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Ultrabright near-infrared light

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Light(Science & Applications) 2024年第4期13卷 609-609页

作者： andries Meijerink Debye Institute for Nanomaterials Science Utrecht UniversityPrincetonplein

Nature Photonics(2024)https://***/10.1038/s41566-024-01400-7 The recent race for bright and broadband near-infrared(NIR)light sources has focused on new luminescent materials(phosphors)for efficient conversion of blue light emitting diode(LED)emission to broadband NIR.For extreme brightness,blue laser diodes(LDs)are superior to LEDs as they maintain efficiency at high current density.However,so far NIR phosphors suffered from severe efficiency droop at these high pumping powers.The team of Zhiguo Xia from South China University of Technology reported a record high 6 Watt NIR output from LDpumped MgO:Cr^(3+)translucent ceramics emitting around 810 nm.Interestingly,MgO:Cr^(3+)is an old phosphor,extensively studied in the past century.The success is based in spark plasma sintering of translucent MgO:Cr^(3+)ceramics using nanosilica as flux,incorporated at grain boundaries.The refractive index contrast between MgO and SiO2 induces scattering of blue light,increasing light absorption,and the dense ceramic has excellent thermal conductivity,reducing thermal quenching of NIR emission.The new scheme using NIR-emitting translucent ceramics for laser-driven NIR light sources will propel photonics applications in night vision,biomedical imaging and sensing.

关键词： ceramics ceramic maintain

Challenges and opportunities in remote sensing-based crop monitoring: a review

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National Science Review 2023年第4期10卷 203-219页

作者： Bingfang Wu Miao Zhang Hongwei Zeng Fuyou Tian andries B Potgieter Xingli Qin Nana Yan Sheng Chang Yan Zhao Qinghan Dong Vijendra Boken Dmitry Plotnikov Huadong Guo Fangming Wu Hang Zhao Bart Deronde Laurent Tits Evgeny Loupian State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences School of Resources and Environment University of Chinese Academy of Sciences Executive Committee of Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Queensland Alliance for Agriculture and Food Innovation The University of Queensland Department of Remote Sensing Flemish Institute of Technological Research Department of Geography and Earth Science University of Nebraska-Kearney Department of Satellite Monitoring Technologies Space Research Institute of Russian Academy of Sciences

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making.Satellite-driven crop monitoring has become a main method to derive crop information at local, regional,and global scales by revealing the spatial and temporal dimensions of crop growth status and production.However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the later can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information.Encouraging users to obtain crop information from multiple sources could prevent unconscious biases.Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.

关键词： crop monitoring crop condition crop production Ground data Remote Sensing

A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

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Plant Phenomics 2019年第1期1卷 1-14页

作者： Sambuddha Ghosal Bangyou Zheng Scott CChapman andries bpotgieter David RJordan Xuemin Wang Asheesh KSingh Arti Singh Masayuki Hirafuji Seishi Ninomiya Baskar Ganapathysubramanian Soumik Sarkar Wei Guo Department of Mechanical Engineering Iowa State UniversityAmesIAUSA 2Department of Computer ScienceIowa State UniversityAmesIAUSA 3CSIRO Agriculture and FoodSt.LuciaQLDAustralia 4School of Agriculture and Food SciencesThe University of QueenslandGattonQLD 4343Australia 5Queensland Alliance for Agriculture and Food Innovation(QAAFI)The University of QueenslandGattonQLDAustralia 6Queensland Alliance for Agriculture and Food Innovation(QAAFI)The University of QueenslandWarwickQLDAustralia 7Department of AgronomyIowa State UniversityAmesIAUSA 8International Field Phenomics Research LaboratoryInstitute for Sustainable Agro-Ecosystem Services

The yield of cereal crops such as sorghum(Sorghum bicolor L.Moench)depends on the distribution of crop-heads in varying branching arrangements.Therefore,counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field.However,measuring such phenotypic traitsmanually is an extremely labor-intensive process and suffers from low efficiency and human errors.Moreover,the process is almost infeasible for large-scale breeding plantations or experiments.Machine learning-based approaches like deep convolutional neural network(CNN)based object detectors are promising tools for efficient object detection and counting.However,a significant limitation of such deep learningbased approaches is that they typically require a massive amount of hand-labeled images for training,which is still a tedious process.Here,we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images.We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance(𝑅2 between human count and machine count is 0.88)by using a semitrained CNN model(i.e.,trained with limited labeled data)to perform synthetic annotation.In addition,we also visualize key features that the network learns.This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes.

