bluuebunny/crossref_metadata_2025_split huggingface dataset:
Silane treatment of coated carbonate apatite scaffold affects bioactivity and cell viability
Authors: Mamat Normahira , Jaafar Mariatti , Hamid Zuratul Ain Abdul , Yahaya Badrul Hisham 
Published: November 2019

Abstract
The aim of this study is to determine effects of surface treatment on coated-carbonate apatite (CO3Ap) scaffold using silane coupling agent of 3-(trimethoxysilyl) propyl methacrylate. The effect of coating properties with and without surface treatment were investigated through in vitro bioactivity and biocompatibility. Firstly, CO3Ap scaffold was fabricated by transforming β-TCP scaffold via hydrothermal treatment. CO3Ap scaffolds were treated in 2% silane solution before coating in chitosan solution (0.5%, 1% and 2%) by dipping method. The coated scaffold without silane treatment denoted as UTR while with silane treatment denoted as TR. It was found that, the presence of Si-OH group enhanced apatite growth on the coated scaffold. TR0.5 has capability to induce apatite growth for 7 days immersion in HBSS compared to UTR0.5. Rougher surface and different surface charge properties have influenced cell viability on the coated CO3Ap scaffold. To be concluded, it is important to note that bioactivity and cell activity were forced by surface energy or topography, composition and electrostatic interaction.
Electrical Stimulation in Combating Pressure Ulcer for Immobilize Subjects: A Conceptual Framework
Authors: Azman Amelia Wong , Fajingbesi Fawwaz Eniola , Ahmad Zuraida , Mustafah Yasir Mohd , Olanrewaju Rashidah Funke , Hamzaid Nur Azah 
Published: November 2019

Abstract
A paradigm shift in health system did intensify the need for research on wearable electronics. There are numerous reasons that could lead to skin ulcers. The most common are lesion due to constant pressure, incontinence control, excessive humidity and postural changes. Over the years, there have been numerous interventions ranging from specific skin treatment, introducing prevention programs as well as to mechanical intervention. On the rise in such clinical rehabilitation program is the use of electrical stimulation (ES) at a therapeutic level, wound care and muscle function restoration. This paper presents a concept development and implementation process of a new pressure ulcer intervention system which can be used for both chairbound and bedridden patients. The concept here is to integrate the use of electrical stimulation, interface pressure mapping and redistribution with antimicrobial protection not only to act as therapeutic but at the same time as a means of prevention of pressure ulcers by improving muscle tone and decreasing atrophy among immobilized subjects.
Assessment of an Arterial Stiffness to Demonstrate AVF Performance: Modelling Approach
Authors: Mien T’ng Chien , Yahud Shuhaida 
Published: November 2019

Abstract
Arteriovenous fistula (AVF) defined as shunting in the arterial system and the venous conduit is a preferred vascular access used in haemodialysis. The AVF provide a sufficient increase in blood flow rates for effective dialysis. However maintaining AVF patency become the greatest challenge for dialysis patient. Complications arise from this includes stenosis, aneurism and thrombosis. In previous studies, several methods are used to evaluate the performance of AVF. This study will focus on the assessment of arterial stiffness to demonstrate the performance of a side-to-side AVF. The condition of arterial stiffness which alter haemodynamic of AVF, in fact, will affect its performance and functionality. To gain insight and understanding for the haemodynamic condition, a healthy AVF (without arterial stiffening) has been modelled in the computational fluid dynamic (CFD) framework. The aim of this project is therefore to develop a modelling approach, suitable for the analyses of blood flow field to predict the performance and patency of AVF. A 3D CFD method is proposed to analysis flow field or haemodynamic in terms of blood flow velocity, blood pressure and wall shear stress (WSS) along the AVF. A laminar flow was introduced for the simulation of healthy AVF. Correlations in these haemodynamic parameters to assess the vascular failure supports hypothesis suggesting flow field as predicator for AVF performance. Haemodynamic effects lead to AVF failure were determined. Significant flow disturbances with abnormally high or low WSS obtained from the simulation is used as a primary indicator.
Prediction of Alzheimer’s disease (AD) Using Machine Learning Techniques with Boruta Algorithm as Feature Selection Method
Authors: Leong Lee Kuok , Abdullah Azian Azamimi 
Published: November 2019

