Search results for: Riener muscle model

Authors: Levicatus Mugenyi, Joaniter Nankabirwa, Emmanuel Arinaitwe, John Rek, Niel Hens, Moses Kamya, Grant Dorsey

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Background: Indoor residual spraying (IRS) reduces vector densities and malaria transmission, however, the most effective spraying intervals for IRS have not been well established. We aim to estimate the optimal timing interval for IRS using a modeling approach. Methods: We use a generalized additive model to estimate the optimal timing interval for IRS using the predicted malaria incidence. The model is applied to post IRS cohort clinical data from children aged 0.5–10 years in selected households in Tororo, historically a high malaria transmission setting in Uganda. Six rounds of IRS were implemented in Tororo during the study period (3 rounds with bendiocarb: December 2014 to December 2015, and 3 rounds with actellic: June 2016 to July 2018). Results: Monthly incidence of malaria from October 2014 to February 2019 decreased from 3.25 to 0.0 per person-years in the children under 5 years, and 1.57 to 0.0 for 5-10 year-olds. The optimal time interval for IRS differed between bendiocarb and actellic and by IRS round. It was estimated to be 17 and 40 weeks after the first round of bendiocarb and actellic, respectively. After the third round of actellic, 36 weeks was estimated to be optimal. However, we could not estimate from the data the optimal time after the second and third rounds of bendiocarb and after the second round of actellic. Conclusion: We conclude that to sustain the effect of IRS in a high-medium transmission setting, the second rounds of bendiocarb need to be applied roughly 17 weeks and actellic 40 weeks after the first round, and the timing differs for subsequent rounds. The amount of rainfall did not influence the trend in malaria incidence after IRS, as well as the IRS timing intervals. Our results suggest that shorter intervals for the IRS application can be more effective compared to the current practice, which is about 24 weeks for bendiocarb and 48 weeks for actellic. However, when considering our findings, one should account for the cost and drug resistance associated with IRS. We also recommend that the timing and incidence should be monitored in the future to improve these estimates.

Keywords: incidence, indoor residual spraying, generalized additive model, malaria

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Authors: Florina Dutt, Subhajit Das, Matthew Swartz

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Lighting design and its impact on indoor comfort conditions are an integral part of good interior design. Impact of lighting in an interior space is manifold and it involves many sub components like glare, color, tone, luminance, control, energy efficiency, flexibility etc. While other components have been researched and discussed multiple times, this paper discusses the research done to understand the glare component from an artificial lighting source in an indoor space. Consequently, the paper discusses a parametric model to convey real time glare level in an interior space to the designer/ architect. Our end users are architects and likewise for them it is of utmost importance to know what impression the proposed lighting arrangement and proposed furniture layout will have on indoor comfort quality. This involves specially those furniture elements (or surfaces) which strongly reflect light around the space. Essentially, the designer needs to know the ramification of the ‘discomfortable glare’ at the early stage of design cycle, when he still can afford to make changes to his proposed design and consider different routes of solution for his client. Unfortunately, most of the lighting analysis tools that are present, offer rigorous computation and analysis on the back end eventually making it challenging for the designer to analyze and know the glare from interior light quickly. Moreover, many of them do not focus on glare aspect of the artificial light. That is why, in this paper, we explain a novel approach to approximate interior glare data. Adding to that we visualize this data in a color coded format, expressing the implications of their proposed interior design layout. We focus on making this analysis process very fluid and fast computationally, enabling complete user interaction with the capability to vary different ranges of user inputs adding more degrees of freedom for the user. We test our proposed parametric model on a case study, a Computer Lab space in our college facility.

Keywords: computational geometry, glare impact in interior space, info visualization, parametric lighting analysis

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Authors: Muhammad Arif Mahmood, Andrei C. Popescu, Mihai Oane, Diana Chioibascu, Carmen Ristoscu, Ion N. Mihailescu

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Powder flow and temperature distributions are recognized as influencing factors during laser melting deposition (LMD) process, that not only affect the consolidation rate but also characteristics of the deposited layers. Herewith, two simplified analytical models will be presented to simulate the powder flow with the inclusion of powder particles size distribution in Gaussian form, under three powder jet nozzles, and temperature analyses during LMD process. The output of the 1st model will serve as the input in the 2nd model. The models will be validated with experimental data, i.e., weight measurement method for powder particles distribution and infrared imaging for temperature analyses. This study will increase the cost-efficiency of the LMD process by adjustment of the operating parameters for reaching optimal powder debit and energy. This research has received funds under the Marie Sklodowska-Curie grant agreement No. 764935, from the European Union’s Horizon 2020 research and innovation program.

