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Authors: Alexander Kospach, Fabian Benezeder, Jürgen Abraham

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E-mobility poses new challenges for inverters (e.g., higher switching frequencies) in terms of thermal behavior and thermal management. Due to even higher switching frequencies, thermal losses become greater, and the cooling of critical components (like insulated gate bipolar transistor and diodes) comes into focus. New manufacturing methods, such as 3D printing, enable completely new pin-fin structures that can handle higher waste heat to meet the new thermal requirements. Based on the geometrical specifications of the industrial partner regarding the manufacturing possibilities for 3D printing, different and completely new pin-fin structures were numerically investigated for their hydraulic and thermal behavior in fundamental studies assuming an indirect liquid cooling. For the 3D computational fluid dynamics (CFD) thermal simulations OpenFOAM was used, which has as numerical method the finite volume method for solving the conjugate heat transfer problem. A steady-state solver for turbulent fluid flow and solid heat conduction with conjugate heat transfer between solid and fluid regions was used for the simulations. In total, up to fifty pinfin structures and arrangements, some of them completely new, were numerically investigated. On the basis of the results of the principal investigations, the best two pin-fin structures and arrangements for the complete module cooling of an automotive inverter were numerically investigated and compared. There are clear differences in the maximum temperatures for the critical components, such as IGTBs and diodes. In summary, it was shown that 3D pin fin structures can significantly contribute to the improvement of heat transfer and cooling of an automotive inverter. This enables in the future smaller cooling designs and a better lifetime of automotive inverter modules. The new pin fin structures and arrangements can also be applied to other cooling applications where 3D printing can be used.

Keywords: pin fin heat sink optimization, 3D printed pin fins, CFD simulation, power electronic cooling, thermal management

Procedia PDF Downloads 76

Authors: David Barney, Francis Pleban, Muna Muday

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The purpose of this study was to explore gender differences of former physical education students related to reflective experiences of competition in physical education learning environment. In the school environment, students are positioned in competitive situations, including in the physical education context. Therefore it is important to prepare future physical educators to address the role of competition in physical education. Participants for this study were 304 college-aged students and young adults (M = 1.53, SD = .500), from a private university and local community located in the western United States. When comparing gender, significant differences (p < .05) were reported for four (questions 5, 7, 12, and 14) of the nine scaling questions. Follow-up quantitative findings reported that males (41%) more than females (27%) witnessed fights in physical education environment during competitive games. Qualitative findings reported fighting were along the lines of verbal confrontation. Female participants tended to experience being excluded from games, when compared to male participants. Both male and female participants (total population; 95%, males; 98%; and females 92%) were in favor of including competition in physical education for students. Findings suggest that physical education teachers and physical education teacher education programs have a responsibility to develop gender neutral learning experiences that help students better appreciate the role competition plays, both in and out of the physical education classroom.

Keywords: competition, physical education, physical education teacher education, gender

Procedia PDF Downloads 483

Authors: Kathryn Watson

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In 2020 North Carolina passed legislation mandating all educators be trained in identifying, referring, and supporting students showing signs of mental health issues, drug use, suicidal ideation, and sex trafficking. This study collected survey responses from 226 educators in North Carolina to better understand their perspectives on the legislation and their self-efficacy in supporting student mental health needs. Key findings of the study reveal that the mandated trainings increased educator awareness of student mental health, and higher awareness was linked to higher self-efficacy in supporting student mental health needs. Additionally, the results showed that educators who identify as Black had lower levels of self-efficacy in supporting student mental health. Additionally, rural educators were least likely to support the legislation in comparison to their urban and suburban counterparts. These findings can help inform policymakers in evaluating the policy and district decision-makers in selecting and implementing school-based mental health training.

Keywords: school-based mental health, education policy, student health, North Carolina, K-12 education

Procedia PDF Downloads 46

Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

Procedia PDF Downloads 247

Authors: Lokanath Barik, Abinash Kumar Swain

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This paper presents vibration analysis of FGM sandwich structure with a complex profile governed by refined higher-order shear deformation theory (RHSDT) using isogeometric analysis (IGA). Functionally graded sandwich plates provide a wide range of applications in aerospace, defence, and aircraft industries due to their ability to distribute material functions to influence the thermo-mechanical properties as desired. In practical applications, these structures generally have intrinsic profiles, and their response to loads is significantly affected due to cut-outs. IGA is primarily a NURBS-based technique that is effective in solving higher-order differential equations due to its inherent C1 continuity imposition in solution space for a single patch. Complex structures generally require multiple patches to accurately represent the geometry, and hence, there is a loss of continuity at adjoining patch junctions. Therefore, patch coupling is desired to maintain continuity requirements throughout the domain. In this work, a novel strong coupling approach is provided that generates a well-defined NURBS-based model while achieving continuity. The methodology is validated by free vibration analysis of sandwich plates with present literature. The results are in good agreement with the analytical solution for different plate configurations and power law indexes. Numerical examples of rectangular and annular plates are discussed with variable boundary conditions. Additionally, parametric studies are provided by varying the aspect ratio, porosity ratio and their influence on the natural frequency of the plate.

