bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influenza Virus in Community-Acquired Pneumonia: Current Understanding and Knowledge Gaps
Authors: Xu Jiuyang , Yu Jiapei , Yang Luning , Zhou Fei , Li Hui , Cao Bin 
Published: June 2020

AbstractInfluenza virus infection poses a heavy burden on global health and economics. With the advancement in viral pathogen detection methods, the role of virus infection in community-acquired pneumonia has been increasingly recognized. The disease spectrum of influenza ranges from asymptomatic infection to severe or even fatal illness. Progress has been made in recent years to identify risk factors including lymphopenia and hypoxia for influenza mortality. Immunopathology plays an important role in influenza pathogenesis. The disturbed homeostasis after virus infection consists of both an excessive inflammatory phase and an immune suppression phase, collectively described as viral sepsis. Multiple antiviral therapies have been tested and some were advanced to late-phase clinical trials, including polymerase inhibitors, hemagglutinin inhibitors, host-acting antivirals, monoclonal antibodies, and adjunctive immunomodulatory therapies. Combination therapies have been shown to increase antiviral efficacy and genetic resistance barrier. In this review, we summarized the recent advances in our understanding of the disease pathogenesis, as well as the progress in antiviral therapy development. We also pointed out current key knowledge gaps in influenza research. Hopefully, experience gained from seasonal influenza research will prepare us for the next influenza pandemic and emerging respiratory pathogens.
Factors Influencing Problem List Use in Electronic Health Records—Application of the Unified Theory of Acceptance and Use of Technology
Authors: Klappe Eva S. , de Keizer Nicolette F. , Cornet Ronald 
Published: June 2020

Abstract
Background Problem-oriented electronic health record (EHR) systems can help physicians to track a patient's status and progress, and organize clinical documentation, which could help improving quality of clinical data and enable data reuse. The problem list is central in a problem-oriented medical record. However, current problem lists remain incomplete because of the lack of end-user training and inaccurate content of underlying terminologies. This leads to modifications of diagnosis code descriptions and use of free-text notes, limiting reuse of data.
Objectives We aimed to investigate factors that influence acceptance and actual use of the problem list, and used these to propose recommendations, to increase the value of problem lists for (re)use.
Methods Semistructured interviews were conducted with physicians, heads of medical departments, and data quality experts, who were invited through snowball sampling. The interviews were transcribed and coded. Comments were fitted in constructs of the validated framework unified theory of acceptance user technology (UTAUT), and were discussed in terms of facilitators and barriers.
Results In total, 24 interviews were conducted. We found large variability in attitudes toward problem list use. Barriers included uncertainty about the responsibility for maintaining the problem list and little perceived benefits. Facilitators included the (re)design of policies, improved (peer-to-peer) training to increase motivation, and positive peer feedback and monitoring. Motivation is best increased through sharing benefits relevant in the care process, such as providing overview, timely generation of discharge or referral letters, and reuse of data. Furthermore, content of the underlying terminology should be improved and the problem list should be better presented in the EHR system.
Conclusion To let physicians accept and use the problem list, policies and guidelines should be redesigned, and prioritized by supervising staff. Additionally, peer-to-peer training on the benefits of using the problem list is needed.
Antibiotic Lock Therapy with Linezolid for the Treatment of Persistent Catheter-Related Infection in Children with Cancer
Authors: Sarı Neriman , Okur Nurettin , Çakmakcı Selma , Aksu Tekin , İlhan İnci Ergürhan 
Published: June 2020

