Incorporating optimization in strategic conflict resolution for UAS traffic management

This study presents an approach to incorporate optimisation in the strategic conflict resolution service for unmanned aircraft systems (UAS) traffic management. A conventional approach in line with the First-Come, First-Served (FCFS) principle is introduced, following the generation of two types of flight plans (i.e., linear and area operations) with uncertainty buffers further taken into account. This approach is based on iteratively checking the availability of the shared airspace volumes. Next, an optimisation model is formulated, using the same common airspace representation, aiming at minimising the overall delay and deviation to the equivalent FCFS solution (i.e., fairness concern), subject to operational constraints including a time-based separation minima. Some potential implementations are also envisioned for the optimisation model under plausible operational scenarios. Finally, simulation experiments are performed where five case studies are designed, including FCFS and optimisation, as well as their hybrid and batch uses depending on the flight plan submission time. Sensitivity analysis is conducted to assess the impact of some specific model assumptions. Results suggest that, compared to FCFS, a notable delay reduction can be achieved with optimisation incorporated, which is due to the FCFS prioritisation scheme that is often not efficient.Single European Sky ATM Research 3 Joint Undertaking (SESAR JU) through the European Union’s Horizon 2020 Research and Innovation Program: Air Mobility Urban-Large Experimental Demonstrations (AMU-LED) (Grant Number: 101017702) Innovate UK: 1002320

Quantitative Comparative Analysis of the Bio-Active and Toxic Constituents of Leaves and Spikes of Schizonepeta tenuifolia at Different Harvesting Times

A GC-MS-Selected Ion Monitoring (SIM) detection method was developed for simultaneous determination of four monoterpenes: (−)-menthone, (+)-pulegone, (−)-limonene and (+)-menthofuran as the main bio-active and toxic constituents, and four other main compounds in the volatile oils of Schizonepeta tenuifolia (ST) leaves and spikes at different harvesting times. The results showed that the method was simple, sensitive and reproducible, and that harvesting time was a possible key factor in influencing the quality of ST leaves, but not its spikes. The research might be helpful for determining the harvesting time of ST samples and establishing a validated method for the quality control of ST volatile oil and other relative products

An integrated approach for on-demand dynamic capacity management service in U-space

This paper presents an integrated approach for on-demand Dynamic Capacity Management (DCM) service to be offered in U-space. The approach involves three main threads, including flight planning (demand), airspace configuration (capacity) and demand-capacity balancing (DCB). The flight planning thread produces UAS (unmanned aerial systems) trajectories for each flight that together reflect the estimated traffic demand. The airspace configuration thread defines the fundamental airspace structure and proposes dynamic adjustment schemes that determine the capacity distribution. It also enables the flight planning to reschedule alternative trajectory options to route away from possible congested areas. The last DCB thread takes the previous inputs and then computes for the optimal slot allocation and trajectory selection, as well as the optimal airspace configuration. Simulation case studies have been performed through mimicking an envisioned U-space operating scenario. Results suggest that the integrated approach can achieve the best outcome in almost all the key performance areas than any other cases where only partial functions are realised

Conflict probability based strategic conflict resolution for UAS traffic management

In this paper, we present a strategic conflict resolution method based on the conflict probability estimation, in the context of Unmanned Aircraft System (UAS) Traffic Management. We first elaborate a classic approach for flight trajectory generation in a designated realistic airspace environment, which is then smoothed by B-spline algorithm to achieve higher realism. The trajectories are extended to 4-dimensional Operational Volumes (OV) following the current UTM development visions. This forms the basis for performing a coarse conflict screening process, as the initial part for conflict detection, primarily based on identifying any OVs overlapping in temporal and spatial. Next, we look into the captured OVs and apply a well-studied conflict probability estimation approach, which contributes to a refined and more accurate conflict detection outcome. To resolve the potential conflicts, we propose two models including First-Come, First-Served (FCFS) and optimisation, both embedded with the probability-based conflict detection. In the FCFS approach, flights are delayed in the order of their submission, while the optimisation model aims at cherry-picking flights to seek the optimal solution. Numerical experiments with various case studies are performed to assess the effects with and without such probability concern, as well as different implementation strategies in real world. Results suggest that, allowing OVs’ overlapping to some extent does not necessarily incur conflict over an acceptable probability, whereas the efficiency of airspace use could be improved.This work was partially funded by the SESAR JU under grant agreement No 101017702, as part of the European Union’s Horizon 2020 research and innovation programme: AMU-LED (Air Mobility Urban - Large Experimental Demonstrations)

ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance

Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view cues embedded in the text modality and fail to weigh the relative importance of different views. In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities. For the text branch, ViewRefer leverages the diverse linguistic knowledge of large-scale language models, e.g., GPT, to expand a single grounding text to multiple geometry-consistent descriptions. Meanwhile, in the 3D modality, a transformer fusion module with inter-view attention is introduced to boost the interaction of objects across views. On top of that, we further present a set of learnable multi-view prototypes, which memorize scene-agnostic knowledge for different views, and enhance the framework from two perspectives: a view-guided attention module for more robust text features, and a view-guided scoring strategy during the final prediction. With our designed paradigm, ViewRefer achieves superior performance on three benchmarks and surpasses the second-best by +2.8%, +1.5%, and +1.35% on Sr3D, Nr3D, and ScanRefer.Comment: Accepted by ICCV 202

3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness

Oxytocin and Pregnancy

Oxytocin, an important neuropeptide, exerts a wide influence on the central nervous system and the peripheral tissues. In the central nervous system, the oxytocin gene expression is mainly shown to be present in neurons in the hypothalamic paraventricular and supraoptic nuclei. Oxytocin gene also transcribes in the peripheral tissues such as uterus, placenta, and amnion. Oxytocin receptors can be founded in many tissues in humans, like the uterine, ovary, testis, kidney, and so on. And just in the same tissue, due to the variation of physiology factors, the amount of oxytocin changes a lot. Oxytocin secretion is closely linked with pregnancy advancing. During labor, the contractions of uterine smooth muscles and oxytocin secretion are inseparable. Moreover, oxytocin is also responsible for stimulating milk ejection after parturition. Oxytocin is associated with many diseases. Poor regulation of oxytocin may cause postpartum depression and infantile autism. In terms of physiology, fatal heart failure and gestational hypertension are concerned with oxytocin level. In this chapter, we will discuss the oxytocin in pregnancy as well as its clinical applications

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space between 3D and multi-modalities, enabling many promising applications, e.g., any-to-3D generation, 3D embedding arithmetic, and 3D open-world understanding. On top of this, we further present Point-LLM, the first 3D large language model (LLM) following 3D multi-modal instructions. By parameter-efficient fine-tuning techniques, Point-LLM injects the semantics of Point-Bind into pre-trained LLMs, e.g., LLaMA, which requires no 3D instruction data, but exhibits superior 3D and multi-modal question-answering capacity. We hope our work may cast a light on the community for extending 3D point clouds to multi-modality applications. Code is available at https://github.com/ZiyuGuo99/Point-Bind_Point-LLM.Comment: Work in progress. Code is available at https://github.com/ZiyuGuo99/Point-Bind_Point-LL

DICER1 regulated let-7 expression levels in p53-induced cancer repression requires cyclin D1.

Let-7 miRNAs act as tumour suppressors by directly binding to the 3\u27UTRs of downstream gene products. The regulatory role of let-7 in downstream gene expression has gained much interest in the cancer research community, as it controls multiple biological functions and determines cell fates. For example, one target of the let-7 family is cyclin D1, which promotes G0/S cell cycle progression and oncogenesis, was correlated with endoribonuclease DICER1, another target of let-7. Down-regulated let-7 has been identified in many types of tumours, suggesting a feedback loop may exist between let-7 and cyclin D1. A potential player in the proposed feedback relationship is Dicer, a central regulator of miRNA expression through sequence-specific silencing. We first identified that DICER1 is the key downstream gene for cyclin D1-induced let-7 expression. In addition, we found that let-7 miRNAs expression decreased because of the p53-induced cell death response, with deregulated cyclin D1. Our results also showed that cyclin D1 is required for Nutlin-3 and TAX-induced let-7 expression in cancer repression and the cell death response. For the first time, we provide evidence that let-7 and cyclin D1 form a feedback loop in regulating therapy response of cancer cells and cancer stem cells, and importantly, that alteration of let-7 expression, mainly caused by cyclin D1, is a sensitive indicator for better chemotherapies response

Demonstrating advanced U-space services for urban air mobility in a co-simulation environment

https://whova.com/xems/whova_backend/get_event_s3_file_api/?event_id=sesar_202212&file_url=https://d1keuthy5s86c8.cloudfront.net/static/ems/upload/files/1670053372_jdpqz_SIDs_2022_paper_112_final_r.pdf&eventkey=4c68ac23dcb73ce4b5be87ca1b5b0ccaed6016dfcefe0c4b6af300caca061224The present paper formalises the development of a co-simulation environment aimed at demonstrating a number of advanced U-space services for the Air Mobility Urban - Large Experimental Demonstrations (AMU-LED) project. The environment has a visionary build that addresses Urban Air Mobility (UAM) challenges to support the High/Standard Performance Vehicles (HPV/SPV) operations within a complex urban environment by proposing an integrated solution that packages advanced services from the pre-flight to the in-flight phase in line with ongoing UAM Concept of Operations (ConOps). This setup opts for a holistic approach by promoting intelligent algorithmic design, artificial intelligence, robust serviceability through either virtual and live elements, and strong cooperation between the different services integrated, in addition to sustain interoperability with external U-space Service providers (USSP), Common Information Service providers (CISPs), and Air Traffic Controllers. The prototype has been recently showcased through the AMU-LED Cranfield (UK) demonstration activities