This paper presents a model for calculating the cost of power system reliability based on the stochastic optimization of long-term security-constrained unit commitment. Random outages of generating ...units and transmission lines as well as load forecasting inaccuracy are modeled as scenario trees in the Monte Carlo simulation. Unlike previous reliability analyses methods in the literature which considered the solution of an economic dispatch problem, this model solves an hourly unit commitment problem, which incorporates spatial constraints of generating units and transmission lines, random component outages, and load forecast uncertainty into the reliability problem. The classical methods considered predefined reserve constraints in the deterministic solution of unit commitment. However, this study considers possible uncertainties when calculating the optimal reserve in the unit commitment solution as a tradeoff between minimizing operating costs and satisfying power system reliability requirements. Loss-of-load-expectation (LOLE) is included as a constraint in the stochastic unit commitment for calculating the cost of supplying the reserve. The proposed model can be used by a vertically integrated utility or an ISO. In the first case, the utility considers the impact of long-term fuel and emission scheduling on power system reliability studies. In the second case, fuel and emission constraints of individual generating companies are submitted as energy constraints when solving the ISO's reliability problem. Numerical simulations indicate the effectiveness of the proposed approach for minimizing the cost of reliability in stochastic power systems.
Diabetic Retinopathy (DR) grading is challenging due to the presence of intra-class variations, small lesions and imbalanced data distributions. The key for solving fine-grained DR grading is to find ...more discriminative features corresponding to subtle visual differences, such as microaneurysms, hemorrhages and soft exudates. However, small lesions are quite difficult to identify using traditional convolutional neural networks (CNNs), and an imbalanced DR data distribution will cause the model to pay too much attention to DR grades with more samples, greatly affecting the final grading performance. In this article, we focus on developing an attention module to address these issues. Specifically, for imbalanced DR data distributions, we propose a novel Category Attention Block (CAB), which explores more discriminative region-wise features for each DR grade and treats each category equally. In order to capture more detailed small lesion information, we also propose the Global Attention Block (GAB), which can exploit detailed and class-agnostic global attention feature maps for fundus images. By aggregating the attention blocks with a backbone network, the CABNet is constructed for DR grading. The attention blocks can be applied to a wide range of backbone networks and trained efficiently in an end-to-end manner. Comprehensive experiments are conducted on three publicly available datasets, showing that CABNet produces significant performance improvements for existing state-of-the-art deep architectures with few additional parameters and achieves the state-of-the-art results for DR grading. Code and models will be available at https://github.com/he2016012996/CABnet .
Plant-pathogen interactions induce a signal transmission series that stimulates the plant's host defense system against pathogens and this, in turn, leads to disease resistance responses. Plant ...innate immunity mainly includes two lines of the defense system, called pathogen-associated molecular pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). There is extensive signal exchange and recognition in the process of triggering the plant immune signaling network. Plant messenger signaling molecules, such as calcium ions, reactive oxygen species, and nitric oxide, and plant hormone signaling molecules, such as salicylic acid, jasmonic acid, and ethylene, play key roles in inducing plant defense responses. In addition, heterotrimeric G proteins, the mitogen-activated protein kinase cascade, and non-coding RNAs (ncRNAs) play important roles in regulating disease resistance and the defense signal transduction network. This paper summarizes the status and progress in plant disease resistance and disease resistance signal transduction pathway research in recent years; discusses the complexities of, and interactions among, defense signal pathways; and forecasts future research prospects to provide new ideas for the prevention and control of plant diseases.
This study presents conventional and artificial neural network‐based data‐driven modeling (DDM) methods to model simultaneously the filtered mesoscale drag, heat transfer and reaction rate in ...gas–particle flows. The dataset used for developing the DDM is filtered from highly resolved simulations closed by our recently formulated microscopic drag and heat transfer coefficients (HTCs). Results reveal that the filtered drag correction is nearly independent of filter size when including the filtered gas phase pressure gradient. We further find that the filtered HTC correction critically depends on the added filtered temperature difference marker while the filtered reaction rate correction shows weak dependence on the additional markers. Moreover, compared with conventional correlations, DDM predictions agree better with filtered resolved data. Comparative analysis is also conducted between existing HTC corrections and our work. Finally, the applicability of conventional and data‐driven models coupled with coarse‐grid computational fluid dynamics simulations for pilot‐scale (reactive) gas–particle flows is validated comprehensively.
Good interfacial compatibility is the key to realize the full potential of metal–organic framework-based mix matrix membranes for gas separation. Here we report a new approach that uses polyimide ...brushes covalently grafted on the MOF surface to engineer the MOF-polymer interface. Benefiting from the strong brush–brush interaction, polyimide grafted MOF particles can form a stand-alone membrane at 88 wt % MOF loading without the addition of polymeric matrix. Compared to traditional mixed-matrix membranes, the modified membranes exhibit improved ductility up to 472%, reduced interfacial tearing phenomenon under shear force, decreased matrix chain mobility, and improved plasticization resistance against CO2. Most importantly, with increasing MOF loading, only the modified membranes exhibit simultaneous increase of selectivity and permeability for CO2/N2 and CO2/CH4 separation, following the trend predicted by the modified Maxwell model.
