As we have all experienced during the COVID pandemic, the storage and transportation of vaccines require a certain temperature control. The tighter the temperature control, the more predictable the time-wise viability of the vaccine can be guaranteed. This paper aims to provide an Adaptive PID (Proportional-Integral-Derivative) control based on reinforcement learning and supported by user usage habits that gives more precise temperature control for sensitive medicines, vaccines and samples. Data collected from IoT fridges will aid acquiring usage habits and ambient conditions from users and devices deployed in the field. Experimental results show that 45%-75% decrease in cabinet temperature oscillation with same energy level for energy tests, 4%-12% decrease in energy consumption, and 10%-24% decrease in cabinet temperature oscillation in user tests when compared with classical cooling method can be obtained.

The correct and flawless functioning of medical devices is of utmost importance for patients and the medical sector. Although medical device manufacturers utilize various protocols and procedures during the development and production of medical devices, the integrated circuits are usually manufactured by other parties. During the manufacture of the semiconductors utilized in the integrated circuits, the performance of the components can vary from batch to batch and even samples within the same batch can have different performance characteristics. For applications in life critical applications the selection of the most fit for purpose components is vital. In this paper, we propose a novel method for the semiconductor industry to verify the quality of transistors. The I-V graphs of the components are evaluated by multiple Convolutional Neural Networks (CNN) using visual data similar to expert evaluation, then these Machine Learning (ML) architectures utilize a multi model ensemble technique where one architecture providing a negative output will overrule the vote of the other architectures is utilized to assure very stringent quality control. Our method is tested on CMOS transistors and the results are comparable to those of experts with 10 years of experience in the industry.

Air pollution is the contamination of the internal or external environment of any chemical, physical or biological material and altering the natural properties of the atmosphere. Air pollution is caused by natural causes such as volcanic eruptions, forest fires, or excessive evaporation, as well as by increasing traffic density, especially by artificial causes such as fuel types used by urban inhabitants and industrialization. Each year, countries keep statistics of deaths caused by different types of direct air pollution or indirectly by diseases caused by these different types of pollution. In this study, the impact of countries' geographical region and population density on mortality rates due to air pollution is analyzed. A dataset of Turkey and the twenty six countries covers information such as the population density, different types of pollution rates, number of deaths caused by each type of pollution and their ratios to current population number throughout a time period between 1990-2017. In the data analysis, air pollution types were examined one by one and it was sought to identify the similarities among countries. As a result of these analysis, it was shown that there are similarities between Turkey and the different neighbour countries on the death cases observed due to different types of air pollution.