Directional Frames for Image Recovery: Multi-scale Discrete Gabor Frames

Sparsity-driven image recovery methods assume that images of interest can be sparsely approximated under some suitable system. As discontinuities of 2D images often show geometrical regularities along image edges with different orientations, an effective sparsifying system should have high orientation selectivity. There have been enduring efforts on constructing discrete frames and tight frames for improving the orientation selectivity of tensor product real-valued wavelet bases/frames. In this paper, we studied the general theory of discrete Gabor frames for finite signals, and constructed a class of discrete 2D Gabor frames with optimal orientation selectivity for sparse image approximation. Besides high orientation selectivity, the proposed multi-scale discrete 2D Gabor frames also allow us to simultaneously exploit sparsity prior of cartoon image regions in spatial domain and the sparsity prior of textural image regions in local frequency domain. Using a composite sparse image model, we showed the advantages of the proposed discrete Gabor frames over the existing wavelet frames in several image recovery experiments.     

Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data With Applications to Cancer Clinical Trials

Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes. In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo-marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this article, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately. Based on this decomposition, we then propose new Bayesian model assessment criteria, namely, ΔDIC and ΔLPML, to determine the importance and contribution of the longitudinal (survival) data to the model fit of the survival (longitudinal) data. Moreover, we develop an efficient Monte Carlo method for computing the conditional predictive ordinate statistics in the joint modeling setting. A simulation study is conducted to examine the empirical performance of the proposed criteria and the proposed methodology is further applied to a case study in mesothelioma. Supplementary materials for this article are available online.     

Precise large deviations for sums of random vectors with dependent components of consistently varying tails

Let {X _i = (X _1,i ,...,X _m,i )^?, i ≥ 1} be a sequence of independent and identically distributed nonnegative m-dimensional random vectors. The univariate marginal distributions of these vectors have consistently varying tails and finite means. Here, the components of X ₁ are allowed to be generally dependent. Moreover, let N(·) be a nonnegative integer-valued process, independent of the sequence {X _i , i ≥ 1}. Under several mild assumptions, precise large deviations for S _n = Σ _i=1 ⁿ X _i and S _N(t) = Σ _i=1 ^N(t) X _i are investigated. Meanwhile, some simulation examples are also given to illustrate the results.     

Semiparametric Bayesian inference for mean-covariance regression models

In this paper, we propose a Bayesian semiparametric mean-covariance regression model with known covariance structures. A mixture model is used to describe the potential non-normal distribution of the regression errors. Moreover, an empirical likelihood adjusted mixture of Dirichlet process model is constructed to produce distributions with given mean and variance constraints. We illustrate through simulation studies that the proposed method provides better estimations in some non-normal cases. We also demonstrate the implementation of our method by analyzing the data set from a sleep deprivation study.     

Rees–Shishikura’s theorem for geometrically finite rational maps

In this paper, we generalize Rees–Shishikura’s theorem to the class of geometrically finite rational maps.     

