Search results for: thyristor controlled series capacitor (TCSC)

Authors: Gu Pang, Bartosz Gebka

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We forecast the demand of total container throughput at the Indonesia’s largest seaport, Tanjung Priok Port. We propose four univariate forecasting models, including SARIMA, the additive Seasonal Holt-Winters, the multiplicative Seasonal Holt-Winters and the Vector Error Correction Model. Our aim is to provide insights into whether forecasting the total container throughput obtained by historical aggregated port throughput time series is superior to the forecasts of the total throughput obtained by summing up the best individual terminal forecasts. We test the monthly port/individual terminal container throughput time series between 2003 and 2013. The performance of forecasting models is evaluated based on Mean Absolute Error and Root Mean Squared Error. Our results show that the multiplicative Seasonal Holt-Winters model produces the most accurate forecasts of total container throughput, whereas SARIMA generates the worst in-sample model fit. The Vector Error Correction Model provides the best model fits and forecasts for individual terminals. Our results report that the total container throughput forecasts based on modelling the total throughput time series are consistently better than those obtained by combining those forecasts generated by terminal-specific models. The forecasts of total throughput until the end of 2018 provide an essential insight into the strategic decision-making on the expansion of port's capacity and construction of new container terminals at Tanjung Priok Port.

Keywords: SARIMA, Seasonal Holt-Winters, Vector Error Correction Model, container throughput

Procedia PDF Downloads 484

Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfil the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption, carbon dioxide (CO2) emissions and gross domestic product (GDP) for Oman using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey Fuller (ADF) test for stationary, Johansen maximum likelihood method for co-integration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. All the variables in this study show very strong significant effects on GDP in the country for the long term. The long-run equilibrium in the VECM suggests positive long-run causalities from CO2 emissions to GDP. Conversely, negative impacts of energy consumption on GDP are found to be significant in Oman during the period. In the short run, there exist negative unidirectional causalities among GDP, CO2 emissions and energy consumption running from GDP to CO2 emissions and from energy consumption to CO2 emissions. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output in Oman over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Oman, time series analysis

Procedia PDF Downloads 440

Authors: Cui Li, Xu Yuping

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Based on the question of, "Are short video creators who are digital workers controlled by platform rules?" this study discusses the specific ways of platform rules control and the impact on short video creators. Based on the theory of digital labor, this paper adopts the method of in-depth interview and participant observation and chooses 24 producers of short video content of Tiktok to conduct in-depth interview. At the same time, through entering the short video creation field, the author carries on the four-month field investigation, obtains the creation process related data, and analyzes how the short video creator, as the digital labor, is controlled by the platform rule, as well as the creator in this process of compromise and resistance, a more comprehensive presentation of the short video creators of the labor process. It is found that the short video creators are controlled by the platform rules, mainly in the control of traffic rules, and the creators create content, compromise and resist under the guidance of traffic. First, while the platform seems to offer a flexible and autonomous way for creators to monetize, the threshold for participating in the event is actually very high for creators, and the rules for monetizing the event are vague. Under the influence of the flow rule, the creator is faced unstable incomes and high costs. Therefore, creators have to follow the rules of traffic to guide their own creation, began to flow-oriented content production, mainly reflected in the need to keep up-to-date, the pursuit of traffic to ride on the hot spots, in order to flow regardless, set up people "Born for the show", by the label solidified content creation. Secondly, the irregular working hours lead to the extension and overwork of the working hours, which leads to the internal friction of the short video creators at the spiritual level, and finally leads to the Rat Race of video creation. Thirdly, the video creator has completed the internalization and compromise of the platform rules in practice, which promotes the creator to continue to create independently, and forms the intrinsic motive force of the creator. Finally, the rule-controlled short video creators resist and fight in flexible ways, make use of the mechanism and rules of the platform to carry on the second creation, carry on the routine production, purchase the false flow, transfer the creation position to maintain own creation autonomy.

