Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Selective hydrogenation of benzene to cyclohexene over Ce-promoted Ru catalysts

Ru-Ce catalysts were prepared by a co-precipitation method.The effects of Ce precursors with different valences and Ce contents on the catalytic performance of Ru-Ce catalysts were investigated in the presence of ZnSO4.The Ce species in the catalysts prepared with different valences of the Ce precursors all exist as CeO2 on the Ru surface.The promoter CeO2alone could not improve the selectivity to cyclohexene of Ru catalysts.However,almost all the CeO2 in the catalysts could react with the reaction modifier ZnSO4 to form(Zn(OH)2)3(ZnSO4)(H2O)3 salt.The amount of the chemisorbed salt increased with the CeO2 loading,resulting in the decrease of the activity and the increase of the selectivity to cyclohexene of Ru catalyst.The Ru-Ce catalyst with the optimum Ce/Ru molar ratio of 0.19 gave a maximum cyclohexene yield of 57.4%.Moreover,this catalyst had good stability and excellent reusability.

作物病虫害遥感监测研究进展与展望

病虫害是农业生产过程中影响粮食产量和质量的重要生物灾害。目前,我国的作物病虫害监测方式以点状的地面调查为主,无法大面积、快速获取作物病虫害发生状况和空间分布信息,难以满足作物病虫害的大尺度科学监测和防控的需求。近年来,随着国内外卫星光谱、时间和空间分辨率的不断提升,利用遥感手段开展高效、无损的病虫害监测成为有效提升我国病虫害测报水平的重要手段。与此同时,多平台、多种方式的作物病虫害遥感监测也为病虫害的有效防治和管理提供了重要科技支撑。本文从作物病虫害光谱特征、遥感监测方法和遥感监测系统等方面阐述了作物病虫害遥感监测研究的进展,分析了当前面临的挑战,并对未来发展趋势进行了展望。

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests.In most previous studies,the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice.If a detection system can be established with a few bands and a relatively simple logic,it would be of great significance for application.This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat,i.e.,powdery mildew,yellow rust and aphid with a few specific bands.Through a comprehensive spectral analysis,only three bands at 570,680 and 750 nm were selected.A novel vegetation index namely Ratio Triangular Vegetation Index(RTVI)was developed for detecting anomalous areas on leaves.Then,the Support Vector Machine(SVM)method was applied to construct the discrimination model based on the spectral ratio analysis.The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy(OA)of 83%.With three bands from the hyperspectral imaging data,the three wheat diseases and pests were successfully detected and discriminated.A stepwise strategy including background removal,damage lesions recognition and stresses discrimination was proposed.The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

Two new species in the genus Heteribalia Sakagami(Hymenoptera: Ibaliidae) from China

The genus Heteribalia Sakagami（1949） is a member of the small parasitic wasp family Ibaliidae（Hymenoptera： Cynipoidea）, and all known species of this genus are from China and its neighbouring countries. In this paper, we report two new species, Heteribalia miltopronotum sp. nov. and Heteribalia sichuanensis sp. nov., and distribution for H. divergens Maa（1949）. These new discoveries update the known distribution of this genus in China from the known east coast areas to a much more extensive region between the eastern coast and Sichuan Province in central China. A key to all known Heteribalia species is provided.

Clinicopathological analysis of solitary fibrous tumor

Objective:The aim of this study was to investigate the clinicopathologic characteristics,diagnosis and differential diagnosis,molecular genetics,treatment and prognosis of solitary fibrous tumor(SFT).Methods:The clinicopathological manifestations were analyzed retrospectively in 22 patients with surgically confirmed SFT.Results:There were 12 male patients and 10 female patients,with the age range 33-67(mean 48.62) years.The SFTs originated from different from parts of the body,including 13 in the chest,2 in the lungs,3 in the abdomen,1 in the lumbosacral area,2 in the pelvis,and 1 in the left shoulder.There were 19 benign and 3 malignant tumors.Major clinical presentations were local masses and compression symptoms.Microscopy:the tumor was composed of areas of alternating hypercellularity and hypocellularity.The tumor cells were spindle to short-spindle shaped and arranged in fascicular or storiform pattern and hemangiopericytoma-like structure was presented.Immunohistochemically,Vimentin positive rate was 100%(22/22),Bcl-2 positive rate was 95.5%(21/22),CD99 positive rate was 86.4%(19/22),CD34 positive rate was 81.8(18/22),focally positive for P53,as well as negative CK,S100 and Desmin.Ki67 labelling index was 2%-30%.Conclusion:SFT is a rare tumor which may be found in various parts of human body.SFT mostly is a benign tumor,but a few could be malignant.Its diagnosis mainly rely on its morphologic features and immunohistochemical profiles.The major treatment is to completely resect it by operation and long-term clinical follow-up is necessary.

Dragon 4-Satellite Based Analysis of Diseases on Permanent and Row Crops in Italy and China

The AMEOS(Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting)project aims to bring together cutting edge research to provide pest and disease monitoring and forecast information,integrating multi-source information(Earth Observation,meteorological,entomological and plant pathological,etc.)to support decision making in the sustainable management of insect pests and diseases in agriculture.The main objective of the project,that is,improving crop diseases and pests monitoring and forecasting,will be achieved by utilizing EO data,developing new algorithms,and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information.The project foresees the assessment of the possibility of using available satellite images datasets to assess the evolution of diseases on permanent(olive groves,vineyards),or row crops(wheat)in Italy and China.The paper describes the results of the research activity which focused on:①improving the classification of the agricultural areas devoted to winter wheat and olive trees,starting from what has been made available from the Corine Land Cover initiative;②developing an approach suitable to be automated for estimating trees by using Sentinel 2 images;③developing a new index,REDSI(consisting of Red,Re 1,and Re 3 bands),for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale.The research activity covers the:Province of Lecce,that is the Italian area strongly affected,since 2015,by the Xylella fastidiosa disease which causes a rapid decline in olive plantations.Province of Anyang,Neihuang county,which was affected by the yellow rust disease in the spring 2017.

The clinical efficacy of Lou’s Wentong method in the treatment of lactational mastitis: A retrospective self-control study with 158 cases

Background:As a common breast disease in lactating women,lactational mastitis is clinically manifested as painful breast lumps and systemic symptoms.To a certain extent,this disease may affect the physical and mental health of lactating women and even the breastfeeding of infants.Objective:This study was conducted to evaluate the clinical efficacy of Lou’s Wentong Method in the comprehensive treatment of lactational mastitis and its effects on relevant symptoms.Methods:A total of 158 patients who were diagnosed with lactational mastitis in The First Affiliated Hospital of Zhejiang Chinese Medical University from January 2022 to January 2024 were included in this study to conduct a retrospective analysis.The treatment method comprised oral administration of traditional Chinese medicine prescriptions guided by the concept of“Wentong zhi yong”(warming and dispersing therapy for treating abscesses),puncture and pus extraction for patients with abscess formation,and milk drainage for patients with milk stagnation.The clinical symptoms of these patients were collected according to the seven indexes in Lou’s quantitative scorecard for evaluating the condition of canker sores before and after treatment.Additionally,the improvement of each symptom after treatment,the total effective rate of the clinical treatment,and the total symptom evaluation score before and after treatment were also analyzed.Results:It was demonstrated that there were significant differences in the overall distribution of different severity symptoms(skin erythema,breast pain,number of lumps,size of lumps,breast abscesses,and body temperature)at three time points(P<0.01).The evaluation of these symptoms at different time points revealed that all symptoms were effectively mitigated over time.Meanwhile,the efficacy was assessed according to the efficacy index to evaluate the efficacy of Lou’s Wentong Method in the comprehensive treatment of lactational mastitis.The results corroborated the pronounced efficacy of Lou’s Wentong Method in treating lactational mastitis,exhibiting statistical significance(P<0.01).Conclusion:Lou’s Wentong Method has significant therapeutic effects in the comprehensive treatment of lactational mastitis.Furthermore,this method is conducive to alleviating the clinical symptoms of these patients as soon as possible and can reduce the pain of patients,thus promoting their recovery.

Rationally pairing photoactive materials for high-performance polymer solar cells with efficiency of 16.53%

The emergence of non-fullerene acceptors(NFA) offers a promising opportunity to develop high-performance donor/acceptor pairs with high power conversion efficiency,as NFAs offer tunable energy levels,broad absorption and suitable aggregation property.In order to enhance light-harvesting capability of active layers,we choose a wide bandgap polymer PTQ10 as the donor to blend with a narrow bandgap NFAY6 as the acceptor.In comparison with PTQ10:IDIC blend,~130 nm red-shifted absorption spectrum is observed in the PTQ10:Y6 blend,which potentially enhance the short-circuit current density(Jsc) for the PSCs.In addition,the optimal PTQ10:Y6 blend shows higher photoluminescence quenching efficiency and more efficient charge separation,higher charge mobilities,as well as weaker bimolecular recombination over the PTQ10:IDIC blend,which leads to an outstanding power conversion efficiency(PCE) of 16.53%,with a notable Jsc of 26.65 mA cm^-2 and fill factor(FF) of 0.751.

