This study established a dog model of acute multiple cauda equina constriction by experimental constriction injury (48 hours) of the lumbosacral central processes in dorsal root ganglia neurons. The ...repair effect of intrathecal injection of brain-derived neurotrophic factor with 15 mg encapsulated biodegradable poly(lactide-co-glycolide) nanoparticles on this injury was then analyzed. Dorsal root ganglion cells (LT) of all experimental dogs were analyzed using hematoxylin-eosin staining and immunohistochemistry at 1,2 and 4 weeks following model induction. Intrathecal injection of brain-derived neurotrophic factor can relieve degeneration and inflammation, and elevate the expression of brain-derived neurotrophic factor in sensory neurons of compressed dorsal root ganglion Simultaneously, intrathecal injection of brain-derived neurotrophic factor obviously improved neurological function in the dog model of acute multiple cauda equina constriction. Results verified that sustained intraspinal delivery of brain-derived neurotrophic factor encapsulated in biodegradable nanoparticles promoted the repair of histomorphology and function of neurons within the dorsal root ganglia in dogs with acute and severe cauda equina syndrome.
We describe herein an organocatalytic enantioselective approach for the construction of axially chiral sulfone-containing styrenes. Various axially chiral sulfone-containing styrene compounds were ...prepared with excellent enantioselectivities (up to >99% ee) and almost complete E/Z selectivities (>99% E/Z). Furthermore, the axially chiral sulfone-containing styrenes could be easily converted into phosphonic acid and S/P ligands, which could be potentially used as organocatalysts or ligands in asymmetric catalysis.
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IJS, KILJ, NUK, PNG, UL, UM
•A deep reinforcement learning-based energy management model for a plug-in hybrid electric bus is proposed.•The model is optimized with a large amount of driving cycles generated from traffic ...simulation.•The traffic information and number of passenger on-board are utilized in the proposed model.•The proposed method outperforms conventional reinforcement learning model in terms of fuel economy and generality.
Hybrid electric vehicles offer an immediate solution for emissions reduction and fuel displacement under the current technique level. Energy management strategies are critical for improving fuel economy of hybrid electric vehicles. In this paper we propose a energy management strategy for a series-parallel plug-in hybrid electric bus based on deep deterministic policy gradients. Specifically, deep deterministic policy gradients is an actor-critic, model-free reinforcement learning algorithm that can assign the optimal energy split of the bus over continuous spaces. We consider that the buses are driving in a fixed bus line, where driving cycle is constrained by the traffic. The traffic information and number of passengers are also incorporated into the energy management system. The deep reinforcement learning based energy management agent is trained with a large amount of driving cycles that generated from traffic simulation. Experiments on the traffic simulation driving cycles show that the proposed approach outperforms conventional reinforcement learning approach and exhibits performance close to the global optimal dynamic programming. Moreover, it also has great generality to the standard driving cycles that are significantly different with the ones that it has been trained with. We also show some interesting attributes of learned energy management strategies through visualizations of the actor and critic. The main contribution of this study is to explore the incorporation of traffic information within hybrid electric vehicle energy managment through advanced intelligent algorithms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The optimization and training processes of deep reinforcement learning (DRL) based energy management strategy (EMS) can be very slow and resource-intensive. In this paper, an improved energy ...management framework that embeds expert knowledge into deep deterministic policy gradient (DDPG) is proposed. Incorporated with the battery characteristics and the optimal brake specific fuel consumption (BSFC) curve of hybrid electric vehicles (HEVs), we are committed to solving the optimization problem of multi-objective energy management with a large space of control variables. By incorporating this prior knowledge, the proposed framework not only accelerates the learning process, but also gets a better fuel economy, thus making the energy management system relatively stable. The experimental results show that the proposed EMS outperforms the one without prior knowledge and the other state-of-art deep reinforcement learning approaches. In addition, the proposed approach can be easily generalized to other types of HEV EMSs.
