Weighted Centroid Localization Algorithm Based on Least Square for Wireless Sensor Networks

Localization algorithm is an important and challenging topic in today’s wireless sensor networks (WSNs). In order to improve the localization accuracy, a weighted centroid localization algorithm based on least square to predict the location of any sensor in WSNs is proposed in this paper. The proposed algorithm proposes a Least-Square-based weight model which can reasonably weigh the proportion of each anchor node in the unknown node. In the weight model, we utilize least square method to compute the weight. Then, we increase the weight of anchor nodes closer to the unknown node, introduce the parameter k into the proposed likelihood model, and we determine the optimal value of the parameter k through our real experiments. Simulation and experimental results show that the proposed weighted centroid algorithm is better than Weighted Centroid Localization (WCL) and Anchor-optimized Modified Weighted Centroid Localization based on RSSI (AMWCL-RSSI) in terms of the localization accuracy

Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation

Hemorrhagic complication (HC) is the most severe complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS). This study aimed to build a machine learning (ML) prediction model and an application system for a personalized analysis of the risk of HC in patients undergoing IVT therapy. We included patients from Chongqing, Hainan and other centers, including Computed Tomography (CT) images, demographics, and other data, before the occurrence of HC. After feature engineering, a better feature subset was obtained, which was used to build a machine learning (ML) prediction model (Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB)), and then evaluated with relevant indicators. Finally, a prediction model with better performance was obtained. Based on this, an application system was built using the Flask framework. A total of 517 patients were included, of which 332 were in the training cohort, 83 were in the internal validation cohort, and 102 were in the external validation cohort. After evaluation, the performance of the XGB model is better, with an AUC of 0.9454 and ACC of 0.8554 on the internal validation cohort, and 0.9142 and ACC of 0.8431 on the external validation cohort. A total of 18 features were used to construct the model, including hemoglobin and fasting blood sugar. Furthermore, the validity of the model is demonstrated through decision curves. Subsequently, a system prototype is developed to verify the test prediction effect. The clinical decision support system (CDSS) embedded with the XGB model based on clinical data and image features can better carry out personalized analysis of the risk of HC in intravenous injection patients

Study on the Synthesis and Properties of Waterborne Polyurea Modified by Epoxy Resin

The most notable features of polyurea are its fast reaction, energy-saving and high efficiency. In order to meet the needs of environmental protection, waterborne polyurea (WPUA) has become a research hotspot. However, the presence of hydrophilic groups in WPUA reduces its solvent resistance, heat resistance and mechanical properties. Therefore, it is necessary and valuable to develop a high-performance WPUA. In this study, epoxy-modified waterborne polyurea (WPUAE) emulsions were prepared using epoxy resin as a modifier. Fourier transform infrared spectroscopy (FT-IR) showed that E44 was successfully introduced into the molecular chain of WPUA. The WPUAE was tested for gel fraction, adhesion, contact angle, solvent resistance, tensile properties and thermal stability. The results showed that when the E44 content was 8 wt%, the performance of WPUAE was best, the adhesion of WPUAE coating film was 1.53 MPa, the gel fraction, water contact angle, water absorption, toluene absorption, tensile strength and decomposition temperature were 96.94%, 70.3°, 16.43%, 131.04%, 9.05 MPa and 365 °C, respectively. The results showed that epoxy resin as an emulsion modifier improved the comprehensive properties of WPUA