The quantificational and normative design is the precondition of improving the design of copper staves for blast furnaces. Based on a 3-dimensional temperature field calculation model, from the view point of heat tran...The quantificational and normative design is the precondition of improving the design of copper staves for blast furnaces. Based on a 3-dimensional temperature field calculation model, from the view point of heat transfer and long campaigns note with the core of forming accretion, the forming-accretion-ability （FAA） and the rib hot surface maximum temperature difference （ATmax） as quantificational indexes to direct and evaluate the design of copper staves for blast furnaces were presented. The application of the two indexes in design essentially embodies the new long campaigns in the stage of design. With the application of the two indexes, good results can be obtained. Firstly, it was suggested that the rib height of a copper stave can be reduced to 15 mm, which is a new method and theory for the reduction of copper staves. Secondly, the influence of insert on FAA and ATmax, is decided by the volume of insert. According to this, the principle of design for the hot surface geometry of copper staves was put forward that the ratio of the rib hot surface to the copper stave hot surface （abbreviated as the ratio of rib to stave） must be maintained in the range of 45% to 55%; for the present copper stave with a 35-40 mm thick rib, the ratio of rib to stave in the range of 50% to 55% can optimize the design of copper staves; for the copper stave with a smaller rib thickness, for example 15 ram, the ratio of rib to stave in the range of 45% to 50% can optimize the design of copper staves. It can be summarized that the thicker the rib thickness, the larger is the ratio of rib to stave. 2008 University of Science and Technology Beijing. All rights reserved.展开更多
The sensitivity and quantification capability of surface-enhanced Raman scattering(SERS)substrates are mutually exclusive,because the ultrasensitive SERS sites(hottest spots)necessary for the sensitivity will signific...The sensitivity and quantification capability of surface-enhanced Raman scattering(SERS)substrates are mutually exclusive,because the ultrasensitive SERS sites(hottest spots)necessary for the sensitivity will significantly magnify the SERS signals of the analyte molecules and thus each of these molecules will be miscounted to be hundreds during the quantification process.We demonstrate a concept to circumvent the above contradiction by engineering a timeshare SERS platform capable of working at the quantitative or the sensitive mode on demand.The timeshare SERS platform was constructed by transferring a monolayer gold nanosphere film onto elastic substrates(e.g.,hydrogel).The volume change of the hydrogel could adjust the inter-nanosphere distance,dynamically controlling the formation or extinction of the SERS hottest spots on the same SERS substrate without influencing the spatial distribution of the analyte molecules.The timeshare SERS platform without the SERS hottest spots showed strong quantification capability,while when equipped with a substantial number of the SERS hottest spots exhibited ultrahigh sensitivity.We demonstrated quantitative and ultrasensitive detection of various analyte molecules using the quantitative and the sensitive mode of the timeshare SERS platform,respectively.We opened an avenue towards designing SERS substrates with both high sensitivity and strong quantification capability.展开更多
This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from...This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from the Dahei River prior to DNA extraction and 16S rRNA gene sequencing,it generates standard curves to convert sequencing data into absolute microbial copy numbers.The method,which is proved highly accurate(R^(2)>0.99),reveals a clear contrast between the river sites:the upstream community has not only a significantly higher total microbial load but also a completely different makeup of species compared to the downstream site.This approach effectively overcomes the limitations of relative abundance analysis,providing a powerful tool for environmental monitoring,and proposes key steps for future standardization to ensure data comparability and integration.展开更多
Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable meas...Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.展开更多
Live imaging enables direct observation of dynamic biological processes,capturing their progression from molecular to organismal scales in space and time.Through high-resolution observation,it provides a powerful mean...Live imaging enables direct observation of dynamic biological processes,capturing their progression from molecular to organismal scales in space and time.Through high-resolution observation,it provides a powerful means to decode biological complexity by revealing dynamic behaviors,spatial patterns,and regulatory changes.This review illustrates the application of live imaging in investigating complex biological processes with spatiotemporal resolution and mechanistic insight.We first highlight the analytical power and integrative strategies of live imaging,and then summarize recent advances that further extend its capacities.We then focus on four complex processes―cell proliferation,lineage regulation,morphogenesis,and atlas construction―to elucidate how live imaging contributes to their decoding through representative studies.We also discuss the conceptual and practical limitations that currently constrain the full interpretive potential of live imaging,underscoring the need for deeper integration between observation,perturbation,and modeling.Looking ahead,live imaging will benefit from both technical refinement and advances in data standardization and visualization,functional quantification,multiscale integration,and the discovery of generalizable principles.Together,these directions advance a more integrative and mechanistic understanding of complex biological processes.展开更多
Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundr...Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.展开更多
Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accur...Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accuracy and robustness.Therefore,this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm.By combining deep learning techniques with traditional template matching methods,the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination.Specifically,in the edge-orientation gradient matching stage,the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are employed for sub-pixel edge extraction,and a five-level Gaussian pyramid structure is introduced to accelerate the matching speed.Experimental results show that the proposed method achieves high-precision displacement monitoring under sufficient illumination,and it maintains stable target localization and displacement quantification performance under conditions of noise interference and extremely low illumination.Notably,under salt-and-pepper noise interference,although YOLOv8n maintains a high level of localization confidence,the accuracy of gradient matching deteriorates,resulting in a root-mean-square error(RMSE)of 0.035 mm.This finding reveals the differential impact of various noise types on different stages of the algorithm.The proposed method offers a novel technological approach for precise structural displacement monitoring in complex environments.展开更多
Software-defined,data-intensive cyber-physical systems and software-defined networks of atmospheric observers are evolving rapidly due to the rapid expansion of sensing diversity,the volume of streaming data,and the d...Software-defined,data-intensive cyber-physical systems and software-defined networks of atmospheric observers are evolving rapidly due to the rapid expansion of sensing diversity,the volume of streaming data,and the demand for low-latency,decision-relevant products.Simultaneously,artificial intelligence(AI)and the continuously evolving state of computing are making it possible to create end-to-end architecture fostering the migrations of the presumably single algorithm to combined intelligent ingestion,quality control,and multi-modal fusion,uncertainty-related retrieval,and scalable service delivery at the edge-to-cloud-high-performance computing(HPC)environment.This overview summarizes AI-based models of future atmospheric observation networks within a single,consolidated taxonomy based on deployment topology,learning and update modes,connectivity to physical models and data assimilation,level of autonomy(passive to adaptive sensing),and model of governance.Next,we consider recurring architectural themes,such as edge intelligence and streaming provenance and machine learning operations(MLOps)/model operations(ModelOps)to continue evaluation and safely update,and we scrutinize integration gateways with physical models,like data-assimilation-oriented outputs,hybrid/physics-informed designs,and simulation of observing systems using digital twins.Lastly,we address evaluation and readiness aspects that are not limited to predictive skill,but also involve calibrated uncertainty,nonstationary and extreme robustness,system latency and reliability,interoperability,security,and demonstrated downstream influence on analyses and forecasts.Through bringing together the cross-cutting issues and prospects,this review provides a road map with respect to trustworthy,interoperable,and sustainable observation infrastructures in which code and climate science will co-evolve.展开更多
Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriter...Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriteria approach for selecting optimal intensity measures(IMs)to assess SR of structures.Eight representative IMs,derived from time histories and response spectrum are evaluated.Incremental dynamic analysis is con-ducted on a reinforced concrete structure,using engineering demand parameters such as the maximum interstory drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model.The optimal IMs are identified through a multi-criteria decision-making process,with scores calculated using the entropy weight method to incorporate factors such as efficiency,proficiency,and uncertainty based on infor-mation entropy.An effective SR framework is derived from fragility results.The findings indicate that peak ground velocity and spectral IMs are the most effective,while energy-related IMs underestimate SR.The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments.The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.展开更多
Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strate...Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strategies.However,traditional ground-based surveys are limited in spatial coverage and efficiency,hindering effective forest management.To overcome these limitations,this study developed an integrated assessment framework that couples ground-based modeling with remote sensing inversion to achieve large-scale site quality mapping.Field investigations on Pingtan Island,Fujian Province,China,were used to establish a ground-based evaluation model.Soil fertility was quantified using Principal Component Analysis(PCA),and principal components were classified into discrete fertility grades through K-means clustering.These grades,together with topographic variables,were incorporated into a site quality classification model constructed using Quantification Theory I.The point-based model was subsequently extrapolated using Landsat 9 imagery to generate a spatially