A CO2 laser has the advantages of being high in power and having many laser lines in the 9–11 µm infrared region. Thus, a CO2 laser photoacoustic spectrometer (PAS) can have a multi-component measurement capability for many gas compounds that have non-zero absorption coefficients at the laser lines, and therefore can be applied for measuring several volatile organic compounds (VOCs) in the human breath. We have developed a CO2 laser PAS system for detecting acetone in the human breath. Although acetone has small absorption coefficients at the CO2 laser lines, our PAS system was able to obtain strong photoacoustic (PA) signals at several CO2 laser lines, with the strongest one being at the 10P20 line. Since at the 10P20 line, ethylene and ammonia also have significant absorption coefficients, these two gases have to be included in a multi-component measurement with acetone. We obtained the lowest detection limit of our system for the ethylene, acetone, and ammonia are 6 ppbv, 11 ppbv, and 31 ppbv, respectively. We applied our PAS system to measure these three VOCs in the breath of three groups of subjects, i.e., patients with lung cancer disease, patients with other lung diseases, and healthy volunteers.

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In this research, an intelligent system for detecting cassava leaf disease has been developed by utilizing the MobileNetV2 deep learning model and displaying it using a python graphical user interface (GUI). There are five disease classes used in this study, namely Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD) and Healthy. The results showed that the overall accuracy of the test data obtained was 65,6%. The GUI application program was made to be operated efficiently for beginners and can be used by cassava farmers in the field.

One example of a multi-objective optimization problem is stock portfolio management. There are at least two objective functions to be achieved simultaneously, namely to maximize returns and minimize risk. The desire to maximize return and minimize risk are conflicting objectives. In this study, the problem of multi-objective optimization in the selection of Islamic stock portfolios will use the Pascoletti-Serafini scalarization modification method. Furthermore, the solution to the multi objective optimization problem is known as the Pareto optimal solution or efficient solution. In the Pascoletti-Serafini scalarization modification method, a set of Pareto optimal solutions can be constructed so that not only one solution is offered to decision makers, but a set of Pareto optimal solutions. From this research, the results obtained in the form of a set of efficient solutions that can be used as investor preferences in choosing the optimal stock portfolio.

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