Long-Term Performance and Microstructural Characterization of Dam Concrete in the Three Gorges Project

This study investigates the long-term performance of laboratory dam concrete in different curing environments over ten years and the microstructure of 17-year-old laboratory concrete and actual concrete cores drilled from the Three Gorges Dam.The mechanical properties of the laboratory dam concrete,whether cured in natural or standard environments,continued to improve over time.Furthermore,the laboratory dam concrete exhibited good resistance to diffusion and a refined microstructure after 17 years.However,curing and long-term exposure to the local natural environment reduced the frost resistance.Microstructural analyses of the laboratory concrete samples demonstrated that moderate-heat cement and fine fly ash(FA)particles were almost fully hydrated to form compact micro structures consisting of large quantities of homogeneous calcium(alumino)silicate hydrate(C-(A)-S-H)gels and a few crystals.No obvious interfacial transition zones were observed in the microstructure owing to the longterm pozzolanic reaction.This dense and homogenous microstructure was the crucial reason for the excellent long-term performance of the dam concrete.A high FA volume also played a significant role in the microstructural densification and performance growth of dam concrete at a later age.The concrete drilled from the dam surface exhibited a loose microstructure with higher microporosity,indicating that concrete directly exposed to the actual service environment suffered degradation caused by water and wind attacks.In this study,both macro-performance and microstructural analyses revealed that the application of moderate-heat cement and FA resulted in a dense and homogenous microstructure,which ensured the excellent long-term performance of concrete from the Three Gorges Dam after 17 years.Long-term exposure to an actual service environment may lead to microstructural degradation of the concrete surface.Therefore,the retained long-term dam concrete samples need to be further researched to better understand its microstructural evolution and development of its properties.

Numerical Simulation of Vacuum Preloading for Chemically Conditioned Municipal Sludge

Municipal sludge is a sedimentation waste produced during the wastewater process in sewage treatment plants.Among recent studies,pilot and field tests showed that chemical conditioning combined with vacuum preloading can effectively treat municipal sludge.To further understand the drainage and consolidation characteristics of the conditioning sludge during vacuum preloading,a large deformation nonlinear numerical simulation model based on the equal strain condition was developed to simulate and analyze the pilot and field tests,whereas the simulation results were not satisfactory.The results of the numerical analysis of the pilot test showed that the predicted consolidation degree was greater than that measured by the field tests,which is attributed to the relatively low permeability layer formed during the preloading process of the prefabricated vertical drain.To better reflect the consolidation process of the conditioned sludge,a simplified analysis method considering the low permeability layer around the prefabricated vertical drain was proposed.The initial permeability coefficient of the low permeability layer is determined via numerical simulations using finite difference method.The predicted settlement curve was in good agreement with the measured results,which indicated that the numerical simulation based on the equal strain condition considering the relatively low permeability layer can better analyze the consolidation process of ferric chloride-conditioning sludge with vacuum preloading.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future metropolises.Here,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality.Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other models.And identifies the ranking of major factors,local meteorological conditions have become a nonnegligible factor.Layer-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of Shanghai.Meanwhile,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 gradually.These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

在不使用数字控制器的情况下,闭合无线充电器接收器和发射器之间的控制环路

小尺寸可穿戴设备越来越多地采用无线充电,因为这样无须使用充电线,在设备上也无须配备外露式接口。对于充电电流小于10 mA的应用,由于功耗很低,因此无须在无线充电器接收器和发射器之间实施闭环控制。但是,要获得更高的充电电流,就需要发射器根据其接收器的需求,以及两端之间的耦合系数,主动调节其输出功率。否则,接收器可能需要以热量的形式消耗多余的功率,这会影响用户体验,并且可能损害电池性能。无线充电发射器和接收器间的控制回路通常用数字通信的方式来实现闭合,但是数字控制会增加总体设计的复杂性和增大应用的大小。本文介绍一种方法,可以在不增加接收器电路板上组件数量(和宝贵的整体尺寸)的情况下,闭合接收器和发射器之间的控制环路。我们使用LTC4125 AutoResonant™发射器和LTC4124无线锂离子充电器接收器来构建闭环控制无线充电器原型,以演示此理念。