continuous site quality map.Spatial autocorrelation(Moran’s Ⅰ)and LISA clustering were further employed to interpret spatial patterns.Results indicate that coastal sandy soils in the study area are generally nutrient-poor,with tree growth primarily constrained by total nitrogen,organic matter,available phosphorus,and total phosphorus.The five most influential site factors,ranked by importance,are soil fertility,distance from the coastline,aspect,slope gradient,and elevation.Optimal conditions for C.equisetifolia growth include fertile soil,location>1000 m from the coastline,south-facing or semi-sunny slopes,slope gradients<15°,and elevations between 10-100 m.Only 11.94%of the area was classified as high-quality(Grade I),while 61.74%fell into moderate or poor grades(Grades Ⅲ and Ⅳ),indicating that most plantations are located on suboptimal sites.This study provides scientific support for improving the precision and sustainability of coastal shelterbelt planning and management,offering practical insights for afforestation strategies,forest restoration,and ecological forestry development in coastal zones.展开更多
To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
Under the“dual carbon”goals,it is imperative to incorporate carbon emissions-related factors into research of power grid risk assessment to meet the green transformation needs of the power grid.Therefore,this paper ...Under the“dual carbon”goals,it is imperative to incorporate carbon emissions-related factors into research of power grid risk assessment to meet the green transformation needs of the power grid.Therefore,this paper conducts a study on the risk assessment of carbon emissions changes in regional power grids based on dynamic carbon emission factors,aiming to quantitatively analyze the impact of random disturbances such as equipment failures or fluctuations in renewable energy generation on the carbon emission intensity of regional power grids.First,carbon emission change risk indicators are constructed from three dimensions:the probability,frequency,and magnitude of carbon emission changes.Second,a dynamic carbon emission factor calculation model is proposed to reflect the spatiotemporal change of carbon emissions in the regional power grid,considering output of different types of generators and the components of inter-area power transmission.Finally,with the premise of ensuring safe and stable operation of power grid,a quantitative assessment model for carbon emission change risks is proposed under the objective of minimizing the electricity loss.The sampling convergence conditions of the model are also derived.The results from the MRTS79 case study demonstrate the proposed method can effectively quantify and analyze the risk of carbon emissions changes in regional power grids,validating the effectiveness of the proposed model.展开更多
Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that ad...Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.展开更多
The development of remote sensing has seen the creation of a global measurement infrastructure of sustainable development due to growing multipolar archives,rising revisit frequency,and the availability of cloud-acces...The development of remote sensing has seen the creation of a global measurement infrastructure of sustainable development due to growing multipolar archives,rising revisit frequency,and the availability of cloud-accessible platforms of Earth observation.This review summarizes how remote sensing big data is being organized into decision-grade sustainability intelligence,the new approaches to analytics,and how Sustainable Development Goals(SDGs)-oriented application pathways inter-relate action pathways that bridge observations with action.The terminologies like new data ecosystem,data readiness and interoperability,changing economics of scalable computation,and detailing the functions of diversity of modalities(optical,Synthetic Aperture Radar—SAR,thermal,Light Detection and Ranging—LiDAR,hyperspectral)have been defined.These themes of analytics,which are transforming the practice of operational analytics,are then condensed:foundations and self-supervised learning of transferable representations,multi-modal fusion to gap fill and richer inference,spatiotemporal intelligence to trend of early warning,physics-aware hybrid methods to enhance robustness and meaning under non-stationary conditions.Across the climate risk,food systems,water resources,sustainable cities,ecosystems and biodiversity,energy transitions,and health exposure pathways,the roles of Earth Observation(EO)products as direct measures and proxies,and concepts of validating,semantic comparability,and communicating uncertainties play a key role in EO products becoming credible when faced with high-stakes deployment decisions.Lastly,we chart world ways of implementation via monitoring services,early warning systems,and systems of multiple regimes,and previously underline cross-cutting priorities,scalable structures in validation,performance,so that domains of shift,agreeable governance,and Dual-use risk safeguards,and sustainable lifecycle support of EO services.These priorities form a realistic set of priorities on the alignment of remote sensing innovation with quantifiable SDGs progress.展开更多
OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulati...OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.展开更多
The mechanisms of mushroom polysaccharides on immune functions and lipid metabolism of aged mammals have not been fully elucidated.In the present study,after assessing the impacts of one type of Lentinula edodes-deriv...The mechanisms of mushroom polysaccharides on immune functions and lipid metabolism of aged mammals have not been fully elucidated.In the present study,after assessing the impacts of one type of Lentinula edodes-derived polysaccharides,named L2,on immune functions and blood lipid profiles,isobaric tags for relative and absolute quantification(iTRAQ)-based proteomic profiling of the small intestinal tissues from aged mice treated with L2 was performed.L2 reversed immune function declines and modulated the lipid metabolism of aged mice evidenced by increased levels of serum TC,HDL-C,and LDL-C,and reduced levels of serum TG.Moreover,a total of 95 differentially regulated proteins(DRPs) were identified,of which75 were up-regulated and 20 were down-regulated.Most of the DRPs were involved in intracellular and extracellular structure organization,and cellular and metabolic regulation.Particularly,approximately 16 and 9 DRPs participated in the regulation of immune functions and lipid metabolism,respectively.Furthermore,protein-protein interaction analysis highlighted that cadherin-1,plectin,cadherin-17,Ras GTPase-activating-like protein IQGAP2,and ezrin might be key proteins in response to L2 treatment.These findings provide new insights into the biological mechanisms of mushroom polysaccharides in anti-aging from a proteomic perspective.展开更多
For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube samplin...For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.展开更多
A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can ...A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.展开更多
Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants h...Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.展开更多
Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devi...Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devising effective control strategies.However,viruses are complex to propagate and quantify.Existing methodologies for viral quantification tend to be expensive and time-consuming.Here,we present a rapid cost-effective approach to quantify viral propagation using an engineered virus expressing a fluorescent reporter.Using a microplate reader,we measured viral protein levels and we validated our findings through comparison by western blot analysis of viral coat protein,the most common approach to quantify viral titer.Our proposed methodology provides a practical and accessible approach to studying virus-host interactions and could contribute to enhancing our understanding of plant virology.展开更多
基金the National Natural Science Foundation of China(No.60672145).
文摘The quantificational and normative design is the precondition of improving the design of copper staves for blast furnaces. Based on a 3-dimensional temperature field calculation model, from the view point of heat transfer and long campaigns note with the core of forming accretion, the forming-accretion-ability （FAA） and the rib hot surface maximum temperature difference （ATmax） as quantificational indexes to direct and evaluate the design of copper staves for blast furnaces were presented. The application of the two indexes in design essentially embodies the new long campaigns in the stage of design. With the application of the two indexes, good results can be obtained. Firstly, it was suggested that the rib height of a copper stave can be reduced to 15 mm, which is a new method and theory for the reduction of copper staves. Secondly, the influence of insert on FAA and ATmax, is decided by the volume of insert. According to this, the principle of design for the hot surface geometry of copper staves was put forward that the ratio of the rib hot surface to the copper stave hot surface （abbreviated as the ratio of rib to stave） must be maintained in the range of 45% to 55%; for the present copper stave with a 35-40 mm thick rib, the ratio of rib to stave in the range of 50% to 55% can optimize the design of copper staves; for the copper stave with a smaller rib thickness, for example 15 ram, the ratio of rib to stave in the range of 45% to 50% can optimize the design of copper staves. It can be summarized that the thicker the rib thickness, the larger is the ratio of rib to stave. 2008 University of Science and Technology Beijing. All rights reserved.
基金supported by the National Science Foundation of China(12304422,52501261,52273233)the China Postdoctoral Science Foundation(512200-X92103)+2 种基金the Natural Science Foundation of Jiangsu Province(BK20230911)the fundamental Research Funds for the Central Universities(30923010209)Natural Science Foundation of Inner Mongolia(2025QN05053).
文摘The sensitivity and quantification capability of surface-enhanced Raman scattering(SERS)substrates are mutually exclusive,because the ultrasensitive SERS sites(hottest spots)necessary for the sensitivity will significantly magnify the SERS signals of the analyte molecules and thus each of these molecules will be miscounted to be hundreds during the quantification process.We demonstrate a concept to circumvent the above contradiction by engineering a timeshare SERS platform capable of working at the quantitative or the sensitive mode on demand.The timeshare SERS platform was constructed by transferring a monolayer gold nanosphere film onto elastic substrates(e.g.,hydrogel).The volume change of the hydrogel could adjust the inter-nanosphere distance,dynamically controlling the formation or extinction of the SERS hottest spots on the same SERS substrate without influencing the spatial distribution of the analyte molecules.The timeshare SERS platform without the SERS hottest spots showed strong quantification capability,while when equipped with a substantial number of the SERS hottest spots exhibited ultrahigh sensitivity.We demonstrated quantitative and ultrasensitive detection of various analyte molecules using the quantitative and the sensitive mode of the timeshare SERS platform,respectively.We opened an avenue towards designing SERS substrates with both high sensitivity and strong quantification capability.
基金supported by the National Natural Science Foundation of China(Grant No.32160172)the Key Science-Technology Project of Inner Mongolia(2023KYPT0010)+1 种基金the Natural Science Foundation of Inner Mongolia Autonomous Region of China(Grant No.2025QN03006)the 2023 Inner Mongolia Public Institution High-level Talent Introduction Scientific Research Support Project.
文摘This study establishes and validates a method for the precise quantification of aquatic microbial loads using microbial diversity absolute quantitative sequencing.By adding synthetic spike-in DNA to water samples from the Dahei River prior to DNA extraction and 16S rRNA gene sequencing,it generates standard curves to convert sequencing data into absolute microbial copy numbers.The method,which is proved highly accurate(R^(2)>0.99),reveals a clear contrast between the river sites:the upstream community has not only a significantly higher total microbial load but also a completely different makeup of species compared to the downstream site.This approach effectively overcomes the limitations of relative abundance analysis,providing a powerful tool for environmental monitoring,and proposes key steps for future standardization to ensure data comparability and integration.
文摘Indeed,a range of systems in the environment requires timely,spatially explicit,and credible information to support its environmental decision-making,but no one observing system can give the complete and reliable measures of the Earth system across scales.This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning,remote sensing,and sensor data fusion to generate decision-ready environmental insights.We use the application-first approach,which considers remote sensing,in situ and Internet of Things(IoT)sensing,and physics-based models as complementary streams of evidence with similar strengths and failures.We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas:atmosphere and air quality,water–land–ecosystem dynamics,and hazards.Rapid-response situational awareness,ecosystem condition metrics,drought and flood indicators,exposure maps,and hazard/extreme indicators are key products.The integrated systems to environment interface in three high impact application areas:atmosphere and air quality,water-land-ecosystem dynamics,and hazard Examine Our operational requirements can often determine real-life value such as latency,time stability,smooth degradation in the presence of missing or degraded inputs,and calibrated uncertainty usable in thresholdbased decisions.These pitfalls are common across fields:mismatch in the scale between a point sensor and a gridded product,objectives on proxies in remotely sensed measurements,domain shift in the extremes and changing baselines,and evaluation aspects,which overestimate generalization because of spatiotemporal autocorrelation.Based on these lessons,we present cross-domain proposals for strong validation,uncertainty quantification,provenance,and versioning,as well as fair performance evaluation.We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness,transparency,and operational responsibility,thus allowing the integrated environmental intelligence system to be deployed,which may be relied on to monitor human health,resource allocation,and survival in a more climate-adapted world.
基金supported by the National Key Research and Development Program of China(2022YFA1303000 and 2021YFA0805800)the National Natural Science Foundation of China(32325032)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(YSBR-073).
文摘Live imaging enables direct observation of dynamic biological processes,capturing their progression from molecular to organismal scales in space and time.Through high-resolution observation,it provides a powerful means to decode biological complexity by revealing dynamic behaviors,spatial patterns,and regulatory changes.This review illustrates the application of live imaging in investigating complex biological processes with spatiotemporal resolution and mechanistic insight.We first highlight the analytical power and integrative strategies of live imaging,and then summarize recent advances that further extend its capacities.We then focus on four complex processes―cell proliferation,lineage regulation,morphogenesis,and atlas construction―to elucidate how live imaging contributes to their decoding through representative studies.We also discuss the conceptual and practical limitations that currently constrain the full interpretive potential of live imaging,underscoring the need for deeper integration between observation,perturbation,and modeling.Looking ahead,live imaging will benefit from both technical refinement and advances in data standardization and visualization,functional quantification,multiscale integration,and the discovery of generalizable principles.Together,these directions advance a more integrative and mechanistic understanding of complex biological processes.
