bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the computational Bayesian survival spatial dengue hemorrhagic fever (DHF) modeling with double-exponential CAR frailty
Authors: Rantini D , Abdullah M N , Iriawan N , Irhamah  , Rusli M 
Published: January 2021

Abstract
In statistics, there are many types of data. Some data carry information about the location where observations occur, so that they can have a spatial effect. Dengue hemorrhagic fever (DHF) data which is easily transmitted, will be consequently has a spatial effect on its patient survival. In this study, we included DHF patient recovery time as a response variable, and several other variables as covariates considered to influence the patient’s recovery time. Our aim in this study is to model how these variables affect the recovery rate for DHF patients with the accompanying patient residence as the spatial effects. Survival analysis is the best method for modeling the recovery rate for DHF patients. A conditional autoregressive (CAR) model is given to explain the relationship between adjacent locations, which is not explained in the general survival analysis. Several researchers have used the Cox model coupled with the Normal CAR. In this study, we used the Cox model using Normal CAR and compared it with the Double-Exponential (DE) CAR. To estimate the regression parameters of the Cox model, we used the Stan software. The advantage of Stan compared to the other Bayesian software such as BUGS and JAGS is the creativity of the researcher in writing the distribution as user-defined, as well as writing the CAR model in the Stan. Based on the WAIC value, modeling the DHF data using the Cox model coupled with the DE CAR is better than coupled with the Normal CAR. Based on the best model, variables that affect the recovery rate of DHF patients are age, the high schools in last education, unemployed in the type of occupations, the stadium II in severity level, pulse, temperature, and leukocytes.
Analysis of factors associated with early stage Parkinson’s disease based on daily activities and sleeping behaviour disorder
Authors: Nastitie F , Abdulllah S , Nurrohmah S 
Published: January 2021

Abstract
Parkinson’s Disease (PD) is a disorder in human movement coordination system that is characterized by motoric and non-motoric symptoms. At the late stage of PD, clinical diagnosis is relatively easy to detect because the symptoms are clear-cut. However, when the symptoms are often incomplete or subtle, in the initial stage, diagnosis becomes difficult and sometimes the subject still remains undiagnosed or even misdiagnosed. This study was aimed at identifying risk factors in early stage PD based on patient daily activities and rapid eye movement behaviour disorder. Daily activities were measured using the Modified Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part I and part II and sleep behaviour disorder was measured using Rapid eye movement sleeping Behaviour Disorder Screening Questionnaire (RBDSQ). Data analysis was conducted using classification trees with CART algorithm, to classify patients into early stage PD patients or healthy control patients. Missing values were handled using k-Nearest Neighbour (kNN) method. The results were satisfactory, with the classification accuracy of 86.5%, sensitivity 80%, specificity 91.57% and AUC 0.858. It is also found that tremor, dressing difficulty, speech difficulty, RBD questionnaire score, and age are important in differentiating early stage PD from healthy control.
Adjustable robust counterpart optimization model for internet shopping online problem
Authors: Chaerani D , Rusyaman E , Mahrudinda  , Marcia A , Fridayana A 
Published: January 2021

Abstract
In this paper, a discussion on deriving the Adjustable Robust Counterpart for Internet Shopping Online (ARC-ISO) is discussed. The problem on Internet Shopping Online is considered as an application of Robust Maximum Flow Problem (RMFP) with circular demand. The decision variables which considered as the adjustable variable is the maximum flow from a destination node in a flow network which back to the source node. In this paper, when the source nodes is multiple, then it is possible to add a dummy node as a single source node. For the multiple destinations, the same design is done. The main challenge is when and how we assume the data can be uncertain and assumed to be lies within a box, an ellipsoidal or polyhedral uncertainty set. In this paper, the uncertain delivering time is assumed to be lied in a polyhedral uncertainty set.
Quantile regression in varying coefficient model of upper respiratory tract infections in Bandung City
Authors: Tantular B , Andriyana Y , Ruchjana B N 
Published: January 2021

AbstractVarying coefficient models are commonly used to obtain effects of covariates that vary over other variables. A special case of varying coefficient model is applied to longitudinal data where the covariates may vary over time. When the function is not easy to specify parametrically, we then need to work on a non-parametric regression technique. In this case, we approximate the function by B-splines. B-splines smoothing tends to overfit with increasing knots, then a penalty is added to the quantile objective function. This estimation procedure is called P-splines. As the objective function, we propose to use quantile loss function. The technique will be implemented to the upper respiratory tract infection data in Bandung City which was measured repeatedly from 30 sub district in Bandung City and hence we have a longitudinal data structure.
Stationary distribution Markov chain for Covid-19 pandemic
Authors: Achmad A L H , Mahrudinda  , Ruchjana B N 
Published: January 2021

Abstract
Coronavirus disease (Covid-19) is a new disease found in the late 2019. The first case was reported on December 31, 2019 in Wuhan, China and spreading all over the countries. The disease was quickly spread to all over the countries. There are 206,900 cases confirmed by March 18, 2020 causing 8,272 death. It was predicted that the number of confirmed cases will continue to increase. On January 30, 2020, World Health Organization (WHO) declared this as Public Health Emergency of International Concern (PHEIC). There are a lot of researchers which discuss pandemic spreading caused by virus with mathematical modelling. In this paper, we discuss a long-term prediction over the Covid-19 spreading using stationary distribution Markov chain. The aim of this paper is to analyze the prediction of infected people in long-term by analyzing the Covid-19 daily cases in an observation interval. By analyzing the daily cases of Covid-19 worldwide from December 31, 2019 until April 16, 2020, result shows that 61.43% of probability that the Covid-19 daily case will incline in long-term, 32.14% of chance will decline, and 6.43% of chance will stagnant.
Factors affecting the number of dengue fever cases in West Sumatra province using the Multivariate Adaptive Regression Splines (MARS) approach
Authors: Sriningsih R , Otok B W , Sutikno  
Published: January 2021

Abstract
The Multivariate Adaptive Regression Splines (MARS) approach is a multivariate nonparametric regression analysis that assumes the form of a functional relationship between response variable and predictors whose patterns are unknown. MARS is a combination of Recursive Partitioning Regression (RPR) and the Spline method which is able to process high-dimensional data (data that has many predictor variables 3 ≤ p ≤ 20), and large data samples (50 ≤ n ≤ 1000). The MARS model is obtained from a combination of Basis Function values (BF), Maximum Interaction (MI), and Minimum Observation (MO) by trial and error. In this study, we describe the use of MARS to analyze the factors that influence the number of dengue cases in West Sumatra Province. The response variable (Y) used was the number of dengue fever cases in West Sumatra Province with several predictor variables, namely the number of health workers (X1), the number of health facilities (X2), the height of an area (X3), and the density of settlements (X4). The data used is secondary data from the Central Statistics Agency (BPS) in 2019. Based on the calculation, the best model for this problem is the model with a combination of BF = 16, MI = 3, and MO = 2 with GCV value = 279.1654. The results showed that all predictor variables had an effect on the number of dengue fever cases according to the order of importance: the number of health workers (X1), the number of health facilities (X2), the density of settlements (X4) and finally the height of an area (X3).
Application of Internet Communications in Agricultural Electromechanical Drainage and Irrigation Pumping Station
Authors: Li Yanman , Huang Kun , Zhu Xingye , Lu Keji 
Published: January 2021

Abstract
With the gradual advancement of electric energy substitution, the power consumption of agricultural electromechanical drainage and irrigation pumping station increases rapidly. It is urgent to update and transform the pumping station. Aiming at the substitution scenario of electric energy for agricultural irrigation and drainage, the technology of internet communications are combined in the application. It focuses on the design of two systems: monitoring and warning of operation conditions, and charging management of pumping stations. An electrical control cabinet integrated with power supply and control is designed by using PLC control system. This control cabinet is installed in the pump house of irrigation and drainage area. Some control terminals can be connected to it, and each terminal can be connected with a load that can work or shut down independently. These two systems store and analyse the operation conditions and electricity charge information. They also establish database and exchange data with external power supply companies and water resources management bureaus through internet communications. They are used to realize intelligent control and management. It is of great significance to establish a perfect integrated power supply control system for agricultural irrigation and drainage.
Advanced Production Plan System of Military Manufacturing Enterprises Based on Linear Programming Model
Authors: Wang Tao , Zhao Yaojun , Zhu Xi 
Published: January 2021

Abstract
The problem encountered by military manufacturing enterprises in the preparation of production plans is mainly attributable to the failure to optimize supply and demand resources in the supply chain. The article innovatively employs the advanced planning system based on linear programming model to solve the problem. The problem is first of all described mathematically; then its linear programming model is constructed, and the latter’s APS system designed; at last, the model is solved and checked. Upon the verification, the model is proven helpful for enterprises to work out production plans quickly and efficiently, and carry out what-if analysis. While winning the time advantage for military manufacturing enterprises in the market competition, it also enhances their operating management efficiency.
Design for Spatiotemporal Information Cloud Platform of Smart City Based on OSGi
Authors: Zhao Tianpu , Yu Zhibo , Han Jiale , Zhang Min , Wang Jingyang 
Published: January 2021

Abstract
In 2009, IBM put forward the development policy of building “smart city” before building “smart earth”. In response to this policy, the National Bureau of Surveying and Mapping issued “The Notification on the Development of Smart City Spatiotemporal Information Cloud Platform”, which formally took the construction spatiotemporal information cloud platform of smart city as a pilot project. In this paper, the OSGi (Open Service Gateway Initiative) technology is used as the framework. Developing component on demand is realized by the SOA architecture and OSGi technology, which improves the efficiency of developing software, shortens the cycle of developing software and saves labor costs. Resource access is achieved through RESTful services, and distributed coordination services are implemented through Zookeeper.
Improving the Performance of Target Tracker by Deepening Neural Network
Authors: Zhao Yifeng , Lang Weimin , Peng Xiangyu , Li Bin 
Published: January 2021

Abstract
Visual target tracking is an important topic in the field of vision research. Its main task is to automatically process and analyze the input video information by computer, so as to obtain the position and key motion information of the target in the input video sequence, and to facilitate the subsequent research and analysis of the track and behavior of the tracking target. At present, researchers in the field of visual target tracking have proposed a series of excellent algorithms and frameworks. However, in view of the problems of deformation, occlusion, illumination change and random motion in the process of target tracking, it is still a great challenge to ensure the real-time, accuracy and robustness of tracking algorithms. In recent years, with the excellent performance of depth neural network in many tasks, the fusion of depth features based on relevant filtering algorithms to achieve target tracking tasks has become a hot research topic. In this paper, an improved target tracking algorithm is proposed based on the framework of classical target tracking methods: (1) Integrating deeper ResNet-101 deep convolution neural network model to detect target features more accurately. (2) Improve the model training method to save the network training cost. Finally, the performance of the proposed tracking algorithm is tested on the OTB target tracking standard dataset, and compared with other mainstream algorithms. The results show that the improved method proposed in this paper can effectively improve the tracking effectiveness and robustness.
Construction of Hierarchical Detail Model of Energy Environment Based on the Integration of Automatic Information Association Technology in Virtual Reality
Authors: Zhang Zhiliang , Bing Liu 
Published: January 2021

Abstract
In this paper, the defects of the existing models in virtual reality, such as the excessively long pretreatment time, time consuming in update operations and so on, when dealing with the large-scale automatic information association technology are analyzed. And a kind of construction method for the hierarchical detail model of energy environment (hereinafter referred to as CHDM for short) based on the integration of automatic information association technology is put forward. In this method, the information is aggregated in virtual reality to establish the hierarchical detail model of energy environment. Then the geometric parameters and form features are integrated to calculate the hierarchical details of each volume element. The topology connection relationship of the hierarchical detail model of energy environment is used to form a “Hierarchical detail model of energy environment”, which is merged and simplified in virtual reality. The experimental results show that for the automatic association of information with a patch complexity of about 10M can improve the average rate of model establishment about 3 times on the premise of guaranteeing certain construction quality on the ordinary PC. And the pretreatment time is controlled within 7min.
Visual inspection method for inner wall of small hole based on internal reflection principle
Authors: Chen Xinyang , Chen Xin , Wei Jiasi , Cai Xiaoyu , Li Yuan 
Published: January 2021

Abstract
This paper introduces a new image acquisition scheme based on the principle of internal reflection for the inner wall of the small hole, and visually inspects small hole workpieces with an inner diameter of 2-10mm. The customized reflector is inserted into the small hole, and the internal wall image is displayed in the CCD industrial camera through the reflection of light, and then the subsequent image processing is performed. The feasibility of the scheme is verified by theoretical calculations, and the obtained inner wall image resolution can reach 10 μm, which has high practical value.
Optimization and improvement of software and theory comprehensive training
Authors: Yu Wenjun , Huang Yu , Hu Bin , Gao Junxiang 
Published: January 2021

Abstract
The rapid development of information science and the transformation of modern teaching concepts have made the teaching work more inclined to the cultivation of students’ practical ability. Comprehensive training is a teaching activity for the better application of students’ computer theory knowledge. The research and practice of practical teaching system, practical teaching content and practical teaching methods are particularly important in the teaching work of colleges and universities. Based on this, we have built and improved the core layer of the practical teaching system, namely the comprehensive training of software and theory.
Study on night transportation organization of high-speed rail express products
Authors: Ma Mingyao , Zhao Peng , Qiao Ke 
Published: January 2021

