bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time series forecasting of ozone levels in the Metropolitan Area of Monterrey, Mexico
Authors: Iglesias-González S , Huertas M , Hernández-Paniagua I , Mendoza A 
Published: May 2020

Abstract
Based on the limitations of air quality models to forecast air pollution, statistical models are convenient and suitable tools to predict pollutant concentrations. This research work proposes a SARMA model to forecast ozone maxima concentrations in five sites of the Metropolitan Area of Monterrey, Mexico. The design of the model includes diverse novel features: meteorological variables were considered as predictors, short-term ozone concentrations (4 times in the day) were forecasted, and appropriated transformations of meteorological parameters were included. The performance measures applied to assess the SARMA model demonstrated that this statistical model is reliable to predict short-term ozone concentrations and it is consistent with the general dynamics of the ozone formation in the metropolitan area.
Distinguish Potential Source Areas of PM2.5 and PM10 by Statistical Data Analysis
Authors: Sooktawee S , Kanchanasuta S , Boonyapitak S , Patpai A , Piemyai N 
Published: May 2020

Abstract
Power plant from biomass in community area has been complained as emission source of many pollutants such as wastewater, noise, odor and polluted air. For air pollution, total suspended particulate (TSP) and particulate matter less than 10 micron (PM10) from rice husk energy process is quite large. Roi Et is a province located in Northeast of Thailand which obtains power plants using rice husk as fuel in the process. According to the land-use pattern of the community area of Roi Et, it revealed that there are many sources of air pollution. This study selected the statistical techniques to distinguish the potential source area influencing the high concentrations of particulate matter less than 2.5 micron (PM2.5), particulate matter 2.5 to 10 micron (PMcoarse), and PM10 which caused the health effects of people around the rice husk power plants. Hourly observed PM10 and PM2.5 data were analysed by time series and bivariate polar plot. Results showed that increasing the concentration of PM10 and PM2.5 were observed in the duration of low wind speed. However, daily average of PM10 was not more than National Ambient Air Quality Standard (NAAQS) which is 120 μg/m3. In contrast, results showed that daily average of PM2.5 performed over NAAQS of 50 μg/m3 in the 20th -22nd December 2018 that high pressure cover Thailand. While sources of these particulate matters could be distinguished into 3 potential areas based on the mobile air quality monitoring station location which were 1) east side and 2) north-west side which regarded as sub-urban and traffic areas, respectively and 3) north-east side which regarded as power plants area. Results revealed that PMcoarse was the major pollutant dispersed from the Northeast direction that the power plants were the major source. Whereas traffic and community were the main sources of PM2.5 coming from the East and the Northwest direction of the mobile air quality monitoring station point. Therefore, discrimination of each point source could be done first before implementing to control and manage the air pollution effectively.
Public perceptions of air pollution and its health impacts in Greater Kuala Lumpur
Authors: Chang F.J. , Ashfold M. J. 
Published: May 2020

Abstract
Air pollution is a globally pervasive health hazard but the risks it poses can be overlooked by the public. To explore the significance of this problem in Greater Kuala Lumpur (KL), Malaysia, we conducted a survey of the public (n = 165). We found (1) an appreciation for the importance of motor vehicles as a source of air pollution; (2) little reported engagement with the official Air Pollution Index (API); (3) a lack of awareness of the long-term health effects of air pollution and (4) reports of a substantial impact of air pollution on day-to-day life. While our survey sample is not representative of the population of Greater KL, our findings are broadly consistent with those of others and underscore a need for improved communication to the public of risks posed by air pollution. Tightened standards underlying the Malaysian API and improved dissemination of real-time air quality updates may be important opportunities to achieve this.
Sensitivity of WRF-Chem model resolution in simulating tropospheric ozone in Southeast Asia
Authors: Adedeji A R , Dagar L , Petra M I , Silva L C De 
Published: May 2020

