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. This selection may be performed automatically depending on the network conditions. The mechanisms that adjust their parameters to the network conditions are called the adaptive ones. The example can be the Adaptive RED (ARED) mechanism, which adjusts its parameters taking into consideration the traffic intensity. In our paper, we propose to use an additional traffic parameter to adjust the AQM parameters-degree of self-similarity-expressed using the Hurst parameter. In our study, we propose the modifications of the well-known AQM algorithms: ARED and fractional order PIαDß and the algorithms based on neural networks that are used to automatically adjust the AQM parameters using the traffic intensity and its degree of self-similarity. We use the Fluid Flow approximation and the discrete event simulation to evaluate the behavior of queues controlled by the proposed adaptive AQM mechanisms and compare the results with those obtained with their basic counterparts. In our experiments, we analyzed the average queue occupancies and packet delays in the communication node. The obtained results show that considering the degree of self-similarity of network traffic in the process of AQM parameters determination enabled us to decrease the average queue occupancy and the number of rejected packets, as well as to reduce the transmission latency.

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. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks.

Diffusion Model of a Non-Integer Order PIγ 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. The controller has been elaborated in accordance with the control theory. The TCP and UDP flows are transmitted simultaneously and are mutually independent. Only the TCP is controlled by the AQM algorithm. Our diffusion model allows a single TCP or UDP flow to start or end at any time, which distinguishes it from those previously described in the literature.

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. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.

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. Biofilm-associated bacteria exhibit a different phenotype that protects them from external factors such as the activity of immune system or antimicrobial substances. Moreover, it has been shown that the majority of persistent and recurrent infections are associated with the presence of the biofilm. Omiganan, an analog of indolicidin - antimicrobial peptide (AMP) derived from bovine neutrophil granules, was found to exhibit high antistaphylococcal and antibiofilm potential. Furthermore, its analog with a reversed sequence (retro-omiganan) was found to display enhanced activity against a variety of pathogens. Based on experience of our group, we found out that counterion exchange can improve the antistaphylococcal activity of AMPs. The aim of this study was to investigate the activity of both compounds against S. aureus biofilm under flow conditions. The advantage of this approach was that it offered the opportunity to form and characterize the biofilm under more controlled conditions. To do this, unique flow cells made of polydimethylsiloxane (PDMS) were developed. The activity against pre-formed biofilm as well as AMPs-treated bacteria was measured. Also, the incorporation of omiganan and retro-omiganan into the channels was conducted to learn whether or not it would inhibit the development of biofilm. The results of the microbiological tests ultimately confirmed the high potential of the omiganan and its retro-analog as well as the importance of counterion exchange in terms of antimicrobial examination. We found out that retro-omiganan trifluoroacetate had the highest biofilm inhibitory properties, however, acetates of both compounds exhibited the highest activity against planktonic and biofilm cultures. Moreover, the developed methodology of investigation under flow conditions allows the implementation of the studies under flow conditions to other compounds.

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. The NIR spectral features were quantitatively discussed in view of proton solid-state magic-angle spinning nuclear magnetic resonance ((1)H MAS NMR) results. We conclude that the NIR spectra of apatites are useful in the structural characterization of synthetic and biogenic apatites.