压力与温度对炭黑填充丁苯橡胶复合材料动静态性质影响的分子模拟

采用分子动力学模拟研究了不同压力与温度对炭黑填充丁苯橡胶复合体系动静态性能的影响。结果表明,复合体系的玻璃化转变温度随着压力升高呈现线性上升;体系自由体积与分子链尺寸随着压力上升或温度下降而减小,这也导致了分子链平动扩散与旋转动力学的降低。通过计算材料储能模量、损耗模量与损耗因子,发现材料储能模量与损耗模量随着压力上升或温度下降而上升,而材料损耗因子随着压力上升或温度下降而逐渐减小。这些结果为理解水声材料在大压力宽温域环境下的性能演变提供了微观机理。

Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food production.Object detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating diseases.However,existing methods are still limited to single crop disease diagnosis.More importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile devices.Nonetheless,reducing the number of model parameters tends to cause a decrease in model accuracy.To solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple crops.In detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher model.We develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing methods.Overall,the multistage knowledge distillation technique can make the model lighter while maintaining high accuracy.Not only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart agriculture.Our code is available at https://github.com/QDH/MSKD.

Constructing origami power generator from one piece of electret thin film and application in AIenabled transmission line vibration monitoring

One of the crucial issues for applying electret/triboelectric power generators in the Internet of Things(IoT)is to take full advantage of specific high voltage signals and enable self-powered sensing.Therefore,inspired by Miura-origami,we present an innovative origami power generator(OPG)constructed from only one piece of electret thin film.The Miura-origami architecture realizes a generator with excellent deformability and stretchability and makes it unnecessary for any auxiliary support structure during the compress-release cycle.Various parameters of the generator are intensively investigated,including the excitation accelerations,excitation displacements,numbers of power generation units and deformation degree of the device.When stimulated with 5.0 g acceleration at 15 Hz frequency,the generator with 8 generation units can obtain an instantaneous peak-to-peak voltage and a remarkable optimum peak power of 328 V and 2152μW at 50MΩ,respectively.In addition,the regulable shape and multiple generation modes of the device greatly improve its applicability in various vibration energy collection requirements.Based on the above results,a hexagonal electret generator integrated with six-phase OPGs is developed as a“Buoy on Sky,”after which the signal waveforms generated from internal power generators are recognized with 92%accuracy through a neural network algorithm that identifies the vibration conditions of transmission lines.This work demonstrates that a fusion of origami art and energy conversion techniques can achieve a multifunctional generator design satisfying the requirements for IoT applications.

CPSF30-L-mediated recognition of mRNA m^(6)A modification controls alternative polyadenylation of nitrate signaling-related gene transcripts in Arabidopsis

N6-methyladenosine(m^(6)A),a ubiquitous internal modification of eukaryotic mRNAs,plays a vital role in almost every aspect of mRNA metabolism.However,there is little evidence documenting the role of m^(6)A in regulating alternative polyadenylation(APA)in plants.APA is controlled by a large protein-RNA complex with many components,including CLEAVAGE AND POLYADENYLATION SPECIFICITY FACTOR30(CPSF30).In Arabidopsis,CPSF30 has two isoforms and the longer isoform(CPSF30-L)contains a YT512-B Homology(YTH)domain,which is unique to plants.In this study,we showed that CPSF30-L YTH domain binds to m^(6)A in v itro.In the cpsf30-2 mutant,the transcripts of many genes including several important nitrate signaling-related genes had shifts in polyadenylation sites that were correlated with m^(6)A peaks,indicating that these gene transcripts carrying m^(6)A tend to be regulated by APA.Wild-type CPSF30-L could rescue the defects in APA and nitrate metabolism in cpsf30-2,but m^(6)A-binding-defective mutants of CPSF30-L could not.Taken together,our results demonstrated that m^(6)A modification regulates APA in Arabidops is and revealed that the m^(6)A reader CPSF30-L affects nitrate signaling by controlling APA,shedding new light on the roles of the m^(6)A modification during RNA 3-end processing in nitrate metabolism.

Enhancing sensitivity of microbial fuel cell sensors for low concentration biodegradable organic matter detection:Regulation of substrate concentration,anode area and external resistance

The relatively low sensitivity is an important reason for restricting the microbial fuel cell(MFC)sensors'application in low concentration biodegradable organic matter(BOM)detection.The startup parameters,including substrate concentration,anode area and external resistance,were regulated to enhance the sensitivity of MFC sensors.The results demonstrated that both the substrate concentration and anode area were positively correlated with the sensitivity of MFC sensors,and an external resistance of 210Ωwas found to be optimal in terms of sensitivity of MFC sensors.Optimized MFC sensors had lower detection limit(1 mg/L)and higher sensitivity(Slope value of the linear regression curve was 1.02),which effectively overcome the limitation of low concentration BOM detection.The essential reason is that optimized MFC sensors had higher coulombic efficiency,which was beneficial to improve the sensitivity of MFC sensors.The main impact of the substrate concentration and anode area was to regulate the proportion between electrogens and nonelectrogens,biomass and living cells of the anode biofilm.The external resistance mainly affected the morphology structure and the proportion of living cells of the anode.This study demonstrated an effective way to improve the sensitivity of MFC sensors for low concentration BOM detection.

Red soil for sediment capping to control the internal nutrient release under flow conditions

Nitrogen （N） and phosphorus （P） released from the sediment to the surface water is a major source of water quality impairment. Therefore, inhibiting sediment nutrient release seems necessary. In this study, red soil （RS） was employed to control the nutrients released from a black-odorous river sediment under flow conditions. The N and P that were released were effectively controlled by RS capping. Continuous-flow incubations showed that the reduction efficiencies of total N （TN）, ammonium （NH4＋-N）, total P （TP） and soluble reactive P （SRP） of the overlying water by RS capping were 77%, 63%, 77% and 92%, respectively, and nitrification and denitrification occurred concurrently in the RS system. An increase in the water velocity coincided with a decrease in the nutrient release rate as a result of intensive water aeration.

Nitrate signaling and use efficiency in crops

Nitrate(NO3–)is not only an essential nutrient but also an important signaling molecule for plant growth.Low nitrogen use efficiency(NUE)of crops is causing increasingly serious environmental and ecological problems.Understanding the molecular mechanisms of NO3–regulation in crops is crucial for NUE improvement in agriculture.During the last several years,significant progress has been made in understanding the regulation of NO3–signaling in crops,and some key NO3–signaling factors have been shown to play important roles in NO3–utilization.However,no detailed reviews have yet summarized these advances.Here,we focus mainly on recent advances in crop NO3–signaling,including short-term signaling,long-term signaling,and the impact of environmental factors.We also review the regulation of crop NUE by crucial genes involved in NO3–signaling.This review provides useful information for further research on NO3–signaling in crops and a theoretical basis for breeding new crop varieties with high NUE,which has great significance for sustainable agriculture.

BiodMHC:an online server for the prediction of MHC class Ⅱ-peptide binding affinity

Effective identification of major histocompatibility complex （MHC） molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class Ⅱ MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler. ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC （http：//biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request）. Using a standard measure of AUC （Area Under the receiver operating characteristic Curves）, we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class Ⅱ MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods.