Search results for: flexibility training

Authors: Omran Alharbi, Victor Lally

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The advantages of the adoption of new technology such as learning management systems (LMSs) in education and teaching methods have been widely recognised. This has led a large number of universities to integrate this type of technology into their daily learning and teaching activities in order to facilitate the education process for both learners and teachers. On the other hand, in some developing countries such as Saudi Arabia, educators have seldom used this technology. As a result, this study was conducted in order to investigate the factors that impede teachers’ use of technology (LMSs) in their teaching in Saudi Arabian institutions. This study used a qualitative approach. Eight participants were invited to take part in this study, and they were asked to give their opinions about the most significant factors that prevented them from integrating technology into their daily activities. The results revealed that a lack of LMS skills, interest in and knowledge about the LMS among teachers were the most significant factors impeding them from using technology in their lessons. The participants suggested that incentive training should be provided to reduce these challenges.

Keywords: LMS, factors, KSA, teachers

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Dominika Collett

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AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

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Authors: Abdelbasit Gadour

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A great number of children in mainstream schools across Libya are currently living with emotional, behavioral difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioral difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Libya find classroom behavior problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with emotional and behavioral difficulties (EBD). The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary schools.

Keywords: children, emotional and behavior difficulties, learning, teachers'

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Authors: Radesh Palakurthi

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The purpose of this study was to determine the impact of experiential learning on learning outcomes in hospitality education. This paper presents the results of an online survey of students from the U.S. studying abroad and their self-reported change in learning outcomes as assessed using the Core Competencies Model for the Hospitality Industry developed by Employment and Training Development Office of the U.S. Department of Labor. The impact of student learning styles on learning outcomes is also evaluated in this study. Kolb’s Learning Styles Inventory Model was used to assess students’ learning style. The results show that students reported significant improvements in their learning outcomes because of engaging in study abroad experiential learning programs. The learning styles of the students had significant effect on one of core learning outcomes- personal effectiveness.

Keywords: hospitality competencies, hospitality education, Kolb’s learning style inventory, learning outcomes, study abroad

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Authors: Chuyi Ye, Minsang Kim, Cheol-Hee Moon

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The organic light emitting diode(OLED), is a potential organic optical functional materials which is considered as the next generation display technology with the advantages such as all-solid state, ultra-thin thickness, active luminous and flexibility. Due to the development of polymer-inorganic substrate, it becomes possible to achieve the flexible OLED display. However the organic light-emitting material is very sensitive to the oxygen and water vapor, and the encapsulation requires water vapor transmission rate(WVTR) and oxygen transmission rate(OTR) as lower as 10-6 g/(m2.d) and 10-5 cm3/(m2.d) respectively. In current situation, the rigorous WVTR and OTR have restricted the application of the OLED display. Traditional epoxy/getter or glass frit approaches, which have been widely applied on glass-substrate-based devices, are not suitable for transparent flexible organic devices, and mechanically flexible thin-film approaches are required. To ensure the OLED’s lifetime, the encapsulation material of the OLED package is very important. In this paper, a low melting point alloy(LMPA)-epoxy mixture in the encapsulation process is introduced. There will be a phase separation when the mixture is heated to the melting of LMPA and the formation of the double line structure between two substrates: the alloy barrier has extremely low WVTR and OTR and the epoxy fills the potential tiny cracks. In our experiment, the PI film is chosen as a flexible transparent substrate, and Mo and Cu are deposited on the PI film successively. Then the two metal layers are photolithographied to the sealing pattern line. The Mo is a transition layer between the PI film and Cu, at the same time, the Cu has a good wettability with the LMPA(Sn-58Bi). At last, pattern is printed with LMPA layer and applied voltage, the gathering Joule heat melt the LMPA and form the double line structure and the OLED package is sealed in the same time. In this research, the double-line encapsulating structure of LMPA and epoxy on the PI film is manufactured for the flexible OLED encapsulation, and in this process it is investigated whether the encapsulation satisfies the requirement of WVTR and OTR for the flexible OLED.