Keywords: laser additive manufacturing, powder particles size distribution in Gaussian form, powder stream distribution, temperature analyses

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Authors: Wee Tong Liaw, Elaine Wong Yee Sing

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The main objective and focus of this study are to establish the significance of a sustained health promoting workplace on stock and portfolio returns focusing on companies listed on the Singapore stock exchange, using a two-factor model comprising of the single factor CAPM and a 'health promoting workplace' factor. The 'health promoting workplace' factor represents the excess returns derived between two portfolios of component stocks that, when combined, would represent a top tier stock market index in Singapore, namely the STI index. The first portfolio represents companies that are independently assessed by the Singapore’s Health Award, SHA, to have a sustained and comprehensive health promoting workplace (SHA-STI portfolio) and the second portfolio represents companies that had not been independently assessed (Non-SHA STI portfolio). Since 2001, many companies in Singapore have voluntarily participated in the bi-annual Singapore HEALTH Award initiated by the Health Promotion Board of Singapore (HPB). The Singapore HEALTH Award (SHA), is an industry-wide award and assessment process. SHA assesses and recognizes employers in Singapore for implementing a comprehensive and sustainable health promotion programme at their workplaces. When using a ten year holding period instead of a one year holding period, excess returns in the SHA-STI portfolio over Non-SHA STI portfolio were consistently being observed over all test periods, during 2001 to 2013. In addition, when applied to the SHA-STI portfolio, results from the Two Factor Model consistently revealed higher explanatory powers across all test periods for the portfolio as well as all the individual component stocks in SHA-STI portfolio, than the single factor CAPM model. However, with respect to attaining higher level of achievement in the Singapore Health Award, this study did not show any incentive for selecting listed companies that have achieved a higher level of award. Results from this study would give further insights to investors and fund managers alike who intend to consider health promoting workplace as a risk factor in their stock or portfolio selection process, in particular for investors who have a preference for STI’s component stocks and with a longer investment horizon. Key micro factors like management abilities, business development strategies and production capabilities that meet the needs of market would create the demand for a company’s product(s) or service(s) and consequently contribute to its top line and profitability. Thereafter, the existence of a sustainable health promoting workplace would be a key catalytic factor in sustaining a productive workforce needed to support the continued success of a profitable business.

Keywords: asset pricing model, company's performance, stock returns, financial risk factor, sustained health promoting workplace

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Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: Claudia Munoz, Henry Laniado

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In this research, the impact of outbound and return flight schedule preferences on airline choice for international trips is quantified. Several studies have used airline choice data to identify preferences and trade-offs of different air carrier service attributes, such as travel time, fare and frequencies. However, estimation of the effect return flight schedules have on airline choice for an international round-trip flight has not yet been studied in detail. The multinomial logit model found shows that airfare, travel time, arrival preference schedule in the outward journey, departure preference in the return journey and the schedule combination of round-trip flights are significantly affecting passenger choice behavior in international round-trip flights. it results indicated that return flight schedule preference plays a substantial role in air carrier choice and has a similar effect to outbound flight schedule preference. Thus, this study provides an analytical tool designed to provide a better understanding of international round-trip flight demand determinants and support carrier decisions.

Keywords: flight schedule, airline choice, return flight, passenger choice behavior

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Authors: Nieves M. C. Talavera-Prieto, Abel G. M. Ferreira, António T. G. Portugal, Rui J. Moreira, Jaime B. Santos

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The knowledge of biodiesel density over large ranges of temperature and pressure is important for predicting the behavior of fuel injection and combustion systems in diesel engines, and for the optimization of such systems. In this study, cottonseed oil was transesterified into biodiesel and its density was measured at temperatures between 288 K and 358 K and pressures between 0.1 MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg.m^-3. Experimental pressure-volume-temperature (pVT) cottonseed data was used along with literature data relative to other 18 biodiesels, in order to build a database used to test the correlation of density with temperarure and pressure using the Goharshadi–Morsali–Abbaspour equation of state (GMA EoS). To our knowledge, this is the first that density measurements are presented for cottonseed biodiesel under such high pressures, and the GMA EoS used to model biodiesel density. The new tested EoS allowed correlations within 0.2 kg•m-3 corresponding to average relative deviations within 0.02%. The built database was used to develop and test a new full predictive model derived from the observed linear relation between density and degree of unsaturation (DU), which depended from biodiesel FAMEs profile. The average density deviation of this method was only about 3 kg.m-3 within the temperature and pressure limits of application. These results represent appreciable improvements in the context of density prediction at high pressure when compared with other equations of state.