Keywords: short videos, tiktok, production, digital labors

Procedia PDF Downloads 44

Authors: Yvette Reisinger

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Relatively little is known about the Arab travel market, especially the outbound travel market from Arab countries in the Middle East. The Kuwaiti travel market is the smallest yet fastest growing in the Gulf Cooperation Council (GCC) region. The Kuwaiti travel market represents a great potential for the international tourism industry. Kuwaiti nationals have a very high spending power due to the Kuwaiti dinar being the highest-valued currency unit in the world. Although Europe, North America, and Asia/Pacific try to attract the Arab tourist market the number of Kuwaiti travellers attracted to these destinations is very low. The success in attracting the Kuwaiti travel market to Western countries must be guided by an analysis of the factors that affect its travel decisions. The objective of the study is to identify major factors that influence Kuwaiti nationals’ intentions to travel to Western countries. A model is developed and empirically tested on a sample of 343 Kuwaiti nationals. A series of regression analyses are run to determine the effects of different factors on Kuwaiti’s travel decisions. A Herman’s single factor test and Durbin-Watson test are used to assess the validity of the regression model. Analysis is controlled for socio-demographics. The results show that the Muslim friendly amenities and destination cognitive image exert significant effects on Kuwaiti nationals’ intentions to travel to Western countries. The study provides a better understanding of the factors that attract Kuwaiti tourists to Western countries. By knowing what encourages Kuwaitis to travel to Western countries marketers can plan and promote these countries accordingly. The study provides a foundation of future empirical research into the Kuwaiti/Arab travel market.

Keywords: Kuwaiti travel market, travel decisions, Western countries

Procedia PDF Downloads 168

Authors: Darren Zou

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Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

Procedia PDF Downloads 104

Authors: Johanna Renny Octavia, Kamila Okta Saarah

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Smartphone nowadays is widely used by many people all over the world. However, people with vision impairment may experience difficulties that interfere with the proper usage of the smartphone. In Indonesia, the population of visually impaired is about 13 million people (estimated 285 million people worldwide). There are a number of mobile applications developed to enhance the use of smartphone by visually impaired. This paper discusses the usability evaluation of a mobile application, namely Ray Vision, designed to help visually impaired in using smartphone. A series of usability testing with a number of Indonesian visually impaired revealed 28 usability problems in the mobile application that led to 14 design recommendations. The redesigned application was then re-evaluated through another usability testing series. The results showed that all five usability criteria assessed were increased (usefulness by 13%, effectiveness by 27%, efficiency by 27%, satisfaction by 23%, and learnability by 12%). The System Usability Score (SUS) was also increased by 14.92%.

Keywords: mobile application, smartphone, usability evaluation, vision impaired

Procedia PDF Downloads 292

Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: climate, reanalysis, renewable energy, solar radiation

Procedia PDF Downloads 190

Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

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Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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Authors: Changhong Guo, Shaomei Fang, Yong He

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In this paper, fractional Black-Scholes models for the European option pricing were established based on the fractional G-Brownian motion (fGBm), which generalizes the concepts of the classical Brownian motion, fractional Brownian motion and the G-Brownian motion, and that can be used to be a tool for considering the long range dependence and uncertain volatility for the financial markets simultaneously. A generalized fractional Black-Scholes equation (FBSE) was derived by using the Taylor’s series of fractional order and the theory of absence of arbitrage. Finally, some explicit option pricing formulas for the European call option and put option under the FBSE were also solved, which extended the classical option pricing formulas given by F. Black and M. Scholes.

Keywords: European option pricing, fractional Black-Scholes equations, fractional g-Brownian motion, Taylor's series of fractional order, uncertain volatility

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Authors: Panudet Saengseedam, Nanthachai Kantanantha

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This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.

Keywords: Bayesian method, linear mixed model, multivariate conditional autoregressive model, spatial time series

Procedia PDF Downloads 377

Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of carbon dioxide (CO2) emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, and electricity), CO2 emissions and gross domestic product (GDP) for Turkey using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey-Fuller (ADF) test for stationarity, Johansen’s maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests no effects of the CO2 emissions and energy use on the GDP in Turkey. There exists a short-run bidirectional relationship between the electricity and natural gas consumption, and also there is a negative unidirectional causality running from the GDP to electricity use. Overall, the results partly support arguments that there are relationships between energy use and economic output; however, the effects may differ due to the source of energy such as in the case of Turkey for the period of 1980-2010. However, there is no significant relationship between the CO2 emissions and the GDP and between the CO2 emissions and the energy use both in the short term and long term.