文摘Also known as imaging spectroscopy,hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability.Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments,water status of the canopy,biochemical composition,mineralogies,and organic matter of the soil,and water quality constituents of aquatic water.These abilities allow one to make a transition between the descriptive mapping and the functional monitoring,the anticipation of stress and disturbance early,and the more accurate attribution of environmental change.This summary encompasses improvements on the entire sensor-to-product pipeline,including field and UAV(Unmanned Aerial Vehicle)system platform developments,airborne campaign and spaceborne mission developments,calibration and analysis-ready preprocessing improvements,empirical learning methodology improvements,radiative transfer-based inversion method,spectral unmixing,deep learning,and hybrid physics-machine learning.We underline the increased importance of the combination of data with LiDAR(Light Detection and Ranging),SAR(Synthetic Aperture Radar),and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience.Applications based on decision are evaluated in terms of biodiversity and habitat evaluation,vegetation functionality and restoration,stress and disturbance,sustainable agricultural production,inland water quality and coastal water quality,land degradation and soil status,and environmental impact assessment.Inhibiting factors to operational adoption have always been perceived to be domain shift by region,season,and sensor,ground truth and validation,mixed pixels and scale mismatch,preprocessing sensitivities,and desirable uncertainty quantification and product output that is interpretable.We conclude with the scalability,sustainability,service priorities,such as harmonization standards,representative benchmarking,uncertainty-aware delivery,and co-design of stakeholders.
基金supported by the National Natural Science Foundation of China(No.52408533)the Natural Science Foundation of Shandong Province(No.ZR2024QE408)+3 种基金the University of Jinan Disciplinary Cross-Convergence Construction Project 2023(XKJC202310)the Municipal and School Integration Development Strategic Project of Jinan City(JNSX2023023)Natural Science Foundation of Tianjin(24JCQNJC00870)Doctoral Fund Support Project of University of Jinan(XRC2563).
文摘Structural displacement monitoring faces significant challenges under complex environmental conditions due to the loss or degradation of target features,making it difficult for traditional methods to ensure high accuracy and robustness.Therefore,this study proposes a structural displacement identification and quantification method that integrates YOLOv8n with an improved edge-orientation gradient-based template matching algorithm.By combining deep learning techniques with traditional template matching methods,the accuracy and robustness of monitoring are enhanced under adverse conditions such as noise and extremely low illumination.Specifically,in the edge-orientation gradient matching stage,the Canny-Devernay sub-pixel edge detection technique and an improved ellipse-fitting method are employed for sub-pixel edge extraction,and a five-level Gaussian pyramid structure is introduced to accelerate the matching speed.Experimental results show that the proposed method achieves high-precision displacement monitoring under sufficient illumination,and it maintains stable target localization and displacement quantification performance under conditions of noise interference and extremely low illumination.Notably,under salt-and-pepper noise interference,although YOLOv8n maintains a high level of localization confidence,the accuracy of gradient matching deteriorates,resulting in a root-mean-square error(RMSE)of 0.035 mm.This finding reveals the differential impact of various noise types on different stages of the algorithm.The proposed method offers a novel technological approach for precise structural displacement monitoring in complex environments.
文摘Software-defined,data-intensive cyber-physical systems and software-defined networks of atmospheric observers are evolving rapidly due to the rapid expansion of sensing diversity,the volume of streaming data,and the demand for low-latency,decision-relevant products.Simultaneously,artificial intelligence(AI)and the continuously evolving state of computing are making it possible to create end-to-end architecture fostering the migrations of the presumably single algorithm to combined intelligent ingestion,quality control,and multi-modal fusion,uncertainty-related retrieval,and scalable service delivery at the edge-to-cloud-high-performance computing(HPC)environment.This overview summarizes AI-based models of future atmospheric observation networks within a single,consolidated taxonomy based on deployment topology,learning and update modes,connectivity to physical models and data assimilation,level of autonomy(passive to adaptive sensing),and model of governance.Next,we consider recurring architectural themes,such as edge intelligence and streaming provenance and machine learning operations(MLOps)/model operations(ModelOps)to continue evaluation and safely update,and we scrutinize integration gateways with physical models,like data-assimilation-oriented outputs,hybrid/physics-informed designs,and simulation of observing systems using digital twins.Lastly,we address evaluation and readiness aspects that are not limited to predictive skill,but also involve calibrated uncertainty,nonstationary and extreme robustness,system latency and reliability,interoperability,security,and demonstrated downstream influence on analyses and forecasts.Through bringing together the cross-cutting issues and prospects,this review provides a road map with respect to trustworthy,interoperable,and sustainable observation infrastructures in which code and climate science will co-evolve.
基金partly supported by Engineering Partners Inter-national,LLC,Richfield,MN 55423(PC13803,482842-58309).
文摘Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriteria approach for selecting optimal intensity measures(IMs)to assess SR of structures.Eight representative IMs,derived from time histories and response spectrum are evaluated.Incremental dynamic analysis is con-ducted on a reinforced concrete structure,using engineering demand parameters such as the maximum interstory drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model.The optimal IMs are identified through a multi-criteria decision-making process,with scores calculated using the entropy weight method to incorporate factors such as efficiency,proficiency,and uncertainty based on infor-mation entropy.An effective SR framework is derived from fragility results.The findings indicate that peak ground velocity and spectral IMs are the most effective,while energy-related IMs underestimate SR.The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments.The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.
