Application of multi-attribute decision-making methods based on normal random variables in supply chain risk management

For the multi-criteria group decision-making problem where the criterion value is a normal interval number and the weight information is incomplete, the normal interval number and its compromise expected value, compromise mean square error, algorithm, weighted arithmetic average of normal interval number (ININWAA) Operator, the ordered weighted average (ININOWA) operator of normal interval numbers and the mixed weighted average (ININHA) operator of normal interval numbers, and a multi-criteria group with incomplete information based on normal interval numbers is proposed. Decision-making methods. This method uses ININWAA operator and INNHA operator to integrate criterion values, uses the compromise mean square error of criterion values, establishes an optimisation model to solve the optimal criterion weights and uses the expectation variance criterion to determine the order of the schemes. The case analysis shows the effectiveness and feasibility of this method.

ТЕОРИЯ И ПРАКТИКА ОЦЕНКИ КАЧЕСТВА ЖИЗНИ НАСЕЛЕНИЯ РОССИИ НА ТЕКУЩЕМ ВЕКОВОМ ИНТЕРВАЛЕ

The objective of the present work is to develop and evaluate workable models for assessing population life quality (PLQ) in Russia based on the statistical theory of pattern recognition. To this end, the following tasks have been done: 1) developing of an alphabet of classes, which is algorithmically associated with the space of classification characteristics of PQL and gradations thereof (description of classes using characteristics vocabulary); 2) selecting of representative characteristics for assessing PLQ in Russia; 3) formulating and implementing of a series of workable statistical models for assessing PLQ in Russia (construction of recognition algorithms); 4) determining of PLQ classes at different stages of the foreign-policy and domestic socioeconomic development of Russia; and 5) developing of an algorithm for choosing an optimal criterion for PQL estimation out of a range of such criteria and for ranking the criteria according to their practical suitability. The suggested algorithm for PLQ recognition system includes training and recognition subsystems using various pattern recognition criteria: arithmetic mean, geometric mean, weighted mean for mortality factors and weighted mean for significance of life quality parameters. An algorithm for optimal selection of PLQ recognition criteria is proposed. To implement the selection of PLQ, a range of data reflecting the socioeconomic and environmental monitoring of the Russian Empire, the USSR and the Russian Federation in 1910 through 2015 were used. It is shown that the representative characteristics of PLQ in Russia differ from those adopted in the Human Development Index, i.e. life expectancy at birth, per capita gross national income, and expected years of schooling and average years of schooling. The PLQ characteristics representative for Russia are the levels of nutrition and health care and of the pollution of freshwater reservoirs and watercourses. The PLQ assessment algorithms using the weighted mean across the significance of life quality parameters and the weighted mean across the main factors of population mortality proved to be optimal. Apparently, the proposed methodology for assessing PLQ must be applicable to all member states of the Commonwealth of Independent States that emerged after the collapse of the USSR.

Construction of Windows for Pharmacokinetic Sampling

This paper describes a method for the construction of pharmacokinetic sampling windows so that they are around the $D$-optimum time points. Here we consider the situation where a pharmacokinetic (PK) study is accompanied by a dose-finding study in phase I clinical trial. The D-optimal criterion is often used to determine the optimal time for collecting blood samples so that they provide maximum information regarding the population PK parameters. However, collecting blood samples at the D-optimal time points is often difficult. Instead, the sampling time point chosen from a suitable time interval or window can ease the process. The proposed method is conceptually simple and considers the average value and standard deviation of D-optimal time points up to create sampling windows. Random time points can be chosen from these windows then to collect blood samples from the next cohort. The nonlinear random-effects model has been used to model the PK data. Also, we employ the continual reassessment method for dose allocation to the patients. Comparisons of the accuracy and precision for the PK parameter estimates obtained at the D-optimal and random time points are also presented. The results are convincing enough to suggest the proposed method as a useful tool for blood sample collection.

External validation of 4C ISARIC mortality score in the setting of a Saudi Arabian ICU. Retrospective study

COVID-19 pandemic has burdened healthcare systems, necessitating the development of mortality prediction scores to guide clinical decisions and resource allocation. 4C ISARIC mortality score was developed and validated on a British cohort.ObjectivesExternal validation of the score in the setting of a large Saudi Arabian ICU.MethodRetrospective chart review of COVID-19 patients admitted to ICU of King Saud Medical City, Riyadh, Saudi Arabia. Collecting data to calculate the score, then fitting a ROC curve against known patients’ outcome.ResultsCohort included 1493 patients with 38% mortality, AUC of the score was 0.81 (95% CI: 0.79 – 0.83, p < 0.001), correctly classifying 72.67% of the cohort. Cut-off value of > 9 had sensitivity of 70.5% (95% CI: 66.6 – 74.3), specificity 73.97% (95% CI: 71 – 76.8), positive predictive value 62.4% (95% CI: 59.5 – 65.2), and negative predictive value 80.2% (95% CI: 78.2 – 82.4).Conclusion4C ISARIC mortality risk score performed well with a good discriminatory ability for critically ill patients admitted to ICU in our setting. Cut-off > 9 was the optimal criterion.

