Extrusion-based 3D Printing of Macro/Microstructures for Advanced Lithium/Sodium Batteries

With the development of electronics and electric vehicles, high-performance batteries with high energy density, high safety, and aesthetic diversity are greatly needed as dominating power sources. However, the electrodes and electrolytes fabricated with traditional techniques have limited form factors, mechanical flexibility, and poor performance. Extrusion-type 3D printing techniques have thus been applied to fabricate 3D batteries with high performance since 3D printing techniques have great advantages in the fabrication of complex 3D structures and geometric shapes from various materials. The research in this thesis aims at fabricating high-performance Li/Na batteries via 3D printing of advanced electrodes and solid electrolytes

Revisit to Non-decoupling MSSM

Dipole operator $\bar{s}\sigma_{\mu\nu}F^{\mu\nu}b$ requires the helicity flip in the involving quark states thus the breaking of chiral $U(3)_{Q}\times U(3)_{d}$. On the other hand, the $b$-quark mass generation is also a consequence of chiral $U(3)_{Q}\times U(3)_{d}$ symmetry breaking. Therefore, in many models, there might be strong correlation between the $b\to s\gamma$ and $b$ quark Yukawa coupling. We use non-decoupling MSSM model to illustrate this feature. The light Higgs boson may evade the direct search experiments at LEPII or Tevatron while the 125 GeV Higgs-like boson is identified as the heavy Higgs boson in the spectrum. A light charged Higgs is close to the heavy Higgs boson which is of 125 GeV and its contribution to $b\to s \gamma$ requires large supersymmetric correction with large PQ and $R$ symmetry breaking. The large supersymmetric contribution at the same time significantly modifies the $b$ quark Yukawa co upling. With combined flavor constraints $B\to X_{s}\gamma$ and $B_{s}\to \mu^{+}\mu^{-}$ and direct constraints on Higgs properties, we find best fit scenarios with light stop of $\cal O$(500 GeV), negative $A_{t}$ around -750 GeV and large $\mu$-term of 2-3 TeV. In addition, reduction in $b\bar{b}$ partial width may also result in large enhancement of $\tau\tau$ decay branching fraction. Large parameter region in the survival space under all bounds may be further constrained by $H\to \tau\tau$ if no excess of $\tau\tau$ is confirmed at LHC. We only identify a small parameter region with significant $H\to hh$ decay that is consistent with all bounds and reduced $\tau\tau$ decay branching fraction.Comment: 18pages, 6 figure

Can the Black Lives Matter Movement Reduce Racial Disparities? Evidence from Medical Crowdfunding

Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%. The reduction is largely attributed to non-Black donors. Those beneficiaries in counties with moderate BLM activities were most impacted. We construct innovative instrumental variable approaches that utilize weekends and rainfall to identify the global and local effects of BLM protests. Results suggest a broad social movement has a greater influence on charitable-giving behavior than a local event. Social media significantly magnifies the impact of protests

Image retrieval based on colour and improved NMI texture features

This paper proposes an improved method for extracting NMI features. This method uses Particle Swarm Optimization in advance to optimize the two-dimensional maximum class-to-class variance (2OTSU) in advance. Afterwards, the optimized 2OUSU is introduced into the Pulse Coupled Neural Network (PCNN) to automatically obtain the number of iterations of the loop. We use an improved PCNN method to extract the NMI features of the image. For the problem of low accuracy of single feature, this paper proposes a new method of multi-feature fusion based on image retrieval. It uses HSV colour features and texture features, where, the texture feature extraction methods include: Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Improved PCNN. The experimental results show that: on the Corel-1k dataset, compared with similar algorithms, the retrieval accuracy of this method is improved by 13.6%; On the AT&T dataset, the retrieval accuracy is improved by 13.4% compared with the similar algorithm; on the FD-XJ dataset, the retrieval accuracy is improved by 17.7% compared with the similar algorithm. Therefore, the proposed algorithm has better retrieval performance and robustness compared with the existing image retrieval algorithms based on multi-feature fusion

A Review on Progress in QSPR Studies for Surfactants

This paper presents a review on recent progress in quantitative structure-property relationship (QSPR) studies of surfactants and applications of various molecular descriptors. QSPR studies on critical micelle concentration (cmc) and surface tension (γ) of surfactants are introduced. Studies on charge distribution in ionic surfactants by quantum chemical calculations and its effects on the structures and properties of the colloids of surfactants are also reviewed. The trends of QSPR studies on cloud point (for nonionic surfactants), biodegradation potential and some other properties of surfactants are evaluated

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

Quality product descriptions are critical for providing competitive customer experience in an e-commerce platform. An accurate and attractive description not only helps customers make an informed decision but also improves the likelihood of purchase. However, crafting a successful product description is tedious and highly time-consuming. Due to its importance, automating the product description generation has attracted considerable interests from both research and industrial communities. Existing methods mainly use templates or statistical methods, and their performance could be rather limited. In this paper, we explore a new way to generate the personalized product description by combining the power of neural networks and knowledge base. Specifically, we propose a KnOwledge Based pErsonalized (or KOBE) product description generation model in the context of e-commerce. In KOBE, we extend the encoder-decoder framework, the Transformer, to a sequence modeling formulation using self-attention. In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc. Experiments on real-world datasets demonstrate that the proposed method out-performs the baseline on various metrics. KOBE can achieve an improvement of 9.7% over state-of-the-arts in terms of BLEU. We also present several case studies as the anecdotal evidence to further prove the effectiveness of the proposed approach. The framework has been deployed in Taobao, the largest online e-commerce platform in China.Comment: KDD 2019 Camera-ready. Website: https://sites.google.com/view/kobe201