Keywords: encapsulation, flexible, low melting point alloy, OLED

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Authors: Pieter van den Berg, Retief Broodryk, Bert Moolman

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Firstly, this study aimed to identify which performance indicators (PIs) discriminate between winning and losing university-level Rugby Union (RU) teams and, secondly, to compare the significant PIs in RU and Rugby Union Sevens (RS) at university level. Understanding the importance of PIs and their effect on match outcomes could assist coaching staff to prioritise specific game aspects during training to increase performance. Twenty randomly selected round-robin matches of the 2018 Varsity Cup (n=20), and Varsity Sports sevens (n=20) tournaments were analysed. A linear mixed model was used to determine statistical significant differences set at p≤0.05 while effect size was reported according to Cohen's d value. Results revealed that various PIs discriminated between winning and losing RU teams and that specific PIs could be observed as significant in both RU and RS. Therefore, specific identified tactical aspects of RU and RS should be prioritised to optimise performance

Keywords: match success, notational analysis, performance analysis, rugby, video analysis

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Authors: Bharath Anantharamaiah, Tomas Bouda, Elke Deckers, Stijn Jonckheere, Wim Desmet, Juan J. Garcia

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Automotive tyres are gaining importance recently in terms of their noise emission, not only with respect to reduction in noise, but also their perception and detection. Tyres exhibit a mechanical noise generation mechanism up to 1 kHz. However, owing to the fact that tyre is a composite of several materials, it has been difficult to model it using finite elements to predict noise at high frequencies. The currently available FE models have a reliability of about 500 Hz, the limit which, however, is not enough to perceive the roughness or sharpness of noise from tyre. These noise components are important in order to alert pedestrians on the street about passing by slow, especially electric vehicles. In order to model tyre noise behaviour up to 1 kHz, its dynamic behaviour must be accurately developed up to a 1 kHz limit using finite elements. Materials play a vital role in modelling the dynamic tyre behaviour precisely. Since tyre is a composition of several components, their precise definition in finite element simulations is necessary. However, during the tyre manufacturing process, these components are subjected to various pressures and temperatures, due to which these properties could change. Hence, material definitions are better described based on the tyre responses. In this work, the hyperelasticity of tyre component rubbers is calibrated, using the design of experiments technique from the tyre characteristic responses that are measured on a stiffness measurement machine. The viscoelasticity of rubbers are defined by the Prony series for rubbers, which are determined from the loss factor relationship between the loss and storage moduli, assuming that the rubbers are excited within the linear viscoelasticity ranges. These values of loss factor are measured and theoretically expressed as a function of rubber shore hardness or hyperelasticities. From the results of the work, there exists a good correlation between test and simulation vibrational transfer function up to 1 kHz. The model also allows flexibility, i.e., the frequency limit can also be extended, if required, by calibrating the Prony parameters of rubbers corresponding to the frequency of interest. As future work, these tyre models are used for noise generation at high frequencies and thus for tyre noise perception.

Keywords: tyre dynamics, rubber materials, prony series, hyperelasticity

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Authors: A. Y. Purnama, N. Yasufuku

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The bridge structure is a building that has to be maintained, especially for the elastomeric bearing. The girder of the bridge needs to be lifted upward to maintain this elastomeric bearing, that needs high cost. Nowadays, integral abutment bridges are becoming popular. The integral abutment bridge is less costly because the elastomeric bearings are eliminated, which reduces the construction cost and maintenance costs. However, when this elastomeric bearing removed, the girder movement due to environmental thermal forces directly support by pile foundation, and it needs to be considered in the design. In case of pile foundation in a stiff soil, in the top area of the pile cannot move freely due to the fixed condition by soil stiffness. Pre-bored pile system can be used to increase the flexibility of pile foundation using a pre-bored hole that filled with elastic materials, but the behavior of soil-pile interaction and soil response due to this system is still rarely explained. In this paper, an experimental study using small-scale laboratory model test conducted in a half size model. Single flexible pile model embedded in sandy soil with the pre-bored ring, which filled with the filler material. The testing box made from an acrylic glass panel as observation area of the pile shaft to monitor the displacement of the pile during the lateral loading. The failure behavior of the soil inside the pre-bored ring and around the pile shaft was investigated to determine the point of pile rotation and the movement of this point due to the pre-bored ring system along the pile shaft. Digital images were used to capture the deformations of the soil and pile foundation during the loading from the acrylic glass on the side of the testing box. The results were presented in the form of lateral load resistance charts against the pile shaft displacement. The failure pattern result also established due to the cyclic lateral loading. The movement of the rotational point was measured due to the pre-bored system filled with appropriate filler material. Based on the findings, design considerations for pre-bored pile system due to cyclic lateral loading can be introduced.

