Search for ultralight dark matter with a frequency adjustable diamagnetic levitated sensor

Among several dark matter candidates, bosonic ultralight (sub meV) dark matter is well motivated because it could couple to the Standard Model (SM) and induce new forces. Previous MICROSCOPE and Eot Wash torsion experiments have achieved high accuracy in the sub-1 Hz region, but at higher frequencies there is still a lack of relevant experimental research. We propose an experimental scheme based on the diamagnetic levitated micromechanical oscillator, one of the most sensitive sensors for acceleration sensitivity below the kilohertz scale. In order to improve the measurement range, we used the sensor whose resonance frequency could be adjusted from 0.1Hz to 100Hz. The limits of the coupling constant are improved by more than 10 times compared to previous reports, and it may be possible to achieve higher accuracy by using the array of sensors in the future

Generating Giant and Tunable Nonlinearity in a Macroscopic Mechanical Resonator from Chemical Bonding Force

Nonlinearity in macroscopic mechanical system plays a crucial role in a wide variety of applications, including signal transduction and processing, synchronization, and building logical devices. However, it is difficult to generate nonlinearity due to the fact that macroscopic mechanical systems follow the Hooke's law and response linearly to external force, unless strong drive is used. Here we propose and experimentally realize a record-high nonlinear response in macroscopic mechanical system by exploring the anharmonicity in deforming a single chemical bond. We then demonstrate the tunability of nonlinear response by precisely controlling the chemical bonding interaction, and realize a cubic elastic constant of \mathversion{bold}$2 \times 10^{18}~{\rm N}/{\rm m^3}$, many orders of magnitude larger in strength than reported previously. This enables us to observe vibrational bistate transitions of the resonator driven by the weak Brownian thermal noise at 6~K. This method can be flexibly applied to a variety of mechanical systems to improve nonlinear responses, and can be used, with further improvements, to explore macroscopic quantum mechanics

Seasonal dynamics of trace elements in sediment and seagrass tissues in the largest Zostera japonica habitat, the Yellow River Estuary, northern China

Trace element accumulation is an anthropogenic threat to seagrass ecosystems, which in turn may affect the health of humans who depend on these ecosystems. Trace element accumulation in seagrass meadows may vary temporally due to, e.g., seasonal patterns in sediment discharge from upstream areas. In addition, when several trace elements are present in sufficiently high concentrations, the risk of seagrass loss due to the cumulative impact of these trace elements is increased. To assess the seasonal variation and cumulative risk of trace element contamination to seagrass meadows, trace element (As, Cd, Cr, Cu, Pb, Hg, Mn and Zn) levels in surface sediment and seagrass tissues were measured in the largest Chinese Zostera japonica habitat, located in the Yellow River Estuary, at three sites and three seasons (fall, spring and summer) in 2014–2015. In all three seasons, trace element accumulation in the sediment exceeded background levels for Cd and Hg. Cumulative risk to Z. japonica habitat in the Yellow River Estuary, from all trace elements together, was assessed as “moderate” in all three seasons examined. Bioaccumulation of trace elements by seagrass tissues was highly variable between seasons and between above-ground and below-ground biomass. The variation in trace element concentration of seagrass tissues was much higher than the variation in trace element concentration of the sediment. In addition, for trace elements which tended to accumulate more in above-ground biomass than below-ground biomass (Cd and Mn), the ratio of above-ground to below-ground trace element concentration peaked at times corresponding to high water discharge and high sediment loads in the Yellow River Estuary. Overall, our results suggest that trace element accumulation in the sediment may not vary between seasons, but bioaccumulation in seagrass tissues is highly variable and may respond directly to trace elements in the water column

Generation of Human Epidermis-Derived Mesenchymal Stem Cell-like Pluripotent Cells and their reprogramming in mouse chimeras

Stem cells can be derived from the embryo (embryonic stem cells, ESCs), from adult tissues (adult stem cells, ASCs), and by induction of fibroblasts (induced pluripotent stem cells, iPSs). Ethical problems, immunological rejection, and difficulties in obtaining human tissues limit the use of ESCs in clinical medicine. Induced pluripotent stem cells are difficult to maintain in vitro and carry a greater risk of tumor formation. Furthermore, the complexity of maintenance and propagation is especially difficult in the clinic. Adult stem cells can be isolated from several adult tissues and present the possibility of self-transplantation for the clinical treatment of a variety of human diseases. Recently, several ASCs have been successfully isolated and cultured in vitro, including hematopoietic stem cells (HSCs) , mesenchymal stem cells (MSCs), epidermis stem cells, neural stem cells (NSCs), adipose-derived stem cells (ADSCs), islet stem cells, and germ line stem cells. Human mesenchymal stem cells originate mainly from bone marrow, cord blood, and placenta, but epidermis-derived MSCs have not yet been isolated. We isolated small spindle-shaped cells with strong proliferative potential during the culture of human epidermis cells and designed a medium to isolate and propagate these cells. They resembled MSCs morphologically and demonstrated pluripotency in vivo; thus, we defined these cells as human epidermis-derived mesenchymal stem cell-like pluripotent cells (hEMSCPCs). These hEMSCPCs present a possible new cell resource for tissue engineering and regenerative medicine

Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data

The classification and extraction of street tree geometry information in road scenes is crucial in urban forest biomass statistics and road safety. To address the problem of 3D fine extraction of street trees in complex road scenes, this paper designs and investigates a method for extracting street tree geometry and forest parameters from vehicle-mounted LiDAR point clouds in road scenes based on a Gaussian distributed regional growth algorithm and Voronoi range constraints. Firstly, a large number of non-tree and other noise points, such as ground points, buildings, shrubs and vehicle points, are filtered by applying multi-geometric features; then, the main trunk of the street tree is extracted based on the vertical linear features of the tree and the region growth algorithm based on Gaussian distribution; secondly, a Voronoi polygon constraint is established to segment the single tree canopy region with the main trunk center of mass; finally, based on the extracted locations of the street trees and their 3D points, the tree growth parameters of individual trees are obtained for informative management and biomass estimation by combining geometric statistical methods. In this paper, the experimental data from vehicle-borne LiDAR point clouds of different typical areas were selected to verify that the proposed Gaussian-distributed regional growth algorithm can achieve fine classification and extraction of tree growth parameters for different types of roadside trees, with accuracy, recall and F1 values reaching 96.34%, 97.22% and 96.45%, respectively. This research method can be used for the extraction of 3D fine classification of street trees in complex road environments, which in turn can provide support for the safety management of traffic facilities and forest biomass estimation in urban environments

Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors

Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning