Search results for: learning delivery modes

Authors: Penn P. Wu, Jeannie Jo

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Blockchain technology has rapidly gained popularity in industry. This paper attempts to assist academia to answer four questions. First, should community colleges begin offering education to nurture blockchain-literate students for the job market? Second, what are the appropriate topical areas to cover? Third, should it be an individual course? And forth, should it be a technical or management course? This paper starts with identifying the knowledge domains of blockchain technology and the topical areas each domain has, and continues with placing them in appropriate academic territories (Computer Sciences vs. Business) and subjects (programming, management, marketing, and laws), and then develops an evaluation model to determine the appropriate topical area for community colleges to teach. The evaluation is based on seven factors: maturity of technology, impacts on management, real-world applications, subject classification, knowledge prerequisites, textbook readiness, and recommended pedagogies. The evaluation results point to an interesting direction that offering an introductory course is an ideal option to guide students through the learning journey of what blockchain is and how it applies to business. Such an introductory course does not need to engage students in the discussions of mathematics and sciences that make blockchain technologies possible. While it is inevitable to brief technical topics to help students build a solid knowledge foundation of blockchain technologies, community colleges should avoid offering students a course centered on the discussion of developing blockchain applications.

Keywords: blockchain, pedagogies, blockchain technologies, blockchain course, blockchain pedagogies

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: George Onomah

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Background: The integration of information communication technology (ICT) in Mathematics education faces notable challenges, which this study aimed to dissect and understand. Objectives: The primary goal was to assess the internal and external factors affecting the adoption of ICT by in-service Mathematics teachers. Internal factors examined included teachers' pedagogical beliefs, prior teaching experience, attitudes towards computers, and proficiency with technology. External factors included the availability of technological resources, the level of ICT training received, the sufficiency of allocated time for technology use, and the institutional culture within educational environments. Methods: A descriptive survey design was employed to methodically investigate these factors. Data collection was carried out using a five-point Likert scale questionnaire, administered to a carefully selected sample of 100 in-service Mathematics teachers through a combination of purposive and convenience sampling techniques. Findings: Results from multiple regression analysis revealed a significant underutilization of ICT in Mathematics teaching, highlighting a pronounced deficiency in current classroom practices. Recommendations: The findings suggest an urgent need for educational department heads to implement regular and comprehensive ICT training programs aimed at enhancing teachers' technological capabilities and promoting the integration of ICT in Mathematics teaching methodologies.

Keywords: ICT, Mathematics, integration, barriers

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Authors: Julie Chuah Suan Choo

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Due to the continuous change in workforce and the demands of the global workplace, many employers had lamented that the majority of university graduates were not prepared in the key areas of employment such as critical thinking, writing, self-direction and global knowledge which are most needed for the purposes of promotion. Further, critical thinking skills are deemed as integral parts of transformational pedagogy which aims at having a more informed society. To add to this, literature teaching has recently been advocated for enhancing students’ critical thinking and reasoning. Thus this study explored the effects of incorporating a few strategies in teaching literature, namely a Shakespeare play, into a course design to enhance these skills. An experiment involving a pretest and posttest using the California Critical Thinking Skills Test (CCTST) were administered on 80 first-year students enrolled in the Bachelor of Arts programme who were randomly assigned into the control group and experimental group. For the next 12 weeks, the experimental group was given intervention which included guided in-class discussion with Socratic questioning skills, learning log to detect their weaknesses in logical reasoning; presentations and quizzes. The results of CCTST which included paired T-test using SPSS version 22 indicated significant differences between the two groups. Findings have significant implications on the course design as well as pedagogical practice in using literature to enhance students’ critical thinking skills.

Keywords: literature teaching, critical thinking, California critical thinking skills test (CCTST), course design

Procedia PDF Downloads 458

Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Shahla Ali

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In the context of promoting green infrastructure development, new opportunities are emerging to re-examine sustainable development practices. This paper presents an initial exploration of the development of community-investor dispute prevention and facilitation mechanisms in the context of the Belt and Road Initiative (BRI) spanning Asia, Africa, and Europe. Given the widescale impact of China’s multi-jurisdictional development initiative, learning how to coordinate with local communities is vital to realizing inclusive and sustainable growth. In the 20 years since the development of the first multilateral community-investor dispute resolution mechanism developed by the International Finance Centre/World Bank, much has been learned about public facilitation, community engagement, and dispute prevention during the early stages of major infrastructure development programs. This paper will explore initial findings as they relate to initiatives underway along the BRI within the Asian Infrastructure Investment Bank and the Asian Development Bank. Given the borderless nature of sustainability concerns, insights from diverse regions are critical to deepening insights into best practices. Drawing on a case-based methodology, this paper will explore the achievements, challenges, and lessons learned in community-investor dispute prevention and resolution for major infrastructure projects in the greater China region.

