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Role of compliant mechanics and motor control in hopping - from human to robot
Full text linkCompliant leg function found during bouncy gaits in humans and animals can be considered a role model for designing and controlling bioinspired robots and assistive devices. The human musculoskeletal design and control differ from distal to proximal joints in the leg. The specific mechanical properties of different leg parts could simplify motor control, e.g., by taking advantage of passive body dynamics. This control embodiment is complemented by neural reflex circuitries shaping human motor control. This study investigates the contribution of specific passive and active properties at different leg joint levels in human hopping at different hopping frequencies. We analyze the kinematics and kinetics of human leg joints to design and control a bioinspired hopping robot. In addition, this robot is used as a test rig to validate the identified concepts from human hopping. We found that the more distal the joint, the higher the possibility of benefit from passive compliant leg structures. A passive elastic element nicely describes the ankle joint function. In contrast, a more significant contribution to energy management using an active element (e.g., by feedback control) is predicted for the knee and hip joints. The ankle and knee joints are the key contributors to adjusting hopping frequency. Humans can speed up hopping by increasing ankle stiffness and tuning corresponding knee control parameters. We found that the force-modulated compliance (FMC) as an abstract reflex-based control beside a fixed spring can predict human knee torque-angle patterns at different frequencies. These developed bioinspired models for ankle and knee joints were applied to design and control the EPA-hopper-II robot. The experimental results support our biomechanical findings while indicating potential robot improvements. Based on the proposed model and the robot’s experimental results, passive compliant elements (e.g. tendons) have a larger capacity to contribute to the distal joint function compared to proximal joints. With the use of more compliant elements in the distal joint, a larger contribution to managing energy changes is observed in the upper joints
Stereoselective polar radical crossover for the functionalization of strained-ring systems
Full text linkRadical-polar crossover of organoborates is a poweful tool that enables the creation of two C-C bonds simultaneously. Small ring systems have become essential motifs in drug discovery and medicinal chemistry. However, step-economic methods for their selective functionalization remains scarce. Here we present a one-pot strategy that merges a simple preparation of strained organoboron species with the recently popularized polar radical crossover of borate derivatives to stereoselectively access tri-substituted azetidines, cyclobutanes and five-membered carbo- and heterocycles
Energy-resolved fast-neutron radiography using an event-mode neutron imaging detector
Full text linkEnergy-resolved fast-neutron radiography is a powerful non-destructive technique that can be used to remotely measure the quantity and distribution of elements and isotopes in a sample. This is done by comparing the energy-dependent neutron transmission of a sample with the known cross-sections of individual isotopes. The reconstruction of the composition is possible due to the unique features (e.g. resonances) in the cross-sections of individual isotopes. At short-pulsed (<~ 1 ns) neutron sources, such information is accessible via time-of-flight neutron imaging in principle, but requires a detector with nanosecond temporal resolution. Conventional neutron detectors can meet this requirement only by heavily compromising spatial resolution or efficiency. Here, we present a unique approach on fast neutron resonance radiography using a scintillator-based event-mode imaging detector at a short-pulsed neutron source, including first results on spatially mapped resonance profiles using MeV neutrons. The event mode approach applied in the presented detector allows recording of individual neutron interactions with nanosecond precision in time and sub-mm resolution in space. As a result, the entire available neutron energy spectrum can be measured for each pulse. At the same time, the use of a thick scintillator screen and lenses to focus the produced light results in a highly flexible field of view and a high interaction probability in the sensitive volume of the detector
Arctic plant-fungus interaction networks show major rewiring with environmental variation
Full text linkGlobal environmental change may lead to changes in community structure and in species interactions, ultimately changing ecosystem functioning. Focusing on spatial variation in fungus–plant interactions across the rapidly changing Arctic, we quantified variation in the identity of interaction partners. We then related interaction turnover to variation in the bioclimatic environment by combining network analyses with general dissimilarity modelling. Overall, we found species associations to be highly plastic, with major rewiring among interaction partners across variable environmental conditions. Of this turnover, a major part was attributed to specific environmental properties which are likely to change with progressing climate change. Our findings suggest that the current structure of plant-root associated interactions may be severely altered by rapidly advancing global warming. Nonetheless, flexibility in partner choice may contribute to the resilience of the system
Stability of step size control based on a posteriori error estimates
Full text linkA posteriori error estimates based on residuals can be used for reliable error control of numerical methods. Here, we consider them in the context of ordinary differential equations and Runge-Kutta methods. In particular, we take the approach of Dedner & Giesselmann (2016) and investigate it when used to select the time step size. We focus on step size control stability when combined with explicit Runge-Kutta methods and demonstrate that a standard I controller is unstable while more advanced PI and PID controllers can be designed to be stable. We compare the stability properties of residual-based estimators and classical error estimators based on an embedded Runge-Kutta method both analytically and in numerical experiments
Ag-only inner electrode Na₀.₅Bi₀.₅TiO₃-based X9R MLCC: achieving high performance and cost efficiency
