Search results for: multimedia learning theory

Authors: Emma M. Iglesias

Abstract:

We have analyzed extreme movements of the main stocks traded in the Eurozone in the 2000-2012 period. Our results can help future very-risk-averse investors to choose their portfolios in the Eurozone for risk management purposes. We find two main results. First, we can clearly classify firms by economic sector according to their different estimated VaR values in five of the seven countries we analyze. In special, we find sectors in general where companies have very high (telecommunications and banking) and very low (petroleum, utilities, energy and consumption) estimated VaR values. Second, we only find differences according to the geographical situation of where the stocks are traded in two countries: (1) all firms in the Irish stock market (the only financially rescued country we analyze) have very high estimated VaR values in all sectors; while (2) in Spain all firms have very low estimated VaR values including in the banking and the telecommunications sectors. All our results are supported when we study also the expected shortfall of the firms.

Keywords: risk management, firms, pareto tail thickness parameter, GARCH-type models, value-at-risk, extreme value theory, heavy tails, stock indexes, eurozone

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Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

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A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

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Authors: Aly Elshafei, Daniela Romano

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With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

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Authors: D. Hassell, D. De Focatiis

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This paper presents work on the application of wiki based coursework for a fourth-year engineering module delivered as part of both a MEng and MSc programme in Chemical Engineering. The module was taught with an equivalent structure simultaneously on two separate campuses, one in the United Kingdom (UK) and one in Malaysia, and the subsequent results were compared. Student feedback was sought via questionnaires, with 45 respondents from the UK and 49 from Malaysia. Results include discussion on; perceived difficulty; student enjoyment and experiences; differences between MEng and MSc students; differences between cohorts on different campuses. The response of students to the use of wiki-based coursework was found to vary based on their experiences and background, with UK students being generally more positive on its application than those in Malaysia.

Keywords: engineering education, student differences, student learning, web based coursework

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Authors: Zheng Baichuan, Li Shenghui, Li Bingqian, Zhang Ning, Chen Kai

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Entity alignment (EA) tasks in knowledge graphs often play a pivotal role in the integration of knowledge graphs, where structural differences often exist between the source and target graphs, such as the presence or absence of attribute information and the types of attribute information (text, timestamps, images, etc.). However, most current research efforts are focused on improving alignment accuracy, often along with an increased reliance on specific structures -a dependency that inevitably diminishes their practical value and causes difficulties when facing knowledge graph alignment tasks with varying structures. Therefore, we propose a universal knowledge graph alignment approach that only utilizes the common basic structures shared by knowledge graphs. We have demonstrated through experiments that our method achieves state-of-the-art performance in fair comparisons.

Keywords: knowledge graph, entity alignment, transformer, deep learning

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

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Authors: Purna Prasad Acharya, Madhav Karki, Sunta B. Tamang, Uttam Basnet, Chhatra Katwal