Keywords: law and development, dispute prevention, sustainable development, mitigation

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

Procedia PDF Downloads 48

Authors: M. M. Hassani, S. Ammann, F. K. Wittel, P. Niemz, H. J. Herrmann

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Application of engineered wood, especially in the form of glued-laminated timbers has increased significantly. Recent progress in plywood made of high strength and high stiffness hardwoods, like European beech, gives designers in general more freedom by increased dimensional stability and load-bearing capacity. However, the strong hygric dependence of basically all mechanical properties renders many innovative ideas futile. The tendency of hardwood for higher moisture sorption and swelling coefficients lead to significant residual stresses in glued-laminated configurations, cross-laminated patterns in particular. These stress fields cause initiation and evolution of cracks in the bond-lines resulting in: interfacial de-bonding, loss of structural integrity, and reduction of load-carrying capacity. Subsequently, delamination of glued-laminated timbers made of hardwood elements can be considered as the dominant failure mechanism in such composite elements. In addition, long-term creep and mechano-sorption under changing environmental conditions lead to loss of stiffness and can amplify delamination growth over the lifetime of a structure even after decades. In this study we investigate the delamination process of adhesively bonded hardwood (European beech) elements subjected to changing climatic conditions. To gain further insight into the long-term performance of adhesively bonded elements during the design phase of new products, the development and verification of an authentic moisture-dependent constitutive model for various species is of great significance. Since up to now, a comprehensive moisture-dependent rheological model comprising all possibly emerging deformation mechanisms was missing, a 3D orthotropic elasto-plastic, visco-elastic, mechano-sorptive material model for wood, with all material constants being defined as a function of moisture content, was developed. Apart from the solid wood adherends, adhesive layer also plays a crucial role in the generation and distribution of the interfacial stresses. Adhesive substance can be treated as a continuum layer constructed from finite elements, represented as a homogeneous and isotropic material. To obtain a realistic assessment on the mechanical performance of the adhesive layer and a detailed look at the interfacial stress distributions, a generic constitutive model including all potentially activated deformation modes, namely elastic, plastic, and visco-elastic creep was developed. We focused our studies on the three most common adhesive systems for structural timber engineering: one-component polyurethane adhesive (PUR), melamine-urea-formaldehyde (MUF), and phenol-resorcinol-formaldehyde (PRF). The corresponding numerical integration approaches, with additive decomposition of the total strain are implemented within the ABAQUS FEM environment by means of user subroutine UMAT. To predict the true stress state, we perform a history dependent sequential moisture-stress analysis using the developed material models for both wood substrate and adhesive layer. Prediction of the delamination process is founded on the fracture mechanical properties of the adhesive bond-line, measured under different levels of moisture content and application of the cohesive interface elements. Finally, we compare the numerical predictions with the experimental observations of de-bonding in glued-laminated samples under changing environmental conditions.

Keywords: engineered wood, adhesive, material model, FEM analysis, fracture mechanics, delamination

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Akiko Ohta, Masahiro Minami, Yusra Qadir, Jennifer York

