中美大陆区域气溶胶成分消光贡献研究综述

气溶胶消光作用是影响大气能见度的主控因素,气溶胶浓度、成分与其散射和吸收特性的非线性关系导致其对能见度的影响存在较大不确定性。1988~2008年美国IMPROVE(Interagency Monitoring of Protected Visual Environments)能见度监测网络各区域的重构细颗粒物(RCFM)浓度范围为1.4~19.4μg m^(-3),重构气溶胶消光系数为10.0~172.5 Mm^(-1)(1 Mm^(-1)=10^(-6) m^(-1))。2006~2018年中国各地区已有观测的平均细颗粒物PM2.5浓度为14.3~188.3μg m^(-3),对应的重构消光系数为52.6~1044.0 Mm^(-1)。美国地区PM2.5浓度水平与我国三亚地区相当;硫酸盐是气溶胶消光的最大贡献成分,占比可高达77%;其次是有机物,最大可达50%;而硝酸盐只有在南加州对气溶胶消光的贡献较大,超过了30%。同时,由于东部的相对湿度高于西部,东部和西部的消光差异比气溶胶浓度的差异更显著。在我国,硫酸盐和有机物同样是消光的主要成分,贡献分别为21%~57%和21%~39%;硝酸盐只在华中地区是主要的消光成分,超过了30%。我国气溶胶浓度和消光水平显著高于美国地区,当前广泛使用的气溶胶消光IMPROVE方程存在较高误差,消光系数低值高估和高值低估范围可达-60%~35%;并且污染越严重,消光低估越显著。采用较大误差的气溶胶消光算法将直接影响我国大气污染防治与蓝天计划的准确实施,随着我国大气污染防治工作的深入开展,急需针对我国各类环境和气候区域进行气溶胶成分消光的精准核算与溯源研究,为我国大气能见度提升提供关键科技支撑。

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Identification of SSR markers linked to the abscission of cotton boll traits and mining germplasm in Cotton

Background Cotton is an economically important crop.It is crucial to find an effective method to improve cotton yield,and one approach is to decrease the abscission of cotton bolls and buds.However,the lack of knowledge of the genetic and molecular mechanisms underlying cotton boll abscission traits has hindered genetic improvements.Results Pearson’s correlation analysis revealed a significant positive correlation between boll abscission rates 1(AR1)and boll abscission rates 2(AR2).A genome-wide association study was conducted on 145 loci that exhibited high polymorphism and were uniformly distributed across 26 chromosomes(pair).The study revealed 18,46,and 62 markers that were significantly associated with boll abscission,fiber quality,and yield traits(P<0.05),explaining 1.75%–7.13%,1.16%–9.58%,and 1.40%–5.44%of the phenotypic variation,respectively.Notably,the marker MON_SHIN-1584b was associated with the cotton boll abscission trait,whereas MON_CGR5732a was associated with cotton boll abscission and fiber quality traits.Thirteen of the marker loci identified in this study had been previously reported.Based on phenotypic effects,six typical cultivars with elite alleles related to cotton boll abscission,fiber quality,and yield traits were identified.These cultivars hold great promise for widespread utilization in breeding programs.Conclusions These results lay the foundation for understanding the molecular regulatory mechanism of cotton boll abscission and provide data for the future improvement of cotton breeding.

An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7

For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.

Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.

Modelling smear effect of vertical drains using a diameter reduction method

Vertical drains are used to accelerate consolidation of clays in ground improvement projects.Smear zones exist around these drains,where permeability is reduced due to soil disturbance caused by the installation process.Hansbo solution is widely used in practice to consider the effects of drain discharge capacity and smear on the consolidation process.In this study,a computationally efficient diameter reduction method(DRM)obtained from the Hansbo solution is proposed to consider the smear effect without the need to model the smear zone physically.Validated by analytical and numerical results,a diameter reduction factor is analytically derived to reduce the diameter of the drain,while achieving similar solutions of pore pressure dissipation profile as the classical full model of the smear zone and drain.With the DRM,the excess pore pressure u obtained from the reduced drain in the original un-disturbed soil zone is accurate enough for practical applications in numerical models.Such performance of DRM is independent of soil material property.Results also show equally accurate performance of DRM under conditions of multi-layered soils and coupled radial-vertical groundwater flow.