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The paper outlines the idea behind achieving sustainable lifestyles based on the spiritual values and teachings of Lord Buddha. This objective is to be achieved by spreading the tenets and teachings of Buddhism throughout the Asia Pacific region and the world from the sacred birth place of Buddha - Lumbini, Nepal. There is an urgent need to advance the relevance of Buddhist philosophy in tackling the triple planetary crisis of climate change, nature’s decline, and pollution. Today, the world is facing an existential crisis due to the above crises, exasperated by hunger, poverty and armed conflict. To address multi-dimensional impacts, the global communities have to adopt simple life styles that respect nature and universal human values. These were the basic teachings of Gautam Buddha. Lumbini, Nepal has the moral obligation to widely disseminate Buddha’s teaching to the world and receive constant feedback and learning to develop human and ecosystem resilience by molding the lifestyles of current and future generations through adaptive learning and simplicity across the geography and nationality based on spirituality and environmental stewardship. By promoting Buddhism, Nepal has developed a pro-nature tourism industry that focuses on both its spiritual and bio-cultural heritage. Nepal is a country rich in ancient wisdom, where sages have sought knowledge, practiced meditation, and followed spiritual paths for thousands of years. It can spread the teachings of Buddha in a way people can search for and adopt ways to live, creating harmony with nature. Using tools of natural sciences and social sciences, the team will package knowledge and share the idea of community well-being within the framework of environmental sustainability, social harmony and universal respect for nature and people in a more holistic manner. This notion takes into account key elements of sustainable development such as food-energy-water-biodiversity interconnections, environmental conservation, ecological integrity, ecosystem health, community resiliency, adaptation capacity, and indigenous culture, knowledge and values. This inclusive concept has garnered a strong network of supporters locally, regionally, and internationally. The key objectives behind this concept are: a) to leverage expertise and passion of a network of global collaborators to advance research, education, and policy outreach in the areas of human sustainability based on lifestyle change using the power of spirituality and Buddha’s teaching, resilient lifestyles, and adaptive living; b) help develop creative short courses for multi-disciplinary teaching in educational institutions worldwide in collaboration with Lumbini Buddha University and other relevant partners in Nepal; c) help build local and regional intellectual and cultural teaching and learning capacity by improving professional collaborations to promote nature based and Buddhist value-based lifestyles by connecting Lumbini to Nepal’s rich nature; d) promote research avenues to provide policy relevant knowledge that is creative, innovative, as well as practical and locally viable; and e) connect local research and outreach work with academic and cultural partners in South Korea so as to open up Lumbini based Buddhist heritage and Nepal’s Karnali River basin’s unique natural landscape to Korean scholars and students to promote sustainable lifestyles leading to human living in harmony with nature.

Keywords: triple planetary crisis, spirituality, sustainable lifestyles, living in harmony with nature, resilience

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Authors: Katie Strong, Brian P. O'Connor, Jacqueline M. Kanippayoor

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To date, self-report measures of psychopathy have largely been conceptualized with a male-focused understanding of the disorder, with the presumption that psychopathy expression is uniform across genders. However, generalizing this understanding to the female population may be misleading. The objective of this research was to explore gender differences in the expression of psychopathy and to assess current self-report psychopathy measures for gender bias. It was hypothesized that some items in commonly used measures of psychopathy may show gender bias and that existing measures may not contain enough items that are relevant to the manifestation of psychopathy in women. An exploratory investigation was conducted on statistical bias in common measures of psychopathy, and novel, relevant, but previously neglected items and measures were included in a new data collection. The participant pool included a sample of 403 university students and 354 participants recruited using Amazon Mechanical Turk. Item Response Theory methods - including Differential Item Functioning - were used to assess for the item- and test- level bias across several common self-report measures of psychopathy. Analyses indicated occasional and modest levels of item-level bias, and that some additional female-relevant items merit consideration for inclusion in measures of psychopathy. These findings suggest that current self-report measures of psychopathy may be demonstrating gender-bias and warrant further examination.

Keywords: gender, measurement bias, personality, psychopathy

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Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Tiberiu Socaciu, Tudor Colomeischi, Eugenia Iancu

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Business failure affects in various proportions shareholders, managers, lenders (banks), suppliers, customers, the financial community, government and society as a whole. In the era in which we have telecommunications networks, exists an interdependence of markets, the effect of a failure of a company is relatively instant. To effectively manage risk exposure is thus require sophisticated support systems, supported by analytical tools to measure, monitor, manage and control operational risks that may arise. As we know, bankruptcy is a phenomenon that managers do not want no matter what stage of life is the company they direct / lead. In the analysis made by us, by the nature of economic models that are reviewed (Altman, Conan-Holder etc.), estimating the risk of bankruptcy of a company corresponds to some extent with its own business cycle tracing of the company. Various models for predicting bankruptcy take into account direct / indirect aspects such as market position, company growth trend, competition structure, characteristics and customer retention, organization and distribution, location etc. From the perspective of our research we will now review the economic models known in theory and practice for estimating the risk of bankruptcy; such models are based on indicators drawn from major accounting firms.