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The Home Instruction for Parents of Preschool Youngsters (HIPPY) is a home instruction program for mothers of children 3 to 5 years old. Using role-play as a method of teaching, the participating mothers work with their home visitors and learn how to deliver the HIPPY curriculum to their children. Applying HIPPY, Reviving Hope and Home for High-risk Refugee Mothers Program (RHH) was created to provide more personalized peer support and to respond to ongoing settlement challenges for isolated and vulnerable Government Assisted Refugee (GAR) mothers. GARs often have greater needs and vulnerabilities than other refugee groups. While the support is available, they often face various challenges and barriers in starting their new lives in Canada, such as inadequate housing, low first-language literacy levels, low competency in English or French, and social isolation. The pilot project was operated by Mothers Matter Centre (MMC) from January 2019 to March 2021 in partnership with the Immigrant Services Society of BC (ISSofBC). The formative evaluation was conducted by a research team at Simon Fraser University. In order to provide more suitable support for GAR mothers, RHH intended to offer more flexibility in HIPPY delivery, supported by a home visitor, to meet the need of refugee mothers facing various conditions and challenges; to have a pool of financial resources to be used for the RHH families when necessitated during the program period; to have another designated staff member, called a community navigator, assigned to facilitate the support system for the RHH families in their settlement; to have a portable device available for each RHH mother to navigate settlement support resources; and to provide other variations of the HIPPY curriculum as an option for the RHH mothers, including a curriculum targeting pre-HIPPY age children. Reflections on each program component was collected from RHH mothers and staff members of MMC and ISSofBC, including frontline workers and management staff, through individual interviews and focus group discussions. Each of the RHH program components was analyzed and evaluated by applying Moore’s four domains framework to identify key information and generate new knowledge (data). To capture RHH mothers’ program experience more in depth based on their own reflections, the photovoice method was used. Some photos taken by the mothers will be shared to illustrate their RHH experience as part of their life stories. Over the period of the program, this evaluation observed how RHH mothers became more confident in various domains, such as communicating with others, taking public transportations alone, and teaching their own child(ren). One of the major factors behind the success was their home visitors’ flexibility and creativity to create a more meaningful and tailored approach for each mother, depending on her background and personal situation. The role of the community navigator was tested out and improved during the program period. The community navigators took the key role to assess the needs of the RHH families and connect them with community resources. Both the home visitors and community navigators were immigrant mothers themselves and owing to their dedicated care for the RHH mothers; they were able to gain trust and work closely and efficiently with RHH mothers.

Keywords: refugee mothers, settlement support, program evaluation, Canada

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

Procedia PDF Downloads 185

Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Mona Isazad Mashinchi, Davood Roshan Sangachin, Francis J. Sullivan, Dietrich Rebholz-Schuhmann

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Background: Decision-making processes are nowadays driven by data, data analytics and Business Intelligence (BI). BI as a software platform can provide a wide variety of capabilities such as organization memory, information integration, insight creation and presentation capabilities. Visualizing data through dashboards is one of the BI solutions (for a variety of areas) which helps managers in the decision making processes to expose the most informative information at a glance. In the healthcare domain to date, dashboard presentations are more frequently used to track performance related metrics and less frequently used to monitor those quality parameters which relate directly to patient outcomes. Providing effective and timely care for patients and improving the health outcome are highly dependent on presenting and visualizing data and information. Objective: In this research, the focus is on the presentation capabilities of BI to design a dashboard for prostate cancer (PC) data that allows better decision making for the patients, the hospital and the healthcare system related to a cancer dataset. The aim of this research is to customize a retrospective PC dataset in a dashboard interface to give a better understanding of data in the categories (risk factors, treatment approaches, disease control and side effects) which matter most to patients as well as other stakeholders. By presenting the outcome in the dashboard we address one of the major targets of a value-based health care (VBHC) delivery model which is measuring the value and presenting the outcome to different actors in HC industry (such as patients and doctors) for a better decision making. Method: For visualizing the stored data to users, three interactive dashboards based on the PC dataset have been developed (using the Tableau Software) to provide better views to the risk factors, treatment approaches, and side effects. Results: Many benefits derived from interactive graphs and tables in dashboards which helped to easily visualize and see the patients at risk, better understanding the relationship between patient's status after treatment and their initial status before treatment, or to choose better decision about treatments with fewer side effects regarding patient status and etc. Conclusions: Building a well-designed and informative dashboard is related to three important factors including; the users, goals and the data types. Dashboard's hierarchies, drilling, and graphical features can guide doctors to better navigate through information. The features of the interactive PC dashboard not only let doctors ask specific questions and filter the results based on the key performance indicators (KPI) such as: Gleason Grade, Patient's Age and Status, but may also help patients to better understand different treatment outcomes, such as side effects during the time, and have an active role in their treatment decisions. Currently, we are extending the results to the real-time interactive dashboard that users (either patients and doctors) can easily explore the data by choosing preferred attribute and data to make better near real-time decisions.

