Evolutionary game theory on complex networks provides an effective theoretical tool to explain the emergence of sustained cooperative behavior. Human society has formed various organizational ...networks. The network structure and individual behavior take on a variety of forms. This diversity provides the basis for choice, so it is crucial for the emergence of cooperation. This article provides a dynamic algorithm for individual network evolution, and calculates the importance of different nodes in the network evolution process. In the dynamic evolution simulation, the probability of the cooperation strategy and betrayal strategy is described. In the individual interaction network, cooperative behavior will promote the continuous evolution of individual relationships and form a better aggregative interpersonal network. The interpersonal network of betrayal has been in a relatively loose state, and its continuity must rely on the participation of new nodes, but there will be certain "weak links" in the existing nodes of the network.
Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to ...predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short‐term memory (LSTM) model and sequence‐to‐sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems by considering long‐term dependencies and multiple outputs. This study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall‐runoff. Focusing on two Midwestern watersheds, namely, Clear Creek and Upper Wapsipinicon River in Iowa, these models were used to predict hourly runoff for a 24‐hr period using rainfall observation, rainfall forecast, runoff observation, and empirical monthly evapotranspiration data from all stations in these two watersheds. The models were evaluated using the Nash‐Sutcliffe efficiency coefficient, the correlation coefficient, statistical bias, and the normalized root‐mean‐square error. The results show that the LSTM‐seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM‐seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short‐term flood forecast applications. In addition, the seq2seq method was demonstrated to be an effective method for time series predictions in hydrology.
Key Points
An hourly runoff model was developed using the LSTM sequence‐to‐sequence learning method for 24‐hr predictions on USGS stations
The proposed model shows better performance than traditional data‐driven models and is applicable to different watersheds
The advantages and limitations of seq2seq models and how this model structure could work on the rainfall‐runoff modeling is presented
This paper concentrates upon the problem of finite-time fault-tolerant control for a class of switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss of ...effectiveness and bias fault in actuator are taken into account. The method developed extends the traditional finite-time convergence from nonswitched lower-triangular nonlinear systems to switched version by designing appropriate controller and adaptive laws. In contrast to the previous results, it is the first time to handle the fault tolerant problem for switched system while the finite-time stability is also necessary. Meanwhile, there exist unknown internal dynamics in the switched system, which are identified by the radial basis function neural networks. It is proved that under the presented control strategy, the system output tracks the reference signal in the sense of finite-time stability. Finally, an illustrative simulation on a resistor-capacitor-inductor circuit is proposed to further demonstrate the effectiveness of the theoretical result.
In this paper, an adaptive controller design is studied for single-input–single-output (SISO) nonlinear systems with parameter uncertainties and the systems are enforced to subject to the full state ...constraints. A remarkable feature of the constrained systems is that the so-called control direction is unknown, or in other words, the signs of control gains are unknown. In the existing results, we discover that all the state constraint control results are required to determine a priori knowledge of control direction. It will be inevitable to bring about a different design procedure and a difficult task when no a priori knowledge of control direction is known. To stabilize this class of systems, two parameter adaptive controllers with Nussbaum gain technique are constructively framed to overcome the unknown control direction problem, and the novel symmetric and asymmetric Barrier Lyapunov Functions (BLFs) are adopted to guarantee that the states are not to violate their constraints. Then, the proposed BLF strategy can be to conquer the conservatism of the traditional BLF-based controls for the full state constraints. Finally, two theorems are provided to show that all the signals in the closed-loop system are bounded, the outputs are driven to follow the reference signals and all the states are ensured to remain in the predefined compact sets. The effectiveness of the proposed scheme is performed via a simulation example.
In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame and contain unknown ...functions and nonsymmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but achieves the optimal control performance as well. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of nonsymmetric dead-zone on the control performance, and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.
Heterojunction engineering, especially 2D/2D heterojunctions, is regarded as a quite promising strategy to manipulate the photocatalytic performance of semiconductor catalysts. In this manuscript, a ...direct Z‐scheme 2D/2D heterojunction of CsPbBr3/Bi2WO6 is designed and fabricated by a simple electrostatic self‐assembly process. By using ultrathin nanosheets with several atomic layers as the building blocks, a close CsPbBr3/Bi2WO6 heterointerface over large area with quite a short charge transport distance is obtained, which enables a valid Z‐scheme interfacial charge transfer between Bi2WO6 and CsPbBr3 and thus boosts charge separation. The CsPbBr3/Bi2WO6 heterojunction exhibits a superior photocatalytic performance toward CO2 reduction. By incorporating Pt nanoparticles as the cocatalyst, a high photoelectron consumption rate of 324.0 µmol g−1 h−1 under AM 1.5G irradiation (150 mW cm−2) is obtained, which is 12.2 fold higher than that of CsPbBr3 nanosheets. Moreover, a stable product yield of up to 1582.0 µmol g−1 and electron consumption yield of 8603.0 µmol g−1 for photocatalytic CO2 reduction to CO (11.4%) and CH4 (84.3%) can be achieved after 30 h of continuous catalytic reaction. The accelerated photogenerated charge transfer and spatial charge separation are investigated in detail by ultrafast spectra, photoelectrochemical test, and Kelvin probe force microscopy.
A Z‐Scheme 2D/2D heterojunction of CsPbBr3/Bi2WO6 is fabricated using a simple electrostatic assembly process. The as‐formed heterojunction possesses a large interface contact area and quite a short charge transport distance, which enable efficient Z‐scheme charge transfer and separation between Bi2WO6 and CsPbBr3, as well as remarkably enhanced performance toward photocatalytic CO2 reduction.