An improved typhoon monitoring model based on precipitable water vapor and pressure

The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

Consolidation of high replacement ratio stone column-reinforced ground:Analytical solutions incorporating clogging effect

The utilization of stone columns has emerged as a popular ground improvement strategy,whereas the drainage performance can be adversely hampered by clogging effect.Despite the ample progress of calculation methods for the consolidation of stone column-improved ground,theoretical investigations into the clogging effect have not been thoroughly explored.Furthermore,it is imperative to involve the column consolidation deformation to mitigate computational error on the consolidation of composite ground with high replacement ratios.In this context,an analytical model accounting for the initial clogging and coupled time and depth-dependent clogging of stone columns is established.Then,the resulting governing equations and analytical solutions are obtained under a new flow continuity relationship to incorporate column consolidation deformation.The accuracy and reliability of the proposed model are illustrated by degradation analysis and case studies with good agreements.Subsequently,the computed results of the current study are juxtaposed against the existing models,and an in-depth assessment of the impacts of several crucial parameters on the consolidation behavior is conducted.The results reveal that ignoring column consolidation deformation leads to an overestimate of the consolidation rate,with maximum error reaching up to 16%as the replacement ratio increases.Furthermore,the initial clogging also has a significant influence on the consolidation performance.Additionally,the increment of depth and time-clogging factors a and b will induce a noticeable retardation of the consolidation process,particularly in the later stage.

Approaches to Improving Selectivity During Photoelectrochemical Transformation of Small Molecules

Photoelectrochemical (PEC) small-molecule oxidation can selectively transform substrates into high-value-added fine chemicals and increase the rate of cathode hydrogen evolution. Nevertheless, achieving high-selectivity PEC oxidation of small molecules to produce specific products is a very challenging task. In general, selectivity can be improved by changing the surface catalyticsites of the photoanode and modulating the interfacial environments of the reactions. Herein, recent advances in approaches to improving selective PEC oxidation of small molecules are introduced. We first briefly discuss the basic concept and fundamentals of small-molecule PEC oxidation. The reported approaches to improving the performance of selective PEC oxidation of small molecules are highlighted from two aspects: (1) changing the surface properties of photoanodes by selecting suitable materials or modifying the photoanodes and (2) mediating the oxidation reactions using redox mediators. The PEC oxidation mechanism of these studies is emphasized. We also discuss the challenges in this research direction and offer a perspective on the further development of selective PEC-based small-molecule transformation.

Mechanical behaviors of warm and ice-rich frozen soil stabilized with sulphoaluminate cement

The warm and ice-rich frozen soil is characterized by high unfrozen water content, low shear strength and large compressibility, which is unreliable to meet the stability requirements of engineering infrastructures and foundations in permafrost regions. In this study, a novel approach for stabilizing the warm and ice-rich frozen soil with sulphoaluminate cement was proposed based on chemical stabilization. The mechanical behaviors of the stabilized soil, such as strength and stress-strain relationship, were investigated through a series of triaxial compression tests conducted at -1.0℃, and the mechanism of strength variations of the stabilized soil was also explained based on scanning electron microscope test. The investigations indicated that the strength of stabilized soil to resist failure has been improved, and the linear Mohr-Coulomb criteria can accurately reflect the shear strength of stabilized soil under various applied confining pressure. The increase in both curing age and cement mixing ratio were favorable to the growth of cohesion and internal friction angle. More importantly, the strength improvement mechanism of the stabilized soil is attributed to the formation of structural skeleton and the generation of cementitious hydration products within itself. Therefore, the investigations conducted in this study provide valuable references for chemical stabilization of warm and ice-rich frozen ground, thereby providing a basis for in-situ ground improvement for reinforcing warm and ice-rich permafrost foundations by soil-cement column installation.

IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

Anti-aging performance improvement and enhanced combustion efficiency of boron via the coating of PDA

Boron is an ambitious fuel in energetic materials since its high heat release values,but its application is prohibited by low combustion efficiency and oxidization during storage.The polydopamine(PDA)was introduced into boron particles,investigating the impact of PDA content on the energetic behavior of boron.The results indicated that the PDA coating formed a fishing net structure on the surface of boron particles.The heat release results showed that the combustion calorific value of B@PDA was higher than that of the raw boron.Specifically,the actual combustion heat of boron powder in B@10%PDA increased by 38.08%.Meanwhile,the DSC peak temperature decreased by 100.65℃under similar oxidation rate compared to raw boron.Simultaneously,the B@PDA@AP and B@AP composites were prepared,and their combustion properties were evaluated.It was demonstrated that B@10%PDA@AP exhibited superior performance in terms of peak pressure and burning time,respectively.The peak pressure is 12.43 kPa more than B@AP and burning time is 2.22 times higher than B@AP.Therefore,the coating of PDA effectively inhibits the oxidization of boron during storage and enhances the energetic behavior of boron and corresponding composites.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Deterioration Reason and Improvement Measure of the Retarding Effect of Protein Retarder on Phosphorus Building Gypsum

