Logistics support planning model in the conditions of limited resources

Introduction/purpose: The paper presents a model of logistics support planning in the conditions of limited logistic resources based on the prioritization of customer requirements and resource allocation. Decisionmakers play a crucial role in the efficient and equitable allocation of resources as they prioritize among different user requirements. Methods: Requirement prioritization techniques that use nominal scale, ordinal scale, and ratio scale, and five methods for converting ranks into weighting coefficients have been applied to determine the degree of significance of user requirements. The Requirements triage method has been used for establishing relative priorities, while the heuristic algorithm determining the Kemeny median was used to consolidate individually ranked requests into a group rank. In order to balance opposing demands of users, consensus measures of group decision making were used. For obtaining an optimal planned solution of logistic support, the methods and techniques of resource allocation were applied. Results: A model for adaptive planning of logistics support in the conditions of limited resource capacities of the logistics system has been developed. Conclusion: The proposed model can be effectively applied in other areas of resource allocation.

About the approximation of the tunnel lining deformations according to the measurement results

The article deals with the approximation of the displacements of the points of the tunnel lining contour, obtained, for example, by geodetic survey. In the future, the processed data can be used to calculate the internal forces in the tunnel lining, which will help in monitoring the technical condition of the bearing structures of underground structures. In the article, calculation formulas have been obtained that allow using the measured displacements of the reference points to find the displacements of any point of the lining. Displacements are determined by approximating deformations with periodic cubic splines. To determine the coefficients of splines, it is proposed to apply the common least squares method. A method for calculating the weighting coefficients of basic functions for a spline with an arbitrary step of the nodes is presented. As an intermediate result, analytical dependences were obtained between the Cartesian coordinates of the tunnel lining points and the arc length. In this case, the design data on the radii of curvature of the lining sections and the coordinates of the points of section conjugation were used. It is assumed that the data obtained will increase the information content of the control and can be used to monitor its technical condition of underground structures at all stages of its life cycle.

Muscle Synergies in People With Chronic Ankle Instability During Anticipated and Unanticipated Cutting Tasks

Context: Although neuromuscular deficits in people with chronic ankle instability (CAI) have been identified, previous studies mostly investigated the activation of multiple muscles in isolation. Investigating muscle synergies in people with CAI would provide information about the coordination and control of neuromuscular activation strategies and could hold important information for understanding and rehabilitating neuromuscular deficits in this population. Objective: The purpose of this study was to investigate muscle synergies in people with CAI and healthy controls as they perform different cutting tasks. Design: Cross-sectional study Setting: Laboratory Participants: Eleven people with CAI (22 ± 3 years, 1.68 ± 0.11 m, 69.0 ± 19.1 kg) and 11 healthy controls (CON) (23 ± 4 years, 1.74 ± 0.11 m, 66.8 ± 15.5 kg) participated in the current study. Main Outcome Measures: Muscle synergies were extracted from the EMG of the soleus, medial gastrocnemius, lateral gastrocnemius, tibialis anterior, and fibularis longus muscles during anticipated and unanticipated cutting tasks. The number of synergies, activation coefficients, and muscle-specific weighting coefficients were compared between groups and across tasks. Results: The number of muscle synergies were the same for each group and task. The CAI group exhibited significantly greater (p = 0.023) tibialis anterior weighting coefficients within Synergy 1 compared to the CON group. In addition, both groups exhibited greater fibularis longus (p = 0.029) weighting coefficients within Synergy 2 during unanticipated cutting compared to anticipated cutting. Conclusion: These results suggest that while both groups used a neuromuscular control strategy of similar complexity / dimensionality to perform the cutting tasks, people with CAI exhibited different muscle-specific weightings characterized by greater emphasis on tibialis anterior function within Synergy 1, which may reflect an effort to increase joint stability to compensate for the presence of ankle instability.

Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.

Data transformations when constructing a composite system quality index

The features of the data distribution can significantly affect the composite characteristics of objects, so composite indexes of objects must necessarily take into account the features of the data. Some types of data are characterized by distributions with a significant anomaly, when the vast majority of observations are concentrated near the boundary values. This type of data cannot always be characterized by an asymmetry coefficient. In addition, if the values of a variable are approximately symmetric with respect to zero or are concentrated near zero, the sample cannot also be characterized by the coefficient of variation. The paper proposes a transformation that allows us to identify the anomalous nature of variables using the signal-to-noise ratio. Variables are evaluated in the standard range, which is shifted to the right relative to zero. If it is necessary to logarithm, such a transformation will avoid the pressure of small values of variables that, after direct logarithm, would have large negative values. The application of logarithmic correction for the detected anomalous variables redistributes the values of the obtained weighting coefficients in the direction of a more correct interpretation and, in particular, solves the problem with the negativity of the weighting coefficients.

An Efficient Numerical Scheme for the Solution of a Stochastic Volatility Model Including Contemporaneous Jumps in Finance

The model of stochastic volatility with contemporaneous jumps is written for pricing under a partial integro-differential equation (PIDE) having a double integral and a nonsmooth initial value. To tackle this problem, first, a new radial basis function (RBF) as a convex combination of two known RBFs is given. Second, the weighting coefficients of the RBF generated finite difference (FD) method are contributed and the associated error equations are derived. To deal with the integral part, the new idea is to apply an estimate for the unknown function for every cell and do an integration of the density function. The contributed approach is competitive and reduces both the calculational efforts and elapsed time.

Blockchain Software Selection as a Fuzzy Multi-Criteria Problem

Increased consumer requirements for quality, safety and traceability of goods in supply chains has accelerated the implementation of blockchain during the COVID-19 pandemic. The right choice of blockchain software is a complicated task and an important prerequisite for successful deployment. In this study, we propose a conceptual framework for group multi-criteria selection of blockchain software in fuzzy environment according to organization needs and experts’ judgements. The applicability of the new framework has been verified through an illustrative example for ranking blockchain systems. The evaluations of compared alternatives were calculated by using measurement of alternatives and ranking according to the compromise solution (MARCOS) method. The robustness of the new framework was proven by sensitivity analysis in which two (crisp and fuzzy) MARCOS models with two different sets of weighting coefficients were compared.

Comprehensive preview decision-making method for direction and speed of intelligent vehicle based on rules and mechanisms

According to the existing driver model, the objective function is coupled with continuity factors and discontinuity factors, which makes it difficult to determine the weighting coefficients in multi-objective optimization, which will cause dangerous situations such as the vehicle rushing out of the road boundary; in response to this problem, this paper proposes a driver model adapted to the complex traffic environment, based on the mechanism modeling of continuity factors and rule modeling of discontinuity factors. In view of the difficulty of traditional optimization algorithms to find a balance between efficiency and accuracy, this paper proposes a grid optimization algorithm that takes into account both efficiency and accuracy. In order to reduce the amount of calculation in the preview decision-making process, this paper proposes a curve integral method based on the laws of vehicle kinematics to predict the position of the vehicle to judge whether a collision will occur. The driver model is established in the Simulink simulation environment, and the C-level prototype model in the vehicle dynamics simulation software CarSim is selected as the control object, the results show that the proposed the preview decision model effectively solves the problem of divergence in the optimization solution, and can also ensure safety and traffic rules in a complex traffic environment, improving the quality of the model.

Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome

Aim. To develop predictive models of obstructive coronary artery disease (OPCA) in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) based on the predictive potential of cardiometabolic risk (CMR) factors.Material and methods. This prospective observational cohort study included 495 patients with NSTE-ACS (median age, 62 years; 95% confidence interval [60; 64]), who underwent invasive coronary angiography (CAG). Two groups of persons were identified, the first of which consisted of 345 (69,6%) patients with OPCA (stenosis ≥50%), and the second — 150 (30,4%) without OPCA (<50%). The clinical and functional status of patients before CAG was assessed including 29 parameters. For data processing and analysis, the Mann-Whitney, Fisher, chi-squared tests and univariate logistic regression (LR) were used. In addition, for the development of predictive models, we used multivariate LR (MLR), support vector machine (SVM) and random forest (RF). The models was assessed using 4 metrics: area under the ROC-curve (AUC), sensitivity, specificity, and accuracy.Results. A comprehensive analysis of functional and metabolic status of patients made it possible to identify the CMR factors that have linear and nonlinear association with OPCA. Their weighting coefficients and threshold values with the highest predictive potential were determined using univariate LR. The quality metrics of the best predictive algorithm based on an ensemble of 10 MLR models were as follows: AUC — 0,82, specificity and accuracy — 0,73, sensitivity — 0,75. The predictors of this model were 7 categorical (total cholesterol (CS) ≥5,9 mmol/L, low-density lipoprotein cholesterol >3,5 mmol/L, waist-to-hip ratio ≥0,9, waist-to-height ratio ≥0,69, atherogenic index ≥3,4, lipid accumulation product index ≥38,5 cm*mmol/L, uric acid ≥356 pmol/L) and 2 continuous (high density lipoprotein cholesterol and insulin resistance index) variables.Conclusion. The developed algorithm for selecting predictors made it possible to determine their significant predictive threshold values and weighting coefficients characterizing the degree of influence on endpoints. The ensemble of MLR models demonstrated the highest accuracy of OPCA prediction before the CAG. The predictive accuracy of the SVM and RF models was significantly lower.