This study examines an atmospheric bore formed from the collision between a gust front(GF)and a sea breeze(SB)over the Bohai sea coast of China during the night of June 12-13,2018,and discusses the role of atmospheric bore on upstream convection initiation(CI).Based on radar data,ground-based observations,and high-resolution numerical simulations,the findings indicate that the SB establishes a stable boundary layer(SBL)offshore,while the GF associated with the eastward-moving mesoscale convective system(MCS)undergoes a transformation due to their interaction.Initially characterized as a density current,the GF gradually evolves into a non-undular bore exhibiting gravity wave properties and eventually to an undular one.The undular bore then propagates within favorable trapping layers associated with the SB,lifting air parcels with high convective available potential energy(CAPE)within the SBL.This process initiates convection upstream and further sustains the MCS.Due to the widespread occurrence of GFs and SBs over the eastern coastal regions of China during the warm season,their interactions provide the possibility for bore formation under various boundary layer conditions,thereby playing a crucial role in the occurrence and development of intense convection both at night and during the daytime.
Orbital angular momentum(OAM)multiplexing has been suggested as a potential solution to increase the data capacity of acoustic communication systems.A method of modulation and demodulation of OAM multiplexed signals using acoustic metasurfaces is proposed in this manuscript.The amplitude and phase of independent acoustic orbital angular momentum(AOAM)multiplexed signal provide orthogonal degrees of freedom.By designing appropriate acoustic metasurfaces and combining them with binary differential phase coding techniques,we demonstrated that efficient modulation and fast demodulation of AOAM multiplexed signals can be achieved.Orthogonal acoustic spiral modes prevent mode coupling and crosstalk,enhancing acoustic multiplexing.The resonance-based structure provides signal frequency selectivity,boosting efficiency and simplifying terminal equipment for practical applications,which leads to the creation of high-capacity acoustic communication systems.
Background: Breast ultrasound is prominently used in diagnosing breast tumors. At present, many automatic systems based on deep learning have been developed to help radiologists in diagnosis. However, training such systems remains challenging because they are usually data-hungry and demand amounts of labeled data, which need professional knowledge and are expensive. Methods: We adopted a triplet network and a self-supervised contrastive learning technique to learn representations from unlabeled breast ultrasound video clips. We further designed a new hard triplet loss to to learn representations that particularly discriminate positive and negative image pairs that are hard to recognize. We also constructed a pretraining dataset from breast ultrasound videos (1,360 videos from 200 patients), which includes an anchor sample dataset with 11,805 images, a positive sample dataset with 188,880 images, and a negative sample dataset dynamically generated from video clips. Further, we constructed a finetuning dataset, including 400 images from 66 patients. We transferred the pretrained network to a downstream benign/malignant classification task and compared the performance with other state-of-the-art models, including three models pretrained on ImageNet and a previous contrastive learning model retrained on our datasets. Results and conclusion: Experiments revealed that our model achieved an area under the receiver operating characteristic curve (AUC) of 0.952, which is significantly higher than the others. Further, we assessed the dependence of our pretrained model on the number of labeled data and revealed that <100 samples were required to achieve an AUC of 0.901. The proposed framework greatly reduces the demand for labeled data and holds potential for use in automatic breast ultrasound image diagnosis.
Finding the causal relationship between variables from observed data is a key issue in many decision analytics researches. Because the traditional Granger causality model is affected by the curse of dimensionality, it is difficult to accurately find causality in high-dimensional time series. To address this problem, we propose a new Granger causality analysis method based on quantile factor model, named QFM-CGC. First, QFM-CGC adopts Akaike information criterion to select models, which avoids setting the lag order relying on human intervention. Then, the quantile factor model is established to reduce the dimensionality of conditional variables in a VAR (Vector Auto-Regressive) model, further reducing the number of coefficients. After that, the reduced-dimensional VAR model is used for a conditional Granger causality analysis. Finally, in order to identify the connectivity structure between the underlying system and observation time series, Monte Carlo simulation is applied to evaluate the performance of different methods. Experiments compared with classical methods on linear simulation systems in different dimensions and two realistic datasets, all demonstrate the effectiveness of QFM-CGC. In simulation experiments, the method proposed in this article has improved the accuracy of identifying causal relationships by an average of 6% and 3.46%. In two real-world data experiments, compared with the optimal comparison methods, QFM-CGC reduced the prediction error of causal relationship sequences.
Subway tunneling construction in shallow buried karst region is a common challenge. Survey boreholes and cross-hole seismic CT data showed that 29 karst caves exist at elevation of −17 m to −37 m in Si–He section of the Wuxi Metro Line 4, which are consisted of 18 filled caves and 11 empty caves and constitute substantial dangers to the stability and quality of metro construction projects. Based on their size, position, and type of filling, we divided these shallow karst caves into four types. Aimed at exploring the instability of the tunnel after excavation, numerical analyses have been developed by using the Finite difference method. Both the effects of the distance between tunnel and karst caves and the effects of the filling of the cavities on the overall stability of the rock mass around the tunnel have been investigated. According to the design criteria, safety distance between tunnel and karst caves was determined. The monitoring data of tunnel during excavation proved the rationality and accuracy of the analysis results. The research results can provide technical reference for the design and construction of other proposed tunnels that are located in the regions of shallow buried karst in Jiangsu Province.
