Simulating the Impacts of Land-Use Land-Cover Changes on Cropland Carbon Fluxes in the Midwest of the United States

Understanding the major drivers of the cropland carbon fluxes is important for carbon management and greenhouse gas mitigation in agriculture. Past studies found that agricultural land-use and land-cover (LULC) changes, such as changes in cropland production technologies, tillage practices, and planted crop species, could have large impacts on carbon fluxes. However, the impacts remain highly uncertain at regional to global scales. Satellite remote sensing is commonly used to create products with geospatial information on LULC changes. This geospatial information can be integrated into biogeochemical models to simulate the spatial and temporal patterns of carbon fluxes. We used the General Ensemble Biogeochemical Modeling System (GEMS) to study LULC change impacts on cropland carbon fluxes in the Midwest USA. First we evaluated the impacts of LULC change on cropland net primary production (NPP) estimates. We found out the high spatial variability of cropland NPP across the study region was strongly related to the changes in crop species. Ignoring information about crop species distributions could introduce large biases into NPP estimates. We then investigated whether the characteristics of LULC change could impact the uncertainties of carbon flux estimates (i.e., NPP, net ecosystem production (NEP) and soil organic carbon (SOC)) using GEMS and two other models. The uncertainties of all three flux estimates were spatial autocorrelated. Land cover characteristics, such as cropland percentage, crop richness, and land cover diversity all showed statistically significant relationships with the uncertainties of NPP and NEP, but not with the uncertainties of SOC changes. The impacts of LULC change on SOC changes were further studied with historical LULC data from 1980 to 2012 using GEMS simulations. The results showed that cropland production increase over time from technology improvements had the largest impacts on cropland SOC change, followed by expansion of conservation tillage. This study advanced the scientific knowledge of cropland carbon fluxes and the impacts of various management practices over an agricultural area. The findings could help future carbon cycle studies to generate more accurate estimates on spatial and temporal changes of carbon fluxes

A STUDY ON THE DURABILITY AND PERFORMANCE OF PHOTOVOLTAIC MODULES IN THE TROPICS

Ph.DDOCTOR OF PHILOSOPH

A high performance ultra-wideband low cost SMA-to-GCPW transition

This letter presents a novel low cost through-the-wall SMA connector and the transition structures from the SMA to a grounded coplanar waveguide (GCPW). The SMA connector has two short metal legs extended from the outer conductor used to reduce the discontinuity of the transition. The parameters of the GCPW are designed to match the geometry of the coaxial. A matching stub is introduced in the center conductor line to further improve the performance of the transition. A prototype device is developed and measured. The measurement results show the return loss of the proposed transition is better than 20dB up to 26.5GHz

Multi-model Fusion Attention Network for News Text Classification

At present, the classification prediction task based on news content or news headline has the problems of inaccurate classification and attention deviation. In this paper, a multi-model fusion attention network for news text classification (MFAN) is proposed to train news content and news titles in parallel. Firstly, the multi-head attention mechanism is used to obtain the category information of news content through a dynamic word vector, focusing on the semantic information that significantly influences the downstream classification task. Secondly, the semantic information of news headlines is obtained by using the improved version of the long-short-term memory network, and the attention is focused on the words that have a great influence on the final results, which improves the effectiveness of model classification. Finally, the classification fusion module fuses the probability scores of news text and news headlines in proportion to improve the accuracy of text classification. The experimental test on the Tenth China Software cup dataset shows that the F1 - Score index of the MFAN model reaches 97.789 %. The experimental results show that the MFAN model can effectively and accurately predict the classification of news texts

A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing

Remote sensing images are essential for many earth science applications, but their quality can be degraded due to limitations in sensor technology and complex imaging environments. To address this, various remote sensing image deblurring methods have been developed to restore sharp, high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined hand-craft prior assumptions, which are difficult to handle in complex applications, and most deep learning-based deblurring methods are designed as a black box, lacking transparency and interpretability. In this work, we propose a novel blind deblurring learning framework based on alternating iterations of shrinkage thresholds, alternately updating blurring kernels and images, with the theoretical foundation of network design. Additionally, we propose a learnable blur kernel proximal mapping module to improve the blur kernel evaluation in the kernel domain. Then, we proposed a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold operator and a multi-scale prior feature extraction block. This module also introduces an attention mechanism to adaptively adjust the prior importance, thus avoiding the drawbacks of hand-crafted image prior terms. Thus, a novel multi-scale generalized shrinkage threshold network (MGSTNet) is designed to specifically focus on learning deep geometric prior features to enhance image restoration. Experiments demonstrate the superiority of our MGSTNet framework on remote sensing image datasets compared to existing deblurring methods.Comment: 12 pages