基金supported by University Key Lab for Geomatics Technology&Optimize Resources Utilization in Fujian Province(KJG20104A)Fujian Forestry Science and Technology project(2023FKJ15)Fuzhou Forestry Science and Technology Research Project(2130206).
文摘Accurate assessment of site quality in coastal Casuarina equisetifolia(C.equisetifolia)plantations is essential for enhancing the protective function of shelterbelts and implementing site-specific afforestation strategies.However,traditional ground-based surveys are limited in spatial coverage and efficiency,hindering effective forest management.To overcome these limitations,this study developed an integrated assessment framework that couples ground-based modeling with remote sensing inversion to achieve large-scale site quality mapping.Field investigations on Pingtan Island,Fujian Province,China,were used to establish a ground-based evaluation model.Soil fertility was quantified using Principal Component Analysis(PCA),and principal components were classified into discrete fertility grades through K-means clustering.These grades,together with topographic variables,were incorporated into a site quality classification model constructed using Quantification Theory I.The point-based model was subsequently extrapolated using Landsat 9 imagery to generate a spatially continuous site quality map.Spatial autocorrelation(Moran’s Ⅰ)and LISA clustering were further employed to interpret spatial patterns.Results indicate that coastal sandy soils in the study area are generally nutrient-poor,with tree growth primarily constrained by total nitrogen,organic matter,available phosphorus,and total phosphorus.The five most influential site factors,ranked by importance,are soil fertility,distance from the coastline,aspect,slope gradient,and elevation.Optimal conditions for C.equisetifolia growth include fertile soil,location>1000 m from the coastline,south-facing or semi-sunny slopes,slope gradients<15°,and elevations between 10-100 m.Only 11.94%of the area was classified as high-quality(Grade I),while 61.74%fell into moderate or poor grades(Grades Ⅲ and Ⅳ),indicating that most plantations are located on suboptimal sites.This study provides scientific support for improving the precision and sustainability of coastal shelterbelt planning and management,offering practical insights for afforestation strategies,forest restoration,and ecological forestry development in coastal zones.
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
基金supported by the Key R&D Program of Hubei Province(2023BAB002)the Science and technology project of State Grid Hubei Electric Power Co.,Ltd.(52153223000A)。
文摘Under the“dual carbon”goals,it is imperative to incorporate carbon emissions-related factors into research of power grid risk assessment to meet the green transformation needs of the power grid.Therefore,this paper conducts a study on the risk assessment of carbon emissions changes in regional power grids based on dynamic carbon emission factors,aiming to quantitatively analyze the impact of random disturbances such as equipment failures or fluctuations in renewable energy generation on the carbon emission intensity of regional power grids.First,carbon emission change risk indicators are constructed from three dimensions:the probability,frequency,and magnitude of carbon emission changes.Second,a dynamic carbon emission factor calculation model is proposed to reflect the spatiotemporal change of carbon emissions in the regional power grid,considering output of different types of generators and the components of inter-area power transmission.Finally,with the premise of ensuring safe and stable operation of power grid,a quantitative assessment model for carbon emission change risks is proposed under the objective of minimizing the electricity loss.The sampling convergence conditions of the model are also derived.The results from the MRTS79 case study demonstrate the proposed method can effectively quantify and analyze the risk of carbon emissions changes in regional power grids,validating the effectiveness of the proposed model.
文摘Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.
