Effect of drying-wetting cycles on pore characteristics and mechanical properties of enzyme-induced carbonate precipitation-reinforced sea sand

Enzyme-induced carbonate precipitation(EICP)is an emanating,eco-friendly and potentially sound technique that has presented promise in various geotechnical applications.However,the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting(D-W)cycles is under-explored yet.This study investigates the evolution of mechanical behavior and pore charac-teristics of EICP-treated sea sand subjected to D-W cycles.The uniaxial compressive strength(UCS)tests,synchrotron radiation micro-computed tomography(micro-CT),and three-dimensional(3D)recon-struction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles.The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis(GRA).Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles.The proportion of mesopores gradually decreases whereas the proportion of macropores in-creases due to the exfoliated calcium carbonate with increasing number of D-W cycles.The micro-structure in EICP-reinforced sea sand was gradually disintegrated,resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched.The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100-1000 m m under the action of D-W cycles.Overall,the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.

Personalized Dialogue Generation Model Based on BERT and Hierarchical Copy Mechanism

Despite the great advances in generative dialogue systems, existing dialogue generation models are still unsatisfactory in maintaining persona consistency. In order to make the dialogue generation model generate more persona-consistent responses, this paper proposes a model named BERT-HCM (Personalized Dialogue Generation Model Based on BERT and Hierarchical Copy Mechanism). The model uses an encoder based on BERT initialization to encode persona information and dialogue queries and subsequently uses a Transformer decoder incorporating a hierarchical copy mechanism to dynamically copy the input-side content to guide the model in generating responses. The experimental results show that the proposed model improves on both automatic and human evaluation metrics compared to the baseline model and is able to generate more fluent, relevant and persona-consistent responses.

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

Supply Chain Demand Forecast Based on SSA-XGBoost Model

Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts. To solve these problems, using artificial intelligence and big data technology to achieve market demand forecasting and intelligent decision-making is becoming a strategic technology trend of supply chain management in the future. Firstly, this paper makes a visual analysis of the historical data of the Stock Keeping Unit (SKU);Then, the characteristic factors affecting the future demand are constructed from the storage level, product level, historical usage of SKU, etc;Finally, a supply chain demand forecasting algorithm based on SSA-XGBoost model has proposed around three aspects of feature engineering, parameter optimization and model integration, and is compared with other machine learning models. The experiment shows that the forecasting result of SSA-XGBoost forecasting model is highly consistent with the actual value, so it is of practical significance to adopt this forecasting model to solve the supply chain demand forecasting problem.

Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model

Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.

Innovations in functional genomics and molecular breeding of pea:exploring advances and opportunities

The garden pea(Pisum sativum L.)is a significant cool-season legume,serving as crucial food sources,animal feed,and industrial raw materials.The advancement of functional genomics over the past two decades has provided substantial theoretical foundations and progress to pea breeding.Notably,the release of the pea reference genome has enhanced our understanding of plant architecture,symbiotic nitrogen fixation(SNF),flowering time,floral organ development,seed development,and stress resistance.However,a considerable gap remains between pea functional genomics and molecular breeding.This review summarizes the current advancements in pea functional genomics and breeding while highlighting the future challenges in pea molecular breeding.

Lead-free perovskite Cs_(2)AgBiBr_(6)photodetector detecting NIR light driven by titanium nitride plasmonic hot holes

Lead-free perovskite Cs_(2)AgBiBr_(6)manifests great potential in developing high-performance,environmentally friendly,solution-processable photodetectors(PDs).However,due to the relatively large energy bandgap,the spectrum responses of Cs_(2)AgBiBr_(6)PDs are limited to the ultraviolet and visible region with wavelengths shorter than 560 nm.In this work,a broadband Cs_(2)AgBiBr_(6)PD covering the ultraviolet,visible,and near infrared(NIR)range is demonstrated by incorporating titanium nitride(TiN)nanoparticles that are prepared with the assistance of self-assembled polystyrene sphere array.In addition,an atomically thick Al2O3layer is introduced at the interface between the Cs_(2)AgBiBr_(6)film and TiN nanoparticles to alleviate the dark current deterioration caused by nanoparticle incorporation.As a result,beyond the spectrum range where Cs_(2)AgBiBr_(6)absorbs light,the external quantum efficiency(EQE)of the TiN nanoparticle incorporated Cs_(2)AgBiBr_(6)PD is enhanced significantly compared with that of the control,displaying enhancement factors as high as 2000 over a broadband NIR wavelength range.The demonstrated enhancement in EQE arises from the photocurrent contribution of plasmonic hot holes injected from TiN nanoparticles into Cs_(2)AgBiBr_(6).This work promotes the development of broadband solution-processable perovskite PDs,providing a promising strategy for realizing photodetection in the NIR region.

Correction To:Intranasal booster using an Omicron vaccine confers broad mucosal and systemic immunity against SARSCoV-2 variants

Correction to:Signal Transduction and Targeted Therapy https://doi.org/10.1038/s41392-023-01423-6,published online 17 April 2023 After online publication of the article^(1),the authors noticed two inadvertent mistakes occurred in References that needs to be corrected.The correct text is provided as follows.

Omicron variants breakthrough infection elicited higher specific memory immunity than third dose booster in healthy vaccinees

Homologous booster,heterologous booster,and Omicron variants breakthrough infection(OBI)could improve the humoral immunity against Omicron variants.Questions concerning about memory B cells(MBCs)and T cells immunity against Omicron variants,features of long-term immunity,after booster and OBI,needs to be explored.Here,comparative analysis demonstrate antibody and T cell immunity against ancestral strain,Delta and Omicron variants in Omicron breakthrough infected patients(OBIPs)are comparable to that in Ad5-nCoV boosted healthy volunteers(HVs),higher than that in inactivated vaccine(InV)boosted HVs.However,memory B cells(MBCs)immunity against Omicron variants was highest in OBIPs,followed by Ad5-nCoV boosted and InV boosted HVs.OBIPs and Ad5-nCoV boosted HVs have higher classical MBCs and activated MBCs,and lower naïve MBCs and atypical MBCs relative to both vaccine boosted HVs.Collectively,these data indicate Omicron breakthrough infection elicit higher MBCs and T cells against SARS-CoV-2 especially Omicron variants relative to homologous InV booster and heterologous Ad5-nCoV booster.

Intranasal booster using an Omicron vaccine confers broad mucosal and systemic immunity against SARS-CoV-2 variants

The highly contagious SARS-CoV-2 Omicron subvariants severely attenuated the effectiveness of currently licensed SARS-CoV-2 vaccines based on ancestral strains administered via intramuscular injection.In this study,we generated a recombinant,replication-incompetent human adenovirus type 5,Ad5-S-Omicron,that expresses Omicron BA.1 spike.Intranasal,but not intramuscular vaccination,elicited spike-specific respiratory mucosal IgA and residential T cell immune responses,in addition to systemic neutralizing antibodies and T cell immune responses against most Omicron subvariants.We tested intranasal Ad5-S-Omicron as a heterologous booster in mice that previously received intramuscular injection of inactivated ancestral vaccine.In addition to inducing serum broadly neutralizing antibodies,there was a significant induction of respiratory mucosal IgA and neutralizing activities against Omicron subvariants BA.1,BA.2,BA.5,BA.2.75,BF.7 as well as pre-Omicron strains Wildtype,Beta,and Delta.Serum and mucosal neutralizing activities against recently emerged XBB,BQ.1,and BQ.1.1 could also be detected but were much lower.Nasal lavage fluids from intranasal vaccination contained multimeric IgA that can bind to at least 10 spike proteins,including Omicron subvariants and pre-Omicron strains,and possessed broadly neutralizing activities.Intranasal vaccination using Ad5-S-Omicron or instillation of intranasal vaccinee’s nasal lavage fluids in mouse nostrils protected mice against Omicron challenge.Taken together,intranasal Ad5-S-Omicron booster on the basis of ancestral vaccines can establish effective mucosal and systemic immunity against Omicron subvariants and multiple SARS-CoV-2 variants.This candidate vaccine warrants further development as a safe,effective,and user-friendly infection and transmission-blocking vaccine.

反向电渗析热机发生单元研究进展

反向电渗析热机技术通过"热能-化学势能"和"化学势能-电能"两个转化过程完成电力生产,是一种新型的低品位余热利用技术.热机由发生单元及反向电渗析电池单元构成,分别对应上述两个转化过程.其中,发生单元对系统性能有显著影响,故本文从提升发生单元效率和系统性能出发,系统地综述了发生单元工质和发生方法的研究进展.通过探究溶剂和溶质特性对发生单元及系统性能的影响机理,凝练出工质筛选原则:反向电渗析热机适合于低汽化潜热、低沸点、高活度系数和高电导率的工质.调研发现,现有发生方法主要有蒸馏法、膜蒸馏法和热分解法,其中蒸馏法为最常见的发生方法.3种发生方法的效率较低,不可逆损失大,未来研究可聚焦于系统最优浓度梯度的构建以及发生单元与发电单元的参数匹配.此外,反向电渗析热机技术除了设计用于回收低品位工业余热,亦可用于回收太阳能和地热能等中低品位可再生能源,促进新能源的高效利用.同时,热机工作时伴随有电极反应,表明采用反向电渗析热机进行发电的同时,亦可进行污水处理、有机物降解、制氢及废酸、废碱中和处理.

A Two-Level Cross-Sectional Optimization Approach for Automotive Body Concept Design

Concept design is vital important in development of auto-body and it has great effects on later design work.In this paper,a twolevel cross-sectional optimization approach is presented to shorten concept design cycles.First,an exact structural analysis approach for spatial semi-rigid framed structures,i.e.,the transfer stiffness matrix method proposed in our previous study,is adopted for both static and dynamic analyses of body-in-white(BIW)structure.A two-level cross-sectional optimization approach is then proposed for an automotive BIW lightweight design,and genetic algorithm is used to solve the optimization models.Afterward,an object-oriented MATLAB toolbox,using distributed parallel computing techniques,is developed to promote the concept design of the BIW structure.Finally,relevant numerical examples demonstrate the validity and accuracy of the proposed method.

Surveillance and Response to Imported Malaria During the COVID-19 Epidemic—Anhui Province,China,2019–2021

Summary What is already known about this topic?China was certified malaria-free on June 30,2021.However,imported malaria continuously threatens the effort to prevent re-establishment of malaria in China.What is added by this report?Measures such as international travel restrictions,entry quarantine,and screening in fever clinics during the coronavirus disease 2019(COVID-19)period were associated with a significant decrease of imported malaria cases in Anhui Province,a higher proportion of non-Plasmodium falciparum(non-P.falciparum)malaria reported infections,and a higher proportion of cases requiring medical attention at their initial visit.What are the implications for public health practices?It is necessary to be vigilant about imported malaria during the COVID-19 epidemic,especially for non-P.falciparum infections which are more difficult to detect,and to promote research,development,and introduction of more sensitive and specific point-ofcare detection methods for non-P.falciparum species.