Search results for: equation model

Authors: Najib K. Dankadai, Ahmad 'Athif Mohd Faudzi, Khairuddin Osman, Muhammad Rusydi Muhammad Razif, IIi Najaa Aimi Mohd Nordin

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Researches in soft actuators are now growing rapidly because of their adequacy to be applied in sectors like medical, agriculture, biological and welfare. This paper presents system identification (SI) and control of the force generated by a two chambers pneumatic soft actuator (PSA). A force mathematical model for the actuator was identified experimentally using data acquisition card and MATLAB SI toolbox. Two control techniques; a predictive functional control (PFC) and conventional proportional integral and derivative (PID) schemes are proposed and compared based on the identified model for the soft actuator flexible mechanism. Results of this study showed that both of the proposed controllers ensure accurate tracking when the closed loop system was tested with the step, sinusoidal and multi step reference input through MATLAB simulation although the PFC provides a better response than the PID.

Keywords: predictive functional control (PFC), proportional integral and derivative (PID), soft actuator, system identification

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Authors: M. Gulam Kibria, Shourav Ahmed, Kais Zaman

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In system analysis, the information on the uncertain input variables cause uncertainty in the system responses. Different probabilistic approaches for uncertainty representation and propagation in such cases exist in the literature. Different uncertainty representation approaches result in different outputs. Some of the approaches might result in a better estimation of system response than the other approaches. The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (MUQC) has posed challenges about uncertainty quantification. Subproblem A, the uncertainty characterization subproblem, of the challenge posed is addressed in this study. In this subproblem, the challenge is to gather knowledge about unknown model inputs which have inherent aleatory and epistemic uncertainties in them with responses (output) of the given computational model. We use two different methodologies to approach the problem. In the first methodology we use sampling-based uncertainty propagation with first order error analysis. In the other approach we place emphasis on the use of Percentile-Based Optimization (PBO). The NASA Langley MUQC’s subproblem A is developed in such a way that both aleatory and epistemic uncertainties need to be managed. The challenge problem classifies each uncertain parameter as belonging to one the following three types: (i) An aleatory uncertainty modeled as a random variable. It has a fixed functional form and known coefficients. This uncertainty cannot be reduced. (ii) An epistemic uncertainty modeled as a fixed but poorly known physical quantity that lies within a given interval. This uncertainty is reducible. (iii) A parameter might be aleatory but sufficient data might not be available to adequately model it as a single random variable. For example, the parameters of a normal variable, e.g., the mean and standard deviation, might not be precisely known but could be assumed to lie within some intervals. It results in a distributional p-box having the physical parameter with an aleatory uncertainty, but the parameters prescribing its mathematical model are subjected to epistemic uncertainties. Each of the parameters of the random variable is an unknown element of a known interval. This uncertainty is reducible. From the study, it is observed that due to practical limitations or computational expense, the sampling is not exhaustive in sampling-based methodology. That is why the sampling-based methodology has high probability of underestimating the output bounds. Therefore, an optimization-based strategy to convert uncertainty described by interval data into a probabilistic framework is necessary. This is achieved in this study by using PBO.

Keywords: aleatory uncertainty, epistemic uncertainty, first order error analysis, uncertainty quantification, percentile-based optimization

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Authors: Mona Lisa M. Oliveira, Nara A. Policarpo, Ana Luiza B. P. Barros, Carla A. Silva

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Selective catalytic reduction systems for nitrogen oxides reduction by ammonia has been the chosen technology by most of diesel vehicle (i.e. bus and truck) manufacturers in Brazil, as also in Europe. Furthermore, at some conditions, over-stoichiometric ammonia availability is also needed that increases the NH₃ slips even more. Ammonia (NH₃) by this vehicle exhaust aftertreatment system provides a maximum efficiency of NOx removal if a significant amount of NH₃ is stored on its catalyst surface. In the other words, the practice shows that slightly less than 100% of the NOx conversion is usually targeted, so that the aqueous urea solution hydrolyzes to NH₃ via other species formation, under relatively low temperatures. This paper presents a model based on neural networks integrated with a road vehicle simulator that allows to estimate NH₃-slip emission factors for different driving conditions and patterns. The proposed model generates high NH₃slips which are not also limited in Brazil, but more efforts needed to be made to elucidate the contribution of vehicle-emitted NH₃ to the urban atmosphere.