Response Bias Reflects Individual Differences in Sensory Encoding

Humans exhibit substantial biases in their decision making even in simple two-choice tasks, but the origin of these biases remains unclear. I hypothesized that one source of bias could be individual differences in sensory encoding. Specifically, if one stimulus category gives rise to an internal-evidence distribution with higher variability, then responses should optimally be biased against that stimulus category. Therefore, response bias may reflect a previously unappreciated subject-to-subject difference in the variance of the internal-evidence distributions. I tested this possibility by analyzing data from three different two-choice tasks ( ns = 443, 443, and 498). For all three tasks, response bias moved in the direction of the optimal criterion determined by each subject’s idiosyncratic internal-evidence variability. These results demonstrate that seemingly random variations in response bias can be driven by individual differences in sensory encoding and are thus partly explained by normative strategies.

ANALYSIS OF STRENGTH CRITERIA IN THE DESIGN OF PRODUCTS FROM COMPOSITE MATERIALS

Technological progress gives rise to the continuous expansion of the class of structural materials and the improvement of their properties. The appearance of new materials is due to the natural desire to increase the efficiency of the structures under development. One of the most striking manifestations of progress in the development of materials, structures and technology is associated with the development and application of composite materials. Composites have a number of obvious advantages over other materials, in particular over metals. Such advantages are high specific strength and rigidity, high corrosion resistance, good ability to withstand alternating loads and others. It should be noted another, perhaps the most important feature of composites - is the ability to change the properties of the material in accordance with the purpose of the structure and the nature of its load during operation. Under the influence of loads on the structure, its strength is estimated by the ultimate state of the materials of the structural elements. When a boundary state arises in a material, its transition to another mechanical state - elastic, plastic, or fracture state - occurs. This article aims to determine the optimal criterion for the strength of composite material that takes into account different values of ultimate stresses not only in different directions of the coordinate axes, but also to stretch and compress and further calculate the maximum allowable load for single-layer unidirectional composite material During the research the main properties of composite materials, methods of manufacturing parts from composite material, their main properties and methods of destruction were considered. The characteristics of the strength criteria of composite materials are given, the most suitable for calculating the maximum value of the allowable load for a single-layer unidirectional composite material is determined. The proposed approach to the optimal design of elements of single-layer composite structures may be of interest to developers of numerous and analytical methods for solving problems of optimal design of more complex structures.

Vortices evolution in ideal (M)HD

&lt;p&gt;Vortices and vortex tubes are ubiquitous in the solar atmosphere and space plasma. In order to identify vortices and to study their evolution, we seek a suitable mathematical criterium for which a dynamical equation exists. So far, the only option available is given by the vorticity, which however is not the optimal criterion since it can be biased by shear flows. Therefore, we look at another criterion, the swirling strength, for which we found an evolution equation, which we suggest as a novel tool for the analysis of vortex dynamics in (magneto-)hydrodynamics. We highlight a few results obtained by applying the swirling strength and its dynamical equation to simulations of the solar atmosphere.&lt;/p&gt;

Diagnostic Value of Clinical Prediction Scores for Acute Appendicitis in Children Younger than 4 Years

Introduction Acute appendicitis can be difficult to diagnose, especially in children < 4 years old. The aim of the present study was to assess the diagnostic value of Alvarado score (AS), appendicitis inflammatory response (AIR) score, and pediatric appendicitis score (PAS) in children younger than 4 years. Materials and Methods All children younger than 4 years who underwent appendicectomy between 2005 and 2019 were included retrospectively. The diagnostic performance of the scores was analyzed using the area under the receiver-operating characteristic (ROC) curve and by calculating the diagnostic performances at optimal criterion value cutoff points. Results In this study, 100 children were included (58 boys and 42 girls) with a median age of 39.5 (12–47) months. Ninety children were diagnosed with pathologically proven acute appendicitis. The area under ROC curve of AS was 0.73, AIR score was 0.79, and PAS was 0.69 (p > 0.05, respectively). In children with low risk of acute appendicitis, negative predictive values were 75.0% for AS, 50.0% for AIR score, and 66.7% for PAS (p < 0.05, respectively). The positive predictive values in children with high risk of acute appendicitis were of 92.7% for AS, 92.6% for AIR score, and 93.6% for PAS (p > 0.05, respectively). AS, AIR score, and PAS plus positive ultrasonography have 0.58, 0.49, and 0.88 area under ROC curve. Conclusion The three scores can be of assistance in the suspicion of acute appendicitis. PAS markedly improved combined with positive ultrasonography, but none can be used in setting the diagnosis of acute appendicitis in young children.