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Action Augmentation of Tactile Perception for Soft-Body Palpation.
No full textMedical palpation is a diagnostic technique in which physicians use the sense of touch to manipulate the soft human tissue. This can be done to enable the diagnosis of possibly life-threatening conditions, such as cancer. Palpation is still poorly understood because of the complex interaction dynamics between the practitioners' hands and the soft human body. To understand this complex of soft body interactions, we explore robotic palpation for the purpose of diagnosing the presence of abnormal inclusions, or tumors. Using a Bayesian framework for training and classification, we show that the exploration of soft bodies requires complex, multi-axis, palpation trajectories. We also find that this probabilistic approach is capable of rapidly searching the large action space of the robot. This work progresses "robotic" palpation, and it provides frameworks for understanding and exploiting soft body interactions
Effects of semi-trailer modifications on HGV fuel consumption
No full textThis article investigates the effects of aerodynamic and lightweight double-deck semi-trailers on fuel consumption of Heavy Goods Vehicles (HGVs). The HGVs were evaluated using in-service data, and computer-based simulations with coefficients of aerodynamic drag and rolling resistance estimated from coast-down tests conducted on a test track. The coast-down tests showed that the aerodynamic features reduced the coefficient of aerodynamic drag by approximately 7.2% and the wide single tyres on the lightweight trailers reduced the coefficient of rolling resistance by approximately 10%. The in-service data showed that the aerodynamic features on the aerodynamic vehicles have a statistical significance on fuel consumption. Computer-based simulations showed that the aerodynamic-lightweight trailer reduces the HGV's fuel consumption by approximately 20.2% for a long-haul drive cycle. As these improvements don't have significant barrier to implementation, which is the case with electrification of HGVs, fleet operators can employ these improvements to reduce their carbon emissions
Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation
No full textCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients
Dynamic characteristics of a YBCO bulk above an electromagnet guideway
No full textFor high-temperature superconducting (HTS) maglev, an electromagnet guideway unit (EMGU) that can form an electromagnet guideway (EMG) with a small gap, or even no gap, between EMGUs has been designed. The longitudinal magnetic fields along a single EMGU and two EMGUs arranged in a line were first investigated through measurement and simulation. The experimentally measured data validated the simulation results from a three-dimensional (3D) EMGU model implemented in COMSOL Multiphysics, indicating that the model is reliable and can be used for further studies. The dynamic responses of a high-temperature superconducting (HTS) bulk above a single EMGU and two EMGUs arranged in a line, including the dynamic levitation force (LF) and traveling directional force (TDF), under different operating conditions were investigated through experiment and simulation using a segregated H-formulation model. The magnetic field and current density distributions inside the superconductor are affected by the external magnetic field generated by the EMGU and are responsible for the dynamic characteristics. Finally, the segmented instant excitation (SIE) mode was investigated through simulation, which shows it is feasible by coordinating the currents of EMGUs
Origins of structural and electronic transitions in disordered silicon
No full textStructurally disordered materials pose fundamental questions1–4, including how different disordered phases (‘polyamorphs’) can coexist and transform from one phase to another5–9. Amorphous silicon has been extensively studied; it forms a fourfold-coordinated, covalent network at ambient conditions and much-higher-coordinated, metallic phases under pressure10–12. However, a detailed mechanistic understanding of the structural transitions in disordered silicon has been lacking, owing to the intrinsic limitations of even the most advanced experimental and computational techniques, for example, in terms of the system sizes accessible via simulation. Here we show how atomistic machine learning models trained on accurate quantum mechanical computations can help to describe liquid–amorphous and amorphous–amorphous transitions for a system of 100,000 atoms (ten-nanometre length scale), predicting structure, stability and electronic properties. Our simulations reveal a three-step transformation sequence for amorphous silicon under increasing external pressure. First, polyamorphic low- and high-density amorphous regions are found to coexist, rather than appearing sequentially. Then, we observe a structural collapse into a distinct very-high-density amorphous (VHDA) phase. Finally, our simulations indicate the transient nature of this VHDA phase: it rapidly nucleates crystallites, ultimately leading to the formation of a polycrystalline structure, consistent with experiments13–15 but not seen in earlier simulations11,16–18. A machine learning model for the electronic density of states confirms the onset of metallicity during VHDA formation and the subsequent crystallization. These results shed light on the liquid and amorphous states of silicon, and, in a wider context, they exemplify a machine learning-driven approach to predictive materials modelling
GI-COVID: Are There COVID-19 Patients with Primary Gastrointestinal SARS-CoV-2 Infection and Symptoms?
No full textSequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods
No full textHierarchy and leadership interactions commonly occur in animal groups, crowds of people, and in vehicle motions. Such interactions are often affected by one or more individuals who possess key domain information (e.g., final destination, environmental constraints, and best routes) or pertinent traits (e.g., better navigation, sensing, and decision making capabilities) compared with the rest of the group. This article presents a framework for the automatic identification of group structure and leadership from noisy sensory observations of tracked groups. Accordingly, a new leader-follower model is developed, which assumes the dynamics of the group to be a multivariate Ornstein-Uhlenbeck process with the designated leader(s) drifting to the destination and followers reverting to the leaders' state. Sequential Monte Carlo approaches, and specifically the sequential Markov chain Monte Carlo approach, are adopted to infer, probabilistically, the evolving leadership structure. A Rao-Blackwellisation scheme is employed such that the kinematic state of the objects in the group is inferred in closed form by Kalman filtering. Experiments show that the proposed techniques can successfully determine the leadership structures in challenging scenarios with a corresponding enhancement in tracking accuracy through direct consideration of the leadership interactions of the group
Experimental realization of an all-(RE)BaCuO hybrid trapped field magnet lens generating a 9.8 T concentrated magnetic field from a 7 T external field
No full textIn this work, we have verified experimentally an all-(RE)BaCuO hybrid trapped field magnet lens (HTFML) using only one cryocooler and a special technique named the 'loose contact method'. In the experimental setup, only the inner magnetic lens was tightly connected to the cold stage and cooled at all times, and the outer trapped field magnet (TFM) cylinder was loosely connected to the cold stage before the magnetizing process by introducing a gap between the outer TFM and cold stage of the cryocooler. As a result, the superconducting state for zero-field cooled magnetization of the inner magnetic lens and the non-superconducting (normal) state for field-cooled magnetization of the outer TFM cylinder can co-exist at the same time. A maximum concentrated field of B c = 9.8 T was achieved for the magnetizing process with an applied field of B app = 7 T in the present HTFML, consistent with the numerical estimation in our previous conceptual study. These results validate the HTFML concept as a compact and desktop-type magnet device that can provide 10 T-class magnetic field enhancement from the viewpoint of the magnetizing method. However, during magnetization with a higher B app of 10 T, thermal instability of the outer stacked TFM cylinder caused flux jumps to occur, resulting in mechanical fracture of multiple bulks. These results suggest that the further development of a practical cooling method that can realize a stable and controllable cooling process for each part of the HTFML is necessary based on fundamental studies relating to the thermal stability of the large stacked TFM cylinder
Simulating the blowoff transient of a swirling, bluff body-stabilized kerosene spray flame using detailed chemistry
No full textThe lean blowoff transient of a Jet-A spray flame in a lab-scale swirl burner is simulated using Large Eddy Simulation (LES) and the Conditional Moment Closure (CMC) combustion model. The objectives are: (i) verify the capability of LES-CMC to predict the blowoff of heavy hydrocarbon spray flames, (ii) to investigate local flame behavior such as local extinction and flame lift-off, and (iii) to analyze species behavior during the blowoff transient. The kerosene in this study is a conventional Jet-A reference fuel standardized under the USA National Jet Fuels Combustion Program. A detailed hybrid chemistry (HyChem) mechanism based on the lumped pyrolysis assumption for high temperatures was used. The flame shape changes as it experiences the transient, shrinking down and then retreating from the edges of the bluff body until mixture fraction is present only in regions along the spray cone. During the simulation of the blowoff transient, local extinctions are identified both visually along the flame stoichiometric isosurface and quantitatively in mixture fraction space by regions of low OH and temperature and high fuel and formaldehyde (CH2 O) mass fractions. Pyrolysis species benzene (C6 H6 ) and ethylene (C2 H4 ) are analyzed and shown to reduce during blowoff, due to reduced temperatures preventing vaporized kerosene from undergoing pyrolysis. Without sufficient pyrolysis products available, the flame experiences fuel starvation. The combination of fuel starvation and increased number of local extinctions causes the global lean blowoff event. CH2 O notably builds up around the edges of the combustion chamber toward the end of blowoff, as less OH mass fraction is present to consume the formaldehyde. LES-CMC simulates blowoff to occur at a bulk air velocity within 5% of the experimental value, affirming that the LES-CMC approach coupled with detailed chemistry is able to accurately predict the blow off of heavy hydrocarbon flames
Allocation of Excitation Signals for Generic Identifiability of Linear Dynamic Networks
No full textA recent research direction in data-driven modeling is the identification of dynamic networks, in which measured vertex signals are interconnected by dynamic edges represented by causal linear transfer functions. The major question addressed in this paper is where to allocate external excitation signals such that a network model set becomes generically identifiable when measuring all vertex signals. To tackle this synthesis problem, a novel graph structure, referred to as , is introduced, and the generic identifiability of a network model set can be featured by a set of disjoint directed pseudotrees that cover all the parameterized edges of an , which includes the correlation structure of the process noises. Thereby, an algorithmic procedure is devised, aiming to decompose the extended graph into a minimal number of disjoint pseudotrees, whose roots then provide the appropriate locations for excitation signals. Furthermore, the proposed approach can be adapted using the notion of to solve a dual problem, that is to select a minimal number of measurement signals for generic identifiability of the overall network, under the assumption that all the vertices are excited