Muhammad Waqas, Song Hua Xu, Syed Masroor Anwar
et al.
Abstract Conventional control charts often assume normality, which may not hold for many engineering processes. In cases where processes follow an Inverse Maxwell $$\left( {{\text{IM}}} \right)$$ IM distribution, as seen in various industrial applications, it becomes crucial to employ suitable monitoring methods. To address this gap, this study introduces the hybrid exponentially weighted moving average ( $${\text{HEWMA}}_{{{\text{IM}}}}$$ HEWMA IM ) chart for the $${\text{IM}}$$ IM distribution. Performance evaluation includes metrics like average run length, median run length, and standard deviation run length. Comparative analysis with existing IM distribution-based charts such as the Shewhart V chart ( $${\text{V}}_{{{\text{IM}}}}$$ V IM ), exponentially weighted moving average ( $${\text{EWMA}}_{{{\text{IM}}}}$$ EWMA IM ), and extended EWMA ( $${\text{EEWMA}}_{{{\text{IM}}}}$$ EEWMA IM ) charts reveal the $${\text{HEWMA}}_{{{\text{IM}}}}$$ HEWMA IM chart’s superior efficiency. Real-world applications in brake pad production and carbon fiber strength testing validate its practicality and engineering applications. In conclusion, $${\text{HEWMA}}_{{{\text{IM}}}}$$ HEWMA IM is a novel tool tailored to monitor $${\text{IM}}$$ IM processes efficiently, offering enhanced process monitoring for diverse industries. Graphical abstract
Emmanuel D. Kpeglo, Michael J. Adjabui, Dioggban Jakperik
In efficacy and safety studies, a vital research concern is to determine the therapeutic window of a pharmaceutical compound. The approach to finding minimum effective dose and maximum safe dose involves multiple comparisons. Generally, in such a problem, a family-wise error rate is inherited if multiplicity adjustment is not handled properly. This article without a need for multiplicity adjustment formulates a stepwise confidence interval method to identify a therapeutic window of a pharmaceutical compound when equality of variances cannot be assumed and when ratios of variances are known. This article employed Fieller’s method and obtained individual confidence intervals for the identification of the minimum effective dose and maximum safe dose. A simulation study was performed and the results show that the confidence interval method proposed in this article controls the family-wise error rate strongly. Power increases whenever the ratio of means increases and decreases against higher clinical relevance margins. The proposed procedure was applied to real data and provided optimum results. It was also observed that as both the ratio of means and the sample sizes increase, power increases accordingly.
Karunia Eka Lestari, Fitriani Agustina, Mokhammad Ridwan Yudhanegara
et al.
The study highlighted three essential roles of retrospective analysis in hypothesis testing, particularly as a priori analysis, post hoc analysis, and sensitivity analysis. These approaches were applied to the Gini ratio data sourced from the National Socioeconomic Survey Indonesia 2023 to examine the income inequality level in Indonesia. The sample size, statistical power, and effect size for the one-sample t-test are evaluated by aid G*Power software. The test results show that for a sample size of 10, at the 95% confidence interval, there is not enough evidence to show that the Gini ratio in 2023 is smaller than 0.4. A retrospective analysis using G*power software reveals that for a sample size of 20 at the same confidence interval, there is enough evidence to suggest that the Gini ratio is statistically significant at less than 0.4 with a power of analysis of 90.8% and an effect size of 0.76. This study has important implications in hypothesis testing, especially in retrospective analysis, since understanding the effect of sample size and effect size makes it possible for academics or practitioners to optimize hypothesis testing and generate more accurate and reliable test results.
Lalu Ramzy Rahmanda, Adji Achmad Rinaldo Fernandes, Solimun Solimun
et al.
Comorbidity between pulmonary tuberculosis and mental health status requires effective psychiatric treatment. This study aims to predict anxiety and depression levels in patients with pulmonary tuberculosis and consider future mental health treatment for patients. A sample of 60 pulmonary tuberculosis patients in Malang were involved and evaluated longitudinally every two weeks over 13 periods. In this study, we use the Generalized Neural Network Mixed Model (GNMM) to obtain better results in predicting anxiety and depression levels in patients with pulmonary tuberculosis and compare the results with the Generalized Linear Mixed Model (GLMM). The flexibility of GLMM in modeling longitudinal data, and the power of neural network in performing a prediction makes GNMM a powerful tool for predicting longitudinal data. The result shows that neural network's prediction performance is better than the classical GLMM with a smaller MSPE and fairly accurate prediction. The MSPEs of the three compared models: 1-Layer GNMM, 2-Layer, and GLMM, respectively are 0.0067, 0.0075, 0.0321 for the anxiety levels, and 0.0071, 0.0002, and 0.0775 for the depression levels. Furthermore, future research needs to investigate the data with a larger sample size or high dimensional data with large network architectures to prove the robustness of GNMM.