关键词： Deep breeding network

赛峰集团:全力以赴支持C919产能爬坡——专访赛峰集团首席执行官安德斯

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大飞机 2024年第3期 11-14页

作者：欧阳亮安德斯不详赛峰集团

疫情三年,航空产业链企业受到了不同程度的冲击,有些公司不得不收缩业务规模甚至裁员来降低成本,但疫情结束后,面对迅速恢复的市场,已经降低的产能一时却难以提升。在此背景下,2023年,赛峰集团以其灵活性获得了良好的业绩。赛峰集团战... 详细信息

疫情三年,航空产业链企业受到了不同程度的冲击,有些公司不得不收缩业务规模甚至裁员来降低成本,但疫情结束后,面对迅速恢复的市场,已经降低的产能一时却难以提升。在此背景下,2023年,赛峰集团以其灵活性获得了良好的业绩。赛峰集团战胜市场的秘诀是什么?2024年,C919飞机将全面转入量产阶段,赛峰集团对确保C919相关工作包有何规划?

关键词：首席执行官航空产业链降低成本集团产能爬坡全力以赴

Parametric study of spark plasma sintering of Al_(20)Cr_(20)Fe_(25)Ni_(25)Mn_(10) high entropy alloy with improved microhardness and corrosion

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International Journal of Minerals,Metallurgy and Materials 2022年第1期29卷 119-127页

作者： andries Mthisi Nicholus Malatji A.Patricia I.Popoola L.Rudolf Kanyane Department of Chemical Metallurgical and Materials EngineeringTshwane University of TechnologyP.M.B.X680PretoriaSouth Africa

Multicomponent Al_(20)Cr_(20)Fe_(25)Ni_(25)Mn_(10) alloys were synthesized using spark plasma sintering at different temperatures(800,900,and 1000℃)and holding times(4,8,and 12 min)to develop a high entropy alloy(HEA).The characteristics of spark plasma-synthesized(SPSed)alloys were experimentally explored through investigation of microstructures,microhardness,and corrosion using scanning electron microscopy coupled with energy dispersive spectroscopy(EDS),Vickers microhardness tester,and potentiodynamic polarization,respectively.Xray diffraction(XRD)characterization was employed to identify the phases formed on the developed alloys.The EDS results revealed that the alloys consisted of elements selected in this work irrespective of varying sintering parameters.The XRD,EDS,and scanning electron microscopy collectively provided evidence that the fabricated alloys were characterized by globular microstructures exhibiting face-centered cubic phase,which was formed on a basis of solid solution mechanism.This finding implies that the SPSed alloy showed the features of HEAs.The alloy produced at 1000℃ and holding time of 12 min portrayed an optimal microhardness of HV 447.97,but the value decreased to HV 329.47 after heat treatment.The same alloy showed an outstanding corrosion resistance performance.The increase in temperature resulted in an Al_(20)Cr_(20)Fe_(25)Ni_(25)Mn_(10) alloy with superior density,microhardness,and corrosion resistance over the other alloys developed at different parameters.

关键词： high entropy alloy spark plasma sintering microhardness corrosion

From Prototype to Inference:A Pipeline to Apply Deep Learning in Sorghum Panicle Detection

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Plant Phenomics 2023年第1期5卷 94-109页

作者： Chrisbin James Yanyang Gu andries Potgieter Etienne David Simon Madec Wei Guo Frédéric Baret Anders Eriksson Scott Chapman School of Agriculture and Food Sciences The University of QueenslandBrisbaneAustralia School of Information Technology and Electrical Engineering The University of QueenslandBrisbaneAustralia Queensland Alliance for Agriculture and Food Innovation The University of QueenslandBrisbaneAustralia Arvalis Institut du VégétalParisFrance Graduate School of Agricultural and Life Sciences The University of TokyoTokyoJapan Institut National de la Recherche Agronomique ParisFrance

Head(panicle)density is a major component in understanding crop yield,especially in crops that produce variable numbers of tillers such as sorghum and wheat.Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation,which is an inefficient and tedious process.Because of the easy availability of red–green–blue images,machine learning approaches have been applied to replacing manual counting.However,much of this research focuses on detection per se in limited testing conditions and does not provide a general protocol to utilize deep-learning-based counting.In this paper,we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for sorghum.This pipeline provides a basis from data collection and model training,to model validation and model deployment in commercial fields.Accurate model training is the foundation of the pipeline.However,in natural environments,the deployment dataset is frequently different from the training data(domain shift)causing the model to fail,so a robust model is essential to build a reliable solution.Although we demonstrate our pipeline in a sorghum field,the pipeline can be generalized to other grain species.Our pipeline provides a high-resolution head density map that can be utilized for diagnosis of agronomic variability within a field,in a pipeline built without commercial software.

