Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

地理大数据揭示中国主要城市建成环境物质存量的空间格局

The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important,yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources,waste,and climate strategies.However,our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited,largely owing to the lack of sufficient high spatial resolution data.This study leveraged multi-source big geodata,machine learning,and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels.The per capita built environment stock of many cities(261 tonnes per capita on average)is close to that in western cities,despite considerable disparities across cities owing to their varying socioeconomic,geomorphology,and urban form characteristics.This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades.China’s urban expansion tends to be more“vertical”(with high-rise buildings)than“horizontal”(with expanded road networks).It trades skylines for space,and reflects a concentration-dispersion-concentration pathway for spatialized built environment stocks development within cities in China.These results shed light on future urbanization in developing cities,inform spatial planning,and support circular and low-carbon transitions in cities.

基于数字化植物表型平台(D3P)的田间小麦冠层光截获算法开发

冠层光截获能力是反映作物品种间差异的重要功能性状,高通量表型冠层光截获对提高作物改良效率具有重要意义。本研究以小麦为研究目标,利用数字化植物表型平台(D3P)模拟生成了100种冠层结构不同的小麦品种在5个生育期的三维冠层场景,记录了从原始冠层结构中提取的绿色叶面积指数(GAI)、平均倾角(AIA)和散射光截获率(FIPAR_(dif))信息作为真实值,进一步利用上述三维小麦场景开展了虚拟的激光雷达(LiDAR)模拟实验,生成了对应的三维点云数据。基于模拟的点云数据提取了其高度分位数特征(H)和绿色分数特征(GF)。最后,利用人工神经网络(ANN)算法分别构建了从不同LiDAR点云特征(H、GF和H+GF)输入到FIPAR_(dif)、GAI和AIA的反演模型。结果表明,对于GAI、AIA和FIPAR_(dif),预测精度从高到低对应的点云特征输入为GF+H> H> GF。由此可见,H特征对提高目标表型特性的估算精度起到了重要作用。输入GF+H特征,在中等测量噪音(10%)情况下,FIPAR_(dif)和GAI的估算均获得了满意精度,R^2分别为0.95和0.98,而AIA的估算精度(R^2=0.20)还有待进一步提升。本研究基于D3P模拟数据开展,算法的实际表现还有待通过田间数据进一步验证。尽管如此,本研究验证了D3P协助表型算法开发的能力,展示了高通量LiDAR数据在估算田间冠层光截获和冠层结构方面的较高潜力。

Opposed multi-burner gasification technology:Recent process of fundamental research and industrial application

Opposed multi-burner(OMB)gasification technology is the first large-scale gasification technology developed in China with completely independent intellectual property rights.It has been widely used around the world,involving synthetic ammonia,methanol,ethylene glycol,coal liquefaction,hydrogen production and other fields.This paper summarizes the research and development process of OMB gasification technology from the perspective of the cold model experiment and process simulation,pilotscale study and industrial demonstration.The latest progress of fundamental research in nozzle atomization and dispersion,mixing enhancement of impinging flow,multiscale reaction of different carbonaceous feedstocks,spectral characteristic of impinging flame and particle characteristics inside gasifier,and comprehensive gasification model are reviewed.The latest industrial application progress of ultralarge-scale OMB gasifier and radiant syngas cooler(RSC)combined with quenching chamber OMB gasifier are introduced,and the prospects for the future technical development are proposed as well.

Effects of antioxidants on homocysteine thiolactone-induced apoptosis in human umbilical vein endothelial cells

