ASEAN Engineering Journal最新文献

{"title":"CUTTING ANALYSIS ON HORIZONTAL DRIILING USING CUTTING CARRY INDEX, CUTTING TRANSPORT RATIO AND CUTTING CONCENTRATION IN ANNULUS METHOD ON A WELL G FIELD S","authors":"A. R. R. Wastu, R. Husla, Ghanima Yasmaniar, P. S. Yulia, S. Samsol, O. Ridaliani","doi":"10.11113/aej.v13.20028","DOIUrl":"https://doi.org/10.11113/aej.v13.20028","url":null,"abstract":"The basic principle of the drilling operation is to grind the rock formations with bits which will later become drill cuttings. In this process, fluid is needed as a conveying medium, commonly known as drilling mud, because the deeper the drilling, the more drill flakes are produced. One of the prominent functions of drilling mud is lifting the drill shale up to surface. The purpose of this analysis is to quantitatively prove whether the cutting has been successfully lifted to the surface or not, and to determine the elements of the fluid’s physical properties and the drilling mud fluid’s rheology. This analysis was carried out using three methods including; the cutting carry index, which the value required is greater than one; the cutting transport ratio, which the value required is greater than 50%; and the cutting concentration in annulus, which the value required is less than 5%. If the value of the quantitative calculation already has a value above the standard, it can be concluded that the drill cuttings can be lifted optimally. The value of the cutting carry index has to be greater than one, because if it is less than one the solid value in the mud is still lacking, causing a low density so it will affect the carrying capacity of cutting in 1 gal of mud. The value of cutting concentration in annulus should not be more than 5, it is feared that if it exceeds this limit, it can affect the rate of penetration and can cause a stuck. As for the value of the cutting transport ratio should not be less than 50% because if it is less than that the hydrostatic pressure below will be large and can cause formation fractures. In this case, we will discuss the analysis of cutting removal on the 8.5 inch route at 4290 to 5174 ft depth, with a slope of 66.29o from the starting point of the borehole.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":"349 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139314512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PROPERTIES AND CHARACTERIZATION OF MAGNESIUM OXYCHLORIDE CEMENT AS CARBON CAPTURE MATERIAL","authors":"Andrie Harmaji, Eva Febrina, Salsabila Ansari Putri","doi":"10.11113/aej.v13.19647","DOIUrl":"https://doi.org/10.11113/aej.v13.19647","url":null,"abstract":"Greenhouse gas emissions produced by steam-powered electric plants can trigger damage to the atmosphere and increase the average surface temperature below it, resulting in global warming as a manifestation of the operation of power plants. A material is needed to capture carbon dioxide (CO2) gas produced by the power plant. Magnesium oxychloride (MOC) cement, commonly called Sorel cement, has the potential to be used as a carbon capture material. MOC is synthesized from magnesium oxide (MgO), magnesium chloride (MgCl2), and water (H2O). This study aimed to find the optimum ratio of MgO:MgCl2:H2O to produce the MOC with highest mechanical properties ranged from 1:1:1, 2:1:1, and 3:1:1. To determine the performance of the resulting MOC, physical, mechanical, X-ray diffraction (XRD), and scanning electron microscope (SEM) characterization tests were carried out. MOC with the highest mechanical properties was exposed to a high CO2 gas environment to determine its carbon capture performance. The mechanical testing shows that the best ratio of MgO:MgCl2:H2O was 3:1:1. This produces a hardness value of 43 VHN, a compressive strength of 57 MPa, a flexural strength of 46 MPa, and a modulus of elasticity of 2 GPa. The MOC 3:1:1 shows a CO2 gas capture effectiveness of 36% after 7 days, proven by XRD and SEM. The results of the tests carried out show that MOC has the potential to reduce carbon emissions produced by the steam-powered electric plant industry.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139314590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DEEP LEARNING METHODOLOGY FOR PLANT SPECIES RECOGNITION USING MORPHOLOGY OF LEAVES","authors":"Deepti Barhate, Sunil Pathak, Ashutosh Kumar Dubey","doi":"10.11113/aej.v13.19461","DOIUrl":"https://doi.org/10.11113/aej.v13.19461","url":null,"abstract":"Plants play a crucial role in supporting all forms of life on Earth, not just humans but every living organism. Understanding the diverse range of plant species that surround us is essential due to their significance in various aspects of human life, including agriculture, the environment, medicine, cosmetics, and more. Advancements in machine learning and computer vision algorithms have opened possibilities for identifying different types of plant species, both within and across classes. Plant species detection typically involves several steps, such as image acquisition, feature extraction, categorization, and pre-processing. In this study, three datasets—namely Flavia, Swedish, and the intelligent computing laboratory (ICL) dataset—were chosen for experimentation purposes. For feature extraction, three different models were employed: k-nearest neighbour (KNN), naive Bayes (NB), and the visual geometry group (VGG)-16 model. These models were used to extract distinctive features such as shape, texture, venation, and margin from the plant images. A multiclass classification task was conducted to categorize the plant species. Among the models tested, the VGG-16 model consistently demonstrated superior performance in terms of accuracy. Specifically, when using the VGG-16 model, the obtained accuracies were 96.68% for the Flavia dataset, 97.65% for the Swedish dataset, and 96.11% for the ICL dataset.