Phillip D. Clausen

The largest antlers of any known deer species belonged to the extinct giant deerMegaloceros giganteus. It has been argued that their antlers were too large for use in fighting, instead being used only in ritualized displays to attract mates. Here, we used finite-element analysis to test whether the antlers ofM. giganteuscould have withstood forces generated during fighting. We compared the mechanical performance of antlers inM. giganteuswith three extant deer species: red deer (Cervus elaphus), fallow deer (Dama dama) and elk (Alces alces). Von Mises stress results suggest thatM. giganteuswas capable of withstanding some fighting loads, provided that their antlers interlocked proximally, and that their antlers were best adapted for withstanding loads from twisting rather than pushing actions, as are other deer with palmate antlers. We conclude that fighting inM. giganteuswas probably more constrained and predictable than in extant deer.

Commercially available high-speed multi-channel data acquisition systems can be expensive to purchase and are generally difficult to tailor to specific applications. Arduino is an open-source prototyping platform that facilitates the design and build of computer controlled systems for a wide variety of uses. This article describes in detail several separate high-speed data acquisition systems that have been integrated to simultaneously acquire data from sensors on the rotor, platform and tower of an operating 5 kW horizontal-axis wind turbine along with wind speed and direction data. Due to the presence of strong magnetic fields at the turbine site, turbine direction and yaw rate were measured using high-definition visual footage from a GoPro^® and analysed in a MATLAB script. The processed data from the GoPro system was time-aligned with data from the Arduino-based systems allowing the response of the turbine to be analysed in detail.

Determining the signal quality of surface electromyography (sEMG) recordings is time consuming and requires the judgment of trained observers. An automated procedure to evaluate sEMG quality would streamline data processing and reduce time demands. This paper compares the performance of two supervised and three unsupervised artificial neural networks (ANNs) in evaluation of sEMG quality. Manually classified sEMG recordings from various lower-limb muscles during motor tasks were used to train (n=28000), test performance (n=12000), and evaluate accuracy (n=47000) of the five ANNs in classifying signals into four categories. Unsupervised ANNs demonstrated a 30-40% increase in classification accuracy (>98%) compared to supervised ANNs. AlexNet demonstrated the highest accuracy (99.55%) with negligible false classifications. Results indicate that sEMG quality evaluation can be automated via an ANN without compromising human-like classification accuracy. This classifier will be publicly available and will be a valuable tool for researchers and clinicians using electromyography.