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High-definition (HD) map provides abundant and precise static environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. In this paper, we present Map TR ansformer, an end-to-end framework for online vectorized HD map construction. We propose a unified permutation-equivalent modeling approach, i . e ., modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process. We design a hierarchical query embedding scheme to flexibly encode structured map information and perform hierarchical bipartite matching for map element learning. To speed up convergence, we further introduce auxiliary one-to-many matching and dense supervision. The proposed method well copes with various map elements with arbitrary shapes. It runs at real-time inference speed and achieves state-of-the-art performance on both nuScenes and Argoverse2 datasets. Abundant qualitative results show stable and robust map construction quality in complex and various driving scenes. Code and more demos are available at https://github.com/hustvl/MapTR for facilitating further studies and applications.

Operational global-scale hydrological forecasting systems are used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/, last access: 3 December 2022) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the continuous ranked probability skill score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93 % of catchments in the short (1 to 3 d) and medium range (5 to 15 d) against a persistence benchmark forecast and skilful in over 80 % of catchments out to the extended range (16 to 30 d) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score available as a new layer on the GloFAS forecast Web Map Viewer to aid user interpretation and understanding of forecast quality.

Active faults serve as potential sources of destructive earthquakes. Studies and investigations of active faults are necessary for earthquake disaster prevention. This study presents a nation-scale database of active faults in China and its adjacent regions, in tandem with an associated web-based query system. This database is an updated version of the active faults data included in the Seismotectonic Map of China and its Adjacent Regions (1:4 000 000), which is one of the four essential maps of the mandatory Chinese standard GB 18306-2015 Seismic Ground Motion Parameter Zonation Maps of China. The data update and integration stem from regional-scale studies and surveys conducted over the past 2 decades (at reference scales from 1:250 000 to 1:50 000). The information amassed from these regional-scale studies and surveys encompasses geophysical probing, drill logging, measurement of offset landforms, sample dating, as well as geometric and kinematic parameters of exposed and blind faults, paleo-earthquake sequences, and recurrence intervals. These data have been acquired and analyzed utilizing a uniform technical standard framework and reviewed by expert panels in both field and laboratory settings. Our system hosts this nation-scale database accessible through a Web Geographic Information System (GIS) application, enabling browsing, querying, and downloading functionalities via a web browser. The system we built also publishes the Open Geospatial Consortium (OGC) Web Feature Service and the OGC Web Map Service of active faults data. Users can incorporate map layers and obtain fault data in OGC-compliant GIS software for further analysis through these services. The Chinese government, research institutions, and companies have widely used the active faults data from the previous versions of the database. The database is available at https://doi.org/10.12031/activefault.china.400.2023.db (Xu, 2023) and via the web system (https://data.activetectonics.cn/arcportal/apps/webappviewer/index.html?id=684737e8849c4170bbca14447608c451, CEFIS, 2023; http://data.activetectonics.cn/arcserver/services/Hosted/CAFD400_2022_WFS/MapServer/WFSServer, CAFD WFS, 2024).

Critically conscious care theories provide a framework for teacher candidates to name, analyze, and challenge structural injustice within and beyond the classroom during their teacher education. To support teacher candidates' enactment of such a critically conscious praxis in the postpandemic era, teacher educators must understand how teacher candidates during the COVID-19 pandemic adopted, appropriated, or resisted discourses of care that may enable or limit how they become critically caring teachers. This study examines how three elementary teacher candidates during the COVID-19 pandemic (co)constructed and (re) negotiated their identities as critically caring teachers through collaborative and individual body mapping exercises during online asynchronous coursework in the southeastern United States. The way that the participants drew and narrated their mapped bodies shows that participants positioned themselves in multiple, evolving, and sometimes conflicting ways in relation to the discourses of care across their education and work contexts and professional relationships. This study contributes to teacher education scholarship by shedding light on the affordances and challenges that discursive and semiotic tools like body mapping pose for teacher educators, who need to scaffold teacher candidates' understanding of care and rehearse pedagogical possibilities during emerging crises like the COVID-19 pandemic.

High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy.

PurposeIn order to facilitate targeted outreach, we sought to identify patient populations with a lower likelihood of returning for breast cancer screening after COVID-19-related imaging center closures.MethodsWeekly total screening mammograms performed throughout 2019 (baseline year) and 2020 (COVID-19-impacted year) were compared. Demographic and clinical characteristics, including age, race, ethnicity, breast density, breast cancer history, insurance status, imaging facility type used, and need for interpreter, were compared between patients imaged from March 16 to October 31 in 2019 (baseline cohort) and 2020 (COVID-19-impacted cohort). Census data and an online map service were used to impute socioeconomic variables and calculate travel times for each patient. Logistic regression was used to identify patient characteristics associated with a lower likelihood of returning for screening after COVID-19-related closures.ResultsThe year-over-year cumulative difference in screening mammogram volumes peaked in week 21, with 2962 fewer exams in the COVID-19-impacted year. By week 47, this deficit had reduced by 49.4% to 1498. A lower likelihood of returning for screening after COVID-19-related closures was independently associated with younger age (odds ratio (OR) 0.78, p < 0.001), residence in a higher poverty area (OR 0.991, p = 0.014), lack of health insurance (OR 0.65, p = 0.007), need for an interpreter (OR 0.68, p = 0.029), longer travel time (OR 0.998, p < 0.001), and utilization of mobile mammography services (OR 0.27, p < 0.001).ConclusionSeveral patient factors are associated with a lower likelihood of returning for screening mammography after COVID-19-related closures. Knowledge of these factors can guide targeted outreach to vulnerable patients to facilitate breast cancer screening.