新型分离纯化技术在天然产物中的应用

天然产物是自然界生物在长久进化过程中经自然选择所保留下来的次生代谢物,因其丰富的药用价值而被广泛应用于多个领域,随着现代科学的发展,对于天然产物高纯度产品的需求也在不断增加。传统的分离方法通常存在有机溶剂消耗大、分离效果差、成本高、周期长等缺点,严重制约了天然产物在各领域的发展使用。新型分离纯化技术的出现为天然产物的提取、分离和应用提供了新的思路。本文在总结现有文献的基础上,对天然产物的新型分离纯化方法进行了综述,最后对天然产物分离纯化的研究瓶颈和未来发展方向进行了总结与讨论。

Inversion of maize leaf nitrogen using UAV hyperspectral imagery in breeding fields

Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of maize.Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture,based on unmanned aerial vehicle(UAV)remote sensing technology.In this study,the hyperspectral images were acquired by UAV and the leaf nitrogen content(LNC)and leaf nitrogen accumulation(LNA)were measured to estimate the N nutrition status of maize.24 vegetation indices(VIs)were constructed using hyperspectral images,and four prediction models were used to estimate the LNC and LNA of maize.The models include a single linear regression model,multivariable linear regression(MLR)model,random forest regression(RFR)model,and support vector regression(SVR)model.Moreover,the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields.The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll(NDchl)had the highest prediction accuracy for LNC(R^(2),RMSE,and RE were 0.72,0.21,and 12.19%,respectively)and LNA(R^(2),RMSE,and RE were 0.77,0.26,and 14.34%,respectively).And then,24 VIs were divided into 13 important VIs and 11 unimportant VIs.Three prediction models for LNC and LNA were constructed using 13 important VIs,and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model,in which RFR model had the highest prediction accuracy for the validation dataset of LNC(R^(2),RMSE,and RE were 0.78,0.16,and 8.83%,respectively)and LNA(R^(2),RMSE,and RE were 0.85,0.19,and 9.88%,respectively).This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.

我国黄金现货、ETF、期货市场动态联动及波动溢出效应

黄金现货、ETF、期货等市场在维护一国经济稳定、增强国家信用、对冲金融市场波动等方面一直发挥着核心作用.最近几年,黄金价格历经多次剧烈波动.黄金现货、ETF、期货之间观察到了不同步不协同现象,加大了市场不确定性和风险.本文选取2015年至2021年黄金现货合约、华安黄金ETF、黄金期货主力合约每日收盘数据,探讨黄金现货、ETF、期货市场的动态联动及波动溢出效应.首先,构建MS-GARCH模型,研究三市场波动及区制转变关系;然后借助DCC-GARCH模型进一步探究三个黄金市场彼此之间的动态联动关系;最后使用溢出指数模型测度三市场间的波动溢出效应.结果表明:1)三市场一体化程度较高.DCC-GARCH模型表明:三市场收益率动态相关性绝大部分时间都维持在了0.9左右的水平.2)不同市场之间的动态联动性具有显著区别.DCC-GARCH模型表明:黄金现货与期货市场收益率之间的相关系数波动更为剧烈更为频繁.溢出指数模型表明:黄金现货市场在多数时期呈现为负向净溢出,绝大部分时间为溢出的接收者,黄金ETF、期货市场收益率多数时期为正向溢出,即绝大部分时间为溢出的传播者.3)样本期内,三市场波动溢出具有动态特性.MS-GARCH模型平滑概率图发现2019年后三市场收益率在高、低波动状态的持续时间都比较短,两种状态的交替更加频繁.DCC-GARCH模型表明三市场收益率相关系数在2015年、2018年后都有显著下降.溢出指数模型表明;三市场的总溢出指数在2019年出现较大波动,一度下探到55%;2019年对外溢出指数(TO)和接收溢出(FROM)均出现了显著的下降,黄金现货市场降幅最大,分别下降到43%、50%;2019年黄金现货、ETF市场收益率的净溢出指数波动较大,也观察到了黄金现货到ETF、现货到期货的净成对溢出指数的大幅波动.

