A Novel FEM-Based Numerical Solver for Interactive Catheter Simulation in Virtual Catheterization

Virtual reality-based simulators are very helpful for trainees to acquire the skills of manipulating catheters and guidewires during the vascular interventional surgeries. In the development of such a simulator, however, it is a great challenge to realistically model and simulate deformable catheters and guidewires in an interactive manner. We propose a novel method to simulate the motion of catheters or guidewires and their interactions with patients' vascular system. Our method is based on the principle of minimal total potential energy. We formulate the total potential energy in the vascular interventional circumstance by summing up the elastic energy deriving from the bending of the catheters or guidewires, the potential energy due to the deformation of vessel walls, and the work by the external forces. We propose a novel FEM-based approach to simulate the deformation of catheters and guidewires. The motion of catheters or the guidewires and their responses to every input from the interventionalist can be calculated globally. Experiments have been conducted to validate the feasibility of the proposed method, and the results demonstrate that our method can realistically simulate the complex behaviors of catheters and guidewires in an interactive manner

Molecularly Imprinted Fluorescent Hollow Nanoparticles as Sensors for Rapid and Efficient Detection λ-Cyhalothrin in Environmental Water

Molecularly imprinted fluorescent polymers have shown great promise in biological or chemical separations and detections, due to their high stability, selectivity and sensitivity. In this work, molecularly imprinted fluorescent hollow nanoparticles, which could rapidly and efficiently detect λ-cyhalothrin (a toxic insecticide) in water samples, was reported. The molecularly imprinted fluorescent sensor showed excellent sensitivity (the limit of detection low to 10.26 nM), rapid detection rate (quantitative detection of λ-cyhalothrin within 8 min), regeneration ability (maintaining good fluorescence properties after 8 cycling operation) and appreciable selectivity over several structural analogues. Moreover, the fluorescent sensor was further used to detect λ-cyhalothrin in real samples form the Beijing-Hangzhou Grand Canal Water. Despite the relatively complex components of the environmental water, the molecularly imprinted fluorescent hollow nanosensor still showed good recovery, clearly demonstrating the potential value of this smart sensor nanomaterial in environmental monitoring

Characteristics and Formation Mechanism of Inclusions in 304L Stainless Steel during the VOD Refining Process

The formation and characteristics of non-metallic inclusions in 304L stainless steel during the vacuum oxygen decarburization (VOD) refining process were investigated using industrial experiments and thermodynamic calculations. The compositional characteristics indicated that two types of inclusions with different sizes (from 1 μm to 30 μm) existed in 304L stainless steel during the VOD refining process, i.e., CaO-SiO2-Al2O3-MgO external inclusions, and CaO-SiO2-Al2O3-MgO-MnO endogenous inclusions. The calculation results obtained using the FactSage 7.1 software confirmed that the inclusions that were larger than 5 μm were mostly CaO-SiO2-Al2O3-MgO; the similarity in composition to the slag indicated that these inclusions originated from the slag entrapment. The CaO-SiO2-Al2O3-MgO-MnO inclusions that were smaller than 5 μm originated mainly from the oxidation reaction with Ca, Al, Mg, Si, and Mn. The changes in the inclusion composition resulting from changes in the Ca, Al, and O contents, and the temperature during the VOD refining process was larger for the smaller inclusions. Generating mechanisms for the CaO-SiO2-Al2O3-MgO-MnO inclusions in the 304L stainless steel were proposed

An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions

Proanthocyanidins as a Potential Novel Way for the Treatment of Hemangioma

Hemangioma, the most common benign vascular tumor, not only affects the appearance and psychology but also has a life-threatening potential. It is considered that clonal vascular endothelial cell proliferation and excessive angiogenesis are responsible for hemangioma pathogenesis, in which abnormal cytokines/pathways are closely implicated, primarily including high expression of hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) as well as their downstream pathways, especially phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt). These further stimulate the migration and proliferation of vascular endothelial cells and promote the formation of new vessels, ultimately leading to the occurrence and development of hemangioma. Proanthocyanidins are naturally active substance from plants and fruits. They possess multiple functions like antiproliferation, antiangiogenesis, and antitumor. It has been demonstrated that proanthocyanidins effectively work in various diseases via inhibiting the expression of various factors, e.g., HIF-1α, VEGF, PI3K, and Akt. Considering the pathogenesis of hemangioma and the effect of proanthocyanidins, we hold a hypothesis that proanthocyanidins would be applied in hemangioma via downregulating cytokine/pathway expression, suppressing vascular cell proliferation and arrest abnormal angiogenesis. Taken together, proanthocyanidins may be a potential novel way for the treatment of hemangioma

A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system

As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real-time (RT) load forecasting. In this article, an edge-cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid-long term mixed sequential mode RT forecasting. In the proposed edge-cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid-short term time-series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid-long term time-series forecasting and peak forecasting

Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision

Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of a target by exploiting both classic principal component analysis (PCA) algorithms with recent deep learning representation architectures. We use learned PCA eigenvectors as filters and develop a novel algorithm to represent a target by composing of a PCA-based filter bank layer, a nonlinear layer, and a patch-based pooling layer, respectively. Then, based on the feature representation, a neural network with one hidden layer is trained in a supervised mode to construct a discriminative appearance model. Finally, to alleviate the tracker drifting problem, a sample update scheme is carefully designed to keep track of the most representative and diverse samples during tracking. We test the proposed tracking method on two standard individual cell/object tracking benchmarks to show our tracker's state-of-the-art performance