Prediction of Road Traffic Accidents Using a Combined Model Based on IOWGA Operator

Traffic accident prediction plays a important role in reducing the likelihood of traffic accidents and improving the management levels of traffic safety. A new combined prediction model based on the induced ordered weighted geometric average (IOWGA) operator was proposed. This new model combines the GM(1,1) model and the Verhulst model with changeable weight coefficients of each single model. A combined model based on the optimal weighted(OW) method is also presented for comparison. An example is given with the number of deaths by road traffic accidents in China from 2003 to 2008. The results indicate that the proposed combined model is better than the other three models

Acoustic assembly of cell spheroids in disposable capillaries

Multicellular spheroids represent a promising approach to mimic 3D tissues in vivo for emerging applications in regenerative medicine, therapeutic screening, and drug discovery. Conventional spheroid fabrication methods, such as the hanging drop method, suffer from low-throughput, long time, complicated procedure, and high heterogeneity in spheroid size. In this work, we report a simple yet reliable acoustic method to rapidly assemble cell spheroids in capillaries in a replicable and scalable manner. Briefly, by introducing a coupled standing surface acoustic wave, we are able to generate a linear pressure node array with 300 trapping nodes simultaneously. This enables us to continuously fabricate spheroids in a high-throughput manner with minimal variability in spheroid size. In a proof of concept application, we fabricated cell spheroids of mouse embryonic carcinoma (P19) cells, which grew well and retained differentiation potential in vitro. Based on the advantages of the non-invasive, contactless and label-free acoustic cell manipulation, our method employs the coupling strategy to assemble cells in capillaries, and further advances 3D spheroid assembly technology in an easy, cost-efficient, consistent, and high-throughput manner. This method could further be adapted into a novel 3D biofabrication approach to replicate compilated tissues and organs for a wide set of biomedical applications

High-Throughput Acoustofluidic Fabrication of Tumor Spheroids

Three-dimensional (3D) culture of multicellular spheroids, offering a desirable biomimetic microenvironment, is appropriate for recapitulating tissue cellular adhesive complexity and revealing a more realistic drug response. However, current 3D culture methods are suffering from low-throughput, poor controllability, intensive-labor, and variation in spheroid size, thus not ready for many high-throughput screening applications including drug discovery and toxicity testing. Herein, we developed a high-throughput multicellular spheroid fabrication method using acoustofluidics. By acoustically-assembling cancer cells with low-cost and disposable devices, our method can produce more than 12 000 multicellular aggregates within several minutes and allow us to transfer these aggregates into ultra-low attachment dishes for long-term culture. This method can generate more than 6000 tumor spheroids per operation, and reduce tumor spheroid formation time to one day. Our platform has advantages in forming spheroids with high throughput, short time, and long-term effectiveness, and is easy-to-operation. This acoustofluidic spheroid assembly method provides a simple and efficient way to produce large numbers of uniform-sized spheroids for biomedical applications in translational medicine, pharmaceutical industry and basic life science research

Direction of Arrival Estimation and Sensor Array Error Calibration Based on Blind Signal Separation

We consider estimating the direction of arrival (DOA) in the presence of sensor array error. In the proposed method, a blind signal separation method, the Joint Approximation and Diagonalization of Eigenmatrices (JADE) algorithm, is implemented to separate the signal vector and the mixing matrix consisting of the array manifold matrix and the sensor array error matrix. Based on a new mixing matrix and the reconstruction of the array output vector of each individual signal, we propose a novel DOA estimation method and sensor array error calibration procedure. This method is independent of array phase errors and performs well against difference of SNR of signals. Numerical simulations verify the effectiveness of the proposed method

Gou Qi Zi inhibits proliferation and induces apoptosis through the PI3K/AKT1 signaling pathway in non-small cell lung cancer

BackgroundGou Qi Zi (Lycium barbarum) is a traditional herbal medicine with antioxidative effects. Although Gou Qi Zi has been used to prevent premature aging and in the treatment of non-small cell lung cancer (NSCLC), its mechanism of action in NSCLC remains unclear. The present study utilized network pharmacology to assess the potential mechanism of action of Gou Qi Zi in the treatment of NSCLC.MethodsThe TCMSP, TCMID, SwissTargetPrediction, DrugBank, DisGeNET, GeneCards, OMIM and TTD databases were searched for the active components of Gou Qi Zi and their potential therapeutic targets in NSCLC. Protein-protein interaction networks were identified and the interactions of target proteins were analyzed. Involved pathways were determined by GO enrichment and KEGG pathway analyses using the Metascape database, and molecular docking technology was used to study the interactions between active compounds and potential targets. These results were verified by cell counting kit-8 assays, BrdU labeling, flow cytometry, immunohistochemistry, western blotting, and qRT-PCR.ResultsDatabase searches identified 33 active components in Gou Qi Zi, 199 predicted biological targets and 113 NSCLC-related targets. A network of targets of traditional Chinese medicine compounds and potential targets of Gou Qi Zi in NSCLC was constructed. GO enrichment analysis showed that Gou Qi Zi targeting of NSCLC was mainly due to the effect of its associated lipopolysaccharide. KEGG pathway analysis showed that Gou Qi Zi acted mainly through the PI3K/AKT1 signaling pathway in the treatment of NSCLC. Molecular docking experiments showed that the bioactive compounds of Gou Qi Zi could bind to AKT1, C-MYC and TP53. These results were verified by experimental assays.ConclusionGou Qi Zi induces apoptosis and inhibits proliferation of NSCLC in vitro and in vivo by inhibiting the PI3K/AKT1 signaling pathway

