Molecular and morphological analyses support recognition of Prostanthera volucris (Lamiaceae), a new species from the Central Tablelands of New South Wales

Research into the systematics of Prostanthera has recently revealed a close evolutionary relationship among P. phylicifolia s. str., the critically endangered P. gilesii, and a population of uncertain identity from the Central Tablelands of New South Wales, Australia. Previous analyses were unable to establish whether genetic boundaries separated these taxa. This study aimed to assess the species boundaries among these three taxa using a combination of single-nucleotide polymorphisms (SNP) sampled at the population-scale and multivariate analysis of morphological characters. Non-parametric and parametric statistics, neighbour-network analysis, phylogenetic analysis, and ancestry coefficient estimates all provided support for discrete genetic differences between the three taxa. Morphological phenetic analysis identified a suite of characters that distinguished each of these taxa. This corroboration of evidence supports the presence of three independently evolving lineages. Prostanthera gilesii and P. phylicifolia s. str. are distinct species independent from the third taxon which is described here as P. volucris R.P.O'Donnell. A detailed description, diagnostic line drawings and photographs are provided. We evaluate P. volucris as satisfying criteria to be considered Critically Endangered.

Drowsiness Detection using Band Power and log Energy Entropy Features Based on EEG Signals

Sleeping on the wheels due to drowsiness is one of the significant causes of death tolls all over the world. The primary reason for the sleepiness is due to lack of sleep and irregular sleep patterns. Several methods such as unobtrusive sensors, vehicle dynamics and obtrusive physiology sensors are used to diagnose drowsiness in drivers. However, the unobtrusive sensors detect drowsiness in the later stage. Whereas the physiological methods use obtrusive sensors such as electro-ocular, electro-myo and electro-encephalograms produce high accuracy in the early detection of drowsiness, which makes them a preferable solution. The objective of this research article is to classify drowsiness with alertness based on the electroencephalographic (EEG) signals using band power and log energy entropy features. A publicly available ULg DROZY database used in this research. The raw multimodal signal is processed to extract the five EEG channels. A passband filter with the cut off frequencies of 0.1 Hz and 50 Hz attenuates the high-frequency components. Another bandpass filter bank is designed to slice the raw signals into eight sub-bands, namely delta, theta, low alpha, high alpha, low beta, mid-beta, high beta and gamma. The preprocessed signals are segmented into an equal number of frames with a frame duration of 2 seconds using a rectangular time windowing approach with an overlap of 50%. Frequency domain features such as log energy entropy and band power were extracted. The extracted feature sets were further normalised between 0 and 1 and labelled as drowsy and alert and then combined to form the final dataset. The K-fold cross-validation method is used to divide the dataset into training and testing sets. The processed dataset is then trained using Discriminant analysis, k-nearest neighbour network, Binary decision tree, ensemble, Naive Bayes and support vector machine classifiers and the results are compared with the literature. The kNN classifier produces 95% classification accuracy. The developed model can provide a tool for drowsiness detection in drivers

Network Analysis of Northern Queensland Tropical Rainforests

The graph theory of a 'two-neighbour network' is explained, and its potential advantages for the elucidation of complex ecological systems are outlined. The method is used for the analysis of a difficult set of 146 tropical rain forest sites, defined by the presence or absence of 740 tree species, in the humid tropics of N. Queensland. It is shown that the results greatly clarify a complex ecological problem, and that they are in unexpectedly good agreement with a preexisting intuitive classification.