Keywords: CO2 emissions, energy consumption, GDP, Turkey, time series analysis

Procedia PDF Downloads 490

Authors: Dana Madalina Pohrib, Elena Irina Ciobanu

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In compaction works, the most often used codes and standards are those for road embankments and refer to a maximum filling height of 3.00m. When filling a height greater than 3.00m, such codes are no longer valid and thus their application may lead to technical difficulties in the process of compaction and to the achievement of a sufficient degree of compaction. For this reason, in the case of controlled fillings with heights greater than 3.00m it is necessary to formulate and apply a number of special techniques, which can be determined by performing a full scale test. This paper presents the results of the studies and full scale tests conducted for the stabilization of a ravine with vertical banks and a depth of about 12.00m. The fillings will support a heavy traffic road connecting the two parts of a village in Vaslui County, Romania. After analyzing two comparative intervention solutions, the variant of a controlled filling bordered by a monolith concrete retaining wall was chosen. The results obtained by the authors highlighted the need to insert a geogrid reinforcement at every 2.00m for creating a 12.00m thick compacted fill.

Keywords: compaction, dynamic probing, stability, soil stratification

Procedia PDF Downloads 294

Authors: Saad Alkentar, Abdulkareem Assalem

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Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points).

Keywords: small targets, drones, trajectory information, TBD, multivariate time series

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Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

Procedia PDF Downloads 65

Authors: Dechrit Maneetham

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The aim of this paper was to design a void and head controlled electric power wheelchair (EPW). A novel activate the control system for quadriplegics with voice, head and neck mobility. Head movement has been used as a control interface for people with motor impairments in a range of applications. Acquiring measurements from the module is simplified through a synchronous a motor. Axis measures the two directions namely x and y. At the same time, patients can control the motorized wheelchair using voice signals (forward, backward, turn left, turn right, and stop) given by it self. The model of a dc motor is considered as a speed control by selection of a PID parameters using genetic algorithm. An experimental set-up constructed, which consists of micro controller as controller, a DC motor driven EPW and feedback elements. This paper is tuning methods of parameter for a pulse width modulation (PWM) control system. A speed controller has been designed successfully for closed loop of the dc motor so that the motor runs very closed to the reference speed and angle. Intelligent wheelchair can be used to ensure the person’s voice and head are attending the direction of travel asserted by a conventional, direction and speed control.

Keywords: wheelchair, quadriplegia, rehabilitation , medical devices, speed control

Procedia PDF Downloads 513

Authors: Z. Adnan, M. M. Habib

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The laboratory tests for measuring the effects of small strain to the shear strength and maximum stiffness development of post-cyclic degradation of hemic peat are reviewed in this paper. A series of laboratory testing has been conducted to fulfil the objective of this research to study the post-cyclic behaviour of peat soil and focuses on the small strain characteristics. For this purpose, a number of strain-controlled static, cyclic and post-cyclic triaxial tests were carried out in undrained condition on hemic peat soil. The shear strength and maximum stiffness of hemic peat are evaluated immediately after post-cyclic monotonic testing. There are two soil samples taken from West Johor and East Malaysia peat soil. Based on these laboratories and field testing data, it was found that the shear strength and maximum stiffness of peat soil decreased in post-cyclic monotonic loading than its initial shear strength and stiffness. In particular, degradation in shear strength and stiffness is more sensitive for peat soil due to fragile and uniform fibre structures. Shear strength of peat soil, τ_max = 12.53 kPa (Beaufort peat, BFpt) and 36.61 kPa (Parit Nipah peat, PNpt) decreased than its initial 58.46 kPa and 91.67 kPa. The maximum stiffness, G_max = 0.23 and 0.25 decreased markedly with post-cyclic, G_max = 0.04 and 0.09. Simple correlations between the G_max and the τ_max effects due to small strain, ε = 0.1, the G_max values for post-cyclic are relatively low compared to its initial G_max. As a consequence, the reported values and patterns of both the West Johor and East Malaysia peat soil are generally the same.

Keywords: post-cyclic, strain, maximum stiffness, shear strength

Procedia PDF Downloads 278

Authors: S. Sudhharani

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Energy distribution networks are the main link between the energy industry and consumers and are subject to the most scrutiny and testing of any category. As a result, it is important to monitor energy levels during the distribution phase. Power distribution networks, on the other hand, remain subject to common problems, including voltage breakdown, power outages, harmonics, and capacitor switching, all of which disrupt sinusoidal waveforms and reduce the quality and power of the network. Using power appliances in the form of custom power appliances is one way to deal with energy quality issues. Dynamic Voltage Restorer (DVR), integrated with network and distribution networks, is one of these devices. At the same time, by injecting voltage into the system, it can adjust the voltage amplitude and phase in the network. In the form of injections and three-phase syncing, it is used to compensate for the difficulty of energy quality. This article examines the recent use of DVR for power compensation and provides data on the control of each DVR in distribution networks.

Keywords: dynamic voltage restorer (DVR), power quality, distribution networks, control systems(PWM)

Procedia PDF Downloads 118

Authors: A. Fettahoglu

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Theory of viscoelasticity is used by many researchers to represent the behavior of many materials such as pavements on roads or bridges. Several researches used analytical methods and rheology to predict the material behaviors of simple models. Today, more complex engineering structures are analyzed using Finite Element Method, in which material behavior is embedded by means of three dimensional viscoelastic material laws. As a result, structures of unordinary geometry and domain can be analyzed by means of Finite Element Method and three dimensional viscoelastic equations. In the scope of this study, rheological models embedded in ANSYS, namely, generalized Maxwell model and Prony series, which are two methods used by ANSYS to represent viscoelastic material behavior, are presented explicitly. Afterwards, a guide is illustrated to ease using of viscoelasticity tool in ANSYS.

Keywords: ANSYS, generalized Maxwell model, finite element method, Prony series, viscoelasticity, viscoelastic material curve fitting

Procedia PDF Downloads 540

Authors: S. Khardazi, H. Zaitouni, A. Neqali, S. Lyubchyk, D. Mezzane, M. Amjoud, E. Choukri, S. Lyubchyk, Z. Kutnjak

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In the present paper, structural, dielectric, ferroelectric, and energy storage properties of pure perovskite lead-free BCZT, BTSn, and BTSn-BCZT ferroelectric ceramics have been investigated. Rietveld refinement of XRD data confirms the coexistence of the rhombohedral and orthorhombic phases at room temperature in the composite BCZT–BTSn ceramic. Remarkably, an improved recoverable energy density of 137.86 mJ/cm³ and a high energy storage efficiency of 86.19 % at 80°C under a moderate applied electric field of 30 kV/cm were achieved in the designed BCZT–BTSn ceramic. Besides, the sample exhibits excellent thermal stability of the energy storage efficiency (less than 3%) in the temperature range of 70 to 130 °C under 30 kV/cm. Such results make the pb-free BCZT–BTSn ferroelectric ceramic a very promising potential matrix for energy storage capacitor applications.

Keywords: sol-gel, ferroelectrics, lead-free, perovskites, energy storage

Procedia PDF Downloads 48

Authors: W. H. Wan Zakiyatussariroh, A. A. Nasuhar, W. Y. Wan Fairos, Z. A. Nazatul Shahreen

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Forecasting dengue incidence in a population can provide useful information to facilitate the planning of the public health intervention. Many studies on dengue cases in Malaysia were conducted but are limited in modeling the outbreak and forecasting incidence. This article attempts to propose the most appropriate time series model to explain the behavior of dengue incidence in Malaysia for the purpose of forecasting future dengue outbreaks. Several seasonal auto-regressive integrated moving average (SARIMA) models were developed to model Malaysia’s number of dengue incidence on weekly data collected from January 2001 to December 2011. SARIMA (2,1,1)(1,1,1)52 model was found to be the most suitable model for Malaysia’s dengue incidence with the least value of Akaike information criteria (AIC) and Bayesian information criteria (BIC) for in-sample fitting. The models further evaluate out-sample forecast accuracy using four different accuracy measures. The results indicate that SARIMA (2,1,1)(1,1,1)52 performed well for both in-sample fitting and out-sample evaluation.

Keywords: time series modeling, Box-Jenkins, SARIMA, forecasting

Procedia PDF Downloads 456

Authors: Jamunarani Appalasamy, Tha Kyi Kyi, Quek Kia Fatt, Joyce Pauline Joseph, Anuar Zaini M. Zain

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Background: The Health Belief Theory had always been associated with chronic disease management. Various health behaviour concepts and perception branching from this Health Belief Theory had involved with medication understanding, use, and self-efficacy which directly link to medication adherence. In a previous quantitative and qualitative study, stroke patients in Malaysia were found to be strongly believing information obtained by various sources such as the internet and social communication. This action leads to lower perception of their stroke preventative medication benefit which in long-term creates non-adherence. Hence, this study intends to pilot an intervention which uses audio-visual concept incorporated with mHealth service to enhance learning and self-reflection among stroke patients to manage their disease. Methods/Design: Twenty patients will be allocated to a proposed intervention whereas another twenty patients are allocated to the usual treatment. The intervention involves a series of developed audio-visual videos sent via mobile phone which later await for responses and feedback from the receiver (patient) via SMS or recorded calls. The primary outcome would be the medication understanding, use and self-efficacy measured over two months pre and post intervention. Secondary outcome is measured from changes of blood parameters and other self-reported questionnaires. Discussion: This study shall also assess uptake/attrition, feasibility, and acceptability of this intervention. Trial Registration: NMRR-15-851-24737 (IIR)

Keywords: health belief, medication understanding, medication use, self-efficacy

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Authors: Manisha Mukherjee, Diptarka Saha

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Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature.

Keywords: time series, ARIMA, auto.arima, ARIMA parameters, forecast, R function

Procedia PDF Downloads 137

Authors: Gobinda Gopal Khan, Ashutosh K. Singh, Debasish Sarkar

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Unique one-dimensional (1D) Ni-NiO and Co-Ni/Co3O4-NiO core/shell nano-heterostructures are fabricated by combining the electrochemical deposition and annealing. The high-performance pseudo-capacitor electrode based on the Ni-NiO and Co-Ni/Co3O4-NiO core/shell nano-heterostructures is designed and demonstrated. The Co-Ni/Co3O4-NiO core/shell nano-heterostructures exhibit high specific capacitance (2013 Fg-1 at 2.5 Ag-1), high energy and power density (23 Wh kg-1 and 5.5 kW kg-1, at the discharge current density of 20.8 A g-1.), good capacitance retention, and long cyclicality. The remarkable electrochemical property of the large surface area nano-heterostructures is demonstrated based on the novel nano-architectural design of the electrode with the coexistence of the two highly redox active materials at the surface supported by highly conducting metal alloy channel at the core for faster charge transport.

Keywords: nano-heterostructures, energy storage, supercapacitors, electrochemical deposition

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Authors: Yoshinori Kitsutaka, Fumiya Ikedo

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In this study, the mechanical model of various anchors embedded in gypsum board subjected cyclic shear loading were investigated. Shear tests for anchors embedded in 200 mm square size gypsum board were conducted to measure the load - load displacement curves. The strength of the gypsum board was changed for three conditions and 12 kinds of anchors were selected which were ordinary used for gypsum board anchoring. The loading conditions were a monotonous loading and a cyclic loading controlled by a servo-controlled hydraulic loading system to achieve accurate measurement. The fracture energy for each of the anchors was estimated by the analysis of consumed energy calculated by the load - load displacement curve. The effect of the strength of gypsum board and the types of anchors on the shear properties of gypsum board anchors was cleared. A numerical model to predict the load-unload curve of shear deformation of gypsum board anchors caused by such as the earthquake load was proposed and the validity on the model was proved.

Keywords: gypsum board, anchor, shear test, cyclic loading, load-unload curve

Procedia PDF Downloads 367

Authors: Selim M. Khan, Dustin D. Pearson, Tryggve Rönnqvist, Markus E. Nielsen, Joshua M. Taron, Aaron A. Goodarzi

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Accumulation of radioactive radon gas in indoor air poses a serious risk to human health by increasing the lifetime risk of lung cancer and is classified by IARC as a category one carcinogen. Radon exposure risks are a function of geologic, geographic, design, and human behavioural variables and can change over time. Using time series and deep machine learning modelling, we analyzed long-term radon test outcomes as a function of building metrics from 25,489 Canadian and 38,596 Swedish residential properties constructed between 1945 to 2020. While Canadian and Swedish properties built between 1970 and 1980 are comparable (96–103 Bq/m³), innate radon risks subsequently diverge, rising in Canada and falling in Sweden such that 21st Century Canadian houses show 467% greater average radon (131 Bq/m³) relative to Swedish equivalents (28 Bq/m³). These trends are consistent across housing types and regions within each country. The introduction of energy efficiency measures within Canadian and Swedish building codes coincided with opposing radon level trajectories in each nation. Deep machine learning modelling predicts that, without intervention, average Canadian residential radon levels will increase to 176 Bq/m³ by 2050, emphasizing the importance and urgency of future building code intervention to achieve systemic radon reduction in Canada.

