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The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp $\gamma$-ray structures in the GeV$-$TeV range thanks to its unprecedented high energy resolution. In this work, we search for $\gamma$-ray line structures using five years of DAMPE data. To improve the sensitivity, we develop two types of dedicated data sets (including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories) and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates are found between 10 and 300 GeV in the Galaxy. The constraints on the velocity-averaged cross section for $\chi\chi \to \gamma\gamma$ and the decay lifetime for $\chi \to \gamma\nu$, both at 95% confidence level, have been calculated and the systematic uncertainties have been taken into account. Comparing to the previous Fermi-LAT results, though DAMPE has an acceptance smaller by a factor of $\sim 10$, similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few. Our results demonstrate the potential of high-energy-resolution observations on dark matter detection., Comment: 14 pages, 8 figures. Update the content to keep up with the published version

In order to solve the problem of intelligent anti-jamming decision-making in battlefield communication, this paper designs an intelligent decision-making method for communication anti-jamming based on deep reinforcement learning. Introducing experience replay and dynamic epsilon mechanism based on PHC under the framework of DQN algorithm, a dynamic epsilon-DQN intelligent decision-making method is proposed. The algorithm can better select the value of epsilon according to the state of the decision network and improve the convergence speed and decision success rate. During the decision-making process, the jamming signals of all communication frequencies are detected, and the results are input into the decision-making algorithm as jamming discriminant information, so that we can effectively avoid being jammed under the condition of no prior jamming information. The experimental results show that the proposed method adapts to various communication models, has a fast decision-making speed, and the average success rate of the convergent algorithm can reach more than 95%, which has a great advantage over the existing decision-making methods.

Recently, Retinex theory based low-light image enhancement (LLIE) algorithms have achieved impressive results in controlled environment. However, the majority of deep learning based LLIE algorithms leverage relighting by enhancing the illumination components that directly determines the image brightness, regretfully, they ignore the information of reflectance components, which may cause problems such as image noise and color distortion in reconstructed images. To tackle this problem, in this letter, we propose a component enhancement network based on Generative Adversarial Network (Component-GAN) for recovering clear images from low-light ones. Specifically, the network is composed of the decomposition part for dividing the paired low/normal-light images into illumination components and reflectance components, and the enhancement part for generating high-quality images. It is worth to note that we provide two branches of component enhancement network, which are parallel to improve the two components simultaneously. Hereby, we treat the reconstruction part as the generative network and adopt discriminative network to boost image reconstruction performance. Through extensive experiments, the proposed approach outperforms some state-of-the-art LLIE methods in terms of visual and subjective qualities.

Due to the nonconvexity feature of optimal controlling such as jamming link selection and jamming power allocation issues, obtaining the optimal resource allocation strategy in communication countermeasures scenarios is challenging. Thus, we propose a novel decentralized jamming resource allocation algorithm based on multiagent deep reinforcement learning (MADRL) to improve the efficiency of jamming resource allocation in battlefield communication countermeasures. We first model the communication jamming resource allocation problem as a fully cooperative multiagent task, considering the cooperative interrelationship of jamming equipment (JE). Then, to alleviate the nonstationarity feature and high decision dimensions in the multiagent system, we introduce a centralized training with decentralized execution framework (CTDE), which means all JEs are trained with global information and rely on their local observations only while making decisions. Each JE obtains a decentralized policy after the training process. Subsequently, we develop the multiagent soft actor-critic (MASAC) algorithm to enhance the exploration capability of agents and accelerate the learning of cooperative policies among agents by leveraging the maximum policy entropy criterion. Finally, the simulation results are presented to demonstrate that the proposed MASAC algorithm outperforms the existing centralized allocation benchmark algorithms.

