ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.

Botanical authentication using one-class modeling

Sample authentication using one-class modeling is simpler and more versatile than the approved AOAC Probability of Identification (POI) method. This approach develops a one-class model for authentic samples and does not identify or model non-authentic or adulterated samples. Unknown samples are classified as either authentic (in the same class as the authentic samples) or not authentic (outside the authentic model class). One-class modeling uses chemometric analysis based on soft independent modeling of class analogy and the specific pre-processing steps of sample vector normalization and autoscaling. Data from flow injection mass spectrometry are used to illustrate the method. Autoscaling reduces the impact of the relative ion intensities and provides much greater sensitivity to changes in the intensities of individual variables. Detection limits for each variable can be predicted based on an average spectrum and types of uncertainty (random or proportional noise). Method validation is readily achieved using cross validation. Detection of adulteration is spectrally oriented and requires no identification of adulterants. Thus, both the data acquisition and the selection of adulterants are untargeted.