Abstract
In this paper, WRF-Chem model response to horizontal resolution has been presented in simulating tropospheric ozone distribution during an intense biomass burning across Southeast Asia. Model resolution is varied between 100 km and 20 km. Enhanced fire emissions were also considered in the 20 km resolution simulation. Evaluations were made against observed meteorology such as temperature, relative humidity, wind speed and direction. Spatio-temporal distribution of ozone precursors such as NO2 and CO at the surface retrieved from OMI and MOPITT instruments respectively, were compared against model outputs. Ozonesonde datasets for ozone profile from SHADOZ campaign at Watukosek-Java, Hanoi and Kuala Lumpur were used in evaluating simulated results. All the model simulations adequately represented the observed meteorology. Except in Watukosek-Java where ozone levels were overrepresented, the levels in other locations such as Kuala Lumpur and Hanoi were captured adequately. For model simulations using low-resolution, high-resolution and high-resolution with enhanced fire emissions in Hanoi, Kuala Lumpur and Watukosek-Java region, normalized bias factors are around -0.06, 0.14 and 0.22; 0.01, 0.28 and 0.18, and; 1.20, 3.36 and 3.21, respectively. Normalized root mean square error obtained is as low as 0.09 in Hanoi, and as high as 1.02 in Watukosek-Java region.
A Hybrid Approach to Representing Chessboard using Bitboard and Compact Chessboard Representation
Authors: Bimonugroho Sigit Kariagil , Maulidevi Nur Ulfa 
Published: May 2020

Abstract
A chess engine is a computer program that can perform position analysis in chess and take which are the most optimal move. The main process in a chess engine can be divided into three: move generation, searching, and evaluation. Each of these processes are interrelated and require efficient board representation methods. Some methods of representation from previous research are efficient in some processes but less efficient in others. This study proposes an efficient representation method with a hybrid approach, which combines Compact Chessboard Representation (C.C.R) and bitboard representation. The C.C.R method is used in move generation and searching processes to reach conciseness, and bitboard representation method is used in the evaluation process to make it faster. The result of the movement validity test for the hybrid representation method shows the move generation is valid, and a hybrid-based chess engine also can do searching and evaluation faster than a pure C.C.R based chess engine.
Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach
Authors: Aulia M N , Khodra M L , Koesoema A P 
Published: May 2020

Abstract
—In Indonesia, malnutrition and overnutrition can still be found especially among children and toddlers who are supposed to get adequate nutrition. The cause of malnutrition is inadequate nutrition intake, both in terms of the quantity and quality of the food. Therefore, mothers need to know whether the food consumed by children has reached the specified nutrition intake recommendation. This research develops a system to predict macronutrient content in baby food using Near-infrared Spectroscopy (NIRS) to obtain the spectral profile of the food and Deep Learning approach such as Deep-belief network (DBN) and Convolutional Neural Network (CNN) to build the prediction model. We used instant-porridge scan data from SCiO to build the model. CNN managed to give the best performance with error for carbohydrate, protein and fat 11.70%, 26.14% and 28.72% respectively.
Establishing the Syntactic Rules of the Kankana-ey Dialect using TensorFlow
Authors: Cabilitazan Angeli A. , Ingosan Jeffrey S. , Irabagon Jason , Jacinto Philip Irving G. , Lam Jan Jefferson O. 
Published: May 2020

AbstractKankana-ey is a widely used dialect in the northern region of the Philippines. Unfortunately, there are documented studies on the syntactic rules of this dialect. This study explored the development of a corpus for the Kankana-ey dialect. Further, the corpus was then used to establish the syntactic rules of Kankana-ey. A Kankana-ey version of the bible, dictionaries, news articles, songs and various online resources were used to collect words for the corpus of the Kankana-ey dialect. These identified words were also tagged using the parts of speech tags of the Penn TreeBank. Using the corpus and TensorFlow, 320 Kankana-ey sentences were analysed to determine the syntactic rules. In addition, 80 sentences were used to test the accuracy of the identified rules. At the end of the study, the created corpus has 3,412 tagged Kankana-ey words, while the analysis of the syntactic rules resulted to 1,722 rules. Testing also showed a 60% accuracy of the syntactic rules. In conclusion, the high number of identified rules from the 320 sentences was due to multiple Kankana-ey words having different possible tags. This also resulted to the low accuracy of the syntactic rules.
Convolutional neural network acoustic model for robust Indonesian speech recognition in noisy environment
Authors: Budiman M J , Lestari D P 
Published: May 2020

Abstract
Noise causes the decreasing accuracy of automatic speech recognition (ASR). Several techniques have been developed and proposed to overcome this problem. Using artificial neural network (ANN) as acoustic model is one of the techniques. Convolutional neural network (CNN) is a variant of ANN that has been used for acoustic modeling. Another approach is to do pre-processing to the speech signal or to the extracted acoustic feature from speech signal, such as cepstral mean and variance normalization (CMVN). On this work, CNN acoustic models were trained by using CMVN pre-processed acoustic feature to make a noise-robust speech recognition system. Two group of models were made, each to handle 2 kinds of noise (babble noise and street noise). Those acoustic models were tested with noisy speech at different SNR (signal-to-noise ratio) value. Testing results from CNN acoustic models were compared with the ones from Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) acoustic models. Testing results showed the increasing accuracy scores of acoustic models when models were trained using more variation of training data. CNN acoustic models that were trained using FBANK feature have higher accuracy scores than GMM-HMM models that were built using the same feature.
The Identification System of Acne Type on Indonesian People’s Face Image
Authors: Darmawan Agus , Rositasari Annisa , Muhimmah Izzati 
Published: May 2020

Abstract
We report the results of the detection of types of acne on the human face that are identified directly by a specialist skin and genitals. The proposed algorithm has been tested on a standard image and gives very reasonable results, there are 5 types of acne identified, namely Blackhead Blackheads, Blackhead Blackheads, Papules, Pustules and Nodules. Work completed using PYTHON.
Recommending Tourism Destination using Collaborative Filtering
Authors: Apriliana Rahmatina Ari , Ahmad Hidayatullah Fathan , Hamzah Almed 
Published: May 2020

Abstract
Tourism is among the interesting area of research as more people traveling to various destinations worldwide. With the vast amount of places to visit, there is a problem for tourist to determine which place is most suitable for them in terms of their preferences. However, it can be solved by the means of technology. This paper propose the use of recommender techniques to help tourist in selecting the most preferable destinations. A web-based application was built as an interface with the tourist. The result shows that the system is functioning as it expected. Future directions are discussed.
Knowledge management system (KMS) framework for outcomes-based education sustainability
Authors: Galeon D H , Palaoag T D 
Published: May 2020

AbstractEtched in official memoranda, the Philippine higher education institutions (HEIs) are in their transformational outcomes-based education (OBE) as a government-prescribed educational reform towards quality and sustainability. The educators in these HEIs have a positive attitude and knowledge towards OBE principles and implementation however they lack resources. The paper aims to understand the knowledge management (KM) practices of the Ifugao State University (IFSU) and design a KMS framework considering the most critical factors affecting its implementation, which shall serve as a reference to its development. Using a descriptive-analytical method, it shows that the institution has a strong KM culture but needs to advance their KM strategy and implement modern KM technologies. It also shows that the stakeholders are excellent in socializing and control activities but need improvement and support in networking, encountering, collaborating and coordinating activities. Thus, the proposed KMS framework was designed to build up their KM practices. It primarily informs the stakeholders on whose knowledge, what type of knowledge, how, and why they need to manage knowledge. Future research includes the development of a KMS prototype based on the framework and tests its applicability for the community of practice.
Analysis of the diagrams and main parameters of the existing structures of the articulated tracked vehicles
Authors: Belyakov V , Makarov V , Klubnichkin V , Klubnichkin E 
Published: May 2020

Abstract
The articles reviews structures of the existing all-terrain articulated tracked vehicles (ATV). It includes the existing classification of the ATVs by the type of pivot coupling mechanism (PCM). It analyses the vehicles produced by the principal manufacturers of ATV available on the Russian market. It offers statistical dependencies for the main relationships of the basic power, dimensional characteristics as well as ground pressure depending on the ATV gross weight. It presents a block diagram of the method of rational selection of the vehicle type, maneuverability control system, provision of the total required power, capacity, maximum travel speed.
Stationary simulation method of road conditions in the problem of interaction prediction of the wheel of the vehicle support surface
Authors: Volskaja N , Zhileykin M , Zakharov A 
Published: May 2020

Abstract
A method for estimating the deformation properties of the support surface of the operation proposed area of a four-wheel drive wheeled vehicle is presented. These properties influence the vehicle efficiency. The method is based on the use of physical characteristics of soils specified in the probabilistic form. The estimation of the vehicle movement efficiency is carried out on the average probabilistic speed on the reference route. Features of vehicle rectilinear and curvilinear movement are considered.
Selection of the combat vehicle individual wheel drive operation modes under required mobility criteria
Authors: Kotiev G , Padalkin B , Miroshnichenko A , Stadukhin A , Kositsyn B 
Published: May 2020

Abstract
With the development of the electric drive systems and vehicle on-board power sources the development of the combat vehicles with individually driven wheels is becoming a promising area. In the presented article, the authors illustrate an approach to selection of the requirements for power plants of such vehicles by an example of an 8x8 combat vehicle with full weight of 25 t. The approach is based on the use of the real-time driving simulator allowing the operator to drive the vehicle along typical routes and gather statistical data on the operation modes of the electric motors in the conditions close to the real operation conditions. The virtual driving for this purpose is performed along the routes which are generated from the known statistical data on the excitation inputs during the vehicle motion. The collected data can be used for the selection of the electric motor mechanical characteristics providing given mobility criteria of the vehicle and for the statement of the requirements for new electric motor specifications which would provide its most effective use on the vehicles being developed.
Mathematical Modeling of a Linear Motion on a Deformable Bearing Surface of a Saddle-Type Road Train with Active Semi-Trailer Element
Authors: Chudakov O , Gorelov V , Padalkin B 
Published: May 2020

Abstract
The paper introduces a mathematical modeling of linear motion on a deformable bearing surface (DBS) of a two-link road train consisting of four-axle truck with independent wheel drive and a three-axle semi-trailer. Predictive modeling based on application of pull-traction and energy efficiency obtained during the field research was used in the course of the model development to predict wheeled vehicle mover properties. Results of the theoretical research are given that relates to road train uphill motion with various wheel drives of the semi-trailer.
Using strain gages to assess the bearing properties of underlying surface with a robot crawler
Authors: Mashkov K , Pozdnyakov T 
Published: May 2020

Abstract
The advent of military robotic systems has raised requirements for control systems, especially independent motion control, in particular, lateral possibility and flotation when moving in partially defined or undefined environment and in the presence of external perturbations. This paper proposes approaches to determine bearing reactions under the dynamic interaction of RV running gear with the bearing surface. It demonstrates a set of gages built into the tracked running gear allowing to evaluate the underlying surface properties. It proposes a computer-aided engineering system based on the finite element method to identify the most efficient locations to install strain gages.
Improvement of the active safety of the vehicle for driving on the irregularities by the method of control of vertical reactions on wheels
Authors: Ryazantsev V , Balabin I , Balsam A 
Published: May 2020