Keywords: Anglo-Saxon models, continental models, national models, statistical models

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Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba

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Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.

Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network

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Authors: Stephen Kirkup

Abstract:

This paper discusses the implementation of the boundary element method (BEM) on an Excel spreadsheet and how it can be used in teaching vector calculus and simulation. There are two separate spreadheets, within which Laplace equation is solved by the BEM in two dimensions (LIBEM2) and axisymmetric three dimensions (LBEMA). The main algorithms are implemented in the associated programming language within Excel, Visual Basic for Applications (VBA). The BEM only requires a boundary mesh and hence it is a relatively accessible method. The BEM in the open spreadsheet environment is demonstrated as being useful as an aid to teaching and learning. The application of the BEM implemented on a spreadsheet for educational purposes in introductory vector calculus and simulation is explored. The development of assignment work is discussed, and sample results from student work are given. The spreadsheets were found to be useful tools in developing the students’ understanding of vector calculus and in simulating heat conduction.

Keywords: boundary element method, Laplace’s equation, vector calculus, simulation, education

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Authors: Thanny Noeem Taiwo

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The study is an attempt to gauge the way political parties advertised their candidates and parties during the 2015 presidential election. It examined the prominence given to political advertisement by newspapers and their patrons. It also looked at the campaign issues that the candidates emphasized in their campaign and also the appeals used in the advertisements. This research work is examined against the backdrop of the Framing Theory. The agenda setting and framing effects theories are media effects theories that explain how the media affects the people, events and situations in the society. Samples comprising of three daily newspapers were selected to represent the newspapers in the Nigeria. The Punch Newspaper, the Nation and the Vanguard will be used for population of this study. These newspapers were chosen because they are widely read national dailies and also because of its affiliation with some political parties. The study revealed that low prominence was given to political messages because most of the advertisements were in the inside pages of the newspapers. The media or the print medium could be a partner in a political setting as the fourth estate of the realm. While the press was trying hard to be professional, its operations were considerably inhibited by the political adventures of the patrons of the media. There is the need for strict professionalism to the ethics of journalism in the commercialization of political messages that would eventually rub off positively on the way the media is perceived.

Keywords: aAdvertisement, election, framing, newspaper

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Authors: Kevin Spencer G. Anglim, Zhenyu Zhang, Qingbin Gao

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Reusable launch vehicles (RLVs) present a more environmentally-friendly approach to accessing space when compared to traditional launch vehicles that are discarded after each flight. This paper studies the recyclable nature of RLVs by presenting a solution method for determining minimum-fuel optimal trajectories using principles from optimal control theory and particle swarm optimization (PSO). This problem is formulated as a minimum-landing error powered descent problem where it is desired to move the RLV from a fixed set of initial conditions to three different sets of terminal conditions. However, unlike other powered descent studies, this paper considers the highly nonlinear effects caused by atmospheric drag, which are often ignored for studies on the Moon or on Mars. Rather than optimizing the controls directly, the throttle control is assumed to be bang-off-bang with a predetermined thrust direction for each phase of flight. The PSO method is verified in a one-dimensional comparison study, and it is then applied to the two-dimensional cases, the results of which are illustrated.

Keywords: minimum-fuel optimal trajectory, particle swarm optimization, reusable rocket, SpaceX

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Authors: Philip A. Szlosek, M. Susan Guyer, Mary G. Barnum, Elizabeth M. Mullin

