Search results for: adaptive educational digital learning environments

Authors: Elena E. Lyakso, Olga V. Frolova, Yuri N. Matveev, Aleksey S. Grigorev, Alexander S. Nikolaev, Viktor A. Gorodnyi

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The study of the children’s emotional sphere depending on age and psychoneurological state is of great importance for the design of educational programs for children and their social adaptation. Atypical development may be accompanied by violations or specificities of the emotional sphere. To study characteristics of the emotional state reflection in the voice and speech features of children, the perceptual study with the participation of adults and the automatic recognition of speech were conducted. Speech of children with typical development (TD), with Down syndrome (DS), and with autism spectrum disorders (ASD) aged 6-12 years was recorded. To obtain emotional speech in children, model situations were created, including a dialogue between the child and the experimenter containing questions that can cause various emotional states in the child and playing with a standard set of toys. The questions and toys were selected, taking into account the child’s age, developmental characteristics, and speech skills. For the perceptual experiment by adults, test sequences containing speech material of 30 children: TD, DS, and ASD were created. The listeners were 100 adults (age 19.3 ± 2.3 years). The listeners were tasked with determining the children’s emotional state as “comfort – neutral – discomfort” while listening to the test material. Spectrographic analysis of speech signals was conducted. For automatic recognition of the emotional state, 6594 speech files containing speech material of children were prepared. Automatic recognition of three states, “comfort – neutral – discomfort,” was performed using automatically extracted from the set of acoustic features - the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) and the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS). The results showed that the emotional state is worse determined by the speech of TD children (comfort – 58% of correct answers, discomfort – 56%). Listeners better recognized discomfort in children with ASD and DS (78% of answers) than comfort (70% and 67%, respectively, for children with DS and ASD). The neutral state is better recognized by the speech of children with ASD (67%) than by the speech of children with DS (52%) and TD children (54%). According to the automatic recognition data using the acoustic feature set GeMAPSv01b, the accuracy of automatic recognition of emotional states for children with ASD is 0.687; children with DS – 0.725; TD children – 0.641. When using the acoustic feature set eGeMAPSv01b, the accuracy of automatic recognition of emotional states for children with ASD is 0.671; children with DS – 0.717; TD children – 0.631. The use of different models showed similar results, with better recognition of emotional states by the speech of children with DS than by the speech of children with ASD. The state of comfort is automatically determined better by the speech of TD children (precision – 0.546) and children with ASD (0.523), discomfort – children with DS (0.504). The data on the specificities of recognition by adults of the children’s emotional state by their speech may be used in recruitment for working with children with atypical development. Automatic recognition data can be used to create alternative communication systems and automatic human-computer interfaces for social-emotional learning. Acknowledgment: This work was financially supported by the Russian Science Foundation (project 18-18-00063).

Keywords: autism spectrum disorders, automatic recognition of speech, child’s emotional speech, Down syndrome, perceptual experiment

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Authors: Unity Chipfupa, Aluwani Tagwi, Edilegnaw Wale

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Climate change adaptation studies are challenged by a limited understanding of how non-cognitive factors such as psychological capital affect adaptation decisions of smallholder farmers. The concept of psychological capital has not been fully applied in the empirical literature on climate change adaptation strategies. Hence, the study was meant to assess how psychological capital endowment affects climate change adaptation among smallholder farmers. A multivariate probit regression model was estimated using data collected from 328 smallholder farmers in KwaZulu-Natal, South Africa. The findings indicate that, among other factors, self-confidence and hope or aspirations in farming influence climate change adaptation decisions of smallholders. The psychological capital theory proved to be comprehensive in identifying specific psychological dimensions associated with adaptation decisions. However, the non-alignment of approaches for measuring non-cognitive factors made it difficult to compare results among different studies. In conclusion, the study recommends the need for practical ways for enhancing smallholders’ endowment with key non-cognitive abilities. Researchers should develop and agree on a comprehensive framework for assessing non-cognitive factors critical for climate change adaptation. This will improve the use of positive psychology theories to advance the literature on climate change adaptation. Other key recommendations include targeted support for communities facing higher risks of climate change, improving smallholders’ ability to adapt, promotion of social networks and the inclusion of farming objectives as an important indicator in climate change adaptation research.

Keywords: adaptive capacity, climate change adaptation, psychological capital, multivariate probit, non-cognitive factors.

