Hasil untuk "Probabilities. Mathematical statistics"

J. Wolfowitz, A. Mood

J. Harris, H. Stöcker

Fadjryani Fadjryani, Iman Setiawan, Hartayuni Sain et al.

The Central Sulawesi government has a Sustainable Development Goals (SDGs) target for 2020-2024, which sets the maternal mortality rate below 70/100,000 KH. However, in 2018-2022, the maternal mortality rate fluctuated by 128/100,000 KH. One of the factors causing maternal mortality is the duration of the labor process. The factors that are thought to have an influence on the duration of labor are gestational age, maternal age, baby height, parity, and hemoglobin levels. Therefore, this study aims to see what modeling and factors affect the duration of birth using Lin-Ying additive hazard regression analysis. Data were obtained from the medical records of normal deliveries between January and December 2023 at Anutapura Palu Hospital. The results showed that the factors that affect the duration of birth are preterm gestational age, aterm gestational age, maternal age 20-35 years, primigravida mothers, multigravida mothers, and mothers who are not anemic.  A limitation of this study is the relatively short data collection period of one year, which may not capture variations or trends in labor outcomes over time.

Idrus Syahzaqi, Sediono Sediono, Aurellia Calista Anggakusuma et al.

Transportation has an important role in supporting the mobility of people in Indonesia. Trains are included in the most widely used transportation category because they are effective and efficient, not only transporting passengers, trains also have a role in the distribution of goods. This study aims to model and forecast total volume of goods transported through rail transportation in Indonesia using the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method because the data has seasonal trend. The data used comes from the Central Statistics Agency (BPS) from January 2013 to April 2024. The results were obtained that the SARIMA (0,1,1)(0,1,1)12  model is the best model with a MAPE value of 0.96% which is included in the category of accurate model. In addition to being an additional insight, this research can also be a reference in the management of railway transportation considering the number of uses both passengers, the distribution of goods that continue to increase, and can be recommendation for other research that same topic with it.

Fatma Arum Fatonah, Evi Noviani, Meliana Pasaribu

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a coronavirus originating from the city of Wuhan in 2019. This disease affects the respiratory system. The city of Pontianak has the highest population density in West Kalimantan. This density results in a higher spread of Covid-19. In this article, the spread of COVID-19 is formulated into a mathematical model, equilibrium points are sought, stability is analyzed, and a delay time is introduced to reduce the spread of COVID-19. The magnitude of the delay time given during quarantine complies with health protocols, which is between 2 – 14 days. This article aims to analyze the influence of the delay time in modeling the spread of Covid-19. The problem of COVID-19 spread is constructed into an SIQR model, with a sub-population of recovered individuals returning to the susceptible sub-population. The population is divided into four sub-populations: susceptible (S), Infected (I), Quarantined (Q), and Recovered (R). The parameters used  include the natural birth rate ( ), the rate of susceptibility to infection ( ), the rate of infection under quarantine ( ), the recovery rate from infection ( ), the recovery rate from infection under quarantine ( ), the death rate from infection ( ), the death rate under quarantine ( ), the delay time from infection to quarantine process ( ), the natural death rate ( ), and the rate of recovered immunity returning to susceptibility ( ). The simulation results show that when the basic reproduction number is less than , the disease-free equilibrium is stable, and when the basic reproduction number is greater than , the endemic equilibrium point is stable. The addition of a time delay ( ) in the SIQR model affects the stability of the endemic equilibrium point but does not affect the stability of the disease-free equilibrium point.

Elisabetta Mazzullo, Okan Bulut, Tarid Wongvorachan et al.

Learning analytics (LA) has the potential to significantly improve teaching and learning, but there are still many areas for improvement in LA research and practice. The literature highlights limitations in every stage of the LA life cycle, including scarce pedagogical grounding and poor design choices in the development of LA, challenges in the implementation of LA with respect to the interpretability of insights, prediction, and actionability of feedback, and lack of generalizability and strong practices in LA evaluation. In this position paper, we advocate for empowering teachers in developing LA solutions. We argue that this would enhance the theoretical basis of LA tools and make them more understandable and practical. We present some instances where process data can be utilized to comprehend learning processes and generate more interpretable LA insights. Additionally, we investigate the potential implementation of large language models (LLMs) in LA to produce comprehensible insights, provide timely and actionable feedback, enhance personalization, and support teachers’ tasks more extensively.

E Ebin Raja Merly, E Anlin Bena

Decomposition of a graph G is the collection of edge-disjoint subgraphs of G. The longest distance between any two vertices of G is its detour distance. A subset S of V (G) is a detour set if every vertex of G lie on some u − v detour path, where u, v ∈ S. If a graph G can be decomposed into subgraphs G1,G2, ...,Gn with same detour number as G then the decomposition Π = (G1,G2, ...,Gn) is called detour self-decomposition. The cardinality of maximum such possibility of detour self-decomposition in G is the detour self-decomposition number of G and is denoted by πsdn(G). The bounds of detour selfdecomposition number of corona product of graphs based on few properties have been discussed here.

Qianyu Dong, David Kline, Staci A. Hepler

AbstractThe opioid epidemic is an ongoing public health crisis. In North Carolina, overdose deaths due to illicit opioid overdose have sharply increased over the last 5–7 years. Buprenorphine is a U.S. Food and Drug Administration approved medication for treatment of opioid use disorder and is obtained by prescription. Prior to January 2023, providers had to obtain a waiver and were limited in the number of patients that they could prescribe buprenorphine. Thus, identifying counties where increasing buprenorphine would yield the greatest overall reduction in overdose death can help policymakers target certain geographical regions to inform an effective public health response. We propose a Bayesian spatio-temporal model that relates yearly, county-level changes in illicit opioid overdose death rates to changes in buprenorphine prescriptions. We use our model to forecast the statewide count and rate of illicit opioid overdose deaths in future years, and we use nonlinear constrained optimization to identify the optimal buprenorphine increase in each county under a set of constraints on available resources. Our model estimates a negative relationship between death rate and increasing buprenorphine after accounting for other covariates, and our identified optimal single-year allocation strategy is estimated to reduce opioid overdose deaths by over 5%. Supplementary materials for this article are available online.

K. Lashgari, K. Lashgari, K. Lashgari et al.

<p>Evaluation of climate model simulations is a crucial task in climate research. Here, a new statistical framework is proposed for evaluation of simulated temperature responses to climate forcings against temperature reconstructions derived from climate proxy data for the last millennium. The framework includes two types of statistical models, each of which is based on the concept of latent (unobservable) variables: <i>confirmatory factor analysis</i> (CFA) models and <i>structural equation modelling</i> (SEM) models. Each statistical model presented is developed for use with data from a single region, which can be of any size. The ideas behind the framework arose partly from a statistical model used in many detection and attribution (D&amp;A) studies. Focusing on climatological characteristics of <i>five specific</i> forcings of natural and anthropogenic origin, the present work theoretically motivates an extension of the statistical model used in D&amp;A studies to CFA and SEM models, which allow, for example, for non-climatic noise in observational data without assuming the additivity of the forcing effects. The application of the ideas of CFA is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. The result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.</p>

mahdieh mirzaie, yunes jahani, abbas bahrampour

Background: Logistic regression is one of the most common models used to predict and classify binary and multiple state responses in medicine. Genetic algorithms search techniques inspired by biology have recently been used successfully as a predictive model. Objective: The aim of this study was to use the genetic algorithm and logistic regression models in diagnosing and predicting factors affecting breast cancer mortality. Method: In this study, data of 2836 people with breast cancer during the years 2014-2018 was examined; their information was recorded in the cancer registration system of Kerman University of Medical Sciences. Death status was considered a dependent variable, while age, morphology, tumor differentiation (grad), residence status, and place of residence were considered independent variables. Sensitivity, specificity, accuracy, and area under the ROC curve were used to compare the models. Results: the logistic regression model determined factors affecting the breast cancer mortality rate, (with sensitivity (0.62), specificity (0.81), area under the ROC curve (0.74), and accuracy (0.84)), and genetic algorithm model (, with sensitivity (0.19), specificity (0.97), area under the ROC curve (0.58) and accuracy (0.87)). Conclusions: The sensitivity and area under the ROC curve of the logistic regression model were higher than those of the genetic algorithm, but the specificity and accuracy of the genetic algorithm were higher than those of the logistic regression. According to the purpose of the study, two models can be used simultaneously.

P. Donnelly

A.T. Assanova

110 years have passed since the birth of the outstanding scientist academician Orymbek Akhmetbekovich Zhautykov. O.A. Zhautykov is a Soviet mathematician and mechanic, Doctor of Physical and Mathematical Sciences, professor, academician of the Academy of Sciences of the Kazakh SSR, Honored Worker of Science and Technology of Kazakhstan, laureate of the State Prize of the Kazakh SSR. Scientific research by O.A. Zhautykov are mainly associated with the theory of infinite systems of differential equations. He has published about 200 scientific, popular science, methodological works, textbooks and teaching aids, magazine and newspaper articles.

L. Bain, M. Engelhardt

R. Plackett, V. Rohatgi

A. Cohen

Siti Hafawati Jamaluddin, Norwaziah Mahmud, Nur Syuhada Muhammat Pazil et al.

Transportation is literally defined as an act, process or instance of transporting or being transported. Many business organizations are relying on transportation in running their business. In these years, the transportation cost has increase from time to time and become a problem to the organization to maintain the cost and profit. To overcome this problem, the optimization of cost is applied in order to make sure the business is in the right financial condition by finding the minimum cost of transporting a single commodity from a given number of sources to a given number of destinations. In this study the modified Particle Swarm Optimization is used to solve the Transportation Cost Problem (TCP) in finding the optimal solution of the amount of product transported with the minimum cost. The model of nonlinear cost function had been used throughout this study. As a result, the minimum cost of transportation is 340.69 with the amount of product transported 17, 15, 32, 16, and 20 following the arc A(x)={(1,5), (2,4), (2,6), (3,4), (3,5)} respectively.

Rabah Khaldi, Mohammed Kouidri

In this paper, we study a boundary value problem at resonance with a multi-integral boundary conditions. By constructing suitable operators, we establish an existence theorem upon the coincidence degree theory of Mawhin. An example is given to show the effectiveness of our results.

H. Bazargan, A. Dehghanzadeh

Sanitary porcelain products might have several defects, causing potential high-grade desirable products be converted into low-grade ones. Some of the defects are such that a few of them in the products will result in a great fall of product rating and consequently reduction of its value and price. Among these defects is a defect called pinhole. In this article, it has been tried to identify, from a list of factors, the most influential factors of the production process which cause the pinhole defect affecting the product rating. It then tries to present a prediction model for the number of pinholes. For this purpose, initially seven factors were chosen to help presenting a suitable prediction model and several statistical tools and artificial intelligence prediction tools were investigated to present a suitable prediction model. The presented model could be used by the company to enhance the product rating through choosing right value for the right factors causing the pinhole defect and to decrease the wastes and the expenditures.

A. Hald

Riza F. Ramadhan, Robert Kurniawan

Overdispersion phenomenon and the influence of location or spatial aspect on data are handled using Binomial Geographically Weighted Regression (GWNBR). GWNBR is the best solution to form a regression analysis that is specific to each observation’s location. The analysis resulted in parameter value which different from one observation to another between location. The Weighting Matrix Selection is done before doing The GWNBR modeling. Different weighting  will resulted in different model. Thus this study aims to  investigate the best fit model using infant mortality data that is produced by some kind of weighting such as fixed kernel Gaussian, fixed kernel Bisquare, adaptive kernel Gaussian and adaptive kernal Bisquare in GWNBR modeling. This region study covers all the districts/municipalities in Java because the number of observations are more numerous and have more diverse characteristics. The study shows that out of four kernel functions, infant mortality data in Java2012, the best fit model is produced by fixed kernel Gaussian function. Besides that GWNBR with fixed kernel Gaussian also shows better result than the poisson regression and negative binomial regression for data modeling on  infant mortality based on the value of AIC and Deviance.                                                                                      Keywords:   GWNBR, infant mortality, fixed gaussian, fixed bisquare, adaptive gaussian, adaptive bisquare.