Organ fibrosis often ends in eventual organ failure and leads to high mortality. Although researchers have identified many effector cells and molecular pathways, there are few effective therapies for fibrosis to date and the underlying mechanism needs to be examined and defined further. Epoxyeicosatrienoic acids (EETs) are endogenous lipid metabolites of arachidonic acid (ARA) synthesized by cytochrome P450 (CYP) epoxygenases. EETs are rapidly metabolized primarily via the soluble epoxide hydrolase (sEH) pathway. The sEH pathway produces dihydroxyeicosatrienoic acids (DHETs), which have lower activity. Stabilized or increased EETs levels exert several protective effects, including pro-angiogenesis, anti-inflammation, anti-apoptosis, and anti-senescence. Currently, intensive investigations are being carried out on their anti-fibrotic effects in the kidney, heart, lung, and liver. The present review provides an update on how the stabilized or increased production of EETs is a reasonable theoretical basis for fibrosis treatment.

The interaction between a shock wave and solid particles involves complex gas&ndash, solid two-phase flow, which is widely used in industrial processes. Theoretical analysis, an experimental test, and simulation were combined to investigate the interaction process between a shock wave and quartz sand particles. The variation of physical parameters of the two phases during the interaction process was considered theoretically. Then, a novel vertical shock tube generator was employed to record the pressure attenuation and dispersion process of solid particles. Finally, the complex gas&ndash, solid two-phase flow was simulated based on the computational fluid dynamics method. The results showed that a nonequilibrium state was formed during the interaction process and momentum exchange generated, resulting in a drag force of the shock wave on the particles. The shock intensity obviously attenuated after the shock wave passed through the solid particles, and this part of the energy was work on the solid particles to drive their dispersion. A three-dimensional annular vortex was generated around the solid particles due to the entrainment effect of airflow. Under the shock wave action of 1.47 Ma, the three types of solid particles with average diameters of 2.5, 0.95, and 0.42 mm presented different motion laws. The particles with smaller size were easier to disperse, and the cloud that formed was larger and more uniform.

To investigate whether the methylene tetrahydrofolate reductase (Infertile Chinese Han women were divided into 2 groups: POF group (The results of gene sequencing showed that the PCR amplification fragment was the target gene fragment, and the sequencing showed that the genotype was 100% consistent with the enzymatic display genotype. TheThe distribution of the

The gas-liquid two-phase flow of liquid dispersing and breaking under the action of shock wave includes complex physical phenomena, such as turbulent mixing of gas-liquid two-phase, instability and breakage of liquid interface, and formation of internal cavity structure after atomization. In order to investigate the shock-wave-caused breaking process of the liquid film, a three-dimensional numerical simulation of the gas-liquid two-phase flow process is performed by using the computational fluid dynamics method. In the simulation, the Mach number of shock wave is 1.5 and the thickness of liquid film is 2 mm. The finite volume method is used to solve the three-dimensional Navier-Stokes equation. The volume of fluid model is applied to the gas-liquid two-phase flow. The k-ε double equation turbulence model is selected for the turbulence calculation. The evolution process of the wave system structure of the shock wave and the deformation, breakage and atomization characteristics of the liquid film are obtained, and compared with the experimental results. The results show that the incidence, reflection, and transmission phenomena occur during the interaction between the shock wave and the liquid film, and the intensity of the transmitted shock wave and the liquid surface tension have an important effect on the breaking process of the liquid film. The transmitted shock wave affects the shape of the broken cloud cluster on the left of the liquid film, while the incident shock wave and reflected shock wave affect the shape of the broken cloud cluster on the right side of the liquid film. The volume of the atomized cloud formed in the breaking process of the liquid film increases rapidly, first reaching 6.7 dm3 within 2.5 ms, then keeping stable basically. After the shock wave exits from the tube, a long narrow jet is formed. The maximum velocity reaches 519 m/s and appears in the interior of the jet, and then decreases continuously. Under the action of the jet, an expanding three-dimensional cavity structure is formed inside the atomizing cloud, and an annular vortex with negative pressure in the core area occurs in the cavity structure. Finally, the annular vortex continuously entrains the surrounding fluid in the process of forward movement, the strength of the vortex decreases and gradually dissipates in the space. This work is conducive to further understanding the interaction process of gas-liquid two-phase flow.

The stock market is one of the most important part of the financial market. However, it is difficult for investors to extract information from data. In the meantime, sentiment analysis is a hot topic in computer science. In this paper, we build an emotion dictionary for finance area. We can calculate the emotional values of finance texts based on the dictionary. Further, with a regression analysis by Fama-French model, we discovered the relationship between the emotional value and stock price by experiments. Simply put, the emotional value and stock’s return rate did not have a significant correlation; the emotional value and stock’s volatility don’t have a significant correlation; but the emotional value and stock’ relative return rates had a positive correlation.