Abstract
Countries with large territories have numerous roads of relatively nobody’s quality, characterized by the presence of large irregularities. There are improved roads, but they are badly damaged by inappropriate exploitation or by geophysical and climatic hazards. Such roads are characterized by unevenness, periodic or randomness. The speedy movement of medical transport, rescue vehicles and special services can be difficult because of the possible loss of vehicle stability, which leads to accidents. Often, the loss of stability in such situations is associated with the appearance of resonance vibrations of the vehicle’s wheels, which leads to a deterioration of the wheel to the road, reducing the transverse stability of the axle and thus allowing it to slide transversely. There are many active safety systems, such as ABS, directional stability system and others, which lose their effectiveness in these circumstances. This inconvenience can be avoided by applying the stabilization system for vertical reactions to the vehicle’s wheels (SVRDK).
Crashworthiness design of truck’s cabin using topological and parametric optimization
Authors: Goncharov R , Zuzov V 
Published: May 2020

Abstract
In the given work the basic problems of search of optimum parameters of a cabin of the truck based on parametrical and topological optimization for the purpose of maintenance of requirements on crashworthiness under the international rules and reception of its minimum mass were considered. The article presents the developed finite element models of the cabin and drummer in relation to the optimization tasks, allowing to obtain results with acceptable accuracy and minimum time of solution when using LS-OPT and LS-TaSC programs with LS-DYNA solver. Steel linings and a foam filler are used to reinforce the cabin. In order to solve the problem and more fully assess the impact of parameters, several options were considered to refine the cabin. Topological optimization was carried out in order to obtain a picture of the best distribution of filler on the frame of the cabin. Parametric optimization was carried out by selection of filler properties (aluminium foam) and thickness of structural elements of the cabin. As a result, the passive safety requirements were achieved with optimal mass distribution as a result of combined use of linings and filler. The cabin’s mass increase was 20%.
The Model of Artificial Neural Network and Nonparametric MARS Regression for Indonesian Composite Index
Authors: Devianto Dodi , Permathasari Putri , Yollanda Mutia , Wirahadi Ahmad Afridian 
Published: May 2020

Abstract
The Indonesian composite stock price index is an indicator of changes in stock prices as a guide for investors to invest in reducing risk. The regression model for Indonesian Composite Index (ICI) has the response variable as stock prices with fluctuation behavior and several financial predictor variables, the model tends to violate the assumptions of normality, homoscedasticity, autocorrelation and multicollinearity. This problem can be overcome by modeling the composite stock price index by using the Artificial Neural Network (ANN) and nonparametric regression of Multivariate Adaptive Regression Spline (MARS). In this study, the time series data from the composite stock price index starting from April 2003 to March 2018 with its predictor variables are crude-oil prices, interest rates, inflation, exchange rates, gold prices, Dow Jones price, and Nikkei 225 Index. The both methods give better goodness of fit, where the coefficient of determination ANN is 0.98925 and the MARS determination coefficient is 0.99427. While based on the Mean Absolute Percentage Error (MAPE) of ANN was obtained 6.16383 and the MAPE value of MARS is 4.51372. This means that the ANN method and nonparametric MARS regression method have good performance to forecast the value of the Indonesian composite stock price index in the future, but in this case of data the nonparametric MARS regression method shows the accuracy of the model is slightly better than ANN.
Expert system on core competency detection and student achievement based on Fuzzy TOPSIS method
Authors: Sutiah  , Supriyono  , Zuhriyah Indah Aminatuz , Arifin Zainul 
Published: May 2020

Abstract
The improvement of information technology is experiencing rapid development. The expert system experienced a rapid increase so that it is helpful to human needs in resolving existing problems. The following research has built intelligent systems based on the fuzzy TOPSIS method intending to help to detect core competencies and student achievements. Testing system on the expert system based on the Fuzzy TOPSIS method the conditions in the Ministry of Education Islamic Religion Faculty of Tarbiyah and Islamic State University Maulana Malik Ibrahim Malang. The results show quite a good result that can help in making process decisions. From testing showed that the fuzzy method of TOPSIS resulted in a pretty good level of accuracy in expert systems on core competency detection and student learning achievements.