Keywords: vibration analysis, FGM sandwich structure, multipatch geometry, patch coupling, IGA

Procedia PDF Downloads 61

Authors: Christopher Thornton, Niina Kolehmainen, Kianoush Nazarpour

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Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations.

Keywords: physical activity, machine learning, under 5s, disability, accelerometer

Procedia PDF Downloads 193

Authors: Igor A. Bessmertny, Bidru C. Enkomaryam

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Active involving patients and communities in health decisions can improve both people’s health and the healthcare system. Adopting artificial intelligence can lead to more accurate and complete patient record management. This review aims to identify the current state of researches conducted using artificial intelligence techniques to improve patient engagement and wellbeing, medical domains used in patient engagement context, and lastly, to assess opportunities and challenges for patient engagement in the wellness process. A search of peer-reviewed publications, reviews, conceptual analyses, white papers, author’s manuscripts and theses was undertaken. English language literature published in 2013– 2022 period and publications, report and guidelines of World Health Organization (WHO) were also assessed. About 281 papers were retrieved. Duplicate papers in the databases were removed. After application of the inclusion and exclusion criteria, 41 papers were included to the analysis. Patient counseling in preventing adverse drug events, in doctor-patient risk communication, surgical, drug development, mental healthcare, hypertension & diabetes, metabolic syndrome and non-communicable chronic diseases are implementation areas in healthcare where patient engagement can be implemented using artificial intelligence, particularly machine learning and deep learning techniques and tools. The five groups of factors that potentially affecting patient engagement in safety are related to: patient, health conditions, health care professionals, tasks and health care setting. Active involvement of patients and families can help accelerate the implementation of healthcare safety initiatives. In sub-Saharan Africa, using digital technologies like artificial intelligence in patient engagement context is low due to poor level of technological development and deployment. The opportunities and challenges available to implement patient engagement strategies vary greatly from country to country and from region to region. Thus, further investigation will be focused on methods and tools using the potential of artificial intelligence to support more simplified care that might be improve communication with patients and train health care professionals.

Keywords: artificial intelligence, patient engagement, machine learning, patient involvement

Procedia PDF Downloads 62

Authors: Jutharat Sunprasert, Ekapong Hirunsirisawat, Narongrit Waraporn, Somporn Peansukmanee

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Metal Ship and Robotic Car is one of the hands-on activities in the course, the Fundamental of Engineering that can be divided into three parts. The first part, the metal ships, was made by using engineering drawings, physics and mathematics knowledge. The second part is where the students learned how to construct a robotic car and control it using computer programming. In the last part, the students had to combine the workings of these two objects in the final testing. This aim of study was to investigate the effectiveness of hands-on activity by integrating Science, Technology, Engineering and Mathematics (STEM) concepts to develop scientific and engineering skills. The results showed that the majority of students felt this hands-on activity lead to an increased confidence level in the integration of STEM. Moreover, 48% of all students engaged well with the STEM concepts. Students could obtain the knowledge of STEM through hands-on activities with the topics science and mathematics, engineering drawing, engineering workshop and computer programming; most students agree and strongly agree with this learning process. This indicated that the hands-on activity: “Metal Ship and Robotic Car” is a useful tool to integrate each aspect of STEM. Furthermore, hands-on activities positively influence a student’s interest which leads to increased learning achievement and also in developing scientific and engineering skills.

