164 research outputs found
Empirical exploration of air traffic and human dynamics in terminal airspaces
Air traffic is widely known as a complex, task-critical techno-social system,
with numerous interactions between airspace, procedures, aircraft and air
traffic controllers. In order to develop and deploy high-level operational
concepts and automation systems scientifically and effectively, it is essential
to conduct an in-depth investigation on the intrinsic traffic-human dynamics
and characteristics, which is not widely seen in the literature. To fill this
gap, we propose a multi-layer network to model and analyze air traffic systems.
A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN)
encapsulate critical physical and operational characteristics; an Integrated
Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network
(ICCN) are formulated to represent air traffic flow transmissions and
intervention from air traffic controllers, respectively. Furthermore, a set of
analytical metrics including network variables, complex network attributes,
controllers' cognitive complexity, and chaotic metrics are introduced and
applied in a case study of Guangzhou terminal airspace. Empirical results show
the existence of fundamental diagram and macroscopic fundamental diagram at the
route, sector and terminal levels. Moreover, the dynamics and underlying
mechanisms of "ATCOs-flow" interactions are revealed and interpreted by
adaptive meta-cognition strategies based on network analysis of the ICCN.
Finally, at the system level, chaos is identified in conflict system and human
behavioral system when traffic switch to the semi-stable or congested phase.
This study offers analytical tools for understanding the complex human-flow
interactions at potentially a broad range of air traffic systems, and underpins
future developments and automation of intelligent air traffic management
systems.Comment: 30 pages, 28 figures, currently under revie
Assessment of Long-Term Watershed Management on Reservoir Phosphorus Concentrations and Export Fluxes.
Source water nutrient management to prevent eutrophication requires critical strategies to reduce watershed phosphorus (P) loadings. Shanxi Drinking-Water Source Area (SDWSA) in eastern China experienced severe water quality deterioration before 2010, but showed considerable improvement following application of several watershed management actions to reduce P. This paper assessed the changes in total phosphorus (TP) concentrations and fluxes at the SDWSA outlet relative to watershed anthropogenic P sources during 2005⁻2016. Overall anthropogenic P inputs decreased by 21.5% over the study period. Domestic sewage, livestock, and fertilizer accounted for (mean ± SD) 18.4 ± 0.6%, 30.1 ± 1.9%, and 51.5 ± 1.5% of total anthropogenic P inputs during 2005⁻2010, compared to 24.3 ± 2.7%, 8.8 ± 10.7%, and 66.9 ± 8.0% for the 2011⁻2016 period, respectively. Annual average TP concentrations in SDWSA decreased from 0.041 ± 0.019 mg/L in 2009 to 0.025 ± 0.013 mg/L in 2016, a total decrease of 38.2%. Annual P flux exported from SDWSA decreased from 0.46 ± 0.04 kg P/(ha·a) in 2010 to 0.25 ± 0.02 kg P/(ha·a) in 2016, a decrease of 44.9%. The success in reducing TP concentrations was mainly due to the development of domestic sewage/refuse collection/treatment and improved livestock management. These P management practices have prevented harmful algal blooms, providing for safe drinking water
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Generalizable 3D part segmentation is important but challenging in vision and
robotics. Training deep models via conventional supervised methods requires
large-scale 3D datasets with fine-grained part annotations, which are costly to
collect. This paper explores an alternative way for low-shot part segmentation
of 3D point clouds by leveraging a pretrained image-language model, GLIP, which
achieves superior performance on open-vocabulary 2D detection. We transfer the
rich knowledge from 2D to 3D through GLIP-based part detection on point cloud
rendering and a novel 2D-to-3D label lifting algorithm. We also utilize
multi-view 3D priors and few-shot prompt tuning to boost performance
significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets
shows that our method enables excellent zero-shot 3D part segmentation. Our
few-shot version not only outperforms existing few-shot approaches by a large
margin but also achieves highly competitive results compared to the fully
supervised counterpart. Furthermore, we demonstrate that our method can be
directly applied to iPhone-scanned point clouds without significant domain
gaps.Comment: CVPR 2023, project page: https://colin97.github.io/PartSLIP_page
Efficacy of a smartphone-based care support programme in improving post-traumatic stress in families with childhood cancer: Protocol of a randomised controlled trial
INTRODUCTION: Diagnosis and treatment represent distressing experiences for the families of children with cancer. Psychosocial challenges are faced by these families in China because of limited health services and resources for psychosocial oncology care. Effective interventions tailored to the knowledge level and cultural values of this population are needed. The goal of this study is to evaluate a smartphone-based care support (SBCS) programme for the families of children with cancer in China.
METHODS AND ANALYSIS: A parallel randomised controlled trial will be conducted to examine the efficacy of an evidence-based and culturally tailored SBCS programme for the families of children with cancer in China. A total of 180 families will be recruited. The intervention will consist of an introduction session and four main sessions and will be conducted sequentially on a single weekend day. Participating families will be included in the intervention group. The post-traumatic stress and quality of life of families will be evaluated at baseline, during the intervention, immediately after the intervention, and 2 and 6 months after the intervention.
ETHICS AND DISSEMINATION: Ethical approval for this protocol has been obtained from the Nursing and Behavioural Medicine Research Ethics Review Committee, Xiangya School of Nursing, Central South University (Protocol #: E2020125). The findings of the trial will be disseminated through conference presentations and publications in peer-reviewed journals.
