Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

On p.p.-rings which are reduced

Denote the 2×2 upper triangular matrix rings over ℤ and ℤp by UTM2(ℤ) and UTM2(ℤp), respectively. We prove that if a ring R is a p.p.-ring, then R is reduced if and only if R does not contain any subrings isomorphic to UTM2(ℤ) or UTM2(ℤp). Other conditions for a p.p.-ring to be reduced are also given. Our results strengthen and extend the results of Fraser and Nicholson on r.p.p.-rings

Baer semisimple modules and Baer rings

We consider Baer rings and Baer semisimple R-modules which are generalizations of semisimple modules. Several characterization theorems of Baer semisimple modules are obtained. In particular, we prove that a ring R is a Baer ring if and only if R itself, regarded as a regular R-module, is Baer semisimple

Compact Primitive Semigroups Having (CEP)

Abstract Compact completely simple semigroups having congruence extension property (in brevity (CEP)) were first studied by Dumesnil in 1997. In this paper, we study the compact primitive semigroups having (CEP) and characterize such semigroups, so that the result of Dumesnil on compact completely simple semigroups having (CEP) is extended to compact primitive semigroups

A co-design-based reliable low-latency and energy-efficient transmission protocol for uwsns

Recently, underwater wireless sensor networks (UWSNs) have been considered as a powerful technique for many applications. However, acoustic communications in UWSNs bring in huge QoS issues for time-critical applications. Additionally, excessive control packets and multiple copies during the data transmission process exacerbate this challenge. Faced with these problems, we propose a reliable low-latency and energy-efficient transmission protocol for dense 3D underwater wireless sensor networks to improve the QoS of UWSNs. The proposed protocol exploits fewer control packets and reduces data-packet copies effectively through the co-design of routing and media access control (MAC) protocols. The co-design method is divided into two steps. First, the number of handshakes in the MAC process will be greatly reduced via our forwarding-set routing strategy under the guarantee of reliability. Second, with the help of information from the MAC process, network-update messages can be used to replace control packages through mobility prediction when choosing a route. Simulation results show that the proposed protocol has a considerably higher reliability, and lower latency and energy consumption in comparison with existing transmission protocols for a dense underwater wireless sensor network.This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. U19A2061, 61772228 and 61902143), National key research and development program of China (Grant No. 2017YFC1502306)

Analysis of risk factors for deep vein thrombosis after spinal infection surgery and construction of a nomogram preoperative prediction model

ObjectiveTo investigate the differences in postoperative deep venous thrombosis (DVT) between patients with spinal infection and those with non-infected spinal disease; to construct a clinical prediction model using patients’ preoperative clinical information and routine laboratory indicators to predict the likelihood of DVT after surgery.MethodAccording to the inclusion criteria, 314 cases of spinal infection (SINF) and 314 cases of non-infected spinal disease (NSINF) were collected from January 1, 2016 to December 31, 2021 at Xiangya Hospital, Central South University, and the differences between the two groups in terms of postoperative DVT were analyzed by chi-square test. The spinal infection cases were divided into a thrombotic group (DVT) and a non-thrombotic group (NDVT) according to whether they developed DVT after surgery. Pre-operative clinical information and routine laboratory indicators of patients in the DVT and NDVT groups were used to compare the differences between groups for each variable, and variables with predictive significance were screened out by least absolute shrinkage and operator selection (LASSO) regression analysis, and a predictive model and nomogram of postoperative DVT was established using multi-factor logistic regression, with a Hosmer- Lemeshow goodness-of-fit test was used to plot the calibration curve of the model, and the predictive effect of the model was evaluated by the area under the ROC curve (AUC).ResultThe incidence of postoperative DVT in patients with spinal infection was 28%, significantly higher than 16% in the NSINF group, and statistically different from the NSINF group (P < 0.000). Five predictor variables for postoperative DVT in patients with spinal infection were screened by LASSO regression, and plotted as a nomogram. Calibration curves showed that the model was a good fit. The AUC of the predicted model was 0.8457 in the training cohort and 0.7917 in the validation cohort.ConclusionIn this study, a nomogram prediction model was developed for predicting postoperative DVT in patients with spinal infection. The nomogram included five preoperative predictor variables, which would effectively predict the likelihood of DVT after spinal infection and may have greater clinical value for the treatment and prevention of postoperative DVT

Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Aripiprazole, Levomilnacipran, Sertraline, Tramadol, Fentanyl, or Fluoxetine, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, were strong indicators for no SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making