Experimental study on filtration performance of the moving bed granular filter with axial flow

The filtration performance of the moving bed granular filter with axial flow (MBGF-AF) is investigated through a large cold experiment. The effect of different operation parameters on the filtration performance (collection efficiency, pressure drop) of the axial-flow moving bed filter is investigated in combination with the dust deposition effect and the mechanism of trapping dust by the capturing particles. The results show that the collection efficiency of MBGF-AF is enhanced by decreasing the superficial gas velocity, increasing the inlet dust concentration properly, or decreasing the moving velocity of the capturing particles. A model covering the above operation parameters is established to calculate the collection efficiency of the moving bed granular filter. It is used in a wide range of operating parameters for the MBGFs.

Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery

The disease of banana Fusarium wilt currently threatens banana production areas all over the world.Rapid and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting adjustments.The objective of this study was to evaluate the performance of supervised classification algorithms such as support vector machine(SVM),random forest(RF),and artificial neural network(ANN)algorithms to identify locations that were infested or not infested with Fusarium wilt.An unmanned aerial vehicle(UAV)equipped with a five-band multi-spectral sensor(blue,green,red,red-edge and near-infrared bands)was used to capture the multi-spectral imagery.A total of 139 ground sample-sites were surveyed to assess the occurrence of banana Fusarium wilt.The results showed that the SVM,RF,and ANN algorithms exhibited good performance for identifying and mapping banana Fusarium wilt disease in UAV-based multi-spectral imagery.The overall accuracies of the SVM,RF,and ANN were 91.4%,90.0%,and 91.1%,respectively for the pixel-based approach.The RF algorithm required significantly less training time than the SVM and ANN algorithms.The maps generated by the SVM,RF,and ANN algorithms showed the areas of occurrence of Fusarium wilt disease were in the range of 5.21-5.75 hm2,accounting for 36.3%-40.1%of the total planting area of bananas in the study area.The results also showed that the inclusion of the red-edge band resulted in an increase in the overall accuracy of 2.9%-3.0%.A simulation of the resolutions of satellite-based imagery(i.e.,0.5 m,1 m,2 m,and 5 m resolutions)showed that imagery with a spatial resolution higher than 2 m resulted in good identification accuracy of Fusarium wilt.The results of this study demonstrate that the RF classifier is well suited for the identification and mapping of banana Fusarium wilt disease from UAV-based remote sensing imagery.The results provide guidance for disease treatment and crop planting adjustments.

Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data

Detection of yellow rust using hyperspectral data is of practical importance for disease control and prevention.As an emerging spectral analysis method,continuous wavelet analysis(CWA)has shown great potential for the detection of plant diseases and insects.Given the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly,it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat.A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected plants.The impacts of the mother wavelet type and spectral interval on disease detection were analyzed.The results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow rust.The Mexh wavelet slightly outperformed the others in estimating disease severity.Although the detecting accuracy generally declined with decreasing of spectral interval,relatively high accuracy levels were maintained(R^(2)>0.7)until a spectral interval of 16 nm.Therefore,it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow rust.The relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.

Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

Leaf area index(LAI)and canopy chlorophyll density(CCD)are key indicators of crop growth status.In this study,we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages.The indices were calculated with Sentinel-2 MSI data and hyperspectral data.Their performances were validated against ground measurements using R2,RMSE,and bias.The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage,head emergence stage,and filling stage.Furthermore,rededge modified indices outperformed the traditional indices for both data genres.Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early joint-ing and milk development stage,while indices with long red-edge wavelength estimate the sought variables better at the middle three stages.The results were consistent with the red-edge inflec-tion point shift at different growth stages.The best indices for Sentinel-2 LAI retrieval,Sentinel-2 CCD retrieval,hyperspectral LAI retrieval,and hyperspectral CCD retrieval at five growth stages were determined in the research.These results are beneficial to crop trait monitoring by providing references for crop biophysical and bio-chemical parameters retrieval.

Chronotype distribution in the Chinese population

Purpose:Individual chronotypes are reported to be closely associated with mood,health status,and even disease progression.However,no reports of chronotype distribution in the Chinese population have been made available to date.Methods:We performed a chronotype survey using the classic Morningness–Eveningness Questionnaire both online and offline.The webpage-based online survey was distributed via a social network application on mobile phones.The offline survey was distributed to local primary and middle schools.A total of 9476 questionnaires were collected,of which 8395 were valid.The mean age of the participants was 30.38±11.47 years,and 37.38%were male.Results:Overall,the Chinese chronotypes showed a near-normal distribution with a slight shift toward eveningness.When analyzed in different age groups,the overall Chinese population was shown to be"latest"in their early twenties.In the young population,two significant points of change in chronotype were identified at the ages of 10 and 16 years.The chronotype composition remained relatively stable during early adulthood(from 17 to 28 years of age).Conclusion:This study generated the first overview of chronotype distribution in the Chinese population and will serve as essential background data for future studies.