Keywords: failure behavior, pre-bored pile system, cyclic lateral loading, sandy soil

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Authors: Surajo Ladan

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There is no doubt nowadays, the media plays a crucial role in the development of languages. Media practitioners influence and set our linguistic norms to a greater extent. Their strategic position makes them influential than school teachers as linguistic pacesetters and models. This is so because of the direct access to the general public that media enjoys being public, oriented and at the same time being patronized by the public, the media is regarded as an authority as far as language use is concerned. In the modern world, listening to the news has become part and parcel of our daily lives. Easy communication has made the world a global village. Contact between countries and people are increasing daily. In Nigeria and indeed the whole of West Africa, radio is the most widely spread out of the three types of media (radio, television, and print). This is because of its (radio) cheapness and less cumbersome and flexibility. Therefore, the positive or negative effect of radio on the lives of a typical Nigerian or African cannot be over emphasized. Hausa language, on the other hand, is one of the most widely spoken languages in West Africa and, of course, the lingua franca in the Northern part of Nigeria and Southern Niger. The language has been in use to a large extent by almost all the popular foreign media houses of BBC, VOA, Deutsche Welle Radio, Radio France International, Radio China, etc. The many people in Nigeria and West Africa depend so much on the news in this language. In fact even government programmes, mobilization, education and sensitization of the populace are done in this language through the broadcast media. It is against this background, for effective and efficient work of this nature it requires the services of a trained translator for the purpose of translating scientific and technological terms. The main thrust of this paper was necessitated for the fact that no nation develops using foreign or borrowed language. This is in lined with UNESCO declaration of 1953 where it says 'the best Language of Instruction (LOI) is the vernacular or the Mother Tongue (MT) of the learner'. This idea is in the right direction especially nowadays that the developing nations have come to terms with realities that their destiny is really in their own hands, not in the hands of the so-called developed nations.

Keywords: translation, scientific, technological, language, radio, media

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Authors: Yijie Wang, Tianyue Wang

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In the past two decades, there has been a great ‘piano lesson frenzy’ among Chinese middle-class families, with a large number of parents adding piano training to children’s extra-curriculum lists. Superficially, the frenzy reflects a rather ‘leisurely’ attitude: parents typically claim that pianos lessons are ‘just for fun’ and will hopefully render children’s life more exciting. However, a closer scrutiny reveals that there is great social-status anxiety hidden beneath this ‘leisurely’ surface. Based on pre-interviews of six Chinese middle-class parents who have enthusiastically signed their children up for piano lessons, several tentative analysis are made: 1. Owing to a series of historical and social factors, the Chinese middle-class have yet to establish their cultural norms in the past few decades, resulting in great confusion concerning how to cultivate cultural tastes in their offspring. And partly due to the fact that the middle-class status of the past Chinese generation is mostly self-acquired rather than inherited, parents are much less confident about their cultural resources—which require long-time accumulation—than material ones. Both factors combine to lead to a sort of blind, overcompensating enthusiasm in culture-related education, and the piano frenzy is but a demonstration. 2. The piano has been chosen to be the object of the frenzy partly because of its inherent characteristics as well as socially-constructed ones. Costly, large in size, imported from another culture and so forth, the piano has acquired the meaning of being exclusive, high-end and exotic, which renders it a token of top-tier status among Chinese people, and piano lessons for offspring have therefore become parents’ paths towards a kind of ‘symbolic elevation’. A child playing piano is an exhibition as well as psychological assurance of the families’ middle-class status. 3. A closer look at children’s piano training process reveals that there is much more anxiety than leisurely elements involved. Despite parents’ claim that ‘piano is mainly for kids to have fun,’ the whole process is evidently of a rather ‘ascetic’ nature, with the demands of diligence and senses of time urgency throughout, and techniques rather than flair or styles are emphasized. This either means that the apparent ‘piano-for-fun’ stance is unauthentic and is only other motives in disguise, or that the Chinese middle-class parents are not yet capable of shaking off the sense of anxiety even if they sincerely intend to. 4. When viewed in relation to Chinese formal school system as well as the job market at large, it can be said that by signing children up for piano lessons, parents are consciously or unconsciously seeking to prepare for, or reduce the risks of, their children’s future social mobility. In face of possible failures in the highly-crucial, highly-competitive formal school system, piano-playing as an extra-curriculum activity may be conveniently transferred into an alternative career path. Besides, in contemporary China, as the occupational structure goes through change, and the school-related certificates decline in value, aspects such as a person’s overall deportment, which can be gained or proved by piano-learning, have been gaining in significance.

