Strain engineering for thermal conductivity of single-walled carbon nanotube forests

We perform classical molecular dynamics simulations to investigate the mechanical compression effect on the thermal conductivity of the single-walled carbon nanotube (SWCNT) forest, in which SWCNTs are closely aligned and parallel with each other. We find that the thermal conductivity can be linearly enhanced by increasing compression before the buckling of SWCNT forests, but the thermal conductivity decreases quickly with further increasing compression after the forest is buckled. Our phonon mode analysis reveals that, before buckling, the smoothness of the inter-tube interface is maintained during compression, and the inter-tube van der Waals interaction is strengthened by the compression. Consequently, the twisting-like mode (good heat carrier) is well preserved and its group velocity is increased by increasing compression, resulting in the enhancement of the thermal conductivity. The buckling phenomenon changes the circular cross section of the SWCNT into ellipse, which causes effective roughness at the inter-tube interface for the twisting motion. As a result, in ellipse SWCNTs, the radial breathing mode (poor heat carrier) becomes the most favorable motion instead of the twisting-like mode and the group velocity of the twisting-like mode drops considerably, both of which lead to the quick decrease of the thermal conductivity with further increasing compression after buckling.     

基于桥连接的词典学习方法的语义解析

现阶段的语义解析方法大部分都基于组合语义,这类方法的核心就是词典。词典是词汇的集合,词汇定义了自然语言句子中词语到知识库本体中谓词的映射。语义解析一直面临着词典中词汇覆盖度不够的问题。针对此问题,该文在现有工作的基础上,提出了基于桥连接的词典学习方法,该方法能够在训练中自动引入新的词汇并加以学习,为了进一步提高新学习到的词汇的准确度,该文设计了新的词语—二元谓词的特征模板,并使用基于投票机制的核心词典获取方法。该文在两个公开数据集(WebQuestions和Free917)上进行了对比实验,实验结果表明,该文方法能够学习到新的词汇,提高词汇的覆盖度,进而提升语义解析系统的性能,特别是召回率。     

Hierarchical Attention for Part-Aware Face Detection

International Journal of Computer Vision - Expressive representations for characterizing face appearances are essential for accurate face detection. Due to different poses, scales, illumination,...     

电喷雾质谱法示踪钨回收过程离子的转化路径

将环隙式离心萃取器(ACCs)与电喷雾飞行时间质谱(ESI-TOF-MS)相结合，在线监测了回收过程中的钨萃取行为(宏观)和钨形态的转化路径(微观)，发现宏观萃取反应和微观离子形态转化同时发生并相互补充。伯胺N1923萃取钨在144 s内即可达到萃取平衡，萃取率高达98%以上，同时，酸钨比n(H)/n(W)是一个关键变量，当酸钨比n(H)/n(W)=2.4时，全流程钨回收率超过93%。最后，得到了基于钨形态监测的萃取机理，同时，减少原料液中W1含量，增加W10含量，可有效提高钨回收效率。     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Mitigating Hydrological Risk with Energy Derivatives

The variability of river inflows affects the energy production of hydropower generators and may result in reductions in revenues that can be financially disruptive for these producers. Recent climatic changes have highlighted the risks involved in hydroelectriciy production in Brazil. In this paper, we propose a different approach to formulating a collar derivative, namely an Inverted Collar, to mitigate hydrological risk considering the particularities of Brazil's energy regulatory environment. In addition, we propose a customized collar-by-difference as a variation of the collar model. The effect of these derivatives is analyzed considering electricity market price and power generation uncertainty for a typical hydro generator. The results suggest that these derivatives are effective tools to manage hydrological risk during period of great climatic volatility, such as the height of the drought period experienced by Brazil in 2016. The results also indicate that our models outperform traditional commercial hedging commonly practiced by hydropower producers in the country.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

Willingness to pay and preferences for alternative incentives to EV purchase subsidies: An empirical study in China

The transport sector's promotion of electric vehicles (EVs) is an important tool in reducing greenhouse gas emissions. The high monetary subsidies widely used to promote EV diffusion in many countries are not sustainable in the long term. Therefore, effective alternative incentives are needed as subsidies are gradually phased out. In this paper, consumers' willingness to pay (WTP) for alternative incentives is studied based on a questionnaire survey. Using a discrete choice experiment, 1719 Chinese consumer questionnaires were collected. Consumers' WTP for nine alternative policies was calculated by applying the multinomial logit model and mixed logit model, and the mixed logit model is used as the main model of this study because of its better statistical performance. Finally, the relationship between the respondent heterogeneity and policy preferences was studied. The results show that alternative incentives can fill the gap produced by subsidy reductions. EV privileges like no restrictions on driving and purchases, which have the lowest implementation cost, are suitable for first- and second- tier cities. For second-tier cities, construction of more charging stations is the most appropriate choice. The heterogeneous features of the respondents, including cities of residence, type of vehicle owned, recognition of the environmental benefits of EVs, gender, and whether the family has children, had a significant impact on policy preferences.     

两阶段领域自适应学习

针对领域自适应问题中源域和目标域的联合分布差异最小化问题,提出两阶段领域自适应学习方法.在第一阶段考虑样本标签和数据结构的判别信息,通过学习一个共享投影变换,使投影后的共享空间中边缘分布的差异最小.第二阶段利用源域标记数据和目标域非标记数据学习一个带结构风险的自适应分类器,不仅能最小化源域和目标域条件分布差异,还能进一步保持源域和目标域边缘分布的流形一致性.在3个基准数据集上的实验表明,文中方法在平均分类准确率和Kappa系数两项评价指标上均表现较优.