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In the United States, athletic training is a healthcare profession that encompasses the prevention, examination, diagnosis, treatment, and rehabilitation of injuries and medical conditions. Athletic trainers work under the direction of or in collaboration with a physician and are recognized by the American Medical Association as allied healthcare professionals. Internationally, this profession is often known as athletic therapy. As healthcare professionals, athletic trainers must be prepared for autonomous practice immediately after graduation. However, new athletic trainers have been shown to have clinical areas of strength and weakness.To better assess professional readiness and improve the preparedness of new athletic trainers, the factors of athletic training professional readiness must be defined. Limited research exists defining the holistic aspects of professional readiness needed for athletic trainers. Confirming the factors of professional readiness in athletic training could enhance the professional preparation of athletic trainers and result in more highly prepared new professionals. The objective of this study was to further explore and confirm the factors of professional readiness in athletic training. Authors useda qualitative design based in grounded theory. Participants included athletic trainers with greater than 24 months of experience from a variety of work settings from each district of the National Athletic Trainer’s Association. Participants took the demographic questionnaire electronically using Qualtrics Survey Software (Provo UT). After completing the demographic questionnaire, 20 participants were selected to complete one-on-one interviews using GoToMeeting audiovisual web conferencing software. IBM Statistical Package for the Social Sciences (SPSS, v. 21.0) was used to calculate descriptive statistics for participant demographics. The first author transcribed all interviews verbatim and utilized a grounded theory approach during qualitative data analysis. Data were analyzed using a constant comparative analysis and open and axial coding. Trustworthiness was established using reflexivity, member checks, and peer reviews. Analysis revealed four overarching themes, including management, interpersonal relations, clinical decision-making, and confidence. Management was categorized as athletic training services not involving direct patient care and was divided into three subthemes, including administration skills, advocacy, and time management. Interpersonal Relations was categorized as the need and ability of the athletic trainer to properly interact with others. Interpersonal relations was divided into three subthemes, including personality traits, communication, and collaborative practice. Clinical decision-making was categorized as the skills and attributes required by the athletic trainer whenmaking clinical decisions related to patient care. Clinical decision-making was divided into three subthemes including clinical skills, continuing education, and reflective practice. The final theme was confidence. Participants discussed the importance of confidence regarding relationships building, clinical and administrative duties, and clinical decision-making. Overall, participants explained the value of a well-rounded athletic trainer and emphasized that athletic trainers need communication and organizational skills, the ability to collaborate, and must value self-reflection and continuing education in addition to having clinical expertise. Future research should finalize a comprehensive model of professional readiness for athletic training, develop a holistic assessment instrument for athletic training professional readiness, and explore the preparedness of new athletic trainers.

Keywords: autonomous practice, newly certified athletic trainer, preparedness for professional practice, transition to practice skills

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Authors: SanXi Wu

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A priori argument is a kind of reductio ad absurdum provided by Colin McGinn on the premise of accepting the basic rules of Dummett. The core part of the theory is to analyze the three joint positions besides realism and make them invalid, thus proving the rationality of the stance of realism. However, this argument strategy is vulnerable to skepticism because it delays the skeptical problem. In addition to McGinn, John Searle offers a linguistic transcendental argument strategy, which regards realism as the comprehensible condition that all normal activities of understanding must assume, while skepticism does not enjoy the same status. However, Searle's transcendental argument does not solve the problem of the relationship between realist presupposition and representational presupposition. Strawson, Hume, Reid, Wittgenstein, and others provide a naturalistic argument for realism. In this argument, realism is either a priori commitment from nature, or our second nature, or even a background belief that is fundamentally immovable. The key to this argument is to provide a quietistic treatment that ultimately invalidates our anti-realist myths. In comparison, the naturalistic argument is a more promising one. In this paper, methods of theoretical analysis and comparative research are used in order to demonstrate that in the face of skepticism, McGinn's a priori strategy and Searle's transcendental strategy are untenable, while Strawson and others' naturalistic strategy may resist the pressure of skepticism and make realism possible.

Keywords: a priori argument, transcendental argument, naturalism, realism, skepticism

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Authors: Carol C. Opara, Olukemi E. Adetuyi-Olu-Francis

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The teaching of French language, like every other language, has its numerous challenges. A multilingual community, however, is a linguistic environment housing diverse languages, each with its peculiarity, both pros, and cones. A foreign language will have to strive hard for survival in an environment where various indigenous languages, as well as an established official language, exist. This study examined the challenges and prospects of the teaching of French as a foreign language in a multilingual community. A 22-item questionnaire was used to elicit information from 40 Nigerian Secondary school teachers of French. One of the findings of this study showed that the teachers of the French language are not motivated. Also, the linguistic environment is not favourable for the teaching and learning of French language in Nigeria. One of the recommendations was that training and re-training of teachers of French should be of utmost importance to the Nigerian Federal Ministry of Education.