Keywords: business intelligence, dashboard, decision making, healthcare, prostate cancer, value-based healthcare

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Huicong Jia, Donghua Pan

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As a key component of disaster reduction management, disaster emergency relief and reconstruction is an important process. Based on disaster system theory, this study analyzed the Jiangsu tornado from the formation mechanism of disasters, through to the economic losses, loss of life, and social infrastructure losses along the tornado disaster chain. The study then assessed the emergency relief and reconstruction efforts, based on an analytic hierarchy process method. The results were as follows: (1) An unstable weather system was the root cause of the tornado. The potentially hazardous local environment, acting in concert with the terrain and the river network, was able to gather energy from the unstable atmosphere. The wind belt passed through a densely populated district, with vulnerable infrastructure and other hazard-prone elements, which led to an accumulative disaster situation and the triggering of a catastrophe. (2) The tornado was accompanied by a hailstorm, which is an important triggering factor for a tornado catastrophe chain reaction. (3) The evaluation index (EI) of the emergency relief and reconstruction effect for the ‘‘6.23’’ tornado disaster in Yancheng was 91.5. Compared to other relief work in areas affected by disasters of the same magnitude, there was a more successful response than has previously been experienced. The results provide new insights for studies of disaster systems and the recovery measures in response to tornado catastrophe in China.

Keywords: China, disaster system, emergency relief, tornado catastrophe

Procedia PDF Downloads 265

Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

Procedia PDF Downloads 130

Authors: Ruwan Gunawardane

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This paper attempts to emphasize the benefits of conducting an intensive course in English for university entrants. In the Sri Lankan university context, an intensive course in English is usually conducted amidst various obstacles. In the 1970s and 1980s, undergraduates had intensive programmes in English for two to three months. Towards the end of the 1990s, a programme called General English Language Training (GELT) was conducted for the new students, and it was done outside universities before they entered their respective universities. Later it was not conducted, and that also resulted in students’ poor performance in English at university. However, having understood its importance, an eight week long intensive course in English was conducted for the new intake of the Faculty of Science, University of Ruhuna. As the findings show, the students heavily benefited from the programme. More importantly, they had the opportunity to refresh their knowledge of English gained at school and private institutions while gaining new knowledge. Another advantage was that they had plenty of time to enjoy learning English since the learners had adequate opportunities to carry out communicative tasks and the course was not exam-oriented, which reduced their fear of making mistakes in English considerably. The data was collected through an open-ended questionnaire given to 60 students, and their oral feedback was also taken into consideration. In addition, a focus group interview with 6 teachers was also conducted to get an idea about their experience and perception. The data were qualitatively analyzed. The findings suggest that an intensive programme in English undoubtedly lays a good foundation for the students’ academic career at university.

Keywords: intensive course, English, teachers, undergraduates, experience, perception

Procedia PDF Downloads 131

Authors: Heitor Oliveira, Gabriele De-Waal, Juergen Schmenger, Lynsey Godfrey, Tibor Kovacs

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Rheolaser MASTER™ makes use of multiple scattering of light, caused by scattering objects in a continuous medium (such as droplets and particles in colloids), to characterize the viscoelasticity of soft materials. It offers an alternative to conventional rheometers to characterize viscoelasticity of products such as hair cosmetics. Up to six simultaneous measurements at controlled temperature can be carried out simultaneously (10-15 min), and the method requires only minor sample preparation work. Conversely to conventional rheometer based methods, no mechanical stress is applied to the material during the measurements. Therefore, the properties of the exact same sample can be monitored over time, like in aging and stability studies. We determined the elastic index (EI) of water/emulsion mixtures (1 ≤ fat alcohols (FA) ≤ 5 wt%) and emulsion/gel-network mixtures (8 ≤ FA ≤ 17 wt%) and compared with the elastic/sorage mudulus (G’) for the respective samples using a TA conventional rheometer with flat plates geometry. As expected, it was found that log(EI) vs log(G’) presents a linear behavior. Moreover, log(EI) increased in a linear fashion with solids level in the entire range of compositions (1 ≤ FA ≤ 17 wt%), while rheometer measurements were limited to samples down to 4 wt% solids level. Alternatively, a concentric cilinder geometry would be required for more diluted samples (FA > 4 wt%) and rheometer results from different sample holder geometries are not comparable. The plot of the rheolaser output parameters solid-liquid balance (SLB) vs EI were suitable to monitor product aging processes. These data could quantitatively describe some observations such as formation of lumps over aging time. Moreover, this method allowed to identify that the different specifications of a key raw material (RM < 0.4 wt%) in the respective gel-network (GN) product has minor impact on product viscoelastic properties and it is not consumer perceivable after a short aging time. Broadening of a RM spec range typically has a positive impact on cost savings. Last but not least, the photon path length (λ*)—proportional to droplet size and inversely proportional to volume fraction of scattering objects, accordingly to the Mie theory—and the EI were suitable to characterize product destabilization processes (e.g., coalescence and creaming) and to predict product stability about eight times faster than our standard methods. Using these parameters we could successfully identify formulation and process parameters that resulted in unstable products. In conclusion, Rheolaser allows quick and reliable characterization of viscoelastic properties of hair cosmetics that are related to their performance and stability. It operates in a broad range of product compositions and has applications spanning from the formulation of our hair cosmetics to fast release criteria in our production sites. Last but not least, this powerful tool has positive impact on R&D development time—faster delivery of new products to the market—and consequently on cost savings.