Keywords: radon health risk, time-series, deep machine learning, lung cancer, Canada, Sweden

Procedia PDF Downloads 64

Authors: S. M. Arciniegas, D. Vargas, L. Gutierrez

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The aim of this study was to develop oral drug presentation of doxycycline hyclate that maintains longer therapeutic levels than conventional forms. A polymethacrylate and acrylic acid based matrix were used in different proportions to obtain controlled-release formulations; DOX1 (1:0.25:0.0035), DOX2 (1:2:0.0225) and DOX-C (without excipients). All were tested in vivo in healthy dogs and their serum concentrations vs. time profile was investigated after its oral administration in this species. DOX1 and DOX2 show therapeutic concentrations for 60 hours, while DOX-C only for 24 hours. The pharmacokinetics values tested were K½el, Cmax, Tmax, AUC, AUC∞, AUCt, AUMC, RT, Kel, Vdss, Clb and Frel. DOX1 does not differ significantly from DOX-C, but shows significant differences in all variables with DOX2 (p<0.05). In conclusion, DOX1 presents best pharmacokinetics for time-dependent drug and longer release time of 60 hours, thereby reducing the frequency of administration, the patient's stress, the occurrence of adverse effects and the cost of treatment.

Keywords: tetracyclines, long-acting, sustained-release, carbopol, eudragit, canine

Procedia PDF Downloads 589

Authors: Luthfia A. Eka, Bertri M. Masita, G. Indah Lestari, Rizka. Ryanindya, Anindita D. Nur, Asih. Setiarini

Abstract:

The preferred habit nowadays is to watch television or listen to the radio rather than reading a newspaper or magazine. The low interest in reading is the reason to the Indonesian government passed a regulation to foster interest in reading early in schoolchildren through literacy programs. Literacy programs are held for the first 10 - 15 minutes before classes begin and children are asked to read books other than textbooks such as storybooks or magazines. In addition, elementary school children have a tendency to buy less healthy snacks around the school and do not know the nutrition fact from the food purchased. Whereas snacks contribute greatly in the fulfillment of energy and nutrients of children every day. The purpose of this study was to increase reading interest as well as knowledge of nutrition and health for elementary school students. This study used quantitative method with experimental study design for four months with twice intervention per week and deepened by qualitative method in the form of interview. The participants were 130 students consisting of 3rd and 4th graders in selected elementary school in Depok City. The Interventions given using KUCERIA (Child Storybook) which were storybooks with pictures consisting of 12 series about nutrition and health given at school literacy hours. There were five questions given by using the crossword method to find out the students' understanding of the story content in each series. To maximize the understanding and absorption of information, two students were asked to retell the story in front of the class and one student to fill the crossword on the board for each series. In addition, interviews were conducted by asking questions about students' interest in reading books. Intervention involved not only students but also teachers and parents in order to optimize students' reading habits. Analysis showed > 80% of student could answer 3 of 5 questions correctly in each series, which showed they had an interest in what they read. Research data on nutrition and health knowledge were analyzed using Wilcoxon and Chi-Square Test to see the relationship. However, only 46% of students completed 12 series and the rest lost to follow up due to school schedule incompatibility with the program. The results showed that there was a significant increase of knowledge (p = 0.000) between before intervention with 66,53 score and after intervention with 81,47 score. Retention of knowledge was conducted one month after the last intervention was administered and the analysis result showed no significant decrease of knowledge (p = 0,000) from 79,17 score to 75,48 score. There is also no relationship between sex and class with knowledge. Hence, an increased interest in reading of elementary school students and nutritional knowledge interventions using KUCERIA was proved successful. These interventions may be replicated in other schools or learning communities.

