Search results for: online teaching and learning

Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

Abstract:

Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

Procedia PDF Downloads 67

Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

Abstract:

Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

Procedia PDF Downloads 167

Authors: Teja Koduru

Abstract:

Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

Procedia PDF Downloads 233

Authors: Shanshan Zhou, Massimo Battaglia

Abstract:

Identity plays a critical role when an organization faces disasters. Individuals reflect on their working identities and identify themselves with the group and the organization, which facilitate collective sensemaking under crisis situations and enable coordinated actions to respond to and recover from disasters. In addition, an organization’s identity links it to its regional community, which fosters the mobilization of resources and contributes to rapid recovery. However, identity is also problematic for disaster prevention because of its persistence. An organization’s ego-defenses system prohibits the rethink of its identity and a rigid identity obstructs disaster prevention. This research aims to tackle the ‘problem’ of identity by study in-depth a case of an Italian multi–utility which experienced the 2012 Northern Italy earthquakes. Collecting data from 11 interviews with top managers and key players in the local community and archived materials, we find that the earthquakes triggered the rethink of the organization’s identity, which got reinforced afterward. This research highlighted the importance of identity in disaster response and recovery. More importantly, it explored the solution of overcoming the barrier of ego-defense that is to transform the organization into a learning organization which constantly rethinks its identity.

Keywords: community identity, disaster, identity, organizational learning

Procedia PDF Downloads 722

Authors: Sarah Beaver, Renee Bryce

Abstract:

Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

Procedia PDF Downloads 77

Authors: Rahman Dashti, Ehsan Gord

Abstract:

In this paper, an improved impedance based fault location method is proposed. In this method, online fault locating is performed using voltage and current information at the beginning of the feeder. Determining precise fault location in a short time increases reliability and efficiency of the system. The proposed method utilizes information about main component of voltage and current at the beginning of the feeder and distributed generation unit (DGU) in order to precisely locate different faults in acceptable time. To evaluate precision and accuracy of the proposed method, a 13-node is simulated and tested using MATLAB.

Keywords: distribution network, fault section determination, distributed generation units, distribution protection equipment

Procedia PDF Downloads 396

Authors: Baladitya Swaika, Rahul Khatry

Abstract:

Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

Procedia PDF Downloads 169

Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

Abstract:

Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

Procedia PDF Downloads 437

Authors: Christopher J. Johnston

Abstract:

This paper presents the finding of a research study in which novice (first and second year) elementary teachers (grades Kindergarten – six) evaluated various mathematics Virtual Manipulatives, websites, and Applets (tools) for use in mathematics instruction. Participants identified the criteria they used for evaluating these types of resources and provided recommendations for or against five pre-selected tools. During the study, participants participated in three data collection activities: (1) A brief Likert-scale survey which gathered information about their attitudes toward technology use; (2) An identification of criteria for evaluating technology tools; and (3) A review of five pre-selected technology tools in light of their self-identified criteria. Data were analyzed qualitatively using four theoretical categories (codes): Software Features (41%), Mathematics (26%), Learning (22%), and Motivation (11%). These four theoretical categories were then grouped into two broad categories: Content and Instruction (Mathematics and Learning), and Surface Features (Software Features and Motivation). These combined, broad categories suggest novice teachers place roughly the same weight on pedagogical features as they do technological features. Implications for mathematics teacher educators are discussed, and suggestions for future research are provided.

Keywords: mathematics education, novice teachers, technology, virtual manipulatives

Procedia PDF Downloads 128

Authors: A. Metioui

Abstract:

Research related to the student’s conceptual difficulties in sciences, particularly in the field of physics, are relatively numerous. In this work, we will analyze the results of qualitative research conducted with 80 elementary preservice teachers from Quebec in Canada on their understandings after studying the free fall bodies. First, we will illustrate the paper-pencil questionnaire built for this purpose. Then we will give the analysis of the experimental data. The results show that, even though there is a continuing physics education, many misconceptions persist despite the teaching provided.

Keywords: pre-service teachers, elementary school, conceptual difficulties, free fall bodies

Procedia PDF Downloads 122

Authors: Noppadol Sritragool

Abstract:

The objectives of this research were to investigate the decision of passengers who choose to fry with low cost airlines and to study marketing factors which have the influence to the decision to choose each low cost airlines. This paper was a quantitative research technique. A total of 400 low cost airlines’ passengers were interviewed via English questionnaire to collect the respondents’ opinions. The findings revealed that respondents were male and female at a similar proportion. The majority had at least an undergraduate degree, have a lower management level jobs, and had income in the range of 25,000 -35,000 baht per month.. In addition, the findings also revealed that the first three marketing factors influencing the decision of the respondents to choose low-cost airlines were low price, direct flight, and online system.