Keywords: colloids, hair cosmetics, light scattering, performance and stability, soft materials, viscoelastic properties

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Authors: Mahfoodh Saleh Al Sabbagh, Venkoba Rao

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There is plenty of research, both in academic and consultancy circles, about the importance and benefits of employee engagement and customer engagement and how it gives organization an opportunity to reduce variability and improve performance. Customer engagement is directly related to the engagement level of the organization's employees. It is therefore important to measure both. This research drawing from the work of Human Sigma by Fleming and Asplund, attempts to assess engagement level of customer and employees - the human systems of business - in an educational setup. Student is important to an educational institute and is a customer to be served efficiently and effectively. Considering student as customer and faculty as employees serving them, in–depth interviews were conducted to analyze the relationship between faculty and student engagement in two leading colleges in Oman, one from private sector and another from public sector. The study relied mainly on secondary data sources to understand the concept of engagement. However, the search of secondary sources was extensive to compensate the limited primary data. The results indicate that high faculty engagement is likely to lead to high student engagement. Engaged students were excited about learning, loved the feeling of they being cared as a person by their faculty and advocated the organization to other. The interaction truly represents an opportunity to build emotional connection to the organization. This study could be of interest to organizations interest in building and maintaining engagement with employees and customers.

Keywords: customer engagement, consumer psychology, strategy, educational institutes

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Authors: Nino Sulava, Brian Gilmour, Nana Rezesidze, Tamar Beridze, Rusudan Chagelishvili

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Studies of ancient metallurgy are a subject of worldwide current interest. Georgia with its famous early metalworking traditions is one of the central parts of in the Caucasus region. The aim of the present study is to introduce the results of archaeometallurgical investigations being undertaken in the mountain region of Colchis, Lechkhumi (the Tsageri Municipality of western Georgia) and establish their place in the existing archaeological context. Lechkhumi (one of the historic provinces of Georgia known from Georgian, Greek, Byzantine and Armenian written sources as Lechkhumi/Skvimnia/Takveri) is the part of the Colchian mountain area. It is one of the important but little known centres of prehistoric metallurgy in the Caucasian region and of Colchian Bronze Age culture. Reconnaissance archaeological expeditions (2011-2015) revealed significant prehistoric metallurgical sites in Lechkhumi. Sites located in the vicinity of Dogurashi Village (Tsageri Municipality) have become the target area for archaeological excavations. During archaeological excavations conducted in 2016-2018 two archaeometallurgical sites – Dogurashi I and Dogurashi II were investigated. As a result of an interdisciplinary (archaeological, geological and geophysical) survey, it has been established that at both prehistoric Dogurashi mountain sites, it was copper that was being smelted and the ore sources are likely to be of local origin. Radiocarbon dating results confirm they were operating between about the 13th and 9th century BC. More recently another similar site has been identified in this area (Dogurashi III), and this is about to undergo detailed investigation. Other prehistoric metallurgical sites are being located and investigated in the Lechkhumi region as well as chance archaeological finds (often in hoards) – copper ingots, metallurgical production debris, slag, fragments of crucibles, tuyeres (air delivery pipes), furnace wall fragments and other related waste debris. Other chance finds being investigated are the many copper, bronze and (some) iron artefacts that have been found over many years. These include copper ingots, copper, bronze and iron artefacts such as tools, jewelry, and decorative items. These show the important but little known or understood the role of Lechkhumi in the late Bronze Age culture of Colchis. It would seem that mining and metallurgical manufacture form part of the local agricultural yearly lifecycle. Colchian ceramics have been found and also evidence for artefact production, small stone mould fragments and encrusted material from the casting of a fylfot (swastika) form of Colchian bronze buckle found in the vicinities of the early settlements of Tskheta and Dekhviri. Excavation and investigation of previously unknown archaeometallurgical sites in Lechkhumi will contribute significantly to the knowledge and understanding of prehistoric Colchian metallurgy in western Georgia (Adjara, Guria, Samegrelo, and Svaneti) and will reveal the importance of this region in the study of ancient metallurgy in Georgia and the Caucasus. Acknowledgment: This work has been supported by the Shota Rustaveli National Science Foundation (grant FR # 217128).