Keywords: hands-on activity, STEM education, computer programming, metal work

Procedia PDF Downloads 449

Authors: Tahmina Huq

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This paper presents the findings of an action research study that aimed to investigate the efficacy of a visual dialogue strategy in assisting autistic students with intellectual disabilities in managing their immediate emotions and improving their academic achievements. The research sought to explore the effectiveness of teaching self-regulation techniques as an alternative to traditional approaches involving segregation. The study identified visual dialogue as a valuable tool for promoting self-regulation in this specific student population. Action research was chosen as the methodology due to its suitability for immediate implementation of the findings in the classroom. Autistic students with intellectual disabilities often face challenges in controlling their emotions, which can disrupt their learning and academic progress. Conventional methods of intervention, such as isolation and psychologist-assisted approaches, may result in missed classes and hindered academic development. This study introduces the utilization of visual dialogue between students and teachers as an effective self-regulation strategy, addressing the limitations of traditional approaches. Action research was employed as the methodology for this study, allowing for the direct application of the findings in the classroom. The study observed two 15-year-old autistic students with intellectual disabilities who exhibited difficulties in emotional regulation and displayed aggressive behaviors. The research question focused on the effectiveness of visual dialogue in managing the emotions of these students and its impact on their learning outcomes. Data collection methods included personal observations, log sheets, personal reflections, and visual documentation. The study revealed that the implementation of visual dialogue as a self-regulation strategy enabled the students to regulate their emotions within a short timeframe (10 to 30 minutes). Through visual dialogue, they were able to express their feelings and needs in socially appropriate ways. This finding underscores the significance of visual dialogue as a tool for promoting emotional regulation and facilitating active participation in classroom activities. As a result, the students' learning outcomes and social interactions were positively impacted. The findings of this study hold significant implications for educators working with autistic students with intellectual disabilities. The use of visual dialogue as a self-regulation strategy can enhance emotional regulation skills and improve overall academic progress. The action research approach outlined in this paper provides practical guidance for educators in effectively implementing self-regulation strategies within classroom settings. In conclusion, the study demonstrates that visual dialogue is an effective strategy for enhancing emotional regulation in autistic students with intellectual disabilities. By employing visual communication, students can successfully regulate their emotions and actively engage in classroom activities, leading to improved learning outcomes and social interactions. This paper underscores the importance of implementing self-regulation strategies in educational settings to cater to the unique needs of autistic students.

Keywords: action research, self-regulation, autism, visual communication

Procedia PDF Downloads 49

Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

Procedia PDF Downloads 131

Authors: Akpokiere Ugbede Roseline

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Chemistry is an activity oriented subject in which many students achievement over the years are not encouraging. Among the reasons found to be responsible for student’s poor performance in chemistry are ineffective teaching strategies. This study, therefore, sought to determine the effect of guided inquiry, guided inquiry with demonstration, and demonstration with conventional approach on pre-service teachers’ cognitive attainment and practical skills acquisition on stoichiometry and chemical reactions in practical chemistry, Two research questions and hypotheses were each answered and tested respectively. The study was a quasi-experimental research involving 50 students in each of the experimental groups and 50 students in the control group. Out of the five instruments used for the study, three were on stimulus and two on response (Test of Cognitive Attainment and Test of Practical Skills in Chemistry) instruments administered, and dataobtained were analyzed with t-test and Analysis of Variance. Findings revealed, among others, that there was a significant effect of treatments on students' cognitive attainment and on practical skills acquisition. Students exposed to guided inquiry (with/without demonstration) strategies achieved better than those exposed to demonstration with conventional strategy. It is therefore recommended, among others, that Lecturers in Colleges of Education should utilize the guided inquiry strategy for teaching concepts in chemistry.

Keywords: instructional strategy, practical chemistry, learning outcomes, pre-service teachers

Procedia PDF Downloads 90

Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

Procedia PDF Downloads 124

Authors: Vinay Kumar Vanjakula, Frank Adam, Nils Goseberg

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Wind power is one of the leading renewable energy generation methods. Due to abundant higher wind speeds far away from shore, the construction of offshore wind turbines began in the last decades. However, installation of offshore foundation-based (monopiles) wind turbines in deep waters are often associated with technical and financial challenges. To overcome such challenges, the concept of floating wind turbines is expanded as the basis from the oil and gas industry. The unfolding of Universal heavyweight gravity anchor (UGA) for floating based foundation for floating Tension Leg Platform (TLP) sub-structures is developed in this research work. It is funded by the German Federal Ministry of Education and Research) for a three-year (2019-2022) research program called “Offshore Wind Solutions Plus (OWSplus) - Floating Offshore Wind Solutions Mecklenburg-Vorpommern.” It’s a group consists of German institutions (Universities, laboratories, and consulting companies). The part of the project is focused on the numerical modeling of gravity anchor that involves to analyze and solve fluid flow problems. Compared to gravity-based torpedo anchors, these UGA will be towed and lowered via controlled machines (tug boats) at lower speeds. This kind of installation of UGA are new to the offshore wind industry, particularly for TLP, and very few research works have been carried out in recent years. Conventional methods for transporting the anchor requires a large transportation crane vessel which involves a greater cost. This conceptual UGA anchors consists of ballasting chambers which utilizes the concept of buoyancy forces; the inside chambers are filled with the required amount of water in a way that they can float on the water for towing. After reaching the installation site, those chambers are ballasted with water for lowering. After it’s lifetime, these UGA can be unballasted (for erection or replacement) results in self-rising to the sea surface; buoyancy chambers give an advantage for using an UGA without the need of heavy machinery. However, while lowering/rising the UGA towards/away from the seabed, it experiences difficult, harsh marine environments due to the interaction of waves and currents. This leads to drifting of the anchor from the desired installation position and damage to the lowering machines. To overcome such harsh environments problems, a numerical model is built to investigate the influences of different outer contours and other fluid governing shapes that can be installed on the UGA to overcome the turbulence and drifting. The presentation will highlight the importance of the Computational Fluid Dynamics (CFD) numerical model in OpenFOAM, which is open-source programming software.

