Comparing Antonovsky's sense of coherence scale across three UK post-industrial cities

Objectives: High levels of ‘excess’ mortality (ie, that seemingly not explained by deprivation) have been shown for Scotland compared to England and Wales and, especially, for its largest city, Glasgow, compared to the similarly deprived English cities of Liverpool and Manchester. It has been suggested that this excess may be related to differences in ‘Sense of Coherence’ (SoC) between the populations. The aim of this study was to ascertain whether levels of SoC differed between these cities and whether, therefore, this could be a plausible explanation for the ‘excess’. Setting: Three post-industrial UK cities: Glasgow, Liverpool and Manchester. Participants: A representative sample of more than 3700 adults (over 1200 in each city). Primary and secondary outcome measures: SoC was measured using Antonovsky's 13-item scale (SOC-13). Multivariate linear regression was used to compare SoC between the cities while controlling for characteristics (age, gender, SES etc) of the samples. Additional modelling explored whether differences in SoC moderated city differences in levels of self-assessed health (SAH). Results: SoC was higher, not lower, among the Glasgow sample. Fully adjusted mean SoC scores for residents of Liverpool and Manchester were, respectively, 5.1 (−5.1 (95% CI −6.0 to −4.1)) and 8.1 (−8.1 (−9.1 to −7.2)) lower than those in Glasgow. The additional modelling confirmed the relationship between SoC and SAH: a 1 unit increase in SoC predicted approximately 3% lower likelihood of reporting bad/very bad health (OR=0.97 (95% CI 0.96 to 0.98)): given the slightly worse SAH in Glasgow, this resulted in slightly lower odds of reporting bad/very bad health for the Liverpool and Manchester samples compared to Glasgow. Conclusions: The reasons for the high levels of ‘excess’ mortality seen in Scotland and particularly Glasgow remain unclear. However, on the basis of these analyses, it appears unlikely that a low SoC provides any explanation

Haptic Sequential Monte Carlo Localization for Quadrupedal Locomotion in Vision-Denied Scenarios

Continuous robot operation in extreme scenarios such as underground mines or sewers is difficult because exteroceptive sensors may fail due to fog, darkness, dirt or malfunction. So as to enable autonomous navigation in these kinds of situations, we have developed a type of proprioceptive localization which exploits the foot contacts made by a quadruped robot to localize against a prior map of an environment, without the help of any camera or LIDAR sensor. The proposed method enables the robot to accurately re-localize itself after making a sequence of contact events over a terrain feature. The method is based on Sequential Monte Carlo and can support both 2.5D and 3D prior map representations. We have tested the approach online and onboard the ANYmal quadruped robot in two different scenarios: the traversal of a custom built wooden terrain course and a wall probing and following task. In both scenarios, the robot is able to effectively achieve a localization match and to execute a desired pre-planned path. The method keeps the localization error down to 10cm on feature rich terrain by only using its feet, kinematic and inertial sensing.Comment: 7 pages, 8 figures, 1 table. Accepted at IEEE/RSJ IROS 202

Potential New Iowa Multi-Use Crops for Oils and Hydrocarbons

Many green plants contain, on a dry weight basis, more than 5 percent oil plus hydrocarbon that is not concentrated in storage organs. In these plants, oils and hydrocarbons (botanochemicals) are distributed throughout major plant tissues often as the major component of a latex. Although there has been little past interest in processing whole plants for botanochemicals, there is now great interest in these new and renewable sources of raw materials and energy. Various plant species are being regarded as potential gasoline trees , as possible domestic sources of natural rubber and plastic, as new sources of industrial feedstocks or even as potential fuels

Using GIS to Teach Placed-Based Mathematics in Rural Classrooms

The purpose of this article is to promote the use of GIS and place-based education (PBE) in rural mathematics classrooms. The pedagogy of place is disappearing from rural communities because of declining enrollments, lack of support, and federal mandates to focus more on basic academic skills. However, PBE does not stand in opposition to standards-based instruction and academic achievement; rather, it enhances instructional strategies for getting at these aims. We present examples of place that can be used to engage rural students in meaningful mathematics activities to improve their content knowledge and problem-solving ability. Barriers exist to full implementation of this work. Yet, we offer a vision of what is possible through the use of technological tools like GIS for teachers who teach in rural communities. Additional studies regarding the effect of using GIS are needed to bring the vision of situated place-based education closer to fruition

Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphs

Visual Inertial Odometry (VIO) is one of the most established state estimation methods for mobile platforms. However, when visual tracking fails, VIO algorithms quickly diverge due to rapid error accumulation during inertial data integration. This error is typically modeled as a combination of additive Gaussian noise and a slowly changing bias which evolves as a random walk. In this work, we propose to train a neural network to learn the true bias evolution. We implement and compare two common sequential deep learning architectures: LSTMs and Transformers. Our approach follows from recent learning-based inertial estimators, but, instead of learning a motion model, we target IMU bias explicitly, which allows us to generalize to locomotion patterns unseen in training. We show that our proposed method improves state estimation in visually challenging situations across a wide range of motions by quadrupedal robots, walking humans, and drones. Our experiments show an average 15% reduction in drift rate, with much larger reductions when there is total vision failure. Importantly, we also demonstrate that models trained with one locomotion pattern (human walking) can be applied to another (quadruped robot trotting) without retraining.Comment: Accepted to Robotics and Automation Letter

Online Estimation of Articulated Objects with Factor Graphs using Vision and Proprioceptive Sensing

From dishwashers to cabinets, humans interact with articulated objects every day, and for a robot to assist in common manipulation tasks, it must learn a representation of articulation. Recent deep learning learning methods can provide powerful vision-based priors on the affordance of articulated objects from previous, possibly simulated, experiences. In contrast, many works estimate articulation by observing the object in motion, requiring the robot to already be interacting with the object. In this work, we propose to use the best of both worlds by introducing an online estimation method that merges vision-based affordance predictions from a neural network with interactive kinematic sensing in an analytical model. Our work has the benefit of using vision to predict an articulation model before touching the object, while also being able to update the model quickly from kinematic sensing during the interaction. In this paper, we implement a full system using shared autonomy for robotic opening of articulated objects, in particular objects in which the articulation is not apparent from vision alone. We implemented our system on a real robot and performed several autonomous closed-loop experiments in which the robot had to open a door with unknown joint while estimating the articulation online. Our system achieved an 80% success rate for autonomous opening of unknown articulated objects