Unusual Sign Reversal of Field-like Spin-Orbit Torque in Pt/Ni/Py with an Ultrathin Ni Spacer

The magnetization manipulation by spin-orbit torques (SOTs) in nonmagnetic-metal (NM)/ferromagnet (FM) heterostructures has provided great opportunities for spin devices. Besides the conventional spin Hall effect (SHE) in heavy metals with strong spin-orbit coupling, the orbital currents have been proposed to be another promising approach to generate strong SOTs. Here, we systematically study the SOTs efficiency and its dependence on the FM thickness and different NM/FM interfaces in two prototypical Pt/Py and Ta/Py systems by inserting an ultrathin magnetic layer (0.4 nm thick ML = Co, Fe, Gd, and Ni). The dampinglike (DL) torque efficiency $\xi_{DL}$ is significantly enhanced by inserting ultrathin Co, Fe, and Ni layers and is noticeably suppressed for the Gd insertion. Moreover, the Ni insertion results in a sign change of the field-like (FL) torque in Pt/Py and substantially reduces $\xi_{DL}$ in Ta/Py. These results are likely related to the additional spin currents generated by combining the orbital Hall effect (OHE) in the NM and orbital-to-spin conversion in the ML insertion layer and/or their interfaces, especially for the Ni insertion. Our results demonstrate that inserting ultrathin ML can effectively manipulate the strength and sign of the SOTs, which would be helpful for spintronics applications

T cell aging and Alzheimer’s disease

The brain has long been considered an immune-privileged organ due to the presence of the blood-brain barrier (BBB). However, recent discoveries have revealed the underestimated role of T cells in the brain through the meningeal lymphatic system. Age is the primary risk factor for Alzheimer’s disease (AD), resulting in marked age-dependent changes in T cells. Manipulating peripheral T cell immune response has been shown to impact AD, but the relationship between T cell aging and AD remains poorly understood. Given the limited success of targeting amyloid beta (Aβ) and the growing evidence of T cells’ involvement in non-lymphoid organ aging, a deeper understanding of the relationship between T cells and AD in the context of aging is crucial for advancing therapeutic progress. In this review, we comprehensively examine existing studies on T cells and AD and offer an integrated perspective on their interconnections in the context of aging. This understanding can inform the development of new interventions to prevent or treat AD

Ship Intention Prediction at Intersections Based on Vision and Bayesian Framework

Due to the high error frequency of the existing methods in identifying a ship’s navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this paper, we propose an algorithm based on the fusion of image sequence and radar information to identify the navigation intention of ships at intersections. Some existing algorithms generally use the Automatic Identification System (AIS) to identify ship intentions but ignore the problems of AIS delay and data loss, resulting in unsatisfactory effectiveness and accuracy of intention recognition. Firstly, to obtain the relationship between radar and image, a cooperative target composed of a group of concentric circles and a central positioning radar angle reflector is designed. Secondly, the corresponding relationship of radar and image characteristic matrix is obtained after employing the RANSAC method to fit radar and image detection information; then, the homographic matrix is solved to realize radar and image data matching. Thirdly, the YOLOv5 detector is used to track the ship motion in the image sequence. The visual measurement model based on continuous object tracking is established to extract the ship motion parameters. Finally, the motion intention of the ship is predicted by integrating the extracted ship motion features with the position information of the shallow layer using a Bayesian framework. Many experiments on real data sets show that our proposed method is superior to the most advanced method for ship intention identification at intersections

A Novel Vision-Based Towing Angle Estimation for Maritime Towing Operations

The demand for non-powered facility towing is increasing with the development of large-scale offshore projects. It is of great interest for its safe operation to measure the state of the towing process in real time. This paper proposed a computer vision algorithm designed to measure the tug yawing during the towing operation by estimating the towing line angle. The geometrical projection of the towing line from 3D to 2D is described in detail. By fixing the camera at specific locations and simplifying the calculation procedure, the towing line angle in the 3D world can be estimated by the line angle in the image. Firstly, the sea–sky line is detected to estimate the rolling angle of the tug in the captured image. Then, the towing line angle is calculated by an image processing method. At the same time, the estimation of the towing angle is achieved through the captured video data analysis. Finally, field experiments were carried out and the results demonstrated that this method is suitable for real-time calculation of the towing angle during the towing operation

Development and external validation of a nomogram for predicting preterm birth at < 32 weeks in twin pregnancy

Abstract The purpose of this study was to develop a dynamic model to predict the risk of spontaneous preterm birth at < 32 weeks in twin pregnancy. A retrospective clinical study of consecutively asymptomatic women with twin pregnancies from January 2017 to December 2019 in two tertiary medical centres was performed. Data from one centre were used to construct the model, and data from the other were used to evaluate the model. Data on maternal demographic characteristics, transvaginal cervical length and funnelling during 20–24 weeks were extracted. The prediction model was constructed with independent variables determined by multivariate logistic regression analyses. After applying specified exclusion criteria, an algorithm with maternal and biophysical factors was developed based on 88 twin pregnancies with a preterm birth < 32 weeks and 639 twin pregnancies with a delivery ≥ 32 weeks. It was then evaluated among 34 pregnancies with a preterm birth < 32 weeks and 252 pregnancies with a delivery ≥ 32 weeks in a second tertiary centre without specific training. The model reached a sensitivity of 80.00%, specificity of 88.17%, positive predictive value of 50.33% and negative predictive value of 96.71%; ROC characteristics proved that the model was superior to any single parameter with an AUC of 0.848 (all P < 0.005). We developed and validated a dynamic nomogram model to predict the individual probability of early preterm birth to better represent the complex aetiology of twin pregnancies and hopefully improve the prediction and indication of interventions

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel&rsquo;s vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision

A Hierarchical Vision-Based UAV Localization for an Open Landing

The localization of unmanned aerial vehicles (UAVs) for autonomous landing is challenging because the relative positions of the landing objects are almost inaccessible and the objects have nearly no transmission with UAVs. In this paper, a hierarchical vision-based localization framework for rotor UAVs is proposed for an open landing. In such a hierarchical framework, the landing is defined into three phases: &ldquo;Approaching&rdquo;, &ldquo;Adjustment&rdquo;, and &ldquo;Touchdown&rdquo;. Object features at different scales can be extracted from a designed Robust and Quick Response Landing Pattern (RQRLP) and the corresponding detection and localization methods are introduced for the three phases. Then a federated Extended Kalman Filter (EKF) structure is costumed and utilizes the solutions of the three phases as independent measurements to estimate the pose of the vehicle. The framework can be used to integrate the vision solutions and enables the estimation to be smooth and robust. In the end, several typical field experiments have been carried out to verify the proposed hierarchical vision framework. It can be seen that a wider localization range can be extended by the proposed framework while the precision is ensured