Tool (AZTool) census output areas with the Small Area Layers (SALs) and SubPlaces based on confidentiality limit, population distribution, and degree of homogeneity, as well as shape compactness, was undertaken. Further, SPSS was employed for validation of the AZTool output results. The results showed that AZTool developed output areas out-perform the existing official SAL and SubPlaces with regard to minimum population threshold, population distribution and to some extent to homogeneity. Therefore, it was concluded that AZTool program provides a new alternative to the creation of optimised census output areas for dissemination of population census data in South Africa.

Keywords: AZTool, enumeration areas, small areal layers, South Africa

Procedia PDF Downloads 188
14710 Efficacy of a Social-Emotional Learning Curriculum for Kindergarten and First Grade Students to Improve Social Adjustment within the School Culture

Authors: Ann P. Daunic, Nancy Corbett

Abstract:

Background and Significance: Researchers emphasize the role that motivation, self-esteem, and self-regulation play in children’s early adjustment to the school culture, including skills such as identifying their own feelings and understanding the feelings of others. As social-emotional growth, academic learning, and successful integration within culture and society are inextricably connected, the Social-Emotional Learning Foundations (SELF) curriculum was designed to integrate social-emotional learning (SEL) instruction within early literacy instruction (specifically, reading) for Kindergarten and first-grade students at risk for emotional and behavioral difficulties. Storybook reading is a typically occurring activity in the primary grades; thus SELF provides an intervention that is both theoretically and practically sound. Methodology: The researchers will report on findings from the first two years of a three-year study funded by the US Department of Education’s Institute of Education Sciences to evaluate the effects of the SELF curriculum versus “business as usual” (BAU). SELF promotes the development of self-regulation by incorporating instructional strategies that support children’s use of SEL related vocabulary, self-talk, and critical thinking. The curriculum consists of a carefully coordinated set of materials and pedagogy designed specifically for primary grade children at early risk for emotional and behavioral difficulties. SELF lessons (approximately 50 at each grade level) are organized around 17 SEL topics within five critical competencies. SELF combines whole-group (the first in each topic) and small-group lessons (the 2nd and 3rd in each topic) to maximize opportunities for teacher modeling and language interactions. The researchers hypothesize that SELF offers a feasible and substantial opportunity within the classroom setting to provide a small-group social-emotional learning intervention integrated with K-1 literacy-related instruction. Participating target students (N = 876) were identified by their teachers as potentially at risk for emotional or behavioral issues. These students were selected from 122 Kindergarten and 100 first grade classrooms across diverse school districts in a southern state in the US. To measure the effectiveness of the SELF intervention, the researchers asked teachers to complete assessments related to social-emotional learning and adjustment to the school culture. A social-emotional learning related vocabulary assessment was administered directly to target students receiving small-group instruction. Data were analyzed using a 3-level MANOVA model with full information maximum likelihood to estimate coefficients and test hypotheses. Major Findings: SELF had significant positive effects on vocabulary, knowledge, and skills associated with social-emotional competencies, as evidenced by results from the measures administered. Effect sizes ranged from 0.41 for group (SELF vs. BAU) differences in vocabulary development to 0.68 for group differences in SEL related knowledge. Conclusion: Findings from two years of data collection indicate that SELF improved outcomes related to social-emotional learning and adjustment to the school culture. This study thus supports the integration of SEL with literacy instruction as a feasible and effective strategy to improve outcomes for K-1 students at risk for emotional and behavioral difficulties.

Keywords: Socio-cultural context for learning, social-emotional learning, social skills, vocabulary development

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14709 Optimal Design and Simulation of a Grid-Connected Photovoltaic (PV) Power System for an Electrical Department in University of Tripoli, Libya

Authors: Mustafa Al-Refai

Abstract:

This paper presents the optimal design and simulation of a grid-connected Photovoltaic (PV) system to supply electric power to meet the energy demand by Electrical Department in University of Tripoli Libya. Solar radiation is the key factor determining electricity produced by photovoltaic (PV) systems. This paper is designed to develop a novel method to calculate the solar photovoltaic generation capacity on the basis of Mean Global Solar Radiation data available for Tripoli Libya and finally develop a system design of possible plant capacity for the available roof area. MatLab/Simulink Programming tools and monthly average solar radiation data are used for this design and simulation. The specifications of equipments are provided based on the availability of the components in the market. Simulation results and analyses are presented to validate the proposed system configuration.

