Cochraneskildsen3596

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. 3-Aminobenzamide research buy We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices' position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, such as singular value decomposition (SVD) and reweighted SVD (RSVD), were proposed recently to solve this problem. However, such technologies also face failure in bearing fault detection due to the poor interpretability of the obtained eigenvector. Non-negative Matrix Factorization (NMF), as a part-based representation algorithm, can extract low-rank basis spaces with natural sparsity from the time-frequency representation. It performs excellent interpretability of the factor matrices due to its non-negative constraints. By this virtue, NMF can extract the fault feature by separating the frequency bands of resonance regions from the amplitude spectrogram automatically. In this paper, a new feature extraction method based on sparse kernel NMF (KNMF) was proposed to extract the fault features from the amplitude spectrogram in greater depth. By decomposing the amplitude spectrogram using the kernel-based NMF model with L1 regularization, sparser spectral bases can be obtained. Using KNMF with the linear kernel function, the time-frequency distribution of the vibration signal can be decomposed into a subspace with different frequency bands. Thus, we can extract the fault features, a series of periodic impulses, from the decomposed subspace according to the sparse frequency bands in the spectral bases. As a result, the proposed method shows a very high performance in extracting fault features, which is verified by experimental investigations and benchmarked by the Fast Kurtogram, SVD and NMF-based methods.This study examines the effects of acute pharmacological modulation of the serotonergic system over zebrafish larvae's cognitive, basic, and defense locomotor behaviors, using a medium to high throughput screening assay. Furthermore, the relationship between behavior, enzyme activity related to neurotransmitter metabolism, neurotransmitter levels, and gene expression was also determined. Modulation of larvae serotonergic system was accomplished by 24 h exposure to single and opposite pharmacodynamics co-exposure to three model psychopharmaceuticals with antagonistic and agonistic serotonin signaling properties 2.5 mM 4-Chloro-DL-phenylalanine (PCPA) and 5 µM deprenyl and 0.5 µM fluoxetine, respectively. Similar behavioral outcome was observed for deprenyl and fluoxetine, which was reflected as hypolocomotion, decrease in larvae defensive responses, and cognitive impairment. Contrarily, PCPA induced hyperlocomotion and increase in larvae escape response. Deprenyl exposure effects were more pronounced at a lower level of organization than fluoxetine, with complete inhibition of monoamine oxidase (MAO) activity, dramatic increase of 5-HT and dopamine (DA) levels, and downregulation of serotonin synthesis and transporter genes. PCPA showed mainly effects over serotonin and dopamine's main degradation metabolites. Finally, co-exposure between agonistic and antagonist serotonin signaling drugs reviled full recovery of zebrafish impaired locomotor and defense responses, 5-HT synthesis gene expression, and partial recovery of 5-HT levels. The findings of this study suggest that zebrafish larvae can be highly sensitive and a useful vertebrate model for short-term exposure to serotonin signaling changes.The highly dynamic legged jumping motion is a challenging research topic because of the lack of established control schemes that handle over-constrained control objectives well in the stance phase, which are coupled and affect each other, and control robot's posture in the flight phase, in which the robot is underactuated owing to the foot leaving the ground. This paper introduces an approach of realizing the cyclic vertical jumping motion of a planar simplified legged robot that formulates the jump problem within a quadratic-programming (QP)-based framework. Unlike prior works, which have added different weights in front of control tasks to express the relative hierarchy of tasks, in our framework, the hierarchical quadratic programming (HQP) control strategy is used to guarantee the strict prioritization of the center of mass (CoM) in the stance phase while split dynamic equations are incorporated into the unified quadratic-programming framework to restrict the robot's posture to be near a desired constant value in the flight phase.