Background and objectives Hyperhomocysteinemia is an independent risk factor for cardiovascular disease.Homocysteine thiolactone(HcyT),one of the homocysteine metabolites in vivo,is toxic both in vivo and in vitro.The aim of this study was to investigate the effect of HcyT on apoptotic damage in human umbilical vein endothelial cells(HUVECs)and the role of antioxidants in the reduction of HcyT-induced apoptosis.Methods HUVECs were cultured in DMEM supplemented with 20%heat inactivated fetal bovine serum cell cultures were maintained in a humidified 5%CO_(2)atmosphere at 37℃.Cytotoxicity was determined by MTT assay,which consists of hypodiploid cells with propidium iodide labeling and intracellular reactive oxygen species levels using 2',7'-dichlorofluorescein diacetate as the probe by flow cytometry.Results HcyT(250-2000μM)induced HUVECs apoptosis in a time-and concentration-dependent manner.Reactive oxygen species levels rose in response to increasing HcyT concentrations at 24-h incubation.The reduction of cell apoptosis by N-acetylcysteine,vitamin E,or pyrrolidine dithiocarbamate,occurred simultaneously with a significant decrease in intracellular reactive oxygen species levels.Conclusion HcyT exerts its cytotoxic effects on endothelial cells through an apoptotic mechanism involving cellular reactive oxygen species production.The capacity of N-acetylcysteine,vitamin E,and pyrrolidine dithiocarbamate to scavenge HcyT-induced cellular reactive oxygen species correlates well with their efficiency to protect against HcyT-promoted apoptotic damage.The protective effect of pyrrolidine dithiocarbamate on cell apoptosis indicates HcyT-generated hydrogen peroxide may provoke cell apoptosis via activating nuclear factor-kappa binding protein.

Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics(spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hypertemporal, and large-volume light detection and ranging(Li DAR) and multispectral data to(i) identify the best machine learning method and prediction stage for wheat yield estimation,(ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and(iii) elucidate the contribution of time-series data fusion and 3 D spatial information to yield estimation. Wheat yield could be accurately(R^(2)= 0.891) and timely(approximately-two months before harvest) estimated from fused Li DAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits(such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits(such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3 D points than from canopy surface points and from integrated multi-stage(especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and timeseries information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.

Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks

High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging(Li DAR) provides a new way to characterize three-dimensional(3 D) plant structure, there is a need to develop robust algorithms for extracting 3 D phenotypic traits from Li DAR data to assist in gene identification and selection. Accurate 3 D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial Li DAR data. We describe a two-stage method that combines both convolutional neural networks(CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the Point CNN model and obtains stem instances by fitting 3 D cylinders to the points. It then segments the field Li DAR point cloud into individual plants using local point densities and 3 D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs(F-score =0.8207) and plants(Fscore =0.9909). The effectiveness of terrestrial Li DAR for phenotyping at organ(including leaf area and stem position) and individual plant(including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position(position error =0.0141 m), plant height(R^(2)>0.99), crown width(R^(2)>0.90), and leaf area(R^(2)>0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.

Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

The coordinated impact of forest internal structural complexity and tree species diversity on forest productivity across forest biomes

Forest structural complexity can mediate the light and water distribution within forest canopies,and has a direct impact on forest biodiversity and carbon storage capability.It is believed that increases in forest structural complexity can enhance tree species diversity and forest productivity,but inconsistent relationships among them have been reported.Here,we quantified forest structural complexity in three aspects(i.e.,horizontal,vertical,and internal structural complexity)from unmanned aerial vehicle light detection and ranging data,and investigated their correlations with tree species diversity and forest productivity by incorporating field measurements in three forest biomes with large latitude gradients in China.Our results show that internal structural complexity had a stronger correlation(correlation coefficient=0.85)with tree species richness than horizontal structural complexity(correlation coefficient=-0.16)and vertical structural complexity(correlation coefficient=0.61),and it was the only forest structural complexity attribute having significant correlations with both tree species richness and tree species evenness.A strong scale effect was observed in the correlations among forest structural complexity,tree species diversity,and forest productivity.Moreover,forest internal structural complexity had a tight positive coordinated contribution with tree species diversity to forest productivity through structure equation model analysis,while horizontal and vertical structural complexity attributes have insignificant or weaker coordinated effects than internal structural complexity,which indicated that the neglect of forest internal structural complexity might partially lead to the current inconsistent observations among forest structural complexity,tree species diversity,and forest productivity.The results of this study can provide a new angle to understand the observed inconsistent correlations among forest structural complexity,tree species diversity,and forest productivity.

