The Study of Antistaphylococcal Potential of Omiganan and Retro-Omiganan Under Flow Conditions.

Staphylococcus aureus is considered one of the leading pathogens responsible for infections in humans and animals. The heterogeneous nature of diseases caused by these bacteria is due to the occurrence of multiple strains, differentiated by several mechanisms of antibiotic resistance and virulence factors. One of these is the ability to form biofilm.

Adaptive Hurst-Sensitive Active Queue Management.

An Active Queue Management (AQM) mechanism, recommended by the Internet Engineering Task Force (IETF), increases the efficiency of network transmission. An example of this type of algorithm can be the Random Early Detection (RED) algorithm. The behavior of the RED algorithm strictly depends on the correct selection of its parameters.

Supervised Learning of Neural Networks for Active Queue Management in the Internet.

The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router's queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism.

Diffusion Model of a Non-Integer Order Controller with TCP/UDP Streams.

In this article, a way to employ the diffusion approximation to model interplay between TCP and UDP flows is presented. In order to control traffic congestion, an environment of IP routers applying AQM (Active Queue Management) algorithms has been introduced. Furthermore, the impact of the fractional controller PIγ and its parameters on the transport protocols is investigated.

Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis.

The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of classifying the synthetic data obtained from the Pareto distribution and the real traffic data.

Near-Infrared (NIR) Spectroscopy of Synthetic Hydroxyapatites and Human Dental Tissues.

Near-infrared spectroscopy (NIR) was used to analyze synthetic hydroxyapatite calcined at various temperatures, synthetic carbonated hydroxyapatite, and human hard dental tissues (enamel and dentin). The NIR bands of those materials in the combination, first-overtone, and second-overtone spectral regions were assigned and evaluated for structural characterization. They were attributed to adsorbed and structural water, structural hydroxyl (OH) groups and surface P-OH groups.