In this work, we use grand canonical Monte Carlo (GCMC) simulation to study methane adsorption in various clay nanopores and analyze different approaches to characterize the absolute adsorption. As ...an important constituent of shale, clay minerals can have significant amount of nanopores, which greatly contribute to the gas-in-place in shale. In previous works, absolute adsorption is often calculated from the excess adsorption and bulk liquid phase density of absorbate. We find that methane adsorbed phase density keeps increasing with pressure up to 80 MPa. Even with updated adsorbed phase density from GCMC, there is a significant error in absolute adsorption calculation. Thus, we propose to use the excess adsorption and adsorbed phase volume to calculate absolute adsorption and reduce the discrepancy to less than 3% at high pressure conditions. We also find that the supercritical Dubinin-Radushkevich (SDR) fitting method which is commonly used in experiments to convert the excess adsorption to absolute adsorption may not have a solid physical foundation for methane adsorption. The methane excess and absolute adsorptions per specific surface area are similar for different clay minerals in line with previous experimental data. In mesopores, the excess and absolute adsorptions per specific surface area become insensitive to pore size. Our work should provide important fundamental understandings and insights into accurate estimation of gas-in-place in shale reservoirs.
To date, venipuncture, the most necessary and fundamental medical means, still remains a challenging task for medical stuff due to significant individual differences in vein condition. Thanks to ...mature development in near-infrared (NIR) imaging technology, a series of venepuncture auxiliary equipment has been devised and put into use. Yet, previous researches concentrated more on vein pattern segmentation, failing to materialize the identification of veins suitable to puncture in an embedded system. Given the above, we propose an approach to detect and locate the optimal veins fully utilizing the state-of-the-art deep learning and image processing technologies in order to provide a more practical reference. Firstly, a dedicated NIR-based puncturable vein positioning system is designed, realizing collection of dorsal hand vein images as well as the rapid and accurate location of veins suitable to puncture. Secondly, considering the limitations of embedded devices on computation ability and memory, an improved network based on YOLO Nano, named YOLO Nano-Vein, is presented with architecture trimmed, output scales reduced, and an atrous spatial pyramid pooling (ASPP) added. Finally, average precision (AP) is increased from 91.68 to 93.23%, and the detection time and parameters of network are reduced by 22% and 17.5%, respectively, which validates the proposed network achieves higher accuracy with less detection time in comparison with YOLO Nano and YOLOv3, indicating stronger applicability for detection tasks on embedded devices.
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Fear behavior is under tight control of the prefrontal cortex, but the underlying microcircuit mechanism remains elusive. In particular, it is unclear how distinct subtypes of inhibitory interneurons ...(INs) within prefrontal cortex interact and contribute to fear expression. We employed a social fear conditioning paradigm and induced robust social fear in mice. We found that social fear is characterized by activation of dorsal medial prefrontal cortex (dmPFC) and is largely diminished by dmPFC inactivation. With a combination of in vivo electrophysiological recordings and fiber photometry together with cell-type-specific pharmacogenetics, we further demonstrated that somatostatin (SST) INs suppressed parvalbumin (PV) INs and disinhibited pyramidal cells and consequently enhanced dmPFC output to mediate social fear responses. These results reveal a previously unknown disinhibitory microcircuit in prefrontal cortex through interactions between IN subtypes and suggest that SST INs-mediated disinhibition represents an important circuit mechanism in gating social fear behavior.
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•Social fear conditioning induces robust and specific social fear in mice•The dmPFC participates in social fear expression•SST INs disinhibit principal neurons via their inhibition of FS-PV INs•The disinhibitory microcircuitry is crucial for social fear expression
Prefrontal cortex plays an essential role in fear expression. Xu et al. reveal a disinhibitory microcircuit in prefrontal cortex through interactions between interneuron subtypes and suggest that SST INs-mediated disinhibition represents an important circuit mechanism in gating social fear behavior.
To accurately identify fatigued driving, establishing a monitoring system is one of the important guarantees of improving traffic safety and reducing traffic accidents. Among many research methods, ...electrooculogram signal (EOG) has unique advantages. This paper presents a systematic literature review of these technologies and summarizes a basic framework of fatigue driving monitoring system based on EOGs. Then we summarize the advantages and disadvantages of existing technologies. In addition, 80 primary references published during the last decade were identified. The multi-feature fusion technique based on EOGs performs better than other traditional methods due to its low cost, low power consumption and low intrusion, while its application is still limited which needs more efforts to obtain good and generalizable results. And then, an overview of the literature on technology is given, revealing a premier and unbiased survey of the existing empirical research of classification techniques that have been applied to fatigue driving analysis. Finally, this paper adds value to the current literature by investigating the application of EOG signals in fatigued driving and the design of related systems, future guidelines have been provided to practitioners and researchers to grasp the major contributions and challenges in the state-of-the-art research.
The global concern regarding the monitoring of construction workers' activities necessitates an efficient means of continuous monitoring for timely action recognition at construction sites. This ...paper introduces a novel approach-the multi-scale graph strategy-to enhance feature extraction in complex networks. At the core of this strategy lies the multi-feature fusion network (MF-Net), which employs multiple scale graphs in distinct network streams to capture both local and global features of crucial joints. This approach extends beyond local relationships to encompass broader connections, including those between the head and foot, as well as interactions like those involving the head and neck. By integrating diverse scale graphs into distinct network streams, we effectively incorporate physically unrelated information, aiding in the extraction of vital local joint contour features. Furthermore, we introduce velocity and acceleration as temporal features, fusing them with spatial features to enhance informational efficacy and the model's performance. Finally, efficiency-enhancing measures, such as a bottleneck structure and a branch-wise attention block, are implemented to optimize computational resources while enhancing feature discriminability. The significance of this paper lies in improving the management model of the construction industry, ultimately aiming to enhance the health and work efficiency of workers.
The basic helix-loop-helix (bHLH) family is the second largest superfamily of transcription factors that belongs to all three eukaryotic kingdoms. The key function of this superfamily is the ...regulation of growth and developmental mechanisms in plants. However, the bHLH gene family in
has not yet been studied. Here, we identified 41 bHLH genes in
that were classified into 23 subgroups. Further, we conducted a phylogenetic analysis and identified 10 conserved protein motifs found in the safflower bHLH family. We comprehensively analyzed a group of bHLH genes that could be associated with flavonoid biosynthesis in safflower by gene expression analysis, gene ontology annotation, protein interaction network prediction, subcellular localization of the candidate CtbHLH40 gene, and real-time quantitative expression analysis. This study provides genome-wide identification of the genes related to biochemical and physiological processes in safflower.
The large amount of nanoscale pores in shale results in the inability to apply Darcy's law. Moreover, the gas adsorption of shale increases the complexity of pore size characterization and thus ...decreases the accuracy of flow regime estimation. In this study, an apparent permeability model, which describes the adsorptive gas flow behavior in shale by considering the effects of gas adsorption, stress dependence, and non-Darcy flow, is proposed. The pore size distribution, methane adsorption capacity, pore compressibility, and matrix permeability of the Barnett and Eagle Ford shales are measured in the laboratory to determine the critical parameters of gas transport phenomena. The slip coefficients, tortuosity, and surface diffusivity are predicted via the regression analysis of the permeability data. The results indicate that the apparent permeability model, which considers second-order gas slippage, Knudsen diffusion, and surface diffusion, could describe the gas flow behavior in the transition flow regime for nanoporous shale. Second-order gas slippage and surface diffusion play key roles in the gas flow in nanopores for Knudsen numbers ranging from 0.18 to 0.5. Therefore, the gas adsorption and non-Darcy flow effects, which involve gas slippage, Knudsen diffusion, and surface diffusion, are indispensable parameters of the permeability model for shale.
Plasmonic nanomaterials, especially Au and Ag nanomaterials, have shown attractive physicochemical properties, such as easy functionalization and tunable optical bands. The development of this active ...subfield paves the way to the fascinating biosensing platforms. In recent years, plasmonic nanomaterials–based sensors have been extensively investigated because they are useful for genetic diseases, biological processes, devices, and cell imaging. In this account, a brief introduction of the development of optical biosensors based on DNA‐functionalized plasmonic nanomaterials is presented. Then the common strategies for the application of the optical sensors are summarized, including colorimetry, fluorescence, localized surface plasmon resonance, and surface‐enhanced resonance scattering detection. The focus is on the fundamental aspect of detection methods, and then a few examples of each method are highlighted. Finally, the opportunities and challenges for the plasmonic nanomaterials–based biosensing are discussed with the development of modern technologies.
DNA‐functionalized plasmonic nanosensors have drawn great research attention in disease diagnosis, cell imaging, and medicine development. In this article, a brief introduction of the development of optical biosensors based on DNA‐functionalized plasmonic nanomaterials is presented. Then the common strategies for the application of the optical sensors are summarized, including colorimetry, fluorescence, localized surface plasmon resonance, and surface‐enhanced resonance scattering detection.
In practical industrial environment, variable working condition can result in shifts in data distributions, and the labeled fault data in various working conditions is difficult to collect because ...rotating machines often works in normal status, and the insufficient labeled fault data brings data samples imbalance and performance degradation of intelligent fault diagnosis model. To overcome these problems, by integrating the superiority of deep learning method and feature-based transfer learning method, this work proposes an innovative cross-domain fault diagnosis framework based on deep transfer convolutional neural network and supervised joint matching. First, the continue wavelet transform is used to process original bearing vibration signals and extract time-frequency images. Second, a deep transfer convolutional neural network is built by the way of fine-tuning, and the trained network is used to extract deep features from different domains. Third, a new domain adaptation approach, supervised joint matching, is developed to conduct joint feature distribution matching and instance reweighting with the consideration of maximum marginal criterion. The intelligent bearing fault diagnosis model is then trained to predict the labels of the target domain's feature data. To verify the performance of the proposed approaches, this study uses two distinct datasets pertaining to bearing defects for conducting cross-domain fault diagnosis in the presence of balanced and imbalanced data. The experimental analysis indicates that the designed methods can achieve desirable diagnostic accuracy and possess robust generalization ability.
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