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Authors: Asma Rebaya, Imen Amari, Kaouther Gasmi, Salem Hasnaoui

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Over these last years, the number of cores witnessed a spectacular increase in digital signal and general use processors. Concurrently, significant researches are done to get benefit from the high degree of parallelism. Indeed, these researches are focused to provide an efficient scheduling from hardware/software systems to multicores architecture. The scheduling process consists on statically choose one core to execute one task and to specify an execution order for the application tasks. In this paper, we describe an efficient scheduler that calculates the optimal number of cores required to schedule an application, gives a heuristic scheduling solution and evaluates its cost. Our proposal results are evaluated and compared with Preesm scheduler results and we prove that ours allows better scheduling in terms of latency, computation time and number of cores.

Keywords: computation time, hardware/software system, latency, optimization, multi-cores platform, scheduling

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Authors: Ali Safaeianpour, Nima Shamandi

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As air traffic expands, many airports have evolved into transit centers for people, information, and commerce, and technology implementation is an absolute part of airport development. Several challenges are in the way of implementing technology in an airport. Airport 4.0 proposes the "Smart Airport" concept, which focuses on using modern technologies such as Big Data, the Internet of Things (IoT), advanced biometric systems, blockchain, and cloud computing to alter and enhance passengers' journeys. Several common IoT concrete topics as partial keys to smart airports are discussed and introduced, ranging from automated check-in systems to exterior tracking processes, with the goal of enlightening more and more insightful ideas and proposals about smart airport solutions. IoT will dramatically alter people's lives by infusing intelligence, boosting the quality of life, and assembling it smarter. This paper reviews the approaches to transforming an airport into a smart airport and describes several enabling components of IoT and challenges that can hinder the implementation of a smart airport's function, which require to be addressed.

Keywords: airport 4.0, digital airport, smart airport, IoT

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Authors: Netsanet Kebede Hundessa

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Substrate modification of thin film composite (TFC) membranes with various crosslinkers is typically necessary for organic solvent nanofiltration (OSN) applications. This modification is aimed at enhancing membrane stability and solvent resistance, but it often results in a decline in permeance. This study introduces a distinct approach by developing a coordination-crosslinked polyimide substrate, which differs from the covalently-crosslinked substrates traditionally used. This developed substrate achieves enhanced solvent resistance, improved hydrophilicity, and optimized porous microstructure simultaneously. The study investigates the effects of an alkaline coagulation bath, subsequent ion exchange, and further solvent activation. The resulting TFC membrane successfully overcomes the typical permeability-selectivity trade-off of OSN membranes. It demonstrates significantly improved solvent permeance (1.5–2 times higher than previously reported data) with values of 65.2 LMH/bar for methanol, 33.1 LMH/bar for ethanol, and 59.1 LMH/bar for acetone while maintaining competitive solute rejection (>98% for Rose Bengal). This research is expected to provide a new direction for developing high-performance OSN composite membranes and other separation applications.

Keywords: metal coordinatiom, thin film composite membrane, organic solvent nanofiltration, solvent activation

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Authors: Mehrnoosh Jafari, Zeinolabedin Amini Sabegh, Habibollah Azimian

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Background: Today variable and dynamic environment doubles importance of using suitable solutions for confronting these changes in th4e organizations. One of the best ways for coping with environmental changes is directing the organization towards agility. Current research aims at investigating status of organizational agility in Tehran municipality (22 districts). Research Methodology: This research is applied research in terms of purpose of study and it is survey in terms of collection of descriptive data. A sample (n = 377) was selected from Tehran Municipality (22 districts) employees using multistage sampling method (cluster and regular). Data were collected using organizational agility standard questionnaire, and they were analyzed using statistical tests in SPSS software as well as inferential statistics such as one-sample t-test and Friedman test and descriptive statistics such as mean and median. Findings: Research findings showed organizational agility status in the organizations under study is in relatively optimal status and competence has highest priority in terms of ranking and priority of organizational agility indexes. Conclusion: It is necessary that managers provide suitable conditions for promoting organizational agility status in the organizations under study by identifying factors affecting change in the organizational environments and using available potentials for better coping with changes and higher flexibility and speed.

