Real-Time Monitoring of Bodily Fluids Using a Novel Electromagnetic Wave Sensor

The use of a novel low power electromagnetic sensor for real-time detection of lactate in cerebrospinal fluid (CSF) is investigated. CSF holds key indicators relating to a patient’s future health. A multipurpose sensor platform is currently being developed with the capability to detect the concentration of materials in volumes =1 ml. This paper presents results from a microwave cavity resonator designed and created for this purpose, using varying concentrations of lactate in water. The work demonstrates the feasibility of monitoring bodily fluids in real-time. Such advancements are essential for improved and cost-effective delivery of healthcare services to patients

Prediction of Flood Severity Level Via Processing IoT Sensor Data Using Data Science Approach

The ‘riverine flooding’ is deemed a catastrophic phenomenon caused by extreme climate changes and other ecological factors (e.g., amount of sunlight), which are difficult to predict and monitor. However, the use of internet of things (IoT), various types of sensing including social sensing, 5G wireless communication and big data analysis have devised advanced tools for early prediction and management of distrust events. To this end, this paper amalgamates machine learning models and data analytics approaches along-with IoT sensor data to investigate attribute importance for the prediction of risk levels in flood. The paper presents three river levels: normal, medium and high-risk river levels for machine learning models. Performance is evaluated with varying configurations and evaluations setup including training and testing of support vector machine and random forest using principal components analysis-based dimension reduced dataset. In addition, we investigated the use of synthetic minority over-sampling technique to balance the class representations within dataset. As expected, the results indicated that a “balanced” representation of data samples achieved high accuracy (nearly 93%) when benchmarked with “imbalanced” data samples using random forest classifier 10-folds cross-validation

Rapid Non-Destructive Prediction of Water Activity in Dry-Cured Meat

Water activity (aw) describes the amount of free water available in a matrix for growth of microbiological pathogens and spoilage flora. It is used to predict the safety of food products, and has particular importance for dry-cured meat manufacturers. Results from tests on dry-cured pork (n = 83) demonstrate a high degree of correlation (R2 = 0.909) with current industry standard equipment. System accuracy at the 95% confidence interval (0.0125) is comparable with existing equipment available to industry. However, the added advantage of the microwave sensor to enable rapid and non-destructive measurement means that it could be used for day-to-day monitoring and optimization of products within the dry-cured meat value chain. This would reduce per-product operating costs and waste, in addition to facilitating recipe development (e.g., reduced salt)