Abstract
Alzheimer’s disease (AD) is the most frequent incurable neurodegenerative disease, a general term for memory loss and other cognitive abilities. Early detection of AD can help with proper treatment and prevent brain tissue damage. Traditional medical tests are time consuming, fail to recognize early signs and lack of diagnosis sensitivity and specificity. To achieve promising prediction accuracy, the best predictive machine learning model is selected based on initial pre-processing step followed by vital attributes selection and performance evaluation for five proposed supervised machine learning algorithms. In the pre-processing, all the missing values have been removed since the overall percentage only covered 5.63%. Boruta algorithm as feature selection method resulted Atlas Scaling Factor, Estimated Total Intracranial Volume, Normalized Whole-brain Volume, Mini-Mental State Examination and Clinical Dementia Rating must be included as primary features. With Boruta algorithm, it has been shown that Random Forest Grid Search Cross Validation (RF GSCV) outperformed with 94.39% of accuracy, 88.24% sensitivity, 100.00% specificity and 94.44% AUC among other 12 models that includes conventional and fine-tuned models even for the small OASIS-2 longitudinal MRI dataset. Finally, our developed Graphical User Interface (GUI) prediction tool was evaluated through prediction over OASIS-1 cross-sectional MRI dataset containing 216 samples of imaging sessions that have been pre-processed. Prediction results were closed with the dementia status provided in OASIS cross-sectional data fact sheet.
Development of a Biomechanical Model to Study the Effects of Lifting Load on Sciatic Pain
Authors: Inam Shehla , Ali Zainab , Wahab Haleema , Shaheen Hina , Amin Faisal , Ur Rehman Muhammad Zia , Toor Hamza , Shafique Muhammad 
Published: November 2019

Abstract
Sciatica is a frequently encountered leg pain that originates in the human lower back and travels down the buttocks through the large sciatic nerve present in the back of the leg. Due to lifting heavy loads at different angles, the humans could acquire sciatica at different levels. Thus, the reason of using biomechanical model was to mimic the human spine in such a way that the increased force while lifting the load could easily be measured. This could help to beware the humans to not lifting load at these angles. This paper presents the findings of an experiment conducted to measure the increased forces at 6 different angles i.e. 95°, 105°, 115°, 125°, 135° and 145°. Experiments were carried out by connecting the model and a load cell with PASCO CAPSTONE. Results were concluded using both statistical and graphical analysis. The resultant force increased as the angle of bending was increased due to increased load on the back model. Stair climbing is one of the unique exercises which is frequently used and helps in maintaining the leg muscles active and healthy.
Predictive Systems: Role of Feature Selection in Prediction of Heart Disease
Authors: Panda Debjani , Ray Ratula , Abdullah Azian Azamimi , Dash Satya Ranjan 
Published: November 2019

AbstractAs per recent trends heart disease has become the major factor for untimely deaths. There are huge amounts of clinical data available from biomedical devices and various applications used by hospitals. Artificial Intelligence is rigorously being used in predicting conditions of heart patients. This is mainly achieved by machine learning where a model is trained with sample cases and is then used for prediction of the ailment as per data available from clinical tests of the patient. This paper focuses in analyzing the accuracy of various classification algorithms, when they are supervised by set of features. Feature selection plays an important role in eliminating redundant and irrelevant features and reduces the training cost and time of the predictive models. The classification algorithms, which have been analyzed include Naive Bayes, Random Forest, Extra Trees and Logistic regression which have been provided with selected features using least absolute shrinkage and selection operator (LASSO) and Ridge regression. The accuracy of the classifiers shows remarkable improvement after using feature selection. The prediction has improved on an average by 33.3% using Lasso regression as compared to 30.73% using ridge regression.
Gait Asymmetry Assessment using Muscle Activity Signal: A Review of Current Methods
Authors: Abdul Halim Husnir Nasyuha , Azaman Aizreena , Manaf Haidzir , Saidin Syafiqah , Zulkapri Izwyn , Yahya Azli 
Published: November 2019