Keywords: ammonia slip, neural-network, vehicles emissions, SCR-NOx

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Authors: Ashkan Zakaryazad, Ekrem Duman

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A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

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Authors: M. A. Ansari, A. Hussain, A. Uddin

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A side weir is a flow diversion structure provided in the side wall of a channel to divert water from the main channel to a branch channel. The trapezoidal labyrinth weir is a special type of weir in which crest length of the weir is increased to pass higher discharge. Experimental and numerical studies related to the coefficient of discharge of trapezoidal labyrinth weir in an open channel have been presented in the present study. Group Method of Data Handling (GMDH) with the transfer function of quadratic polynomial has been used to predict the coefficient of discharge for the side trapezoidal labyrinth weir. A new model is developed for coefficient of discharge of labyrinth weir by regression method. Generalized models for predicting the coefficient of discharge for labyrinth weir using Group Method of Data Handling (GMDH) network have also been developed. The prediction based on GMDH model is more satisfactory than those given by traditional regression equations.

Keywords: discharge coefficient, group method of data handling, open channel, side labyrinth weir

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Authors: Mahesh Koti

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In this paper, we present a quantum transport study of a quantum dot in steady state in the presence of static gate potential. We consider a quantum dot coupled to the two metallic leads. The quantum dot under study is modeled through Anderson Impurity Model (AIM) with hopping parameter modulated through voltage drop between leads and the central dot region. Based on the Landauer's formula derived from Nonequilibrium Green's Function and Single Electron Theory, the essential ingredients of transport properties are revealed. We show that the results out of two approaches closely agree with each other. We demonstrate that Landauer current response derived from single electron approach converges with non-zero interaction through gate potential whereas Landauer current response derived from Nonequilibrium Green's Function (NEGF) hits a pole.

Keywords: Anderson impurity model (AIM), nonequilibrium Green's function (NEGF), Landauer's formula, single electron analysis

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Authors: Y. H. Wu, Ji-Yuan Lin, Yu-Ming Liou

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This study aims to set up the landslide susceptibility map of County Route 89 at Ren-Ai Township in Nantou County using the Instability Index Method and Logistic regression. Seven susceptibility factors including Slope Angle, Aspect, Elevation, Distance to fold, Distance to River, Distance to Road and Accumulated Rainfall were obtained by GIS based on the Typhoon Toraji landslide area identified by Industrial Technology Research Institute in 2001. To calculate the landslide percentage of each factor and acquire the weight and grade the grid by means of Instability Index Method. In this study, landslide susceptibility can be classified into four grades: high, medium high, medium low and low, in order to determine the advantages and disadvantages of the two models. The precision of this model is verified by classification error matrix and SRC curve. These results suggest that the logistic regression model is a preferred method than instability index in the assessment of landslide susceptibility. It is suitable for the landslide prediction and precaution in this area in the future.

Keywords: instability index method, logistic regression, landslide susceptibility, SRC curve

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Authors: Mohamed Chakroun

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In this paper, we analyze the macroeconomic impacts of Oman’s strategy to transition to a green economy by 2050. Our objective is to determine the most effective climate policy instrument to facilitate this transition. By utilizing a Dynamic Stochastic General Equilibrium (DSGE) model, we assess the effectiveness of three climate policy tools: a carbon tax, subsidies to green assets, and taxes on brown assets. Our results indicate that a combination of a carbon tax, along with differentiated taxes and subsidies on green and brown assets, proves to the most effective policy in reducing emissions while maintaining macroeconomic stability. The findings of this study demonstrate the need for policymakers to balance the immediate goals of reducing emissions with the economic costs involved. Implementing a gradual transition strategy may be preferable as it allows for mitigating the negative economic impacts while facilitating the shift towards a green economy.

Keywords: green economy, carbon tax, DSGE model, climate policy, sustainable growth

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Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee

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Water pipe network is installed underground and once equipped; it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using Matlab. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.

Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform

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Authors: Saurabh Chanana, Monika Arora

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Demand response is getting increased attention these days due to the increase in electricity demand and introduction of renewable resources in the existing power grid. Traditionally demand response programs involve large industrial consumers but with technological advancement, demand response is being implemented for small residential and commercial consumers also. In this paper, demand response program aims to reduce the peak demand as well as overall energy consumption of the residential customers. Air conditioners are the major reason of peak load in residential sector in summer, so a dynamic model of air conditioning load with thermostat action has been considered for applying demand response programs. A programmable communicating thermostat (PCT) is a device that uses real time pricing (RTP) signals to control the thermostat setting. A new model incorporating PCT in air conditioning load has been proposed in this paper. Results show that introduction of PCT in air conditioner is useful in reducing the electricity payments of customers as well as reducing the peak demand.

Keywords: demand response, home energy management, programmable communicating thermostat, thermostatically controlled appliances

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Chen Niu, Xuesong Zhang, Dejiang Shang, Yongwei Liu

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Fishes can secrete high molecular weight fluid on their body skin to enable their rapid movement in the water. In this work, we employ a hybrid method that combines Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) to investigate the effects of different mucus viscosities and injection velocities on fluctuation pressure in the boundary layer and flow-induced structural vibration noise of a flat plate model. To accurately capture the transient flow distribution on the plate surface, we use Large Eddy Simulation (LES) while the mucus inlet is positioned at a sufficient distance from the model to ensure effective coverage. Mucus injection is modeled using the Volume of Fluid (VOF) method for multiphase flow calculations. The results demonstrate that mucus control of pulsating pressure effectively reduces flow-induced structural vibration noise, providing an approach for controlling flow-induced noise in underwater vehicles.

Keywords: mucus, flow control, noise control, flow-induced noise

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Authors: Rupu Yang, Cong Toan Tran, Assaad Zoughaib

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The work provides an iterative nonlinear programming method to synthesize a heat exchanger network by manipulating the trade-offs between the heat load of process heat exchangers (HEs) and utilities. We consider for the synthesis problem two cases, the first one without fixed cost for HEs, and the second one with fixed cost. For the no fixed cost problem, the nonlinear programming (NLP) model with all the potential HEs is optimized to obtain the global optimum. For the case with fixed cost, the NLP model is iterated through adding/removing HEs. The method was applied in five case studies and illustrated quite well effectiveness. Among which, the approach reaches the lowest TAC (2,904,026$/year) compared with the best record for the famous Aromatic plants problem. It also locates a slightly better design than records in literature for a 10 streams case without fixed cost with only 1/9 computational time. Moreover, compared to the traditional mixed-integer nonlinear programming approach, the iterative NLP method opens a possibility to consider constraints (such as controllability or dynamic performances) that require knowing the structure of the network to be calculated.

Keywords: heat exchanger network, synthesis, NLP, optimization

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Ahmad Nazrun Shuid, Isa Naina Mohamed, Nurul Izzah Ibrahim, Norazlina Mohamed

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Studies have shown that oral tocotrienol, a potent vitamin E, promoted fracture healing of osteoporotic bone. In this study, tocotrienol was combined with a polymer carrier (PLGA), and injected to the fracture site. The slow and constant release of tocotrienol particles would promote fracture healing of post-menopausal osteoporosis rat model. Fracture healing was assessed using micro-CT. Twenty-four Sprague-Dawley rats were ovariectomised or sham-operated and the left tibiae were fractured and fixed with plate and screws. The fractures were created at the upper third of the left tibiae. The rats were divided into 3 groups: sham-operated (SO), ovariectomised-control (OVxC) and PLGA-incorporated tocotrienol treatment (OVx + TT) groups. After 4 weeks, the OVx + TT group showed significantly better callus fracture healing than the OVxC group. In conclusion, tocotrienol-incorporated PLGA was able to promote fracture healing of osteoporotic bone.

Keywords: osteoporosis, micro-CT, tocotrienol, PLGA, fracture

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Authors: S. Chuenchooklin, S. Taweepong, U. Pangnakorn

