Optimization of Food Industry Production Using the Monte Carlo Simulation Method: A Case Study of a Meat Processing Plant

The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level of complexity by presenting it in a specific form. The calculation of such production schemes, in most enterprises, is currently done manually, which significantly limits the possibilities for optimization. This article proposes a model based on the Monte Carlo method to automate the budgeting process. The application of this model is described using an example of a typical meat processing enterprise. Approbation of the model showed its high applicability and the ability to transform the process of making management decisions and the potential to increase the profits of the enterprise, which is unattainable using other methods. As a result of the study, we present a methodology for modeling industrial production that can significantly speed up the formation and optimization of an enterprise’s budget. In our demonstration case, the profit increased by over 30 percentage points

EPIDEMIOLOGY, CLINICAL CHARACTERISTICS, AND VIROLOGIC FEATURES OF COVID-19 PATIENTS IN KAZAKHSTAN: A NATION-WIDE RETROSPECTIVE COHORT STUDY

Background: The earliest coronavirus disease-2019 (COVID-19) cases in Central Asia were announced in March 2020 by Kazakhstan. Despite the implementation of aggressive measures to curb infection spread, gaps remain in the understanding of the clinical and epidemiologic features of the regional pandemic. Methods: We did a retrospective, observational cohort study of patients with laboratory-confirmed COVID-19 hospitalized in Kazakhstan between February and April 2020. We compared demographic, clinical, labora tory and radiological data of patients with different COVID-19 severities on admission. Logistic regression was used to assess factors associated with disease severity and in-hospital death. Whole-genome SARS-CoV 2 analysis was performed in 53 patients. Findings: Of the 1072 patients with laboratory-confirmed COVID-19 in March-April 2020, the median age was 36 years (IQR 24 50) and 484 (45%) were male. On admission, 683 (64%) participants had asymptomatic/ mild, 341 (32%) moderate, and 47 (4%) severe-to-critical COVID-19 manifestation; 20 in-hospital deaths (1 87%) were reported by 5 May 2020. Multivariable regression indicated increasing odds of severe disease associated with older age (odds ratio 1 05, 95% CI 1 03-1 07, per year increase; p<0 001), the presence of comorbidities (2 34, 95% CI 1 18-4 85; p=0 017) and elevated white blood cell count (WBC, 1 13, 95% CI 1 00- 1 27; p=0 044) on admission, while older age (1 09, 95% CI 1 06-1 13, per year increase; p<0 001) and male sex (5 63, 95% CI 2 06-17 57; p=0 001) were associated with increased odds of in-hospital death. The SARS CoV-2 isolates grouped into seven phylogenetic lineages, O/B.4.1, S/A.2, S/B.1.1, G/B.1, GH/B.1.255, GH/B.1.3 and GR/B.1.1.10; 87% of the isolates were O and S sub-types descending from early Asian lineages, while the G, GH and GR isolates were related to lineages from Europe and the Americas. Interpretation: Older age, comorbidities, increased WBC count, and male sex were risk factors for COVID-19 disease severity and mortality in Kazakhstan. The broad SARS-CoV-2 diversity suggests multiple importa tions and community-level amplification predating travel restrictio

Optimization of Food Industry Production Using the Monte Carlo Simulation Method: A Case Study of a Meat Processing Plant

The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level of complexity by presenting it in a specific form. The calculation of such production schemes, in most enterprises, is currently done manually, which significantly limits the possibilities for optimization. This article proposes a model based on the Monte Carlo method to automate the budgeting process. The application of this model is described using an example of a typical meat processing enterprise. Approbation of the model showed its high applicability and the ability to transform the process of making management decisions and the potential to increase the profits of the enterprise, which is unattainable using other methods. As a result of the study, we present a methodology for modeling industrial production that can significantly speed up the formation and optimization of an enterprise&rsquo;s budget. In our demonstration case, the profit increased by over 30 percentage points

Growth Process, Structure and Electronic Properties of Cr₂GeC and Cr_2-xMn_xGeC Thin Films Prepared by Magnetron Sputtering

The growth and phase formation features, along with the influence of structure and morphology on the electronic, optical, and transport properties of Cr2GeC and Cr2-xMnxGeC MAX phase thin films synthesized by magnetron sputtering technique, were studied. It was found that the Cr:Ge:C atomic ratios most likely play the main role in the formation of a thin film of the MAX phase. A slight excess of carbon and manganese doping significantly improved the phase composition of the films. Cr2GeC films with a thicknesses exceeding 40 nm consisted of crystallites with well-developed facets, exhibiting metallic optical and transport properties. The hopping conduction observed in the Cr2-xMnxGeC film could be attributed to the columnar form of crystallites. Calculations based on a two-band model indicated high carrier concentrations N, P and mobility μ in the best-synthesized Cr2GeC film, suggesting transport properties close to single crystal material. The findings of this study can be utilized to enhance the growth technology of MAX phase thin films