Keywords: anchor lowering, towing, waves, currrents, computational fluid dynamics

Procedia PDF Downloads 154

Authors: Al-Sayed A. Bakr, W. A. Makled

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The precipitated low magnesium calcite ooids in assembled softening unit from natural Mediterranean seawater samples were used as adsorbent media in a comparative study with granular activated carbon media in a two separated single-media filtration vessels (operating in parallel) for removal of iron from aqueous solutions. In each vessel, the maximum bed capacity, which required to be filled, was 13.2 l and the bed filled in the vessels of ooids and GAC were 8.6, and 6.6 l, respectively. The operating conditions applied to the semi-pilot filtration unit were constant pH (7.5), different temperatures (293, 303 and 313 k), different flow rates (20, 30, 40, 50 and 60 l/min), different initial Fe(II) concentrations (15–105 mg/ l) and the calculated adsorbent masses were 34.1 and 123 g/l for GAC and calcite ooids, respectively. At higher temperature (313 k) and higher flow rate (60 l/min), the maximum adsorption capacities for ferrous ions by GAC and calcite ooids filters were 3.87 and 1.29 mg/g and at lower flow rate (20 l/min), the maximum adsorption capacities were 2.21 and 3.95 mg/g, respectively. From the experimental data, Freundlich and Langmuir adsorption isotherms were used to verify the adsorption performance. Therefore, the calcite ooids could act as new highly effective materials in iron removal from aqueous solutions.

Keywords: water treatment, calcite ooids, activated carbon, Fe(II) removal, filtration

Procedia PDF Downloads 137

Authors: Mahsa Tavakoli, Seyed Mohammad Hojjati, Yahya Kooch

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Considering that coal mining is one of the important industrial activities, it may cause damages to environment. According to the author’s best knowledge, the effect of traditional coal mining activities on plant biodiversity has not been investigated in the Hyrcanian forests. Therefore, in this study, the effect of coal mining activities on vegetation and tree diversity was investigated in Hyrcanian forest, North Iran. After filed visiting and determining the mine, 16 plots (20×20 m²) were established by systematic-randomly (60×60 m²) in an area of 4 ha (200×200 m²-mine entrance placed at center). An area adjacent to the mine was not affected by the mining activity, and it is considered as the control area. In each plot, the data about trees such as number and type of species were recorded. The biodiversity of vegetation cover was considered 5 square sub-plots (1 m²) in each plot. PAST software and Ecological Methodology were used to calculate Biodiversity indices. The value of Shannon Wiener and Simpson diversity indices for tree cover in control area (1.04±0.34 and 0.62±0.20) was significantly higher than mining area (0.78±0.27 and 0.45±0.14). The value of evenness indices for tree cover in the mining area was significantly lower than that of the control area. The value of Shannon Wiener and Simpson diversity indices for vegetation cover in the control area (1.37±0.06 and 0.69±0.02) was significantly higher than the mining area (1.02±0.13 and 0.50±0.07). The value of evenness index in the control area was significantly higher than the mining area. Plant communities are a good indicator of the changes in the site. Study about changes in vegetation biodiversity and plant dynamics in the degraded land can provide necessary information for forest management and reforestation of these areas.

