Abstract: Herbal composition having an effect of reducing uric acid, which is made from the following herbal materials in specified portions by weight: 4-30 portions of Glabrous greenbrier rhizome, 2-15 portions of Chicory, 2-15 portions of Herba Plantaginis, 2-20 portions of Coix seed, and 2-10 portions of Kudzuvine Root. The composition may be used in combination with modern Western medicines to achieve optimal effects.

Abstract: An optical module includes a shell, a circuit board, at least one of a light-transmitting chip or a light-receiving chip, a lens assembly, an optical fiber ferrule assembly and a fixing plate. The circuit board is disposed in the shell. The light-transmitting chip and/or the light-receiving chip is disposed on the circuit board. The lens assembly is disposed on the circuit board, covers the light-transmitting chip and/or the light-receiving chip, and is configured to change a propagation direction of an optical signal incident into the lens assembly. The optical fiber ferrule assembly is connected to the lens assembly, and is configured to transmit an optical signal incident into the optical fiber ferrule assembly. The fixing plate is configured to fix the optical fiber ferrule assembly to the lens assembly.

Abstract: This application is a transportation network speed forecasting method using deep capsule networks with nested LSTM models. The method includes the following steps: (1) This method divides the transport network into road links, calculates average speeds of each road link, maps the average speeds into a grid system, and generate traffic images representing traffic state at time intervals; (2) the method uses a CapsNet to capture the spatial relationship between road links. The learn patterns are represented in vectors; (3) The vectors of CapsNet are feed into a NLSTM model to learn temporal relationships between road links; (4) The model is trained using and training dataset, and predicts future traffic states using testing dataset. This application uses a new and advanced CapsNet neural structure, while can more efficiently deal with complex traffic networks than CNN models.

Abstract: The present invention discloses an optimization method for joint scheduling of manned buses and autonomous buses, which fully considers the possibility of improving bus service quality and reducing operating costs using the variable capacity characteristics of autonomous buses. For passengers, the optimization method for joint scheduling dynamically adjusts the capacity and departure frequency of autonomous buses according to passenger demands, shortens passenger waiting time, and lowers the risk that a passenger cannot get on a bus during the peak period; and for bus management departments, the optimization method for joint scheduling ensures full utilization of manned buses and autonomous buses, improves scheduling efficiency, and saves operating costs by dynamically adjusting the capacity of autonomous buses during the peak period and flat peak period.

Abstract: This application is a transportation network speed forecasting method using deep capsule networks with nested LSTM models. The method includes the following steps: (1) This method divides the transport network into road links, calculates average speeds of each road link, maps the average speeds into a grid system, and generate traffic images representing traffic state at time intervals; (2) the method uses a CapsNet to capture the spatial relationship between road links. The learn patterns are represented in vectors; (3) The vectors of CapsNet are feed into a NLSTM model to learn temporal relationships between road links; (4) The model is trained using and training dataset, and predicts future traffic states using testing dataset. This application uses a new and advanced CapsNet neural structure, while can more efficiently deal with complex traffic networks than CNN models.

Abstract: A process for the production of organic chemicals and fuels from lignin in the presence of a molybdenum or tungsten based catalyst, comprising mixing the lignin with the catalyst and a solvent in a sealed reactor, introducing an inert gas or hydrogen to the reactor to replace oxygen therein, and heating the sealed reactor to perform a depolymerization reaction at a reaction temperature of above 200° C. to obtain liquid products, which include aromatic compounds, esters, alcohols, monophenols and benzyl alcohols.

Abstract: A subgroup VI element to prepare a catalyst for the production of organic chemicals and fuels from lignin with the involvement of solvent molecules. The catalytic reaction use a catalyst composed of a molybdenum or tungsten compound as the active phase, with mixing a kind of lignin, a catalyst, and a reactive solvent. An inert or reductive gas such as H2, N2 or Ar is used to purge or fill the reaction vessel. The temperature is above 200° C., the reaction time is sufficient. The liquid product is separated and analyzed; a catalytic process with a very high product yield, up to 90% if calculated accounting the parts from lignin of the product molecules, or up to over 100% if calculated as the mass products. The product includes aromatic compounds, esters, alcohols, monophenols and benzyl alcohols in different ratios according to the composition, the solvent and the other reaction conditions.