Rendra Gustriansyah, Deshinta Arrova Dewi, Shinta Puspasari
et al.
This study aims to accurately forecast monthly native chicken egg production using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model with parameter optimization. The optimization process was conducted through a combination of auto.arima() initialization and an exhaustive grid search across the parameter space, evaluated using multiple performance metrics. The dataset comprised monthly production data from Magelang City, Indonesia, spanning the period from 2016 to 2022. The best-performing model, SARIMA (2,1,2)(1,0,1,12), achieved an R² of 0.89, MAE of 82.13, RMSE of 92.92, MAPE of 7.21%, and MASE of 0.67 on the testing set, indicating satisfactory forecasting performance. Compared with the non-optimized SARIMA baseline, the optimized model showed improved predictive accuracy. However, the residuals did not follow a normal distribution, suggesting potential limitations in model assumptions. Moreover, the study is limited by its focus on a single geographic location and native chicken production data, which may restrict its generalizability. Despite these limitations, the findings demonstrate that parameter optimization in SARIMA enhances forecast accuracy and can support better planning for food security initiatives.
Usually, convolution refers to Laplace convolution in the literature, but Mellin convolutions can yield very ueful results. This aspect is illustrated in the coming sections. This study deals with Mellin convolutions of products and ratios. Functions belonging to the pathway family of functions are considered. Several types of integral representations, their equivalent representations in terms of G and H-functions, and their equivalent computable series representations are examined in this study.Mathematics Subject Classification 2010: 26A33, 44A10, 33C60, 35J10.
This paper presents a numerical study of source identification problems for one-dimensional telegraphparabolic equations subject to Dirichlet and Neumann boundary conditions. In these inverse problems, the unknown source terms are assumed to be space-dependent, which introduces both analytical and computational challenges. The study begins by discretizing the considered problems using the finite difference method – first in space and subsequently in time – resulting in a system of discrete equations. Stability results for the solutions of the resulting finite difference schemes are established to ensure the reliability of the numerical approach. A numerical algorithm is proposed for solving the discrete inverse problems. The algorithm begins by eliminating the unknown source terms, which transforms the original discretized problem into a new nonlocal problem with unknown initial data. To approximate this initial data, an iterative procedure based on fixed-point iterations is constructed. Once the transformed nonlocal problem is solved, the solution of the main finite difference scheme and approximations of the unknown source term are recovered. Numerical results for two test problems are presented to illustrate the proposed method in practice. The findings confirm the accuracy of the approach in solving space-dependent inverse source problems.
Mahfudzah binti Othman, Aznoora Osman, Siti Zulaiha Ahmad
et al.
Designing a gamified e-assessment that tailored to the learning requirements of low-achieving students in introductory programming always remains a challenge. By focusing on the integration of gamification and assessment design principles in e-learning platforms for learning programming, this study uses a comparative analysis approach that comprises of three main phases, i) literature search, ii) elicitation and filtration, and iii) review, analysis and extraction of the design principles. Through this qualitative approach, the gamification principles such as the achievement, progression and rules and challenges have been extracted together with the elements of levels, points, badges, progress bars, and leader board. The assessment principles have also been derived consisting of the assessment structure and assessment composition with sub principles of problem-based instructional strategy, formative assessment, post-test strategy, assessment levels, and feedback strategy. The proposed design principles have also been demonstrated through the application in the gamified e-assessment module in an e-learning system.
Propensity score is one of the most commonly used score functions in adjusting for covariates effect in statistical inference. It is important to understand the impact with propensity score in case some of the prespecified covariates are severely imbalanced. In this article, we performed simulation evaluation the empirical type 1 error and empirical power under scenario of imbalanced covariates in several nonparametric two sample tests with propensity score or with other covariate adjustments. Our results suggest common propensity score approaches might have type 1 error inflation at scenarios with severe imbalanced covariates or model is mis-specified.
Nadiasadat Taghavi Razavizadeh, Maryam Salari, Mostafa Jafari
et al.
Introduction: The present study discusses the importance of having a predictive method to determine the prognosis of patients with diseases like Covid-19. This method can assist physicians in making treatment decisions that improve survival rates and avoid unnecessary treatments. This research also highlights the importance of calibration, which is often overlooked in model evaluation. Without proper calibration, incorrect decisions can be made in disease treatment and preventive care. Therefore, the current study compares two highly accurate machine learning algorithms, Gradient boosting and Extreme gradient boosting, not only in terms of prediction accuracy but also in terms of model calibration and speed.
Methods: This study involved analyzing data from Covid-19 patients who were admitted to two hospitals in Mashhad city, Razavi Khorasan province, over a span of 18 months. The k-fold cross-validation method was employed on the training dataset (K=5) to conduct the study. The accuracy and calibration of two methods (Gradient boosting and Extreme gradient boosting) in predicting survival were compared using the Concordance Index and calibration.
Results: The Concordance Index values obtained for gradient boosting and Extreme gradient boosting models were 0.734 and 0.736, in the imbalanced and In the balanced data, the Concordance Index values were 0.893 for gradient boosting and 0.894 for Extreme gradient boosting. The surv.calib_beta index, the gradient boosting model had an estimated value of 0.59 in the imbalanced data and 0.66 in the balanced data. The Extreme gradient boosting model had an estimated value of 0.86 in the balanced data and 0.853 in the imbalanced data. The Extreme gradient boosting model was faster in the learning process compared to the gradient boosting model.
Conclusion: The Gradient boosting and Extreme gradient boosting models exhibited similar prediction accuracy and discrimination power, but the Extreme gradient boosting model demonstrated relatively good calibration compare to Gradient boosting model.
Ori Becher, Mira Marcus-Kalish, David M. Steinberg
The age of big data has fueled expectations for accelerating learning. The availability of large data sets enables researchers to achieve more powerful statistical analyses and enhances the reliability of conclusions, which can be based on a broad collection of subjects. Often such data sets can be assembled only with access to diverse sources; for example, medical research that combines data from multiple centers in a federated analysis. However these hopes must be balanced against data privacy concerns, which hinder sharing raw data among centers. Consequently, federated analyses typically resort to sharing data summaries from each center. The limitation to summaries carries the risk that it will impair the efficiency of statistical analysis procedures. In this work, we take a close look at the effects of federated analysis on two very basic problems, non-parametric comparison of two groups and quantile estimation to describe the corresponding distributions. We also propose a specific privacy-preserving data release policy for federated analysis with the K-anonymity criterion, which has been adopted by the Medical Informatics Platform of the European Human Brain Project. Our results show that, for our tasks, there is only a modest loss of statistical efficiency.
The advancement of teaching and learning across the majority of courses is made possible by information and communication technology (ICT), and Islamic studies is no exception. Education has been among the many industries that information and communication technology (ICT) has revolutionized. Islamic education has embraced ICT to improve teaching and learning processes since it is based on the Quran's teachings and the traditions of the Prophet Muhammad (peace be upon him). This article aims to address the role of digital technology, or ICT, in Islamic teaching and learning, as well as ICT applications for spreading Islamic education, which are becoming increasingly significant over time. Overall, this review offers insightful information about the state of ICT integration in Islamic education today and shows the upcoming directions for additional study and advancement in this area.
An element r in a commutative ring R is called regular if there exist s∈R such that rsr=r. Ring R is called vN (von-Neumann)-regular ring if every element is regular. Recall that for any ring R always can be considered as module over itself. Using the fact, it is natural to generalize the definition of vN-regular ring to vN-regular module. Depend on the ways in generalizing there will be some different version in defining the vN-regular module. The first who defined the concept of regular module is Fieldhouse. Secondly Ramamuthi and Rangaswamy defined the concept of strongly regular module of Fieldhouse by giving stronger requirement. Afterward Jayaram and Tekir defined the concept of vN-regular module by generalizing the regular element in ring to regular element in R-module M. In this paper we investigate the properties of each module regular and the linkages between each vN-regular module.
This study investigates the efficiency of a modified exponentially weighted moving average (EWMA) control method using conditional expected delay to improve its efficiency in detecting changes in a process over time. While the modified EWMA control method is commonly used for this purpose, it can sometimes experience delays in detecting changes. The proposed method aims to address this limitation by incorporating conditional expected delay. The study utilizes simulations to conduct a performance comparison between the modified EWMA control method and the conventional EWMA control, employing the metric of conditional expected delay. Simulation results demonstrate the modified EWMA control method with conditional expected delay in terms of accurately and rapidly detecting changes. Overall, this study concludes that the integration of conditional expected delay into the modified EWMA control method can increase its effectiveness in detecting changes in a process. This has significant practical implications for a variety of industries that require timely and accurate detection of changes to maintain product quality and optimize processes.
The paper describes an integrated/centralised supply chain model consisting of one supplier, one manufacturer and one retailer within a finite time horizon. The manufacturer produces, at a finite rate, in each lot. The lot production rate in a batch increases with a rate λ in successive batch and the produced items are supplied to the retailer. The objective of the proposed model is to optimize the average total profit under the consideration of the proportional increase in the size of successive shipments within a batch production run and the production time of the supplier. The corresponding average profits of the supplier, the manufacturer and the retailer and the average total profit of integrated model are obtained. The results obtained in the numerical examples clearly establish that it is always beneficial in terms of profit when the size of the successive shipment is a variable. Therefore, size of the successive shipment should be variable in order to get more profit. A sensitivity analysis of the optimal solution with respect to changes of the parameter values is also carried out to strengthen the proposed model.
One of the tools, to research and investigation the structural dependence of various properties and some activities of chemical structures and networks is the topological indices of graphs. In this research work, we introduce novel indices of graphs which they based on the uphill degree of the vertices termed as uphill Zagreb topological indices. Exact formulae of these new indices for some important and famous families of graphs are established.
Nor Syazwina Binti Mohd Hanafiah, Nor Hayati Binti Shafii, Nur Fatihah Binti Fauzi
et al.
The stock market has always been a contentious topic in society, and it is a place where economic standards are established. The stock market is incredibly unpredictable and turbulent. This means that the shares may fluctuate for reasons that are sometimes difficult to understand. Due to this uncertainty, many investors believe the stock market as a risky investment. Therefore, having an accurate picture of future market environment is crucial to minimising losses. Forecasting is a technique of predicting the future based on the outcome of the previous data. There are a wide range of forecasting algorithms, however, this study only focuses on these two techniques: Auto Regressive Moving Average (ARIMA) model and Fuzzy Time Series (FTS) Model. The goal of this study is to evaluate and compare the effectiveness of the ARIMA model and the FTS model in predicting sample data of stock prices of Top Glove Corporation Berhad since this company is the largest glove supplier in the world and plays a significant role in the Covid-19 global pandemic crisis. The error measures that were taken into consideration consist of Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). These measurements were computed numerically and graphically using a statistical programme called EViews. The outcome shows that the ARIMA model performs better than the FTS model in terms of forecasting accuracy and provides the lowest values of MAPE, MSE, and RMSE, which are 10.58757, 0.926354, and 0.962473, respectively.
In this paper the properties of the modified EWMA control chart for detecting changes in the mean of an ARFIMA process are discussed. The central question is related to the false alarm probability and its behavior for different autocorrelation structures and parameters of the underlying process. It is shown under which conditions the false alarm probability of an ARFIMA(p,d,q) process is larger than that of the pure ARFIMA(0,d,0) process. Furthermore, it is shown that the false alarm probability for ARFIMA(0,d,0) and ARFIMA(1,d,1) is monotonic in d for common parameter values of the processes.
Vladimir Reinharz, Alexander Churkin, Harel Dahari
et al.
The multiscale model of hepatitis C virus (HCV) dynamics, which includes intracellular viral RNA (vRNA) replication, has been formulated in recent years in order to provide a new conceptual framework for understanding the mechanism of action of a variety of agents for the treatment of HCV. We present a robust and efficient numerical method that belongs to the family of adaptive stepsize methods and is implicit, a Rosenbrock type method that is highly suited to solve this problem. We provide a Graphical User Interface that applies this method and is useful for simulating viral dynamics during treatment with anti-HCV agents that act against HCV on the molecular level.
We have found a new version of well known Ostrowski inequality in a very simple and antique way via Riemann-Liouville fractional integrals. Also some related results have been derived.
The Business Register of Statistics Austria is the basic instrument for all surveys conducted in economic statistics. For the maintenance mainly four different administrative sources are used. Unfortunately, the units of the different registers do not agree exactly and there is no unique numerical key in the business register and the administrative registers. Each register uses its own key. The units of an administrative register belonging to a certain
unit of the business register have to be found by comparing alphanumerical items like name and address. For that purpose we use the method of Ngrams after some parsing and standardising of the texts. With that method above 90% of the profit-oriented units of the business register could be linked with a corresponding unit of the tax register (these linked units account for 99% of total turnover). 80% of the links were found fully
automatically, the rest was checked manually.
The closedness type regularity conditions have proven during the last decade to be viable alternatives to their more restrictive interiority type counterparts, in both convex optimization and different areas where it was successfully applied. In this review article we de- and reconstruct some closedness type regularity conditions formulated by means of epigraphs and subdifferentials, respectively, for general optimization problems in order to stress that they arise naturally when dealing with such problems. The results are then specialized for constrained and unconstrained convex optimization problems. We also hint towards other classes of optimization problems where closedness type regularity conditions were successfully employed and discuss other possible applications of them.
We generalize a method for proving uniform in bandwidth consistency results for kernel type estimators developed by the two last named authors. Such results are shown to be useful in establishing consistency of local polynomial estimators of the regression function.