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.

Medical students and stigma of depression. Part 2. Self-stigma. / Studenci medycyny a stygmatyzacja z powodu depresji. Czesc II. Autostygmatyzacja.

Up to 30% of medical students suffer from depression. They have better access to healthcare, but still receive appropriate treatment less frequently than people with depression in the general population. Most of them do not seek medical help as depression is perceived as a stigmatizing disorder, which leads to self-stigma and hampers early diagnosis and treatment. Thus, self-stigma means less effective therapy, unfavorable prognosis and relapses. According to the literature, self-stigma results in lowered self-esteem and is a major obstacle in the performance of social roles at work and in personal life. Stigmatization and self-stigma of depression among medical students are also associated with effects in their later professional life: they can lead to long-term consequences in the process of treating their patients in the future. Currently there are no unequivocal research results indicating the most effective ways of reducing stigmatization and self-stigma. It is necessary to educate about the symptoms and treatment of depression and to implement diverse intervention techniques to change behaviors and attitudes as early as possible.