•A rule-interposing DRL-based energy management system is proposed.•The embedded knowledge is utilized to solve the optimization problems of EMS.•Extensive comparative experiments between RI DDPG and RI DQL are conducted.•The average fuel economy of RI DDPG is reduced by 8.9% than RI DQL, reaching 93.8% of DP’s.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Conventional high-rise building surface inspection is usually inefficient and requires the inspectors to work at heights with high risk. Unmanned aerial vehicles (UAVs) carrying optical or thermal ...cameras are currently widely utilized as an effective tool for inspection. The UAV-based data collection, especially for unreachable inspection areas, is the basis of unmanned inspection of building surface. In addition, building information modeling (BIM) with rich geometric and semantic information can also be instrumental in building surface inspection. Therefore, this paper presented an automatic inspection method of building surface, especially for the inspection data collection, by integrating UAV and BIM. To minimize the length of UAV flight while collecting complete and high-quality image data considering the limited endurance capability, the coverage path planning problem is solved using genetic algorithm (GA). The required inspection areas are obtained from the BIM model of the target building to be inspected. To further enhance the automation of building surface inspection, the optimized UAV flight mission parameters are rapidly calculated based on the BIM model and proposed algorithm. A real office building in Shenzhen University campus is used to validate the presented automatic method. The quality of the collected inspection images using the UAV with optimized flight mission are evaluated. The results show that this method leads to time-efficient, accurate, and high-quality inspection data collection for building surface.
•UAV flight path of inspecting high-raised building is optimized based on BIM.•Target inspection areas are efficiently generated from BIM.•Inspection flight path of UAV is optimized based on GA.•BIM-based flight mission parameters can be automatically transformed for execution.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cd, Pb and As stand as the most prominent contaminants prevailing in Chinese soils. In the present study, biochars derived from water hyacinth (BCW) and rice straw (BCR) were investigated regarding ...their applicability and durability in soil Cd, Pb, and As immobilization under acid precipitation. Total Cd, Pb, and As in both BCs were below the maximum allowed threshold according to biochar toxicity standard recommended by International Biochar Initiative. To evaluate BCs effect on Cd, Pb, As bioavailability and mobility, CaCl2, KH2PO4 and SPLP extractions were firstly carried out. In neutral extraction with CaCl2 and KH2PO4, significantly reduced Cd/Pb concentrations in CaCl2 extract along with elevated KH2PO4-extractable As were recorded with either BC at 2% or 5%. In SPLP with simulated acid rainwater as extractant, comparable Cd, Pb and As levels were determined in SPLP extract with 2% BCW, while slight to significant increase in SPLP-Cd, Pb or As was recorded with other treatments. Longer-term leaching column test further confirmed the high durability of 2% BCW in Cd immobilization under continuous acid exposure. In parallel, little increase in As concentrations in eluate was determined with 2% BCW compared to no-biochar control, indicating a lowered risk of As mobilization with acid input. However, remarkably higher Pb in leachate from both BCW-only control and 2% BCW-amended soils were noticed at the initial stage of acid leaching, indicating a higher acid-solubility of Pb minerals in BCW (most probably PbO) than in tested soil (PbO2, PbAs2O6). Taken together, BCW exhibited important potential for soil Cd sequestration with little effect on As mobilization under acid precipitation. But it may simultaneously load highly acid-soluble Pb minerals into soils, resulting in elevated Pb mobility upon acid exposure. Therefore, more stringent threshold for Pb content in biochar need to be put forward to secure biochar application in soils subject to anthropogenic acidification.
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•Prolonged Cd immobilization was achieved with BCW under acid precipitation.•BCW application increased soil Pb leachability upon acid exposure.•Higher KH2PO4-extractable As was obtained with BCW addition.•BCW incorporation induced little increase in As mobilization with acid input.•More stringent Pb threshold allowed in biochar need to be proposed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•A deep reinforcement learning based energy management strategy of PHEB is proposed.•The proposed approach fundamentally avoid the discretization error and the dimensionality curse.•The robustness of ...the proposed approach was verified by three typical driving cycles.•The results show that the proposed control strategy achieved similar performance and less computation load with DP.
