Abstract: A multiple output relaxation (MOR) machine learning model. In one example embodiment, a method for employing an MOR machine learning model to predict multiple interdependent output components of a multiple output dependency (MOD) output decision may include training a classifier for each of multiple interdependent output components of an MOD output decision to predict the component based on an input and based on all of the other components. The method may also include initializing each possible value for each of the components to a predetermined output value. The method may further include running relaxation iterations on each of the classifiers to update the output value of each possible value for each of the components until a relaxation state reaches an equilibrium or a maximum number of relaxation iterations is reached. The method may also include retrieving an optimal component from each of the classifiers.

Abstract: The disclosure relates to a method to produce electricity in a cement clinker production utilizing a kiln and/or a precalciner as combustion chambers to generate electricity, the method including: a. supplying fuel to the precalciner and/or the kiln in a quantity corresponding to at least 110% of a heat value requirement for clinker production operation of the precalciner and/or the rotary kiln per unit weight of clinker, respectively; b. bypassing a portion of hot flue gases from at least one of (i) the kiln and/or (ii) the precalciner; c. leading hot flue gases to a heat recovery steam generator producing steam; d. producing electricity with a power island including a steam turbine equipped with an electrical generator.

Abstract: A substantially constant pressure injection molding method and machine that forms molded parts by injecting molten thermoplastic material into a mold cavity at a substantially constant pressure. As a result, the mold cavity is filled with molten thermoplastic material by advancing a continuous flow front of thermoplastic material from a gate to an end of the mold cavity.

Abstract: A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method of employing an HBS machine learning model to predict multiple interdependent output components of an MOD output decision may include determining an order for multiple interdependent output components of an MOD output decision. The method may also include sequentially training a classifier for each component in the selected order to predict the component based on an input and based on any previous predicted component(s).

Abstract: An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

Abstract: An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model may include identifying a temporal sequence of reinforcement learning machine learning training instances with each of the training instances including a state-action pair, determining a first quality value for a first training instance in the temporal sequence of reinforcement learning machine learning training instances determining a second quality value for a second training instance in the temporal sequence of reinforcement learning machine learning training instances, associating the first quality value with the first training instance, and associating the second quality value with the second training instance. In this example embodiment, the first quality value is higher than the second quality value.

Abstract: An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

Abstract: An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

Abstract: A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method of employing an HBS machine learning model to predict multiple interdependent output components of an MOD output decision may include determining an order for multiple interdependent output components of an MOD output decision. The method may also include sequentially training a classifier for each component in the selected order to predict the component based on an input and based on any previous predicted component(s).

Abstract: A reaction engine is disclosed mainly intended for aviation, but which as described herein can be adapted for an industrial engine, which makes use of spherical chambers and a pressure system in the blades of the rotor-stator unit which permits a perfect adjustment between said blades and the inner face of the stator, preventing pressure losses. While edges likewise articulated in the same way as the blades, execute a labyrinth seal.

Abstract: A substantially constant pressure injection molding method and machine that forms molded parts by injecting molten thermoplastic material into a mold cavity at a substantially constant pressure. As a result, the mold cavity is filled with molten thermoplastic material by advancing a continuous flow front of thermoplastic material from a gate to an end of the mold cavity.

Abstract: A substantially constant pressure injection molding method and machine that forms molded parts by injecting molten thermoplastic material into a mold cavity at a substantially constant pressures. As a result, the mold cavity is filled with molten thermoplastic material by advancing a continuous flow front of thermoplastic material from a gate to an end of the mold cavity.

Abstract: A multiple output relaxation (MOR) machine learning model. In one example embodiment, a method for employing an MOR machine learning model to predict multiple interdependent output components of a multiple output dependency (MOD) output decision may include training a classifier for each of multiple interdependent output components of an MOD output decision to predict the component based on an input and based on all of the other components. The method may also include initializing each possible value for each of the components to a predetermined output value. The method may further include running relaxation iterations on each of the classifiers to update the output value of each possible value for each of the components until a relaxation state reaches an equilibrium or a maximum number of relaxation iterations is reached. The method may also include retrieving an optimal component from each of the classifiers.

Abstract: The present invention provides a closed composite material structure for aircraft fuselage shaped on a male jig from which it can be separated in a certain direction, said structure comprising a single outer panel and a plurality of inner longitudinal stiffeners integrated in said panel, such that the expansion coefficient of the male jig is greater than the expansion coefficient of the composite material of the structure, thus being able to remove the already manufactured structure, formed by the panel and the integrated inner stiffeners, in a single operation. The present invention further provides a process for manufacturing such a closed structure.

Abstract: A protective sleeve includes a plurality of cords braided together. Each of the plurality of cords is formed from yarn that are knitted together. The plurality of cords form a substantially complete external surface of the protective sleeve.

Abstract: A process for manufacturing stiffened structures (11) in composite materials formed by an outer coating (13) and a plurality of stiffeners (15) the cross-section of which has a closed form delimiting an inner opening (23), comprising the following steps: a) Providing a shaping tool (31); b) Providing auxiliary male tools (37) with an inert material inside them coated with membranes suitable for curing c) Providing stiffeners (15) in fresh or cured condition; d) Arranging the stiffeners (15) in the tool (31) and the auxiliary male tools (37) in their inner openings (23); e) Laminating the outer coating (13); f) Curing the stiffened structure (11) with high temperature and pressure; g) Removing the auxiliary male tools (37) after reducing their volume by withdrawing the inert material thereof; h) Separating the cured stiffened structure (11) from the tool (31). The invention also relates to the auxiliary male tools.

Abstract: The disclosure relates to a method to produce electricity in a cement clinker production utilizing a kiln and/or a precalciner as combustion chambers to generate electricity, the method including: a. supplying fuel to the precalciner and/or the kiln in a quantity corresponding to at least 110% of a heat value requirement for clinker production operation of the precalciner and/or the rotary kiln per unit weight of clinker, respectively; b. bypassing a portion of hot flue gases from at least one of (i) the kiln and/or (ii) the precalciner; c. leading hot flue gases to a heat recovery steam generator producing steam; d. producing electricity with a power island including a steam turbine equipped with an electrical generator.

Abstract: The invention relates to the development of a method and pharmaceutical compositions for obtaining plasmatic progesterone levels in humans and for maintaining a plasmatic progesterone concentration between 42 and 3.5 ng/mL for eight days as well as maximum plasmatic concentrations (Cmax) between 12 and 42 ng/mL, sufficient for use in different therapeutic options that require said progesterone concentrations.

Abstract: A method for capturing CO2 produced by cement plants by using the calcium cycle method comprising the integration of the process known as calcium cycle to the cement plant by using the cement plant raw materials and sub products in the calcium cycle plant and by using the calcium cycle plant raw materials sub products and residual energy in the cement plant

Abstract: The present invention relates to a tubular-shaped component (19) for an aeronautical fuselage made of composite materials comprising an outer skin (1) without joints having a cylindrical or cylindrical-conical shape, a plurality of stiffeners or stringers (2) longitudinally arranged in said outer skin (1) and a plurality of anchoring elements (3) for anchoring other elements to subsequently be incorporated transversally arranged between said stiffeners or stringers (2). The invention also comprises a jig (18) comprising a support body (12) and a plurality of panels (9) that can shift between an extended position (10) and a retracted position (10?) in both directions and a process for manufacturing said component (19) in the last step of which once the tubular component (19) is formed and cured, it is separated from the jig (18) by means of a longitudinal movement after the shifting of those elements of the jig (18) preventing it, and particularly the panels (9) that can be shifted.