Keywords: organizational, municipality, employer, agility

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Authors: Laszlo Vari

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In the digital age, the spread of the mobile world and the nature of the cyberspace, offers many new opportunities for the prevalence of the fundamental right to free expression, and therefore, for free speech and freedom of the press; however, these new information communication technologies carry many new challenges. Defamation, censorship, fake news, misleading information, hate speech, breach of copyright etc., are only some of the violations, all of which can be derived from the harmful exercise of freedom of expression, all which become more salient in the internet. Here raises the question: how can we eliminate these problems, and practice our fundamental freedom rightfully? To answer this question, we should understand the elements and the characteristic of the nature of freedom of expression, and the role of the actors whose duties and responsibilities are crucial in the prevalence of this fundamental freedom. To achieve this goal, this paper will explore the European practice to understand instructions found in the case-law of the European Court of Human rights for the rightful exercise of freedom of expression.

Keywords: collision of rights, European case-law, freedom opinion and expression, media law, freedom of information, online expression

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Authors: Elisabet Liljeblad, Tomas Karlsson, Torbjorn Sundberg, Anita Kullen

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The magnetospheric magnetic field data from the MESSENGER spacecraft is investigated to establish the presence of ultra-low frequency (ULF) waves in connection to 131 previously observed nonlinear Kelvin-Helmholtz waves (KHWs) at Mercury. Distinct ULF signatures are detected in 44 out of the 131 magnetospheric traversals prior to or after observing a KHW. In particular, 39 of these 44 ULF events are highly coherent at the frequency of maximum power spectral density. The waves observed at the dayside, which appears mainly at the duskside and naturally following the KHW occurrence asymmetry, are significantly different to the events behind the dawn-dusk terminator and have the following distinct wave characteristics: they oscillate clearly in the perpendicular (azimuthal) direction to the mean magnetic field with a wave normal angle more in the parallel than the perpendicular direction, increase in absolute ellipticity with distance from noon, are almost exclusively right-hand polarized, and are observed mainly for frequencies in the range 0.02-0.04 Hz. These results indicate that the dayside ULF waves are likely to shear Alfvén waves driven by KHWs at the magnetopause, which in turn manifests the importance of the Kelvin-Helmholtz instability in terms of mass transport throughout the Mercury magnetosphere.

Keywords: ultra-low frequency waves, kelvin-Helmholtz instability, magnetospheric processes, mercury, messenger, energy and momentum transfer in planetary environments

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Authors: Abdul Rohim Tualeka, Erick Caravan K. Betekeneng, Ramdhoni Zuhro, Reko Triyono, M. Sahri

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It has been widely reported incidence caused by acute and chronic effects of exposure to ammonia in the working environment in Indonesia, but ammonia concentration was found to be below the threshold value. The purpose of this study was to determine the safety limit concentration of ammonia in the working environment through the expression of CD8 as a reference for determining the threshold value of ammonia in the working environment. This research was a laboratory experimental with post test only control group design using experimental animals as subjects experiment. From homogeneity test results indicated that the weight of white rats exposed and control groups had a homogeneous variant with a significant level of p (0.701) > α (0.05). Description of the average breathing rate is 0.0013 m³/h. Average weight rats based group listed exposure is 0.1405 kg. From the calculation IRS CD8, CD8 highest score in the doses contained 0.0154, with the location of the highest dose of ammonia without any effect on the lungs of rats is 0.0154 mg/kg body weight of mice. Safe Human Dose (SHD) ammonia is 0.002 mg/kg body weight workers. The conclusion of this study is the safety limit concentration of ammonia gas in the working environment of 0,025 ppm.

Keywords: ammonia, CD8, rats, safe limits concentration

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Authors: Subodh Kumar, Amit Varde

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Typical on-premises high-performance computing (HPC) environments consist of a fixed number and a fixed set of computing hardware. During the design of the HPC environment, the hardware components, including but not limited to CPU, Memory, GPU, and networking, are carefully chosen from select vendors for optimal performance. High capital cost for building the environment is a prime factor influencing the design environment. A class of software called “Job Schedulers” are critical to maximizing these resources and running multiple workloads to extract the maximum value for the high capital cost. In principle, schedulers work by preventing workloads and users from monopolizing the finite hardware resources by queuing jobs in a workload. A cloud-based HPC environment does not have the limitations of fixed (type of and quantity of) hardware resources. In theory, users and workloads could spin up any number and type of hardware resource. This paper discusses the limitations of using traditional scheduling algorithms for cloud-based HPC workloads. It proposes a new set of features, called “HPC optimizers,” for maximizing the benefits of the elasticity and scalability of the cloud with the goal of cost-performance optimization of the workload.