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139314806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GREEN SYNTHESIS AND CHARACTERIZATION OF FE DOPED TIO2 NANOPARTICLES USING LAWSONIA INERMIS LEAF AQUEOUS EXTRACTS AS REDUCTANT FOR PHOTOCATALYTIC ACTIVITY","authors":"Syamsutajri Syamsol Bahri, Zawati Harun, Wan Norhayati Wan Salleh, Rosniza Hussin, Nur Hanis Hayati Hairom, Noor Hasliza Kamaruddin, Hatijah Basri, Nurul Izwanie Rasli, Afiqah Rosman, Mohd Riduan Jamaluddin, Ainun Rahmahwati Ainuddin","doi":"10.11113/aej.v13.19690","DOIUrl":"https://doi.org/10.11113/aej.v13.19690","url":null,"abstract":"Recently, the improvement and advancement in synthesizing nanoparticles via eco-friendly technique have been intensively explored since the used conventional physical and chemical methods always associated to the intensive energy usage and toxic waste pollution Therefore, nanoparticles synthesized from green route technique has initiate an interest among researchers due to its clean and eco-friendliness approach. In this study, the Fe doped TiO2 were synthesized using lawsonia inermis aqueous leaf extracts that act as reducing agent as well as capping and stabilizing agent. The green synthesized powder was then calcined and characterized using XRD, FESEM, EDX, FTIR and UV-Vis while photocatalytic activity was evaluated based on photodegradation efficiency of methylene blue (MB). The new obtained green calcined powder was found to have a crystalline phase (anatase structure) with crystallite size, 6.79 nm identified by XRD. The FESEM test shows the average particle size of the green synthesized calcined Fe doped TiO2 is in the range of 54.5 nm with capping agent (phyto-constituents) authorized by FTIR spectra. From EDX analysis, Fe ions was successfully incorporated into TiO2 compound during synthesis process confirmed by the presence of Fe element. It was observed that the band gap energy for green calcined Fe doped TiO2 is approximately 2.66 eV. The green synthesized calcined Fe doped TiO2 sample presented the highest photocatalytic activity efficiency under uv light irradiation for 3 hour which is 92.2% with only 7.8% of MB remained, this value is slightly higher than that of commercial P25 powder which is 90.7%. In conclusion, the green synthesis technique of using lawsonia inermis as natural resources as reduction agent was able to produce nanoparticles Fe doped TiO2. Moreover, the properties of Fe doped TiO2 nanoparticles has strong potential to be used as a photocatalyst since this sustainable green synthesis technique able to produce better nanoparticles properties as compared to conventional synthesis.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136081744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRITICAL RISK ANALYSIS OF TOLL ROAD PUBLIC-PRIVATE PARTNERSHIP (PPP) PROJECT CONSTRUCTION PHASE","authors":"Fahira Rhomianti Putri, B. Susanti, Mona Foralisa Toyfur","doi":"10.11113/aej.v13.19462","DOIUrl":"https://doi.org/10.11113/aej.v13.19462","url":null,"abstract":"Limited funding in meeting the financing needs for the provision of toll road infrastructure makes the government need to look for alternative financing so that the project can still be carried out. Public Private Partnership (PPP) is an alternative option to overcome this problem. However, infrastructure projects with PPP schemes require large investments and a relatively long concession period, thus allowing risk uncertainty to arise as a result of decisions made during the project. Risk identification in a construction project is important at the beginning of the project. This study was aimed at identifying critical risks, determining risk allocation and risk mitigation strategies, and analyzing the interrelationship between critical risks. The case study was conducted on the Trans Sumatra toll road project in South Sumatra. Quantitative data (questionnaire survey) and qualitative data (interview) were collected from the toll-road private sector. The results of the study identified five critical risks in the construction phase (CP) of the toll road project along with the allocation of risks, namely the risks of geographical conditions (shared), delays in work progress (private sector), design errors (private sector), force majeure (shared) and weather conditions (shared). The mitigation strategy for the risks of geographical conditions and design errors was to redesign the construction structure by adjusting with the land conditions of the toll road. Force majeure (Covid-19) causing delays in work progress required rescheduling and recalculation of construction costs. Weather conditions (heavy rain) occurring on swampy land caused puddles/floods; this condition caused delays in construction work. The mitigation strategy that could be carried out was by coordinating with the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) in the region and preparing disaster management Standard Operating Procedures (SOPs). The cause and effect of each risk showed that the critical risks in the construction phase of the toll road project were interrelated to each other which the risks would finally contribute to the increase in the construction costs of the toll road project.