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To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be...
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To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.
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The fiscal policy environment central banks operate in can be radically different with respect to debt levels, maturity structures and whether or not fiscal adjustments are spending-or tax-based. Despite this, most analyses of mon...
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The fiscal policy environment central banks operate in can be radically different with respect to debt levels, maturity structures and whether or not fiscal adjustments are spending-or tax-based. Despite this, most analyses of monetary policy delegation schemes typically ignore the behavior of the fiscal policy maker. This paper investigates whether delegating either nominal income or price level targets to a monetary authority yields social gains in an economy with government debt, where the fiscal policymaker, acting strategically, may support or undermine the policies of the central bank. We argue that the fiscal environment plays an important role in determining the performance of monetary policy. The gains to price level targeting typically found in the literature can be overturned at empirically relevant debt-to-GDP ratios, when debt stabilization is achieved through spending cuts. In contrast these gains are retained if the fiscal authorities utilize taxes to respond to shocks and stabilize debt. (C) 2017 Elsevier B.V. All rights reserved.
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We investigated the regulatory effects of hypoxia-inducible factor-1 alpha (HIF-1 alpha) on glycolysis metabolism in esophageal carcinoma (ESCA) cells. A series of bioinformatics databases and tools were used to investigate the ex...
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We investigated the regulatory effects of hypoxia-inducible factor-1 alpha (HIF-1 alpha) on glycolysis metabolism in esophageal carcinoma (ESCA) cells. A series of bioinformatics databases and tools were used to investigate the expression and role of HIF-1 alpha in ESCA. The expression of HIF-1 alpha in ESCA tissues and adjacent tissues was validated by real-time PCR. Small interfering RNA (siRNA) was used to inhibit HIF-1 alpha-related genes in human ESCA cells (Eca109 and KYSE150). Cell proliferation was detected by the CCK-8 assay. The expression of HIF-1 alpha and glycolytic enzymes were investigated by real-time PCR and Western blot. HIF-1 alpha is highly expressed in ESCA and is involved in many biological processes such as cell hypoxia reaction, glucose metabolic process. Further in vitro experiments showed that expression of HIF-1 alpha in Eca109 and KYSE150 significantly increased under hypoxia compared with normoxia conditions. Also, the glucose uptake and lactate production under hypoxia were higher. The expression levels of hexokinase 2 (HK2) and pyruvate dehydrogenase kinase 1 (PDK1), glycolysis-related genes, were significantly increased under hypoxia. After siRNA knockdown of HIF-1 alpha in Eca109 and KYSE150, the glucose uptake and lactate production, as well as cell proliferation were significantly decreased under hypoxia, and HK2 and PDK1 were significantly downregulated. HIF-1 alpha promotes glycolysis of ESCA cells by upregulating the expression of HK2 and PDK1 under hypoxia.
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Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its per...
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Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the Smart Agriculture System.
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Spinal cord injury (SCI) often leads to impaired motor and sensory functions, partially because the injury-induced neuronal loss cannot be easily replenished through endogenous mechanisms. In vivo neuronal reprogramming has emerge...
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Spinal cord injury (SCI) often leads to impaired motor and sensory functions, partially because the injury-induced neuronal loss cannot be easily replenished through endogenous mechanisms. In vivo neuronal reprogramming has emerged as a novel technology to regenerate neurons from endogenous glial cells by forced expression of neurogenic transcription factors. We have previously demonstrated successful astrocyte-to-neuron conversion in mouse brains with injury or Alzheimer’s disease by overexpressing a single neural transcription factor NeuroD1. Here we demonstrate regeneration of spinal cord neurons from reactive astrocytes after SCI through AAV NeuroD1-based gene therapy. We find that NeuroD1 converts reactive astrocytes into neurons in the dorsal horn of stab-injured spinal cord with high efficiency (~95%). Interestingly, NeuroD1-converted neurons in the dorsal horn mostly acquire glutamatergic neuronal subtype, expressing spinal cord-specific markers such as Tlx3 but not brain-specific markers such as Tbr1, suggesting that the astrocytic lineage and local microenvironment affect the cell fate after conversion. Electrophysiological recordings show that the NeuroD1-converted neurons can functionally mature and integrate into local spinal cord circuitry by displaying repetitive action potentials and spontaneous synaptic responses. We further show that NeuroD1-mediated neuronal conversion can occur in the contusive SCI model with a long delay after injury, allowing future studies to further evaluate this in vivo reprogramming technology for functional recovery after SCI. In conclusion, this study may suggest a paradigm shift from classical axonal regeneration to neuronal regeneration for spinal cord repair, using in vivo astrocyte-to-neuron conversion technology to regenerate functional new neurons in the grey matter.
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Water quality assessment analysis is an important technical means for water pollution prevention and control. In this research area, mechanism models and real observation data of water quality evolution are always used to perform ...