Keywords: vegetation biodiversity, species composition, traditional coal mining, Caspian forest

Procedia PDF Downloads 172

Authors: Manana Lobzhanidze

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The COVID-19 pandemic has made significant changes in migration processes, notably changes in the study migration process. The constraints caused by the COVID-19 pandemic led to changes in the studying process, which negatively affected its efficiency. The educational process has partially or completely shifted to distance learning; Both labor and study migration have increased significantly in the world. The employment and education market has become global and consequently, a number of challenges have arisen for employers, researchers, and businesses. The role of preparing qualified personnel in achieving high productivity is justified, the benefits for employers and employees are assessed on the one hand, and the role of study migration for the country’s development is examined on the other hand. Research methods. The research is based on methods of analysis and synthesis, quantitative and qualitative, groupings, relative and mean quantities, graphical representation, comparison, analysis and etc. In-depth interviews were conducted with experts to determine quantitative and qualitative indicators. Research findings. Factors affecting study migration are analysed in the paper and the environment that stimulates migration is explored. One of the driving forces of migration is considered to be the desire for receiving higher pay. Levels and indicators of study migration are studied by country. Comparative analysis has found that study migration rates are high in countries where the price of skilled labor is high. The productivity of individuals with low skills is low, which negatively affects the economic development of countries. It has been revealed that students leave the country to improve their skills during study migration. The process mentioned in the article is evaluated as a positive event for a developing country, as individuals are given the opportunity to share the technology of developed countries, gain knowledge, and then introduce it in their own country. The downside of study migration is the return of a small proportion of graduates from developed economies to their home countries. The article concludes that countries with emerging economies devote less resources to research and development, while this is a priority in developed countries, allowing highly skilled individuals to use their skills efficiently. The paper studies the national education system examines the level of competition in the education market and the indicators of educational migration. The level of competition in the education market and the indicators of educational migration are studied. The role of qualified personnel in achieving high productivity is substantiated, the benefits of employers and employees are assessed on the one hand, and the role of study migration in the development of the country is revealed on the other hand. The paper also analyzes the level of competition in the education and labor markets and identifies indicators of study migration. During the pandemic period, there was a great demand for the digital technologies. Open access to a variety of comprehensive platforms will significantly reduce study migration to other countries. As a forecast, it can be said that the intensity of the use of e-learning platforms will be increased significantly in the post-pandemic period. The paper analyzes the positive and negative effects of study migration on economic development, examines the challenges of study migration in light of the COVID-19 pandemic, suggests ways to avoid negative consequences, and develops recommendations for improving the study migration process in the post-pandemic period.

Keywords: study migration, COVID-19 pandemic, factors affecting migration, economic development, post-pandemic migration

Procedia PDF Downloads 116

Authors: Anamika Devi

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Based on Vygotsky’s cultural, historical theory and Hedegaard’s concept of transition, this study aims to investigate to propose strategies to maintain digital wellbeing of children and parents during and post COVID pandemic. Due COVID 19 pandemic, children and families have been facing new challenges and sudden changes in their everyday life. While children are juggling to adjust themselves in new circumstance of onsite and online learning settings, parents are juggling with their work-life balance. A number of papers have identified that the COVID-19 pandemic has affected the lives of many families around the world in many ways, for example, the stress level of many parents increased, families faced financial difficulties, uncertainty impacted on long term effects on their emotional and social wellbeing. After searching and doing an intensive literature review from 2020 and 2021, this study has found some scholarly articles provided solution or strategies of reducing stress levels of parents and children in this unprecedented time. However, most of them are not underpinned by proper theoretical lens to ensure they validity and success. Therefore, this study has proposed strategies that are underpinned by theoretical lens to ensure their impact on children’s and parents' emotional wellbeing during and post COVID-19 era. The strategies will highlight on activities for positive coping strategies to the best use of family values and digital technologies.

Keywords: onsite and online learning, strategies, emotional wellbeing, tips, and strategies, COVID19

Procedia PDF Downloads 154

Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

Procedia PDF Downloads 126

Authors: Richard White, Anne Drabble, Maureen O’Neill

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The University of the Sunshine Coast (USC) offers secondary school students in the final two years of school (Years 11 and 12, 16 – 18 years of age) an opportunity to participate in a program which provides an accelerated pathway to tertiary studies. Whilst still at secondary school, the students undertake two first year university subjects that are required subjects in USC undergraduate degree programs. The program is called Integrated Learning Pathway (ILP) and offers a range of disciplines, including business, design, drama, education, and engineering. Between 2010 and 2014, 38% of secondary students who participated in an ILP program commenced undergraduate studies at USC following completion of secondary school studies. The research reported here considers “before and after” literacy and numeracy competencies of students to determine what impact participation in the ILP program has had on their academic skills. Qualitative and quantitative data has been gathered via numeracy and literacy testing of the students, and a survey asking the students to self-evaluate their numeracy and literacy skills, and reflect on their views of these academic skills. The research will enable improved targeting of teaching strategies so that students will acquire not only course-specific learning outcomes but also collateral academic skills. This enhancement of academic skills will improve undergraduate experience and improve student retention.

