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Unmanned aerial vehicles (UAVs) are widely used for various applications, such as military surveillance and reconnaissance; delivery of packages; monitoring of plants and buildings; and search and rescue. Besides basic battery-electric propulsion, in order to improve range and endurance, hybrid electric propulsion systems based on combinations of batteries, fuel cells, solar cells, and ultracapacitors are increasingly being applied to these UAVs. For small- and medium-scale UAVs, the solar and fuel cell converters have non-isolated DC-DC converters that include a high-frequency switching device. In this paper, a novel switch fault detection technique based on virtual admittance is proposed for DC-DC converters. A fault index function is formulated based on the virtual admittance to minimize potential influence by highly dynamic load change while reducing computation complexity to implement the technique in cost-effective UAVs. The proposed technique has been verified by simulations and experiments to validate the feasibility of the approach. 

BLDC motors are widely employed in various applications because of their high efficiency, reliability, and long operational life. For BLDC motors, any electric malfunctions in the commutation signal creation with or without Hall sensors can lead to unexpected vibrations, crashes, and accidents according to application areas. Therefore, fast detection and diagnosis of these faults are crucial for the reliable operation of BLDC motor drive systems. In this paper, a unique approach has been explored for developing fault signatures to detect commutation signal faults accurately and rapidly in the BLDC motor drive system under highly dynamic loads. After the fault detection, a commutation signal is indirectly reconstructed based on healthy commutation signals to continuously drive the motor drive system to avoid serious electrical and mechanical issues due to the faults. The proposed approach and feasibility of the method have been verified both by simulation and experimental studies. The results of the proposed method will significantly improve the accuracy of the commutation signal fault detection and eventually enhance the reliability of the BLDC motor drive. 

A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the reward prediction error, which resembles dopamine neuron firing, to learn the best action in response to a set of cues. Though these models can explain many facets of behavior, reproducing some types of goal-directed behavior, such as renewal and reversal, require additional model components. Here we present a reinforcement learning model, TD2Q, which better corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing indirect pathway neurons (N). Unlike previous two-Q architectures, a novel and critical aspect of TD2Q is to update the G and N matrices utilizing the temporal difference reward prediction error. A best action is selected for N and G using a softmax with a reward-dependent adaptive exploration parameter, and then differences are resolved using a second selection step applied to the two action probabilities. The model is tested on a range of multi-step tasks including extinction, renewal, discrimination; switching reward probability learning; and sequence learning. Simulations show that TD2Q produces behaviors similar to rodents in choice and sequence learning tasks, and that use of the temporal difference reward prediction error is required to learn multi-step tasks. Blocking the update rule on the N matrix blocks discrimination learning, as observed experimentally. Performance in the sequence learning task is dramatically improved with two matrices. These results suggest that including additional aspects of basal ganglia physiology can improve the performance of reinforcement learning models, better reproduce animal behaviors, and provide insight as to the role of direct- and indirect-pathway striatal neurons. 

Abstract Failure to direct axon regeneration to appropriate targets is a major barrier to restoring function after nerve injury. Development of strategies that can direct targeted regeneration of neurons such as retinal ganglion cells (RGCs) are needed to delay or reverse blindness in diseases like glaucoma. Here, we demonstrate that a new class of asymmetric, charge balanced (ACB) waveforms are effective at directing RGC axon growth, in vitro, without compromising cell viability. Unlike previously proposed direct current (DC) stimulation approaches, charge neutrality of ACB waveforms ensures the safety of stimulation while asymmetry ensures its efficacy. Furthermore, we demonstrate the relative influence of pulse amplitude and pulse width on the overall effectiveness of stimulation. This work can serve as a practical guideline for the potential deployment of electrical stimulation as a treatment strategy for nerve injury. 

Abstract The absorption by neutral hydrogen in the intergalactic medium (IGM) produces the Ly α forest in the spectra of quasars. The Ly α forest absorbers have a broad distribution of neutral hydrogen column density N H I and Doppler b parameter. The narrowest Ly α absorption lines (of lowest b ) with neutral hydrogen column density above ∼10 13 cm −2 are dominated by thermal broadening, which can be used to constrain the thermal state of the IGM. Here we constrain the temperature-density relation T = T 0 ( ρ / ρ ¯ ) γ − 1 of the IGM at 1.6 < z < 3.6 by using N H I and b parameters measured from 24 high-resolution and high-signal-to-noise quasar spectra and by employing an analytic model to model the N H I -dependent low- b cutoff in the b distribution. In each N H I bin, the b cutoff is estimated using two methods, one non-parametric method from computing the cumulative b distribution and a parametric method from fitting the full b distribution. We find that the IGM temperature T 0 at the mean gas density ρ ¯ shows a peak of ∼1.5 × 10 4 K at z ∼ 2.7–2.9. At redshift higher than this, the index γ approximately remains constant, and it starts to increase toward lower redshifts. The evolution in both parameters is in good agreement with constraints from completely different approaches, which signals that He ii reionization completes around z ∼ 3. 

Conventionally, the size, shape, and biomechanics of cartilages are determined by their voluminous extracellular matrix. By contrast, we found that multiple murine cartilages consist of lipid-filled cells called lipochondrocytes. Despite resembling adipocytes, lipochondrocytes were molecularly distinct and produced lipids exclusively through de novo lipogenesis. Consequently, lipochondrocytes grew uniform lipid droplets that resisted systemic lipid surges and did not enlarge upon obesity. Lipochondrocytes also lacked lipid mobilization factors, which enabled exceptional vacuole stability and protected cartilage from shrinking upon starvation. Lipid droplets modulated lipocartilage biomechanics by decreasing the tissue’s stiffness, strength, and resilience. Lipochondrocytes were found in multiple mammals, including humans, but not in nonmammalian tetrapods. Thus, analogous to bubble wrap, superstable lipid vacuoles confer skeletal tissue with cartilage-like properties without “packing foam–like” extracellular matrix. 

The Sun emits a stream of charged particles called the solar wind, which is the primary driver of space weather and geomagnetic disturbances. Modeling and observations complement each other to help us identify and understand the physical processes governing the solar wind dynamics on different scales. Numerical models of the solar wind have greatly improved in recent years with advances in computational infrastructure and by employing data-driven or data-assimilative approaches. Designed primarily for modeling the partially ionized space plasma using adaptive mesh refinement technique on Cartesian or spherical grids, the Multi-scale Fluid-kinetic Simulation Suite (MS-FLUKSS) is arguably one of the most sophisticated numerical codes for simulating the solar wind flow. To inform potential users and interested members of the space weather community, we present a brief summary of the current state of the solar wind models developed in the MS-FLUKSS framework, with an emphasis on the 3D heliospheric MHD models driven and constrained by remote/in situ observations and empirical coronal models such as the Wang-Sheeley-Arge model. We also discuss potential scientific and operational applications of our solar wind models on prediction of space weather (e.g., high speed streams, coronal mass ejections, and interplanetary shocks) throughout the solar system.