关键词： crops breeding utilize

A physically active lifestyle is related to a lower level of skin autofluorescence in a large population with chronic-disease(LifeLines cohort)

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Journal of Sport and Health Science 2022年第2期11卷 260-265页

作者： Saskia Corine van de Zande Jeroen Klaas de Vries Inge van den Akker-Scheek Johannes Zwerver andries Jan Smit Department of Internal Medicine Division of Vascular MedicineUniversity of GroningenUniversity Medical Center GroningenGroningen9700 RBthe Netherlands Department of Internal Medicine Antonius Hospital SneekSneek8601 ZKthe Netherlands Department of Orthopaedics University of GroningenUniversity Medical Center GroningenGroningen9700 RBthe Netherlands Center for Human Movement Sciences University of GroningenUniversity Medical Center GroningenGroningen9700 RBthe Netherlands Sports Valley Gelderse Vallei HospitalEde6716 RPthe Netherlands

Background:Physical activity(PA)has substantial health benefits and is important in combatting chronic diseases,which have been associated with elevated levels of advanced glycation endproducts(AGEs).AGEs play a role in the aging process,and an association between PA and AGEs has been reported.We aimed to investigate the relationship between PA and AGE accumulation in a general population and in a population with chronic diseases.Methods:This large cross-sectional population study used data from adult participants in the LifeLines project,with participant information drawn from the LifeLines database as well data from patients with diabetes mellitus or renal and/or cardiovascular diseases.Tissue AGE accumulation was assessed non-invasively by skin-autofluorescence(SAF)using an AGE reader(DiagnOptics Technologies BV,Groningen,the Netherlands).PA was assessed using the short questionnaire to assess health-enhancing physical activity(SQUASH).Multivariate linear regression analyses were adjusted for age,body mass index,sex,and smoking status.Results:Data from 63,452 participants(general population n=59,177,chronic disease n=4275)were analyzed.The general population was significantly younger(43.58±11.77 years,mean±SD)and had significantly lower SAF(1.90±0.42 arbitrary units(AU))compared to the population with chronic disease(age:55.51±12.07 years;SAF:2.27±0.51 AU).In the group with chronic disease,more hours of moderate to vigorous physical activities per week were associated with lower SAF(β=-0.002,95%confidence interval(95%CI):-0.002 to-0.001).For the general population,there was no association between hours of moderate to vigorous activity and SAF(β=3.2×10^(-5),95%CI:0.000-0.001,p=0.742).However,there was an association in the general population between total hours of PA per week and SAF(β=4.2×10^(-4),95%CI:0.000-0.001,p<0.001),but this association was not found in the chronic disease population(β=-3.2×10^(-4),95%CI:-0.001 to 0.000,p=0.347).Conclusion:Our study demonstrates that an inverse relationship exists betwe

关键词： Advanced glycation endproducts Chronic disease population General population Physical activity Skin autofluorescence

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

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Plant Phenomics 2021年第1期3卷 337-350页

作者： Yan Zhao Bangyou Zheng Scott CChapman Kenneth Laws Barbara George-Jaeggli Graeme LHammer David RJordan andries B.Potgieter The University of Queensland Queensland Alliance for Agriculture and Food InnovationCentre for Crop ScienceGattonQueensland 4343Australia CSIRO Agriculture and Food St.LuciaQueensland 4072Australia The University of Queensland School of Agriculture and Food SciencesSt.LuciaQueensland 4072Australia Department of Agriculture and Fisheries Agri-Science QueenslandWarwickQueensland 4370Australia

In plant breeding,unmanned aerial vehicles(UAVs)carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping(HTP)to aid the interpretation of genotype and environment effects on morphological,biochemical,and physiological traits.A key constraint remains the reduced resolution and quality extracted from“stitched”mosaics generated from UAV missions across large areas.This can be addressed by generating high-quality reflectance data from a single nadir image per plot.In this study,a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications.Sequential steps involved(i)imagery calibration,(ii)spectral band alignment,(iii)backward calculation,(iv)plot segmentation,and(v)application.Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot.Using a derived nadir image of each plot,the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads,respectively.Furthermore,the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours(i.e.,red and white).Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.

关键词： breeding backward Plant

Emerging substance class with narrow-band blue/green-emitting rare earth phosphors for backlight display application

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Science China Materials 2019年第1期62卷 146-148页

作者： andries Meijerink Condensed Matter and Interfaces Debye Institute for Nanomaterials Science Utrecht University

Solid state lighting (SSL)on the basis of phosphorconverted white light-emitting diodes (pc-wLEDs)is rapidly changing the lighting and display industry.New materials and concepts provide light sources and systems that have unmatched performance and/or functionality [1,2].In addition to the well-known application of pcwLEDs in home and office lighting,these efficient white light sources are also increasingly applied as alternative backlight units in liquid-crystal displays (LCDs),where there is a strong focus on improving resolution,wider color gamut,and decreased power consumption [3].

关键词： Emerging substance class narrow-band blue