Keywords: challenges, french as foreign language, multilingual community, teaching

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Authors: Diane Ghanem, Oscar Covarrubias, Michael Raad, Dawn LaPorte, Babar Shafiq

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Introduction: Standardized exams have long been considered a cornerstone in measuring cognitive competency and academic achievement. Their fixed nature and predetermined scoring methods offer a consistent yardstick for gauging intellectual acumen across diverse demographics. Consequently, the performance of artificial intelligence (AI) in this context presents a rich, yet unexplored terrain for quantifying AI's understanding of complex cognitive tasks and simulating human-like problem-solving skills. Publicly available AI language models such as ChatGPT have demonstrated utility in text generation and even problem-solving when provided with clear instructions. Amidst this transformative shift, the aim of this study is to assess ChatGPT’s performance on the orthopaedic surgery in-training examination (OITE). Methods: All 213 OITE 2021 web-based questions were retrieved from the AAOS-ResStudy website. Two independent reviewers copied and pasted the questions and response options into ChatGPT Plus (version 4.0) and recorded the generated answers. All media-containing questions were flagged and carefully examined. Twelve OITE media-containing questions that relied purely on images (clinical pictures, radiographs, MRIs, CT scans) and could not be rationalized from the clinical presentation were excluded. Cohen’s Kappa coefficient was used to examine the agreement of ChatGPT-generated responses between reviewers. Descriptive statistics were used to summarize the performance (% correct) of ChatGPT Plus. The 2021 norm table was used to compare ChatGPT Plus’ performance on the OITE to national orthopaedic surgery residents in that same year. Results: A total of 201 were evaluated by ChatGPT Plus. Excellent agreement was observed between raters for the 201 ChatGPT-generated responses, with a Cohen’s Kappa coefficient of 0.947. 45.8% (92/201) were media-containing questions. ChatGPT had an average overall score of 61.2% (123/201). Its score was 64.2% (70/109) on non-media questions. When compared to the performance of all national orthopaedic surgery residents in 2021, ChatGPT Plus performed at the level of an average PGY3. Discussion: ChatGPT Plus is able to pass the OITE with a satisfactory overall score of 61.2%, ranking at the level of third-year orthopaedic surgery residents. More importantly, it provided logical reasoning and justifications that may help residents grasp evidence-based information and improve their understanding of OITE cases and general orthopaedic principles. With further improvements, AI language models, such as ChatGPT, may become valuable interactive learning tools in resident education, although further studies are still needed to examine their efficacy and impact on long-term learning and OITE/ABOS performance.

Keywords: artificial intelligence, ChatGPT, orthopaedic in-training examination, OITE, orthopedic surgery, standardized testing

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Authors: Falek Kamel

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Various approaches are usually used in the dynamic analysis of beams vibrating transversally. For this, numerical methods allowing the solving of the general eigenvalue problem are utilized. The equilibrium equations describe the movement resulting from the solution of a fourth-order differential equation. Our investigation is based on the finite element method. The findings of these investigations are the vibration frequencies obtained by the Jacobi method. Two types of the elementary mass matrix are considered, representing a uniform distribution of the mass along with the element and concentrated ones located at fixed points whose number is increased progressively separated by equal distances at each evaluation stage. The studied beams have different boundary constraints representing several classical situations. Comparisons are made for beams where the distributed mass is replaced by n concentrated masses. As expected, the first calculus stage is to obtain the lowest number of beam parts that gives a frequency comparable to that issued from the Rayleigh formula. The obtained values are then compared to theoretical results based on the assumptions of the Bernoulli-Euler theory. These steps are used for the second type of mass representation in the same manner.