Keywords: photovoltaic (PV), grid, Simulink, solar energy, power plant, solar irradiation

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14708 Facilitating Written Biology Assessment in Large-Enrollment Courses Using Machine Learning

Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

Abstract:

Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

Procedia PDF Downloads 283
14707 Deep Learning to Improve the 5G NR Uplink Control Channel

Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

Abstract:

The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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14706 Delivering Distance Educational Services in Difficult Areas: Universitas Terbuka’s Case

Authors: Ida Zubaidah

Abstract:

With the advancement of information and communication technologies, in many cases, geographical distance is no longer considered as a main barrier in distance education. Geographical distance, even from a continent to another, between students and their instructor or students and their campus can be connected by the Internet, telephone or any other means of communication technology. Managing distance learning in an archipelagic country like Indonesia, however, has some different stories. Comprising more than 17,000 islands and 6.000 of them inhabited, Indonesia is considered as one of the most archipelagic countries in the world. In some areas or islands that have adequate public transportation and communication facilities the courses can be delivered quite well. In other areas that geographically very remote and dispersed islander, Universitas Terbuka, an open university in Indonesia, has to have very different strategies in overcoming the specific and even emergency situations in learning delivery. This ongoing research paper aims to share experiences of how Universitas Terbuka makes serious and unique efforts in overcoming the barriers and obstacles in providing educational service in part of difficult areas, especially in eastern areas of Indonesia. The data collection methods are observation of sample areas and in-depth interview with the head of regional offices of Universitas Terbuka in eastern Indonesia, staff, and tutors. Conducting educational deliveries in in difficult areas with no regular and adequate transportation has made the regional office have specific strategies in making the learning process run as smooth as possible. Sending a tutor to an area to meet some students and conducting a series of tutorial, which are supposed to be weekly, in several days is one of the strategies. Recruiting local people to manage the students in the area is another strategy. The absence of regular transportation from island to island, high tides, hurricanes, are among the obstacles faced by the regional offices in doing their job. Non geographical barriers such as unavailability of qualified tutor, inadequate tutor payment, are problems as well. The learning process, however, has to be done in any way, otherwise the distance education mission to reach unreachable cannot be achieved.

Keywords: distance education, Terbuka University, difficult area, geographical barrier, learning services

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14705 Robust Barcode Detection with Synthetic-to-Real Data Augmentation

Authors: Xiaoyan Dai, Hsieh Yisan

Abstract:

Barcode processing of captured images is a huge challenge, as different shooting conditions can result in different barcode appearances. This paper proposes a deep learning-based barcode detection using synthetic-to-real data augmentation. We first augment barcodes themselves; we then augment images containing the barcodes to generate a large variety of data that is close to the actual shooting environments. Comparisons with previous works and evaluations with our original data show that this approach achieves state-of-the-art performance in various real images. In addition, the system uses hybrid resolution for barcode “scan” and is applicable to real-time applications.

Keywords: barcode detection, data augmentation, deep learning, image-based processing

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14704 Machine Learning Based Smart Beehive Monitoring System Without Internet

Authors: Esra Ece Var

Abstract:

Beekeeping plays essential role both in terms of agricultural yields and agricultural economy; they produce honey, wax, royal jelly, apitoxin, pollen, and propolis. Nowadays, these natural products become more importantly suitable and preferable for nutrition, food supplement, medicine, and industry. However, to produce organic honey, majority of the apiaries are located in remote or distant rural areas where utilities such as electricity and Internet network are not available. Additionally, due to colony failures, world honey production decreases year by year despite the increase in the number of beehives. The objective of this paper is to develop a smart beehive monitoring system for apiaries including those that do not have access to Internet network. In this context, temperature and humidity inside the beehive, and ambient temperature were measured with RFID sensors. Control center, where all sensor data was sent and stored at, has a GSM module used to warn the beekeeper via SMS when an anomaly is detected. Simultaneously, using the collected data, an unsupervised machine learning algorithm is used for detecting anomalies and calibrating the warning system. The results show that the smart beehive monitoring system can detect fatal anomalies up to 4 weeks prior to colony loss.