Mohammed Wamique Hisam, Amir Ahmad Dar, Mohammed Osman Elrasheed
et al.
Abstract The Taguchi method, a robust experimental design technique, establishes a strong connection between input and output variables. Known for its capacity to yield precise results with fewer trials and minimized errors, this method has gained widespread application in various fields such as engineering, physics, chemistry, economics, finance, and more. In this paper, the authors examine the importance of the Taguchi orthogonal array method, its step-by-step optimization procedure, and its potential for future applications. Through a thorough literature review, the authors investigate how the Taguchi method has been effectively employed to identify key factors influencing response variables. The versatility of the Taguchi method becomes apparent when considering its applications across diverse disciplines. Researchers in engineering have successfully utilized this technique to optimize processes and enhance product quality. Furthermore, in scientific fields like physics and chemistry, the Taguchi method has proven invaluable for conducting experiments efficiently, resulting in more accurate and reproducible outcomes. Researchers gain critical insights into the effects of factors on the response variable by employing statistical tools such as mean analysis, variance analysis, and signal-to-noise ratio. The Taguchi method remains a valuable and broadly applicable tool for optimizing experiments and identifying influential factors across multiple disciplines. This paper’s extensive literature review emphasizes its significance in various fields and outlines the step-by-step procedure to leverage its potential for optimization.
Tectonic earthquakes are disasters that often cause damage to buildings and loss of life. The Province of North Sulawesi and its surroundings are earthquake-prone areas because they are located at the confluence of the major plates. This study aims to analyze the level of earthquake activity in the Province of North Sulawesi and its surroundings using the maximum likelihood method and the Guttenberg-Richter law. The distribution of the earthquake studied for the period 1941-2021 in the Province of North Sulawesi and its surroundings with a Mag ≥3.0 Mw and a depth ≤60 km. The data used comes from the Meteorological, Climatological, and Geophysical Agency Indonesia and ISC (International Seismological Center). The level of earthquake activity was reviewed based on nine groups of year intervals, three groups of depth levels, and the overall research area. The results show that the level of earthquake activity, rock fragility, and local stress at certain year intervals is sometimes high or low for the 1941-2021 earthquake period in North Sulawesi and its surroundings. The same condition for certain earthquake depths. On the other hand, the level of earthquake activity is quite high and the level of rock fragility is moderate in the research area as a whole.
The aim of this paper, we investigate some Lattice sets such as soft lattice exterior, soft lattice interior, soft lattice boundary and soft lattice border sets in soft lattice topological spaces which are defined over a soft lattice L with a fixed set of parameter A and it is also a generalization of soft topological spaces. Further, we develop and continue the initial views of some soft lattice sets, which are deep-seated for further research on soft lattice topology and will consolidate the origin of the theory of soft topological spaces.
Zani Anjani Rafsanjani, Ratna Herdiana, R Heru Tjahjana
et al.
In this work, we propose a mathematical model to represent traffic congestion in the street under some consideration. A congestion problem in a city highway becomes a critical issue since congestion at one point affected congestion propagation on the other points. We focus on the propagation of traffic propagation by adopting the concept of disease spread using the SIR model. We consider that the disease in traffic problems is congestion. Meanwhile, vehicles that enter the highway are susceptible to congestion. In contrast, vehicles free from traffic jams represent individuals free from disease. The SIR model can explain the spread of congestion by looking at the congestion variable as an infected variable. We discuss and analyze the existence and stability of the equilibrium points. The local stability equilibrium point is verified using the Routh-Hurwitz criteria. At the same time, the global stability is analyzed using Lyapunov function. The numerical simulation is provided in the last section to validate the discussion results.
Investment is the process of investing money or capital for profit or material results. The investor carefully calculates the investment object to minimize losses and maximize profits. One of the essential investment objects is the futures price of natural gas considered a commodity that plays a vital role in the Indonesian economy. The movement of natural gas futures prices can be modeled using a time series model. The data in the time series model is believed to have particular pattern to model the data in the future. The natural gas futures price is modeled into a time series method by using fuzzy time series (FTS) approach of the FTS Chen, Lee and Tsaur. Model accuracy is calculated using the criteria of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The three FTS methods have good performance of accuracy for this time series data, where FTS Tsaur as fuzzy times series approach with average based method shows the best results with the smallest error rate to the data of natural gas future price.
Two-associate class triangular designs have been explored greatly but the Tm-type PBIB designs (m ≥ 3) largely remains unexplored. The paper is written with an objective to construct a new series of Tm-type PBIB designs and to derive some more series of PBIB designs based on these PBIB designs, which we have called as Tm-assisted PBIB designs. For this, we begin by first constructing a series of Tm-type PBIB designs and then based on these designs, three series of Tm-assisted PBIB designs have been constructed. The association schemes of Tm-type and Tm-assisted PBIB designs have been discussed in their complete generalized form.
This research deals with the study of the partial hierarchical Poisson regression model (with a random intercept), where this model is one of the most important models widely applied in analyzing data that is characterized by the fact that the observations take a hierarchical form. Where it the full maximum likelihood (FML) method is used to estimate the model parameters. The model was applied to the covid-9 deaths in Mosul city, were recorded during the period (1/1/202 - 1/9/2021), where four major hospitals in the city were selected to represent the group of second level of data (Ibn Sina Hospital, Al Salam Hospital, Shifa Hospital, General Mosul Hospital).<br /> <strong>*</strong><strong>طالب دبلوم عالی/ قسم الاحصاء والمعلوماتیة/ کلیة علوم الحاسوب والریاضیات/ جامعة الموصل.</strong><br /> <strong>**استاذ مساعد/ قسم الاحصاء والمعلوماتیة/ کلیة علوم الحاسوب والریاضیات/ جامعة الموصل.</strong><br /> <strong>تاریخ استلام البحث</strong>: 4/3/2022 <strong>تاریخ القبول</strong>: 9/4/2022 <strong>تاریخ النشر</strong>: 1/12/2022<br /> The research found the adequate of the model for this type of data, as it was found that there are some factors that contribute to the increase in the number of deaths in the epidemic, such as the advanced age of the patient, the length of stay in the hospital, the percentage of oxygen in the patient's blood, in addition to the incidence of some chronic diseases such as asthma. The study recommended a more in-depth study of other types of these models, and the use of other estimation methods, in addition to paying attention to the methods of data recording by the city health department.<br /> <strong> </strong>
Nuttapong Wattanasiripong, Jirapong Mekwian, Hataikhan Sanpan
et al.
Tripolar fuzzy sets are a concept that deals with tripolar information. This idea is a generalization of bipolar and intuitionistic fuzzy sets. In this paper, the notions of tripolar fuzzy pure ideals in ordered semigroups are introduced, and some algebraic properties of tripolar fuzzy pure ideals are studied. We obtain some characterizations of weakly regular ordered semigroups in terms of tripolar fuzzy pure ideals. Finally, we introduce the concepts of tripolar weakly pure ideals and prove that the tripolar fuzzy ideals are tripolar weakly pure ideals if such tripolar fuzzy ideals satisfy the idempotent property.
Otto Rendón, Otto Rendón, Gilberto D. Avendaño
et al.
The local consistency of the method of Smoothed Particle Hydrodynamics (SPH) is proved for a multidimensional continuous mechanical system in the context of measure theory. The Wasserstein distance of the corresponding measure-valued evolutions is used to show that full convergence is achieved in the joint limit N → ∞ and h → 0, where N is the total number of particles that discretize the computational domain and h is the smoothing length. Using an initial local discrete measure given by μ0N=∑b=1Nm(xb,h)δ0,xb(0), where mb = m(xb, h) is the mass of particle with label b at position xb(t) and δ0,xb(t) is the xb(t)-centered Dirac delta distribution, full consistency of the SPH method is demonstrated in the above joint limit if the additional limit N → ∞ is also ensured, where N is the number of neighbors per particle within the compact support of the interpolating kernel.
The proliferation of vast quantities of available datasets that are large and complex in nature has challenged universities to keep up with the demand for graduates trained in both the statistical and the computational set of skills required to effectively plan, acquire, manage, analyze, and communicate the findings of such data. To keep up with this demand, attracting students early on to data science as well as providing them a solid foray into the field becomes increasingly important. We present a case study of an introductory undergraduate course in data science that is designed to address these needs. Offered at Duke University, this course has no prerequisites and serves a wide audience of aspiring statistics and data science majors as well as humanities, social sciences, and natural sciences students. We discuss the unique set of challenges posed by offering such a course, and in light of these challenges, we present a detailed discussion into the pedagogical design elements, content, structure, computational infrastructure, and the assessment methodology of the course. We also offer a repository containing all teaching materials that are open-source, along with supplementary materials and the R code for reproducing the figures found in the article.
Probabilities. Mathematical statistics, Special aspects of education
In medical research, a two‐phase study is often used for the estimation of the area under the receiver operating characteristic curve (AUC) of a diagnostic test. However, such a design introduces verification bias. One of the methods to correct verification bias is inverse probability weighting (IPW). Since the probability a subject is selected into phase 2 of the study for disease verification is known, both true and estimated verification probabilities can be used to form an IPW estimator for AUC. In this article, we derive explicit variance formula for both IPW AUC estimators and show that the IPW AUC estimator using the true values of verification probabilities even when they are known are less efficient than its counterpart using the estimated values. Our simulation results show that the efficiency loss can be substantial especially when the variance of test result in disease population is small relative to its counterpart in nondiseased population.
The APPLICATION part to be noted as follows: 1. RANDOM COIL BIOPOLYMERS. 2. BACTERIAL GROWTH. 3. PHYSICO-CHEMICAL ANALYSIS / ANALYTICAL CHEMISTRY. It is an experimental finding between Physical Chemistry and Analytical Chemistry Teaching methods .CITATION -Datta, Jaydip & Chatterjee, S & Bhattacharya, T. (2015). HYDRODYNAMIC MODELLING OF BIOPOLYMERS : A CORRELATION APPRAOCH ( ISSN : 0020-3254 ). 87. 2016. 10.6084/m9.figshare.12167607. In the earlier work 1 by Datta et al the important hydrodynamic properties like radius of gyration (R g) and root mean square end to end separation (R rms) can be calculated through theknown value of molecular weight (M). The mathematical modelling was done by polynomial regression through a standard software like curve finder. The present work correlates the most important viscometric property , Diffusion coefficient (D) 2,3 with the same hydrodynamic properties like radius of gyration (R g) i.e. D (R g) of the biopolymers. It is expected that a bulky biopolymer of Random Coil with high molecular weight diffuse more slowly through a solvent than the Random Coil of low.
The Academic Reminder System is built to help users, which focuses for the students in UiTM Tapah to manage their scheduled class and assignment deadline via their mobile smartphones. The application was built for android - based platform. The user only needs to update their schedule after getting logged into the system and list any important date for their assignment deadline to the system, Then  the system will give an alert to remind the user for the upcoming class and task deadline. The System Development Life Cycle (SDLC) which is the waterfall model has been chosen as the methodology for this project that
containing five phases. The result of the discussion shows that Academic Reminder System receives positive feedback through usability testing that has been completed. A total 30 students completed the usability survey and 46.7% of the respondents are strongly agreed that this application meets their satisfaction with interface, usability, and performance. Future work recommendation for this project is to make a sharing application tool in the system so that the user can simply share the applica
tion to their friends. Besides that, the application can engage with new technology, which is using near - field communication (NFC) in the log in page to easier for students log into the system.
Muhammad Aslam Noor, Khalida Inayat Noor, Sabah Iftikhar
et al.
The main aim of this paper is to introduce a new class of harmonic convex functions with respect to non-negative function h, which is called generalized (h,r)-harmonic convex functions. We derive some new Fejer-Hermite-Hadamard type inequalities for generalized harmonic convex functions. Some special cases are also discussed. The ideas and techniques of this paper may stimulate further research.
Background & Aim:A meta-analysis refers to the statistical synthesis of results from a series of studies and has been used to estimate pooled prevalence of human diseases. In this study, we review some statistical issues regarding the meta-analysis of the prevalence of human diseases such as statistical software and programs, transformations of prevalence rate, assessment of heterogeneity, and publication bias.