Keywords: extra-curriculum activities, middle class, piano lesson frenzy, status anxiety

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Authors: Soumya Mishra, Elena Neiterman

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Aligned with the growing interest in the impact of the pandemic on academic experiences of university students, this study aimed to examine the challenges Canadian undergraduate students experienced during the university closures due to COVID-19. Using qualitative methodological approach, the study utilized semi-structured interviews conducted with 20 undergraduate students enrolled in an Ontario university to explore their thoughts and experience regarding online learning during the peak of the COVID-19 pandemic, from January 2021 to March 2021. The interviews yielded four major themes with the following associated subthemes: Personal Challenges Associated with Adapting to the Pandemic (Change in the Type of Stress Experienced, Unique Impact on Certain Groups of Students, Decreased Motivation, Crucial Role of Resilience), Social Challenges Associated with Adapting to the Pandemic (Increased Loneliness, Challenges Faced while Communicating, Perception of Group work, Role of Living Conditions), Challenges associated with Accessing University Resources (Crucial Role of Professors, Perception of Virtual Events, Importance of Physical Spaces). Overall, the analysis showed that the COVID-19 pandemic fostered resilience and psychological flexibility amongst all students. However, the mental health and social wellbeing of students deteriorated during the COVID-19 pandemic and they reported experiencing chronic stress, anxiety and loneliness. International students, first year and final year students experienced a unique set of challenges. It was hard for participants in our study to make strong new connections with their classmates and maintain existing friendships with their peers. The importance of professors in facilitating learning was amplified in the online environment due to the lack of in-person interaction with other students. Despite these challenges, most participants reported that they received high grades during online learning. The findings from this study could be helpful for organizations and individuals working towards fostering the wellbeing of undergraduate students. They can also help in making post-secondary institutions more resilient to future emergencies by creating contingency plans regarding online instructions and risk management techniques.

Keywords: Canadian, COVID-19, university students, wellbeing

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Authors: Janki Shankar, Shu-Ping Chen

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This Canadian study explored the perspectives of recent immigrant workers regarding occupational health and safety (OHS) and workplace conditions that increase workers’ vulnerability to sustaining injury or illness. Using an interpretive research approach and semi structured qualitative interviews, 42 recent immigrant workers from a range of industries operating in two cities in a province in Canada were interviewed. A constant comparative approach was used to identify key themes across the workers’ experiences. The findings revealed that these workers have an incomplete understanding of OHS. In many workplaces, poor job training, little worker support, lack of power in the workplace, and a poor workplace safety culture make it difficult for recent immigrant workers to acquire OHS information and implement safe work practices. This study proposes workplace policies and practices that will improve worker OHS awareness and make workplaces safer for immigrant workers.

Keywords: new immigrant workers, occupational health and safety, workplace challenges, policy, practice

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Authors: Matan Cohen, Maxim Shoshany

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

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

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Magdalena Chrabąszcz, Teresa Wolniewicz, Cezary Maciejewski, Joanna Czuwara

