Preparation and absorption performance of CNTs/PUR honeycomb composite absorbing material

In this paper, a carbon nanotubes/polyurethane resin (CNTs/PUR) honeycomb composite absorbing material was prepared, and the influence of the content of carbon nanotubes on the absorbing performance of single-layer and double-layer honeycomb composite absorbing materials was investigated. The mechanical properties of single-layer and double-layer materials are discussed. The honeycomb core and carbon nanotubes are compounded together by the impregnation method to form a CNTs/PUR honeycomb composite absorbing material. The microstructure shows that the carbon nanotubes are uniformly dispersed in the water-based polyurethane resin, and the impregnated layer and the honeycomb wall are well combined. The reflectivity of the material shows that as the content of carbon nanotubes increases, the absorption performance of the material first increases and then decreases; when the content of carbon nanotubes is 5.6%, the single-layer honeycomb composite absorbing material has the best absorption performance. The effective absorption bandwidth is 10.6 GHz (2~18GHz), and the maximum absorption strength is -24.5 dB; when the combination mode is 2-4, the double-layer honeycomb composite absorbing material has good absorption performance, and the maximum absorbing strength is -32.2dB, the bandwidth is 13.7GHz (2~18GHz).

REGULARIZED REBUILD WORKFLOW OF HBIM FOR BUILT HERITAGE DOCUMENTATION

Historic Building Information Modelling is a continuous process based on reverse engineering of built heritage. By reviewing the research on HBIM with the case study, this article analyzes the combination logic between different components, based on which designs an algorithm program for automatic model generation, and proposes a regularized rebuild workflow to realize the informatization and parameterized documentation of built heritage. This article proposes the parametric workflow based on Rhino + Grasshopper + Revit / OpenBuildings Designer, establishes the information index framework under the guidance of the HBIM model, and proposes the key technologies of informatization and parameterization of architectural heritage protection records. With reference to the point cloud, mapping map, survey photos, and documents, the regularized rebuild is carried out, and all the parameter nodes are visualized to facilitate error correction and modification. The framework of the regularized rebuild workflow is defined, and the problems of packet grouping principle, component combination mode, and output type are solved, and the algorithm principle is described in detail. According to the construction logic, the single building consists of six parts: tile roof, rafter, wooden carpentry, wall, decoration, and foundation. The work of investigation, modeling, and additional professional data are carried out by parts and items to create a "digital twin". This article solves the modeling problem of complex shape and node, and further improves the working method during the survey, and proposes to use an algorithm module to realize real-time association between professional data and model. Taking the grid system, wall brick, balustrade, tile roof, rafter as examples, through compiling and debugging in Grasshopper compiler environment, according to different input parameters, the program automatically outputs the corresponding model and contains professional data, which proves that the program is fast and accurate. The regularized rebuild workflow for HBIM by reference to point cloud is realized.

A Bird's-Eye View of Exercise Intervention in Treating Depression Among Teenagers in the Last 20 Years: A Bibliometric Study and Visualization Analysis

Background: Exercise is medicine. Multiple studies on the effects and mechanisms of exercise in treating depression among teenagers and adolescents have been widely reported. However, literature involving scientometric analysis of this topic is sparse. Here, we endeavored to conduct a bibliometric study and visualization analysis to give a bird's-eye view of publications between 2000 and 2020 on exercise therapy treating depression.Methods: Relevant original publications were obtained from the Science Citation Index Expanded in the Web of Science Core Collection (WoSCC) database between 2000 and 2020. CiteSpace (5.7.R 5) and VOSviewer (1.6.16) software were used to perform bibliometric analysis of countries, institutions, categories, journals, authors, references, and keywords involved in this topic.Results: A total number of 975 articles on this field were retrieved from the WoSCC database and we identified an overall increase in the amount of publications over the past two decades, with the United States and Harvard University leading the field. Most related publications were published in the journals with a focus on sport, medicine, rehabilitation, psychology, and health, as represented by the dual-map overlay. A series of authors and co-cited authors were identified as main contributors in the exercise-depression-teenager domain. Three major clusters were explored based on the reference co-citation analysis: “exercise,” “suicide,” and “concussion”.Conclusions: Current concerns and hotspots of exercise intervention in depression treatments were summarized by “individual level,” “social level,” “role of exercise,” and “research quality.” We considered that the following four directions were potential future perspectives: “research on the effect of specific exercise intervention,” “research on the essence of exercise and sports,” “research on the combination mode of ‘exercise + X',” and “research on the micro and molecular level,” which should receive more attention.