Keywords: beekeeping, smart systems, machine learning, anomaly detection, apiculture

Procedia PDF Downloads 246
14703 Moodle-Based E-Learning Course Development for Medical Interpreters

Authors: Naoko Ono, Junko Kato

Abstract:

According to the Ministry of Justice, 9,044,000 foreigners visited Japan in 2010. The number of foreign residents in Japan was over 2,134,000 at the end of 2010. Further, medical tourism has emerged as a new area of business. Against this background, language barriers put the health of foreigners in Japan at risk, because they have difficulty in accessing health care and communicating with medical professionals. Medical interpreting training is urgently needed in response to language problems resulting from the rapid increase in the number of foreign workers in Japan over recent decades. Especially, there is a growing need in medical settings in Japan to speak international languages for communication, with Tokyo selected as the host city of the 2020 Summer Olympics. Due to the limited number of practical activities on medical interpreting, it is difficult for learners to acquire the interpreting skills. In order to eliminate the shortcoming, a web-based English-Japanese medical interpreting training system was developed. We conducted a literature review to identify learning contents, core competencies for medical interpreters by using Pubmed, PsycINFO, Cochrane Library, and Google Scholar. Selected papers were investigated to find core competencies in medical interpreting. Eleven papers were selected through literature review indicating core competencies for medical interpreters. Core competencies in medical interpreting abstracted from the literature review, showed consistency in previous research whilst the content of the programs varied in domestic and international training programs for medical interpreters. Results of the systematic review indicated five core competencies: (a) maintaining accuracy and completeness; (b) medical terminology and understanding the human body; (c) behaving ethically and making ethical decisions; (d) nonverbal communication skills; and (e) cross-cultural communication skills. We developed an e-leaning program for training medical interpreters. A Web-based Medical Interpreter Training Program which cover these competencies was developed. The program included the following : online word list (Quizlet), allowing student to study online and on their smartphones; self-study tool (Quizlet) for help with dictation and spelling; word quiz (Quizlet); test-generating system (Quizlet); Interactive body game (BBC);Online resource for understanding code of ethics in medical interpreting; Webinar about non-verbal communication; and Webinar about incompetent vs. competent cultural care. The design of a virtual environment allows the execution of complementary experimental exercises for learners of medical interpreting and introduction to theoretical background of medical interpreting. Since this system adopts a self-learning style, it might improve the time and lack of teaching material restrictions of the classroom method. In addition, as a teaching aid, virtual medical interpreting is a powerful resource for the understanding how actual medical interpreting can be carried out. The developed e-learning system allows remote access, enabling students to perform experiments at their own place, without being physically in the actual laboratory. The web-based virtual environment empowers students by granting them access to laboratories during their free time. A practical example will be presented in order to show capabilities of the system. The developed web-based training program for medical interpreters could bridge the gap between medical professionals and patients with limited English proficiency.

Keywords: e-learning, language education, moodle, medical interpreting

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14702 Enhancing Precision Agriculture through Object Detection Algorithms: A Study of YOLOv5 and YOLOv8 in Detecting Armillaria spp.

Authors: Christos Chaschatzis, Chrysoula Karaiskou, Pantelis Angelidis, Sotirios K. Goudos, Igor Kotsiuba, Panagiotis Sarigiannidis

Abstract:

Over the past few decades, the rapid growth of the global population has led to the need to increase agricultural production and improve the quality of agricultural goods. There is a growing focus on environmentally eco-friendly solutions, sustainable production, and biologically minimally fertilized products in contemporary society. Precision agriculture has the potential to incorporate a wide range of innovative solutions with the development of machine learning algorithms. YOLOv5 and YOLOv8 are two of the most advanced object detection algorithms capable of accurately recognizing objects in real time. Detecting tree diseases is crucial for improving the food production rate and ensuring sustainability. This research aims to evaluate the efficacy of YOLOv5 and YOLOv8 in detecting the symptoms of Armillaria spp. in sweet cherry trees and determining their health status, with the goal of enhancing the robustness of precision agriculture. Additionally, this study will explore Computer Vision (CV) techniques with machine learning algorithms to improve the detection process’s efficiency.

Keywords: Armillaria spp., machine learning, precision agriculture, smart farming, sweet cherries trees, YOLOv5, YOLOv8

Procedia PDF Downloads 119
14701 Information Tree: Establishment of Lifestyle-Based IT Visual Model

Authors: Chiung-Hui Chen

Abstract:

Traditional service channel is losing its edge due to emerging service technology. To establish interaction with the clients, the service industry is using effective mechanism to give clients direct access to services with emerging technologies. Thus, as service science receives attention, special and unique consumption pattern evolves; henceforth, leading to new market mechanism and influencing attitudes toward life and consumption patterns. The market demand for customized services is thus valued due to the emphasis of personal value, and is gradually changing the demand and supply relationship in the traditional industry. In respect of interior design service, in the process of traditional interior design, a designer converts to a concrete form the concept generated from the ideas and needs dictated by a user (client), by using his/her professional knowledge and drawing tool. The final product is generated through iterations of communication and modification, which is a very time-consuming process. Although this process has been accelerated with the help of computer graphics software today, repeated discussions and confirmations with users are still required to complete the task. In consideration of what is addressed above a space user’s life model is analyzed with visualization technique to create an interaction system modeled after interior design knowledge. The space user document intuitively personal life experience in a model requirement chart, allowing a researcher to analyze interrelation between analysis documents, identify the logic and the substance of data conversion. The repeated data which is documented are then transformed into design information for reuse and sharing. A professional interior designer may sort out the correlation among user’s preference, life pattern and design specification, thus deciding the critical design elements in the process of service design.

