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In this paper, mathematical model of cancer treatments have been presented and analyzed using coupled ordinary differential equations (ODEs). This model describes cancer growth on a cell population level with combination immunotherapy and chemotherapy treatments are often called biochemotherapy. This model also describes the effect of tumor infiltrating lymphocytes (TIL), interleukin-2 (IL-2) and interferon alpha (INF-α) on dynamics of tumor cells under the influence of immunotherapy, chemotherapy and biochemotherapy. Through this mathematical model, numerical simulations of immunotherapy, chemotherapy and biochemotherapy for some cases such as variation of tumor size and variation of parameter among patient 9 and patient 10 were presented. Our result shown that for parameter set patient 9 and patient 10, the biochemotherapy more effective than the immunotherapy and chemotherapy.

Spatial distribution of tuna (Thunnus.sp) in Indian Ocean south of Java has been investigated. Tuna was scientifically known as thermo-conformer species, thus their distribution were strongly influenced by sub-surface temperature. Tuna species in this study comprise of bigeye tuna (Thunnus obesus), albacore tuna (Thunnus alalunga), yellowfin tuna (Thunnus albacares) and southern bluefin tuna (Thunnus maccoyii). The study was conducted in the area between 100 o E -127 o E and 7 o S -20 o S during 2013 covering of southeast monsoon (April -September) and northwest monsoon (October -March) data. About 1200 coordinate of ARGO Float data and actual catch of tuna from fishing fleet in the same day were processed to obtain the polynomial equation and correlation coefficient. ARGO Float data were processed using kriging method. Correlation coefficient method that used in the study was Empirical Cumulative Distribution Function (ECDF), while spatial distribution equation was developed by polynomial regression equation. Sub-surface temperature in Indian Ocean south of Java fluctuates seasonally. Temporal distribution of dataset indicates that sub-surface temperature was warmer in northwest monsoon than in southeast monsoon. Seasonal fluctuation of sub-surface temperature may vary due to occurrence of upwelling. T. alalunga, T. Albacares and T. Obesus were found to be more favour in the depth around 150m with optimum temperature between 16 o C -21 o C, while T. maccoyii were found in the dept around 250m with optimum temperature between 13 o C -16 o C. Potential fishing zone for Thunnus.sp in southeast monsoon was wider than in northwest monsoon. This condition was according to seasonal variability of sub-surface temperature.

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Photovoltaic (PV) system is one of the promising renewable energy technologies. Although the energy conversion efficiency of the system is still low, but it has the advantage that the operating cost is free, very low maintenance and pollution-free. Maximum power point tracking (MPPT) is a significant part of PV systems. This paper presents a novel intelligent MPPT controller for PV systems. For the MPPT algorithm, an optimized fuzzy logic controller (FLC) using the Hopfield neural network is proposed. It utilizes an automatically tuned FLC membership function instead of the trial-and-error approach. The MPPT algorithm is implemented in a new variant of coupled inductor soft switching boost converter with high voltage gain to increase the converter output from the PV panel. The applied switching technique, which includes passive and active regenerative snubber circuits, reduces the insulated gate bipolar transistor switching losses. The proposed MPPT algorithm is implemented using the dSPACE DS1104 platform software on a DS1104 board controller. The prototype MPPT controller is tested using an agilent solar array simulator together with a 3 kW real PV panel. Experimental test results show that the proposed boost converter produces higher output voltages and gives better efficiency (90%) than the conventional boost converter with an RCD snubber, which gives 81% efficiency. The prototype MPPT controller is also found to be capable of tracking power from the 3 kW PV array about 2.4 times more than that without using the MPPT controller.

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