Geography-aware Optimal UAV 3D Placement for LOS Relaying: A Geometry Approach

Many emerging technologies for the next generation wireless network prefer line-of-sight (LOS) propagation conditions to fully release their performance advantages. This paper studies 3D unmanned aerial vehicle (UAV) placement to establish LOS links for two ground terminals in deep shadow in a dense urban environment. The challenge is that the LOS region for the feasible UAV positions can be arbitrary due to the complicated structure of the environment. While most existing works rely on simplified stochastic LOS models and problem relaxations, this paper focuses on establishing theoretical guarantees for the optimal UAV placement to ensure LOS conditions for two ground users in an actual propagation environment. It is found that it suffices to search a bounded 2D area for the globally optimal 3D UAV position. Thus, this paper develops an exploration-exploitation algorithm with a linear trajectory length and achieves above 99% global optimality over several real city environments being tested in our experiments. To further enhance the search capability in an ultra-dense environment, a dynamic multi-stage algorithm is developed and theoretically shown to find an $\epsilon$-optimal UAV position with a search length $O(1/\epsilon)$. Significant performance advantages are demonstrated in several numerical experiments for wireless communication relaying and wireless power transfer

Online search for UAV relay placement for free-space optical communication under shadowing

Unmanned aerial vehicle (UAV) relaying is promising to overcome the challenge of signal blockage in free-space optical (FSO) communications for users in dense urban area. Existing works on UAV relay placement are mostly based on simplified line-of-sight (LOS) channel models or probabilistic channel models, and thus fail to capture the actual LOS status of the optical communication link. By contrast, this paper studies three-dimensional (3D) online placement for a UAV to construct relay links to two ground users in deep shadow with LOS guarantees. By analyzing the properties of the UAV relay placement problem, it is found that searching on a plane that approximates the equipotential surface can achieve a good performance and complexity trade-off for a good placement of the UAV relay in 3D. Based on these insights, a two-stage online search algorithm on an equipotential plane (TOSEP) is developed for a special case where the equipotential surface turns out to be an equipotential plane. For the general case, a strategy called gradient projected online search algorithm on an approximated equipotential plane (GOSAEP) is developed, which approximates the equipotential surface with a perpendicular plane using the gradient projection method. Numerical experiments are conducted over a real-world city topology, and it is shown that the GOSAEP achieves over 95% of the performance of the exhaustive 3D search scheme within a 300-m search length

Identification of risk factors for infection after mitral valve surgery through machine learning approaches

BackgroundSelecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.MethodsParticipants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance.ResultsWe identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79).ConclusionsKey features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk

Structure and Filling Characteristics of Paleokarst Reservoirs in the Northern Tarim Basin, Revealed by Outcrop, Core and Borehole Images

The Ordovician paleokarst reservoirs in the Tahe oilfield, with burial depths of over 5300 m, experienced multiple phases of geologic processes and exhibit strong heterogeneity. Core testing can be used to analyse the characteristics of typical points at the centimetre scale, and seismic datasets can reveal the macroscopic outlines of reservoirs at the >10-m scale. However, neither method can identify caves, cave fills and fractures at the meter scale. Guided by outcrop investigations and calibrations based on core sample observations, this paper describes the interpretation of high longitudinal resolution borehole images, the identification of the characteristics of caves, cave fills (sedimentary, breccia and chemical fills) and fractures in single wells, and the identification of structures and fill characteristics at the meter scale in the strongly heterogeneous paleokarst reservoirs. The paleogeomorphology, a major controlling factor in the distribution of paleokarst reservoirs, was also analysed. The results show that one well can penetrate multiple cave layers of various sizes and that the caves are filled with multiple types of fill. The paleogeomorphology can be divided into highlands, slopes and depressions, which controlled the structure and fill characteristics of the paleokarst reservoirs. The results of this study can provide fundamental meter-scale datasets for interpreting detailed geologic features of deeply buried paleocaves, can be used to connect core- and seismic-scale interpretations, and can provide support for the recognition and development of these strongly heterogeneous reservoirs

Effects of 3‐HAA on HCC by Regulating the Heterogeneous Macrophages—A scRNA‐Seq Analysis

Abstract Kynurenine derivative 3‐hydroxyanthranilic acid (3‐HAA) is known to regulate the immune system and exhibit anti‐inflammatory activity by inhibiting T‐cell cytokine secretion and influencing macrophage activity. However, the definite role of 3‐HAA in the immunomodulation of hepatocellular carcinoma (HCC) is largely unexplored. An orthotopic HCC model and treated with 3‐HAA by intraperitoneal injection is developed. Furthermore, cytometry by time‐of‐flight (CyTOF) and single‐cell RNA sequencing (scRNA‐seq) analyses are carried out to define the immune landscape of HCC. It is found that 3‐HAA treatment can significantly suppress tumor growth in the HCC model and alter the level of various cytokines in plasma. CyTOF data shows that 3‐HAA significantly increases the percentage of F4/80hiCX3CR1loKi67loMHCIIhi macrophages and decreases the percentage of F4/80loCD64+PD‐L1lo macrophages. scRNA‐seq analyses demonstrate that 3‐HAA treatment is proved to regulate the function of M1 macrophages, M2 macrophages, and proliferating macrophages. Notably, 3‐HAA inhibits the proinflammatory factors TNF and IL‐6 in multiple cell subsets, including resident macrophages, proliferating macrophages, and pDCs. This study reveals the landscape of immune cell subsets in HCC in response to 3‐HAA, indicating that 3‐HAA may be a promising therapeutic target for HCC