Developing theoretical frameworks for vibrational strong coupling (VSC) beyond the single-mode approximation is crucial for a comprehensive understanding of experiments with planar Fabry-Pérot ...cavities. Herein, a generalized cavity molecular dynamics (CavMD) scheme is developed to simulate VSC of a large ensemble of realistic molecules coupled to an arbitrary 1D or 2D photonic environment. This approach is built upon the Power-Zienau-Woolley Hamiltonian in the normal mode basis and uses a grid representation of the molecular ensembles to reduce the computational cost. When simulating the polariton dispersion relation for a homogeneous distribution of molecules in planar Fabry-Pérot cavities, our data highlight the importance of preserving the in-plane translational symmetry of the molecular distribution. In this homogeneous limit, CavMD yields the consistent polariton dispersion relation as an analytic theory, i.e., incorporating many cavity modes with varying in-plane wave vectors (k∥) produces the same spectrum as the system with a single cavity mode. Furthermore, CavMD reveals that the validity of the single-mode approximation is challenged when nonequilibrium polariton dynamics are considered, as polariton-polariton scattering occurs between modes with the nearest neighbor k∥. The procedure for numerically approaching the macroscopic limit is also demonstrated with CavMD by increasing the system size. Looking forward, our generalized CavMD approach may facilitate understanding vibrational polariton transport and condensation.Developing theoretical frameworks for vibrational strong coupling (VSC) beyond the single-mode approximation is crucial for a comprehensive understanding of experiments with planar Fabry-Pérot cavities. Herein, a generalized cavity molecular dynamics (CavMD) scheme is developed to simulate VSC of a large ensemble of realistic molecules coupled to an arbitrary 1D or 2D photonic environment. This approach is built upon the Power-Zienau-Woolley Hamiltonian in the normal mode basis and uses a grid representation of the molecular ensembles to reduce the computational cost. When simulating the polariton dispersion relation for a homogeneous distribution of molecules in planar Fabry-Pérot cavities, our data highlight the importance of preserving the in-plane translational symmetry of the molecular distribution. In this homogeneous limit, CavMD yields the consistent polariton dispersion relation as an analytic theory, i.e., incorporating many cavity modes with varying in-plane wave vectors (k∥) produces the same spectrum as the system with a single cavity mode. Furthermore, CavMD reveals that the validity of the single-mode approximation is challenged when nonequilibrium polariton dynamics are considered, as polariton-polariton scattering occurs between modes with the nearest neighbor k∥. The procedure for numerically approaching the macroscopic limit is also demonstrated with CavMD by increasing the system size. Looking forward, our generalized CavMD approach may facilitate understanding vibrational polariton transport and condensation.
Feature selection is one of the key problems for machine learning and data mining. In this review paper, a brief historical background of the field is given, followed by a selection of challenges ...which are of particular current interests, such as feature selection for high-dimensional small sample size data, large-scale data, and secure feature selection. Along with these challenges, some hot topics for feature selection have emerged, e.g., stable feature selection, multi-view feature selection, distributed feature selection, multi-label feature selection, online feature selection, and adversarial feature selection. Then, the recent advances of these topics are surveyed in this paper. For each topic, the existing problems are analyzed, and then, current solutions to these problems are presented and discussed. Besides the topics, some representative applications of feature selection are also introduced, such as applications in bioinformatics, social media, and multimedia retrieval.
We simulate vibrational strong coupling (VSC) and vibrational ultrastrong coupling (V-USC) for liquid water with classical molecular dynamics simulations. When the cavity modes are resonantly coupled ...to the O—H stretch mode of liquid water, the infrared spectrum shows asymmetric Rabi splitting. The lower polariton (LP) may be suppressed or enhanced relative to the upper polariton (UP) depending on the frequency of the cavity mode. Moreover, although the static properties and the translational diffusion of water are not changed under VSC or V-USC, we do find the modification of the orientational autocorrelation function of H2O molecules especially under V-USC, which could play a role in ground-state chemistry.
In this paper, we propose a self-supervised contrastive learning method to learn video feature representations. In traditional self-supervised contrastive learning methods, constraints from anchor, ...positive, and negative data pairs are used to train the model. In such a case, different samplings of the same video are treated as positives, and video clips from different videos are treated as negatives. Because the spatio-temporal information is important for video representation, we set the temporal constraints more strictly by introducing intra-negative samples. In addition to samples from different videos, negative samples are extended by breaking temporal relations in video clips from the same anchor video. With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn feature representations from videos. Strong data augmentations, residual clips, as well as head projector are utilized to construct an improved version. Three kinds of intra-negative generation functions are proposed and extensive experiments using different network backbones are conducted on benchmark datasets. Without using pre-computed optical flow data, our improved version can outperform previous IIC by a large margin, such as 19.4% (from 36.8% to 56.2%) and 5.2% (from 15.5% to 20.7%) points improvements in top-1 accuracy on UCF101 and HMDB51 datasets for video retrieval, respectively. For video recognition, over 3% points improvements can also be obtained on these two benchmark datasets. Discussions and visualizations validate that our IICv2 can capture better temporal clues and indicate the potential mechanism.
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