Energy management is a fundamental task in hybrid electric vehicle community. Efficient energy management of hybrid electric vehicle is challenging owning to its enormous search space, multitudinous control variables and complicated driving conditions. Most existing methods apply discretization to approximate the continuous optimum in real driving conditions, which results in relatively low performance with the discretization error and curse of dimensionality. We introduce a novel energy management strategy with a deep reinforcement learning framework Actor-Critic to address these challenges. Actor-Critic uses a deep neural network, named as actor network, to directly output continuous control signals. Another deep neural network, named as critic network, evaluates the control signals generated by the actor network.The actor and critic neural network are trained by reinforcement learning from self-play in a continuous action space. Several comprehensive experiments are conducted in this paper, the proposed method surpasses discretization-based strategies by directly optimizing in the continuous space, which improves energy management performance while blackucing computation load. The simulation results indicate that the AC achieve the optimal energy distribution in comparison with the discretization-based strategies, especially surpassing the existing baseline DP by 5.5%, 2.9%, 9.5% in CTUDC, WVUCITY and WVUSUB in one-tenth of the computational cost.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, SAZU, SBCE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
We examine the real effect of partial privatization on corporate innovation. To establish causality, we explore plausibly exogenous variation in the expectation of further partial privatization ...generated by China's split share structure reform, which mandatorily converts non-tradable shares into freely tradable shares and opens up the gate to the further privatization of state-owned enterprises. We find that partial privatization prospects have a positive effect on corporate innovation. A better alignment of the interests of government agents with those of private shareholders and improved stock price informativeness appear to be two plausible underlying mechanisms. Our paper sheds new light on the real effects of partial privatization.
•Partial privatization prospects have a positive effect on corporate innovation.•A better alignment of the interests of government agents with those of private shareholders is a plausible underlying mechanism.•Improved stock price informativeness is another plausible underlying mechanism.•Our paper sheds new light on the real effects of partial privatization and has important policy implications for policy makers who aim to promote technological innovation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A highly diastereo- and enantioselective methodology for the asymmetric synthesis of vicinal diaxial styrenes and multiaxis system was achieved by organocatalysis. Various vicinal diaxial styrenes ...and multiaxis systems were obtained in excellent enantioselective manners. The mechanism studies revealed that a new tetra-substituted vinylidene ortho-quinone methide (VQM) intermediate was likely involved and accounted for the excellent enantioselectivity.
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Mutations in
cause Kufor-Rakeb syndrome, an autosomal recessive form of juvenile-onset atypical Parkinson's disease (PD). Recent work tied
to autophagy and other cellular features of ...neurodegeneration, but how ATP13A2 governs numerous cellular functions in PD pathogenesis is not understood. In this study, the ATP13A2-deficient mouse developed into aging-dependent phenotypes resembling those of autophagy impairment. ATP13A2 deficiency impaired autophagosome-lysosome fusion in cultured cells and in in vitro reconstitution assays. In ATP13A2-deficient cells or
or mouse tissues, lysosomal localization and activity of HDAC6 were reduced, with increased acetylation of tubulin and cortactin. Wild-type HDAC6, but not a deacetylase-inactive mutant, restored autophagosome-lysosome fusion, antagonized cortactin hyperacetylation, and promoted lysosomal localization of cortactin in ATP13A2-deficient cells. Mechanistically, ATP13A2 facilitated recruitment of HDAC6 and cortactin to lysosomes. Cortactin overexpression in cultured cells reversed ATP13A2 deficiency-associated impairment of autophagosome-lysosome fusion. PD-causing ATP13A2 mutants failed to rescue autophagosome-lysosome fusion or to promote degradation of protein aggregates and damaged mitochondria. These results suggest that ATP13A2 recruits HDAC6 to lysosomes to deacetylate cortactin and promotes autophagosome-lysosome fusion and autophagy. This study identifies ATP13A2 as an essential molecular component for normal autophagy flux in vivo and implies potential treatments targeting HDAC6-mediated autophagy for PD.