Effect of change management capability in real-time environment: an information orientation perspective in supply chain management

Supply chain management (SCM) is an important strategic tool that requires careful planning and management. While the availability of real-time information is a critical aspect of an SCM system, it possesses a potential to disrupt supply chain participants. Thus, the success of an SCM system hinges on how well participants deal with changes taking place in a real-time environment. This study adopts the concept of the real-time enterprise to understand the role of change management on SCM performance based on the concept of the information orientation. The study empirically validated the role of change management on SCM performance.     

Interrelation of families of points of high order on the Edwards curve over a prime field

We propose a modification of the addition law on the Edwards curve over a prime field. We prove three theorems on properties of coordinates of high-order points and on a degenerate pair of twisted curves. We propose an algorithm for reconstructing all unknown points kP of the Edwards curve when only 1/8 of the points are known.     

Adaptive methods of solving inverse problems for improvement of fidelity of molecular DNA computations

Elaboration of methods of monitoring of biochemical reactions with DNA strands is necessary to solve one of the main problems in creation of biocomputers—improvement of fidelity of molecular DNA computations. In this paper, the results of solution of inverse two-parameter problems of laser Raman spectroscopy on determination of the types and concentration of DNA nitrogenous bases in multicomponent solutions are presented. Comparative analysis of the three used methods of solving these problems has demonstrated convincing advantages of artificial neural networks and of the method of projection to latent structures. Use of adaptive methods allowed achieving the accuracy of determining the concentration of each base in two-component solutions about 0.2–0.4 g/L.     

Depth-Based Classification Method Underlain by a Remote Concentration Measure for Processing Asymmetric Data

A depth-based classification method underlain by a remote concentration measure for processing asymmetric data is developed and investigated. The motivation for the construction of the method was the inefficient use of affine invariant classifiers in combination with depth functions vanishing outside the convex hull of data. The idea of the proposed method is to map a remote space using a remote concentration measure, the Stahel–Donoho remoteness measure, and a corrected remoteness measure.     

Recognition of Actions of Medical Workers on the Basis of Accelerometer Readings with the Use of a Deep Belief Network

This paper analyzes a real set of large-volume medico-statistical data used for the recognition of actions of medical workers on the basis of accelerometer readings at definite moments of time. During the recognition process, a deep belief network was applied to unlabeled data, and then it was supervisedly learned using the error backpropagation method. The obtained results demonstrate higher recognition accuracy in comparison with basic methods. A considerable improvement in medical staff productivity is also achieved.