Keywords: archaeometallurgy, Colchis, copper, Lechkhumi

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

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Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

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Authors: Waleed Alanzi

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The purpose of this qualitative study was to add to the existing literature and provide a more detailed understanding of the individual experiences and perceptions of 15 Deans at the University of Kuwait regarding their first year of planning, developing, and implementing crisis distance education (CDE) in response to the COVID-19 epidemic. An interpretative phenomenological approach was applied, using the thematic analysis of interview transcripts to describe the challenging journeys taken by each of the Deans from the first-person point of view. There was objective evidence, manifested by four primary themes (“Obstacles to the implementation of CDE”; “Planning for CDE”; “Training for CDE,” and “Future Directions”) to conclude that the faculty members, technical staff, administrative staff, and students generally helped each other to overcome the obstacles associated with planning and implementing CDE. The idea that CDE may turn homes into schools and parents into teachers was supported. The planning and implementation of CDE were inevitably associated with a certain amount of confusion, as well as disruptions in the daily routines of staff and students, as well as significant changes in their responsibilities. There were contradictory ideas about the future directions of distance education after the pandemic. Previous qualitative research on the implementation of CDE at higher education institutions in the Arab world has focused mainly on the experiences and perceptions of students; however, little is known about the experiences and perceptions of the students at the University of Kuwait during the COVID19 pandemic, providing a rationale and direction for future research.

Keywords: distance learning, qualitative research, COVID-19 epidemic, Kuwait university

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Christopher Leung, Tony Kim, Rebecca Lendzion, Scott Mackenzie

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Retroperitoneal abscesses are a rare but serious condition with often delayed diagnosis, non-specific symptoms, multiple causes and high morbidity/mortality. With the advent of more readily available cross-sectional imaging, retroperitoneal abscesses are treated earlier and better outcomes are achieved. Occasionally, a retroperitoneal abscess is present as a huge retroperitoneal abscess, as evident in this 53-year-old male. With a background of chronic renal disease and left partial nephrectomy, this gentleman presented with a one-month history of left flank pain without any other symptoms, including fevers or abdominal pain. CT abdomen and pelvis demonstrated a huge retroperitoneal abscess spanning from the diaphragm, abutting the spleen, down to the iliopsoas muscle and abutting the iliac vessels at the pelvic brim. This large retroperitoneal abscess required open drainage as well as drainage by interventional radiology. A long course of intravenous antibiotics and multiple drainages was required to drain the abscess. His blood culture and fluid culture grew Proteus species suggesting a urinary source, likely from his non-functioning kidney, which had a partial nephrectomy. Such a huge retroperitoneal abscess has rarely been described in the literature. The learning point here is that the basic principle of source control and antibiotics is paramount in treating retroperitoneal abscesses regardless of the size of the abscess.

Keywords: retroperitoneal abscess, retroperitoneal mass, sepsis, genitourinary infection

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Authors: Greg Turner, Bin Lu, Cheer-Sun Yang

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As computing technology advances, smartphone applications can assist in student learning in a pervasive way. For example, the idea of using a mobile apps for the PA Common Trees, Pests, Pathogens, in the field as a reference tool allows middle school students to learn about trees and associated pests/pathogens without bringing a textbook. In the past, some researches study the mobile software Mobile Application Software Development Life Cycle (MADLC) including traditional models such as the waterfall model, or more recent Agile Methods. Others study the issues related to the software development process. Very little research is on the development of three heterogenous mobile systems simultaneously in a small university where the availability of developers is an issue. In this paper, we propose to use a hybride model of Waterfall Model and the Agile Model, known as the Relay Race Methodology (RRM) in practice, to reflect the concept of racing and relaying for scheduling. Based on the development project, we observe that the modeling of the transition between any two phases is manifested naturally. Thus, we claim that the RRM model can provide a de fecto rather than a de jure basis for the core concept in the MADLC. In this paper, the background of the project is introduced first. Then, the challenges are pointed out followed by our solutions. Finally, the experiences learned and the future work are presented.