遥感在生物多样性研究中的应用进展

随着人口的持续增长,人类经济活动对自然资源的利用强度不断升级以及全球气候变暖,全球物种正以前所未有的速度丧失,生物多样性成为了全球关注的热点问题。传统生物多样性研究以地面调查方法为主,重点关注物种或样地水平,但无法满足景观尺度、区域尺度以及全球尺度的生物多样性保护和评估需求。遥感作为获取生物多样性信息的另一种手段,近年来在生物多样性领域发展迅速,其覆盖广、序列性以及可重复性等特点使之在大尺度生物多样性监测和制图以及评估方面具有极大优势。本文主要通过文献收集整理,从观测手段、研究尺度、观测对象和生物多样性关注点等方面综述了遥感在生物多样性研究中的应用现状,重点分析不同遥感平台的技术优势和局限性,并探讨了未来遥感在生物多样性研究的应用趋势。遥感平台按观测高度可分为近地面遥感、航空遥感和卫星遥感,能够获取样地–景观–区域–洲际–全球尺度的生物多样性信息。星载平台在生物多样性研究中应用最多,航空遥感的应用研究偏少主要受飞行成本限制。近地面遥感作为一个新兴平台,能够直接观测到物种的个体,获取生物多样性关注的物种和种群信息,是未来遥感在生物多样性应用中的发展方向。虽然遥感技术在生物多样性研究中的应用存在一定的局限性,未来随着传感器发展和多源数据融合技术的完善,遥感能更好地从多个尺度、全方位地服务于生物多样性保护和评估。

中国生物多样性研究的30个核心问题

在联合国《生物多样性公约》生效30年和《生物多样性》创刊30周年之际,我们通过问卷调查从281名中国研究人员收集到763个生物多样性相关的研究问题,通过归纳与整理,并参考英国生态学会提出的100个生态学基本问题,从中筛选出30个核心问题。这些问题涉及7个方面:演化与生态(6个问题)、种群(4个问题)、群落与多样性(7个问题)、生态系统与功能(3个问题)、人类影响与全球变化(4个问题)、方法与监测(4个问题)、生物多样性保护(2个问题)。前5个方面主要聚焦在物种形成、生物多样性维持等的关键过程与机制、生物多样性与生态功能关系、全球变化对生物多样性的影响机制等,第6方面主要涉及生物监测与预测、数据共享等,第7方面涉及多样性保护、自然与人类健康关系这两个与公众息息相关的重要话题。这30个问题的筛选难免存在偏颇,希望能以此为契机,促进我国生物多样性研究人员对本领域核心问题的深入思考与探讨。

遥感已经成为生物多样性研究保护与变化监测不可或缺的技术手段

自1985年美国国家生物多样性论坛第一次筹备会议提出＂biodiversity＂一词至今（Harper＆Hawksworth, 1995）,生物多样性科学（马克平, 2016）历经30余年的学科建设发展和保护实践活动,其维持生态系统功能、提供生态系统服务、延续人类福.

Crop 3D a LiDAR based platform for 3D high-throughput crop phenotyping

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis.As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging(LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional(3 D) data accurately,and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China,we developed a high-throughput crop phenotyping platform, named Crop 3 D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3 D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs,functions and testing results of the Crop 3 D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

An updated Vegetation Map of China(1:1000000)

Vegetation maps are important sources of information for biodiversity conservation,ecological studies,vegetation management and restoration,and national strategic decision making.The current Vegetation Map of China(1:1000000)was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s.However,the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades,and it urgently needs to be updated to better represent the distribution of current vegetation types.Here,we describe the process of updating the Vegetation Map of China(1:1000000)generated in the 1980s using a‘‘crowdsourcing-change detection-classification-expert knowledge"vegetation mapping strategy.A total of 203,024 field samples were collected,and 50 taxonomists were involved in the updating process.The resulting updated map has 12 vegetation type groups,55 vegetation types/subtypes,and 866 vegetation formation/sub-formation types.The overall accuracy and kappa coefficient of the updated map are 64.8%and 0.52 at the vegetation type group level,61%and 0.55 at the vegetation type/subtype level and 40%and 0.38 at the vegetation formation/sub-formation level.When compared to the original map,the updated map showed that 3.3 million km^2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change.We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.