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46660495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALYSIS OF RWANDA’S GRID POINT OF STABILITY LOSS","authors":"Silas Bizimungu, Francis Njoka, Churchhill Saoke, Clement Siame","doi":"10.11113/aej.v13.18556","DOIUrl":"https://doi.org/10.11113/aej.v13.18556","url":null,"abstract":"E\u0000Electricity stability is the key component in ensuring reliable power supply which is a major hurdle in most developing countries. Power angle being part of grid stability pillars, this work sought to theoretically and numerically investigate the maximum power angle variation that the power system in Rwanda could experience while maintaining transient stability at an acceptable range beyond which generators lose synchronism affecting overall system stability. A steady state and dynamic stability assessment of the lightly loaded Rwandan grid is conducted using PSS/E while MATLAB is used to obtain input values for calculating the system stability power angle. This involved collecting actual network parameters, creating an accurate model of the entire Rwandan grid and validating the model simulation results using regularly metered values at various critical busbars of the network. Results from the study show that a sudden drop in load of 11 MW and 6 MVAr leads to a variation of power angle of the largest power plant by 23o triggering transient stability problems leading to loss of synchronism. Both calculated and simulation results show that, a sudden power angle variation of between 23o to 23.5o at the swing bus power plant, causes the monitored generating units to trip triggering a long-term instability. This was done while also interrogating possible mitigation measures based on the developed and validated model. Based on the system status, the study revealed that the grid point of stability can be determined through simulation using software and to further obtain optimum stability, installation of active devices such as shunt reactors helps the grid to counter sudden variation of power angle due to line and load tripping.\u0000B","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42199189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AN ENSEMBLE APPROACH FOR COFFEE CROP YIELD PREDICTION BASED ON AGRONOMIC FACTORS","authors":"C. S. Santhosh, Kattekyathanalli Kalegowda Umesh","doi":"10.11113/aej.v13.18846","DOIUrl":"https://doi.org/10.11113/aej.v13.18846","url":null,"abstract":"Coffee is the most burned-through handled drink beside water, which is said to be the most exchanged cultivating product followed by oil in the entire globe. The two most significant sorts of coffee assortment filled in India are Arabica and Robusta out of 103 assortments of class coffee bean variety, which are economically exchanged around the planet. In this regard, we are taking major plantation crop in India i.e., Coffee for our research to explore and develop a predictive model for the development of coffee planters to take precise decisions in time during adverse situations in advance. Hence we propose a framework for coffee yield prediction which using machine learning ensemble approach to estimate the influence of agronomic factors to get a good coffee yield. Here, for our research work, the historic dataset is considered which is obtained from Central Coffee Research Institute (CCRI), Karnataka for the year (2008-2019). For the coffee yield prediction, we are considering agronomic factors like Age, Soil Nutrients: Organic carbon (OC), Phosphorus (P), Potassium (K), Alkaline (pH), Zone and Respective yield obtained in chikkamagaluru   region, Karnataka state, India. Different classifiers are used namely, Extra Tree Classifier, Random Forest Classifier, Decision Tree and Boosting Algorithms for prediction and performance of each is compared and analyzed. Our results shown that Extra Tree Classifier and Random forest (RF) classifier with a precision of 91% with good results based on performance metrics considered respectively is an effective and versatile machine-learning method compared to other algorithms used.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47608487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A PROPOSED TECHNIQUE FOR IMPROVING THE ACCURACY OF TIDAL MODELING OF RIVER NETWORKS CONNECTING TO THE DADAHUP IRRIGATION AREA","authors":"A. Zevri, A. P. Rahardjo, D. Legono","doi":"10.11113/aej.v13.19393","DOIUrl":"https://doi.org/10.11113/aej.v13.19393","url":null,"abstract":"Optimizing the operational management of tidal irrigation networks is a significant factor in the reactivation program of the swamp irrigation areas in Central Kalimantan Province. Appropriate water level and flow rate control in a tidal environment would provide a better solution for this effort. Monitoring water parameters and hydraulic modeling is an intelligent technique for evaluating irrigation canals' gate system operations. This paper focuses on providing more accurate hydraulic modeling that