IoT-based incubator monitoring and machine learning powered alarm predictions.

BACKGROUND: Incubators, especially the ones for babies, require continuous monitoring for anomaly detection and taking action when necessary. OBJECTIVE: This study aims to introduce a system in which important information such as temperature, humidity and gas values being tracked from incubator environment continuously in real-time. METHOD: Multiple sensors, a microcontroller, a transmission module, a cloud server, a mobile application, and a Web application were integrated Data were made accessible to the duty personnel both remotely via Wi-Fi and in the range of the sensors via Bluetooth Low Energy technologies. In addition, potential emergencies were detected and alarm notifications were created utilising a machine learning algorithm. The mobile application receiving the data from the sensors via Bluetooth was designed such a way that it stores the data internally in case of Internet disruption, and transfers the data when the connection is restored. RESULTS: The obtained results reveal that a neural network structure with sensor measurements from the last hour gives the best prediction for the next hour measurement. CONCLUSION: The affordable hardware and software used in this system make it beneficial, especially in the health sector, in which the close monitoring of baby incubators is vitally important.

Influence of different cooking methods on quality characteristics and nutritional value of gluten-free beef burger patties formulated with walnut oil, safflower oil and buckwheat.

In this study, it was aimed to develop gluten-free beef burger patties with walnut and safflower oils and to examine the effects of different cooking methods on the quality and nutritional value of the product. Two different cooking methods (oven and pan cooking) and 60 days of storage were applied to the patties that were produced by replacing 50% animal fat content with walnut and safflower oils and using buckwheat flour instead of rusk. The highest MUFA+PUFA and MUFA+PUFA/SFA values were determined in walnut oil added oven cooked samples at the beginning of the storage and safflower oil added oven cooked samples at the end of the storage (P < 0.05). The nutritional quality indexes (NVI, HH, AI, HPI) of fat of beef burger patties improved with the replacement of fat with safflower and walnut oil and preserved better with the oven-cooked method according to the pan cooking method. The addition of walnut oil significantly increased the vitamin E values compared to those of the control sample and these values were preserved during storage (P < 0.05). However, the flavor and overall acceptability scores of the safflower oil samples were higher than those of the walnut oil samples during 30 days of storage (P < 0.05). It was concluded that safflower-added samples could be preferred in terms of lower hardness, oxidation value, total saturated fatty acid, higher cooking yield and sensory evaluation scores.