Geographical variation in bill size across bird species provides evidence for Allen's rule

Allen’s rule proposes that the appendages of endotherms are smaller, relative to body size, in colder climates, in order to reduce heat loss. Empirical support for Allen’s rule is mainly derived from occasional reports of geographical clines in extremity size of individual species. Interspecific evidence is restricted to two studies of leg proportions in seabirds and shorebirds. We used phylogenetic comparative analyses of 214 bird species to examine whether bird bills, significant sites of heat exchange, conform to Allen’s rule. The species comprised eight diverse taxonomic groups—toucans, African barbets, Australian parrots, estrildid finches, Canadian galliforms, penguins, gulls, and terns. Across all species, there were strongly significant relationships between bill length and both latitude and environmental temperature, with species in colder climates having significantly shorter bills. Patterns supporting Allen’s rule in relation to latitudinal or altitudinal distribution held within all groups except the finches. Evidence for a direct association with temperature was found within four groups (parrots, galliforms, penguins, and gulls). Support for Allen’s rule in leg elements was weaker, suggesting that bird bills may be more susceptible to thermoregulatory constraints generally. Our results provide the strongest comparative support yet published for Allen’s rule and demonstrate that thermoregulation has been an important factor in shaping the evolution of bird bills.

Genetic algorithm based PI controller tuning for induction motor drive with ANN flux estimator

This paper presents a Genetic Algorithm (GA) based fast speed response controller for poly-phase induction motor drive. Here the proportional and integral gains of PI controller are optimized by GA to achieve quick speed response. An adaptive Recurrent Neural Network (RNN) with Real Time Recurrent Learning (RTRL) algorithm is proposed to estimate rotor flux. An online tuning scheme to update the weight of RNN is presented to overcome stator resistance variation problem. This tuning scheme requires torque estimator to calculate the torque error. Space vector modulation (SVM) technique is used to produce the motor input voltage. Simulation tests have been performed to study the dynamic performances of the drive system for both the classical PI and the genetic algorithm based PI controllers.

Disadvantaged locations and transition: lived lives of students demonstrating resilience

This study answered what biographical experiences shape the trajectory of students demonstrating resilience during a time of transition. It charted the material conditions of agency and structures. The findings pointed to supportive social relationships, community and school resources and educational policies being critical to shaping agency.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.