Abstract: Training an ordinal mapping deep neural network (OMDNN) can include receiving multiple samples, each a computer-processable data structure corresponding to a real-world object and including a data element indicating one of n predefined classes to which each sample is linked. Each sample can be mapped by the OMDNN to sample points of a multidimensional space. The OMDNN can predicts the class of each sample based on an ordinal mapping. Parameters of the OMDNN can be iteratively adjusted in response to misclassifying one or more samples. Iteratively adjusting the parameters can be based on an expected loss determined by an ordinal mapping loss function that measures (a) distances between each sample point in the multidimensional space and each other sample point of the same class and (b) overlap between sample points of different classes.

Abstract: A fuel composition contains (a) a medium substantially free of to free of sulphur; (b) a detergent/dispersant additive; and (c) a liquid fuel where the medium substantially free of to free of sulphur is an aliphatic hydrocarbon solvent, and where the aliphatic hydrocarbon solvent is present from at least about 50 wt % to about 100 wt % of the total amount of the medium. The fuel composition is prepared by a process and is useful in a process, where the fuel composition includes a medium that is a hydrocarbon or a nonhydrocarbon or a mixture thereof, to increase the efficiency of an exhaust after-treatment device of an internal combustion engine.

Abstract: A fuel composition contains (a) a medium substantially free of to free of sulphur; (b) a detergent/dispersant additive; and (c) a liquid fuel where the medium substantially free of to free of sulphur is an aliphatic hydrocarbon solvent, and where the aliphatic hydrocarbon solvent is present from at least about 50 wt % to about 100 wt % of the total amount of the medium. The fuel composition is prepared by a process and is useful in a process, where the fuel composition includes a medium that is a hydrocarbon or a nonhydrocarbon or a mixture thereof, to increase the efficiency of an exhaust after-treatment device of an internal combustion engine.

Abstract: A fuel composition contains (a) a medium substantially free of to free of sulphur; (b) a detergent/dispersant additive; and (c) a liquid fuel where the medium substantially free of to free of sulphur is an aliphatic hydrocarbon solvent, and where the aliphatic hydrocarbon solvent is present from at least about 50 wt % to about 100 wt % of the total amount of the medium. The fuel composition is prepared by a process and is useful in a process, where the fuel composition includes a medium that is a hydrocarbon or a nonhydrocarbon or a mixture thereof, to increase the efficiency of an exhaust after-treatment device of an internal combustion engine.

Abstract: Internal combustion engines exhibit improved efficiency of the emissions control system by using a lubricating oil composition of a base oil, an alkali or alkaline earth metal-containing detergent; a metal salt of one or more phosphorus-containing compounds represented by the formula (I), wherein X1 and X2 are independently O or S, and R1 and R2 are independently hydrocarbyl groups, the average total number of carbon atoms in R1 and R2 for the one or more phosphorus-containing compounds being at least 10.4; and an acylated nitrogen-containing compound having at least about 10 alphatic carbon atoms and a TBN of at least about 2; the lubricating oil composition being characterized by a phosphorus concentration of up to about 0.12% by weight and the substantial absence of copper, thereby generating a lean-phosphorus containing exhaust gas.