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Satellites detect the distribution of meteorological data worldwide. However, due to the orbital constraints, the satellite can only reach the same area again after one orbiting cycle. The interval between two detections in the same area is long, and the variation of meteorological data between the two detections is unknown. Moreover, meteorological satellite data are only located near the orbit in one cycle, while the global distribution of meteorological data is unknown. Our method allows to train a regression model with only few meteorological satellite data by taking advantage of the recent advances in deep learning. In detail, we train a model-agnostic meta-learning (MAML) model with data from ground stations instead of meteorological satellites and get the initial network parameters. Based on the initial network parameters trained by MAML, we train the regression models again for different areas. We sample the regression curves of all areas by time and get a time series of global meteorological data distribution. Through case studies conducted together with domain experts, we validate the effectiveness of our method.

The FY-3C satellite returns more than 250,000 occultation data each year, each of which records high vertical resolution profiles of various climatic parameters in the range of 0–1000 km above the Earth. These long-term, stable, and globally distributed observations can be used to improve climate modeling and analyze trends in spatial climate. However, traditional methods based on small samples meet challenges in analyzing the multi-dimensional occultation data in large volume. In this paper, we developed OccVis-a visual analytics system for multi-dimensional and multi-scale comparative analysis of occultation data. With a novel workflow, a series of data processing methods are proposed to support data correcting, patching, and clustering. Moreover, a matrix view with two modes is presented for overview, and a detail view along with time series view is provided for further analysis. Domain scientists can easily utilize our system to visually and interactively explore multivariable occultation data at different time and space scales. Finally, we conduct case studies in climate modeling of ionosphere and obtain several preliminary results to demonstrate the usage and effectiveness of our system.

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

Indoor positioning systems have been playing an important role in positioning technology research. This kind of technology can be used in many fields in our daily life. For example, people can use it to help firefighters find people in the fire scene, help managers to monitor robots' track in a factory, and so on. And there are many technologies that could be used to locate people or items indoors, such as Bluetooth, ZigBee, and Ultra-wideband (UWB). UWB technology has characteristics of high precision and low power consumption. In this project, we compare the performance of other technologies with UWB in an indoor positioning system, especially their precision in indoor positioning. Some channel characteristics such as Time of Arrival (TOA), direction of arrival (DOA), received signal strength (RSS) will be measured to analyze the position and motion of people or items indoors. In this paper, we improve the performance of a swarm intelligence algorithm in order to make it have higher efficiency and more accuracy to locate indoor items with static states. And then, for dynamic items, we use the Kalman filter algorithm and try to improve its performance to make the track we get in the positioning system closer to the actual track. Through simulation of position and track of items, we could prove the high precision of this indoor positioning system.

Narrowing the mechanical mismatch between biological tissues (typically soft) and neural interfaces (hard) is essential for maintaining signal quality for the electrical recording of neural activity. However, only a few materials can satisfy all requirements for such electronics, which need to be both biocompatible and sufficiently soft. Here, a highly stretchable electrode array (SEA) is introduced, based on the liquid metal-polymer conductor (MPC), achieving high mechanical flexibility and good cytocompatability for neural interfaces. By utilizing the MPC, the SEA exhibits high stretchability (≈100%) and excellent cycling stability (>400 cycles). The cytocompatability of the SEA can allow for long-term culturing of primary neurons and enable signal recording of primary hippocampal neurons. In the future, the SEA could serve as a reliable and robust platform for diagnostics in neuronal tissues and greatly advance brain-machine interfaces.

