Preparation of Silver Nanowires via a Rapid,Scalable and Green Pathway

Rapid synthesis of silver nanowires(Ag NWs) with high quality and a broad processing window is challenging because of the low selectivity of the formation of multiply twinned particles at the nucleation stage for subsequent Ag NWs growth.Herein we report a systematic study of the water-involved heterogeneous nucleation of Ag NWs with high rate(less than 20 min) in a simple and scalable preparation method.Using glycerol as a reducing agent and a solvent with a high boiling point,the reaction is rapidly heated to 210 ℃ in air to synthesize Ag NWs with a very high yield in gram level.It is noted that the addition of a small dose of water plays a key role for obtaining highly pure Ag NWs in high yield,and the optimal water/glycerol ratio is0.25%.After investigating a series of forming factors including reaction temperature and dose of catalysts,the formation kinetics and mechanism of the Ag NWs are proposed.Compared to other preparation methods,our strategy is simple and reproducible.These Ag NWs show a strong Raman enhancement effect for organic molecules on their surface.     

Recent progress of green sorbents-based technologies for low concentration CO2 capture

The increased concentration of CO₂ due to continuous breathing and no discharge of human beings in the manned closed space, like spacecraft and submarines, can be a threat to health and safety. Effective removal of low concentration CO₂ from the manned closed space is essential to meet the requirements of long-term space or deep-sea exploration, which is an international frontier and trend. Ionic liquids (ILs), as a widespread and green solvent, already showed its excellent performance on CO₂ capture and absorption, indicating its potential application in low concentration CO₂ capture. In this review, we first summarized the current methods and strategies for direct capture from low concentration CO₂ in both the atmosphere and manned closed spaces. Then, the multi-scale simulation methods of CO₂ capture by ionic liquids are described in detail, including screening ionic liquids by COSMO-RS methods, capture mechanism by density functional theory and molecular dynamics simulation, and absorption process by computational fluid dynamics simulation. Lastly, some typical IL-based green technologies for low concentration CO₂ capture, such as functionalized ILs, co-solvent systems with ILs, and supported materials based on ILs, are introduced, and analyzed the subtle possibility in manned closed spaces. Finally, we look forward to the technology and development of low concentration CO₂ capture, which can meet the needs of human survival in closed space and proposed that supported materials with ionic liquids have great advantages and infinite possibilities in the vital area.     

Quantitative analysis of Ni effect on CCT diagrams of novel air-cooled bainite steels using artificial neural network models

The quantitative effect of Ni content on continuous cooling transformation （CCT） diagrams of novel air-cooled bainite steels was analyzed using artificial neural network models. The results showed that Ni may retard the high- and medium-temperature transformation and martensite transformation. The results conform to the materials science theories.     

A NEW LARGE CAVITATION CHANNEL IN CHINA SHIP SCIENTIFIC RESEARCH CENTER

1 .　INTRODUCTIONIntherecentdecades ,itisrealizedthatitisquitenecessarytocarryouttestsathighReynoldsnumberwithalowbackgroundnoiselevelinordertomeetmoreandmoreurgentneedsofmodernnavyandmaritimeindustry .Anewlargecavita tionchannelisdevelopedinCSSRCfortestingcompletehull/ propulsor/appendagesarrange ments[1] .Itrepresentsoneofthelatestintechno logicallyadvancedlargecavitationtestfacilities.Inthefollowingsections ,thefeaturesandthefirsttestresultsofthechannelwillbedescribed .2 .　DICRIP…     

Parallel Algorithms for Image Template Matching on Hypercube SIMD Computers

This correspondence presents several parallel algorithms for image template matching on an SIMD array processor with a hypercube interconnection network. For an N by N image and an M by M window, the time complexity is reduced from O(N2M2) for the serial algorithm to O(M2/K2 + M * log2 N/K + log2 N * log2 K) for the N2K2-PE system (1 ? K ? M), or to O(N2M2/L2) for the L2-PE system (L ? N). With efficient use of the inter-PE communication network, each PE requires only a small local memory, many unnecessary data transmissions are eliminated, and the time complexity is greatly reduced.     

Equivalent model of multi-type distributed generators under faults with fast-iterative calculation method based on improved PSO algorithm

There are various types of distributed generators (DGs) with diferent grid integration strategies. The transient char? acteristics of the fault currents provided by the DGs are diferent to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specifc operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efciency while avoid? ing local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simula? tion system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efciency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.     

Single-molecule biotechnology for protein researches

Cells employ proteins to perform metabolic functions and maintain active physiological state through charge transfer and energy conversion. These processes are carried out in a narrow space precisely and rapidly, which, no doubt, bring great difficulty for their detection and dissection. Fortunately, in recent years, the development and expansion of single-molecule technique in protein research make monitoring the dynamical changes of protein at single-molecule level a reality, which also provides a powerful tool for the further exploration of new phenomena and new mechanisms of life activities. This paper aims to summarize the working principle and essential achievements of single-molecule technique in protein research in recent five years. We focus on not only dissecting the difference of nanopores, atomic force microscope, scanning tunneling microscope, and optical tweezers technique, but also discussing the great significance of these single-molecule techniques in investigating intramolecular and intermolecular interactions, electron transport, and conformational changes. Finally, the opportunities and challenges of the single-molecule technique in protein research are discussed, which provide a new door for single-molecule protein research.     

Contrastive Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation

In recommender systems, the cold-start issue is challenging due to the lack of interactions between users or items. Such an issue can be alleviated via data-level and model-level strategies. Traditional data-level methods employ auxiliary information like feature information to enhance the learning of user and item embeddings. Recently, Heterogeneous Information Networks (HINs) have been incorporated into the recommender system as they provide more fruitful auxiliary information and meaningful semantics. However, these models are unable to capture the structural and semantic information comprehensively and neglect the unlabeled information of HINs during training. Model-level methods propose to apply the meta-learning framework which naturally fits into the cold-start issue, as it learns the prior knowledge from similar tasks and adapts to new tasks quickly with few labeled samples. Therefore, we propose a contrastive meta-learning framework on HINs named CM-HIN, which addresses the cold-start issue at both data level and model level. In particular, we explore meta-path and network schema views to describe the higher-order and local structural information of HINs. Within meta-path and network schema views, contrastive learning is adopted to mine the unlabeled information of HINs and incorporate these two views. Extensive experiments on three benchmark datasets demonstrate that CM-HIN outperforms all state-of-the-art baselines in three cold-start scenarios.     

Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming

In this paper, a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time (DT) systems based on adaptive dynamic programming (ADP) algorithm. First, an augmented system composed of the original system and the command generator is constructed and then an augmented stochastic algebraic equation is derived based on the augmented system. Next, to obtain the optimal control strategy, the stochastic case is converted into the deterministic one by system transformation, and then an ADP algorithm is proposed with convergence analysis. For the purpose of realizing the ADP algorithm, three back propagation neural networks including model network, critic network and action network are devised to guarantee unknown system model, optimal value function and optimal control strategy, respectively. Finally, the obtained optimal control strategy is applied to the original stochastic system, and two simulations are provided to demonstrate the effectiveness of the proposed algorithm.