Application of deep learning in ecological resource research:Theories, methods, and challenges

Ecological resources are an important material foundation for the survival,development,and self-realization of human beings.In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society.Advances in observation technology have improved the ability to acquire long-term,cross-scale,massive,heterogeneous,and multi-source data.Ecological resource research is entering a new era driven by big data.Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data.Deep learning is a method for automatically extracting complex high-dimensional nonlinear features,which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data.To promote the application of deep learning in the field of ecological resource research,here,we first introduce the relationship between deep learning theory and research on ecological resources,common tools,and datasets.Second,applications of deep learning in classification and recognition,detection and localization,semantic segmentation,instance segmentation,and graph neural network in typical spatial discrete data are presented through three cases:species classification,crop breeding,and vegetation mapping.Finally,challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning.It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms,and enrich applications with the development of hardware.

Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest ecosystem.There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution.In this study,we presented a method to map wall-to-wall forest tree height(defined as Lorey’s height)across the SN at 70-m resolution by fusing multi-source datasets,including over 1600 in situ tree height measurements and over 1600 km^(2) airborne light detection and ranging(LiDAR)data.Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements,and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System(GLAS)footprints.Finally,the random forest algorithm was used to model the SN tree height from these GLAS tree heights,optical imagery,topographic data,and climate data.The results show that our fine-resolution SN tree height product has a good correspondence with field measurements.The coefficient of determination between them is 0.60,and the root-mean-squared error is 5.45 m.

Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment.Terrestrial laser scanning(TLS)is a well-suited tool to study structural rhythm under field conditions.Recent studies have used TLS to describe the structural rhythm of trees,but no consistent patterns have been drawn.Meanwhile,whether TLS can capture structural rhythm in crops is unclear.Here,we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS.The seasonal rhythm was studied using TLS data collected at four key growth periods,including jointing,bell-mouthed,heading,and maturity periods.Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions.Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels.(1)Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage.Leaf azimuth was stable after the jointing stage.(2)Some individual-level structural rhythms(e.g.,azimuth and projected leaf area/PLA)were consistent with leaf-level structural rhythms.(3)The circadian rhythms of some traits(e.g.,PLA)were not consistent under standard and cold stress conditions.(4)Environmental factors showed better correlations with leaf traits under cold stress than standard conditions.Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth.This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.

The influence of meteorology and phenology on net ecosystem exchange in an eastern Siberian boreal larch forest

Aims Boreal forests play an important role in the global carbon cycle.Compared with the boreal forests in North America and Europe,relatively few research studies have been conducted in Siberian boreal forests.Knowledge related to the role of Siberian forests in the global carbon balance is thus essential for a full understanding of global carbon cycle.Methods This study investigated the net ecosystem exchange(NEE)during growing season(May-September)in an eastern Siberian boreal larch forest for a 3-year period in 2004-2006 with contrasting meteorological conditions.Important FindingsThe study found that the forest served as a carbon sink during all of the 3 studied years;in addition,the meteorological conditions essentially influenced the specific annual value of the strength of the carbon sinks in each year.Although 2005 was the warmest year and much wetter than 2004,2005 also featured the greatest amount of ecosystem respiration,which resulted in a minimum value of NEE.The study also found that the phenological changes observed during the three study years had a relatively small effect on annual NEE.Leaf expansion was 26 days earlier in 2005 than in the other 2 years,which resulted in a longer growing season in 2005.However,the NEE in 2005 was counterbalanced by the large rate of ecosystem respiration that was caused by the higher temperatures in the year.This study showed that meteorological variables had larger influences on the interannual variations in NEE for a Siberian boreal larch forest,as compared with phenological changes.The overall results of this study will improve our understanding of the carbon balance of Siberian boreal larch forests and thus can help to forecast the response of these forests to future climate change.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics(PP)has been recognized as a bottleneck in studying the interactions of genomics and environment on plants,limiting the progress of smart breeding and precise cultivation.High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits.Proximal and remote sensing(PRS)techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis.Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications.This progress covers most aspects of PRS application in PP,including patterns of global spatial distribution and temporal dynamics,specific PRS technologies,phenotypic research fields,working environments,species,and traits.Subsequently,we demonstrate how to link PRS to multi-omics studies,including how to achieve multi-dimensional PRS data acquisition and processing,how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance,and how to link PP to multi-omics association analysis.Finally,we identify three future perspectives for PRS-based PP:(1)strengthening the spatial and temporal consistency of PRS data,(2)exploring novel phenotypic traits,and(3)facilitating multi-omics communication.