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This research was conducted in the Mae Sot Watershed whereas located in the Moei River Basin at the Upper Salween River Basin in Tak Province, Thailand. The Mae Sot Municipality is the largest urbanized in Tak Province and situated in the midstream of the Mae Sot Watershed. It usually faces flash flood problem after heavy rain due to poor flood management has been reported since economic rapidly bloom up in recently years. Its catchment can be classified as ungauged basin with lack of rainfall data and no any stream gaging station was reported. It was attached by most severely flood event in 2013 as the worst studied case for those all communities in this municipality. Moreover, other problems are also faced in this watershed such shortage water supply for domestic consumption and agriculture utilizations including deterioration of water quality and landslide as well. The research aimed to increase capability building and strengthening the participation of those local community leaders and related agencies to conduct better water management in urban area was started by mean of the data collection and illustration of appropriated application of some short period rainfall forecasting model as the aim for better flood relief plan and management through the hydrologic model system and river analysis system programs. The authors intended to apply the global rainfall data via the integrated data viewer (IDV) program from the Unidata with the aim for rainfall forecasting in short period of 7 - 10 days in advance during rainy season instead of real time record. The IDV product can be present in advance period of rainfall with time step of 3 - 6 hours was introduced to the communities. The result can be used to input to either the hydrologic modeling system model (HEC-HMS) or the soil water assessment tool model (SWAT) for synthesizing flood hydrographs and use for flood forecasting as well. The authors applied the river analysis system model (HEC-RAS) to present flood flow behaviors in the reach of the Mae Sot stream via the downtown of the Mae Sot City as flood extents as water surface level at every cross-sectional profiles of the stream. Both models of HMS and RAS were tested in 2013 with observed rainfall and inflow-outflow data from the Mae Sot Dam. The result of HMS showed fit to the observed data at dam and applied at upstream boundary discharge to RAS in order to simulate flood extents and tested in the field, and the result found satisfied. The result of IDV’s rainfall forecast data was compared to observed data and found fair. However, it is an appropriate tool to use in the ungauged catchment to use with flood hydrograph and river analysis models for future efficient flood relief plan and management.

Keywords: global rainfall, flood forecast, hydrologic modeling system, river analysis system

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Authors: M. K. Witek-Hajduk, T. M. Napiórkowski

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A number of studies discussed the topic of benefits of retailers-manufacturers cooperation and coopetition. However, there are only few publications focused on the benefits of cooperation and coopetition between retailers and their suppliers of durable consumer goods; especially in the context of business model of cooperating partners. This paper aims to provide a clustering approach to segment retailers selling consumer durables according to the benefits they obtain from their cooperation with key manufacturers and differentiate the said retailers’ in term of the business models of cooperating partners. For the purpose of the study, a survey (with a CATI method) collected data on 603 consumer durables retailers present on the Polish market. Retailers are clustered both, with hierarchical and non-hierarchical methods. Five distinctive groups of consumer durables’ retailers are (based on the studied benefits) identified using the two-stage clustering approach. The clusters are then characterized with a set of exogenous variables, key of which are business models employed by the retailer and its partnering key manufacturer. The paper finds that the a combination of a medium sized retailer classified as an Integrator with a chiefly domestic capital and a manufacturer categorized as a Market Player will yield the highest benefits. On the other side of the spectrum is medium sized Distributor retailer with solely domestic capital – in this case, the business model of the cooperating manufactrer appears to be irreleveant. This paper is the one of the first empirical study using cluster analysis on primary data that defines the types of cooperation between consumer durables’ retailers and manufacturers – their key suppliers. The analysis integrates a perspective of both retailers’ and manufacturers’ business models and matches them with individual and joint benefits.

Keywords: benefits of cooperation, business model, cluster analysis, retailer-manufacturer cooperation

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Authors: Katarzyna Baruch, Agata Szelag, Jaroslaw Rubacha, Bartlomiej Chojnacki, Tadeusz Kamisinski

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Methodology used to measure the reduction of transmitted impact sound by floor coverings situated on a massive floor is described in ISO 10140-3: 2010. To carry out such tests, the standardised reverberation room separated by a standard floor from the second measuring room are required. The need to have a special laboratory results in high cost and low accessibility of this measurement. The authors propose their own engineering method to measure the impact sound improvement with floor coverings. This method does not require standard rooms and floor. This paper describes the measurement procedure of proposed engineering method. Further, verification tests were performed. Validation of the proposed method was based on the analytical model, Statistical Energy Analysis (SEA) model and empirical measurements. The received results were related to corresponding ones obtained from ISO 10140-3:2010 measurements. The study confirmed the usefulness of the engineering method.