Keywords: biodiesel density, correlation, equation of state, prediction

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Authors: Lam Pham, Peter Hubber, Russell Tytler

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Students’ conceptual understandings of chemistry concepts/phenomena involve capability to coordinate across the three levels of Johnston’s triangle model. This triplet model is based on reasoning about chemical phenomena across macro, sub-micro and symbolic levels. In chemistry education, there is a need for further examining inquiry-based approaches that enhance students’ conceptual learning and problem solving skills. This research adopted a directed inquiry pedagogy based on students constructing and coordinating representations, to investigate senior school students’ capabilities to flexibly move across Johnston’ levels when learning dilution and molar concentration concepts. The participants comprise 50 grade 11 and 20 grade 10 students and 4 chemistry teachers who were selected from 4 secondary schools located in metropolitan Melbourne, Victoria. This research into classroom practices used ethnographic methodology, involved teachers working collaboratively with the research team to develop representational activities and lesson sequences in the instruction of a unit on solution chemistry. The representational activities included challenges (Representational Challenges-RCs) that used ‘representational tools’ to assist students to move across Johnson’s three levels for dilution phenomena. In this report, the ‘representational tool’ called ‘cross and portion’ model was developed and used in teaching and learning the molar concentration concept. Students’ conceptual understanding and problem solving skills when learning with this model are analysed through group case studies of year 10 and 11 chemistry students. In learning dilution concepts, students in both group case studies actively conducted a practical experiment, used their own language and visualisation skills to represent dilution phenomena at macroscopic level (RC1). At the sub-microscopic level, students generated and negotiated representations of the chemical interactions between solute and solvent underpinning the dilution process. At the symbolic level, students demonstrated their understandings about dilution concepts by drawing chemical structures and performing mathematical calculations. When learning molar concentration with a ‘cross and portion’ model (RC2), students coordinated across visual and symbolic representational forms and Johnson’s levels to construct representations. The analysis showed that in RC1, Year 10 students needed more ‘scaffolding’ in inducing to representations to explicit the form and function of sub-microscopic representations. In RC2, Year 11 students showed clarity in using visual representations (drawings) to link to mathematics to solve representational challenges about molar concentration. In contrast, year 10 students struggled to get match up the two systems, symbolic system of mole per litre (‘cross and portion’) and visual representation (drawing). These conceptual problems do not lie in the students’ mathematical calculation capability but rather in students’ capability to align visual representations with the symbolic mathematical formulations. This research also found that students in both group case studies were able to coordinate representations when probed about the use of ‘cross and portion’ model (in RC2) to demonstrate molar concentration of diluted solutions (in RC1). Students mostly succeeded in constructing ‘cross and portion’ models to represent the reduction of molar concentration of the concentration gradients. In conclusion, this research demonstrated how the strategic introduction and coordination of chemical representations across modes and across the macro, sub-micro and symbolic levels, supported student reasoning and problem solving in chemistry.

Keywords: cross and portion, dilution, Johnston's triangle, molar concentration, representations

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Authors: Saltanat Zhilkibayeva, Muratulla Utenov, Nurzhan Utenov

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The distributed inertia forces of complex nature appear in links of rod mechanisms within the motion process. Such loads raise a number of problems, as the problems of destruction caused by a large force of inertia; elastic deformation of the mechanism can be considerable, that can bring the mechanism out of action. In this work, a new analytical approach for the definition of internal forces in links of robotic systems and mechanisms with statically indeterminate and determinate structures taking into account the distributed inertial and concentrated forces is proposed. The relations between the intensity of distributed inertia forces and link weight with geometrical, physical and kinematic characteristics are determined in this work. The distribution laws of inertia forces and dead weight make it possible at each position of links to deduce the laws of distribution of internal forces along the axis of the link, in which loads are found at any point of the link. The approximation matrixes of forces of an element under the action of distributed inertia loads with the trapezoidal intensity are defined. The obtained approximation matrixes establish the dependence between the force vector in any cross-section of the element and the force vector in calculated cross-sections, as well as allow defining the physical characteristics of the element, i.e., compliance matrix of discrete elements. Hence, the compliance matrixes of an element under the action of distributed inertial loads of trapezoidal shape along the axis of the element are determined. The internal loads of each continual link are unambiguously determined by a set of internal loads in its separate cross-sections and by the approximation matrixes. Therefore, the task is reduced to the calculation of internal forces in a final number of cross-sections of elements. Consequently, it leads to a discrete model of elastic calculation of links of rod mechanisms. The discrete model of the elements of mechanisms and robotic systems and their discrete model as a whole are constructed. The dynamic equilibrium equations for the discrete model of the elements are also received in this work as well as the equilibrium equations of the pin and rigid joints expressed through required parameters of internal forces. Obtained systems of dynamic equilibrium equations are sufficient for the definition of internal forces in links of mechanisms, which structure is statically definable. For determination of internal forces of statically indeterminate mechanisms (in the way of determination of internal forces), it is necessary to build a compliance matrix for the entire discrete model of the rod mechanism, that is reached in this work. As a result by means of developed technique the programs in the MAPLE18 system are made and animations of the motion of the fourth class mechanisms of statically determinate and statically indeterminate structures with construction on links the intensity of cross and axial distributed inertial loads, the bending moments, cross and axial forces, depending on kinematic characteristics of links are obtained.