The dissemination of bla[sub.KPC]-harboring Pseudomonas aeruginosa (KPC-Pa) is considered a serious public health problem. This study provides an overview of the epidemiology of these isolates to try to elucidate novel mobilization platforms that could contribute to their worldwide spread. A systematic review in PubMed and EMBASE was performed to find articles published up to June 2022. In addition, a search algorithm using NCBI databases was developed to identify sequences that contain possible mobilization platforms. After that, the sequences were filtered and pair-aligned to describe the bla[sub.KPC] genetic environment. We found 691 KPC-Pa isolates belonging to 41 different sequence types and recovered from 14 countries. Although the bla[sub.KPC] gene is still mobilized by the transposon Tn4401, the non-Tn4401 elements (NTE[sub.KPC]) were the most frequent. Our analysis allowed us to identify 25 different NTE[sub.KPC], mainly belonging to the NTE[sub.KPC]-I, and a new type (proposed as IVa) was also observed. This is the first systematic review that consolidates information about the behavior of the bla[sub.KPC] acquisition in P. aeruginosa and the genetic platforms implied in its successful worldwide spread. Our results show high NTE[sub.KPC] prevalence in P. aeruginosa and an accelerated dynamic of unrelated clones. All information collected in this review was used to build an interactive online map.

This study focuses on the recently emerged Internet of Vehicles (IoV) concept to provide an integrated agricultural vehicle/machinery tracking system through two leading low power wide area network (LPWAN) technologies, namely LoRa and NB-IoT. The main aim is to investigate the theoretical coverage limits by considering the urban, suburban, and rural environments. Two vehicle tracking units (VTUs) have been designed for LoRa and NB-IoT connectivity technologies that can be used as reference hardware in coverage analysis. On this basis, the closed-form explicit analytical expressions of the maximum transmission range have been derived using the Hata path loss model. Besides, the computer simulation results have been validated via the maps from XIRIO online radio planning tool. In light of the obtained findings, several evaluations have been made to enhance the LPWAN-based agricultural vehicle tracking feasibility in smart farms.

Real-time cooperative control of quadrotor swarms in cluttered environments requires balancing formation maintenance, obstacle avoidance, inter-UAV safety, and per-step computational cost. This paper proposes a multilayer perceptron (MLP) surrogate for high-level objective-weight selection in a modified multi-objective pigeon-inspired optimization (modified MPIO) distributed controller. The proposed MLP surrogate learns the state-to-weight mapping of the online search and directly predicts the two-dimensional objective-weight vector, while the original flocking, gap-based obstacle-avoidance, and command generation rules are retained unchanged. The surrogate is trained from teacher-generated weight labels using randomized scenes, DAgger-based state aggregation, and risk-weighted supervision. On a fixed closed-loop benchmark, the proposed controller increases the true collision free rate from 48.00% to 86.89% and the safe success rate from 38.67% to 74.22% relative to modified MPIO, while reducing the mean per-step decision latency for the whole swarm from 8494.70 ms to 0.92 ms. The improvement is most pronounced in safety-related and runtime metrics, while the formation-related gain is comparatively modest. Ablation results show that the final benchmark performance is not explained by DAgger or risk weighting alone, and that the medium-sized surrogate provides the best safety-latency tradeoff among the tested network architectures. A qualitative AirSim case study further indicates that the same high-level surrogate controller can be executed in a higher-fidelity asynchronous multirotor simulator.

Background Shenzhen has rapidly grown into a megacity in the recent decades. It is a challenging task for the Shenzhen government to provide sufficient healthcare services. The spatial configuration of healthcare services can influence the convenience for the consumers to obtain healthcare services. Spatial accessibility has been widely adopted as a scientific measurement for evaluating the rationality of the spatial configuration of healthcare services. Methods The multi-modal two-step floating catchment area (2SFCA) method is an important advance in the field of healthcare accessibility modelling, which enables the simultaneous assessment of spatial accessibility via multiple transport modes. This study further develops the multi-modal 2SFCA method by introducing online map APIs to improve the estimation of travel time by public transit or by car respectively. Results As the results show, the distribution of healthcare accessibility by multi-modal 2SFCA shows significant spatial disparity. Moreover, by dividing the multi-modal accessibility into car-mode and transit-mode accessibility, this study discovers that the transit-mode subgroup is disadvantaged in the competition for healthcare services with the car-mode subgroup. The disparity in transit-mode accessibility is the main reason of the uneven pattern of healthcare accessibility in Shenzhen. Conclusions The findings suggest improving the public transit conditions for accessing healthcare services to reduce the disparity of healthcare accessibility. More healthcare services should be allocated in the eastern and western Shenzhen, especially sub-districts in Dapeng District and western Bao’an District. As these findings cannot be drawn by the traditional single-modal 2SFCA method, the advantage of the multi-modal 2SFCA method is significant to both healthcare studies and healthcare system planning.