Keywords: literation, reading interest, nutrition knowledge, school children

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Authors: Arpita Saha, Apoorv Jain, Satish Chandra, Indrajit Ghosh

Abstract:

Level of service at a signal controlled intersection is directly measured from the delay. Similarly, saturation flow rate is a fundamental parameter to measure the intersection capacity. The present study calculates vehicle arrival rate, departure rate, and queue length for every five seconds interval in each cycle. Based on the queue lengths, the total delay of the cycle has been calculated using Simpson’s 1/3rd rule. Saturation flow has been estimated in terms of veh/hr of green/lane for every five seconds interval of the green period until at least three vehicles are left to cross the stop line. Vehicle composition shows an immense effect on total delay and saturation flow rate. The increase in two-wheeler proportion increases the saturation flow rate and reduces the total delay per vehicle significantly. Additionally, an increase in the heavy vehicle proportion reduces the saturation flow rate and increases the total delay for each vehicle.

Keywords: delay, saturation flow, signalised intersection, vehicle composition

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Authors: Farnaz Farhangi

Abstract:

Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Natalia Caldeira De Carvalho, Tassia Batista Pessato, Luis Gustavo R. Fernandes, Ricardo L. Zollner, Flavia Maria Netto

Abstract:

Protein hydrolysates are ingredients of enteral diets and hypoallergenic formulas. Enzymatic hydrolysis is the most commonly used method for reducing the antigenicity of milk protein. The antigenicity and physicochemical characteristics of the protein hydrolysates depend on the reaction parameters. Among them, pH has been pointed out as of the major importance. Hydrolysis reaction in laboratory scale is commonly carried out under controlled pH (pH-stat). However, from the industrial point of view, controlling pH during hydrolysis reaction may be infeasible. This study evaluated the impact of pH control on the physicochemical properties and antigenicity of the hydrolysates of whey proteins with Alcalase. Whey protein isolate (WPI) solutions containing 3 and 7 % protein (w/v) were hydrolyzed with Alcalase 50 and 100 U g-1 protein at 60°C for 180 min. The reactions were carried out under controlled and uncontrolled pH conditions. Hydrolyses performed under controlled pH (pH-stat) were initially adjusted and maintained at pH 8.5. Hydrolyses carried out without pH control were initially adjusted to pH 8.5. Degree of hydrolysis (DH) was determined by OPA method, peptides profile was evaluated by HPLC-RP, and molecular mass distribution by SDS-PAGE/Tricine. The residual α-lactalbumin (α-La) and β-lactoglobulin (β-Lg) concentrations were determined using commercial ELISA kits. The specific IgE and IgG binding capacity of hydrolysates was evaluated by ELISA technique, using polyclonal antibodies obtained by immunization of female BALB/c mice with α-La, β-Lg and BSA. In hydrolysis under uncontrolled pH, the pH dropped from 8.5 to 7.0 during the first 15 min, remaining constant throughout the process. No significant difference was observed between the DH of the hydrolysates obtained under controlled and uncontrolled pH conditions. Although all hydrolysates showed hydrophilic character and low molecular mass peptides, hydrolysates obtained with and without pH control exhibited different chromatographic profiles. Hydrolysis under uncontrolled pH released, predominantly, peptides between 3.5 and 6.5 kDa, while hydrolysis under controlled pH released peptides smaller than 3.5 kDa. Hydrolysis with Alcalase under all conditions studied decreased by 99.9% the α-La and β-Lg concentrations in the hydrolysates detected by commercial kits. In general, β-Lg concentrations detected in the hydrolysates obtained under uncontrolled pH were significantly higher (p<0.05) than those detected in hydrolysates produced with pH control. The anti-α-La and anti-β-Lg IgE and IgG responses to all hydrolysates decreased significantly compared to WPI. Levels of specific IgE and IgG to the hydrolysates were below 25 and 12 ng ml-1, respectively. Despite the differences in peptide composition and α-La and β-Lg concentrations, no significant difference was found between IgE and IgG binding capacity of hydrolysates obtained with or without pH control. These results highlight the impact of pH on the hydrolysates characteristics and their concentrations of antigenic protein. Divergence between the antigen detection by commercial ELISA kits and specific IgE and IgG binding response was found in this study. This result shows that lower protein detection does not imply in lower protein antigenicity. Thus, the use of commercial kits for allergen contamination analysis should be cautious.

Keywords: allergy, enzymatic hydrolysis, milk protein, pH conditions, physicochemical characteristics

Procedia PDF Downloads 280