文摘The development of remote sensing has seen the creation of a global measurement infrastructure of sustainable development due to growing multipolar archives,rising revisit frequency,and the availability of cloud-accessible platforms of Earth observation.This review summarizes how remote sensing big data is being organized into decision-grade sustainability intelligence,the new approaches to analytics,and how Sustainable Development Goals(SDGs)-oriented application pathways inter-relate action pathways that bridge observations with action.The terminologies like new data ecosystem,data readiness and interoperability,changing economics of scalable computation,and detailing the functions of diversity of modalities(optical,Synthetic Aperture Radar—SAR,thermal,Light Detection and Ranging—LiDAR,hyperspectral)have been defined.These themes of analytics,which are transforming the practice of operational analytics,are then condensed:foundations and self-supervised learning of transferable representations,multi-modal fusion to gap fill and richer inference,spatiotemporal intelligence to trend of early warning,physics-aware hybrid methods to enhance robustness and meaning under non-stationary conditions.Across the climate risk,food systems,water resources,sustainable cities,ecosystems and biodiversity,energy transitions,and health exposure pathways,the roles of Earth Observation(EO)products as direct measures and proxies,and concepts of validating,semantic comparability,and communicating uncertainties play a key role in EO products becoming credible when faced with high-stakes deployment decisions.Lastly,we chart world ways of implementation via monitoring services,early warning systems,and systems of multiple regimes,and previously underline cross-cutting priorities,scalable structures in validation,performance,so that domains of shift,agreeable governance,and Dual-use risk safeguards,and sustainable lifecycle support of EO services.These priorities form a realistic set of priorities on the alignment of remote sensing innovation with quantifiable SDGs progress.
基金Modernization of Traditional Chinese Medicine Project of National Key R&D Program of China:The construction of the theoretical system of Traditional Chinese Medicine nonpharmacological therapy based on body surface stimulation(2023YFC3502704)Sichuan Provincial Science and Technology Program Project:Research and Development of Chinese Medicine Intelligent Tongue Diagnosis Equipment for Digestive System Chinese Medicine Advantageous Diseases(2023YFS0327)+2 种基金Research and Development of Chinese Medicine Intelligent Detection System for Intestinal Functions(2024YFFK0044)Research and Application of Chinese Medicine Diagnosis and Treatment Program for Herpes Zoster Treated by Shu Pai Fire Acupuncture(2024YFFK0089)Major Research and Development Project of The China Academy of Chinese Medical Sciences Innovation:Construction and application of the theoretical research mode of Traditional Chinese Medicine diagnosis and treatment of modern diseases(CI2021A00104)。
文摘OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.
基金supported by the Key-Area Research and Development Program of Guangdong Province (2021B0707060001)the Program for Scientific Research Start-Up Funds of Guangdong Ocean UniversityChina Postdoctoral Science Foundation (2016T90787)。
文摘The mechanisms of mushroom polysaccharides on immune functions and lipid metabolism of aged mammals have not been fully elucidated.In the present study,after assessing the impacts of one type of Lentinula edodes-derived polysaccharides,named L2,on immune functions and blood lipid profiles,isobaric tags for relative and absolute quantification(iTRAQ)-based proteomic profiling of the small intestinal tissues from aged mice treated with L2 was performed.L2 reversed immune function declines and modulated the lipid metabolism of aged mice evidenced by increased levels of serum TC,HDL-C,and LDL-C,and reduced levels of serum TG.Moreover,a total of 95 differentially regulated proteins(DRPs) were identified,of which75 were up-regulated and 20 were down-regulated.Most of the DRPs were involved in intracellular and extracellular structure organization,and cellular and metabolic regulation.Particularly,approximately 16 and 9 DRPs participated in the regulation of immune functions and lipid metabolism,respectively.Furthermore,protein-protein interaction analysis highlighted that cadherin-1,plectin,cadherin-17,Ras GTPase-activating-like protein IQGAP2,and ezrin might be key proteins in response to L2 treatment.These findings provide new insights into the biological mechanisms of mushroom polysaccharides in anti-aging from a proteomic perspective.
基金co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015)the Natural Science Foundation of Beijing Municipality,China(No.L221008)+1 种基金Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005)the Tianmushan Laboratory Project,China(No.TK2023-B-001)。
文摘For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.
文摘A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.
基金supported by the key project at the central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(Grant number 2060302)the National Natural Science Foundation of China(Grant number 82373982,82173929).
文摘Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.
基金Funding from Natural Sciences and Engineering Research Council of Canada award number RGPIN/4002-2020.
文摘Viral diseases are an important threat to crop yield,as they are responsible for losses greater than US$30 billion annually.Thus,understanding the dynamics of virus propagation within plant cells is essential for devising effective control strategies.However,viruses are complex to propagate and quantify.Existing methodologies for viral quantification tend to be expensive and time-consuming.Here,we present a rapid cost-effective approach to quantify viral propagation using an engineered virus expressing a fluorescent reporter.Using a microplate reader,we measured viral protein levels and we validated our findings through comparison by western blot analysis of viral coat protein,the most common approach to quantify viral titer.Our proposed methodology provides a practical and accessible approach to studying virus-host interactions and could contribute to enhancing our understanding of plant virology.