Plasmonic Nanosensors and Metasensors Based on New Physical Mechanisms

Plasmonics can bind light to their surface while increasing its intensity. The confinement and enhancement of light allows high–density, independent, subwavelength sensor elements to be constructed in micrometer–sized arrays. Plasmonic nanostructures have been widely used in the sensing field because of their fast, real–time and label–free characteristics. Numerous plasmonic metasensors have been configured for next–generation technologies since the emergence of metamaterials and metasurfaces. Among these applications, the development of high–sensitivity sensors based on new physical mechanisms has received tremendous interest recently. This review focuses on high–sensitivity plasmonic nanosensors and metasensors based on new physical mechanisms, especially based on Fano resonance and the exceptional point (EP). The asymmetric Fano resonance generated by the interference of different resonance modes has a narrower bandwidth, while an EP occurs whenever two resonant modes coalesce both in their resonant frequency and their rate of decay or growth. Both physical mechanisms could tremendously improve the sensitivity of the plasmonic sensors. We summarize the working principles, the latest development status and the development trends of these plasmonic nanosensors and metasensors. It is believed that these new sensing mechanisms can inspire more fruitful scientific research

Calibration of Phased-Array High-Frequency Radar on an Anchored Floating Platform

Prior studies have highlighted the importance of calibrating receiver antennas in target direction-of-arrival (DOA) estimation and surface current measurement for high-frequency (HF) radar systems. It is worth noting that the calibration contributes to the performance of both shore-based HF radar and platform-mounted HF radar. Compared with shore-based HF radar, the influence of six-degrees-of-freedom (six-DOF) platform motion should be considered in the calibration of platform-mounted HF radar. This paper initially describes a calibration scheme that receives phasedarray antennas for an anchored platform-mounted HF radar incorporating a model of free rotation, which is called yaw rotation and dominates the six-DOF platform motion in this study. In the presence of yaw rotation, the amplitude and phase of the source calibration signal from the other shore-based radar sites reveal the directional sensitivity of the receiver phased-array antennas. The calibration of receiver phased-array antennas is composed of channel calibration (linking cables and receiver hardware calibration) and antenna pattern calibration. The antenna pattern at each bearing can be represented by the Fourier series. The estimation of channel calibration and antenna pattern calibration depends on an overdetermined HF radar system consisting of observed values and theoretical constraints, so the least-squares fits of the channel calibration coefficients and antenna pattern calibration coefficients are obtained. The experimental results show that the target DOA estimation and surface current measurement can be improved if the phased-array platform-mounted HF radar system is calibrated

SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection

Roller bearings are some of the most critical and widely used components in rotating machinery. Appearance defect inspection plays a key role in bearing quality control. However, in real industries, bearing defects are usually extremely subtle and have a low probability of occurrence. This leads to distribution discrepancies between the number of positive and negative samples, which makes intelligent data-driven inspection methods difficult to develop and deploy. This paper presents a small data-driven convolution neural network (SDD-CNN) for roller subtle defect inspection via an ensemble method for small data preprocessing. First, label dilation (LD) is applied to solve the problem of an imbalance in class distribution. Second, a semi-supervised data augmentation (SSDA) method is proposed to extend the dataset in a more efficient and controlled way. In this method, a coarse CNN model is trained to generate ground truth class activation and guide the random cropping of images. Third, four variants of the CNN model, namely, SqueezeNet v1.1, Inception v3, VGG-16, and ResNet-18, are introduced and employed to inspect and classify the surface defects of rollers. Finally, a rich set of experiments and assessments is conducted, indicating that these SDD-CNN models, particularly the SDD-Inception v3 model, perform exceedingly well in the roller defect classification task with a top-1 accuracy reaching 99.56%. In addition, the convergence time and classification accuracy for an SDD-CNN model achieve significant improvement compared to that for the original CNN. Overall, using an SDD-CNN architecture, this paper provides a clear path toward a higher precision and efficiency for roller defect inspection in smart manufacturing

Calibration of Phased-Array High-Frequency Radar on an Anchored Floating Platform

Prior studies have highlighted the importance of calibrating receiver antennas in target direction-of-arrival (DOA) estimation and surface current measurement for high-frequency (HF) radar systems. It is worth noting that the calibration contributes to the performance of both shore-based HF radar and platform-mounted HF radar. Compared with shore-based HF radar, the influence of six-degrees-of-freedom (six-DOF) platform motion should be considered in the calibration of platform-mounted HF radar. This paper initially describes a calibration scheme that receives phasedarray antennas for an anchored platform-mounted HF radar incorporating a model of free rotation, which is called yaw rotation and dominates the six-DOF platform motion in this study. In the presence of yaw rotation, the amplitude and phase of the source calibration signal from the other shore-based radar sites reveal the directional sensitivity of the receiver phased-array antennas. The calibration of receiver phased-array antennas is composed of channel calibration (linking cables and receiver hardware calibration) and antenna pattern calibration. The antenna pattern at each bearing can be represented by the Fourier series. The estimation of channel calibration and antenna pattern calibration depends on an overdetermined HF radar system consisting of observed values and theoretical constraints, so the least-squares fits of the channel calibration coefficients and antenna pattern calibration coefficients are obtained. The experimental results show that the target DOA estimation and surface current measurement can be improved if the phased-array platform-mounted HF radar system is calibrated