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Flood wave superposition (FWS) of upstream and tributary rivers, as a typical compound hydrological event, may lead to considerable downstream flood hazards. In spite of this, the quantitative identification of FWS classification ...
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Flood wave superposition (FWS) of upstream and tributary rivers, as a typical compound hydrological event, may lead to considerable downstream flood hazards. In spite of this, the quantitative identification of FWS classification so far remains elusive. In this study, we quantitatively examined the typical matching patterns of FWS based on flood peak magnitude and timing between the upstream and tributary discharge, to evaluate the flood severity for both present-day conditions and future climate projections. The future FWS projection was realized using hydrological modeling coupled with multiple outputs of global climate models (GCMs) under the Representative Concentration Pathway (RCP) 2.6 and 8.5 emission scenarios. A triple point of discharge stations, including upstream, downstream and tributary stations, located at a river confluence of the Poyang Lake Basin, China, was selected as the study area. The results revealed that the frequency of perfect temporal matching (0-day time lag) of projected peaks between upstream and tributary floods increased from 61% in the reference period to >68% and > 66% in the future under RCPs 2.6 and 8.5, respectively. Furthermore, both the future numbers and frequencies of the projected peaks between upstream and tributary floods with both perfect temporal and magnitude matching (the classification associated with the most damage in FWS) would substantially increase across all GCMs under RCPs 2.6 and 8.5. These findings indicate that future FWS is expected to experience increasing severity due to the changing climate under no matter RCP 2.6 or 8.5 emission scenarios. Overall, it is suggested that effective adaptation strategies be developed in order to stagger the timing of upstream and tributary floods in the future.
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A domain decomposition-based lattice Boltzmann-cell automation probabilistic model (DDLB-CA model) has been developed to investigate soot particle filtration process in wall-flow diesel particulate filters. In order to obtain usef...
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A domain decomposition-based lattice Boltzmann-cell automation probabilistic model (DDLB-CA model) has been developed to investigate soot particle filtration process in wall-flow diesel particulate filters. In order to obtain useful information for optimization of the porous structure, non-virtual porous walls are considered. Nine different porous walls are generated by a self-developed reconstruction scheme based on the pore size distribution (PSD) and porosity. The DDLB-CA model is validated with the results of previous studies. A clear pressure gradient and a spatial inhomogeneous velocity distribution can be seen for each porous wall. For the porous wall with the smallest mean pore size and the largest porosity, a lower initial pressure gradient and a better initial homogeneous velocity distribution can be achieved. Particles tend to deposit at the front of the porous wall with a PSD of a smaller mean pore size. Besides, particles also have an obvious tendency of depositing on the surface of narrow porous channels. Particle capture probability is obviously affected by the PSD. Therefore, adjustment of the PSD is recommended for optimization of the particle distribution and filtration efficiency. The solid nodes composed of deposited soot particles appear on the surface of narrow porous channels first and then form dendritic structures. Finally, the dendrite structures construct a bridge and block the narrow porous channel. The distributions of solid nodes are affected obviously by the structure of porous media. The locations of solid nodes affect the distribution of pressure and the uniformity of velocity distribution. The subsequent particles are more inclined to deposit at the front of the porous wall and the particle deposition efficiency eta increases after the formation of solid nodes. For the porous wall with a PSD of a smaller mean pore size, the solid nodes in front of porous walls (x/L-w < 0) are more concentrated, which means the cake layer will form m
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A two-dimensional mesoscopic gas-solid two-phase flow model has been developed to investigate the flow and soot loading in the micro-channel of diesel particulate filters. Soot particle size examined is in the range of 10 nm-1 mu ...
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A two-dimensional mesoscopic gas-solid two-phase flow model has been developed to investigate the flow and soot loading in the micro-channel of diesel particulate filters. Soot particle size examined is in the range of 10 nm-1 mu m. The flow is solved by an incompressible lattice Boltzmann model and the transport of solid particles is described by the cell automation probabilistic model. The lattice Boltzmann-cell automation probabilistic model (LB-CA model) is validated with the results of previous studies. The effects of different upstream velocities on the flow field in channels are investigated. The distribution and deposition of soot particles with different sizes in clean channels are simulated based on the LB-CA method and the LB-Lagrangian method respectively. The effects of deposited soot particles on flow field are evaluated in real soot particle capture process. The results show that the distributions of velocity field and pressure field in the channel are significantly affected by the upstream velocity. Compared with the effect of the particle size, the upstream velocity is more influential on the particle deposition distributions. The profiles of deposition distribution from the LB-CA method are in close agreement with those from the LB-Lagrangian method. The deposition distributions of particles with different diameters at the top of the porous wall are similar to the distributions of wall velocity along the channel length. Generally, the deposited soot particles increase the axial pressure and decrease the axial velocity in the inlet channel. The evolution trend of the areas where wall velocity undergoes changes is consistent with that of the solid nodes made of the captured soot particles. (C) 2019 Elsevier Ltd. All rights reserved.
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Dysfunction of gamma-aminobutyric acid A (GABA(A)) receptors (GABA(A)Rs) is a prominent factor affecting intractable epilepsy. Plic-1, an ubiquitin-like protein enriched in the inhibitory synapses connecting GABA(A)Rs and the ubiq...
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Dysfunction of gamma-aminobutyric acid A (GABA(A)) receptors (GABA(A)Rs) is a prominent factor affecting intractable epilepsy. Plic-1, an ubiquitin-like protein enriched in the inhibitory synapses connecting GABA(A)Rs and the ubiquitin protease system (UPS), plays a key role in the modification of GABA(A)R functions. However, the relationship between Plic-1 and epileptogenesis is not known. In the present study, we aimed to investigate Plic-1 levels in patients with temporal lobe epilepsy, as well as the role of Plic-1 in regulating onset and progression of epilepsy in animal models. We found that Plic-1 expression was significantly decreased in patients with epilepsy as well as pilocarpine- and pentylenetetrazol (PTZ)-induced rat epileptic models. Intrahippocampal injection of the PeP alpha peptide, which disrupts Plic-1 binding to GABA(A)Rs, significantly shortened the latency of seizure onset, and increased the seizure severity and duration in these two epileptic models. Overexpressed Plic-1 through lentivirus transfection into a PTZ model resulted in a reduction in both seizure severity and generalized tonic-clonic seizure duration. Whole-cell clamp recordings revealed that the PeP alpha peptide decreased miniature inhibitory postsynaptic currents (mIPSCs) whereas overexpressed Plic-1 increased mIPSCs in the pyramidal neurons of the hippocampus. These effects can be blocked by picrotoxin, a GABA(A)R inhibitor. Our results indicate that Plic-1 plays an important role in managing epileptic seizures by enhancing seizure inhibition through regulation of GABA(A)Rs at synaptic sites.
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The uncertainty of streamflow forecast will cause the ineffectiveness on the short-term hydropower station optimal operation (SHSOO) and lead to the risk of producing less hydropower than planned. Traditionally, stochastic optimal...
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The uncertainty of streamflow forecast will cause the ineffectiveness on the short-term hydropower station optimal operation (SHSOO) and lead to the risk of producing less hydropower than planned. Traditionally, stochastic optimal theory represented by the stochastic dynamic programming model (SDPM) is commonly used for SHSOO, which is focus on the uncertainty during a single lead time and lack of an overall consideration of multiple independent lead times of differing length. Thus, this paper proposes a short-term hydropower station optimal operation model based on mean-variance theory (MV-SHSOOM) for reducing operational risk. The proposed multi-step model considers the forecasting uncertainty of different lead times as an integral component to guide a station's optimal operation. Specifically, the forecast uncertainty was integrated by the joint probability distribution of forecast error using a multivariate hydrologic uncertainty processor (HUP) at first. Then, possible observed streamflow sequences were obtained using the joint probability distribution of forecast error and taken as input to the short-term optimal operation model of the hydropower station. Finally, the mean-variance theory was applied to construct the optimal operation model for balancing the power generation and opera-tional risk. The proposed model was applied to the Jinxi hydropower station, China, to determine its performance. The results show that: 1) the dependent structure among the forecast errors at different lead times is significant in terms of Spearman correlation, and the joint probability distribution of forecast error can be effectively described by a multivariate HUP; 2) the proposed MV-SHSOOM is su-perior to the traditional deterministic dynamic programming model (DDPM) and SDPM, and can more effectively reduce operational risk while increasing power generation.(c) 2021 Elsevier Ltd. All rights reserved.