Keywords: information design, life model-based, aesthetic computing, communication

Procedia PDF Downloads 302
14700 Compare Online Metacognitive Reading Strategies Used by Iranian Postgraduate Students with Internal and External Locus of Control

Authors: Mitra Mesgar

Abstract:

Online learning environment is becoming more popular among learners because of their multiple information representations. Despite the growing importance of online reading strategies among adult learners, little attention has been carried out to postgraduate EFL learners. This study is quantitative research designed and aimed to investigate metacognitive reading strategies employed by Iranian postgraduate learners to read online academic texts. This study is conducted by over 50 Iranian postgraduate students studying in different Malaysian universities. This study used two different survey questionnaires, namely, 1) background questionnaire and 2) OSORS questionnaire. The collected data were analyzed using SPSS. The findings of the study emphasized metacognitive reading strategies used by different aged adult learners. The results of the survey questionnaires revealed that adult learners use global reading strategies as well as problem-solving strategies and support reading strategies. Also, through one-way analysis of variance toward age factor revealed that it has no meaningful changes on metacognitive reading strategy usage. This means that metacognitive reading strategies used by adult learners are independent of age variable. Drawing from findings, adult learners have learning goals, and since they have more exposure to online academic texts, they are able to use different metacognitive online reading strategies that affect their understanding of academic texts.

Keywords: online reading strategies, metacognitive strategies, online learning, independent students, locus of control

Procedia PDF Downloads 92
14699 The Effect of the Andalus Knowledge Phases and Times Model of Learning on the Development of Students’ Academic Performance and Emotional Quotient

Authors: Sobhy Fathy A. Hashesh

Abstract:

This study aimed at investigating the effect of Andalus Knowledge Phases and Times (ANPT) model of learning and the effect of 'Intel Education Contribution in ANPT' on the development of students’ academic performance and emotional quotient. The society of the study composed of Andalus Private Schools, elementary school students (N=700), while the sample of the study composed of four randomly assigned groups (N=80) with one experimental group and one control group to study "ANPT" effect and the "Intel Contribution in ANPT" effect respectively. The study followed the quantitative and qualitative approaches in collecting and analyzing data to answer the study questions. Results of the study revealed that there were significant statistical differences between students’ academic performances and emotional quotients for the favor of the experimental groups. The study recommended applying this model on different educational variables and on other age groups to generate more data leading to more educational results for the favor of students’ learning outcomes.

Keywords: Al Andalus, emotional quotient, students, academic performance development

Procedia PDF Downloads 239
14698 Event Data Representation Based on Time Stamp for Pedestrian Detection

Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

Abstract:

In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

Procedia PDF Downloads 104
14697 Analysis of Real Time Seismic Signal Dataset Using Machine Learning

Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

Abstract:

Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

Procedia PDF Downloads 133
14696 Transitioning Classroom Students to Working Learners: Lived Experiences of Senior High School Work Immersion Students

Authors: Rico Herrero

Abstract:

The study looked into the different lived experiences of senior high school to work immersion and how they were able to cope up in the transition stage from being classroom students into immersion students in work immersion site. The participants of the study were the ten senior high school students from Punta Integrated School. Using interview guide questions, the researchers motivated the participants to reveal their thoughts, feelings, and experiences in the interviews via video recording. The researchers utilized the qualitative research design, but the approach used was grounded theory. The findings revealed the participants’ lived experiences on how to cope or overcome the transition stage during the work immersion program. They unanimously responded to the interview questions. And based on the themes that emerged from the testimonies of the Senior High School students, the classroom learners benefited a lot from authentic learning opportunity of immersion program. Work immersion provides the students the opportunity to learn and develop their skills/ competencies related to the field of specialization. The hands-on training provides them simulation of work. They realized that theoretical learning in school is not enough to be equipped to work. Immersion program also provides venue for values and standard transformation. Senior High School students felt a high demand of self-confidence at the beginning of their race. Good thing, self-esteem of an individual helps bring out one’s potential at its best. Students find it challenging to get along with people in all ages. But, the endeavour absolutely helps them to grow maturely. Participants also realized that it’s not easy to deal with time pressure. Hence, the immersion program taught them to learn about time management. Part of the best training is to expose the learners to the harsh reality. Despite of the things that the school had taught them, still, students realized that they are not yet ready to deal with the demands of work. Furthermore, they also found out that they need to develop an interpersonal skill to improve their human relationships.

