A crucial part to any warehouse workflow is the process of order picking. Orders can significantly vary in the number of items, mass, volume and the total path needed to collect all the items. Some orders can be picked by just one worker, while others are required to be split up and shrunk down, so that they can be assigned to multiple workers. This paper describes the complete process of optimal order splitting. The process consists of evaluating if a given order requires to be split, determining the number of orders it needs to be split into, assigning items for every worker and optimizing the order picking routes. The complete order splitting process can be used both with and without the logistic data (mass and volume), but having logistic data improves the accuracy. Final step of the algorithm is reduction to Vehicle Routing Problem where the total number of vehicles is known beforehand. The process described in this paper is implemented in some of the largest warehouses in Bosnia and Herzegovina.

This letter presents a topology inference technique for neuronal networks of the cortex of the human brain based on network tomography theory. We envision that this technique will be used for high-resolution and high-precision brain tissue tomography and imaging using principles of the Internet of Bio-Nano Things. Our network tomography solution relies on the classification of processed data of spike delay and synaptic weight functions of neuronal network activity. For a 6-layer cortical neural network, we achieved 99.27% of accuracy using the Decision Tree machine learning technique for individual neurons, 2-leaf and 4-leaf star topologies of neuronal networks.

We demonstrate that a single 6mm line sample of simulated near-field speckle intensity suffices for accurate estimation of the concentration of dielectric micro-particles over a range from 104 to 6⋅106 particles per ml. For this estimation, we analyze the speckle using both standard methods (linear principal component analysis, support vector machine (SVM)) and a neural network, in the form of a sparse stacked autoencoder (SSAE) with a softmax classifier or with an SVM. Using an SSAE with SVM, we classify line speckle samples according to particle concentration with an average accuracy of over 78%, with other methods close behind.

The paper explores the nature and trend of migration during 2018, on the example of Bosnia and Herzegovina. The politicization of migrants' issues as well as a number of other factors contributed to the attitude of criminalization and securitization of migrants. Most authors and researchers dealing with the issue of migrants as well as the security representatives themselves argue that migrants are not a security threat to Bosnia and Herzegovina and that the state should treat them humanely and treat them according to international standards. In this paper we have determined that it is necessary to work on building additional capacities to accommodate migrants who have not found any accommodation or are on routes of Bosnia and Herzegovina, and that is important to undertake activities on technical, material and financial support for migration management in Bosnia and Herzegovina. We have used a qualitative research approach with the use of interview techniques and desk analysis for the purpose of collecting and processing primary and secondary data.

Constant improvement and evolution is one of the basic components of human nature. It is a requirement for one’s survival and well-being of future g