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Fast-charging is highly desired for lithium-ion batteries but is hindered by potential hazardous lithium plating and the associated parasitic reactions. Here, the authors report a nondestructive differential pressure sensing method for early detection and mitigation of lithium plating in fast-charging batteries.

Abstract Community detection is a vital task in many fields, such as social networks, and financial analysis, to name a few. The Louvain method, the main workhorse of community detection, is a popular heuristic method based on modularity. But it is difficult for the sequential Louvain method to deal with large-scale graphs. In order to overcome the drawback, researchers have proposed several parallel Louvain methods (Parallel Louvain Method, PLM), which suffer two challenges: (1) latency in the information synchronization and (2) communities swap. To tackle these two challenges, we propose a graph partition algorithm for the parallel Louvain method. Different from existing graph partition algorithms, our graph partition algorithm divides the graph into subgraphs called isolate sets, in which vertices are relatively decoupled from others, and the PLM computes and synchronizes information without delay and communities swap. We first describe concepts and properties of isolate sets. In the second place, we propose an algorithm to divide the graph into isolate sets, which enjoys the same computation complexity as the breadth-first search. Finally, we propose the isolate-set-based parallel Louvain method, which calculates and updates vertices information without latency and communities swap. We implement our method with OpenMP on an 8-cores PC. Experiments on 18 graphs show that our parallel method achieves a maximum 4.62 $$\times $$ × speedup compared with the sequential method, and outputs higher modularity on 14 graphs.

IntroductionSchizophrenia is a severe psychiatric disorder with a high prevalence worldwide, however, its pathogenesis remains poorly understood.Methods and resultsIn this study, we used the non-competitive NMDA receptor antagonist MK-801 to induce schizophrenia-like behaviors and confirmed that mice exhibited stereotypic rotational behavior and hyperlocomotion, social interaction defects and cognitive dysfunction, similar to the clinical symptoms in patients. Here, the anterior cingulate cortex (ACC) and basolateral amygdala (BLA) were involved in the schizophrenia-like behaviors induced by MK-801. Furthermore, we confirmed BLA sent glutamatergic projection to the ACC. Chemogenetic and optogenetic regulation of BLA-ACC projecting neurons affected social and cognitive deficits but not stereotypic rotational behavior in MK-801-treated mice.DiscussionOverall, our study revealed that the BLA-ACC circuit plays a major role and may be a potential target for treating schizophrenia-related symptoms.

Schizophrenia is a severe mental disorder characterized by positive, negative, and cognitive symptoms. Cognitive symptoms are a kind of symptoms with high incidence and great impact on patients. There is no effective treatment in clinical practice. N-methyl-d-aspartic acid (NMDA) receptor hypofunction may be an important cause of cognitive symptoms. MK-801 (also named Dizocilpine), a noncompetitive antagonist of NMDA receptor, is often used to construct a model of NMDA receptor dysfunction. In terms of treatment, environmental enrichment (EE) as an environmental intervention can effectively improve the symptoms of cognitive impairment in rodents. In this paper, we first briefly introduce the background of cognitive symptoms and EE in schizophrenia, and then investigate the manifestations of MK-801 induced cognitive impairment, the improvement of EE on these cognitive impairments based on the MK-801 induced schizophrenia rodent models, and the possible mechanism of EE in improving cognitive symptoms. This article reviews the literature in recent years, which provides an important reference for MK-801 to construct a cognitive symptom model of schizophrenia and the mechanism of EE in improving cognitive symptoms of schizophrenia.

Speaker recognition is a crucial bio-identification technology, which is extensively used in our daily life. With the development of deep learning, convolutional neural networks (CNNs) are applied to speaker recognition tasks given their excellent performance. However, in real life, speaker recognition systems are frequently deployed on end-devices. Therefore, while obtaining recognition accuracy, the model of speaker recognition is expected to be as simple as possible. Inspired by 1-max pooling CNN and Gaussian mixture model-universal background model (GMM-UBM), this study proposes a one dimension convolutional neural networks (1D CNN) on the basis of original 2D CNN. The proposed model reduces the computational complexity of ResNet20 by 64% and the amount of parameters by 53%. In comparison with the original ResNet20 models, the recognition accuracy will be reduced by about one percent on the 15s data set. Then, on the basis of the 1D CNN, we propose a pyramid layer-folding pipeline structure and implement it on the Xilinx VC709 platform. According to the time-dimension partition, the proposed pyramid pipeline structure can process speech data of various lengths. Moreover, our accelerator is 5.1× faster on 3s dataset and 6.8× quicker on 15s dataset than those of the CPU platform.

Manipulating liquids with tunable shape and optical functionalities in real time remains a great challenge. Here, the authors demonstrate electrotunable liquid sulfur microdroplets in an electrochemical cell and tune its characteristics in a fast, repeatable, and controlled manner.

Self-grooming is an innate, cephalo-caudal progression of body cleaning behaviors that are found in normal rodents but exhibit repetitive and stereotyped patterns in several mouse models, such as autism spectrum disorders (ASDs). It is also recognized as a marker of stress and anxiety. Mice with Shank3B gene knockout (KO) exhibit typical ASD-like behavioral abnormalities, including stereotyped self-grooming and increased levels of anxiety. However, the exact relationship between anxiety and stereotyped self-grooming in certain types of animal models is not clear. We selected three animal models with high anxiety to compare their self-grooming parameters. First, we confirmed that Shank3B KO mice (ASD model), acute restraint stress mouse model (stress model), and chronic inflammatory pain mouse model (pain model) all showed increased anxiety levels in the open field test (OFT) and elevated plus maze (EPM). We found that only the ASD model and the stress model produced increased total grooming duration. The pain model only exhibited an increasing trend of mean self-grooming duration. We used the grooming analysis algorithm to examine the self-grooming microstructure and assess the cephalo-caudal progression of grooming behavior. The results showed distinct self-grooming microstructures in these three models. The anxiolytic drug diazepam relieved the anxiety level and the total time of grooming in the ASD and stress models. The grooming microstructure was not restored in Shank3B KO mice but was partially relieved in the stress model, which suggested that anxiety aggravated stereotyped self-grooming duration but not the grooming microstructure in the ASD mouse model. Our results indicated that stereotyped behavior and anxiety may be shared by separate, but interacting, neural circuits in distinct disease models, which may be useful to understand the mechanisms and develop potential treatments for stereotyped behaviors and anxiety.

Soil pollution by heavy metals is a problem of global concern, requiring the development of remediation technologies. Here the authors report a method based on asymmetrical alternating current electrochemistry, which enables recycling of soil washing chemicals and eliminates secondary pollution.

Operation of lithium batteries at high, non-uniform temperatures can lead to safety issues, but the effects of localized high temperatures are difficult to probe. Here the authors use micro-Raman spectroscopy to show that local-temperature hotspots can induce lithium metal growth and trigger circuit shorting.

Recently, the face detection and alignment is so popular and widely used in many research and application fields. Many superior face detection algorithms such as multi-task cascade convolutional network have been presented. However, it has difficulty in predicting faces among the big scale images in real time due to its three stages cascade architecture with less optimization. In this paper, we propose a full GPU-based batch multi-task cascade convolutional network which is carefully designed and optimized in each step to gain a superior speed performance. In addition, we present a novel parallel memory allocation strategy, which further enables our algorithm to support the batch operation, so that the system throughput increases significantly. In the experiment, our method achieves up to 300fps, over 600% speedup with an equal accuracy over the state-of-the-art methods on the face detection benchmarks.

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.