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BACKGROUND: Dermoscopy is a clinical tool known to improve the early detection of melanoma and other malignancies of the skin. Over the past few years melanoma has grown into a disease of socio-economic importance due to the increasing incidence and persistently high mortality rates. Early diagnosis remains the best method to reduce melanoma and non-melanoma skin cancer– related mortality and morbidity. Dermoscopy is a noninvasive technique that consists of viewing pigmented skin lesions through a hand-held lens. This simple procedure increases melanoma diagnostic accuracy by up to 35%. Dermoscopy is currently the standard for clinical differential diagnosis of cutaneous melanoma and for qualifying lesion for the excision biopsy. Like any clinical tool, training is required for effective use. The introduction of small and handy dermoscopes contributed significantly to the switch of dermatoscopy toward a first-level useful tool. Non-dermatologist physicians are well positioned for opportunistic melanoma detection; however, education in the skin cancer examination is limited during medical school and traditionally lecture-based. AIM: The aim of this randomized study was to determine whether the adjunct of dermoscopy to the standard fourth year medical curriculum improves the ability of medical students to distinguish between benign and malignant lesions and assess acceptability and satisfaction with the intervention. METHODS: We performed a prospective study in 2 cohorts of fourth-year medical students at Medical University of Warsaw. Groups having dermatology course, were randomly assigned to:  cohort A: with limited access to dermatoscopy from their teacher only – 1 dermatoscope for 15 people  Cohort B: with a full access to use dermatoscopy during their clinical classes:1 dermatoscope for 4 people available constantly plus 15-minute dermoscopy tutorial. Students in both study arms got an image-based test of 10 lesions to assess ability to differentiate benign from malignant lesions and postintervention survey collecting minimal background information, attitudes about the skin cancer examination and course satisfaction. RESULTS: The cohort B had higher scores than the cohort A in recognition of nonmelanocytic (P < 0.05) and melanocytic (P <0.05) lesions. Medical students who have a possibility to use dermatoscope by themselves have also a higher satisfaction rates after the dermatology course than the group with limited access to this diagnostic tool. Moreover according to our results they were more motivated to learn dermatoscopy and use it in their future everyday clinical practice. LIMITATIONS: There were limited participants. Further study of the application on clinical practice is still needed. CONCLUSION: Although the use of dermatoscope in dermatology as a specialty is widely accepted, sufficiently validated clinical tools for the examination of potentially malignant skin lesions are lacking in general practice. Introducing medical students to dermoscopy in their fourth year curricula of medical school may improve their ability to differentiate benign from malignant lesions. It can can also encourage students to use dermatoscopy in their future practice which can significantly improve early recognition of malignant lesions and thus decrease melanoma mortality.

Keywords: dermatoscopy, early detection of melanoma, medical education, skin cancer

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Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

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Authors: Oladimeji Adetona Adeyeye, Emmanuel Rotimi Sadiku, Oluwaseun Olayinka Adeyeye

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In order to resuscitate soybean production and post-harvest processing especially, in term of threshing, there is need to develop an affordable threshing machine which will reduce drudgery associated with manual soybean threshing. Soybean thresher was fabricated and evaluated at Institute of Agricultural Research and Training IAR&T Apata Ibadan. The machine component includes; hopper, threshing unit, shaker, cleaning unit and the seed outlet, all working together to achieve the main objective of threshing and cleaning. TGX1835 - 10E variety was used for evaluation because of its high resistance to pests, rust and pustules. The final moisture content of the used sample was about 15%. The sample was weighed and introduced into the machine. The parameters evaluated includes moisture content, threshing efficiency, cleaning efficiency, machine capacity and speed. The threshing efficiency and capacity are 74% and 65.9kg/hr respectively. All materials used were sourced locally which makes the cost of production of the machine extremely cheaper than the imported soybean thresher.

Keywords: efficiency, machine capacity, speed, soybean, threshing

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Authors: Ana Maghfiroh

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Learning a foreign language needs a big effort from the learner itself to make their language ability grows better day by day. Among those, they also need a support from all around them including teacher, friends, as well as activities which support them to speak the language. When those activities developed well as a habit which are done regularly, it will help improving the students’ language competence. It was a qualitative research which aimed to find out and describe some activities implemented in Pesantren Al Mawaddah, Ponorogo, in order to teach the students a foreign language. In collecting the data, the researcher used interview, questionnaire, and documentation. From the study, it was found that Pesantren Al Mawaddah had successfully built the language habit on the students to speak the target language. More than 15 hours a day students were compelled to speak foreign language, Arabic or English, in turn. It aimed to habituate the students to keep in touch with the target language. The habit was developed through daily language activities, such as dawn vocabs giving, dictionary handling, daily language use, speech training and language intensive course, daily language input, and night vocabs memorizing. That habit then developed the students awareness towards the language learned as well as promoted their language mastery.

