Heatwaves are consecutive hot days with devastating impacts on human health and the environment. These events may evolve across both space and time, characterizing a spatiotemporally contiguous propagation pattern that has not been fully understood. Here, we track the spatiotemporally contiguous heatwaves in both reanalysis datasets and model simulations and examine their moving patterns (i.e., moving distance, speed, and direction) in different continents and periods. Substantial changes in contiguous heatwaves have been identified from 1979 to 2020, with longer persistence, longer traveling distance, and slower propagation. These changes have been amplified since 1997, probably due to the weakening of eddy kinetic energy, zonal wind, and anthropogenic forcing. The results suggest that longer-lived, longer-traveling, and slower-moving contiguous heatwaves will cause more devastating impacts on human health and the environment in the future if greenhouse gas emissions keep rising and no effective measures are taken immediately. Our findings provide important implications for the adaption and mitigation of globally connected extreme heatwaves.

Measuring the volume of bird eggs is a very important task for the poultry industry and ornithological research due to the high revenue generated by the industry. In this paper, we describe a prototype of a new metrological system comprising a 3D range camera, Microsoft Kinect (Version 2) and a point cloud post-processing algorithm for the estimation of the egg volume. The system calculates the egg volume directly from the egg shape parameters estimated from the least-squares method in which the point clouds of eggs captured by the Kinect are fitted to novel geometric models of an egg in a 3D space. Using the models, the shape parameters of an egg are estimated along with the egg’s position and orientation simultaneously under the least-squares criterion. Four sets of experiments were performed to verify the functionality and the performance of the system, while volumes estimated from the conventional water displacement method and the point cloud captured by a survey-grade laser scanner serve as references. The results suggest that the method is straightforward, feasible and reliable with an average egg volume estimation accuracy 93.3% when compared to the reference volumes. As a prototype, the software part of the system was implemented in a post-processing mode. However, as the proposed processing techniques is computationally efficient, the prototype can be readily transformed into a real-time egg volume system.

Urban vibrancy contributes towards a successful city and high-quality life for people as one of its vital elements. Therefore, the association between service facilities and vibrancy is crucial for urban managers to understand and improve city construction. Moreover, the rapid development of information and communications technology (ICT) allows researchers to easily and quickly collect a large volume of real-time data generated by people in daily life. In this study, against the background of emerging multi-source big data, we utilized Tencent location data as a proxy for 24-h vibrancy and adopted point-of-interest (POI) data to represent service facilities. An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables. Empirical results show that (1) spatiotemporal variations exist in the impact of service facilities on urban vibrancy across Guangzhou, China; and (2) GTWR models exhibit a higher degree of explanatory capacity on vibrancy than the OLS models. In addition, our results can assist urban planners to understand spatiotemporal patterns of urban vibrancy in a refined resolution, and to optimize the resource allocation and functional configuration of the city.

The Velodyne LiDAR series is one of the most popular spinning beam LiDAR systems currently available on the market. In this paper, the temporal stability of the range measurements of the Velodyne HDL-32E LiDAR system is first investigated as motivation for the development of a new automatic calibration method that allows quick and frequent recovery of the inherent time-varying errors. The basic principle of the method is that the LiDAR’s internal systematic error parameters are estimated by constraining point clouds of some known and automatically detected cylindrical features such as lamp poles to fit to the 3D cylinder models. This is analogous to the plumb-line calibration method in which the lens distortion parameters are estimated by constraining the image points of straight lines to fit to the 2D line model. The calibration can be performed at every measurement epoch in both static and kinematic modes. Four real datasets were used to verify the method, two of which were captured in static mode and the other two in kinematic mode. The overall results indicate that up to approximately 72% and 41% accuracy improvement were realized as a result of the calibration for the static and kinematic datasets, respectively.