Microfluidic devices for high-throughput plant phenotyping and bioenergy harvesting from microbes and living plants

Microfluidics and micro/nanofabrication techniques provide powerful technological platforms to develop miniature bioassay devices for studying cellular and multicellular organisms. Microfluidic devices have many advantages over traditional counterparts, including good throughput due to parallel experiments, low infrastructural cost, fast reaction, reduced consumption of agent and reagent, and avoidance of contamination. This thesis is focused on the development of a microfluidic toolkit with several miniature devices to tackle important problems that the fields of plant phenotyping and bioenergy harvesting are facing. The ultimate goal of this research is to realize high-throughput screening methods for studying environment-genomics of plants through phenomics, and understanding microbial and plant metabolisms that contribute to harvesting bioenergy from microbes and living plants in different environments. First, we develop vertical microfluidic plant chips and miniature greenhouses for high throughput phenotyping of Arabidopsis plants. The vertical design allows for gravitropic growth of multiple plants and continuous monitoring of seed germination and plant development at both the whole-plant and cellular levels. An automatic seed trapping method is developed to facilitate seed loading process. Also, electrospun nanofibrous membranes are incorporated with a seed germination chip to obtain a set of incubation temperatures on the device. Furthermore, miniature greenhouses are designed to house the plant and seed chips and to flexibly change temperature and light conditions for high-throughput plant phenotyping on a multi-scale level. Second, to screen bacteria and mutants for elucidating mechanisms of electricity generation, we develop two types of miniature microbial fuel cells (µMFCs) using conductive poly(3,4-ethylenedioxythiophene) nanofibers and porous graphene foam (GF) as three-dimensional (3D) anode materials. It is demonstrated that in the nanofiber-based µMFC, the nanofibers are suitable for rapid electron transfer and Shewanella oneidensis can fully colonize the interior region of the nanofibers. The GF-based µMFC is featured with a porous anolyte chamber formed by embedding a GF anode inside a microchannel. The interconnected pores of the GF provide 3D scaffolds favorable for cell attachment, inoculation and colonization, and more importantly, allow flowing nutritional and bacterial media throughout the anode with minimal waste. Therefore, the nutrients in bio-convertible substrates can be efficiently used by microbes for sustainable production of electrons. Last, we develop a first miniature plant-MFC or µPMFC device as a technological interface to study bioenergy harvesting from microbes and living plants. A pilot research is conducted to create the µPMFC device by sandwiching a hydrophilic semi-permeable membrane between a µMFC and a plant growth chamber. Mass transport of carbon-containing organic exudates from the plant roots to the µMFC is quantified. This work represents an important step towards screening plants, microbes, and their mutants to maximize energy generation of PMFCs

High Range Resolution Profile Construction Exploiting Modified Fractional Fourier Transformation

This paper addresses the discrimination of closely spaced high speed group targets with radar transmitting linear frequency modulation (LFM) pulses. The high speed target motion leads to range migration and target dispersion and thereby the discriminating capability of the high range resolution profile (HRRP) deteriorating significantly. An effective processing approach composed of stretch processing (SP), modified fractional Fourier transform (FrFT), and multiple signal classification (MUSIC) algorithm is proposed to deal with this problem. Firstly, SP is adopted to transform the received LFM with Doppler distortions into narrow band LFM signals. Secondly, based on the two-dimensional range/velocity plane constructed by the modified FrFT, the velocity of the high speed group target is estimated and compensated with just one single pulse. After the compensation of range migration and target dispersion simultaneously, the resolution of the HRRP achieved by single pulse transmission improves significantly in the high speed group targets scenarios. Finally, MUSIC algorithm with superresolution capability is utilized to make a more explicit discrimination between the scatterers in comparison with the conventional SP method. Simulation results show the effectiveness of the proposed scheme

Can the digital economy promote the development of the energy economy? Evidence from China

In this paper, 22 indexes are selected at three levels, including the informatization development level, the Internet development level, and the digital transaction development level, based on China’s provincial panel data from 2011 to 2020, so as to build a digital economy development index system. Moreover, 28 basic indexes are selected from three aspects, including energy construction, energy production and energy consumption, so as to develop an energy economy development evaluation index system. The development index of China’s digital economy and energy economy are measured by using the entropy weight method. The effect of the digital economy on the energy economy and its mechanism are tested by the static panel, the dynamic panel, and the mediating effect and regulating effect models. The results indicate that the digital economy has pronouncedly promoted the development of China’s energy economy, and the development of the digital economy can have an effect on the rationalization of the industrial structure and then affect the development of the energy economy, and there is an intermediary effect. Moreover, the upgrading of the industrial structure is conducive to regulating the digital economy and facilitates the development of the energy economy. The development of the energy economy can be better promoted by focusing on the coordinated regional layout of the digital economy development, building a reliable energy commodity trading platform, and expediting the optimization and upgrading of the industrial structure

Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets.Comment: 11 pages, 6 figure

GUS-YOLO Remote Sensing Target Detection Algorithm Introducing Context Information and Attention Gate

At present, there are still some problems in the remote sensing target detection algorithm based on the general YOLO (you only look once) series, such as not making full use of the global context information of the image, not narrowing the semantic gap in the feature fusion pyramid part, and not suppressing the interference of redundant information. On the basis of combining the advantages of YOLO algorithms, this paper proposes GUS-YOLO (network of global context extraction unit and attention gate-based YOLOS) algorithm. It has a backbone network Global Backbone that can make full use of global context information. Other than that, this algorithm introduces the Attention Gate module into the top-down structure of the fused feature pyramid, which can emphasize the necessary feature information and suppress redundant information. Furthermore, this paper designs the best network structure for the Attention Gate module and proposes the feature fusion structure U-Net of proposed network.  Finally, because the ReLU activation function may lead to the problem that the model gradient is no longer updated, the Attention Gate module uses a learnable SMU (smooth maximum unit) activation function, which can improve the robustness of the model. On the NWPU VHR-10 remote sensing dataset, this algorithm achieves 1.64 percentage points and 9.39 percentage points performance improvement on mAP0.50 and mAP0.75 respectively compared with YOLOV7. Compared with the current 7 mainstream detection algorithms, this algorithm achieves better detection performance

Monitoring the Invasion of Spartina alterniflora

Spartina alterniflora was introduced to Beihai, Guangxi (China), for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR) imagery from the unmanned aerial vehicle (UAV). The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS) detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population