Abstract
Along with the rapid development of high-speed railway, high-speed railway express products gradually mature, the electronic commerce industry of high-speed rise in demand for goods, the implementation of the high-speed rail freight trains is imperative, but freight trains operation organization faces new challenges at night. In this paper, we study the freight trains, different types of high-speed rail transportation express product process, the purpose is to effectively reduce skylight influence on train operation organization and freight trains for passenger train interference, and improve transportation efficiency, increase freight trains, operation specification, create a good environment for the emu operation. Based on the characteristics of different types of high-speed rail express products, this paper puts forward suggestions on rationalizing the transport process of freight emu, so as to give full play to the transport capacity advantages of freight emu, which is conducive to the high-quality development of freight emu.
Dynamic Evaluation of Discrete Zoning over Time Using Spatial Big Data
Authors: Liu Chenyu , Liao Zhouxing 
Published: January 2021

Abstract
Zoning is widely used in land-use control and the criteria for a ‘good’ zoning scheme varies dramatically for different purposes of zoning. Studies during the past decades have presented many theoretical frameworks for the design and evaluation of zoning schemes designed for dynamic urban processes especially the housing market, but challenges remain. Therefore, this paper attempts to explore the robust zoning over time help derive boundaries that better fit with changing housing market. It starts with the preliminary introduction of the zoning and a review of spatial big data and existing studies on evaluating the suitability of zoning schemes over time. Three indicators are adopted to evaluate the current zoning framework. A new index called RHD that can evaluate zoning changing dynamics over time is proposed, followed by the discussion of the empirical findings of the impacts of the zoning on land supply and house prices in the territory. The proposed framework for evaluating zoning can benefit future studies of zoning regarding dynamic housing market in the world.
Hyperspectral anomaly detection method based on linear background removal
Authors: Du Huiting , Fan Yanguo , Xu Mingming 
Published: January 2021

Abstract
In view of the fact that the existing hyperspectral image anomaly detection algorithms only pay attention to distinguish between target and background by using spectral differences and ignore the correlation between pixels, and that local RX algorithm is easy to detect global non-anomalies as anomalies when the background is relatively complex, a hyperspectral anomaly detection method based on linear background removal is proposed. In this method, the edge information of hyperspectral image is obtained first, then the line part of edge image is extracted by Hough transform, and finally the local anomaly detection is carried out based on the linear background removal. By comparing the ROC curve and AUC value of this algorithm with RX algorithm and local RX algorithm, it shows that the algorithm of this paper can effectively reduce the false alarm rate of detection and has a better detection effect.
Automatic Text Summarization for Marine Natural Products Literature
Authors: Zhang Xukai , Liu Sue 
Published: January 2021

Abstract
Based on the Encoder-Decoder framework, an automatic summarization model MNPAS for marine natural products literature is proposed. Instead of directly using the structure of the common encoder in the past, a certain degree of innovation has been made to make Gru model complete the coding work. This advantage is that data information is not so easy to lose. In decoder, attention mechanism is integrated to improve the quality of predicted output word sequence. Compared with the traditional methods, the method proposed in this paper can complete the automatic generation of marine natural product literature abstracts better.
Design and implementation of partition Operating System based on ARINC653 standard
Authors: Tao Yongchao , Song Qilong , Piao Songhao 
Published: January 2021

Abstract
The ARINC series of standards are mainly used to define how to use the computing resources of avionics. The ARINC 653 standard applies to the service interface of the spacecraft’s integrated electronic operating system to the upper-layer application software. It isolates the application software from the underlying processor architecture and meets the requirements of the on-board real-time system with good scalability. This article will follow the ARINC 653 standard, design and implement a partition operating system, providing basic system static configuration capabilities, space partition capabilities, time sharing capabilities, inter-zone communication capabilities, and security monitoring capabilities
Design and Simulation of information processing system based on digital PCR
Authors: Shi Weizhan , Wang Junbo 
Published: January 2021

Abstract
In order to meet the demand of real-time data acquisition and analysis in the process of digital PCR detection, this paper proposes a high-speed data acquisition system and design scheme based on FPGA. The system uses FPGA as the signal acquisition platform, USB interface as the data transmission module, develops a set of real-time acquisition and processing software, and proposes a peak extraction algorithm based on Chebyshev inequality. The algorithm improves the peak detection accuracy, shortens the peak extraction time, and reduces the requirements for software and hardware. The system can monitor and analyze the data signal. It provides a reliable basis for the later data processing, and has strong engineering practice.
Research on Hybrid Real-Time System Scheduling Algorithm for Multiprocessor Environment
Authors: Cai Ren Pu Cuo , Hong JianChao 
Published: January 2021

Abstract
Firstly, the mixed key system task model of compatible with periodic tasks and sporadic tasks is studied. Based on the model compatible with these two tasks, a semi-divided real-time task scheduling mechanism is proposed. This process also considers the aggressive handling of Low critical task to improve resource utilization. Finally, the relevant program code is designed and developed, and the performance of the proposed algorithm is evaluated by means of simulation and simulation. The correctness of the model and theory is verified, and the theory and protocol design are further improved.