Keywords: distributed inertial forces, internal forces, statically determinate mechanisms, statically indeterminate mechanisms

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Authors: Laras Tursilowati, Bambang Siswanto

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Urban climate change has short- and long-term implications for decision-makers in urban development. The problem for this important metropolitan regional of population and economic value is that there is very little usable information on climate change. Research about urban climate change has been carried out in Bali Indonesia by using Conformal Cubic Atmospheric Model (CCAM) that runs with Representative Concentration Pathway (RCP)4.5. The history data means average data from 1975 to 2005, climate projections with RCP4.5 scenario means average data from 2006 to 2099, and anomaly (urban climate change) is RCP4.5 minus history. The results are the history of temperature between 22.5-27.5 OC, and RCP4.5 between 25.5-29.5 OC. The temperature anomalies can be seen in most of northern Bali that increased by about 1.6 to 2.9 OC. There is a reduced humidity tendency (drier) in most parts of Bali, especially the northern part of Bali, while a small portion in the south increase moisture (wetter). The comfort index of Bali region in history is still relatively comfortable (20-26 OC), but on the condition RCP4.5 there is no comfortable area with index more than 26 OC (hot and dry). This research is expected to be useful to help the government make good urban planning.

Keywords: CCAM, comfort index, IPCC AR5, temperature, urban climate change

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Authors: Yamina Boughari, Ruxandra Mihaela Botez, Florian Theel, Georges Ghazi

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Clearance of flight control laws of a civil aircraft is a long and expensive process in the Aerospace industry. Thousands of flight combinations in terms of speeds, altitudes, gross weights, centers of gravity and angles of attack have to be investigated, and proved to be safe. Nonetheless, in this method, a worst flight condition can be easily missed, and its missing would lead to a critical situation. Definitively, it would be impossible to analyze a model because of the infinite number of cases contained within its flight envelope, that might require more time, and therefore more design cost. Therefore, in industry, the technique of the flight envelope mesh is commonly used. For each point of the flight envelope, the simulation of the associated model ensures the satisfaction or not of specifications. In order to perform fast, comprehensive and effective analysis, other varying parameters models were developed by incorporating variations, or uncertainties in the nominal models, known as Linear Fractional Representation LFR models; these LFR models were able to describe the aircraft dynamics by taking into account uncertainties over the flight envelope. In this paper, the LFRs models are developed using the speeds and altitudes as varying parameters; The LFR models were built using several flying conditions expressed in terms of speeds and altitudes. The use of such a method has gained a great interest by the aeronautical companies that have seen a promising future in the modeling, and particularly in the design and certification of control laws. In this research paper, we will focus on the Cessna Citation X open loop stability analysis. The data are provided by a Research Aircraft Flight Simulator of Level D, that corresponds to the highest level flight dynamics certification; this simulator was developed by CAE Inc. and its development was based on the requirements of research at the LARCASE laboratory. The acquisition of these data was used to develop a linear model of the airplane in its longitudinal and lateral motions, and was further used to create the LFR’s models for 12 XCG /weights conditions, and thus the whole flight envelope using a friendly Graphical User Interface developed during this study. Then, the LFR’s models are analyzed using Interval Analysis method based upon Lyapunov function, and also the ‘stability and robustness analysis’ toolbox. The results were presented under the form of graphs, thus they have offered good readability, and were easily exploitable. The weakness of this method stays in a relatively long calculation, equal to about four hours for the entire flight envelope.