Keywords: high performance computing, HPC, cloud computing, optimization, schedulers

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Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

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Authors: Zohreh Mohammadi, Jan Kaspar, Peter Lohmander, Robert Marusak, Harald Vacik, Ljusk Ola Eriksson

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Storms are important damaging agents in European forest ecosystems. In the latest decades, significant economic losses in European forestry occurred due to storms. This study investigates the problem of optimal harvest planning when forest stands risk to be felled by storms. One of the most applicable mathematical methods which are being used to optimize forest management is stochastic dynamic programming (SDP). This method belongs to the adaptive optimization class. Sequential decisions, such as harvest decisions, can be optimized based on sequential information about events that cannot be perfectly predicted, such as the future storms and the future states of wind protection from other forest stands. In this paper, stochastic dynamic programming is used to maximize the expected present value of the profits from an area consisting of several forest stands. The region of analysis is the Czech Republic. The harvest decisions, in a particular time period, should be simultaneously taken in all neighbor stands. The reason is that different stands protect each other from possible winds. The optimal harvest age of a particular stand is a function of wind speed and different wind protection effects. The optimal harvest age often decreases with wind speed, but it cannot be determined for one stand at a time. When we consider a particular stand, this stand also protects other stands. Furthermore, the particular stand is protected by neighbor stands. In some forest stands, it may even be rational to increase the harvest age under the influence of stronger winds, in order to protect more valuable stands in the neighborhood. It is important to integrate wind risk in forestry decision-making.

Keywords: Czech republic, forest stands, stochastic dynamic programming, wind risk

Procedia PDF Downloads 139

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Stephen Bahooshy

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Our understanding of gender inequality has advanced in recent years. Differences in pay and societal gendered behaviour expectations have been emphasized. It is acknowledged globally that gender shapes everyone’s experiences of health and social care, including access to care, use of services and products, and the interaction with care providers. NHS Digital in England collects data from local authorities on the number of carers and people with support needs and the services they access. This data does not provide a gender breakdown. Caring can have many positive and negative impacts on carers’ health and wellbeing. For example, caring can improve physical health, provide a sense of pride and purpose, and reduced stress levels for those who undertake a caring role by choice. Negatives of caring include financial concerns, social isolation, a reduction in earnings, and not being recognized as a carer or involved and consulted by health and social care professionals. Treating male and female carers differently is by definition unequitable and precludes one gender from receiving the benefits of caring whilst potentially overburdening the other with the negatives of caring. In order to explore the issue on a preliminary basis, five local authorities who provide statutory adult social care services in England were sent Freedom of Information requests in 2019. The authorities were selected to include county councils and London boroughs. The authorities were asked to provide data on the amount of money spent on care at home packages to people over 65 years, broken down by gender and carer gender for each financial year between 2013 and 2019. Results indicated that in each financial year, female carers supporting someone over 65 years received less financial support for care at home support packages than male carers. Over the six-year period, this difference equated to a £9.5k deficit in financial support received on average per female carer when compared to male carers. An example of a London borough with the highest disparity presented an average weekly spend on care at home for people over 65 with a carer of £261.35 for male carers and £165.46 for female carers. Consequently, female carers in this borough received on average £95.89 less per week in care at home support than male carers. This highlights a real and potentially detrimental disparity in the care support received to female carers in order to support them to continue to care in parts of England. More research should be undertaken in this area to better explore this issue and to understand if these findings are unique to these social care providers or part of a wider phenomenon. NHS Digital should request local authorities collect data on gender in the same way that large employers in the United Kingdom are required by law to provide data on staff salaries by gender. People who allocate social care packages of support should consider the impact of gender when allocating support packages to people with support needs and who have carers to reduce any potential impact of gender bias on their decision-making.

Keywords: caregivers, carers, gender equality, social care

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Authors: Sally J. Price, Greg F. Walker, Weiyi Liu, Craig R. Bunt

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Plastics, due to their long lifespan, are becoming more of an environmental concern once their useful life has been completed. There are a vast array of different types of plastic, and they can be found in almost every ecosystem on earth and are of particular concern in terrestrial environments where they can become incorporated into the food chain. Hence bioplastics have become more of interest to manufacturers and the public recently as they have the ability to (bio)degrade in commercial and in home composting situations. However, tools in which to quantify how they degrade in response to environmental variables are still being developed -one such approach is texture analysis using a TA.XT Texture Analyser, Stable Microsystems, was used to determine the force required to break or punch holes in standard ASTM D638 Type IV 3D printed bioplastic “dogbones” depending on the thicknesses of them. Manufacturers’ recommendations for calibrating the Texture Analyser are one such approach for standardising results; however, an independent technique using dummy dogbones and a substitute for the bioplastic was used alongside the samples. This approach was unexpectedly more valuable than realised at the start of the trial as irregular results were later discovered with the substitute material before valuable samples collected from the field were lost due to possible machine malfunction. This work will show the value of having an independent approach to machine calibration for accurate sample analysis with a Texture Analyser when analysing bioplastic samples.

Keywords: bioplastic, degradation, environment, texture analyzer

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Authors: Deyadeen Ali Alshibani

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In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

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Authors: Sergio Saponara, Luca Fanucci, Riccardo Cassettari, Ruggero Piernicola, Marco Righetto

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The paper presents an innovative networked radar system for detection of obstacles in a railway level crossing scenario. This Monitoring System (MS) is able to detect moving or still obstacles within the railway level crossing area automatically, avoiding the need of human presence for surveillance. The MS is also connected to the National Railway Information and Signaling System to communicate in real-time the level crossing status. The architecture is compliant with the highest Safety Integrity Level (SIL4) of the CENELEC standard. The number of radar sensors used is configurable at set-up time and depends on how large the level crossing area can be. At least two sensors are expected and up four can be used for larger areas. The whole processing chain that elaborates the output sensor signals, as well as the communication interface, is fully-digital, was designed in VHDL code and implemented onto a Xilinx Virtex 6.

Keywords: radar for safe mobility, railroad crossing, railway, transport safety

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Sergio Andres Rojas, Julian Benavides Franco, Juan Tomas Sayago

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An increasing amount of research demonstrates how market mood affects financial markets, but their primary goal is to demonstrate how Trump's tweets impacted US interest rate volatility. Following that lead, this work evaluates the effect that Trump's tweets had during his presidency on local and international stock markets, considering not just volatility but the direction of the movement. Three indexes for Trump's tweets were created relating his activity with movements in the S&P500 using natural language analysis and machine learning algorithms. The indexes consider Trump's tweet activity and the positive or negative market sentiment they might inspire. The first explores the relationship between tweets generating negative movements in the S&P500; the second explores positive movements, while the third explores the difference between up and down movements. A pseudo-investment strategy using the indexes produced statistically significant above-average abnormal returns. The findings also showed that the pseudo strategy generated a higher return in the local market if applied to intraday data. However, only a negative market sentiment caused this effect on daily data. These results suggest that the market reacted primarily to a negative idea reflected in the negative index. In the international market, it is not possible to identify a pervasive effect. A rolling window regression model was also performed. The result shows that the impact on the local and international markets is heterogeneous, time-changing, and differentiated for the market sentiment. However, the negative sentiment was more prone to have a significant correlation most of the time.

Keywords: market sentiment, Twitter market sentiment, machine learning, natural dialect analysis

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Authors: Kelly Jaunzems

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Social media is a powerful instrument of communication, enabling the presentation of information in multiple forms and modes, amplifying the interactions between people, organisations, and stakeholders, and increasing the range of communication channels available. Younger generations are highly engaged with social media and more likely to use this channel than any other to seek information. Given this, it may appear extraordinary that occupational safety and health professionals have yet to seriously engage with social media for communicating safety messages to younger audiences who, in many industries, might be statistically more likely to encounter more workplace harm or injury. Millennials, defined as those born between 1981-2000, have distinctive characteristics that also impact their interaction patterns rendering many traditional occupational safety and health communication channels sub-optimal or near obsolete. Used to immediate responses, 280-character communication, shares, likes, and visual imagery, millennials struggle to take seriously the low-tech, top-down communication channels such as safety noticeboards, toolbox meetings, and passive tick-box online inductions favoured by traditional OSH professionals. This paper draws upon well-established communication findings, which argue that it is important to know a target audience and reach them using their preferred communication pathways, particularly if the aim is to impact attitudes and behaviours. Health practitioners have adopted social media as a communication channel with great success, yet safety practitioners have failed to follow this lead. Using a digital ethnography approach, this paper examines seven organisations’ Facebook posts from two one-month periods one year apart, one in 2018 and one in 2019. Each of the years informs organisation-based case studies. Comparing, contrasting, and drawing upon these case studies, the paper discusses and evaluates the (non) use of social media communication of safety information in terms of user engagement, shareability, and overall appeal. The success of health practitioners’ use of social media provides a compelling template for the implementation of social media into organisations’ safety communication strategies. Highly visible content such as that found on social media allows an organization to become more responsive and engage in two-way conversations with their audience, creating more engaged and participatory conversations around safety. Further, using social media to address younger audiences with a range of tonal qualities (for example, the use of humour) can achieve cut through in a way that grim statistics fail to do. On the basis of 18 months of interviews, filed work, and data analysis, the paper concludes with recommendations for communicating safety information via social media. It proposes exploration of the social media communication formula that, when utilised by safety practitioners, may create an effective social media presence. It is anticipated that such social media use will increase engagement, expand the number of followers and reduce the likelihood and severity of safety-related incidents. The tools offered may provide a path for safety practitioners to reach a disengaged generation of workers to build a cohesive and inclusive conversation around ways to keep people safe at work.

