Vibration characteristics of a cylinder partially filled with liquid with an attached elastic drain pipe

Liquid and ullage gas effects of partially filled cylinder with attached elastic drain pip

Robot public speakers' effect on audience affective reaction and attention allocation

Social robots delivering public speeches have a wide range of practical applications as stand-ins for educators, experts, or entertainers. The goal of our work is to investigate how a social robot should be programmed to deliver an effective public speech. Applying a mixed methods research design to collect quantitative and qualitative data, we have conducted a study, which compares a human speaker with a semi-Anthropomorphic social robot speaker (the SoftBank Pepper robot). The robot was programmed to mimic the behaviour patterns of the human delivering the same speech. The study results show that the robot is perceived as intelligent and rational, which contributes to effective delivery of the message content. However, the robot struggles with actively engaging the audience and with establishing an emotional connection. In addition, the behavioural patterns that appear natural in the human speaker tend to be distracting in the robot. Suggestions for the improved design of robot public speakers are discussed, which include implementing rhetoric skills, exploiting and synchronising the robot's specific communication channels, and creating a robot persona

Estimating offsets for avian displacement effects of anthropogenic impacts

Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland ecosystems to compensate for unavoidable environmental damage from anthropogenic developments such as transportation infrastructure, urbanization, and energy development. Pursuit of energy independence in the United States will expand domestic energy production. Concurrent with this increased growth is increased disruption to wildlife habitats, including avian displacement from suitable breeding habitat. Recent studies at energy-extraction and energy-generation facilities have provided evidence for behavioral avoidance and thus reduced use of habitat by breeding waterfowl and grassland birds in the vicinity of energy infrastructure. To quantify and compensate for this loss in value of avian breeding habitat, it is necessary to determine a biologically based currency so that the sufficiency of offsets in terms of biological equivalent value can be obtained. We describe a method for quantifying the amount of habitat needed to provide equivalent biological value for avifauna displaced by energy and transportation infrastructure, based on the ability to define five metrics: impact distance, impact area, pre-impact density, percent displacement, and offset density. We calculate percent displacement values for breeding waterfowl and grassland birds and demonstrate the applicability of our avian-impact offset method using examples for wind and oil infrastructure. We also apply our method to an example in which the biological value of the offset habitat is similar to the impacted habitat, based on similarity in habitat type (e.g., native prairie), geographical location, land use, and landscape composition, as well as to an example in which the biological value of the offset habitat is dissimilar to the impacted habitat. We provide a worksheet that informs potential users how to apply our method to their specific developments and a framework for developing decision-support tools aimed at achieving landscape-level conservation goals

Multi-modal robotic visual-tactile localisation and detection of surface cracks

We present and validate a method to detect surface cracks with visual and tactile sensing. The proposed algorithm localises cracks in remote environments through videos/photos taken by an on-board robot camera. The identified areas of interest are then explored by a robot with a tactile sensor. Faster R-CNN object detection is used for identifying the location of potential cracks. Random forest classifier is used for tactile identification of the cracks to confirm their presence. Offline and online experiments to compare vision only and combined vision and tactile based crack detection are demonstrated. Two experiments are developed to test the efficiency of the multi-modal approach: online accuracy detection and time required to explore a surface and localise a crack. Exploring a cracked surface using combined visual and tactile modalities required four times less time than using the tactile modality only. The accuracy of detection was also improved with the combination of the two modalities. This approach may be implemented also in extreme environments since gamma radiation does not interfere with the sensing mechanism of fibre optic-based sensors

Financing SME growth in the UK: meeting the challenges after the global financial crisis

In the aftermath of the Global Financial Crisis new forms of SME finance are emerging in the place of traditional banking and equity finance sources. This Special Issue has its origins in a conference organised in June 2014 by the Centre for Enterprise and Economic Development Research (CEEDR) at Middlesex University Business School, where all but the final two papers were presented. The Conference was designed to provide a timely forum for leading academics, practitioners and policy makers to disseminate current research and practitioner knowledge exploring finance gaps and how best to address the financing needs of small high growth potential businesses

The evaluation criteria used by venture capitalists:evidence from a UK fund

GRAHAM BOOCOCK AND MARGARET WOODS are Lecturers in Banking and Finance, and Financial Management, respectively, at Loughborough University Business School, England. The paper examines how venture fund managers select their investee companies, by exploring the evaluation criteria and the decision-making process adopted at one United Kingdom regional venture fund (henceforth referred to as the Fund). The analysis confirms that relatively consistent evaluation criteria are applied across the industry and corroborates previous models which suggest that the venture capitalist's decision-making consists of several stages. With the benefit of access to the Fund's internal records, however, this paper adds to the current literature by differentiating the evaluation criteria used at each successive stage of the decision-making process. The paper presents a model of the Fund's activities which demonstrates that the relative importance attached to the evaluation criteria changes as applications are systematically processed. Proposals have to satsfy different criteria at each stage of the decision-making process before they receive funding. In the vast majority of cases, applications are rejected by the fund managers. In addition, the length of time taken by the fund managers in appraising propositions can lead to withdrawal of applications at an advanced stage

Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping

We present an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or motion-captured data and represented as sequences of 3D poses. Given the motion on each joint in the pose at each time step extracted from 3D pose sequences, we hierarchically pool these joint motions in a bottom-up manner in the encoder, following the kinematic chains in the human body. We also constrain the latent embeddings of the encoder to contain the space of psychologically-motivated affective features underlying the gaits. We train the decoder to reconstruct the motions per joint per time step in a top-down manner from the latent embeddings. For the annotated data, we also train a classifier to map the latent embeddings to emotion labels. Our semi-supervised approach achieves a mean average precision of 0.84 on the Emotion-Gait benchmark dataset, which contains both labeled and unlabeled gaits collected from multiple sources. We outperform current state-of-art algorithms for both emotion recognition and action recognition from 3D gaits by 7%--23% on the absolute. More importantly, we improve the average precision by 10%--50% on the absolute on classes that each makes up less than 25% of the labeled part of the Emotion-Gait benchmark dataset.Comment: In proceedings of the 16th European Conference on Computer Vision, 2020. Total pages 18. Total figures 5. Total tables