Keywords: decision to choose, marketing factors, low-cost airlines

Procedia PDF Downloads 423

Authors: Vinay P., Srinivas K. S.

Abstract:

Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown.

Keywords: low light image enhancement, real-time video, computer vision, machine learning

Procedia PDF Downloads 196

Authors: Yooncheol Lee, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

This paper deals with the establishment of a short-term operational plan for an air conditioner for efficient energy management of exhibition hall. The short-term operational plan is composed of a time series of operational schedules, which we have searched using genetic algorithms. Establishing operational schedule should be considered the future trends of the variables affecting the exhibition hall environment. To reflect continuously changing factors such as external temperature and occupant, short-term operational plans should be updated in real time. But it takes too much time to evaluate a short-term operational plan using EnergyPlus, a building emulation tool. For that reason, it is difficult to update the operational plan in real time. To evaluate the short-term operational plan, we designed prediction models based on machine learning with fast evaluation speed. This model, which was created by learning the past operational data, is accurate and fast. The collection of operational data and the verification of operational plans were made using EnergyPlus. Experimental results show that the proposed method can save energy compared to the reactive control method.

Keywords: exhibition hall, energy management, predictive model, simulation-based optimization

Procedia PDF Downloads 333

Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

Abstract:

Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

Procedia PDF Downloads 19

Authors: Yassine Jadi, Lin Jie

Abstract:

In order to serve the society in an electronic manner, many developing countries have launched tremendous e-government projects. The strategies of development and implementation e-government system have reached different levels, and to ensure consistency of development, the governments need to evaluate e-government performance. The United nation has design e-government development ranking index (EGDI) that rely on three indexes, Online service index (OSI), Telecommunication Infrastructure index (TII), and human capital index( HCI) which are not reflecting the interaction between a government and their citizens. Based on data envelopment analyses (DEA) technique, we are using E-participating index (EPI) as an output of government effort to evaluate the performance of e-government system. Therefore, the ranking index can be achieved in efficiency manner.

Keywords: e-government, DEA, efficiency measurement, EGDI

Procedia PDF Downloads 370

Authors: Tarik Elaujali

Abstract:

This study examines a new media effective theory was developed by the author, it is called ‘The Effervescent Disk Theory’ (EDT). The theory main goal is to affect the unaffected audience who are either not exposing to a particular message or do not show interest in it. EDT suggest melting down messages that means to be affected within the media materials which are selected willingly by the audience themselves. A certain set of procedures to test EDT hypotheses were taken and illustrated in this study. A sample of 342 respondents (males & females) was collected from Tripoli University in Libya during the academic year 2013-2014. The designated sample is representing students who were failing to pass the English module for beginners’. This study aims to change the students’ negative notion about the importance of learning English, and to put their new idea into action. The theory seeks to affect audience cognition, emotions, and behaviors. EDT was applied in the present study alongside the media dependency theory. EDT hypotheses were confirmed, study results denoted that 73.6 percentage of the students responded positively and passed their English exam for beginners after being exposed selectively to their favorite TV program that contains a dissolved messages about the importance and vitality of learning English language.

Keywords: effervescent disk theory, selective exposure, media dependency, Libyan students

Procedia PDF Downloads 243

Authors: Merih Welay Welesilassie, Marianne Nikolov

Abstract:

Research into second and foreign language (L2) acquisitions has demonstrated that L2 anxiety, perceived proficiency, and L2 willingness to communicate (L2WTC) profoundly impact language learning outcomes. However, the complex interplay between these variables has yet to be fully explored, as these factors are dynamic and context-specific and can vary across different learners and learning environments. This study, therefore, utilized a cross-sectional quantitative survey research design to scrutinise the causal relationships between L2 anxiety, English proficiency, and L2WTC of 609 Ethiopian preparatory school students. The model for the L2WTC, both inside and outside the classroom, has been expanded to include an additional sub-scale known as the L2WTC in a digital setting. Moreover, in contrast to the commonly recognised debilitative-focused L2 anxiety, the construct of L2 anxiety has been divided into facilitative and debilitative anxiety. This method allows to measure not only the presence or absence of anxiety but also evaluate if anxiety helps or hinders the L2 learning experience. A self-assessment proficiency measure was also developed specifically for Ethiopian high school students. The study treated facilitative and debilitative anxiety as independent variables while considering self-assessed English proficiency and L2WTC in the classroom, outside the classroom, and in digital settings as dependent variables. Additionally, self-assessed English proficiency was used as an independent variable to predict L2WTC in these three settings. The proposed model, including these variables, was tested using structural equation modelling (SEM). According to the descriptive analysis, the mean scores of L2WTC in the three settings were generally low, ranging from 2.30 to 2.84. Debilitative anxiety casts a shadow on the positive aspects of anxiety. Self-assessed English proficiency was also too low. According to SEM, debilitative anxiety displayed a statistically significant negative impact on L2WTC inside the classroom, outside the classroom, in digital settings, and in self-assessed levels of English proficiency. In contrast, facilitative anxiety was found to positively contribute to L2WTC outside the classroom, in digital settings, and in self-assessed English proficiency. Self-assessed English proficiency made a statistically significant and positive contribution to L2WTC within the classroom, outside the classroom, and in digital contexts. L2WTC inside the classroom was found to positively contribute to L2WTC outside the classrooms and in digital contexts. The findings were systematically compared with existing studies, and the pedagogical implications, limitations, and potential avenues for future research were elucidated. The outcomes of the study have the potential to significantly contribute to the advancement of theoretical and empirical knowledge about improving English education, learning, and communication not only in Ethiopia but also in similar EFL contexts, thereby providing valuable insights for educators, researchers, and policymakers.

Keywords: debilitative anxiety, facilitative anxiety, L2 willingness to communicate, self-assessed English proficiency

Procedia PDF Downloads 8

Authors: Yiwei Li, Mingyu Gao

Abstract:

Integrating hybrid memories with heterogeneous processors could leverage heterogeneity in both compute and memory domains for better system efficiency. To ensure performance isolation, we introduce Hydrogen, a hardware architecture to optimize the allocation of hybrid memory resources to heterogeneous CPU-GPU systems. Hydrogen supports efficient capacity and bandwidth partitioning between CPUs and GPUs in both memory tiers. We propose decoupled memory channel mapping and token-based data migration throttling to enable flexible partitioning. We also support epoch-based online search for optimized configurations and lightweight reconfiguration with reduced data movements. Hydrogen significantly outperforms existing designs by 1.21x on average and up to 1.31x.

Keywords: hybrid memory, heterogeneous systems, dram cache, graphics processing units

Procedia PDF Downloads 75

Authors: Spanova Yerkezhan, Amantay Ayan, Alimzhanova Laura

Abstract:

This article discusses the concept of rebranding and its relationship to cybersecurity. Rebranding is the process of changing the appearance and image of a company or organization in order to appeal to new customers or change the perception of a company. It can be a powerful tool for businesses looking to renew their reputation or expand into new markets. In today's digital age, companies increasingly rely on technology and the internet to conduct business; rebranding can also present significant cybersecurity risks. This is because a rebranding effort can create new vulnerabilities for companies, particularly in terms of their online presence. This article explores the potential hazards associated with rebranding and provides recommendations for mitigating those risks. It also highlights the importance of considering cybersecurity in the rebranding process and how it can be integrated into the overall strategy for a successful and secure rebranding.

Keywords: rebranding, cybersecurity, cyberattack, logo, vulnerability

Procedia PDF Downloads 161

Authors: Pijitra Jomsri

Abstract:

Currently, the sources of cultures and tourist attractions are presented in online documentary form only. In order to make them more virtual, the researcher then collected and presented them in the form of Virtual Temple. The prototype, which is a replica of the actual location, was developed to the website and allows people who are interested in Rattanakosin Island can see in form of Panorama Pan View. By this way, anyone can access the data and appreciate the beauty of Rattanakosin Island in the virtual model like the real place. The result from the experiment showed that the levels of the knowledge on Thai temples in Rattanakosin Island increased; moreover, the users were highly satisfied with the systems. It can be concluded that virtual temples can support to publicize Thai arts, cultures and travels, as well as it can be utilized effectively.

Keywords: virtual sight-seeing, Rattanakosin Island, Thai temples, virtual temple

Procedia PDF Downloads 330

Authors: Mehran Memari, Bita Asadi

Abstract:

Second language (L2) classrooms in academic contexts usually consist of learners with diverse L2 proficiency levels. One solution for managing such heterogeneous classes and addressing individual needs of students is to improve learner autonomy by using technological innovations such as blogging. The focus of this study is on investigating the effects of blogging on improving the quality of Iranian university students' writings. For this aim, twenty-six Iranian university students participated in the study. Students in the experimental group (n=13) were required to blog daily while the students in the control group (n=13) were asked to write a daily schedule using paper and pencil. After a 3-month period of instruction, the five last writings of the students from both groups were rated by two experienced raters. Also, students' attitudes toward the traditional method and blogging were surveyed using a questionnaire and a semi-structured interview. The research results showed evidences in favor of the students who used blogging in their writing program. Also, although students in the experimental group found blogging more demanding than the traditional method, they showed an overall positive attitude toward the use of blogging as a way of improving their writing skills. The findings of the study have implications for the incorporation of computer-assisted learning in L2 academic contexts.