This paper improves the methods of observation reduction in non-deterministic planning(NDP) in three aspects:in finding MOS(minimal observation set);in finding out the optimal observation set(OOS) when observations have different costs;and in finding fault-tolerant OOS.A MOS problem is similar to a minimal set cover(MSC) problem,so it can be proved that finding MOS is NP-hard.Inspired by MSC methods,an O(2mm2) but ?(2m?1) algorithm for MOS is presented,where m is the number of observations.By using integer programming(IP) technologies,OOS or fault tolerant OOS can be found out.Proofs are provided to show that these algorithms can guarantee finding optimal solutions.

According to the characteristics of large delay, large inertia and nonlinear of coagulant dosage, a two-layered predictive control strategy is proposed for the coagulant dosing systems. Model predictive controller(MPC) calculation is separated into steady-state and dynamic optimization, through the objective function to reflect economic performance. The proposed method only requires little information of the controlled plant, and its algorithm is simple, easy to implement. Theoretical analysis and simulation results show that this method reduces operating costs and achieve maximum economic efficiency.

In order to improve the detonation performance of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) explosive, addictives with high heat values were used, and magnesium hydride (MgH2) is one of the candidates. However, it is important to see whether MgH2is a safe addictive. In this paper, the thermal and kinetic properties of RDX and mixture of RDX/MgH2were investigated by differential scanning calorimeter (DSC) and accelerating rate calorimeter (ARC), respectively. The apparent activation energy (E) and frequency factor (A) of thermal explosion were calculated based on the data of DSC experiments using the Kissinger and Ozawa approaches. The results show that the addition of MgH2decreases bothEandAof RDX, which means that the mixture of RDX/MgH2has a lower thermal stability than RDX, and the calculation results obtained from the ARC experiments data support this too. Besides, the most probable mechanism functions about the decomposition of RDX and RDX/MgH2were given in this paper which confirmed the change of the decomposition mechanism.

Utilizing measurability of water quality disturbance, a nonlinear regression prediction model-based forward compensation control algorithm with measurable disturbance is proposed for the process of water treatment coagulant dosage. To reject the second disturbance caused by coagulant dosage inverter modification, a mode of double cycle control is presented by combining PFC and PID. Effectiveness of the proposed control strategy is verified through the experimental results of turbidity control of a water factory.

Because of some "inherent" limitations, such as specifications of initial state or structures of domain actions, some goals can't be arrived together, which we call a planning task with goal conflicts. This planning task can hardly be resolved until goal conflicts are eliminated. And in such cases, people often need to know which goal conflicts a planning task contains and choose a new set of goals. In this paper, we present a technique for automatically detecting conflicts among goals by construction of a causal-link graph that captures the interactions between actions relevant to a given goal. By statically analyzing "competitive" preconditions of actions in a causal-link graph, we may inference which goals can't be reached together, without trying all best to resolve such an irresolvable planning task. Our method is sound and scaleable, but not complete; however, it may provide important information when people think about how to reconstruct an appropriate goal set.

Partial satisfaction or Over-subscription Planning problems arise in many real world applications, and these soft problem goals have been defined as preferences in PDDL3.0. Planning with over-subscription problem and preferences is one of the most challenging problems in planning. Inspired by the following two facts, we come up with a method to handle soft goals problem, and it also efficient with classical planning problems. Firstly, using external information has turned out to be a fruitful approach for classical planning; secondly, strategies in incremental planning for ordering and grouping subproblems partitioned by the subgoals of a planning problem have recently draw a lot attention from planning community. In this paper we present an approach to combining incremental planning with control knowledge and this approach can be used to handle soft goals. Our method improved incremental planning using goal agenda with more ordering for atomic goal and grouping subproblems partitioned by the subgoals. We also provide preliminary empirical results in a planner called FFps which demonstrate the effectiveness of our approach in comparison to the original FF and it can handle soft goals problem.