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arXiv 2024年

作者： Liu, A-Ming Guo, Wenbin Safonov, Vasily G. Skiba, Alexander N. School of Mathematics and Statistics Hainan University Hainan Haikou570228 China Institute of Mathematics The National Academy of Sciences of Belarus Minsk220072 Belarus Department of Mechanics and Mathematics Belarusian State University Minsk220030 Belarus Department of Mathematics and Technologies of Programming Francisk Skorina Gomel State University Gomel246019 Belarus

Let σ = {σi | i ∈ I} be some partition of the set of all primes. A subgroup A of a finite group G is said to be: (i) σ-subnormal in G if there is a subgroup chain A = A0 ≤ A1 ≤ · · · ≤ An = G suc...

Let σ = {σi | i ∈ I} be some partition of the set of all primes. A subgroup A of a finite group G is said to be: (i) σ-subnormal in G if there is a subgroup chain A = A0 ≤ A1 ≤ · · · ≤ An = G such that either Ai−1 E Ai or Ai/(Ai−1)Ai is a σj-group, j = j(i), for all i = 1, ..., n;(ii) modular in G if the following conditions are held: (1) 〈X, A ∩ Z〉 = 〈X, A〉 ∩ Z for all X ≤ G, Z ≤ G such that X ≤ Z, and (2) 〈A, Y ∩ Z〉 = 〈A, Y 〉 ∩ Z for all Y ≤ G, Z ≤ G such that A ≤ Z;(iii) σ-quasinormal in G if A is σ-subnormal and modular in G. We obtain a description of finite groups in which σ-quasinormality (respectively, modularity) is a transitive relation. Some known results are *** Codes 20D10, 20D15, 20D30 Copyright © 2024, The Authors. All rights reserved.

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Evaluating carbon cap and trade policy effects on a multi-period bi-objective closed-loop supply chain in retail management under mixed uncertainty: Towards greener horizons

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Expert Systems with Applications 2024年 250卷

作者： Bhunia, Satyajit Das, Soumen Kumar Jablonsky, Josef Roy, Sankar Kumar Department of Pure and Applied Science Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Econometrics Faculty of Informatics and Statistics Prague University of Economics and Business Praha 3 Prague Czech Republic Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India

In response to the growing imperative of addressing environmental concerns and aligning with governmental regulations in supply chain management, this study navigates the optimization landscape of closed-loop supply chains through the lens of carbon cap and trade policy. The optimization model aims to account for the impact of consumer demands, distribution networks, and retail operations on the overall closed-loop supply chain dynamics. The innovative approach introduces an optimization model designed to grapple with the challenges posed by imprecise information within an intuitionistic type-2 fuzzy framework. The bi-objective mixed-integer model integrates pivotal objectives, including carbon emissions reduction, total cost minimization, and trade policy compliance. For a nuanced comprehension of the proposed model, we present three numerical examples, offering clarity on its complexities. Following this, a sensitivity analysis visually depicts the impact of fluctuations in demand and capacity on the defined objective functions. Furthermore, these conclusions unveil promising avenues for future research and exploration within the ambit of this study. © 2024 Elsevier Ltd

关键词： Multiobjective optimization

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Analysis of an incomplete binary outcome dichotomized from an underlying continuous variable in clinical trials

arXiv

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arXiv 2021年

作者： Ma, Chenchen Shen, Xin Qu, Yongming Du, Yu Statistics Data and Analytics Eli Lilly and Company IndianapolisIN United States SAS Programming Brightech International SomersetNJ United States

In many clinical trials, outcomes of interest include binary-valued endpoints. It is not uncommon that a binary-valued outcome is dichotomized from a continuous outcome at a threshold of clinical interest. To reach the objective, common approaches include (a) fitting the generalized linear mixed model (GLMM) to the dichotomized longitudinal binary outcome and (b) imputation method (MI): imputing the missing values in the continuous outcome, dichotomizing it into a binary outcome, and then fitting the generalized linear model for the "complete" data. We conducted comprehensive simulation studies to compare the performance of GLMM with MI for estimating risk difference and logarithm of odds ratio between two treatment arms at the end of study. In those simulation studies, we considered a range of multivariate distribution options for the continuous outcome (including a multivariate normal distribution, a multivariate t-distribution, a multivariate log-normal distribution, and the empirical distribution from a real clinical trial data) to evaluate the robustness of the estimators to various data-generating models. Simulation results demonstrate that both methods work well under those considered distribution options, but MI is more efficient with smaller mean squared errors compared to GLMM. We further applied both the GLMM and MI to 29 phase 3 diabetes clinical trials, and found that the MI method generally led to smaller variance estimates compared to GLMM. © 2021, CC BY.