Abstract
Gait asymmetry is a type of gait characteristics when there is difference in gait parameters statistically, measured bilaterally between left and right limbs. Gait asymmetry assessment is used to observe changes or deviation in gait due to pathological condition, effect of rehabilitation program or to give insight on effect of gait on stability and fall-risk. The assessments of gait asymmetry could be measured by using spatiotemporal, kinetics, kinematics parameters or by analysis of muscle activity signals obtained from surface electromyography (EMG). However, EMG-based assessment for gait asymmetry is not well explored compared to assessment using other gait parameters. This review aims to compare research designs, methods and procedure of previous studies that utilized EMG for gait asymmetry analysis. Therefore, any research in the future that involved gait asymmetry measurement could take note on and produce more reliable findings.
Prediction system of carbon monoxide toxic gas monitoring using double moving average method
Authors: Hasibuan Eriansyah , Harahap H , Hardi S , Suherman S , Fahmi F 
Published: November 2019

Abstract
Health information at this time is very essential to support the continuity improvement of human life. One of the causes of problems to health in humans is the air pollution. It is necessary to have a monitoring system to provide information on air pollution. In this research, a monitoring system for air pollutant gas, specifically Carbon Monoxide (CO) was developed and calibrated with a prediction system using the double moving average method. The prototype for monitoring CO gas used MQ7 sensor, connected with an Arduino microcontroller and GPRS module. Data readings and prediction results were then displayed in real time connected to the web server. The result of developed system were carried out in the KIM area Port Belawan I, by measuring using CO gas prototypes and sending data in real time to the web server in the internal time of 1 hour 13 minutes. We collected 44 log data from the measurement results of data received from CO gas prototypes. The time range of predictive data with real-time gas CO data is 4 minutes on average. The prediction results using the double moving average method have a maximum mean average percentage error MAPE of 3.39% and a minimum MAPE of 0.74%. While the maximum mean square error MSE is 5.84 and minimum MSE of 0.27.
Harmonic Analysis of Ionospheric Total Electron Content (TEC) Using Kalman Filter
Authors: Muslim Buldan , Charisma Juni K , Erlansyah  
Published: November 2019

Abstract
The ionospheric diurnal variation from TEC Global Ionospheric Maps (GIM) data at the epicenter coordinates of the Lombok earthquake on July 29 2018 (-8.4°, 116.5°) has been analyzed using harmonic function which is the sum of the sine and cosine functions with estimated harmonic coefficients obtained using Kalman Filter (KF) method. The initial value of the estimated KF method was obtained from Least Square Estimation (LSE) on the first day data. The estimated TEC value by the KF method shows that there are seasonal variations of the peak values of diurnal variations which is a result of variation in the zenith angle of the sun that change over a year with peaks in March and November in the equator. The results of the first-order harmonic amplitude analysis, namely the diurnal TEC variation (A24) which is the root value of the sum of the squared values of the A1 and B1 coefficients were further analyzed by estimating polynomials with order 17 to eliminate the seasonal effect on A24. The difference in value of A24 from its estimation with order 17 polynomial (delta A24) shows the influence of geomagnetic and seismic activity. A24 analysis based on solar radiation flux at wavelength 10.7 cm (F10.7) does not indicate a strong influence or relationship> Equatorial geomagnetic disturbance index (etc.) shows the influence of geomagnetic storms on diurnal amplitude and earthquake data around Lombok indicating symptoms of pre-earthquake that affects the ionospheric TEC through a decrease in diurnal amplitude a few days before the earthquake.
Effect of Premium-Butanol Blends on Fuel Consumption and Emissions on Gasoline Engine with Cold EGR System
Authors: Sanjaya Firman Lukman , Syaiful  , Sinaga dan Nazarudin 
Published: November 2019