Keywords: social media, workplace safety, communication strategies, young workers

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Martina I. Reinhold, Theresa Bacon-Baguley

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A core challenge of physician assistant education is preparing professionals for lifelong learning. While this conventionally has encompassed scientific advances, students must also embrace new care delivery models and technologies. Telemedicine, the provision of care via two-way audio and video, is an example of a technological advance reforming health care. During a three-semester sequence of Hospital Community Experiences, physician assistant students were assigned experiences with Answer Health on Demand, a telemedicine collaborative. Preceding the experiences, the agency lectured on the application of telemedicine. Students were then introduced to the technology and partnered with a provider. Prior to observing the patient-provider interaction, patient consent was obtained. Afterwards, students completed a reflection paper on lessons learned and the potential impact of telemedicine on their careers. Thematic analysis was completed on the students’ reflection papers (n=13). Preceding the lecture and experience, over 75% of students (10/13) were unaware of telemedicine. Several stated they were 'skeptical' about the effectiveness of 'impersonal' health care appointments. After the experience, all students remarked that telemedicine will play a large role in the future of healthcare and will provide benefits by improving access in rural areas, decreasing wait time, and saving cost. More importantly, 30% of students (4/13) commented that telemedicine is a technology they can see themselves using in their future practice. Initial results indicate that collaborative interaction between students and telemedicine providers enhanced student learning and exposed students to technological advances in the delivery of care. Further, results indicate that students perceived telemedicine more favorably as a viable delivery method after the experience.

Keywords: collaboration, physician assistant education, teaching innovative health care delivery method, telemedicine

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Authors: Emilie Zimet

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During our department meetings for teachers of children with learning disabilities and difficulties, we often discuss the best practices for supporting students who come to school with literacy difficulties. After completing Sounds Write and Writing Revolution courses, it seems there is a possibility to link approaches and still maintain fidelity to a program and provide individualised instruction to support students with such difficulties and disabilities. In this case study, the researcher has been focussing on how best to use the knowledge acquired to provide quality intervention that targets the varied areas of challenge that students require support in. Students present to school with a variety of co-occurring reading and writing deficits and with complementary approaches, such as The Writing Revolution and Sounds Write, it is possible to support students to improve their fundamental skills in these key areas. Over the next twelve weeks, the researcher will collect data on current students with whom this approach will be trialled and then compare growth with students from last year who received support using Sounds-Write only. Maintaining fidelity may be a potential challenge as each approach has been tested in a specific format for best results. The aim of this study is to determine if approaches can be combined, so the implementation will need to incorporate elements of both reading (from Sounds Write) and writing (from The Writing Revolution). A further challenge is the time length of each session (25 minutes), so the researcher will need to be creative in the use of time to ensure both writing and reading are targeted while ensuring the programs are implemented. The implementation will be documented using student work samples and planning documents. This work will include a display of findings using student learning samples to demonstrate the importance of co-targeting the reading and writing challenges students come to school with.