Abstract
Objective Central venous catheter (CVC) colonization is a common problem in the pediatric oncology department. Initial colonization of CVC by coagulase-negative staphylococci (CoNS), Staphylococcus aureus, and enterococci is followed by the growth of intraluminal bioﬁlm formation, and results in antibiotic therapy failure. The removal of the old CVC and insertion of new CVC is a difficult and expensive procedure in small children with cancer. The present article aimed to study our treatment results of antibiotic lock therapy (ALT) with linezolid in pediatric cancer patients.
Methods This study was planned as retrospective presentation of case series with eight pediatric cancer patients treated with 11 courses of systemic and linezolid lock therapy. Demographic information, clinical findings, laboratory data, blood culture results, complications, and outcome were collected for each patient retrospectively and descriptive statistical methods were used.
Results Prior to treatment, peripheral and CVC blood culture results showed Staphylococcus epidermidis in seven patients and Staphylococcus hominis in four patients. All pathogens were susceptible to vancomycin and teicoplanin; first-line treatment was vancomycin in six and teicoplanin in five patients. After first-line treatment, peripheral blood cultures of all patients were negative, whereas blood cultures from CVC remained positive. During second-line therapy with linezolid, microbiological eradication was achieved on the fourth day of treatment in each patient. Median catheter survival time for all patients was 14 (range: 8–30) months. No side effects were observed during the treatment and no resistant organisms were documented.
Conclusion Although multicentric prospective controlled trials will be required to provide more generalizable results, we suggest that systemic antibiotics combined with linezolid lock therapy used in pediatric cancer patients may be an effective option in treating catheter-related bloodstream infection (CRBSI) and prolonging CVC survival when CoNS are identified.
Numerical analysis of air pollutant dispersion in steam power plant area using the finite volume method
Authors: Fatahillah A , Masyhudi M A , Setiawan T B 
Published: June 2020

Abstract
In this research a mathematical model will be developed for the distribution of air pollutants. The model is built on the basis of momentum equations and mass continuity equations solved using the finite volume method. Based on the model obtained, the effect of wind speed and initial concentration on the distribution of pollutants in the air will be analyzed. An air pollutant with a certain concentration released through the chimney into the atmosphere will undergo a process of transportation and transformation. The characteristics of emission sources such as concentration when the emission and wind speed around the chimney really determine the spreading of the concentration. The case study in this research is located in the Paiton-Probolinggo Steam Power Plant area. The analysis of the distribution of pollutants was solved numerically using the Gauss Seidel iteration method assisted by MATLAB software and simulated using a CFD approach assisted by FLUENT software. The results obtained in this research indicated that the greater the wind speed around the chimney, the lower the concentration of SO2 pollutants in the air, while the effect of the initial concentration showed that the greater the initial concentration, the greater the concentration of pollutants scattered in the air.
Modelling mixed geographically weighted poisson regression for tuberculosis disease in Surabaya
Authors: Khaulasari Hani 
Published: June 2020

Abstract
Tuberculosis is one of the deadly infectious diseases caused by mycobacterium tuberculosis. Patients with Tuberculosis will experience pain and organ damage leading to death. Surabaya is one of the cities with the most densely populated settlements and the highest number of Tuberculosis sufferers in East Java. In this study, we want to get the factors of the spread of the number of tuberculosis sufferers in Surabaya using Mixed Geographically Weighted Poisson Regression (MGWPR) method. The advantage of the MGWPR method is to pay attention to the differences in each location where it is estimated to affect the spread of Tuberculosis locally and globally. The results show that the Gaussian kernel fixed weighting MGWPR model forms four groups with different factors. Global factors that influence Tuberculosis sufferers are the number of HIV/AIDS sufferers, the percentage of households applying PHBS, the ratio of health education, The percentage of the population receives Tuberculosis information, the number of medical personnel, the number of the population not completed elementary and The number of population graduated from high school while the influenced locally variables on the spread of Tuberculosis was the percentage of healthy homes. Model MGWPR is better than model GWPR and Regression Poisson, because model MGWPR get AIC value is the smallest and R2 is the biggest.
Forecasting amount of passanger of ships in Madura strait port using ARIMA Box-Jenkins method
Authors: Prastuti M , Ulama B S S , Nurindraprasta P P 
Published: June 2020

Abstract
Madura Island and the city of Surabaya, used to be only connected by Ujung-Kamal Port. Later in 2009, Suramadu Bridge began operating. Some people would rather drive over the Suramadu bridge than using the ships so that the amount of ships passanger decreases. This has made PT. ASDP Indonesia Ferry, as the port manager, lose some of the port’s revenue that leads into loss. Along with the free access to pass the Suramadu bridge, it is feared that the amount of ships passanger will continue to decrease. Therefore PT. ASDP Indonesia Ferry requires effort to prevent the greater loss. One of the efforts is to reschedule the ship trips. The ship trips can be affected by the amount of ships passanger. Therefore this research aims to forecast the amount of ships passanger, includes pedestrian, motorbike rider, and car rider. The forecasting is done by the ARIMA Box-Jenkins method. The result shows that ARIMA ([1,12,13], 1,0), ARIMA (0,1,[1,11,12]), and ARIMA (0,1,1) are the best model to forecast the amount of padestrian, motorbike rider, and car rider. The forecast results show that the amount of padestrian passenger is continue to decrease, while the amount of motorbike and car rider are constant.
Modeling of toddler stunting in the province of east nusa tenggara using multivariate adaptive regression splines (mars) method
Authors: Azizah D M , Permatasari E O 
Published: June 2020

Abstract
Stunting is a condition of failure to thrive in children under five (babies under five years old) as a result of chronic malnutrition so that the child is too short for his age. Malnutrition occurs since the baby is in the womb and in the early period after the baby is born. However, the condition of stunting only appears after the baby is 2 years old. Based on Basic Health Research in 2017 the province with the highest prevalence of short and very short toddlers in Indonesia is East Nusa Tenggara which is equal to 40.3 percent. This shows that the prevalence of stunting toddlers in NTT Province is still far from the overall prevalence of stunting toddlers in Indonesia. Cases of toddler stunting can be analyzed using the Multivariate Adaptive Regression Splines (MARS) method because the data used are high-dimensional data and do not show clear patterns of relationships between response variables and predictor variables. The data used is the percentage of stunting toddlers in NTT in 2017 with the district / city research unit. Based on the analysis, it was found that the district in NTT with the highest percentage of stunting children was South East Timor and the district with the lowest percentage was East Manggarai. The best MARS model is formed from 3 basis functions with 2 predictor variables included in the model, namely the percentage of pregnant women at risk of chronic lack of energy (X1) and the percentage of infants with low birth weight (X6). And after the classification results obtained the accuracy of the classification of 77.27% with an error of 22.72%.
The mathematical model of Eunice Siciliensis lifecycle: a study on Lombok Baunyale Festival Preserving Effort
Authors: Ratianingsih R. , Puspita J.W. , Farida  , Jaya A.I. 
Published: June 2020

Abstract
Baunyale Festival or sea worms (nyales) catching event is one of the traditions or heritage culture of the Lombok Sasak tribe community. The appearance of the colorful nyales on the sea surface in large quantities is actually a mass spawning of nyales. The most number of appearing nyales founded during the Baunyale festival is Eunice (palola) siciliensis. It was reported an excessive catching of nyales happened at the event in 2016 that endanger its existence. This paper is a scientific study of the dynamics of Eunice (palola) siciliensis growth represented in an ordinary differential equations system with a harvesting function. The system has an existing critical point that determined by the parameter that represents the number of population to be maintained (M). The stability of the system is evaluated locally at the critical point using the linearization method. The critical point is stable for a specific condition of M and σ, where the second parameter represent the predation rate of phytoplankton as the prey of nyales. The simulation shows that the population of the solution converges to the some amount of nyales populations. It means that the proportional type of harvesting function prevents the number of existing nyales for the festival.
A Generalized Space-Time Autoregressive Moving Average (GSTARMA) Model for Forecasting Air Pollutant in Surabaya
Authors: Akbar M S , Setiawan  , Suhartono  , Ruchjana B N , Prastyo D D , Muhaimin A , Setyowati E 
Published: June 2020