Keywords: habit, communicative competence, daily language activities, Pesantren

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Authors: Lenka Mynaříková, Hedvika Boukalová

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The study looks at the ability to detect nonverbal deception among police officers and management students in the Czech Republic. Respondents from police departments (n=197) and university students of human resources (n=161) completed a deception detection task and evaluated veracity of the statements of suspects in 21 video clips from real crime investigations. Their evaluations were based on nonverbal behavior. Voices in the video clips were modified so that words were not recognizable, yet paraverbal voice characteristics were preserved. Results suggest that respondents have a tendency to lie bias based on their profession. In the evaluation of video clips, stereotypes also played a significant role. The statements of suspects of a different ethnicity, younger age or specific visual features were considered deceitful more often. Research might be beneficial for training in professions that are in need of deception detection techniques.

Keywords: deception detection, police officers, human resources, forensic psychology, forensic studies, organizational psychology

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Authors: Anas H. Aljemely, Jianping Xuan

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

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

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Gaurav Sancheti

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This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Mariia Karizhenskaia, Tanvi Nandani, Ali Tafazoli Moghadam

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Hospice palliative care (HPC) is one of the most complicated philosophies of care in which physical, social/cultural, and spiritual aspects of human life are intermingled with an undeniably significant role in every aspect. Among these dimensions of care, culture possesses an outstanding position in the process and goal determination of HPC. This study shows the importance of cultural elements in the establishment of effective and optimized structures of HPC in the Canadian healthcare environment. Our systematic search included Medline, Google Scholar, and St. Lawrence College Library, considering original, peer-reviewed research papers published from 1998 to 2023 to identify recent national literature connecting culture and palliative care delivery. The most frequently presented feature among the articles is the role of culture in the efficiency of the HPC. It has been shown frequently that including the culturespecific parameters of each nation in this system of care is vital for its success. On the other hand, ignorance about the exclusive cultural trends in a specific location has been accompanied by significant failure rates. Accordingly, implementing a culture-wise adaptable approach is mandatory for multicultural societies. The following outcome of research studies in this field underscores the importance of culture-oriented education for healthcare staff. Thus, all the practitioners involved in HPC will recognize the importance of traditions, religions, and social habits for processing the care requirements. Cultural competency training is a telling sample of the establishment of this strategy in health care that has come to the aid of HPC in recent years. Another complexity of the culturized HPC nowadays is the long-standing issue of racialization. Systematic and subconscious deprivation of minorities has always been an adversity of advanced levels of care. The last part of the constellation of our research outcomes is comprised of the ethical considerations of culturally driven HPC. This part is the most sophisticated aspect of our topic because almost all the analyses, arguments, and justifications are subjective. While there was no standard measure for ethical elements in clinical studies with palliative interventions, many research teams endorsed applying ethical principles for all the involved patients. Notably, interpretations and projections of ethics differ in varying cultural backgrounds. Therefore, healthcare providers should always be aware of the most respectable methodologies of HPC on a case-by-case basis. Cultural training programs have been utilized as one of the main tactics to improve the ability of healthcare providers to address the cultural needs and preferences of diverse patients and families. In this way, most of the involved health care practitioners will be equipped with cultural competence. Considerations for ethical and racial specifications of the clients of this service will boost the effectiveness and fruitfulness of the HPC. Canadian society is a colorful compilation of multiple nationalities; accordingly, healthcare clients are diverse, and this divergence is also translated into HPC patients. This fact justifies the importance of studying all the cultural aspects of HPC to provide optimal care on this enormous land.

Keywords: cultural competence, end-of-life care, hospice, palliative care

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Authors: Rahul Saraswat

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More recently, a focus has been given to replacing machined stainless steel metal flow fields with inexpensive wire mesh current collectors. The flow fields are based on simple woven wire mesh screens of various stainless steels, which are sandwiched between a thin metal plate of the same material to create a bipolar plate/flow field configuration for use in a stack. Major advantages of using stainless steel wire screens include the elimination of expensive raw materials as well as machining and/or other special fabrication costs. The objective of the project is to improve the performance of the passive direct methanol fuel cell without increasing the cost of the cell and to make it as compact and light as possible. From the literature survey, it was found that very little is done in this direction, and the following methodology was used. 1. The passive direct methanol fuel cell (DMFC) can be made more compact, lighter, and less costly by changing the material used in its construction. 2. Controlling the fuel diffusion rate through the cell improves the performance of the cell. A passive liquid feed direct methanol fuel cell (DMFC) was fabricated using a given MEA (Membrane Electrode Assembly) and tested for different current collector structures. Mesh current collectors of different mesh densities along with different support structures, were used, and the performance was found to be better. Methanol concentration was also varied. Optimisation of mesh size, support structure, and fuel concentration was achieved. Cost analysis was also performed hereby. From the performance analysis study of DMFC, we can conclude with the following points: Area specific resistance (ASR) of wire mesh current collectors is lower than the ASR of stainless steel current collectors. Also, the power produced by wire mesh current collectors is always more than that produced by stainless steel current collectors. 1. Low or moderate methanol concentrations should be used for better and stable DMFC performance. 2. Wiremesh is a good substitute for stainless steel for current collector plates of passive DMFC because of its lower cost (by about 27 %), flexibility, and light in weight characteristics of wire mesh.