Keywords: blogging, computer-assisted learning, paper and pencil, writing

Procedia PDF Downloads 397

Authors: Chia Yu Chen, Shu Fen Wu, Chen-Fuh Lam, I-Ling Tsai, Shu Jiuan Chen, Yen Ling Liu

Abstract:

Aim: It remains undetermined that whether increased work stress may affect the job satisfaction and career loyalty among nursing staffs in the operating room. The long-term goal of this study is to lengthen the professional life of operating room nurses by attenuating the work stress and enhancing their contentment in work. Method: This was a cross-sectional, descriptive study performed in a metropolitan teaching hospital in the southern Taiwan between May 2017 to July 2017. A structured self-administered questionnaire, modified from the Occupational Stress Indicator-2 (OSI-2) and Maslach Burnout Inventory (MBI) manual was collected from the operating room nurses. Chi-square test was used to analyze the categorical data and Pearson correlation was used to analyze the association between two numerical datasets (SPSS version 20.0). Results: The response rate was 80% (80/100) and a total of 73 (73%) completed forms were eventually proceeded for analysis. The average scores for work stress and job satisfaction of the operating room nurses were 145.96±32.91 and 47.38±6.07, respectively. The correlation coefficients of work stress versus job satisfaction and organizational identity were (r=-0.338, p=0.003 and r=-0.354, p=0.002), respectively. There were more nurses who took rotating shift quitted works from the operating room than those who took only dayshift (2＝5.176, p<0.05). Nurses who reported of having lower job satisfaction were associated with significantly higher turnover intention (t=3.714, p< 0.01). Following multivariate regression analysis, rotating shift and low job satisfaction were identified as the two independent predictors of intention to quit from working in the operating room. Conclusion: Our study clearly demonstrates that increased work stress significantly attenuates job satisfaction and organizational identity. Rotating shift is associated with higher work stress, lower job satisfaction, and higher turnover intention, which is consistent with the previous surveys carried out in the department of medical technology. Therefore, improvement of working quality in the operating rooms is essential to increase the retain intention of the well-trained nursing staffs. Further investigation into types of work shifts and other strategies of attenuating stress in workplace is currently undertaken in order to improve the job satisfaction and to decrease turnover intention in the operating room.

Keywords: rotating shift, work stress, job satisfaction, turnover intention

Procedia PDF Downloads 193

Authors: Kafeero Frank

Abstract:

This study aimed at developing a frame work for open source software adoption in an institution of higher learning in Uganda, with the case of KIU as a study area. There were mainly four research questions based on; individual staff interaction with open source software forum, perceived FOSS characteristics, organizational characteristics and external characteristics as factors that affect open source software adoption. The researcher used causal-correlation research design to study effects of these variables on open source software adoption. A quantitative approach was used in this study with self-administered questionnaire on a purposively and randomly sampled sample of university ICT staff. Resultant data was analyzed using means, correlation coefficients and multivariate multiple regression analysis as statistical tools. The study reveals that individual staff interaction with open source software forum and perceived FOSS characteristics were the primary factors that significantly affect FOSS adoption while organizational and external factors were secondary with no significant effect but significant correlation to open source software adoption. It was concluded that for effective open source software adoption to occur there must be more effort on primary factors with subsequent reinforcement of secondary factors to fulfill the primary factors and adoption of open source software. Lastly recommendations were made in line with conclusions for coming up with Kyambogo University frame work for open source software adoption in institutions of higher learning. Areas of further research recommended include; Stakeholders’ analysis of open source software adoption in Uganda; Challenges and way forward. Evaluation of Kyambogo University frame work for open source software adoption in institutions of higher learning. Framework development for cloud computing adoption in Ugandan universities. Framework for FOSS development in Uganda IT industry

Keywords: open source software., organisational characteristics, external characteristics, cloud computing adoption

Procedia PDF Downloads 68

Authors: Laura de Zwaan, Tracey West

Abstract:

There is a sustained observable gender gap in financial literacy, with females consistently having lower levels than males. This research explores the knowledge and experiences of high school students in Australia aged 14 to 18 in order to understand how this gap can be improved. Using a predominantly qualitative approach, we find evidence to support impacts on financial literacy from financial socialization and socio-economic environment. We also find evidence that current teaching and assessment approaches to financial literacy may disadvantage female students. We conclude by offering recommendations to improve the way financial literacy education is delivered within the curriculum.

