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应用数学与计算数学学报最新文献

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Differencing effect in series-parallel architecture of NARX model for time series forecasting 时间序列预测中NARX模型串并联结构的差分效应
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0117393
Hermansah, D. Rosadi, Abdurakhman, Herni Utami, G. Darmawan
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引用次数: 1
A construction of generalized quasi-cyclic codes over finite field using gray map 用灰色映射构造有限域上的广义拟循环码
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0114988
Muhammad Irfan Hidayat, Irwansyah, I. G. A. W. Wardhana
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引用次数: 0
Modified EKF for Covid-19 prediction with 3 mobility restrictions (Study Case: Indonesia) 改进EKF预测Covid-19的3个移动限制(研究案例:印度尼西亚)
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0117110
H. N. Fadhilah, Amalia Nur Alifah, Mohammad Hamim Zajuli Al Faroby, D. K. Arif
In this paper, the spread of the Covid-19 in.Indonesia is described by the SIRD epidemiological mathematical model. The mathematical model used in this paper is Susceptible, Infected, Recovered, Death (SIRD). The modified extended Kalman filter algorithm is applied to predict the spread of Covid-19 in.the future. We modified the algorithm by generating real data based on the previous estimation results. The real data generated from the generation is used at the correction stage to obtain prediction results in a fairly long period. Simulations were carried out with three types of mobility restrictions, namely mobility 100%, mobility 75%, and mobility 50%. Based on the simulation results, it can be concluded that mobility restrictions in Indonesia, which starts on September 4, 2020, can reduce the number of infected and death individuals and can increase the number of individuals who recover from Covid-19. © 2022 Author(s).
本文介绍了新型冠状病毒在中国的传播情况。印度尼西亚是由SIRD流行病学数学模型描述的。本文使用的数学模型是易感、感染、恢复、死亡(SIRD)。应用改进的扩展卡尔曼滤波算法预测新冠肺炎在北京的传播。未来。我们通过在之前估计结果的基础上生成真实数据来改进算法。在校正阶段使用生成的真实数据，以获得相当长时间内的预测结果。模拟采用100%、75%和50%三种不同的移动性限制。根据模拟结果，可以得出结论，从2020年9月4日开始，印度尼西亚的流动限制可以减少感染和死亡人数，并可以增加从Covid-19中康复的人数。©2022作者。
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引用次数: 0
The reduced GTH-gene expression caused by mHtt on Sp1 will ultimately result in cell death Sp1上mHtt导致gth基因表达降低，最终导致细胞死亡
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0112750
Jingmo Qu, Dingchen Yang, Yuxuan Wu, Haodong Yu
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引用次数: 0
Review of NIR spectroscopy applications in medical field 近红外光谱在医学领域的应用综述
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0112957
Tong Xu
{"title":"Review of NIR spectroscopy applications in medical field","authors":"Tong Xu","doi":"10.1063/5.0112957","DOIUrl":"https://doi.org/10.1063/5.0112957","url":null,"abstract":"","PeriodicalId":56955,"journal":{"name":"应用数学与计算数学学报","volume":"53 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72615241","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}
引用次数: 0
Prediction of seawater salinity using truncated spline regression method 截断样条回归法预测海水盐度
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0115009
Faisol, T. Yulianto, M. Yaqin, A. Basuki, M. Zainuddin
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引用次数: 1
Determinant regional tax revenue in fear of COVID-19 in East Java: Spatial Durbin Model (SDM) spillover approach 东爪哇对COVID-19的恐惧决定区域税收:空间德宾模型(SDM)溢出方法
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0115580
N. Atikah, B. Widodo, S. Rahardjo, Mardlijah, N. Kholifia, D. L. Afifah
The COVID-19 pandemic has caused economic, social and political crises in the infected countries. These countries implemented a lockdown policy to anticipate the spread, resulting in decreased financial revenues, including tax revenues. It is not easy to achieve tax revenue during a pandemic because almost all sectors have declined. Spatial interactions influence tax revenue, so a spatial model must be applied in its analysis. One of the spatial models is Spatial Durbin Model (SDM) is the development of the spatial autoregressive model (SAR). In the SDM model, the dependent variable and the independent variable both contain spatial effects. Spillover is the tendency of a changes in one area are following changes in another areas. This study's main objective is to estimate spillover's effect on districts/cities tax revenue and its spread in East Java. Researchers found a positive spatial spillover of GRDP, inflation, number of industries, and total population on tax revenue in East Java. © 2022 Author(s).
COVID-19大流行在受感染国家造成了经济、社会和政治危机。这些国家实施了封锁政策，以预测疫情的蔓延，导致包括税收在内的财政收入减少。在大流行期间实现税收并不容易，因为几乎所有部门都出现了衰退。空间相互作用影响税收收入，因此在分析空间相互作用时必须采用空间模型。其中一种空间模式是空间杜宾模式(SDM)，是空间自回归模式(SAR)的发展。在SDM模型中，因变量和自变量都包含空间效应。溢出效应是指一个地区的变化引起另一个地区变化的趋势。本研究的主要目的是估计溢出效应对东爪哇地区/城市税收的影响及其蔓延。研究发现，东爪哇省的gdp、通货膨胀、产业数量和总人口对税收有正向的空间溢出效应。©2022作者。
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引用次数: 0
Mathematical model in Islamic mortgage financing with murabahah and musharakah mutanaqisah contracts 考虑穆拉巴哈和穆沙拉夫合同的伊斯兰抵押贷款融资数学模型
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0116095
Wulan Nurul Kamilah, N. Sumarti, K. A. Sidarto
{"title":"Mathematical model in Islamic mortgage financing with murabahah and musharakah mutanaqisah contracts","authors":"Wulan Nurul Kamilah, N. Sumarti, K. A. Sidarto","doi":"10.1063/5.0116095","DOIUrl":"https://doi.org/10.1063/5.0116095","url":null,"abstract":"","PeriodicalId":56955,"journal":{"name":"应用数学与计算数学学报","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82157623","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}
引用次数: 0
Bivariate zero-inflated generalized Poisson regression on modelling stillbirth and maternal death 双变量零膨胀广义泊松回归模拟死产和孕产妇死亡
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0115756
Dewi Novita Sari, Purhadi, S. Rahayu, Irhamah
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引用次数: 0
Determining the online learning challenges during COVID-19 pandemic at the University of Mataram using Principal Component Analysis 利用主成分分析确定马塔兰大学COVID-19大流行期间在线学习面临的挑战
应用数学与计算数学学报
Pub Date : 2022-01-01 DOI: 10.1063/5.0115590
Ena Setiawana, Joji Ardian Pembargi, Windia Cantika Sari, Baiq Siti Patimah Zohrah, Aanisah Rifdah Rihhadatul Aisy, N. Fitriyani
The COVID-19 pandemic had an impact on the world of education and it leads to the cancellation of all educational activities. An online learning system was an educational system or concept that utilizes information technology in the teaching and learning process. The basic principles in the online learning process are clarity of messages, learning strategies, interactivity, growth of motivation and creativity, and the use of media for effective communication. The purpose of this study was to determine what factors are hindering students in online lectures by using Principal Component Analysis. The research was conducted using a survey method, namely by filling in forms for undergraduate students at the University of Mataram. The method used to analyze the data was a quantitative descriptive technique which was expressed in the distribution of scores and percentages. This form contains 15 observed variables, after factor analysis was carried out, and obtained 3 factors that most hamper online lectures. The dominant factor is Factor 1 that can explain 28.957% of the variation. The variables included in Factor 1 are the lack of concentration, material understanding, not direct discussion, unconcern (boredom), and lack of study companion variables. The results obtained can be used as a consideration to maximize online lectures during the COVID-19 pandemic. © 2022 Author(s).
新冠肺炎疫情对教育界产生了影响，导致所有教育活动被取消。在线学习系统是在教学过程中利用信息技术的一种教育系统或概念。在线学习过程的基本原则是信息清晰，学习策略，互动性，动机和创造力的增长，以及有效沟通媒体的使用。本研究的目的是通过主成分分析来确定哪些因素阻碍了学生的在线讲座。本研究采用问卷调查法，即对马塔兰大学的本科生进行问卷调查。用于分析数据的方法是一种定量描述技术，用分数和百分比的分布来表示。该表格包含15个观察变量，经过因子分析，得到3个最阻碍网络讲座的因素。显性因子为因子1，可以解释28.957%的变异。因素1中的变量包括:注意力不集中、对材料理解不深入、不直接讨论、不关心(无聊)和缺乏学习伴变量。获得的结果可以作为在COVID-19大流行期间最大化在线讲座的考虑因素。©2022作者。
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引用次数: 0
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