requires proper boundary conditions and calibration of the canal roughness coefficient. This study aims to obtain a more precise downstream boundary condition data calibration method with an appropriate river cross-sectional roughness coefficient value to improve the overall hydraulic modeling accuracy of the river network connecting to the Dadahup Irrigation Area. The hydraulic modeling utilized the HEC-RAS Software, where input data preparation used geometric data derived from the National Geospatial Agency's DEM in the form of river channel network chain, cross-section, and long-section data. Boundary condition data evaluation compared and selected data generated from references and the Tides Application Software. The results show that the tide prediction from the Tides Application Software provides the slightest difference between the predicted tide and the measured ones. The river network's channel roughness coefficient calibration utilized the generated boundary condition tide and simultaneously measured water level data at several locations. The results show that the most minor Root Mean Squared Error (RMSE) of the Manning roughness coefficient differences of the river channel network can reach 0.04 with a minimum RMSE value of 0.027.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42046000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3.5 GHZ VIVALDI ANTENNAS: A COMPREHENSIVE PARAMETRIC ANALYSIS FOR UNLEASHING 5G COMMUNICATION TECHNOLOGY","authors":"M. Othman, Nur Aishah Shahirah Ruslan, M. H. Misran, M. A. Meor Said, R. Abdul Manap, A. Jaafar, N. I. Hassan, S. Suhaimi","doi":"10.11113/aej.v13.19774","DOIUrl":"https://doi.org/10.11113/aej.v13.19774","url":null,"abstract":"In this study, we discuss the design and testing of a Vivaldi antenna operating at 3.5 GHz, which is well-suited for mobile mid-band 5G connection. CST Microwave Studio software was used to simulate and evaluate the suggested antenna design, which was printed utilising state-of-the-art 3D printing processes and materials (polylactic acid (PLA) and FR-4 circuit board material). The measured results show that the antenna has a reflection frequency of 3.51 GHz and a gain of -23.695 dB. Parametric analysis was carried out to examine the relationship between antenna performance and design parameters, with special focus on the separation between the antenna and the PLA material in the middle of the spherical construction. The Vivaldi antenna is an attractive choice for 5G mid-band applications because of its wideband features, ease of manufacture using typical industrial processes, and simplicity of impedance matching to the feeding line using microstrip line modelling.\u0000 ","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44819449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MACHINE LEARNING METHODS FOR EARLY-STAGE DIAGNOSIS OF PARKINSON'S DISEASE THROUGH HANDWRITING DATA","authors":"Matthew Dionela, Carey Louise B. Arroyo, Mhica S. Torres, Miguel P. Alaan, Sandy C. Lauguico, R. R. Vicerra, R. Concepcion II","doi":"10.11113/aej.v13.18777","DOIUrl":"https://doi.org/10.11113/aej.v13.18777","url":null,"abstract":"Parkinson's disease (PD) deteriorates human cognitive and motor functions, causing slowness of movements and postural shakiness. PD is currently incurable, and managing symptoms in its late stages is difficult. PD diagnosis also has gaps in accuracy due to several clinical challenges. Thus, early-stage detection of PD through its symptoms, such as handwriting abnormality, has become a popular research area using machine learning. Since most related studies focus on advanced algorithms, this study aims to determine the classification accuracies of simpler classical models using the NewHandPD-NewMeander dataset. This study used the 9 features extracted from the meanders drawn by healthy participants and participants diagnosed with Parkinson’s disease and 3 features about the individual. The same features were reduced to the 8 best according to univariate selection and recursive feature elimination. The machine learning algorithms used for the models in this study are Logistic regression, Multilayer perceptron, and Naive Bayes. Additionally, hyperparameter optimization was done. Results have shown that feature selection improved the performances of the default model, while optimization had varying effects depending on the feature selection method used. Among 15 models built, Multilayer perceptron, which utilized top 8 features from univariate selection with default hyperparameters (MLPU8), performed best. It yielded an accuracy of 84.4% in cross-validation, 87.5% in holdout validation, and an F1-score of 87.5%. Remaining models had accuracies ranging from 81.4% - 84.4% in cross-validations and 82.5% - 85.0% in holdout validations. Other studies done on diagnosing PD using similar handwritten datasets resulted in lower accuracies of 87.14% and 77.38% despite utilizing complex algorithms for its models. This proved that the 15 models built using simple architecture can outperform complex classification methods. The 15 models built accurately classify meander data and can be used as an early assessment tool for detecting PD.","PeriodicalId":36749,"journal":{"name":"ASEAN Engineering Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45135959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}