Keywords: structural elements, beams vibrating, dynamic analysis, finite element method, Jacobi method

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Authors: Shunsuke Saito, Kazuho Yoshimoto, Shunichi Ohmori, Sirawadee Arunyanart

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In this paper, we suggest a mechanism of assessment that rater and Ratee (or employees) to convince. There are many problems exist in the personnel assessment. In particular, we were focusing on the three. (1) Raters are not sufficiently recognized assessment point. (2) Ratee are not convinced by the mechanism of assessment. (3) Raters (or Evaluators) and ratees have empathy. We suggest 1: Setting of "understanding of the assessment points." 2: Setting of "relative assessment ability." 3: Proposal of two-way assessment mechanism to solve these problems. As a prerequisite, it is assumed that there are multiple raters. This is because has been a growing importance of multi-faceted assessment. In this model, it determines the weight of each assessment point evaluators by the degree of understanding and assessment ability of raters and ratee. We used the ANP (Analytic Network Process) is a theory that an extension of the decision-making technique AHP (Analytic Hierarchy Process). ANP can be to address the problem of forming a network and assessment of Two-Way is possible. We apply this technique personnel assessment, the weights of rater of each point can be reasonably determined. We suggest absolute assessment for Two-Way assessment by ANP. We have verified that the consent of the two approaches is higher than conventional mechanism. Also, human resources consultant we got a comment about the application of the practice.

Keywords: personnel evaluation, pairwise comparison, analytic network process (ANP), two-ways

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Authors: Sevgi Balkan-Sahin

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The Justice and Development Party (AKP) government has included nuclear power as a major component of Turkey’s new energy strategy by promoting it as the only alternative for Turkey to diversify energy resources, trigger economic growth, and boost competitiveness of the country. The effective promotion of such a framing has created a hegemonic discourse around nuclear energy in Turkey. However, fiercely opposing the nuclear initiative of the government, the Turkish anti-nuclear platform (ANP) composed of more than 50 civil society groups has challenged the hegemonic discourse of the AKP government by presenting nuclear energy as dangerous for human health, human rights, and the protection of environment. Based on an engagement between Gramscian perspective and Laclau and Mouffe’s discourse theory, this paper considers the discourses of the Turkish anti-nuclear platform and its associated activities as a counter-hegemonic strategy to change the ‘common sense’ on nuclear energy in Turkey. Analyzing the data from interviews with the representatives of the anti-nuclear platform coupled with primary sources, such as Parliamentary Records and official statements by civil society organizations, the paper highlights how the anti-nuclear platform exercises power through counter-hegemonic discourses in terms of the delegitimization of nuclear energy in Turkey.

Keywords: counter-hegemony, discourse, nuclear energy, Turkey

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Authors: Maryam Kalkatechi

Abstract:

Architecture students spent most of their qualitative time in studios during their years of study. The studio table’s importance as furniture in the studio is that it elevates the quality of the projects and positively influences the student’s productivity. This paper first describes the aspects considered in designing comprehensive studio table and later details on each aspect. Comprehensive studio tables are meant to transform the studio space to an efficient yet immense place of learning, collaboration, and participation. One aspect of these tables is that the surface transforms to a place of accommodation for design conversations, the other aspect of these tables is the efficient interactive platform of the tools. The discussion factors of the comprehensive studio include; the comprehensive studio setting of workspaces, the arrangement of the comprehensive studio tables, the collaboration aspects in the studio, the studio display and lightings shaped by the tables and lighting of the studio.

Keywords: studio tables, student performance, productivity, hologram, 3D printer

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Authors: Tzong-Shing Cheng, Chen Pei Chen, Shu-Wei Chen

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The purpose of this research is to apply Unified Theory of Acceptance and Use of Technology (UTAUT) model to investigate the factors that influence elementary school teacher’s behavior of using e-books. Based on the literature review, a questionnaire was modified and used to test the elementary school teachers in Changhua. A total of 420 questionnaires were administered and 364 of them were returned, including 328 valid and 36 invalid questionnaires. The effective response rate is 78%. The methods of data analysis include descriptive statistics, factor analysis, Pearson’s correlation coefficient, one way analysis of variance (ANOVA) and simple regression analysis. The results show that: 1. There were significant difference in the Elementary school teachers’ “Performance Expectancy”, “Effort Expectancy”, “Social Influence”, and “Facilitating Conditions” depending on their different “Demographic Variables”. 2. “Performance Expectancy” and “Behavioral Intention to Use” are positively correlated. 3. “Effort Expectancy” and “Behavioral Intention to Use” are positively correlated. 4. There was no significant relationship between “Social Influence” and “Behavioral Intention to Use”. 5. There was significant relationship between “Facilitating Conditions” and “Use Behavior”.

Keywords: e-books, UTAUT, elementary school teacher, behavioral intention to use

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Authors: Jabulani Sibanda, Clemence Chikiwa

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This paper traces diverse language practices representative of equally diverse conceptions of language. To be dynamic with languaging practices, one needs to appreciate nuanced languaging practices, their challenges, prospects, and opportunities. The paper presents what we envision as three major conceptions of language that give impetus to diverse language practices. It examines theoretical models of the bilingual mental lexicon and how they inform language practices. The paper explores classroom languaging practices that have been promulgated and experimented with. The paper advocates the deployment of multisensory semiotic systems to complement linguistic classroom communication and the acknowledgement of learners’ linguistic and semiotic resources as valid in the learning enterprise. It recommends the enactment of specific clauses on language in education policies and curriculum documents that empower classroom interactants to exercise discretion in languaging practices.

Keywords: languaging, monolingual, multilingual, semiotic and linguistic repertoire

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Authors: Fereshteh Darash

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Reflective tax management corresponds to the Agency Theory since it determines the motivation of managers for tax management actions and short-term and long-term consequences. Therefore, selection of tax strategy contributes to the tax and financial position of the firm in the future. The aim of the present research is to evaluate the effect of tax management components on risk-taking of firms listed in Tehran stock exchange by using regression analysis method. Results show that tax effective rate, tax risk and tax planning have no significant effect on the firm's future risk. Results suggest that stakeholders assess the effective tax rate and delay in tax payment in line with their benefits. They tend to accept the higher risk cost for reduction of tax payments and benefits of higher liquidity in current period. Hence, effective tax rate and tax risk have no significant effect on future risk of the firm. Moreover, tax planning yields no information regarding the predictability of the future profits and as a result, it has no significant effect on the future risk of the firm since specific goals of financial reporting are in priority for the stakeholders and regardless of the firm’s data analysis, they take investment decisions and they less intend to purchase the stocks in a rational manner.

Keywords: tax management, tax effective rate, tax risk, tax planning, firm risk

Procedia PDF Downloads 131

Authors: Chinaza Uleanya, Martin Duma, Bongani Gamede

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The research investigated pedagogic malpractices by lecturers in sub-Sahara African universities. The population of the study consisted of undergraduates and lecturers in selected universities in Nigeria and South Africa. Mixed method approach was adopted for data collection. The sample population of the study was 480 undergraduate students and 16 lecturers. Questionnaires with 4 point Likert-scale were administered to 480 respondents while interviews were conducted with 6 lecturers. In addition, the teaching strategies of 10 lecturers were observed. Data analyses indicated that poor work environment demotivates lecturers and makes them involved in pedagogic malpractice which is one of the causes of learning challenges faced by undergraduates. The finding of the study also shows that pedagogic malpractice contributes to the high rate of dropout in sub-Sahara African universities. Based on the results, it was recommended that qualified lecturers be employed and given conducive environments to work.