Multi-Task Collaboration Deep Learning Framework for Infrared Precipitation Estimation

Infrared observation is an all-weather, real-time, large-scale precipitation observation method with high spatio-temporal resolution. A high-precision deep learning algorithm of infrared precipitation estimation can provide powerful data support for precipitation nowcasting and other hydrological studies with high timeliness requirements. The “classification-estimation” two-stage framework is widely used for balancing the data distribution in precipitation estimation algorithms, but still has the error accumulation issue due to its simple series-wound combination mode. In this paper, we propose a multi-task collaboration framework (MTCF), i.e., a novel combination mode of the classification and estimation model, which alleviates the error accumulation and retains the ability to improve the data balance. Specifically, we design a novel positive information feedback loop composed of a consistency constraint mechanism, which largely improves the information abundance and the prediction accuracy of the classification branch, and a cross-branch interaction module (CBIM), which realizes the soft feature transformation between branches via the soft spatial attention mechanism. In addition, we also model and analyze the importance of the input infrared bands, which lay a foundation for further optimizing the input and improving the generalization of the model on other infrared data. Extensive experiments based on Himawari-8 demonstrate that compared with the baseline model, our MTCF obtains a significant improvement by 3.2%, 3.71%, 5.13%, 4.04% in F1-score when the precipitation intensity is 0.5, 2, 5, 10 mm/h, respectively. Moreover, it also has a satisfactory performance in identifying precipitation spatial distribution details and small-scale precipitation, and strong stability to the extreme-precipitation of typhoons.

Tropical Indo-Pacific SST influences on vegetation variability in eastern Africa

Mechanisms by which tropical Pacific and Indian Ocean sea surface temperatures (SST) influence vegetation in eastern Africa have not been fully explored. Here, we use a suite of idealized Earth system model simulations to elucidate the governing processes for eastern African interannual vegetation changes. Our analysis focuses on Tanzania. In the absence of ENSO-induced sea surface temperature anomalies in the Tropical Indian Ocean (TIO), El Niño causes during its peak phase negative precipitation anomalies over Tanzania due to a weakening of the tropical-wide Walker circulation and anomalous descending motion over the Indian Ocean and southeastern Africa. Resulting drought conditions increase the occurrence of wildfires, which leads to a marked decrease in vegetation cover. Subsequent wetter La Niña conditions in boreal winter reverse the phase in vegetation anomalies, causing a gradual 1-year-long recovery phase. The 2-year-long vegetation decline in Tanzania during an ENSO cycle can be explained as a double-integration of the local rainfall anomalies, which originate from the seasonally-modulated ENSO Pacific-SST forcing (Combination mode). In the presence of interannual TIO SST forcing, the southeast African precipitation and vegetation responses to ENSO are muted due to Indian Ocean warming and the resulting anomalous upward motion in the atmosphere.

Tropical Drivers of Interannual Vegetation Variability in Eastern Africa

Mechanisms by which tropical Pacific and Indian Ocean sea surface temperatures influence vegetation in Eastern Africa and which role drought-induced fires play have not been fully explored. Here, we use a suite of idealized Earth system model simulations to elucidate the governing processes for eastern African interannual vegetation changes. Our analysis focuses on Tanzania. In the absence of ENSO-induced sea surface temperature (SST) anomalies in the Tropical Indian Ocean (TIO), El Niño causes during its peak phase negative precipitation anomalies over Tanzania due to a weakening of the tropical-wide Walker circulation and anomalous descending motion over the Indian Ocean and southeastern Africa. Resulting drought conditions increase the occurrence of wildfires, which leads to a marked decrease in vegetation cover. Subsequent wetter La Niña conditions in boreal winter reverse the trend in vegetation, causing a gradual 1-year-long recovery phase. The 2-year-long vegetation response in Tanzania can be explained as a double-integration of the local rainfall anomalies, which originate from the seasonally-modulated ENSO Pacific-SST forcing (Combination mode). In the presence of interannual TIO SST forcing, the southeast African precipitation and vegetation responses to ENSO are muted due to Indian Ocean warming and the resulting anomalous upward motion in the atmosphere.