关键词： Mean square error

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Using the inverse Cauchy problem of the Laplace equation for wave propagation to implement a numerical regularization homotopy method

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AIP Conference Proceedings 2023年第1期2977卷

作者： H. K. Al-Mahdawi Zainalabideen Albadran Hussein Alkattan Mostafa Abotaleb Khder Alakkari Ali J. Ramadhan Electronic Computer Centre University of Diyala Diyala Iraq Department of Computer Techniques Engineering College of Technical Engineering University of Alkafeel Najaf Iraq Department of System Programming South Ural State University South Ural State Russia Department of Statistics and Programming Faculty of Economics University of Tishreen Latakia Syria

In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for communication networks. We considered the method of Homotopy Perturbation Metheod (HPM) for solving the integral equations of the first kind named Fredholm. In order to formulate the Laplace equation into the first-kind integral equation (Fredholm) the Fourier series used. Then the discretization method used to reduce the integral equation into a linear operator equation for the first kind. It is clear that this kind of problem is callsified as an ill-posed and the direct way to solve it unacceptably. Tikhonov’s regularization method with Homotopy Perturbation algorithm used for obtaing the approximation solution for the Laplace differential equation. Finally, the numerical example is proposed.

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Modeling of rainfall time series using NAR and ARIMA model over western Himalaya, India

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Arabian Journal of Geosciences 2022年第23期15卷 1-26页

作者： Rawat, Deepa Mishra, Pradeep Ray, Soumik Warnakulasooriya, Himasha Harshani Fernando Sati, Sarswati Prakash Mishra, Gaurav Alkattan, Hussein Abotaleb, Mostafa College of Forestry VCSG Uttarakhand University of Horticulture and Forestry Tehri Garhwal India College of Agriculture Rewa India Centurion University of Technology and Management Paralakhemundi India Department of Statistics & Computer Science Faculty of Science University of Kelaniya Kelaniya Sri Lanka Centre of Excellence on Sustainable Land Management Indian Council of Forestry Research and Education (MoEF & CC Govt. of India) Dehradun India Department of System Programming South Ural State University Chelyabinsk Russia

The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian subcontinent. It also affects extreme weather phenomena such as western disturbances in the region during winter. The work presented here describes the trends in 117-year precipitation changes and their impact on the western Himalayas and suggests some possible explanations in the context of changing rainfall patterns. Under the investigation, the forecasting efficiency and the prediction pattern of artificial neural network (ANN) and seasonal autoregressive integrated moving average (SARIMA) models for rainfall series in the western Himalayan states of India have been assessed. The results revealed significant changes in the monthly, seasonal, and annual rainfall series data for the three states of the Western Himalayan regions from the years 1900 to 2017. The study also concludes that the nonlinear autoregressive neural network (NARNN) models can be used to forecast the western Himalayan region data series well. According to the result interpretation, the highest rainfall may be estimated in August, 1632.63 mm (2023), whereas the lowest rainfall can be obtained in October (0.43 mm) during 2023. The model predicted a gradual decrease in annual rainfall trends in Uttarakhand and Himachal Pradesh from 2018 to 2023 despite heavy rainfall prediction in the monsoon season, whereas Jammu and Kashmir increase in annual rainfall has been predicted from 2018 to 2023. Possible explanations for the change in precipitation over the western Himalayas have also been proposed and explained.

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GenTwoArmsTrialSize: An R Statistical Software Package to estimate Generalized Two Arms Randomized Clinical Trial Sample Size

arXiv

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arXiv 2024年

作者： Soltanifar, Mohsen Lee, Chel Hee Shirazi, Amin Behnke, Martha Raymond-Loher, Ilfra Dagne, Getachew A. Analytics Division College of Professional Studies Northeastern University 1400-410 West Georgia Street VancouverBCV6B 1Z3 Canada Department of Mathematics and Statistics University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Statistics College of Liberal Arts and Sciences Iowa State University 2438 Osborn Dr AmesIA50011-1090 United States MPH Biostatistics Program College of Public Health University of South Florida 3010 USF Banyan Circle TampaFL United States Department of Critical Care Medicine Alberta Heath Services University of Calgary 3260 Hospital Dr NW CalgaryABT2N 4Z6 Canada Biostatistics & Programming Division Biometrics Department ClinChoice Inc. 750-2 Robert Speck Parkway MississaugaONL4Z 1H8 Canada Biostatistics & Programming Division Biometrics Department ClinChoice Inc. 11300 Virginia Dr. 4th Floor Fort WashingtonPA19034 United States

The precise calculation of sample sizes is a crucial aspect in the design of clinical trials particularly for pharmaceutical statisticians. While various R statistical software packages have been developed by researchers to estimate required sample sizes under different assumptions, there has been a notable absence of a standalone R statistical software package that allows researchers to comprehensively estimate sample sizes under generalized scenarios. This paper introduces the R statistical software package"GenTwoArmsTrialSize," available on the Comprehensive R Archive Network (CRAN), designed for estimating the required sample size in two-arm clinical trials. The package incorporates four endpoint types, two trial treatment designs, four types of hypothesis tests, as well as considerations for noncompliance and loss of follow-up, providing researchers with the capability to estimate sample sizes across 24 scenarios. To facilitate understanding of the estimation process and illuminate the impact of noncompliance and loss of follow-up on the size and variability of estimations, the paper includes four hypothetical examples and one applied example. The discussion encompasses the package’s limitations and outlines directions for future extensions and *** Codes 62-04, 62-08, 62K05 © 2024, CC BY-NC-SA.

关键词： Software packages

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Hybrid multi-criteria decision-making method with a bipolar fuzzy approach and its applications to economic condition analysis

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Engineering Applications of Artificial Intelligence 2024年 132卷

作者： Jana, Chiranjibe Simic, Vladimir Pal, Madhumangal Sarkar, Biswajit Pamucar, Dragan Tamil Nadu Chennai602105 India Faculty of Transport and Traffic Engineering University of Belgrade VojvodeStepe 305 Belgrade11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City320315 Taiwan Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore721102 India Department of Industrial Engineering Yonsei University 50 Yonsei-ro Sinchon-dong Seodaemun-gu Seoul03722 Korea Republic of Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu 600077 India Department of Industrial Engineering and Management National Taipei University of Technology Taipei City 106344 Taiwan Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Computer Science and Mathematics Lebanese American University Byblos 1102 2801 Lebanon

The bipolar fuzzy set and interval-valued bipolar fuzzy set efficiently analyse real-world problems where for each input of an object, there has counter information. This study's main objective is to lay a foundation for considering a new approach to the bipolar fuzzy hybrid operators tool applied in a realistic frame that contains uncertainties. In this view, some mixed aggregation operators, namely, the hybrid bipolar fuzzy weighted averaging geometric (H-BFWAG), hybrid bipolar fuzzy order weighted averaging geometric (H-BFOWAG), hybrid interval-valued bipolar fuzzy weighted averaging geometric (H-IVBFWAG), and hybrid interval-valued bipolar fuzzy order weighted averaging geometric (H-IVBFOWAG) operators are generated in bipolar fuzzy environment. A decision making approach is developed to build the multi-criteria decision making model. A numerical example is considered to study the different effects on various Indian economic sectors in the COVID-19 environment. Managerial implications are highlighted in between many industrial sectors on which the Indian economy depends and the country's gross domestic product depends on their inter-relationship. This study investigates COVID-19's impact on society and the economy in the Indian context. A comparative investigation is introduced with some pre-existing operators and the TOPSIS (technique for order of preference by similarity to ideal solution) approach to justify the proposed method. © 2024 Elsevier Ltd

关键词： COVID-19

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Modeling and forecasting of wheat of South Asian region countries and role in food security

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Advances in Computational Intelligence 2021年第6期1卷 1-8页

作者： Yonar, Aynur Yonar, Harun Mishra, Pradeep Kumari, Binita Abotaleb, Mostafa Badr, Amr Department of Statistics Faculty of Science Selçuk University Konya Turkey Department of Biostatistics Faculty of Veterinary Medicine Selçuk University Konya Turkey College of Agriculture J.N.K.V.V. Powarkheda India Department of Agricultural Economics Rashtriya Kisan (PG) College (affiliated to Chaudhary Charan Singh University Meerut) Shamli India Department of System Programming South Ural State University Chelyabinsk Russia Faculty of Science School of Science and Technology University of New England Armidale Australia

Wheat is the most important source of food on earth and vital for food safety. It contains 75–80% carbohydrates, 9–18% protein, fiber, many vitamins (especially B vitamins), calcium, iron, and many macronutrients and micronutrients. According to data from the International Grain Council (IGC), wheat has continued to be the most important food grain source for humans in the world. Therefore, determining wheat production behavior has a very important role in food security. In this study, we have modeled and forecasted the production of wheat for 6 years from 2020 to 2025 using ARIMA and Holt’s linear trend models in Afghanistan, Bangladesh, Bhutan, China, India, Nepal, and Pakistan, which are all countries in the South Asian region. Since there is an expectation of a decrease in wheat production in some of these countries, this study can provide these countries with the information they need to take appropriate decisions to prevent the occurrence of food problems in the future and to help deal with food security. Moreover, this projection helps with policy implications and planning.

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AB1033 UNDERSTANDING THE IMPACT OF BELIMUMAB ON EARLY (≤2 YEARS) SYSTEMIC LUPUS ERYTHEMATOSUS IN ADULTS: BeEARLY, A PHASE 4, PROSPECTIVE, OPEN-LABEL STUDY

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Annals of the Rheumatic Diseases 2024年 83卷 1837-1837页

作者： M. Petri K. Costenbader M. Mosca S. Collingborn R.A. Levy C. O’shea H.A. Quasny John Hopkins University School of Medicine Division of Rheumatology Baltimore MD United States of America Brigham & Women’s Hospital Division of Rheumatology Inflammation and Immunity Boston MA United States of America University of Pisa Department of Clinical and Experimental Medicine Pisa Italy Veramed Statistics and Programming Frankfurt Germany GSK Global Medical Affairs Collegeville PA United States of America GSK Rheumatology Global Medical Affairs Dublin Ireland GSK Research & Development Durham NC United States of America

Background: Approximately 40% of patients with systemic lupus erythematosus (SLE) develop organ damage within the first 5 years of diagnosis due to uncontrolled disease activity and cumulative steroid exposure, resulting in a greater risk of early mortality. [1] Timely intervention with disease-modifying biologics such as belimumab (BEL) in the early stages of SLE might modify the disease course and prevent irreversible organ damage while avoiding the need for high-dose glucocorticoids (GCs) or immunosuppressants (IS). Previous studies suggested that BEL may have benefit when used earlier in the course of the disease, especially in patients with minimal or no organ damage at baseline who received BEL within 2 years of classification. [2,3] However, the use of BEL early in the treatment pathway has been limited in clinical practice, with the majority of BEL use following the failure of ≥2 IS. Objectives: Present the design of the BeEARLY (BEL in Early SLE) study, which will evaluate the efficacy and safety of BEL in adult SLE patients with early disease. Methods: In this Phase 4, prospective, open-label, single-arm, 3-year clinical study (GSK Study 219240), approximately 350 adult patients with active, autoantibody-positive, early SLE (≤2 years since diagnosis) with no organ damage and with inadequate response to initial standard therapy (ST; with/without conventional IS) will be enrolled. Key eligibility criteria are shown in Table 1 . Eligible participants will receive open-label BEL 200 mg/week subcutaneously as an add-on to their initial ST for 52 weeks (Part A), with a long-term extension of up to 104 weeks (end of study is the safety follow-up visit after Week 156; Part B). Key primary and secondary endpoints are shown in Table 2 . Participants will be required to taper the average oral GC (OGC; prednisone equivalent) dose from Week 12 to ≤7.5 mg/day by Week 26, and to ≤5 mg/day by Week 48 at the discretion of the investigator if they continue to maintain stabl

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Robust Model Selection and Estimation for Censored Survival Data with High Dimensional Genomic Covariates

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Acta biotheoretica 2019年第3期67卷 225-251页

作者： Guorong Chen Sijian Wang Guannan Sun Huanxue Pan Department of Finance Beijing Forestry University Beijing China. Department of Statistics and Biostatistics Rutgers University New Brunswick NJ USA. Department of Biostatistics and Programming Sanofi China Beijing China. Department of Finance Beijing Forestry University Beijing China. hxpbjfu@***.

When relating genomic data to survival outcomes, there are three main challenges that are the censored survival outcomes, the high-dimensionality of the genomic data, and the non-normality of data. We propose a method to tackle these challenges simultaneously and obtain a robust estimation of detecting significant genes related to survival outcomes based on Accelerated Failure Time (AFT) model. Specifically, we include a general loss function to the AFT model, adopt model regularization and shrinkage technique, cope with parameters tuning and model selection, and develop an algorithm based on unified Expectation-Maximization approach for easy implementation. Simulation results demonstrate the advantages of the proposed method compared with existing methods when the data has heavy-tailed errors and correlated covariates. Two real case studies on patients are provided to illustrate the application of the proposed method.

关键词： AFT model General loss function Regularized robust model Survival data

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