Keywords: academic skills enhancement, accelerated pathways, improved teaching, student retention

Procedia PDF Downloads 294

Authors: Shilpagauri Prasad Ganpule

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The teaching of drama is considered as the most significant and noteworthy area in an ESL classroom. Diverse innovative techniques can be used to make the teaching of drama worthwhile and interesting. The paper presents the different innovative techniques that can be used while teaching Henrik Ibsen’s A Doll’s House [2007]. The innovative techniques facilitate students’ understanding and comprehension of the text. The use of the innovative techniques makes them explore the dramatic text and uncover a multihued arena of meanings hidden in it. They arouse the students’ interest and assist them overcome the difficulties created by the second language. The diverse innovative techniques appeal to the imagination of the students and increase their participation in the classroom. They help the students in the appreciation of the dramatic text and make the teaching learning situation a fruitful experience for both the teacher and students. The students successfully overcome the problem of L2 comprehension and grasp the theme, story line and plot-structure of the play effectively. The innovative techniques encourage a strong sense of participation on the part of the students and persuade them to learn through active participation. In brief, the innovative techniques promote the students to perform various tasks and expedite their learning process. Thus the present paper makes an attempt to present varied innovative techniques that can be used while teaching drama. It strives to demonstrate how the use of innovative techniques improve and enhance the students’ understanding and appreciation of Ibsen’s A Doll’s House [2007].

Keywords: ESL classroom, innovative techniques, students’ participation, teaching of drama

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Authors: Ratna Kuswardani

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This paper aims to study the impact of recent Global Financial Crisis (GFC) on 6 selected emerging Asian economies (Indonesia, Malaysia, Thailand, Philippines, Singapore, and South Korea). We first figure out the contagion of GFC from the US and Europe to the selected emerging Asian countries by studying the tail dependence of market stock returns between those countries. We apply the concept of Extreme Value Theory (EVT) to model the dependence between multiple returns series of variables under examination. We explore the factors causing the contagion between the regions. We find dependencies between markets that are influenced by their size, especially for large markets in emerging Asian countries that tend to have a higher dependency to the market in the more advanced country such as the U.S. and some countries in Europe. The results also suggest that the dependencies between market returns and bank stock returns in the same region tend to be higher than dependencies between these returns across two different regions. We extend our analysis by studying the impact of GFC on the systemic in the banking system. We also find that larger institution has more dependencies with the market stock, suggesting that larger size bank can cause disruption in the market. Further, the higher probability of extreme loss can be seen during the crisis period, which is shown by the non-linear dependency between the pre-crisis and the post-crisis period. Finally, our analysis suggests that systemic risk appears in the domestic banking systems in emerging Asia, as shown by the extreme dependencies within banks in the system. Overall, our results provide caution to policy makers and investors alike on the possible contagion of the impact of global financial crisis across different markets.

Keywords: contagion, extreme value theory, global financial crisis, systemic risk