Keywords: financial literacy, financial socialization, gender, maths

Procedia PDF Downloads 72

Authors: Taissir Fekih Romdhane

Abstract:

This paper proposes a mobile web application named Pharmacy-Station that sells medicines and permits user to search for medications based on their symptoms, making it is easy to locate a specific drug online without the need to visit a pharmacy where it may be out of stock. This application is developed using the jQuery Mobile framework, which uses many web technologies and languages such as HTML5, PHP, JavaScript and CSS3. To test the proposed application, we used data from popular pharmacies in Saudi Arabia that included important information such as location, contact, and medicines in stock, etc. This document describes the different steps followed to create the Pharmacy-Station application along with screenshots. Finally, based on the results, the paper concludes with recommendations and further works planned to improve the Pharmacy-Station mobile application.

Keywords: pharmacy, mobile application, jquery mobile framework, search, medicine

Procedia PDF Downloads 151

Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

Abstract:

Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

Procedia PDF Downloads 87

Authors: Sujin Hwang, Hyerang Park, Hyunchul Kim

Abstract:

The purpose of this study is to find the effectiveness of the admission simplification policy. The number of online news articles about ‘high school record’ was collected and semantically analyzed to identify and analyze the social awareness during 2014 to 2015. The main results of the study are as follows: First, there was a difference in expectations that the burden of the examinees would decrease as announced by KCUE. Thus, there was still a strain on the university entrance exam after the enforcement of the policy. Second, private tutoring is expanding in different forms, rather than reducing the policy. It is different from the prediction that examinees can prepare for university admissions without the private tutoring. Thus, the college admission rules currently enforced needs to be improved. The reasonable college admission system changes are discussed.

Keywords: education policy, private tutoring, shadow education, education admission policy

Procedia PDF Downloads 221

Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

Abstract:

The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

Procedia PDF Downloads 86

Authors: Boonsri Lertviriyachit

Abstract:

The objective of this research is to investigate the treatment of psoriasis through Thai traditional medicine in the selected areas of 2 east coast provinces; Samudprakarn Province and Chantaburi Province. The informants in this study were two famous and accepted Thai traditional doctors, who have more than 20 year experiences. Data were collected by in depth interviews and participant-observation method. The research instrument included unstructured interviews, camera, and cassette tape to collect data analyzed by descriptive statistics. The results revealed that the 2 Thai traditional doctors were 54 and 85 years old with 25 and 45 years of treatment experiences. The knowledge of Thai traditional medicine was transferred from generations to generations in the family. The learning process was through close observation as an apprentice with the experience ones and assisted them in collecting herbs and learning by handling real case in individual situations. Before being doctors, they had to take exam to get the Thai traditional medical certificate. Knowledge of being Thai traditional doctors included diagnosis and find to the suitable way of treatment. They have to look into disorder physical fundamental factors such as blood circulation, lymph, emotion, and food consumption habit. It is important that the treatment needs to focus on balancing the fundamental factors and to observe contraindication.

Keywords: Thai traditional medicine, psoriasis, Samudprakarn Province, Chantaburi Province

Procedia PDF Downloads 361

Authors: Ya Ting Tang

Abstract:

Social learning theory has told us that the cognitions of gender roles come from learning. Thus, the images of gender roles which media describes will shape our cognitions. Taiwan and Japan are both in the East Asian cultural Sphere, and more or less influenced by the traditional Chinese culture. But our social structure and changes must be different. Others, the study also concerns that, with the rise of female consciousness in society, whether the female stereotype in drama of Taiwan and Japan are improved. This research first uses content analysis to analyze drama of Taiwan and Japan in 2003 and 2013 on how to shape female roles. Then use text analysis to conduct a qualitative analysis. Result of this study showed that drama on how to depict women indeed have changed, women are no longer just talk about love, but can serve as president or doctor, and show its mettle in the workplace. In Japanese drama, the female roles have diverse occupation than Taiwanese drama, and not just a background character set. But in most Taiwanese drama, female roles are given a career, but it always put emphasis on women emotionally. To include, although the stereotype in the drama of Taiwan and Japan are improved, female will still be derided due to their ages, love or marriage situations. Taiwanese drama must depict the occupation of female more diverse and let the female roles have more space to play, rather than focusing on romance which women of any occupation can have.

Keywords: female images, stereotype, TV drama, gender roles

Procedia PDF Downloads 271