Mountain bike rear suspension design: utilizing a magnetorheological damper for active vibration isolation and performance

The introduction of suspension systems to mountain bikes began in the late 1980's and early 1990's. These suspensions created two types of mountain bikes; the hardtail and the full suspension mountain bike. However, designers of full suspension bikes must balance the need for pedaling efficiency, which calls for a stiff suspension, with comfort and trail contact, which calls for a soft suspension. This thesis presents experimental and theoretical results from the development of a rear suspension system that has been designed for a mountain bike. A magnetorheological (MR) damper is used to design a rear suspension system that can balance the need of ride comfort through shock absorption and performance characteristics such as handling and pedaling efficiency by using active control. Two control algorithms have been tested in this study – on/off control and proportional control. The damping was adjusted by setting the damper current to different levels in order to measure the effects of the change in response of the bike. The rear suspension system has been integrated into an existing bike frame and tested on a shaker table as well as a mountain trail. Shaker table testing demonstrates the effectiveness of the damper, while the trail testing indicates that the MR damper-based shock absorber can be used to implement different control algorithms. The shaker table and trail testing results indicate that active damping control can be implemented using an MR damper. Using the results of these experimental tests, a theoretical test was simulated using a mathematical model; which was used to represent the mountain bike mounted to the shaker table. The results were plotted using transmissibility, power spectrum density, and frequency mode shape plots which indicated three applicable natural frequencies near 5, 9, and 10 Hz, when applying the mountain bike, rear suspension system, and rider weight/distribution used for this experiment. Upon the analysis using MATLAB, the mathematical model was determined to correctly represent the overall dynamics of the bicycle pertaining to the sprung mass. Additional accelerometers will need to be placed throughout the bicycle to determine if the mathematical model correctly represented the overall dynamics of the bicycle as a whole

Cryptic species and independent origins of allochronic populations within a seabird species complex (Hydrobates spp.)

Humans are inherently biased towards naming species based on morphological differences, which can lead to reproductively isolated species being mistakenly classified as one if they are morphologically similar. Recognising cryptic diversity is needed to understand drivers of speciation fully, and for accurate estimates of global biodiversity and assessments for conservation. We investigated cryptic species across the range of band-rumped storm-petrels (Hydrobates spp.): highly pelagic, nocturnal seabirds that breed on tropical and sub-tropical islands in the Atlantic and Pacific Oceans. In many breeding colonies, band-rumped storm-petrels have sympatric but temporally isolated (allochronic) populations; we sampled all breeding locations and allochronic populations. Using mitochondrial control region sequences from 754 birds, cytochrome b sequences from 69 birds, and reduced representation sequencing of the nuclear genomes of 133 birds, we uncovered high levels of genetic structuring. Population genomic analyses revealed up to seven unique clusters, and phylogenomic reconstruction showed that these represent seven monophyletic groups. We uncovered up to six independent breeding season switches across the phylogeny, spanning the continuum from genetically undifferentiated temporal populations to full allochronic species. Thus, band-rumped storm-petrels encompass multiple cryptic species, with non-geographic barriers potentially comprising strong barriers to gene flow

Automatic analysis of facilitated taste-liking

This paper focuses on: (i) Automatic recognition of taste-liking from facial videos by comparatively training and evaluating models with engineered features and state-of-the-art deep learning architectures, and (ii) analysing the classification results along the aspects of facilitator type, and the gender, ethnicity, and personality of the participants. To this aim, a new beverage tasting dataset acquired under different conditions (human vs. robot facilitator and priming vs. non-priming facilitation) is utilised. The experimental results show that: (i) The deep spatiotemporal architectures provide better classification results than the engineered feature models; (ii) the classification results for all three classes of liking, neutral and disliking reach F1 scores in the range of 71%-91%; (iii) the personality-aware network that fuses participants’ personality information with that of facial reaction features provides improved classification performance; and (iv) classification results vary across participant gender, but not across facilitator type and participant ethnicity.EPSR

Ancient DNA of the Extinct Lava Shearwater (Puffinus olsoni) from the Canary Islands Reveals Incipient Differentiation within the P. puffinus Complex

The loss of species during the Holocene was, dramatically more important on islands than on continents. Seabirds from islands are very vulnerable to human-induced alterations such as habitat destruction, hunting and exotic predators. For example, in the genus Puffinus (family Procellariidae) the extinction of at least five species has been recorded during the Holocene, two of them coming from the Canary Islands

Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to interact with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user’s needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots were able to adequately demonstrate these skills, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye-tracking, electroencephalography, or functional near-infrared spectroscopy embedded in interactive human–robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive processes and mechanisms involved in human–human interactions, and highlight the importance of perceiving others as intentional agents to activate these social brain areas. We then discuss how attribution of intentionality can positively affect human–robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human–robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles

Administrative problems related to athletics in the secondary schools

Call number: LD2668 .R4 1951 F91