To provide and improved remote sensing a system based on an autonomous UAV was developed. The system was based on an easily transportable helicopter platform weighing less than 14 kg. Equipped with a ...multi-spectral camera and autonomous system, the UAV system was capable of acquiring multi-spectral images at the desired locations and times. An extended Kalman filter (EKF) based UAV navigation system was designed and implemented using sensor fusion techniques. A ground station was designed to be the interface between a human operator and the UAV to carry out mission planning, flight command activation, and real-time flight monitoring. Based on the navigation data, and the waypoints generated by the ground station, the UAV could be automatically navigated to the desired waypoints and hover around each waypoint to collect field image data. An experiment using the UAV system to monitor turf grass glyphosate application demonstrated the system, which indicated the UAV system provides a flexible and reliable method of sensing agricultural field with high spatial and temporal resolution of image data.
► A low cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle is developed. ► A ground base station for mission planning, flight command activation and flight monitoring is developed. ► Anavigation system for measuring position and attitude of the UAV is developed. ► A flight path planning method to determine the necessary overlaps of the UAV images while the UAV needs to cover a larger field area with many images is developed. ► The temporal monitoring capability through evaluation of an experiment applying glyphosate to a turf grass field is investigated.
A novel polymer is designed to serve as the conductive binder for high-capacity silicon anodes in lithium ion batteries (LIBs), aiming to address fast capacity fade and poor cycle life of silicon ...anodes caused by large volume change during repeated cycles. Abundant carboxyl groups in the polymer chain can effectively enhance the binding force to Si nanoparticles (NPs) and the n-type polyfluorene backbones of the polymer significantly promote the electronic conductivity under the reducing environment for anodes, dual-features of which can maintain electronic integrity during lithiation/delithiation cycles. Notably, the polymer can react with the polar groups on the surface of Si NPs to form strong chemical bonds, thus truly maintaining the electrode mechanical integrity and good electronic conductivity after repeated charge/discharge process. The as-assembled batteries based on the polymer without any conductive additive exhibit a high reversible capacity (2806mAhg−1 at 420mAg−1) and good cycle stability (85.2% retention of the initial capacity) after 100 cycles.
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•A novel water-based conductive binder PF-COONa is reported.•The conductive binder can form strong chemical bond with the Si particles.•The binder can be cathodically doped under the reducing environment for anodes.•The binder ensures the electrode mechanical integrity and electronic conductivity.•Remarkable electrochemical performances of Si/PF-COONa were obtained.
Deep learning has been broadly applied to imaging in scattering applications. A common framework is to train a descattering network for image recovery by removing scattering artifacts. To achieve the ...best results on a broad spectrum of scattering conditions, individual "expert" networks need to be trained for each condition. However, the expert's performance sharply degrades when the testing condition differs from the training. An alternative brute-force approach is to train a "generalist" network using data from diverse scattering conditions. It generally requires a larger network to encapsulate the diversity in the data and a sufficiently large training set to avoid overfitting. Here, we propose an adaptive learning framework, termed dynamic synthesis network (DSN), which dynamically adjusts the model weights and adapts to different scattering conditions. The adaptability is achieved by a novel "mixture of experts" architecture that enables dynamically synthesizing a network by blending multiple experts using a gating network. We demonstrate the DSN in holographic 3D particle imaging for a variety of scattering conditions. We show in simulation that our DSN provides generalization across a continuum of scattering conditions. In addition, we show that by training the DSN entirely on simulated data, the network can generalize to experiments and achieve robust 3D descattering. We expect the same concept can find many other applications, such as denoising and imaging in scattering media. Broadly, our dynamic synthesis framework opens up a new paradigm for designing highly adaptive deep learning and computational imaging techniques.