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Authors: Aileen O'loughlin

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‘Is Everybody Going WeLL?’ (IEGW?) is a 33-hour classroom-based initiative created to a) explore values and how they impact on well-being, b) encourage adolescents to connect with their community, and c) provide them with the education to encourage and maintain a lifetime love of physical activity (PA) to ensure beneficial effects on their personal well-being. This initiative is also aimed at achieving sustainable education and aligning with the United Nation’s Sustainable Development Goals numbers 3 and 4. The classroom is a unique setting in which adolescents’ PA participation can be positively influenced through fun PA policies and initiatives. The primary purpose of this research is to evaluate a range of psychosocial and PA outcomes following the 33-hour education programme. This research examined the impact of a PA and well-being programme consisting of either a 60minute or 80minute class, depending on the timetable structure of the school, delivered once a week. Participant outcomes were measured using validated questionnaires regarding Self-esteem, Mental Health Literacy (MHL) and Daily Physical Activity Participation. These questionnaires were administered at three separate time points; baseline, mid-intervention, and post intervention. Semi-structured interviews with participating teachers regarding adherence and participants’ attitudes were completed post-intervention. These teachers were randomly selected for interview. This perspective analytical cohort study included 235 post-primary school students between 11-13 years of age (100 boys and 135 girls) from five public Irish post-primary schools. Three schools received the intervention only; a 33hour interactive well-being learning unit, one school formed a control group and one school had participants in both the intervention and control group. Participating schools were a convenience sample. Data presented outlines baseline data collected pre-participation (0 hours completed). N = 18 junior certificate students returned all three questionnaires fully completed for a 56.3% return rate from 1 school, Intervention School #3. 94.4% (n = 17) of participants enjoy taking part in some form of PA, however only 5.5% (n = 1) of the participants took part in PA every day of the previous 7 days and only 5.5% (n = 1) of those surveyed participated in PA every day during a normal week. 55% (n = 11) had a low level of self-esteem, 50% (n = 9) fall within the normal range of self-esteem, and n = 0 surveyed demonstrated a high level of self-esteem. Female participants’ Mean score was higher than their male counterparts when MHL was compared. Correlation analyses revealed a small association between Self-esteem and Happiness (r = 0.549). Positive correlations were also revealed between MHL and Happiness, MHL and Self-esteem and Self-esteem and 60+ minutes of PA completed daily. IEGW? is a classroom-based with simple methods easy to implement, replicate and financially viable to both public and private schools. It’s unique dataset will allow for the evaluation of a societal approach to the psycho-social well-being and PA participation levels of adolescents. This research is a work in progress and future work is required to learn how to best support the implementation of ‘Is Everybody Going WeLL?’ as part of the school curriculum.

Keywords: education, life-long learning, physical activity, psychosocial well-being

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Authors: Irina Deputat, Anatoly Gribanov, Yuliya Dzhos, Alexandra Nekhoroshkova, Tatyana Yemelianova, Irina Bolshevidtseva, Irina Deryabina, Yana Kereush, Larisa Startseva, Tatyana Bagretsova, Irina Ikonnikova

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In the modern world the number of elderly people increases. Preservation of functionality of an organism in the elderly becomes very important now. During aging the higher cortical functions such as feelings, perception, attention, memory, and ideation are gradual decrease. It is expressed in the rate of information processing reduction, volume of random access memory loss, ability to training and storing of new information decrease. Perspective directions in studying of aging neurophysiological parameters are brain imaging: computer electroencephalography, neuroenergy mapping of a brain, and also methods of studying of a neurodynamic brain processes. Research aim – to study features of a brain aging in elderly people by electroencephalogram (EEG) and the DC-potential level. We examined 130 people aged 55 - 74 years that did not have psychiatric disorders and chronic states in a decompensation stage. EEG was recorded with a 128-channel GES-300 system (USA). EEG recordings are collected while the participant sits at rest with their eyes closed for 3 minutes. For a quantitative assessment of EEG we used the spectral analysis. The range was analyzed on delta (0,5–3,5 Hz), a theta - (3,5–7,0 Hz), an alpha 1-(7,0–11,0 Hz) an alpha 2-(11–13,0 Hz), beta1-(13–16,5 Hz) and beta2-(16,5–20 Hz) ranges. In each frequency range spectral power was estimated. The 12-channel hardware-software diagnostic ‘Neuroenergometr-KM’ complex was applied for registration, processing and the analysis of a brain constant potentials level. The DC-potential level registered in monopolar leads. It is revealed that the EEG of elderly people differ in higher rates of spectral power in the range delta (р < 0,01) and a theta - (р < 0,05) rhythms, especially in frontal areas in aging. By results of the comparative analysis it is noted that elderly people 60-64 aged differ in higher values of spectral power alfa-2 range in the left frontal and central areas (р < 0,05) and also higher values beta-1 range in frontal and parieto-occipital areas (р < 0,05). Study of a brain constant potential level distribution revealed increase of total energy consumption on the main areas of a brain. In frontal leads we registered the lowest values of constant potential level. Perhaps it indicates decrease in an energy metabolism in this area and difficulties of executive functions. The comparative analysis of a potential difference on the main assignments testifies to unevenness of a lateralization of a brain functions at elderly people. The results of a potential difference between right and left hemispheres testify to prevalence of the left hemisphere activity. Thus, higher rates of functional activity of a cerebral cortex are peculiar to people of early advanced age (60-64 years) that points to higher reserve opportunities of central nervous system. By 70 years there are age changes of a cerebral power exchange and level of electrogenesis of a brain which reflect deterioration of a condition of homeostatic mechanisms of self-control and the program of processing of the perceptual data current flow.