Keywords: direct methanol fuel cell, membrane electrode assembly, mesh, mesh size, methanol concentration, support structure

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Authors: Stephen O. Amaraihu

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This research investigated the effect of Computer Based Instruction on Students’ interest, achievement, and retention in Algebra in Abia State College of Education (Technical), Arochukwu. Three research questions and two hypotheses guided the study. Two instruments, Maths Achievement Test (MAT) and Maths Interest Inventory were employed, to test a population of three hundred and sixteen (316) NCE 1 students in algebra. It is expected that this research will lead to the improvement of students’ performance and enhance their interest and retention of basic algebraic concept. It was found that the majority of students in the college are not proficient in the use of ICT as a result of a lack of trained personnel. It was concluded that the state government was not ready to implement the usage of mathematics in Abia State College of Education. The paper recommends, amongst others, the employment of mathematics Lectures with competent skills in ICT and the training of lecturers of mathematics.

Keywords: achievement, computer based instruction, interest, retention

Procedia PDF Downloads 189

Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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In biomedical researches and randomized clinical trials, the most commonly interested outcomes are time-to-event so-called survival data. The importance of robust models in this context is to compare the effect of randomly controlled experimental groups that have a sense of causality. Causal estimation is the scientific concept of comparing the pragmatic effect of treatments conditional to the given covariates rather than assessing the simple association of response and predictors. Hence, the causal effect based semiparametric transformation model was proposed to estimate the effect of treatment with the presence of possibly time-varying covariates. Due to its high flexibility and robustness, the semiparametric transformation model which shall be applied in this paper has been given much more attention for estimation of a causal effect in modeling left-truncated and right censored survival data. Despite its wide applications and popularity in estimating unknown parameters, the maximum likelihood estimation technique is quite complex and burdensome in estimating unknown parameters and unspecified transformation function in the presence of possibly time-varying covariates. Thus, to ease the complexity we proposed the modified estimating equations. After intuitive estimation procedures, the consistency and asymptotic properties of the estimators were derived and the characteristics of the estimators in the finite sample performance of the proposed model were illustrated via simulation studies and Stanford heart transplant real data example. To sum up the study, the bias of covariates was adjusted via estimating the density function for truncation variable which was also incorporated in the model as a covariate in order to relax the independence assumption of failure time and truncation time. Moreover, the expectation-maximization (EM) algorithm was described for the estimation of iterative unknown parameters and unspecified transformation function. In addition, the causal effect was derived by the ratio of the cumulative hazard function of active and passive experiments after adjusting for bias raised in the model due to the truncation variable.

Keywords: causal estimation, EM algorithm, semiparametric transformation models, time-to-event outcomes, time-varying covariate

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Authors: Aparajita Mukherjee, D. P. Mukherjee

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This paper critically assessed pollution problems in small and medium enterprises with unique references to foundries and sponge iron industries to survey the adverse impact on human societies and the environment. The objective of this paper was to show how cleaner production concept was implemented in one foundry through improvisation of existing technology in India. Incremental advancement of existing technology minimized environmental issues and resource utilization. This study depicted that poor fiscal help, poor enforcement of government regulations, owners’ attitude and lacking specialized technical workers were the significant hindrances towards cleaner production. The paper explored the possible ways to overcome these hindrances for cleaner production. On a more general level, findings raise important questions regarding the need for a new paradigm for the implementation of cleaner production. Improvisation of existing technology in these enterprises would be cost effective towards sustainable development.

Keywords: SME pollution, ecological crisis, sustainable development, cleaner production, training

Procedia PDF Downloads 353