Abstract
The GSTAR model is a multivariate time series model that has time and location dependencies. The implementation of the GSTAR model was developed using the GSTARMA model by referring to the Autoregressive Moving Average (ARMA) model. This research provides the early warning stage of air pollution in Surabaya by forecasting the content of pollutant, especially the CO (Carbon monoxide). The data used in this research are CO data are taken from three monitoring stations in Surabaya with a period from January, 1st to December 31st, 2018. There are two stages in this research, the first stage is time series regression with a dummy variable from the data pattern, and the second step is modeling residual time series regression with GSTAR and GSTARMA. This research is using two weights, uniform and inverse weight the distances, with two-parameter estimates, namely OLS and SUR. The results given the RMSE values tend to be small by using GSTARMAX(21,[7]1:) model with an inverse weight the distance and OLS parameter estimation for SUF 1, GSTAR(21) model with an inverse weight the distance using SUR parameter estimation on SUF 6, and GSTARMA(21,[7]1) model using an inverse weight the distance and SUR parameter estimation on SUF 7. Based on the results of this research, the GSTARMA model can correct prediction errors from the GSTAR model on CO data.
Rainfall forecasting with climate change detection and its pattern relationship to rice production
Authors: Putri Endah R.M. , Riska Astari I Gusti Ayu , Wahyuningsih Nuri 
Published: June 2020

Abstract
Rainfall as one of main factor of climate change, has a significant effect to agriculture. Prediction modelling of rainfall intensity is used to identify and to provide rainfall future description as a reference of agriculture planning regarding to food security. This study aims to provide the modelling of rainfall intensity using Seasonal Autoregressive Integrated Moving Average. Climate change in the rainfall intensity is detected using the existence of heavy-tail phenomena and therefore a Generalized Pareto Distribution is used to model it. To know the relationship between the rainfall intensity and its impact to rice production, Copula method is used as the extension. The study found: SARIMA ([1; 2; 6]; 1; 0)(0; 1; 1)12 is the best model of the rainfall forecasting, climate change indication is detected but it occurs not significantly in area of Tabanan, Bali, and the relationship of the rainfall and the rice production indicates that a decreasing of rainfall intensity will lead to a decreasing rice production.
Kernel perceptron algorithm for sinusitis classification
Authors: Rustam Z , Hartini S , Pandelaki J 
Published: June 2020

Abstract
Sinusitis is one of the most commonly diagnosed diseases in the world. Its diagnosis is usually based on clinical signs and symptoms, which led to the development and use of many machine learning methods to provide a better diagnosis. This research, therefore, proposed a kernel perceptron method applied to the sinusitis dataset, consisting of 102 acute and 98 chronic samples, obtained from Cipto Mangunkusumo Hospital in Indonesia. This research utilized the RBF and polynomial kernel function for several k values in k-fold cross-validation and compared the results in accuracy, sensitivity, precision, specificity, and Fl-Score. From the experiments, it was concluded that the kernel parameter σ = 0.0001 obtained excellent performance in every k-fold, with a better performance achieved using 10-fold cross-validation. Meanwhile, the polynomial degree did not affect the kernel perceptron performance. However, the use of 7-fold cross-validation can be considered to obtain better performance of kernel perceptron based on polynomial kernel.
Breast cancer clustering using modified spherical K-Means
Authors: Rustam Zuherman , Leudityara Fijri Ajeng 
Published: June 2020

Abstract
Clustering is one of common techniques to group dataset into subsets based on distance measure. It has been applied in machine learning, pattern recognition, data mining, image analysis, and bioinformatics. Spherical k-means is one of clustering methods to address computational efficiency and solution quality in terms of deciding an action. In this paper, we used modified spherical k-means by using kernel radial basis function (RBF) by inner product measures in spherical k-means to cluster breast cancer Coimbra dataset from UCI machine learning into clusters. A new clusters will defined to healthy control cluster and patient cluster based on medical records. The highest accuracy results of kernel spherical k-means (SPKM) clustering method with radial basis function (RBF) kernel in breast cancer Coimbra (BCC) dataset is 72,41%. Addition of kernel to spherical k-means makes the results of accuracy be stable than using spherical k-means.
Acute sinusitis classification using support and fuzzy support vector machines
Authors: Rustam Z , Angie N , Pandelaki J , Yunus R E 
Published: June 2020