Keywords: brain, DC-potential level, EEG, elderly people

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Authors: M. Mousavi-Daramoroudi, H. Yousefnia, F. Abbasi-Davani, S. Zolghadri, S. Kakaei

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Despite the appropriate characteristics of ¹⁷⁷Lu and DOTATOC, to our best knowledge, the therapeutic benefit of ¹⁷⁷Lu-DOTATOC complex in breast cancer has not been reported until now. In this study, biodistribution of ¹⁷⁷Lu-DOTA-TOC in mouse tumor model for evaluation of possible utilization of this complex in breast cancer treatment was investigated.¹⁷⁷Lu was prepared with the specific activity of 2.6-3 GBq.mg^-1 and radionuclidic purity higher than 99%. The radiolabeled complex was prepared in the optimized conditions with the radiochemical purity higher than 99%. The final solution was injected to the BALB/c mice with adenocarcinoma breast cancer. The biodistribution results showed major accumulation in the kidneys as the major excretion route and the somatostatin receptor-positive tissues such as pancreas compared with the other tissues. Also, significant uptake was observed in tumor even in longer time after injection. According to the results obtained in this research study, somatostatin receptors expressed in breast cancers can be targeted with DOTATOC analogues especially with ¹⁷⁷Lu-DOTATOC as an ideal therapeutic agent.

Keywords: ¹⁷⁷Lu, adenocarcinoma breast cancer, DOTATOC, BALB/c mice

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Authors: Claire Xu, Welton Wang, Manasvi Pinnaka, Anqi Pan, Michael Han

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Hospitalizations account for one-third of the total health care spending in the US. Early risk detection and intervention can reduce this high cost and increase the satisfaction of both patients and physicians. Due to the lack of awareness of the potential arising risks in home environment, the opportunities for patients to seek early actions of clinical visits are dramatically reduced. This research aims to offer a highly personalized remote patients monitoring and risk assessment AI framework to identify the potentially preventable hospitalization for both acute as well as chronic diseases. A hybrid-AI framework is trained with data from clinical setting, patients surveys, as well as online databases. 20+ risk factors are analyzed ranging from activities, biometric info, demographic info, socio-economic info, hospitalization history, medication info, lifestyle info, etc. The AI model yields high performance of 87% accuracy and 88 sensitivity with 20+ features. This hybrid-AI framework is proven to be effective in identifying the potentially preventable hospitalization. Further, the high indicative features are identified by the models which guide us to a healthy lifestyle and early intervention suggestions.

Keywords: hospitalization prevention, machine learning, remote patient monitoring, risk prediction

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Kang-In Lee, Woo-Jin Lee, Keun-Deuk Lee, Ju-Seung Chae

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In this study, small scale gap test (SSGT) was performed to measure shock sensitivity of nano-scaled 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) samples. The shock sensitivity of energetic materials is usually evaluated by the method of large-scale gap test (LSGT) that has a higher reliability than other methods. But LSGT has the disadvantage that it takes a high cost and time by using a large amount of explosive. In this experiment, nano-scaled RDX samples were prepared by spray crystallization in two different drying methods. In addition, 30μm RDX sample produced by precipitation crystallization and 5μm RDX sample produced by fluid energy mill process were tested to compare shock sensitivity. The study of shock sensitivity measured by small-scale gap test shows that small sized RDX particles have greater insensitivity. As a result, we infer SSGT method has higher reliability compared to the literature as measurement of shock sensitivity of energetic materials.

Keywords: nano-scaled RDX, SSGT(small scale gap test), shock sensitivity, RDX

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Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: Gianne Bianca P. Manalo, Ernesto A. Martin, Vanessa V. Velasco

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The laying performance, egg quality, egg classification, and income over feed cost of Improved Philippine Mallard duck (Itik Pinas) were examined as influenced by garlic powder in drinking water. A total of 48 ducks (42 females and 6 males) were used in the study. The ducks were allocated into two treatments - with garlic powder (GP) and without garlic powder (control) in drinking water. Each treatment had three replicates with eight ducks (7 females and 1 male) per replication. The results showed that there was a significant (P = 0.03) difference in average egg weight where higher values were attained by ducks with GP (77.67 g ± 0.64) than the control (75.64 g ± 0.43). The supplementation of garlic powder in drinking water, however, did not affect the egg production, feed intake, FCR, egg mass, livability, egg quality and egg classification. The Itik Pinas with GP in drinking water had numerically higher income over feed cost than those without. GP in drinking water can be considered in raising Itik Pinas. Further studies on increasing level of GP and long feeding duration also merit consideration to substantiate the findings.

Keywords: phytogenic, garlic powder, Itik-Pinas, egg weight, egg production

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

Procedia PDF Downloads 535