International audience; Urban leaf area measurement is crucial to properly determining the effect of urban trees on micro-climate regulation, heat island effect, building cooling, air quality improvement, and ozone formation. Previous works on the leaf area measurement have mainly focused on the stand level, although the presence of individual trees is more common than forests in urban areas. The only feasible ways for an operational non-destructive leaf area measurement, namely, optical indirect methods, are mostly limited in urban areas because light path is constantly intercepted by surrounding buildings or other objects. A terrestrial laser scanner (TLS), which can extract an individual tree by using its unique distance information, provides a possibility for indirectly measuring the leaf area index (LAI) in urban areas. However, indirect LAI measurement theory, which uses the cosine of an observation zenith angle for path-length correction, is incompatible for an individual tree because the representative projected area of LAI changes while the observation zenith angle changes, thus making the results incomparable and ambiguous. Therefore, we modified a path length distribution model for the leaf area measurement of an individual tree by replacing the traditional cosine path length correction for a continuous canopy with real path length distribution. We reconstructed the tree crown envelope from a TLS point cloud and calculated a real path length distribution through laser pulse-envelope intersections. Consequently, leaf area density was separated from the path length distribution model for leaf area calculation. Comparisons with reference measurement for an individual tree showed that the TLS-derived leaf area using the path length distribution is insensitive to the scanning resolution and agrees well with an allometric measurement with an overestimation from 5 m 2 to 18 m 2 (3-10%, respectively). Results from different stations are globally consistent, and using a weighted mean for different stations by sample numbers further improves the universality and efficiency of the proposed method. Further automation of the proposed method can facilitate a rapid and operational leaf area extraction of an individual tree for urban climate modeling.

We develop a rapid and facile method to fabricate tubular scaffolds by a single-step rolling operation. With the aid of fibrin medical glue and a smooth expanded polytetrafluoroethylene (ePTFE) mandrel, we can wrap a piece of flat thin film into a three-dimensional (3D), multi-layered tubular structure with well-controlled diameter, wall thickness, and mechanical strength within 10 min. By patterning different cells in a pre-designed area on the film, after rolling, we can obtain blood vessel-mimicking tissues with well-arranged, multilayered 3D architectures within 70 min. Our strategy provides an excellent platform to rapidly fabricate tubular scaffolds essentially with no equipment and straightforward manipulations.

Summary Stretchable, biocompatible devices can bridge electronics and biology. However, most stretchable conductors for such devices are toxic, costly, and regularly break/degrade after several large deformations. Here we show printable, highly stretchable, and biocompatible metal-polymer conductors by casting and peeling off polymers from patterned liquid metal particles, forming surface-embedded metal in polymeric hosts. Our printable conductors present good stretchability (2,316 S/cm at a strain of 500%) and repeatability (ΔR/R, Graphical Abstract, Highlights • A straightforward method for sintering liquid metal particles with high efficiency • Liquid metal can be patterned on different substrates with high resolution • Low-cost, high-throughput, stretchable printed conductors can be fabricated • Conductors are biocompatible and have potentials in implantable electronics, Polymers; Alloys; Electronic Materials

In this paper, we investigate essential mechanical properties and cell behaviors of the scaffolds fabricated by rolling polylactic-co-glycolic acid (PLGA) electrospinning (ES) films for small-diameter vascular grafts (inner diameter < 6 mm). The newly developed strategy can be used to fabricate small diameter vascular grafts with or without pre-seeded cells, which are two main branches for small diameter vascular engineering. We demonstrate that the mechanical properties of our rolling-based scaffolds can be tuned flexibly by the number of layers. For cell-free scaffolds, with the increase of layer number, burst pressure and suture retention increase, elastic tensile modulus maintains unchanged statistically, but compliance and liquid leakage decrease. For cell-containing scaffolds, seeding cells will significantly decrease the liquid leakage, but there are no statistical differences for other mechanical properties; moreover, cells live and proliferate well in the scaffold after a 6-day culture.

A method is developed that can rapidly produce blood vessel-like structures by bonding cell-laden electrospinning (ES) films layer by layer using fibrin glue within 90 min. This strategy allows control of cell type, cell orientation, and material composition in separate layers. Furthermore, ES films with thicker fibers (polylactic-co-glycolic acid, fiber diameter: ≈3.7 µm) are used as cell-seeding layers to facilitate the cell in-growth; those with thinner fibers (polylactic acid, fiber diameter: ≈1.8 µm) are used as outer reinforcing layers to improve the mechanical strength and reduce the liquid leakage of the scaffold. Cells grow, proliferate, and migrate well in the multilayered structure. This design aims at a new type of blood vessel substitute with flexible control of parameters and implementation of functions.