Keywords: flight control clearance, LFR, stability analysis, robustness analysis

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Authors: Mozhgan Mohammadi, Arezoo Ghadi

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Arsenic is known as a potential threat to the environment. Therefore, the aim of this research is to assess the arsenic removal efficiency from an aqueous solution, with a new biosorbent composed of a black seed pulp (BSP). To treat BSP, the combination of two methods (i.e. treating with mineral acids and use at high temperature) was used and designed bio-sorbent called BSP-activated/carbonized. The BSP-activated and BSP-carbonized were also prepared using HCL and 400°C temperature, respectively, to compare the results of each three methods. Followed by, adsorption parameters such as pH, initial ion concentration, biosorbent dosage, contact time, and temperature were assessed. It was found that the combination method has provided higher adsorption capacity so that up to ~99% arsenic removal was observed with BSP-activated/carbonized at pH of 7.0 and 40°C. The adsorption capacity for BSP-carbonized and BSP-activated were 87.92% (pH: 7, 60°C) and 78.50% (pH: 6, 90°C), respectively. Moreover, adsorption kinetics data indicated the best fit with the pseudo-second-order model. The maximum biosorption capacity, by the Langmuir isotherm model, was also recorded for BSP-activated/carbonized (53.47 mg/g). It is notable that arsenic adsorption on studied bio sorbents takes place as spontaneous and through chemisorption along with the endothermic nature of the biosorption process and reduction of random collision in the solid-liquid phase.

Keywords: black seed pulp, bio-sorbents, treatment of sorbents, adsorption isotherms

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Authors: Nada Abdelsadig Moahamed Saied

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Female Genital Mutilation (FGM) and other similar traditional cultural practices pose a significant problem for Sudanese society. Such actions are severe and seriously detrimental to people's health since they are based on false social perceptions. To address these problems, numerous institutions and organizations were compelled to act rapidly. Female circumcision, or FGM, is one of the riskiest practices. It is referred to as the excision of the genitalia. Any surgeries involving the total or partial removal of the external female genitalia for non-medical reasons fall under this category. The results of FGM can vary depending on the kind and degree of the operation. These can be categorized as short-term, mid-term, or long-term issues. Infections, including the Human, blood, discomfort, and difficulty urinating are the immediate effects. FGM is defined by the World Health Organization (WHO) as practices that purposefully damage or modify female genital organs for non-medical purposes. It often takes place between the ages of one and fifteen. The girl's right to decide on important choices affecting her sexual and reproductive health is violated because the act is usually performed without her consent and frequently against her will. UNICEF, the United Nations International Children's Emergency Fund, aggressively combats the issue of FGM in Sudan. Numerous programs were started by NGOs to stop the practice. To our knowledge, no scientific study has been conducted to evaluate the effects of such social marketing techniques on simulating and comprehending society’s feelings surrounding FGM. This study proposes the development of a structural equation model aiming to determine the impact of awareness programs on people’s intentions to adopt the behavior of abandoning FGM based on theoretical models of behavior change. The model incorporates all the relevant factors that contribute to FGM and possible strategic actions to tackle this problem. The theoretical backdrop for FGM is presented in the next section, which also explains the practice's history, justifications, and potential treatments. The methodology section that follows describes the structural equation model. The proposed model, which compiles all the pertinent elements into a single image, is presented in the fourth part. Finally, conclusions are reached, and suggestions for further research are made.

Keywords: social marketing, policy-making, behavioral change, female genital mutilation, culture

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Authors: Nenashev Yaroslav, Russkin Oleg

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The mixing process is one of the most important and critical stages in many industrial sectors, such as chemistry, pharmaceuticals, and the food industry. When designing equipment with mixing impellers, technology developers often encounter working environments with complex physical properties and rheology. In such cases, the use of computational fluid dynamics tools is an excellent solution to mitigate risks and ensure the stable operation of the equipment. The research focuses on one of the designed reactors with mixing impellers intended for polymer synthesis. The study describes an approach to modeling reactors of similar configurations, taking into account the complex properties of the mixed liquids using the computational fluid dynamics (CFD) method. To achieve this goal, a complex 3D model was created, accurately replicating the functionality of chemical equipment. The model allows for the assessment of the hydrodynamic behavior of the reaction mixture inside the reactor, consideration of heat release due to the reaction, and the heat exchange between the reaction mixture and the cooling medium. The results indicate that the choice of the type and size of the mixing device significantly affects the efficiency of the mixing process inside the chemical reactor.

Keywords: CFD, mixing, blending, chemical reactor, non-Newton liquids, polymers

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Authors: Mário Ernesto Sitoe, Orlando Zacarias

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University education is influenced by several factors that range from the adoption of strategies to strengthen the whole process to the academic performance improvement of the students themselves. This work uses data mining techniques to develop a predictive model to identify students with a tendency to evasion and retention. To this end, a database of real students’ data from the Department of University Admission (DAU) and the Department of Mathematics and Informatics (DMI) was used. The data comprised 388 undergraduate students admitted in the years 2009 to 2014. The Weka tool was used for model building, using three different techniques, namely: K-nearest neighbor, random forest, and logistic regression. To allow for training on multiple train-test splits, a cross-validation approach was employed with a varying number of folds. To reduce bias variance and improve the performance of the models, ensemble methods of Bagging and Stacking were used. After comparing the results obtained by the three classifiers, Logistic Regression using Bagging with seven folds obtained the best performance, showing results above 90% in all evaluated metrics: accuracy, rate of true positives, and precision. Retention is the most common tendency.

Keywords: evasion and retention, cross-validation, bagging, stacking

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Authors: Rehan Ali Shah, A. M. Siddiqui, T. Haroon

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Slip boundary value problem in wire coating analysis with heat transfer is examined. The fluid is assumed to be viscoelastic PTT (Phan-Thien and Tanner). The rheological constitutive equation of PTT fluid model simulates various polymer melts. Therefore, the current consequences are valuable in a number of realistic situations. Effects of slip parameter γ as well as εDec^2 (viscoelastic index) on the axial velocity, shear stress, normal stress, average velocity, volume flux, thickness of coated wire, shear stress, force on the total wire and temperature distribution profiles have been investigated. A new direction is explored to analyze the flow with the slip parameter. The slippage at the boundaries plays an important role in thickness of coated wire. It is noted that as the slip parameter increases the flow rate and thickness of coated wire increases while, temperature distribution decreases. The results reduce to no slip when the slip parameter is vanished. Furthermore, we can obtain the results for Maxwell and viscous model by setting ε and λ equal to zero respectively.

Keywords: wire coating, straight annular die, PTT fluid, heat transfer, slip boundary conditions

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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: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

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Authors: Aliya Grig, Konstantin Sokolov, Igor Shatalin

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The report describes the methodology for collecting data and building an ML model for determining the personality psychotype using profiling and personality traits methods based on several short messages of a user communicating on an arbitrary topic with a chitchat bot. In the course of the experiments, the minimum amount of text was revealed to confidently determine aspects of personality. Model accuracy - 85%. Users' language of communication is English. AI for a personalized communication with a user based on his mood, personality, and current emotional state. Features investigated during the research: personalized communication; providing empathy; adaptation to a user; predictive analytics. In the report, we describe the processes that captures both structured and unstructured data pertaining to a user in large quantities and diverse forms. This data is then effectively processed through ML tools to construct a knowledge graph and draw inferences regarding users of text messages in a comprehensive manner. Specifically, the system analyzes users' behavioral patterns and predicts future scenarios based on this analysis. As a result of the experiments, we provide for further research on training AI models to be empathetic, creating personalized communication for a user

Keywords: AI, empathetic, chatbot, AI models

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Authors: Omar Al Denali, Abdelaziz Badi

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The ASTM A516 Grade 70 steel is a suitable material used for the fabrication of boiler pressure vessels working in moderate and lower temperature services, and it has good weldability and excellent notch toughness. The post-weld heat treatment (PWHT) or stress-relieving heat treatment has significant effects on avoiding the martensite transformation and resulting in high hardness, which can lead to cracking in the heat-affected zone (HAZ). An adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the material tensile strength of post-weld heat treatment (PWHT) experiments. The ANFIS models presented excellent predictions, and the comparison was carried out based on the mean absolute percentage error between the predicted values and the experimental values. The ANFIS model gave a Mean Absolute Percentage Error of 0.556 %, which confirms the high accuracy of the model.

Keywords: prediction, post-weld heat treatment, adaptive neuro-fuzzy inference system, mean absolute percentage error

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Authors: Victor William Harris

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This research is a validation study of the Social Emotional Ability Inventory (SEAI), a multi-dimensional self-report instrument informed by positive psychology, emotional intelligence, social intelligence, and sociocultural learning theory. Designed for use in tandem with the Social Emotional Development (SEAD) theoretical model, the SEAI provides diagnostic-level guidance for professionals and individuals interested in investigating, identifying, and understanding social, emotional strengths, as well as remediating specific social competency deficiencies. The SEAI was shown to be psychometrically sound, exhibited strong internal reliability, and supported the a priori hypotheses of the SEAD. Additionally, confirmatory factor analysis provided evidence of goodness of fit, convergent and divergent validity, and supported a theoretical model that reflected SEAD expectations. The SEAI and SEAD hold potentially far-reaching and important practical implications for theoretical guidance and diagnostic-level measurement of social, emotional competency across a wide range of domains. Strategies researchers, practitioners, educators, and individuals might use to deploy SEAI in order to improve quality of life outcomes are discussed.