The retarding effect of protein retarder on phosphorus building gypsum(PBG)and desulfurization building gypsum(DBG)was investigated,and the results show that protein retarder for DBG can effectively prolong the setting time and displays a better retarding effect,but for PBG shows a poor retarding effect.Furthermore,the deterioration reason of the retarding effect of protein retarder on PBG was investigated by measuring the pH value and the retarder concentration of the liquid phase from vacuum filtration of PBG slurry at different hydration time,and the measure to improve the retarding effect of protein retarding on PBG was suggested.The pH value of PBG slurry(<5.0)is lower than that of DBG slurry(7.8-8.5).After hydration for 5 min,the concentration of retarder in liquid phase of DBG slurry gradually decreases,but in liquid phase of PBG slurry continually increases,which results in the worse retarding effect of protein retarder on PBG.The liquid phase pH value of PBG slurry can be adjusted higher by sodium silicate,which is beneficial to improvement in the retarding effect of the retarder.By adding 1.0%of sodium silicate,the initial setting time of PBG was efficiently prolonged from 17 to 210 min,but little effect on the absolute dry flexural strength was observed.

Enhancing the crystallinity and stability of perovskite solar cells with 4-tert-butylpyridine induction for efficiency exceeding 24%

Perovskite solar cells(PSCs)have emerged as a promising photovoltaic technology because of their high light absorption coefficient,long carrier diffusion distance,and tunable bandgap.However,PSCs face challenges such as hysteresis effects and stability issues.In this study,we introduced a novel approach to improve film crystallization by leveraging 4-tert-butylpyridine(TBP)molecules,thereby enhancing the performance and stability of PSCs.Our findings demonstrate the effective removal of PbI_(2)from the perovskite surface through strong coordination with TBP molecules.Additionally,by carefully adjusting the concentration of the TBP solution,we achieved enhanced film crystallinity without disrupting the perovskite structure.The TBP-treated perovskite films exhibit a low defect density,improved crystallinity,and improved carrier lifetime.As a result,the PSCs manufactured with TBP treatment achieve power conversion efficiency(PCE)exceeding 24%.Moreover,we obtained the PCE of 21.39%for the 12.25 cm^(2)module.

一种基于血液检测的诊断和预测HBV相关疾病的弹性方法

Chronic hepatitis B virus(HBV)infection,which threatens global public health,is a major contributor to liver-related morbidity and mortality.Examinations for liver diseases related to chronic HBV infection-including laboratory tests,ultrasounds,computed tomography(CT),and liver biopsies-may take up medical resources,particularly since they overlap in most instances.Thus,there is an urgent need to establish an economical and effective diagnosis method in order to streamline the medical process for HBV-related disea ses.Using complex network models constructed based on clinical blood tests,we provide such a method by defining the novel measure of functional resilience to assess patients’liver conditions.By combining network models and dynamics,we discovered the pivotal items and their corresponding thresholds,which can guide further research on preventing disease deterioration in critical states of these diseases.The macro-averaged precision of our method,functional resilience,is84.74%,whereas the macro-averaged precision of physicians’experience without assistance from imaging or biopsy is 55.63%.From an economic perspective,our approach could save the equivalent of at least30 USD per visit for most Chinese patients and at least 400 USD per visit for most US patients,compared with general diagnostic methods.Globally,this will add to savings of at least 10.5 billion USD annually.Our method can comprehensively evaluate the condition of patients’livers and help avert the waste of medical resources during the diagnosis of liver disease by reducing excessive imaging exams.

An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data

Due to environmental noise and human factors,magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation.Empirical Mode Decomposition(EMD),an adaptive multiscale analysis method for nonlinear and non-stationary signals,is widely used in geophysical and geodetic data processing.Compared with traditional EMD,Improved Complete Ensemble EMD with Adaptive Noise(ICEEMDAN)is more effective in addressing the problem of mode mixing.Based on the principles of 1D ICEEMDAN,this paper presents an alternative algorithm for 2D ICEEMDAN,extending its application to two-dimensional scenarios.The effectiveness of the proposed approach is demonstrated through synthetic signal experiments,which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD(BEMD)method.Furthermore,to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components,an improved soft thresholding technique is introduced.Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods.To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN,it is applied to ground magnetic survey data in the Yandun area of Xinjiang.The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data.In particular,practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD.In conclusion,as a potential tool for multi-scale decomposition,the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.