Abstract
Butanol is renewable fuel that can be used as a solution for the increasing fuel consumption and emissions. This is because of the high octane number in butanol increase the effectiveness of the combustion. In addition, high oxygen content in butanol can reduce CO and HC emissions. The purpose of this study is to determine the optimal mixture of premium and butanol on fuel consumption and exhaust emissions on gasoline engines. This study uses a Toyota 7K engine equipped with an Cold EGR system. The percentage of addition butanol in gasoline fuels is 5%, 10%, and 15%. The experiment was carried out at a variation of 2500 rpm to 4000 rpm engine speed. The experimental results show that the addition of butanol in gasoline reduces fuel consumption by 1.9%. The use of the Cold EGR system results in fuel consumption of 0.31 kG.kW/hours in the P85B15 mixture. This value is 16.8% lower than without using the Cold EGR system. Addition of butanol to fuel also increases the EGT. However, the EGT value decreases with the use of the Cold EGR System. The use of a mixture of gasoline and butanol reduced carbon monoxide (CO) and hydrocarbons (HC) by 74.2% and 46.3%. The percentage of CO2 also showed an increase of 58.6 %. However, the use of the Cold EGR system increases the value of HC and CO. The use of the Cold EGR system also increases the value of CO2 emissions by 19.04% compared without using the Cold EGR system.
The Valuation of Deposit Insurance with Risk Using Fourier Transform
Authors: Afnia Nur , Yunus Mahmud , Putri Endah R. M. 
Published: November 2019

Abstract
Deposit insurance is a tool to stabilize a banking system. In deposit insurance, the insured risk is the risk of the bank’s failure to fulfill its obligations to the customer because of its revoked license. The application of a flat rate deposit insurance system will lead to moral hazard problems, and cause a banking crisis as the banks get involved to risky activities. To anticipate this, it is necessary to design a deposit insurance premium which calculating the different risk levels of each bank. One method that can be used is Fourier transforms. The purpose of this study is to obtain a deposit insurance with risk premium model from the Black-Scholes Model by using Fourier transform. Based on the simulation results, the research found the value of volatility and interest rate is directly proportional to the value of deposit insurance premiums, as well as the value of debt obligations. The high value of deposit insurance premiums is also influenced by the value of dividends, however the value of insurance premiums has changed insignificantly prior to n period, therefore the value of deposit insurance premiums from zero to n period has changed insignificantly.
Indonesian community welfare levels clustering using the fuzzy subtractive clustering (FCM) method
Authors: Cahyaningrum P Laras Sakti , Irsalinda Nursyiva 
Published: November 2019

Abstract
Clustering is a technique used to classify objects or cases into groups based on their similarity, called clusters or groups. Objects in each group tend to resemble each other and differ greatly (not the same) with objects from other clusters. Public welfare is a condition of fulfilling the material, spiritual and social needs of citizens in order to be able to live properly. Fuzzy Subtractive Clustering (FSC) method is a clustering algorithm that can form the number and centroid of clusters in accordance with data conditions. This study aims to determine the FSC results in grouping the level of welfare of the Indonesian people in 2017. The testing results of the cluster validity index show 2 values of Partition Entropy and Classification Entropy forming into 2 clusters that have the best value, indicating that the provincial group has a high welfare level and the provincial group has a low welfare level.
Mushroom Images Identification Using Orde 1 Statistics Feature Extraction with Artificial Neural Network Classification Technique
Authors: Fadlil Abdul , Umar Rusydi , Gustina Sapriani 
Published: November 2019