The implementation of large-scale vegetation restoration over the Chinese Loess Plateau has achieved clear improvements in vegetation fraction, as evidenced by large areas of slopes and plains being ...restored to grassland or forest. However, such large-scale vegetation restoration has altered land-atmosphere exchanges of water and energy, as the land surface characteristics have changed. These variations could affect regional climate, especially local precipitation. Quantitatively evaluating this feedback is an important scientific question in hydrometeorology. This study constructs a coupled land-atmosphere model incorporating vegetation dynamics, and analyzes the spatio-temporal changes of different land use types and land surface parameters over the Loess Plateau. By considering the impacts of vegetation restoration on the water-energy cycle and on land-atmosphere interactions, we quantified the feedback effect of vegetation restoration on local precipitation across the Loess Plateau, and discussed the important underlying processes. To achieve a quantitative evaluation, we designed two simulation experiments, comprising a real scenario with vegetation restoration and a hypothetical scenario without vegetation restoration. These enabled a comparison and analysis of the net impact of vegetation restoration on local precipitation. The results show that vegetation restoration had a positive effect on local precipitation over the Loess Plateau. Observations show that precipitation on the Loess Plateau increased significantly, at a rate of 7.84 mm yr
−2
, from 2000 to 2015. The simulations show that the contribution of large-scale vegetation restoration to the precipitation increase was about 37.4%, while external atmospheric circulation changes beyond the Loess Plateau contributed the other 62.6%. The average annual precipitation under the vegetation restoration scenario over the Loess Plateau was 12.4% higher than that under the scenario without vegetation restoration. The above research results have important theoretical and practical significance for the ecological protection and optimal development of the Loess Plateau, as well as the sustainable management of vegetation restoration.
Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes ...of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.
For the first time, organic semiconducting polymer dots (Pdots) based on poly(9,9′‐dioctylfluorenyl‐2,7‐diyl)‐co‐(1,4‐benzo‐{2,1′,3} thiadiazole) (PFBT) and polystyrene grafting with ...carboxyl‐group‐functionalized ethylene oxide (PS‐PEG‐COOH) are introduced as a photocatalyst towards visible‐light‐driven hydrogen generation in a completely organic solvent‐free system. With these organic Pdots as the photocatalyst, an impressive initial rate constant of 8.3 mmol h−1 g−1 was obtained for visible‐light‐driven hydrogen production, which is 5‐orders of magnitude higher than that of pristine PFBT polymer under the same catalytic conditions. Detailed kinetics studies suggest that the productive electron transfer quench of the excited state of Pdots by an electron donor is about 40 %. More importantly, we also found that the Pdots can tolerate oxygen during catalysis, which is crucial for further application of this material for light‐driven water splitting.
Organic polymer dots were used as a photocatalyst for visible‐light‐driven hydrogen generation for the first time and showed impressive activity with an initial hydrogen generation rate of 8.3 mmol h−1 g−1 without the assistance of any co‐catalysts. Do=donor.
Advances in phenotyping technology are critical to ensure the genetic improvement of crops meet future global demands for food and fuel. Field-based phenotyping platforms are being evaluated for ...their ability to deliver the necessary throughput for large scale experiments and to provide an accurate depiction of trait performance in real-world environments. We developed a dual-camera high throughput phenotyping (HTP) platform on an unmanned aerial vehicle (UAV) and collected time course multispectral images for large scale soybean Glycine max (L.) Merr. breeding trials. We used a supervised machine learning model (Random Forest) to measure crop geometric features and obtained high correlations with final yield in breeding populations (r=0.82). The traditional yield estimation model was significantly improved by incorporating plot row length as covariate (p<0.01). We developed a binary prediction model from time-course multispectral HTP image data and achieved over 93% accuracy in classifying soybean maturity. This prediction model was validated in an independent breeding trial with a different plot type. These results show that multispectral data collected from the UAV-based HTP platform could improve yield estimation accuracy and maturity recording efficiency in a modern soybean breeding program.
•A budget friendly airborne high throughput phenotyping platform was developed.•Canopy geometric features measured at plot-level highly correlated with yield.•Plot row length assessed from image improved yield estimation accuracy.•Time course multispectral data predicted soybean plot maturity with high accuracy.•The UAV-based high throughput phenotyping platform improved breeding efficiency.
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