Keywords: emotion, emotional ability, positive psychology-social emotional ability, social emotional ability, social emotional ability instrument

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Authors: Anders Troedsson

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For us humans, risks are intimately linked to human vulnerabilities - where there is vulnerability, there is potentially insecurity, and risk. Reducing vulnerability through compensatory measures means increasing security and decreasing risk. The paper suggests that a meaningful way to approach the study of risks (including threats, assaults, crisis etc.), is to understand the vulnerabilities these external phenomena evoke in humans. As is argued, the basis of risk evaluation, as well as responses, is the more or less subjective perception by the individual person, or a group of persons, exposed to the external event or phenomena in question. This will be determined primarily by the vulnerability or vulnerabilities that the external factor are perceived to evoke. In this way, risk perception is primarily an inward dynamic, rather than an outward one. Therefore, a route towards an understanding of the perception of risks, is a closer scrutiny of the vulnerabilities which they can evoke, thereby approaching an understanding of what in the paper is called the essence of risk (including threat, assault etc.), or that which a certain perceived risk means to an individual or group of individuals. As a necessary basis for gauging the wide spectrum of potential risks and their meaning, the paper proposes a model of human vulnerabilities, drawing from i.a. a long tradition of needs theory. In order to account for the subjectivity factor, which mediates between the innate vulnerabilities on the one hand, and the event or phenomenon out there on the other hand, an ensuing ontological discussion about the timespace characteristics of risk/threat/assault as perceived by humans leads to the positing of two dimensions. These two dimensions are applied on the vulnerabilities, resulting in a modelling effort featuring four realms of vulnerabilities which are related to each other and together represent a dynamic whole. In approaching the problem of risk perception, the paper thus defines the relevant realms of vulnerabilities, depicting them as a dynamic whole. With reference to a substantial body of literature and a growing international policy trend since the 1990s, this model is put in the language of human security - a concept relevant not only for international security studies and policy, but also for other academic disciplines and spheres of human endeavor.

Keywords: human security, timespace, vulnerabilities, risk perception

Procedia PDF Downloads 336

Authors: Givi Kupatadze

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Backward economies are facing a challenge of achieving sustainable high economic growth rate. Dominant growth models represent a roadmap in framing economic development strategy. This paper examines a relevance of the dominant growth models for backward economies. Cobb-Douglas production function, the Harrod-Domar model of economic growth, the Solow growth model and general formula of gross domestic product are examined to undertake a comprehensive study of the dominant growth models. Deductive research method allows to uncover major weaknesses of the dominant growth models and to come up with practical implications for economic development strategy. The key finding of the paper shows, contrary to what used to be taught by textbooks of economics, that constant returns to scale property of the dominant growth models are a mere coincidence and its generalization over space and time can be regarded as one of the most unfortunate mistakes in the whole field of political economy. The major suggestion of the paper for backward economies is that understanding and considering taxonomy of economic activities based on increasing and diminishing returns to scale represent a cornerstone of successful economic development strategy.

Keywords: backward economies, constant returns to scale, dominant growth models, taxonomy of economic activities

Procedia PDF Downloads 375

Authors: Yosr Ghozzi

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The Cyber-Physical Systems terminology has been well received by the industrial community and specifically appropriated in educational settings. Indeed, our latest educational activities are based on the development of experimental platforms on an industrial scale. In fact, we built a collaborative learning model because of an international market study that led us to place ourselves at the heart of this technology. To align with these findings, a competency-based approach study was conducted, and program content was revised by reflecting the projectbased approach. Thus, this article deals with the development of educational devices according to a generated curriculum and specific educational activities while respecting the repository of skills adopted from what constitutes the educational cyber-physical production systems and the laboratories that are compliant and adapted to them. The implementation of these platforms was systematically carried out in the school's workshops spaces. The objective has been twofold, both research and teaching for the students in mechatronics and logistics of the electromechanical department. We act as trainers and industrial experts to involve students in the implementation of possible extension systems around multidisciplinary projects and reconnect with industrial projects for better professional integration.