Abstract
There are many kinds of mushrooms difficult to identified manually. Because of that, a certain system that can be used to identify mushrooms is needed. One feature that artificial intelligence has is image identification. One image that can be identified mushroom image. Mushroom image identification can contribute to artificial intelligence technology development. Computer-based mushroom image identification can be done by conducting a segmentation process that converts the original image to a grayscale image. The mushroom image pattern characteristics are selected and separated using a feature extraction process. Mushrooms feature extraction conducted by using orde 1 statistics. Feature extraction results are classified using the Artificial Neural Network method with the Backpropagation Algorithm. Classification process carried out by training and testing with neurons variations 5, 10, 15 and 20, while hidden layers are 0.1, 0.3, 0.5, 0.7, and 0.9 with 10,000 times iteration. 30 images that are consist of 15 images for training data and 15 images for test data. From research can be seen that mushroom image identification using orde 1 statistics features extraction with artificial neuron network has the best result with 93% accuracy on neuron 20. Mushroom’s image identification system that is developed can be implemented in other applications.
Identification of Cyber Bullying by using Clustering Methods on Social Media Twitter
Authors: Meliana Nina , Sunardi  , Fadlil Abdul 
Published: November 2019

Abstract
In today’s era social media is very important for some people, because it is the nature of social media that can make people addicted to using social media, this has been widely proven in terms of medical, social interaction has been reduced due to social media, things related to the physical to be reduced, for example going out, and playing outdoors for children, for adults social media can be positive and negative, positive if used to offer old friends who don’t meet for long, but negative things can be used to crime or things that are not good, what else will soon be the presidential election, social media becomes a place or means of defamation of one party with another, the initial goal of social media is to build good relations Actually, there is a sentence that says “Yout thumb your tiger” with comments that are not good on social media. If we take us to the criminal section, and our words on social media can be legally justified, one example is intimidation, intimidation is one of the ITE Law articles, intimidation will be lifted from Twitter’s social media, with Twitter we can see examples how much of this intimidation. In retrieving data on social media there are several methods used in retrieving data, the method used is the clustering method or data grouping method. The study used the Naive Bayes and Decision Tree J48 classification method. The results to be obtained in this study are the Naive Bayes method which obtained an average value of the overall scenario of 92% success rate and 8% not detected and the result of Decision Tree J48 method with an accuracy value of 100%. The most widely used type of cyberbullying is psychology related bullying or related to one’s ignorance or nature.
Stability Analysis on the Spread of the Ebola Virus Considering Changes in Human Behavior and Climate (Case Study: Democratic Republic of the Congo)
Authors: Sitti Rosnafian S , Mardlijah  , Hariyanto  
Published: November 2019

Abstract
In this paper construction of the SEIR (Susceptible-Exposed-Infected-Recovery) type mathematical model consider account the influence of changes in human behavior and climate in the Democratic Republic of the Congo (DR Congo) in the form of differential equations to describe the spread of the Ebola virus. The relationship between climate and behavior change is represented in the form of parameters (α). The constructed system is then validated with existence and uniqueness. Furthermore, an analysis of the spread of the virus was carried out with stability analysis. Based on the results of stability analysis, the system around the endemic equilibrium point is unstable. In the final stage a simulation was carried out to obtain a visual picture of the spread of the Ebola virus in DR Congo.
Design of Automatic Braking System Using Interval Type-2 Fuzzy Logic System
Authors: Setiady Dwi , Wahyunggoro Oyas , Nugroho Prapto 
Published: November 2019

Abstract
The car braking system is a very important part of car safety. The effectiveness of Conventional braking system is strongly influenced by the ability and the driver’s experience. It is very vulnerable to accidents if the driver fails to operate the braking system. Because of that, an automation of the braking system is needed that can decrease the accident’s impact. The Automatic braking systems require the reasoning to manage information before making a decision that brakes or slows down. There is uncertainty in determining membership function of distance and velocity. It is difficult to determine an exact membership function. In this paper, the system use Interval type-2 fuzzy logic control that can work well in an unstructured environment and have the ability to overcome the uncertainty of information. This system will be compared with the braking system using T1FLS. The result is that the pressure on the brake pads is gradually increased. At a distance of 34 m, the pressure is at maximum value. The changes produced by the automatic braking system that uses IT2FLS increase the average pressure of 4,167% while in T1FLS system, a change in growth greater pressure by an average of 5%. In IT2FLS braking system, braking is gradually increasing smoother than T1FLS.