Keywords: grounded theory, lived experiences, senior high school, work immersion

Procedia PDF Downloads 145
14695 Teaching Accounting through Critical Accounting Research: The Origin and Its Relevance to the South African Curriculum

Authors: Rosy Makeresemese Qhosola

Abstract:

South Africa has maintained the effort to uphold its guiding principles in terms of its constitution. The constitution upholds principles such as equity, social justice, peace, freedom and hope, to mention but a few. So, such principles are made to form the basis for any legislation and policies that are in place to guide all fields/departments of government. Education is one of those departments or fields and is expected to abide by such principles as outlined in their policies. Therefore, as expected education policies and legislation outline their intentions to ensure the development of students’ clear critical thinking capacity as well as their creative capacities by creating learning contexts and opportunities that accommodate the effective teaching and learning strategies, that are learner centered and are compatible with the prescripts of a democratic constitution of the country. The paper aims at exploring and analyzing the progress of conventional accounting in terms of its adherence to the effective use of principles of good teaching, as per policy expectations in South Africa. The progress is traced by comparing conventional accounting to Critical Accounting Research (CAR), where the history of accounting as intended in the curriculum of SA and CAR are highlighted. Critical Accounting Research framework is used as a lens and mode of teaching in this paper, since it can create a space for the learning of accounting that is optimal marked by the use of more learner-centred methods of teaching. The Curriculum of South Africa also emphasises the use of more learner-centred methods of teaching that encourage an active and critical approach to learning, rather than rote and uncritical learning of given truths. The study seeks to maintain that conventional accounting is in contrast with principles of good teaching as per South African policy expectations. The paper further maintains that, the possible move beyond it and the adherence to the effective use of good teaching, could be when CAR forms the basis of teaching. Data is generated through Participatory Action Research where the meetings, dialogues and discussions with the focused groups are conducted, which consists of lecturers, students, subject heads, coordinators and NGO’s as well as departmental officials. The results are analysed through Critical Discourse Analysis since it allows for the use of text by participants. The study concludes that any teacher who aspires to achieve in the teaching and learning of accounting should first meet the minimum requirements as stated in the NQF level 4, which forms the basic principles of good teaching and are in line with Critical Accounting Research.

Keywords: critical accounting research, critical discourse analysis, participatory action research, principles of good teaching

Procedia PDF Downloads 314
14694 Adaptable Buildings for More Sustainable Housing: Energy Life Cycle Analysis

Authors: Rafael Santos Fischer, Aloísio Leoni Schmid, Amanda Dalla-Bonna

Abstract:

The life cycle analysis and the energy life cycle analysis are useful design support tools when sustainability becomes imperative. The final phase of buildings life cycle is probably the least known, on which less knowledge is available. In the Brazilian building industry, the lifespan of a building design rarely is treated as a definite design parameter. There is rather a common sense attitude to take any building demands as permanent, and to take for granted that buildings solutions are durable and solid. Housing, being a permanent issue in any society, presents a real challenge to the choice of a design lifespan. In Brazilian history, there was a contrast of the native solutions of collective, non-durable houses built by several nomadic tribes, and the stone and masonry buildings introduced by the sedentary Portuguese conquerors. Durable buildings are commonly associated with welfare. However, social dynamics makes traditional families of both parents and children be just one of several possible arrangements. In addition, a more liberal attitude towards family leads to an increase in the number of people living in alternative arrangements. Japan is an example of country where houses have been made intentionally ephemeral since the half of 20th century. The present article presents the development of a flexible housing design solution on the basis of the Design Science Research approach. A comparison in terms of energy life cycle shows how flexibility and dematerialization may point at a feasible future for housing policies in Brazil.

Keywords: adaptability, adaptable building, embodied energy, life cyclce analysis, social housing

Procedia PDF Downloads 592
14693 Spatio-Temporal Data Mining with Association Rules for Lake Van

Authors: Tolga Aydin, M. Fatih Alaeddinoğlu

Abstract:

People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: apriori algorithm, association rules, data mining, spatio-temporal data

Procedia PDF Downloads 377