In this study, an adaptive control technique is developed for a class of uncertain nonlinear parametric systems. The considered systems can be viewed as a class of nonlinear pure-feedback systems and ...the full state constraints are strictly required in the systems. One remarkable advantage is that only less adjustable parameters are used in the design. This advantage is first to take into account the pure-feedback systems with the full state constraints. The characteristics of the considered systems will lead to a difficult task for designing a stable controller. To this end, the mean value theorem is employed to transform the pure-feedback systems to a strict-feedback structure but non-affine terms still exist. For the transformed systems, a novel recursive design procedure is constructed to remove the difficulties for avoiding non-affine terms and guarantee that the full state constraints are not violated by introducing Barrier Lyapunov Function (BLF) with the error variables. Moreover, it is proved that all the signals in the closed-loop system are global uniformly bounded and the tracking error is remained in a bounded compact set. Two simulation studies are worked out to show the effectiveness of the proposed approach.
Formic acid (HCOOH) is one of the most promising chemical fuels that can be produced through CO2 electroreduction. However, most of the catalysts for CO2 electroreduction to HCOOH in aqueous solution ...often suffer from low current density and limited production rate. Herein, we provide a bismuth/cerium oxide (Bi/CeOx) catalyst, which exhibits not only high current density (149 mA cm−2), but also unprecedented production rate (2600 μmol h−1 cm−2) with high Faradaic efficiency (FE, 92 %) for HCOOH generation in aqueous media. Furthermore, Bi/CeOx also shows favorable stability over 34 h. We hope this work could offer an attractive and promising strategy to develop efficient catalysts for CO2 electroreduction with superior activity and desirable stability.
The limited current density, production rate as well as selectivity hinder the improvement of HCOOH production from CO2 electroreduction. Here, bismuth/cerium oxide (Bi/CeOx) displays outstanding performances for CO2 electroreduction to HCOOH, which not only shows excellent selectivity, but also achieves a high current density (149 mA cm−2) and especially the maximum HCOOH production rate (2600 μmol h−1 cm−2) ever reported.
Reactive species produced in the cell during normal cellular metabolism can chemically react with cellular biomolecules such as nucleic acids, proteins, and lipids, thereby causing their oxidative ...modifications leading to alterations in their compositions and potential damage to their cellular activities. Fortunately, cells have evolved several antioxidant defense mechanisms (as metabolites, vitamins, and enzymes) to neutralize or mitigate the harmful effect of reactive species and/or their byproducts. Any perturbation in the balance in the level of antioxidants and the reactive species results in a physiological condition called “oxidative stress.” A catalase is one of the crucial antioxidant enzymes that mitigates oxidative stress to a considerable extent by destroying cellular hydrogen peroxide to produce water and oxygen. Deficiency or malfunction of catalase is postulated to be related to the pathogenesis of many age-associated degenerative diseases like diabetes mellitus, hypertension, anemia, vitiligo, Alzheimer’s disease, Parkinson’s disease, bipolar disorder, cancer, and schizophrenia. Therefore, efforts are being undertaken in many laboratories to explore its use as a potential drug for the treatment of such diseases. This paper describes the direct and indirect involvement of deficiency and/or modification of catalase in the pathogenesis of some important diseases such as diabetes mellitus, Alzheimer’s disease, Parkinson’s disease, vitiligo, and acatalasemia. Details on the efforts exploring the potential treatment of these diseases using a catalase as a protein therapeutic agent have also been described.
The aberrant expression of myotubularin-related protein 2 (MTMR2) has been found in some cancers, but little is known about the roles and clinical relevance. The present study aimed to investigate ...the roles and clinical relevance of MTMR2 as well as the underlying mechanisms in gastric cancer (GC).
MTMR2 expression was examined in 295 GC samples by using immunohistochemistry (IHC). The correlation between MTMR2 expression and clinicopathological features and outcomes of the patients was analyzed. The roles of MTMR2 in regulating the invasive and metastatic capabilities of GC cells were observed using gain-and loss-of-function assays both in vitro and in vivo. The pathways involved in MTMR2-regulating invasion and metastasis were selected and identified by using mRNA expression profiling. Functions and underlying mechanisms of MTMR2-mediated invasion and metastasis were further investigated in a series of in vitro studies.
MTMR2 was highly expressed in human GC tissues compared to adjacent normal tissues and its expression levels were significantly correlated with depth of invasion, lymph node metastasis, and TNM stage. Patients with MTMR2
had significantly shorter lifespan than those with MTMR2
. Cox regression analysis showed that MTMR2 was an independent prognostic indicator for GC patients. Knockdown of MTMR2 significantly reduced migratory and invasive capabilities in vitro and metastases in vivo in GC cells, while overexpressing MTMR2 achieved the opposite results. MTMR2 knockdown and overexpression markedly inhibited and promoted the epithelial-mesenchymal transition (EMT), respectively. MTMR2 mediated EMT through the IFNγ/STAT1/IRF1 pathway to promote GC invasion and metastasis. Phosphorylation of STAT1 and IRF1 was increased by MTMR2 knockdown and decreased by MTMR2 overexpression accompanying with ZEB1 down-regulation and up-regulation, respectively. Silencing IRF1 upregulated ZEB1, which induced EMT and consequently enhanced invasion and metastasis in GC cells.
Our findings suggest that MTMR2 is an important promoter in GC invasion and metastasis by inactivating IFNγ/STAT1 signaling and may act as a new prognostic indicator and a potential therapeutic target for GC.
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