Keywords: agile methods, mobile apps, software process model, waterfall model

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Authors: Soofia Malik

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The purpose of this study is to develop an instrument to measure undergraduate students’ attitudes toward mathematics (MAT) and to assess the data collected from the instrument for validity and reliability. The instrument is developed using five subscales: anxiety, enjoyment, self-confidence, value, and technology. The technology dimension is added as the fifth subscale of attitude toward mathematics because of the recent trend of incorporating online homework in mathematics courses as well as due to heavy reliance of higher education on using online learning management systems, such as Blackboard and Moodle. The sample consists of 163 (M = 82, F = 81) undergraduates enrolled in College Algebra course in the summer 2017 semester at a university in the USA. The data is analyzed to answer the research question: if and how do undergraduate students’ attitudes toward mathematics load using Principal Components Analysis (PCA)? As a result of PCA, three subscales emerged namely: anxiety/self-confidence scale, enjoyment, and value scale. After deleting the last five items or the last two subscales from the initial MAT scale, the Cronbach’s alpha was recalculated using the scores from 20 items and was found to be α = .95. It is important to note that the reliability of the initial MAT form was α = .93. This means that employing the final MAT survey form would yield consistent results in repeated uses. The final MAT form is, therefore, more reliable as compared to the initial MAT form.

Keywords: college algebra, Cronbach's alpha reliability coefficient, Principal Components Analysis, PCA, technology in mathematics

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Authors: Qiao Mao

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There is a growing number of studies concerning STEM (Science, Technology, Engineering, and Mathematics) and STEAM (Science, Technology, Engineering, Arts, and Mathematics). However, the research is little on STEAM competitions from Arts' perspective. This study takes the annual PowerTech STEAM competition in Taiwan as an example. In this activity, students are asked to make wooden bionic mechanical beasts on the spot and participate in a model and speed competition. This study aims to explore how Arts influences STEM after it involves in the making of mechanical beasts. A case study method is adopted. Through expert sampling, five prize winners in the PowerTech Youth Science and Technology Creation Competition and their supervisors are taken as the research subjects. Relevant data which are collected, sorted out, analyzed and interpreted afterwards, derive from observations, interview and document analyses, etc. The results of the study show that in the PowerTech Youth Science and Technology Creation Competition, when Arts involves in STEM, (1) it has an impact on the athletic performance, balance, stability and symmetry of mechanical beasts; (2) students become more interested and more creative in making STEAM mechanical beasts, which can promote students' learning of STEM; (3) students encounter more difficulties and problems when making STEAM mechanical beasts, and need to have more systematic thinking and design thinking to solve problems.

Keywords: PowerTech, STEAM contest, mechanical beast, arts' role

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Authors: Kitda Praraththajariya

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The main purpose of this research was to study how to communicate the identity of the Mueang district, Samut Songkram province for ecotourism. The qualitative data was collected through studying related materials, exploring the area, in-depth interviews with three groups of people: three directly responsible officers who were key informants of the district, twenty foreign tourists and five Thai tourist guides. A content analysis was used to analyze the qualitative data. The two main findings of the study were as follows: 1. The identity of Amphur (District) Mueang, Samut Songkram province. This establishment was near the Mouth of Maekong River for normal people and tourists, consisting of rest accommodations. There are restaurants where food and drinks are served, rich mangrove forests, Hoy Lod (Razor Clam) and mangrove trees. Don Hoy Lod, is characterized by muddy beaches, is a coastal wetland for Ramsar Site. It is at 1099th ranging where the greatest number of Hoy Lod (Razor Clam) can be seen from March to May each year. 2. The communication of the identity of Amphur Mueang, Samut Songkram province which the researcher could find and design to present in English materials can be summed up in 4 items: 1) The history of Amphur Mueang, Samut Songkram province 2) Wat Phet Samut Worrawihan 3) The Learning source of Ecotourism: Don Hoy Lod and Mangrove forest 4) How to keep Amphur Mueang, Samut Songkram province for ecotourism.

Keywords: foreigner tourists, signified, semiotics, ecotourism

Procedia PDF Downloads 236