Keywords: building acoustic, impact noise, impact sound insulation, impact sound transmission, reduction of impact sound

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Authors: Melissa de Zeeuw, Peter Smolianov, Arnold Bohl

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This study was designed to help and improve international performance as well increase swimming participation in the Netherlands. Over 200 sources of literature on sport delivery systems from 28 Australasian, North and South American, Western and Eastern European countries were analyzed to construct a globally applicable model of high performance swimming integrated with mass participation, comprising of the following seven elements and three levels: Micro level (operations, processes, and methodologies for development of individual athletes): 1. Talent search and development, 2. Advanced athlete support. Meso level (infrastructures, personnel, and services enabling sport programs): 3. Training centers, 4. Competition systems, 5. Intellectual services. Macro level (socio-economic, cultural, legislative, and organizational): 6. Partnerships with supporting agencies, 7. Balanced and integrated funding and structures of mass and elite sport. This model emerged from the integration of instruments that have been used to analyse and compare national sport systems. The model has received scholarly validation and showed to be a framework for program analysis that is not culturally bound. It has recently been accepted as a model for further understanding North American sport systems, including (in chronological order of publications) US rugby, tennis, soccer, swimming and volleyball. The above model was used to design a questionnaire of 42 statements reflecting desired practices. The statements were validated by 12 international experts, including executives from sport governing bodies, academics who published on high performance and sport development, and swimming coaches and administrators. In this study both a highly structured and open ended qualitative analysis tools were used. This included a survey of swim coaches where open responses accompanied structured questions. After collection of the surveys, semi-structured discussions with Federation coaches were conducted to add triangulation to the findings. Lastly, a content analysis of Dutch Swimming’s website and organizational documentation was conducted. A representative sample of 1,600 Dutch Swim coaches and administrators was collected via email addresses from Royal Dutch Swimming Federation' database. Fully completed questionnaires were returned by 122 coaches from all key country’s regions for a response rate of 7,63% - higher than the response rate of the previously mentioned US studies which used the same model and method. Results suggest possible enhancements at macro level (e.g., greater public and corporate support to prepare and hire more coaches and to address the lack of facilities, monies and publicity at mass participation level in order to make swimming affordable for all), at meso level (e.g., comprehensive education for all coaches and full spectrum of swimming pools particularly 50 meters long), and at micro level (e.g., better preparation of athletes for a future outside swimming and better use of swimmers to stimulate swimming development). Best Dutch swimming management practices (e.g., comprehensive support to most talented swimmers who win Olympic medals) as well as relevant international practices available for transfer to the Netherlands (e.g., high school competitions) are discussed.

Keywords: sport development, high performance, mass participation, swimming

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Authors: M. Bazrafshan, M. B. de Rooij, D. J. Schipper

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Frictional hysteresis is the phenomenon in which mechanical contacts are subject to small (compared to contact area) oscillating tangential displacements. In the presence of adhesion at the interface, the contact repulsive force increases leading to a higher static friction force and pre-sliding displacement. This paper proposes a boundary element model (BEM) for the adhesive frictional hysteresis contact at the interface of two contacting bodies of arbitrary geometries. In this model, adhesion is represented by means of a Dugdale approximation of the total work of adhesion at local areas with a very small gap between the two bodies. The frictional contact is divided into sticking and slipping regions in order to take into account the transition from stick to slip (pre-sliding regime). In the pre-sliding regime, the stick and slip regions are defined based on the local values of shear stress and normal pressure. In the studied cases, a fixed normal force is applied to the interface and the friction force varies in such a way to start gross sliding in one direction reciprocally. For the first case, the problem is solved at the smooth interface between a ball and a flat for different values of work of adhesion. It is shown that as the work of adhesion increases, both static friction and pre-sliding distance increase due to the increase in the contact repulsive force. For the second case, the rough interface between a glass ball against a silicon wafer and a DLC (Diamond-Like Carbon) coating is considered. The work of adhesion is assumed to be identical for both interfaces. As adhesion depends on the interface roughness, the corresponding contact repulsive force is different for these interfaces. For the smoother interface, a larger contact repulsive force and consequently, a larger static friction force and pre-sliding distance are observed.

Keywords: boundary element model, frictional hysteresis, adhesion, roughness, pre-sliding

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Authors: Felix Brito

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With the growth of online learning, several higher education institutions have attempted to incorporate technology in their curriculum. Successful technology implementation projects really on technology infrastructure and on the acceptance of education professionals towards innovation. This research study is aimed at illustrating the relevance of the human component in technology implementation projects in higher education by describing the Learning Management System implementation project executed by instructional designers working for a higher education institution in the southeast region of the United States. An analysis of the Transformative Leadership Theory, the Technology Acceptance Model, and the Diffusion of Innovation Process provide the support for a solid understanding of this issue and address recommendations for future technology implementation projects in higher education institutions.

