Abstract: A MSE-type zeolite which has a Si/Al ratio of 5 or more, is a proton-type zeolite, and is obtained by transforming a raw material MSE-type zeolite synthesized without using a structure directing agent into an ammonium-type zeolite through ion exchange, then, exposing the MSE-type zeolite to water vapor, and subjecting the exposed MES-type zeolite to an acid treatment.

Abstract: A multi-core fiber for accommodating multiple single mode cores in one optical fiber is provided with multiple types of non-identical cores having different propagation constants. Each of the multiple types of non-identical cores includes a core part and a cladding part covering an outer periphery of the core part. The cladding part has a double cladding structure including a first cladding for surrounding an outer periphery of the core part and a second cladding on the outer side of the first cladding. In the multiple types of non-identical cores, an optical electromagnetic profile in the core part and the first cladding is confined within a range of the core part and the first cladding, restricting a leakage thereof to the second cladding, and the propagation constant of each non-identical core is made different using the refractive index of the first cladding as a parameter.

Abstract: Provided is a beta-type zeolite which has a high catalytic activity and is not easily deactivated. The beta-type zeolite of the invention has a substantially octahedral shape, has a Si/Al ratio of 5 or more, and is a proton-type zeolite. The Si/Al ratio is preferably 40 or more. This beta-type zeolite is preferably obtained by transforming a raw material beta-type zeolite synthesized without using a structure directing agent into an ammonium-type zeolite through ion exchange, then, exposing the beta-type zeolite to water vapor, and subjecting the exposed beta-type zeolite to an acid treatment.

Abstract: Reinforcement learning is one of the intellectual operations applied to autonomously moving robots etc. It is a system having excellent sides, for example, enabling operation in unknown environments. However, it has the basic problem called the “incomplete perception problem”. A variety of solution has been proposed, but none has been decisive. The systems also become complex. A simple and effective method of solution has been desired. A complex value function defining a state-action value by a complex number is introduced. Time series information is introduced into a phase part of the complex number value. Due to this, the time series information is introduced into the value function without using a complex algorithm, so the incomplete perception problem is effectively solved by simple loading of the method.