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources,for which mathematical modeling is commonly adopted.In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling,we employed an ensemble machine learning(ML)model to identify the key nitrogen and phosphorus sources of lakes.Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality,environmental input,and meteorological conditions,among which the XGBoost model stood out as the best model for total nitrogen(TN)and total phosphorus(TP)prediction.The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality,while the lake TP is predominantly from endogenous sources.The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control.Finally,one-month-ahead prediction of lake TN and TP concentrations(R2 of 0.85 and 0.95,respectively)was achieved based on this model with sliding time window lengths of 9 and 6 months,respectively.Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction,which may provide valuable references for early warning and rational control of lake eutrophication.

Non-covalent glycosylated gold nanoparticles/peptides nanovaccine as potential cancer vaccines

Herein,we firstly developed a non-covalent glycosylated gold nanoparticles/peptides nanovaccine which is assembled byβ-cyclodextrin(β-CD)based host-guest recognitions.This nanovaccine can generate significant titers of antibodies and improve the therapeutic effect against melanoma,suggesting the immunogenicity of peptide antigens can be improved by loading with this carrier.The novel vaccine carrier provides a platform for the transport of various antigens especially T cell-independent antigens.

Mechanisms behind the accelerated extracellular electron transfer in Geobacter sulfurreducens DL-1 by modifying gold electrode with self-assembled monolayers

Modification of electrode surface with carboxylic acid terminated alkanethiol self-assembled monolayers （SAMs） has been found to be an effective approach to improve the extracellular electron transfer （EET） of electrochemically active bacteria （EAB） on electrode surface, but the underlying mechanism behind such enhanced EET remains unclear. In this work, the gold electrodes modified by mercapto-acetic acid and mercapto- ethylamine （Au-COOH, Au-NH2） were used as anodes in microbial electrolysis cells （MECs） inoculated with Geobacter sulfurreducens DL- 1, and their electrochemical performance and the bacteria-electrode interactions were investigated. Results showed that the Fe（CN）6^3-/4^- redox reaction occurred on the Au-NH2 with a higher rate and a lower resistance than that on the Au or the Au-COOH. Both the MECs with the Au-COOH and Au-NH2 anodes exhibited a higher current density than that with a bare Au anode. The biofilm formed on the Au-COOH was denser than that on bare Au, while the biofilm on the Au-NH2 had a greater thickness, suggesting a critical role of direct EET in this system. This work suggests that functional groups such as --COOH and-NH2 could promote electrode performance by accelerating the direct EET of EAB on electrode surface.

Human extracellular matrix(ECM)-like collagen and its bioactivity

Collagen,the most abundant structural protein in the human extracellular matrix(ECM),provides essential support for tissues and guides tissue development.Despite its widespread use in tissue engineering,there remains uncertainty regarding the optimal selection of collagen sources.Animal-derived sources pose challenges such as immunogenicity,while the recombinant system is hindered by diminished bioactivity.In this study,we hypothesized that human ECM-like collagen(hCol)could offer an alternative for tissue engineering.In this study,a facile platform was provided for generating hCol derived from mesenchymal stem cells with a hierarchical structure and biochemical properties resembling native collagen.Our results further demonstrated that hCol could facilitate basal biological behaviors of human adipose-derived stem cells,including viability,proliferation,migration and adipocyte-like phenotype.Additionally,it could promote cutaneous wound closure.Due to its high similarity to native collagen and good bioactivity,hCol holds promise as a prospective candidate for in vitro and in vivo applications in tissue engineering.