Keywords: malpractice, pedagogy, pedagogic malpractice, correlates

Procedia PDF Downloads 301

Authors: Ankit Kumar, Balram Dubey

Abstract:

This study proposes a three dimensional prey-predator model with additional food, provided to predator individuals, including gestation delay in predators and delay in supplying the additional food to predators. It is assumed that the interaction between prey and predator is followed by Holling type-II functional response. We discussed the steady states and their local and global asymptotic behavior for the non-delayed system. Hopf-bifurcation phenomenon with respect to different parameters has also been studied. We obtained a range of predator’s tendency factor on provided additional food, in which the periodic solutions occur in the system. We have shown that oscillations can be controlled from the system by increasing the tendency factor. Moreover, the existence of periodic solutions via Hopf-bifurcation is shown with respect to both the delays. Our analysis shows that both delays play an important role in governing the dynamics of the system. It changes the stability behavior into instability behavior. The direction and stability of Hopf-bifurcation are also investigated through the normal form theory and the center manifold theorem. Lastly, some numerical simulations and graphical illustrations have been carried out to validate our analytical findings.

Keywords: additional food, gestation delay, Hopf-bifurcation, prey-predator

Procedia PDF Downloads 126

Authors: Érick Aragão Ribeiro, George André Pereira Thé, José Marques Soares

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Graphical Human Interface, also known as supervision software, are increasingly present in industrial processes supported by Supervisory Control and Data Acquisition (SCADA) systems and so it is evident the need for qualified developers. In order to make engineering students able to produce high quality supervision software, method for the development must be created. In this paper we propose model, based on the international standards ISO/IEC 25010 and ISO/IEC 25040, for the development of graphical human interface. When compared with to other methods through experiments, the model here presented leads to improved quality indexes, therefore help guiding the decisions of programmers. Results show the efficiency of the models and the contribution to student learning. Students assessed the training they have received and considered it satisfactory.

Keywords: software development models, software quality, supervision software, fuzzy logic

Procedia PDF Downloads 369

Authors: Rezwana Rahman

Abstract:

Various simulations have been conducted to understand the particle's macroscopic behavior in the solid-gas multiphase flow in rotating drums in the past. In these studies, the particle-wall no-slip boundary condition was usually adopted. However, the non-slip boundary condition is rarely encountered in real systems. A little effort has been made to investigate the particle behavior at slip boundary conditions. The paper represents a study of the gas-solid flow in a horizontal rotating drum at a slip boundary wall condition. Two different sizes of particles with the same density have been considered. The Eulerian–Eulerian multiphase model with the kinetic theory of granular flow was used in the simulations. The granular pressure at the rolling flow regime with specularity coefficient 1 was examined and compared with that obtained based on the no-slip boundary condition. The results reveal that the profiles of granular pressure distribution on the transverse plane of the drum are similar for both boundary conditions. But, overall, compared with those for the no-slip boundary condition, the values of granular pressure for specularity coefficient 1 are larger for the larger particle and smaller for the smaller particle.

Keywords: boundary condition, eulerian–eulerian, multiphase, specularity coefficient, transverse plane

Procedia PDF Downloads 216

Authors: Frank C. Amaechi, Adeyinka A. Aderinto, Usman A. Ojedokun, Oludayo Tade

Abstract:

Illicit trading in new naira notes has become a common practice in most communities in Nigeria despite the Central Bank Act’s in 2007 proscription of all forms of naira abuse. This study investigated the socio-economic factors sustaining illicit trading in new naira notes in Ibadan metropolis. The study was exploratory and cross-sectional in design. Neutralization theory was adopted as theoretical framework. Data were generated through the combination of in-depth interview and key informant interview methods. The purposive sampling technique was utilised to select five illicit traders of new naira notes, 32 patrons of the trade and six bank officials. Findings revealed that illicit trading in Nigeria’s national currency is flourishing because of the frequent demand for new naira notes that are not readily available in Nigerian banks. Also, the norm of cash spraying at social events is sustaining the illicit markets for new naira notes in Ibadan metropolis. In addition, a chain of network, comprising three principal actors, is behind the illegal business. A strict enforcement of the law banning cash spraying is advocated as a means of arresting this phenomenon.

Keywords: illicit trading, naira notes, national currency, Nigeria

Procedia PDF Downloads 305