TRIAL REGISTRATION NUMBER: ChiCTR2000040510
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
We introduce OpenShape, a method for learning multi-modal joint
representations of text, image, and point clouds. We adopt the commonly used
multi-modal contrastive learning framework for representation alignment, but
with a specific focus on scaling up 3D representations to enable open-world 3D
shape understanding. To achieve this, we scale up training data by ensembling
multiple 3D datasets and propose several strategies to automatically filter and
enrich noisy text descriptions. We also explore and compare strategies for
scaling 3D backbone networks and introduce a novel hard negative mining module
for more efficient training. We evaluate OpenShape on zero-shot 3D
classification benchmarks and demonstrate its superior capabilities for
open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy
of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than
10% for existing methods. OpenShape also achieves an accuracy of 85.3% on
ModelNet40, outperforming previous zero-shot baseline methods by 20% and
performing on par with some fully-supervised methods. Furthermore, we show that
our learned embeddings encode a wide range of visual and semantic concepts
(e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D
and image-3D interactions. Due to their alignment with CLIP embeddings, our
learned shape representations can also be integrated with off-the-shelf
CLIP-based models for various applications, such as point cloud captioning and
point cloud-conditioned image generation.Comment: Project Website: https://colin97.github.io/OpenShape
Automatic single fish detection with a commercial echosounder using YOLO v5 and its application for echosounder calibration
Nowadays, most fishing vessels are equipped with high-resolution commercial echo sounders. However, many instruments cannot be calibrated and missing data occur frequently. These problems impede the collection of acoustic data by commercial fishing vessels, which are necessary for species classification and stock assessment. In this study, an automatic detection and classification model for echo traces of the Pacific saury (Cololabis saira) was trained based on the algorithm YOLO v5m. The in situ measurement value of the Pacific saury was measured using single fish echo trace. Rapid calibration of the commercial echo sounder was achieved based on the living fish calibration method. According to the results, the maximum precision, recall, and average precision values of the trained model were 0.79, 0.68, and 0.71, respectively. The maximum F1 score of the model was 0.66 at a confidence level of 0.454. The living fish calibration offset values obtained at two sites in the field were 116.30 dB and 118.19 dB. The sphere calibration offset value obtained in the laboratory using the standard sphere method was 117.65 dB. The differences between in situ and laboratory calibrations were 1.35 dB and 0.54 dB, both of which were within the normal range
Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements
Different modes and potencies of translational repression by sequence-specific RNA–protein interaction at the 5′-UTR
To determine whether sequence-specific RNA–protein interaction at the 5′-untranslated region (5′-UTR) can potently repress translation in mammalian cells, a bicistronic translational repression assay was developed to permit direct assessment of RNA–protein interaction and translational repression in transiently transfected living mammalian cells. Changes in cap-dependent yellow fluorescent protein (YFP) and internal ribosome entry sequence (IRES)-dependent cyan fluorescent protein (CFP) translation were monitored by fluorescence microscopy. Selective repression of YFP or coordinate repression of both YFP and CFP translation occurred, indicating two distinct modes by which RNA-binding proteins repress translation through the 5′-UTR. Interestingly, a single-stranded RNA-binding protein from Bacillus subtilis, tryptophan RNA-binding attenuation protein (TRAP), showed potent translational repression, dependent on the level of TRAP expression and position of its cognate binding site within the bicistronic reporter transcript. As the first of its class to be examined in mammalian cells, its potency in repression of translation through the 5′-UTR may be a general feature for this class of single-stranded RNA-binding proteins. Finally, a one-hybrid screen based on translational repression through the 5′-UTR identified linkers supporting full-translational repression as well as a range of partial repression by TRAP within the context of a fusion protein
Systematic identification of conserved motif modules in the human genome
<p>Abstract</p> <p>Background</p> <p>The identification of motif modules, groups of multiple motifs frequently occurring in DNA sequences, is one of the most important tasks necessary for annotating the human genome. Current approaches to identifying motif modules are often restricted to searches within promoter regions or rely on multiple genome alignments. However, the promoter regions only account for a limited number of locations where transcription factor binding sites can occur, and multiple genome alignments often cannot align binding sites with their true counterparts because of the short and degenerative nature of these transcription factor binding sites.</p> <p>Results</p> <p>To identify motif modules systematically, we developed a computational method for the entire non-coding regions around human genes that does not rely upon the use of multiple genome alignments. First, we selected orthologous DNA blocks approximately 1-kilobase in length based on discontiguous sequence similarity. Next, we scanned the conserved segments in these blocks using known motifs in the TRANSFAC database. Finally, a frequent pattern mining technique was applied to identify motif modules within these blocks. In total, with a false discovery rate cutoff of 0.05, we predicted 3,161,839 motif modules, 90.8% of which are supported by various forms of functional evidence. Compared with experimental data from 14 ChIP-seq experiments, on average, our methods predicted 69.6% of the ChIP-seq peaks with TFBSs of multiple TFs. Our findings also show that many motif modules have distance preference and order preference among the motifs, which further supports the functionality of these predictions.</p> <p>Conclusions</p> <p>Our work provides a large-scale prediction of motif modules in mammals, which will facilitate the understanding of gene regulation in a systematic way.</p
Short/medium-term prediction for the aviation emissions in the en route airspace considering the fluctuation in air traffic demand
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