Indoor Localization Algorithm Based on a High-Order Graph Neural Network.

Given that fingerprint localization methods can be effectively modeled as supervised learning problems, machine learning has been employed for indoor localization tasks based on fingerprint methods. However, it is often challenging for popular machine learning models to effectively capture the unstructured data features inherent in fingerprint data that are generated in diverse propagation environments. In this paper, we propose an indoor localization algorithm based on a high-order graph neural network (HoGNNLoc) to enhance the accuracy of indoor localization and improve localization stability in dynamic environments.

Inflammation-related nomogram for predicting survival of patients with unresectable hepatocellular carcinoma received conversion therapy.

Background: The efficacy of conversion therapy for patients with unresectable hepatocellular carcinoma (HCC) is a common clinical concern.

Aim: To analyse the prognostic factors of overall survival (OS) in patients with unresectable HCC who received conversion therapy.

Methods: One hundred and fifty patients who met the inclusion criteria were enrolled and divided into a training cohort ( = 120) and a validation cohort ( = 30).

Microfluidic paper-based aptasensor devices for multiplexed detection of pathogenic bacteria.

Foodborne pathogens are major public health concerns worldwide. Paper-based microfluidic devices are versatile, user friendly and low cost. We report a novel paper-based single input channel microfluidic device that can detect more than one whole-cell foodborne bacteria at the same time, and comes with quantitative reading via image analysis.