Keywords: literacy difficulties, intervention, individual differences, methods of provision

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Authors: Piyush V. Surjagade, Jiamei Deng, Paul Doney, S. R. Shimjith, A. John Arul

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In sophisticated and complex systems such as nuclear power plants, maintaining the system's stability in the presence of uncertainties and disturbances and obtaining a fast dynamic response are the most challenging problems. Thus, to ensure the satisfactory and safe operation of nuclear power plants, this work proposes a new fast, robust optimal switching control strategy for pressurized water reactor-type nuclear power plants. The proposed control strategy guarantees a substantial degree of robustness, fast dynamic response over the entire operational envelope, and optimal performance during the nominal operation of the plant. To improve the robustness, obtain a fast dynamic response, and make the system optimal, a bank of controllers is designed. Various controllers, like a baseline proportional-integral-derivative controller, an optimal linear quadratic Gaussian controller, and a robust adaptive L1 controller, are designed to perform distinct tasks in a specific situation. At any instant of time, the most suitable controller from the bank of controllers is selected using the switching logic unit that designates the controller by monitoring the health of the nuclear power plant or transients. The proposed switching control strategy optimizes the overall performance and increases operational safety and efficiency. Simulation studies have been performed considering various uncertainties and disturbances that demonstrate the applicability and effectiveness of the proposed switching control strategy over some conventional control techniques.

Keywords: switching control, robust control, optimal control, nuclear power control

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Authors: Liang Weichien

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Without adequate information and mitigation plan for natural disaster, the risk to urban populated areas will increase in the future as populations grow, especially in Taiwan. Taiwan is recognized as the world's high-risk areas, where an average of 5.7 times of floods occur per year should seek to strengthen coherence and consensus in how cities can plan for flood and climate change. Therefore, this study aims at understanding the vulnerability to flooding in Taipei city, Taiwan, by creating indicators and calculating the vulnerability of each study units. The indicators were grouped into sensitivity and adaptive capacity based on the definition of vulnerability of Intergovernmental Panel on Climate Change. The indicators were weighted by using Principal Component Analysis. However, current researches were based on the assumption that the composition and influence of the indicators were the same in different areas. This disregarded spatial correlation that might result in inaccurate explanation on local vulnerability. The study used Geographically Weighted Principal Component Analysis by adding geographic weighting matrix as weighting to get the different main flood impact characteristic in different areas. Cross Validation Method and Akaike Information Criterion were used to decide bandwidth and Gaussian Pattern as the bandwidth weight scheme. The ultimate outcome can be used for the reduction of damage potential by integrating the outputs into local mitigation plan and urban planning.

Keywords: flood vulnerability, geographically weighted principal components analysis, GWPCA, highly urbanized area, spatial correlation

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Authors: Yanping Liao, Congcong He, Ruigang Zhao

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The application of magnetocardiography signals to detect cardiac electrical function is a new technology developed in recent years. The magnetocardiography signal is detected with Superconducting Quantum Interference Devices (SQUID) and has considerable advantages over electrocardiography (ECG). It is difficult to extract Magnetocardiography (MCG) signal which is buried in the noise, which is a critical issue to be resolved in cardiac monitoring system and MCG applications. In order to remove the severe background noise, the Total Variation (TV) regularization method is proposed to denoise MCG signal. The approach transforms the denoising problem into a minimization optimization problem and the Majorization-minimization algorithm is applied to iteratively solve the minimization problem. However, traditional TV regularization method tends to cause step effect and lacks constraint adaptability. In this paper, an improved TV regularization method for denoising MCG signal is proposed to improve the denoising precision. The improvement of this method is mainly divided into three parts. First, high-order TV is applied to reduce the step effect, and the corresponding second derivative matrix is used to substitute the first order. Then, the positions of the non-zero elements in the second order derivative matrix are determined based on the peak positions that are detected by the detection window. Finally, adaptive constraint parameters are defined to eliminate noises and preserve signal peak characteristics. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

Keywords: constraint parameters, derivative matrix, magnetocardiography, regular term, total variation

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Authors: Mairo H. Ipadeola, Catherine James Atteng

