G-α-preinvex functions and non-smooth vector optimization problems

In this paper, we proposed the non-smooth G-?-preinvexity by generalizing ?-invexity and G-preinvexity, and discussed some solution properties about non-smooth vector optimization problems and vector variational-like inequality problems under the condition of non-smooth G-?-preinvexity. Moreover, we also proved that the vector critical points, the weakly efficient points and the solutions of the non-smooth weak vector variational-like inequality problem are equivalent under non-smooth pseudo-G-?-preinvexity assumptions.

Experimental study of combustion strategy for jet ignition on a natural gas engine

To explore a suitable combustion strategy for natural gas engines using jet ignition, lean burn with air dilution, stoichiometric burn with EGR dilution and lean burn with EGR dilution were investigated in a single-cylinder natural gas engine, and the performances of two kinds of jet ignition technology, passive jet ignition (PJI) and active jet ignition (AJI), were compared. In the study of lean burn with air dilution strategy, the results showed that AJI could extend the lean limit of excess air ratio (λ) to 2.1, which was significantly higher than PJI’s 1.6. In addition, the highest indicated thermal efficiency (ITE) of AJI was shown 2% (in absolute value) more than that of PJI. Although a decrease of NOx emission was observed with increasing λ in the air dilution strategy, THC and CO emissions increased. Stoichiometric burn with EGR was proved to be less effective, which can only be applied in a limited operation range and had less flexibility. However, in contrast to the strategy of stoichiometric burn with EGR, the strategy of lean burn with EGR showed a much better applicability, and the highest ITE could achieve 45%, which was even higher than that of lean burn with air dilution. Compared with the most efficient points of lean burn with pure air dilution, the lean burn with EGR dilution could reduce 78% THC under IMEP = 1.2 MPa and 12% CO under IMEP = 0.4 MPa. From an overall view of the combustion and emission performances under both low and high loads, the optimum λ would be from 1.4 to 1.6 for the strategy of lean burn with EGR dilution.

PEMBENTUKAN PORTOFOLIO OPTIMAL DENGAN METODE RESAMPLED EFFICIENT FRONTIER UNTUK PERHITUNGAN VALUE AT RISK DILENGKAPI APLIKASI GUI MATLAB

The purpose of investors in investing is to get a return, but investors also have to bear the risks that might exist. There are 3 types of investors in investment based on their preference for risk, namely risk aversion (risk averter), moderate risk takers (risk moderate), and high risk takers (risk takers). To obtain an optimal portfolio for each type of investor, the Resampled Efficient Frontier Method is used with Monte Carlo Simulation as much as 700 times, to obtain more parameter estimates. The results of the Resampled Efficient Frontier from Efficient Frontier will take 51 efficient points to determine the optimal portfolio for each type of investor. The efficient point taken is the 1st, 26th and 51st efficient points for the investor risk averter type, risk moderate, and risk taker. To determine the estimated loss in investment, the VaR value is calculated based on the monthly return data of BBNI, UNTR, INKP, and KLBF shares for the period February 2013 to March 2017, with a capital allocation of Rp 100,000,000.00, a holding period of 20 days, and a level of trust of 95%. The Matlab GUI is used to facilitate users in processing data.Keywords: Efficient Frontier, Monte-Carlo Simulation, Normal Distribution, VaR, Matlab GUI