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Many rumors spread quickly and widely on social media, affecting social stability. The rumors of most current detection methods only use textual information or introduce external auxiliary information (such as user information and...
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Many rumors spread quickly and widely on social media, affecting social stability. The rumors of most current detection methods only use textual information or introduce external auxiliary information (such as user information and propagation information) to enhance the detection effect, and the inherent statistical features of the corpus have not been fully used and compared with the external auxiliary features; in addition, statistical features are more certain and can only be obtained from textual information. Therefore, we adopted a method based on the adaptive fusion of word frequency distribution features and textual features to detect rumors. Statistical features were extracted by encoding statistical information through a variational autoencoder. We extracted semantic features and sequence features as textual features through a parallel network comprising a convolutional neural network and a bidirectional long-term memory network. In addition, we also designed an adaptive valve to only fuse useful statistical features with textual features according to the credibility of textual features, which can solve the over-fitting problem caused by the excessive use of statistical features. The accuracy of the model in three public datasets (Twitter15, Twitter16, and Weibo) reached 87.5%, 88.6%, and 95.8%, respectively, and the F1 value reached 87.4%, 88.5%, and 95.8%, respectively, showing that the model can effectively improve the performance of rumor detection.
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Difficulty remains to represent water propagation impact to construct water balance constraints for the Short-Term Cascade Reservoirs Optimal Operation (SCROD), due to the complex water propagation mechanism existing between casca...
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Difficulty remains to represent water propagation impact to construct water balance constraints for the Short-Term Cascade Reservoirs Optimal Operation (SCROD), due to the complex water propagation mechanism existing between cascade reservoirs. The imprecise representation of water propagation impact hinders the implementation of hydropower generation schemes and leads to operational risks. Thus, a modified SCROO model considering water propagation impact was proposed in this study to improve the accuracy of cascade operation. Specifically, water propagation is expressed using the Muskingum method in the modified SCROO model, and characterized by two variables, including propagation time related to the magnitude of the upstream reservoir's outflow and flow attenuation related to the propagation time, which were identified using the successive approximation method under water balance constraints within the modified SCROO model. Subsequently, this research applied the Nested Progressive Optimality Algorithm (NPOA) in the model calculation innovatively, in which the stage benefit function and nest computation could be improved. The modified model and algorithm developed herein were applied and validated on cascade reservoirs along the Yalung River in China by comparing it with traditional methods and algorithms. The results show that: 1) Compared to traditional methods, considering water propagation within water balance constraints as a dynamic parameter can calculate the inflow of downstream reservoir more accurately and mitigate risks in the actual operation of cascade reservoirs. Besides, the reservoir inflow calculated by traditional methods will lead to different magnitudes of benefit loss; and 2) the NPOA can significantly reduce dimensionality problems caused by the water propagation impact and greatly improve the actual hydropower generation than that of a traditional optimal algorithm, Multi-Stage Dynamic Programming (MSDP). Compared to the MSDP, the NOPA is more applicable in a cascade system with a larger number of reservoirs. The modified model and algorithm can significantly improve the effectiveness of cascade reservoir optimal operation schemes and provide more information for decision-makers.
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Bioaugmentation was conducted using a bacterial consortium of Pseudomonas putida SW-3 and Rhodococcus ruber SS-4, to test their ability to degrade benzene, toluene, and styrene (BTS). SW-3 and SS-4 were isolated from domestic slud...
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Bioaugmentation was conducted using a bacterial consortium of Pseudomonas putida SW-3 and Rhodococcus ruber SS-4, to test their ability to degrade benzene, toluene, and styrene (BTS). SW-3 and SS-4 were isolated from domestic sludge and sewage samples to establish a synthetic consortium with an optimized ratio of 2:1 to reach a degradation efficiency of 82.5-89.8% of BTS. The bacterial consortium was inoculated with sludge and sewage samples at a ratio of 2:1, resulting in a degradation efficiency of 97.9% and 92.7%, respectively, at a BTS concentration of 1800 mg.L-1. Analysis of bacterial community structure following bioaugmentation indicated an increase in abundance of BTS-degrading bacteria, particularly Acinetobacter and Pseudoxanthomonas in sludge and Pseudomonas in sewage, enhancing the collective BTS degradation ability of the bacterial community. Principal component analysis demonstrated that a more balanced bacterial community structure was established following intervention. This indicated that the selected bacteria are excellent candidates for bioaugmentation.
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Repulsive guidance molecule a (RGMa) is a membrane-bound protein that inhibits axon outgrowth in the central nervous system. Temporal lobe epilepsy (TLE) is a common neurological disorder characterized by recurrent spontaneous sei...
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Repulsive guidance molecule a (RGMa) is a membrane-bound protein that inhibits axon outgrowth in the central nervous system. Temporal lobe epilepsy (TLE) is a common neurological disorder characterized by recurrent spontaneous seizures. To explore the role of RGMa in epilepsy, we investigated the expression of RGMa in patients with TLE, pilocarpine-induced rat model, and pentylenetetrazol kindling model of epilepsy, and then we performed behavioral, histological, and electrophysiological analysis by lentivirus-mediated overexpression of RGMa in the hippocampus of animal model. We found that RGMa was significantly decreased in TLE patients and in experimental rats from 6 h to 60 days after pilocarpine-induced seizures. In two types of epileptic animal models, pilocarpine-induced model and pentylenetetrazol kindling model, overexpression of RGMa in the hippocampus of rats exerted seizure-suppressant effects. The reduced spontaneous seizures were accompanied by attenuation of hippocampal mossy fiber sprouting. In addition, overexpression of RGMa inhibited hyperexcitability of hippocampal neurons via suppressing NMDAR-mediated currents in Mg2+-free-induced organotypic slice model. Collectively, these results demonstrate that overexpression of RGMa could be an alternative strategy for epilepsy therapy.
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The multidimensional dynamic programming (MDP) algorithm is a traditional method used to solve cascade reservoir operation optimization (CROO) problems, but the high dimensionality called the curse of dimensionalitycannot be ignor...
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The multidimensional dynamic programming (MDP) algorithm is a traditional method used to solve cascade reservoir operation optimization (CROO) problems, but the high dimensionality called the curse of dimensionalitycannot be ignored. In order to alleviate this problem, this paper proposes a new MDP algorithm named multilayer nested multidimensional dynamic programming (MNDP), which is based on a multilayered, nested structure. MNDP is mainly used to deal with computer memory space and computation complexity problems of MDP in CROO, and its recursive equation of reverse recursion calculation and specific calculation steps are presented in detail. This paper takes the cascade reservoirs of the Li Xianjiang River in China as an example to solve the CROO problem with the proposed MNDP. By comparing with the dynamic programming with successive approximations (DPSA) method, MNDP presents better performance in terms of power generation and the assurance rate in wet, normal, dry, and average years. The global optimality of MNDP is validated by MDP, and the parallel computing results of MNDP are shown in a case study. The MNDP proposed in this paper can reduce not only the programming complexity of MDP, but also the storage of intermediate variables during calculation, thus effectively solving the curse of dimensionality of MDP in CROO and keeping the global convergence feature of MDP.
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