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Water quality assessment analysis is an important technical means for water pollution prevention and control. In this research area, mechanism models and real observation data of water quality evolution are always used to perform water quality assessment. However, the existing water quality evolution mechanism modeling researches commonly use a single time-invariant model to model the water quality evolution process. It is inappropriate to directly describe the complex behavior of long-term water quality evolution with the existing models, since the evolution process contain different feature states, and the water quality evolution characteristics under these states are different. In addition, the existing water quality assessment methods are mostly methods for directly processing and calculating the observation data of water quality. This makes the existing methods difficult to effectively compensate for the contingency and randomness in the water quality evolution process, which leads to deviations and errors in performing the water quality assessment. Considering these deficiencies, this paper proposes a water quality evolution mechanism modeling and health risk assessment method based on stochastic hybrid dynamic systems (SHDS). Firstly, a hybrid water quality evolution mechanism (H-WQEM) model is established based on SHDS, and a hybrid improved fruit fly optimization algorithm (H-IFFOA) is proposed to identify the unknown parameters of the H-WQEM model. Then, an improved interacting multiple model extended Kalman filter algorithm (IIMM-EKF) is employed to estimate the probability distribution of the hybrid state of the H-WQEM model, including the probability distribution of different feature states of water bodies and the probability distribution of water quality indexes under these states. Finally, the health degree of water quality is proposed as an indicator to achieve a quantitative assessment of the water health risk status. Real observation data from a monitoring station at Baiyangwan in China is used to validate the effectiveness of the proposed method. The results show that the method can effectively describe the complex water quality evolution process, and reasonably assess the water quality health risk status.
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The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to pre...
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The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict. An improved deep prediction method is proposed herein based on the dual variational mode decomposition of a nonstationary time series. First, criteria were determined based on information entropy and frequency statistics to determine the quantity of components in the variational mode decomposition, including the number of subsequences and the conditions for dual decomposition. Second, a deep prediction model was built for the subsequences obtained after the dual decomposition. Third, a general framework was proposed to integrate the data decomposition and deep prediction models. The method was verified on practical time series data with some contrast methods. The results show that it performed better than single deep network and traditional decomposition methods. The proposed method can effectively extract the characteristics of a nonstationary time series and obtain reliable prediction results.
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To solve the problem of path planning for an unmanned surface vessel in an unknown environment, this paper proposes a path planning algorithm based on the improved D*Lite algorithm. First, to improve the computational efficiency o...
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To solve the problem of path planning for an unmanned surface vessel in an unknown environment, this paper proposes a path planning algorithm based on the improved D*Lite algorithm. First, to improve the computational efficiency of the traditional D*Lite algorithm, the path cost function is improved to reduce the expansion range of nodes, and the node expansion direction is limited to avoid double node computation. Second, the path planned by the traditional D*Lite algorithm on the grid map is suboptimal; so, the inverse distance weighted (IDW) interpolation method is applied to shorten the path length. To improve the smoothness of the planned path by the traditional D*Lite algorithm, the Dubins algorithm is introduced for local path smoothing, and a smooth collision-free path that conforms to the motion dynamics of the unmanned surface vessel is obtained. The proposed algorithm was verified by experiments in known and unknown environments. The performance of the proposed algorithm was compared with the traditional D*Lite algorithm in terms of planning time, path length, and path smoothness. The results revealed that the proposed algorithm has the shortest path length, a shorter planning time, and the best smooth path.
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Cyclic steam stimulation (CSS) is successfully applied to increase heavy oil recovery in heavy oil reservoirs in Bohai Bay, China. However, during the CSS processes, hydrogen sulfide (H2S) was detected in some heavy oil reservoirs...
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Cyclic steam stimulation (CSS) is successfully applied to increase heavy oil recovery in heavy oil reservoirs in Bohai Bay, China. However, during the CSS processes, hydrogen sulfide (H2S) was detected in some heavy oil reservoirs. The existing literature mainly focused on the H2S generation of onshore heavy oil. There is no concrete experimental data available, especially about the level of H2S generation during CSS of offshore heavy oil. In addition, there is still a lack of effective reaction kinetic models and numerical simulation methods to simulate H2S generation during the CSS of offshore heavy oil. Therefore, this paper presents a case study from Bohai Bay, China. First, the laboratory aquathermolysis tests were conducted to simulate the gases that are produced during the CSS processes of heavy oil. The effects of the reaction temperature and time on the H2S generation were studied. Then, a one-dimensional CSS experiment was performed to predict H2S generation under reservoir conditions. A kinetic model for the prediction of H2S generation during the CSS of heavy oil was presented. The developed model was calibrated with the experimental data of the one-dimensional CSS experiment at a temperature of 300 °C. Finally, a reservoir model was developed to predict H2S generation and investigate the effects of soaking time, steam quality, and steam injection volume on H2S generation during CSS processes. The results show that the H2S concentration increased from 0.77 ppm in the first cycle to 1.94 ppm in the eighth cycle during the one-dimensional CSS experiment. The average absolute error between the measured and simulated H2S production was 12.46%, indicating that the developed model can accurately predict H2S production. The H2S production increase with soaking time, steam quality, and steam injection volume due to the strengthened aquathermolysis reaction. Based on the reservoir simulation, the H2S production was predicted in the range of 228 m3 to 2895 m3 within the parameters of this study.
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The hypoxia in tumor microenvironment (TME) can upregulate the HIF-1 alpha and PD-L1 expression and cause immunosuppression of tumor. In this study, a carboxymethyl chitosan-based pH/hypoxia-responsive and gamma-Fe2O3/isosorbide d...
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The hypoxia in tumor microenvironment (TME) can upregulate the HIF-1 alpha and PD-L1 expression and cause immunosuppression of tumor. In this study, a carboxymethyl chitosan-based pH/hypoxia-responsive and gamma-Fe2O3/isosorbide dinitrate carrying micelle was designed, and it could catalyze endogenous H2O2 to generate oxygen and relieve hypoxia in TME, so as to relieve the overexpression of HIF-1 alpha and PD-L1 in tumor; meanwhile, it could react with H2O2 to release ROS via Fenton reaction and induce cytotoxicity in tumor. Along with these multiple effects, this carboxymethyl chitosan-based micelles could provide a comprehensive strategy for tumor treatment.
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