During the years 2019 and 2020, I conducted a bird survey transect in the Bohemian Forest. I did not record any changes in habitat structure or weather conditions between the two years. The two surveys differed in sampling effort, which was significantly lower in 2020 (n = 5 visits) than 2019 (n = 14 visits). I found that sampling effort affected the assessment of avian community diversity but did not affect the total number of individuals recorded. I also recorded a similar pattern in the cumulative number of species between the two breeding seasons, but 80% of species were recorded ten days earlier with the higher sampling effort. In the year with the lower sampling effort, I recorded fewer species than in the year with higher sampling effort. In both study periods, avian community diversity peaked during May and June. These results suggest that even a sampling effort three times lower is still sufficient to detect most species if the minimal number of visits are conducted. The pattern of detectability during the breeding season differed significantly among species. Most species (n = 24) showed a decreasing linear detectability throughout the summer months (e.g. Turdidae or Muscicapidae), most probably due to their breeding activities. In two species (willow tit Poecile montanus and European goldfinch Carduelis carduelis), this linear relationship was reversed, probably due to singing of young birds from the previous breeding season and the effect of the autumn equinox on birdsong activity. Many species (n = 21) did not show any trend and the rest, mainly migratory species, showed non-linear relationships with the peak in the middle of the breeding season. The differences in trends of detectability (i.e. song activity) among bird species are therefore directly linked with their life history.
Borrelia burgdorferi sensu lato (s.l.) is the etiological agent of Lyme disease, transmitted by ticks of the genus Ixodes Latreille. Diagnosis of Lyme disease in humans is often difficult and a detailed knowledge of the circulation of B. burgdorferi s.l. in tick hosts is therefore fundamental to support clinical procedures. Here we developed a molecular approach for the detection of B. burgdorferi s.l. in North Italian Ixodes ricinus (Linnaeus). The method is based on the amplification of a fragment of the groEL gene, which encodes a heat-shock protein highly conserved among B. burgdorferi s.l. species. The tool was applied in both qualitative and Real-time PCR approaches testing ticks collected in a North Italian area. The obtained results suggest that this new molecular tool could represent a sensitive and specific method for epidemiological studies aimed at defining the distribution of B. burgdorferi s.l. in I. ricinus and, consequently, the exposure risk for humans.
In the current study 744 cloacal samples were collected from mallards (Anas platyrhynchos) in the Czech Republic and tested for the presence of influenza virus between 2008 and 2010. Of the total number of 744 mallards tested nine were positive (prevalence 1.2 %) for influenza virus. All the mallards were up to 1.5 years old and the majority (89 %) were killed by hunters.
Slow fluctuating radar targets have shown to be very difficult to classify by means of neural networks. This paper deals with the application of time-frequency decompositions for improving the performance of neural networks for this kind of targets. Several topics, such as dimensionality reduction of the time-frequency representations and the optimum value of SNR for training are discussed. The proposed detector is compared with a single neural network for radar detection, showing that he performance is improved for slow fluctuating radar targets, especially for low values of the probability of false alarm.
As the UAV industry grows all over the world there are many new companies getting involved in various aspects from manufacturing, training, consulting, and providing aerial services. Commercial and recreational use represent unintentional risk to public safety through accidental collisions and crashes and deliberate use of UAVs to inflict harm is largely unmitigated due to the absence of effective UAV detection technology. and Prudký rozvoj bezpilotních prostředků po celém světě způsobil, že mnoho nových společností se zapojilo do vývoje a výroby v této oblasti. Jedná se o široké spektrum služeb, od výroby, ovládání UAV, přes monitoring a vyhodnocování dat až po údržbu těchto prostředků. Komerční a rekreační využití těchto prostředků sebou přináší ohrožení veřejné bezpečnosti prostřednictvím náhodných kolizí a pádů a může docházet i ke zneužití bezpilotních prostředků vzhledem k absenci účinné technologie odhalení UAV.
The coexistence of discrete morphs within a species, with one morph more conspicuous than the other(s) is often thought to result from both sexual selection and predation. In many damselflies, sexual dimorphism occurs jointly with multiple female colour morphs. Typically, one morph is coloured like the male (andromorph), while the other(s) is not (gynomorph(s)). The mechanisms contributing to the maintenance of such female polymorphism in damselflies remain poorly understood, especially the role of predation. We tested the detectability of two different female colour morphs of the damselfly, Enallagma cyathigerum, using human observers as model predators; andromorphs were detected more frequently than gynomorphs. Field data on mortality of males and the two different female morphs due to predation or drowning were also collected, and these observations support morph-specific mortality. In natural populations predation risk was higher in males than females; gynomorphs, however, were more prone to predation than andromorphs. Differences in behaviour between morphs, rather than colour, may explain this result.
With the gradual improvement of the telecommunication infrastructure in China, the Internet and other new technologies have been frequently used. The Internet technology also brings many network security threats, for example, botnet, while bringing convenience. Botnet is a network formed between hosts controlled by malicious code. One of the most serious threat to network security faced by the Internet is a variety of malicious network attacks on the carrier of botnet. Back propagation (BP) neural network is proposed to detect botnet threat transmission. In this study, a botnet detection model was established using BP neural network system. BP neural network classifier could identify the botnet traffic and normal traffic. Moreover a test was carried out to detect botnet traffic using BP neural network; the performance of the BP neural network classifier was evaluated by the detection rate and false positive rate. The results showed that it had high detection rate and low false positive rate, which indicated that the BP neural network had a good performance in detecting the traffic of botnet threat transmission.