Keywords: education 4.0, competency-based learning, teaching factory, project-based learning, cyber-physical systems, industry 4.0

Procedia PDF Downloads 107

Authors: Sarantos Psycharis

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Physical Computing, as an instructional model, is applied in the framework of the Engineering Pedagogy to teach “transversal/cross-cutting ideas” in a STEM content approach. Labview and Arduino were used in order to connect the physical world with real data in the framework of the so called Computational Experiment. Tertiary prospective engineering educators were engaged during their course and Computational Thinking (CT) concepts were registered before and after the intervention across didactic activities using validated questionnaires for the relationship between self-efficacy, computer programming, and CT concepts when STEM content epistemology is implemented in alignment with the Computational Pedagogy model. Results show a significant change in students’ responses for self-efficacy for CT before and after the instruction. Results also indicate a significant relation between the responses in the different CT concepts/practices. According to the findings, STEM content epistemology combined with Physical Computing should be a good candidate as a learning and teaching approach in university settings that enhances students’ engagement in CT concepts/practices.

Keywords: arduino, computational thinking, computer programming, Labview, self-efficacy, STEM

Procedia PDF Downloads 113

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

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

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

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Authors: N. Borjalilu, P. Rabiei, A. Enjoo

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Flight Data Monitoring (FDM) program assists an operator in aviation industries to identify, quantify, assess and address operational safety risks, in order to improve safety of flight operations. FDM is a powerful tool for an aircraft operator integrated into the operator’s Safety Management System (SMS), allowing to detect, confirm, and assess safety issues and to check the effectiveness of corrective actions, associated with human errors. This article proposes a model for safety risk assessment level of flight data in a different aspect of event focus based on fuzzy set values. It permits to evaluate the operational safety level from the point of view of flight activities. The main advantages of this method are proposed qualitative safety analysis of flight data. This research applies the opinions of the aviation experts through a number of questionnaires Related to flight data in four categories of occurrence that can take place during an accident or an incident such as: Runway Excursions (RE), Controlled Flight Into Terrain (CFIT), Mid-Air Collision (MAC), Loss of Control in Flight (LOC-I). By weighting each one (by F-TOPSIS) and applying it to the number of risks of the event, the safety risk of each related events can be obtained.

Keywords: F-topsis, fuzzy set, flight data monitoring (FDM), flight safety

Procedia PDF Downloads 168

Authors: Hsin-Yang Wang, K. M. A. Salam, Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao

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Detecting user intent from natural language expression has a wide variety of use cases in different natural language processing applications. Recently few-shot training has a spike of usage on commercial domains. Due to the lack of significant sample features, the downstream task performance has been limited or leads to an unstable result across different domains. As a state-of-the-art method, the pre-trained BERT model gathering the sentence-level information from a large text corpus shows improvement on several NLP benchmarks. In this research, we are proposing a method to change multi-class classification tasks into binary classification tasks, then use the confidence score to rank the results. As a language model, BERT performs well on sequence data. In our experiment, we change the objective from predicting labels into finding the relations between words in sequence data. Our proposed method achieved 71.0% accuracy in the internal intent detection dataset and 63.9% accuracy in the HuffPost dataset. Acknowledgment: This work was supported by NCKU-B109-K003, which is the collaboration between National Cheng Kung University, Taiwan, and SoftBank Corp., Tokyo.

Keywords: OSML, BERT, text classification, one shot

Procedia PDF Downloads 101

Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Li Long, Thomas Ortlepp

Abstract:

A theoretical model for the optimization of thermopile sensor performance is developed for thermoelectric-based infrared radiation detection. It is shown that the performance of polycrystalline silicon film thermopile sensor can be optimized according to the thermoelectric quality factor, sensor layer structure factor, and sensor layout geometrical form factor. Based on the properties of electrons, phonons, grain boundaries, and their interactions, the thermoelectric quality factor of polycrystalline silicon is analyzed with the relaxation time approximation of the Boltzmann transport equation. The model includes the effect of grain structure, grain boundary trap properties, and doping concentration. The layer structure factor is analyzed with respect to the infrared absorption coefficient. The optimization of layout design is characterized by the form factor, which is calculated for different sensor designs. A double-layer polycrystalline silicon thermopile infrared sensor on a suspended membrane has been designed and fabricated with a CMOS-compatible process. The theoretical approach is confirmed by measurement results.

Keywords: polycrystalline silicon, relaxation time approximation, specific detectivity, thermal conductivity, thermopile infrared sensor

Procedia PDF Downloads 140