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The term social inclusion refers to a process by which those disadvantaged in society can have access to fully participate in education like others. Student with special needs are expected to learn along with their peers within the some educational institutions which provide adequate access for all. There for, the study sort to understand the typical ways in which students with disabilities (SWD) were denied from fully participating as students in Kaduna Polytechnic. In doing this, two (2) objectives and research questions were raised. Firstly, to explore the attitudes of others towards students with disabilities in the institutions and secondly, to ascertain the extent of social participation and physical accessibility for students with disabilities (SWD) while in the institutions. Based on the objectives the paper postulated the research questions: what are the attitudes of management, teachers, and students towards students with special need in Kaduna Polytechnic and to what extent did the students with disabilities experience social participation and physical accessibility within Kaduna Polytechnic school environment? The study area was Kaduna Polytechnic. The study engaged the interview for the data collected which were transcribed and analyzed by thematic coding. The findings were categorized under themes, sub-themes, and codes. The findings revealed that the perception, behavior, and association experiences of students with disabilities within Kaduna Polytechnic were not encouraging. Their experiences were characterized by negative attitudes, feelings of rejection, neglect, and bullying. Data generated on social participation indicated that 71% of the respondents believed that learning, school activities, recreations, and student politics between SWD and the other student were in the direction of low / very low. All the respondents, particularly students with blindness and physical challenges faced difficulty with environmental and physical access above all within the school environment, classroom, walkways and ramps, Also, directions were none existent in most departments with physical access to classrooms, toilets, cafeterias, and school shops absent or very low (71% and 29% of the respondents). The conclusion was that the physical barriers limited the possibilities of social participation of SWD.The paper made some recommendations such as mass public enlightenment on radio and television to change the perception of society about people with disability. Also, the federal, state, and local governments enact building acts for fresh builders and adopted measures and time frames for existing public buildings to be made accessible for people with disabilities. All stakeholders should ensure that the five (5) percent budget set aside by State Universal Basic Education Board (SUBEB) and/or Tertiary Education Trust Fund (TETFUND) for the provision of specialized equipment and facilities for the student with special needs should be used prudently spent and monitored by the board.cm.

Keywords: social inclusion, students with disability, social participation, environmental/physical access

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Authors: Maryam Alkhalosi, Ahmad Naddaf, Rana Alani

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Qatar National Library has its main accounts on social media, which presents the general image of the library and its daily news. A paper will be presented based on a case study researching the outcome of having a separate Instagram account led by librarians, not the Communication Department of the library. The main purpose of the librarians-led account is to promote librarians’ services and events, such as research consultation, reference questions, community engagement programs, collection marketing, etc. all in the way that librarians think it reflects their role in the community. Librarians had several obstacles to help users understanding librarians' roles. As was noticed that Instagram is the most popular social media platform in Qatar, it was selected to promote how librarians can help users through a focused account to create a direct channel between librarians and users. Which helps librarians understand users’ needs and interests. This research will use a quantitative approach depending on the case study, librarians have used their case in the department of Research and learning to find out the best practices might help in promoting the librarians' services and reaching out to a bigger number of users. Through the descriptive method, this research will describe the changes observed in the numbers of community users who interact with the Instagram account and engaged in librarians’ events. Statistics of this study are based on three main sources: 1. The internal monthly statistics sheet of events and programs held by the Research and Learning Department. 2. The weekly tracking of the Instagram account statistics. 3. Instagram’s tools such as polls, quizzes, questions, etc. This study will show the direct effect of a librarian-led Instagram account on the number of community members who participate and engage in librarian-led programs and services. In addition to highlighting the librarians' role directly with the community members. The study will also show the best practices on Instagram, which helps reaching a wider community of users. This study is important because, in the region, there is a lack of studies focusing on librarianship, especially on contemporary problems and its solution. Besides, there is a lack of understanding of the role of a librarian in the Arab region. The research will also highlight how librarians can help the public and researchers as well. All of these benefits can come through one popular easy channel in social media. From another side, this paper is a chance to share the details of this experience starting from scratch, including the phase of setting the policy and guidelines of managing the social media account, until librarians reached to a point where the benefits of this experience are in reality. This experience had even added many skills to the librarians.

Keywords: librarian’s role, social media, instagram and libraries, promoting libraries’ services

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Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

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The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

Procedia PDF Downloads 357

Authors: Parham Bakhtiari

Abstract:

This document follows the development of the prelude genre by analyzing specific pieces from a representative selection of composer from Bach to Shostakovich in every era. The research aims to prove the existence of an evolutionary axis in the genre of prelude's history, which is believed to be fading. In this research, Bach, Chopin, and Debussy's works are demonstrated and explored by creating a definition of the genre on his own terms and having an impact on future composers in the following generations. Taking into account, the educational aspect of the prelude and its connection to the genre of study, a brief conversation about it is also provided with an assessment of shorter versions of the genre, for instance, Chopin's preludes.

Keywords: music, piano, prelude, Bach, Chopin, Debussy

Procedia PDF Downloads 44