Abstract
The medical sector is currently in need of a method to aid in the classification of diseases, which contemporarily progresses into varying types. Therefore, the role of technology is highly relevant in the process of overcoming this challenge. This report discusses acute sinusitis, which is one of the most common forms of sinusitis, possibly caused by viruses, bacteria, fungi, pollutants, allergies, and also autoimmune reactions. Furthermore, the Support Vector Machines (SVM) and Fuzzy Support Vector Machines (FSVM) are used as a classification method to diagnose a person of acute sinusitis, therefore, this research aims to compare how both work, using Radial Basis Function (RBF) and Polynomial Kernel. Data of CT scan from Cipto Mangunkusumo Hospital, Indonesia was used to evaluate acute sinusitis, in terms of Accuracy, Sensitivity, Precision, and F1-Score. Thus, the final results indicate a better performance for FSVM than SVM in all perspectives, especially using the RBF kernel.
Analyzing cerebral infarction using support vector machine with artificial bee colony and particle swarm optimization feature selection
Authors: Rustam Z , Utami D A , Pandelaki J , Yunus R E 
Published: June 2020

Abstract
Early diagnosis of cerebral infarction is essential since many patients cannot be cured where the diagnosis is made at an advanced stage. In case an infarct occurs, the tissue in the brain die and stop the circulation of blood, which carries oxygen and nutrients to the body. Therefore, this study uses a machine learning Support Vector Machine (SVM) for early detection of the disorder. To produce the best classification accuracy and fast computing time, feature selection is performed on cerebral infarction data, including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). After classification, infarction data with the best features are classified using SVM. The classification results of ABC-SVM and PSO-SVM methods are compared with the accuracy of 90.36% for ABC-SVM and 86.74% for PSO-SVM. Therefore, the best approach used in classification is the SVM method with ABC feature selection.
Effects of vaccination on dengue transmission dynamics
Authors: Ndii Meksianis Z. , Messakh Jakobis J. , Djahi Bertha S. 
Published: June 2020

Abstract
Around 390 million dengue cases happen annually and two thirds of the world’s population are at risk of attracting dengue. Dengue is caused by four distinct serotypes where infection by one of the serotypes provides lifelong immunity to that serotype but has a higher chance of attracting the more dangerous forms of dengue (Dengue Hemorrhagic Fever(DHF) or Dengue Shock Syndrome (DSS)) in subsequent infections. Therefore, a perfect strategy against all dengue serotypes is required to reduce the number of dengue infections. A dengue vaccine with the efficacy of 54–77% has been approved for use in reducing dengue transmission. The use of this ‘imperfect’ vaccine may increase the possibility of individuals to attract DHF or DSS. Using a deterministic mathematical model, we assess the impact of the use of dengue vaccine. The results showed that vaccinating seronegative individuals may increase the number of secondary infections. On the other hand, the number of secondary infections decrease if we vaccinate seropositive individuals. This indicates that the risk of attracting DHF or DSS increases if we vaccinate seronegative individuals. Our results imply that the vaccination program may be successful when we vaccinate seropositive individuals.
Factors affecting adolescent nutritional status in Banda Aceh, Indonesia
Authors: Kesuma Zurnila Marli , Rusyana Asep , Rahayu Latifah 
Published: June 2020

AbstractThe purpose of this study was to examine factors affecting nutritional status, anemia status, and adolescent blood pressure in Banda Aceh, Indonesia. A cross-sectional study was designed using interviewed questionnaire on nutritional status, knowledge about nutrition, and anemia status. National Health Research Data (Riskesdas) in 2013 shows the prevalence of obesity in adolescents 13-15 years based on Body Mass Index (BMI) was 8.3% and for very fat was reaches 2.5%. Although these prevalences are still lower than the national prevalence, which is 8.2% and 1.7%, there are still many other issues that need serious attention related to nutrition in adolescents. For this reason, we use adolescent BMI to see adolescent nutritional status. Moreover, to find out the factors affecting adolescent nutritional status, multivariate regression analysis is used. Before estimating parameters by multivariate regression, predictor variables were reduced using factor analysis and five new factors were generated from the reduction. The results shows that factors affecting the nutritional status are dietary factors, pocket money and age factors, and knowledge about nutrition. Factors affecting anemia Status are allowance and age, income, and a number of family member. While the factors that affect the blood pressure is allowance and age and the number of family member.