Full text linkThe demand for high-power electronic applications is set to drive the necessity for robust components like multi-layer ceramic capacitors (MLCCs). These MLCCs must endure a broad temperature range and withstand high electric fields. Simultaneously, the production cost of these components is a crucial concern for manufacturers. The regularly used Ag/Pd inner electrodes constitute the most significant cost factor. Hence, this study showcases the fabrication of a sodium bismuth titanate (NBT)-based MLCC using only Ag inner electrodes. This could be achieved by reducing the sintering temperatures with the help of sintering aids, but still maintaining excellent dielectric properties of the ceramic. This MLCC demonstrates an exceptional operational temperature range (− 90 to 310 °C), high energy density (up to 5.1 J/cm³), higher efficiency (92%) at 217 kV/cm, and robust capacitance stability (variation less than 10%) even under high temperatures and electric fields
Biomechanical models in the lower-limb exoskeletons development: a review
Full text linkLower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This paper provides an overview of these models and a comprehensive review of the current applications of them in assistive device development. It also critically analyzes the existing literature to identify research gaps and suggest future directions. Biomechanical models can be broadly classified as conceptual and detailed models and can be used for the design, control, and assessment of exoskeletons. Also, these models can estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. We identified the validation of simulation studies and the enhancement of the accuracy and fidelity of biomechanical models as key future research areas for advancing the development of assistive devices. Additionally, we suggest using exoskeletons as a tool to validate and refine these models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. In addition, increasing the availability of open source resources accelerates the advancement of the exoskeleton and biomechanical models. Although biomechanical models are widely applied to improve movement assistance and rehabilitation, their full potential in developing human-compatible exoskeletons remains underexplored and requires further investigation. This review aims to reveal existing needs and cranks new perspectives for developing more effective exoskeletons based on biomechanical models
Crystal structure of decapotassium hexaarsenidodistannate(IV), K₁₀[Sn₂As₆]
Full text linkAs₆Ki₀Sn₂, monoclinic, P12₁/n1 (No. 14), a = 15.1959(9) Å, b = 8.2988(7) Å, c = 9.219(1) Å, β = 90.00(1)°, V = 1162.6 ų, Z = 2, Rgt(F) = 0.041, wRreft(F²) = 0.135, Τ = 293
Unit‐cell parameters determination from a set of independent electron diffraction zonal patterns
No full textDue to the short de Broglie wavelength of electrons compared with X‐rays, the curvature of their Ewald sphere is low, and individual electron diffraction patterns are nearly flat in reciprocal space. As a result, a reliable unit‐cell determination from a set of randomly oriented electron diffraction patterns, an essential step in serial electron diffraction, becomes a non‐trivial task. Here we describe an algorithm for unit‐cell determination from a set of independent electron diffraction patterns, as implemented in the program PIEP (Program for Interpreting Electron diffraction Patterns), written in the early 1990s. We evaluate the performance of the algorithm by unit‐cell determination of two known structures – copper perchlorophthalocyanine (CuPcCl₁₆) and lysozyme, challenging the algorithm by high‐index zone patterns and long crystallographic axes. Finally, we apply the procedure to a new, structurally uncharacterized five amino acid peptide
Inverse Reinforcement Learning for Human Decision-Making Under Uncertainty
No full textHuman decision-making in the real world is characterized by uncertainty, continuous learning, and adaptation. In the past, reinforcement learning and stochastic optimal control have been widely used as normative frameworks to model, reproduce, and predict human behavior. However, interpreting observed behavior requires inverse approaches to infer the underlying decision-making mechanisms. Existing inverse approaches, such as inverse reinforcement learning and inverse optimal control, commonly make assumptions, such as full knowledge of the environment and stationary policies, which often do not align with human behavior in real-world scenarios. This dissertation introduces novel inverse approaches for sequential decision-making that account for the adaptive and dynamic nature of human behavior arising from uncertainty. The contributions are organized into three main parts:
First, we address the problem of inferring local knowledge of human subjects in navigation tasks. Seemingly suboptimal routes taken by humans can be explained by incomplete knowledge of the environment, offering insights into their knowledge and beliefs. We describe a Bayesian inference method for systematically inferring a subject's knowledge of the environmental structure based on their navigation behavior. The approach combines approximate sampling methods with a navigation model based on shortest path reduction with an additional cost for uncertainty for efficient inference. We evaluate the approach using both simulated data and real human trajectories collected in an online experiment.
Second, we consider the problem of inferring time-varying preferences in the form of discount functions, which arise when individuals face uncertainty about risks. These varying preferences can be explained by individuals adapting their risk beliefs over time and manifest as preference inconsistencies and hyperbolic discounting. We derive a normative model of hyperbolic discounting for the discrete-time setting and discuss how beliefs about the risk can be inferred in a human discounting experiment. Additionally, we extend this analysis to continuous-time stochastic optimal control, for which we define a formulation with non-exponential discounting, and present an approach to infer the discount function based on observed decision data.
Finally, we address the problem of inferring latent quantities in sensorimotor control tasks, which can be formulated as partially observable stochastic optimal control problems. In these formulations, subjects receive only partial, noisy observations of their state and are uncertain about the future evolution of the stochastic environment.
The inverse problem is particularly challenging, as the subjects' beliefs and control signals are usually latent in the observed trajectory data. For linear-quadratic-Gaussian (LQG) systems with multiplicative noise, we derive an approximate likelihood using an assumed density approach to find the most likely parameters given the observed data. Additionally, for general non-linear stochastic systems, we introduce a linearization-based approximation to enable efficient parameter inference. The methods are evaluated on a range of different simulated tasks and on animal reaching data