Keywords: diffusion of innovation process, instructional design, leadership, learning management systems, online learning, technology acceptance model, transformative leadership theory

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Authors: W. Badla

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A numerical investigation has been carried out to examine the behaviour of reinforced concrete slabs to uniform blast loading. The aim of this work is to determine the effects of various parameters on the results. Finite element simulations were performed in the non linear dynamic range using an elasto-plastic damage model. The main parameters considered are: the negative phase of blast loading, time duration, equivalent weight of TNT, distance of the explosive and slab dimensions. Numerical modelling has been performed using ABAQUS/Explicit. The results obtained in terms of displacements and propagation of damage show that the above parameters influence considerably the nonlinear dynamic behaviour of reinforced concrete slabs under uniform blast loading.

Keywords: blast loading, reinforced concrete slabs, elasto-plastic damage model, negative phase, time duration, equivalent weight of TNT, explosive distance, slab dimensions

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Authors: Era Dorihi Kale, Sabina Gero, Uly Agustine

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Asylum seekers are people who come to other countries to get asylum. In line with that, they also carry the culture and health behavior of their country, which is very different from the new country they currently live in. This situation raises problems, also in the health sector. The approach taken must also be a culturally sensitive approach, where the culture and habits of the refugee's home area are also valued so that the health services provided can be right on target. Some risk factors that already exist in this group are lack of activity, consumption of fast food, smoking, and stress levels that are quite high. Overall this condition will increase the risk of an increased incidence of cardiovascular disease. This research is a descriptive and experimental study. The purpose of this study is to identify health status and develop a culturally sensitive health promotion model, especially related to the risk of cardiovascular disease for asylum seekers in detention homes in the city of Kupang. This research was carried out in 3 stages, stage 1 was conducting a survey of health problems and the risk of asylum seeker cardiovascular disease, Stage 2 developed a health promotion model, and stage 3 conducted a testing model of health promotion carried out. There were 81 respondents involved in this study. The variables measured were: health status, risk of cardiovascular disease and, health promotion models. Method of data collection: Instruments (questionnaires) were distributed to respondents answered for anamnese health status; then, cardiovascular risk measurements were taken. After that, the preparation of information needs and the compilation of booklets on the prevention of cardiovascular disease is carried out. The compiled booklet was then translated into Farsi. After that, the booklet was tested. Respondent characteristics: average lived in Indonesia for 4.38 years, the majority were male (90.1%), and most were aged 15-34 years (90.1%). There are several diseases that are often suffered by asylum seekers, namely: gastritis, headaches, diarrhea, acute respiratory infections, skin allergies, sore throat, cough, and depression. The level of risk for asylum seekers experiencing cardiovascular problems is 4 high risk people, 6 moderate risk people, and 71 low risk people. This condition needs special attention because the number of people at risk is quite high when compared to the age group of refugees. This is very related to the level of stress experienced by the refugees. The health promotion model that can be used is the transactional stress and coping model, using Persian (oral) and English for written information. It is recommended for health practitioners who care for refugees to always pay attention to aspects of culture (especially language) as well as the psychological condition of asylum seekers to make it easier to conduct health care and promotion. As well for further research, it is recommended to conduct research, especially relating to the effect of psychological stress on the risk of cardiovascular disease in asylum seekers.

Keywords: asylum seekers, health status, cardiovascular disease, health promotion

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Authors: Lim Chen Kim, Tan Kian Lam, Chan Yi Chee

Abstract:

This research presents a multi-modal simulation in the reconstruction of the past and the construction of present in digital cultural heritage on mobile platform. In bringing the present life, the virtual environment is generated through a presented scheme for rapid and efficient construction of 360° panoramic view. Then, acoustical heritage model and crowd model are presented and improvised into the 360° panoramic view. For the reconstruction of past life, the crowd is simulated and rendered in an old trading port. However, the keystone of this research is in a virtual walkthrough that shows the virtual present life in 2D and virtual past life in 3D, both in an environment of virtual heritage sites in George Town through mobile device. Firstly, the 2D crowd is modelled and simulated using OpenGL ES 1.1 on mobile platform. The 2D crowd is used to portray the present life in 360° panoramic view of a virtual heritage environment based on the extension of Newtonian Laws. Secondly, the 2D crowd is animated and rendered into 3D with improved variety and incorporated into the virtual past life using Unity3D Game Engine. The behaviours of the 3D models are then simulated based on the enhancement of the classical model of Boid algorithm. Finally, a demonstration system is developed and integrated with the models, techniques and algorithms of this research. The virtual walkthrough is demonstrated to a group of respondents and is evaluated through the user-centred evaluation by navigating around the demonstration system. The results of the evaluation based on the questionnaires have shown that the presented virtual walkthrough has been successfully deployed through a multi-modal simulation and such a virtual walkthrough would be particularly useful in a virtual tour and virtual museum applications.

Keywords: Boid Algorithm, Crowd Simulation, Mobile Platform, Newtonian Laws, Virtual Heritage

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Authors: Huamin Xu, Wenting Jia, Hong Jiang, Junxia Xie

Abstract:

Rosmarinic acid (RA) is a natural acid that is found in a variety of herbs, such as rosemary and has multiple biological activities such as antioxidative, anti-inflammatory and antiviral activities. In this study, we investigated the neuroprotective effects of RA on dopaminergic system in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced mouse model of Parkinson’s disease (PD). The mice received oral administration of RA before MPTP injection. Results showed that the tyrosine hydroxylase expression in SN reduced and the levels of dopamine and its metabolites in the striatum decreased in MPTP intoxicated PD mice. Pretreatment with RA significantly inhibited these changes. Further studies demonstrated that MPTP treatment increased the iron content, which was counteracted by pre-treatment with RA. In addition, RA could restore the decrease of superoxide dismutase (SOD) induced by MPTP. This study provides evidence that RA could suppress MPTP-induced degeneration of the nigrostriatal dopaminergic system by regulating iron content and the expression of SOD. Thus, RA might be clinically evaluated for the prevention of neurodegenerative diseases.

Keywords: rosmarinic acid, Parkinson's disease, MPTP, dopaminergic system

Procedia PDF Downloads 205

Authors: Sharifa Alshahrani

Abstract:

Comics is a multimodal text wherein meaning is created by taking in all modes of expression at once. It uses two different semiotic modes, the verbal and the visual modes, together to make meaning and these different semiotic modes can be socially and culturally shaped to give meaning. Therefore, comics translation cannot treat comics as a monomodal text by translating only the verbal mode inside or outside the speech balloons as the cultural differences are encoded in the visual mode as well. Due to the development of the internet and editing software, comics translation is not anymore confined to the publishing houses and official translation as scanlation, or the fan translation took the initiative in translating comics for being emotionally attracted to the culture and genre. Scanlation is carried out by volunteering fans who translate out of passion. However, quality is one of the debatable issues relating to scanlation and fan translation. This study will investigate how the dynamic multimodal relationship in comics is exploited and interpreted in the translation by exploring the translation strategies and procedures adopted by the publishing houses and scanlation in interpreting comics into Arabic using three analytical frameworks; cultural references model, multimodal relation model and translation strategies and procedures models.

Keywords: comics, multimodality, translation, scanlation

Procedia PDF Downloads 212

Authors: Sudha T., Naveen C.

Abstract:

Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: F. Atheer, Al-Saoudi, Robin Kalfat, Riadh Al-Mahaidi

Abstract:

Over the last two decades, externally bonded fiber reinforced polymer (FRP) composites bonded to concrete substrates has become a popular method for strengthening reinforced concrete (RC) highway and railway bridges. Such structures are exposed to severe cyclic loading throughout their lifetime often resulting in fatigue damage to structural components and a reduction in the service life of the structure. Since experimental and numerical results on the fatigue performance of FRP-to-concrete joints are still limited, the current research focuses on assessing the fatigue performance of externally bonded FRP-to-concrete joints using a direct shear test. Some early results indicate that the stress ratio and the applied cyclic stress level have a direct influence on the fatigue life of the externally bonded FRP. In addition, a calibrated finite element model is developed to provide further insight into the influence of certain parameters such as: concrete strength, FRP thickness, number of cycles, frequency and stiffness on the fatigue life of the FRP-to-concrete joints.

Keywords: FRP, concrete bond, control, fatigue, finite element model

Procedia PDF Downloads 449