碱性电解液中K3[Fe(CN)6]在锌阳极上的自发还原和吸附延长锌镍电池的循环寿命

In view of the continuously worsening environmental problems, fossil fuels will not be able to support the development of human life in the future. Hence, it is of great importance to work on the efficient utilization of cleaner energy resources. In this case, cheap, reliable, and eco-friendly grid-scale energy storage systems can play a key role in optimizing our energy usage. When compared with lithium-ion and lead-acid batteries, the excellent safety, environmental benignity, and low toxicity of aqueous Zn-based batteries make them competitive in the context of large-scale energy storage. Among the various Zn-based batteries, due to a high open-circuit voltage and excellent rate performance, Zn-Ni batteries have great potential in practical applications. Nevertheless, the intrinsic obstacles associated with the use of Zn anodes in alkaline electrolytes, such as dendrite, shape change, passivation, and corrosion, limit their commercial application. Hence, we have focused our current efforts on inhibiting the corrosion and dissolution of Zn species. Based on a previous study from our research group, the failure of the Zn-Ni battery was caused by the shape change of the Zn anode, which stemmed from the dissolution of Zn and uneven current distribution on the anode. Therefore, for the current study, we selected K₃[Fe(CN)₆] as an electrolyte additive that would help minimize the corrosion and dissolution of the Zn anode. In the alkaline electrolyte, [Fe(CN)₆]^3– was reduced to [Fe(CN)₆]^4– by the metallic Zn present in the Zn-Ni battery. Owing to its low solubility in the electrolyte, K₄[Fe(CN)₆] adhered to the active Zn anode, thereby inhibiting the aggregation and corrosion of Zn. Ultimately, the shape change of the anode was effectively eliminated, which improved the cycling life of the Zn-Ni battery by more than three times (i.e., from 124 cycles to more than 423 cycles). As for capacity retention, the Zn-Ni battery with the pristine electrolyte only exhibited 40% capacity retention after 85 cycles, while the Zn-Ni battery with the modified electrolyte (i.e., containing K₃[Fe(CN)₆]) showed 72% capacity retention. Moreover, unlike conventional organic additives that increase electrode polarization, the addition of K₃[Fe(CN)₆] not only significantly reduced the charge-transfer resistance in a simplified three-electrode system, but also improved the discharge capacity and rate performance of the Zn-Ni battery. Importantly, considering that this strategy was easy to achieve and minimized additional costs, K₃[Fe(CN)₆], as an electrolyte additive with almost no negative effect, has tremendous potential in commercial Zn-Ni batteries.     

纳米SiC基光催化剂研究进展

Industrialization undoubtedly boosts economic development and improves the standard of living; however, it also leads to some serious problems, including the energy crisis, environmental pollution, and global warming. These problems are associated with or caused by the high carbon dioxide (CO₂) and sulfur dioxide (SO₂) emissions from the burning of fossil fuels such as coal, oil, and gas. Photocatalysis is considered one of the most promising technologies for eliminating these problems because of the possibility of converting CO₂ into hydrocarbon fuels and other valuable chemicals using solar energy, hydrogen (H₂) production from water (H₂O) electrolysis, and degradation of pollutants. Among the various photocatalysts, silicon carbide (SiC) has great potential in the fields of photocatalysis, photoelectrocatalysis, and electrocatalysis because of its good electrical properties and photoelectrochemistry. This review is divided into six sections: introduction, fundamentals of nanostructured SiC, synthesis methods for obtaining nanostructured SiC photocatalysts, strategies for improving the activity of nanostructured SiC photocatalysts, applications of nanostructured SiC photocatalysts, and conclusions and prospects. The fundamentals of nanostructured SiC include its physicochemical characteristics. It possesses a range of unique physical properties, such as extreme hardness, high mechanical stability at high temperatures, a low thermal expansion coefficient, wide bandgap, and superior thermal conductivity. It also possesses exceptional chemical characteristics, such as high oxidation and corrosion resistance. The synthesis methods for obtaining nanostructured SiC have been systematically summarized as follows: Template growth, sol-gel, organic precursor pyrolysis, solvothermal synthesis, arc discharge, carbon thermal reduction, and electrospinning. These synthesis methods require high temperatures, and the reaction mechanism involves SiC formation via the reaction between carbon and silicon oxide. In the section of the review involving the strategies for improving the activity of nanostructured SiC photocatalysts, seven strategies are discussed, viz., element doping, construction of Z-scheme (or S-scheme) systems, supported co-catalysts, visible photosensitization, construction of semiconductor heterojunctions, supported carbon materials, and construction of nanostructures. All of these strategies, except element doping and visible photosensitization, concentrate on enhancing the separation of holes and electrons, while suppressing their recombination, thus improving the photocatalytic performance of the nanostructured SiC photocatalysts. Regarding the element doping and visible photosensitization strategies, element doping can narrow the bandgap of SiC, which generates more holes and electrons to improve photocatalytic activity. On the other hand, the principle of visible photosensitization is that photo-induced electrons move from photosensitizers to the conduction band of SiC to participate in the reaction, thus enhancing the photocatalytic performance. In the section on the applications of nanostructured SiC, photocatalytic H₂ production, pollutant degradation, CO₂ reduction, photoelectrocatalytic, and electrocatalytic applications will be discussed. The mechanism of a photocatalytic reaction requires the SiC photocatalyst to produce photo-induced electrons and holes during irradiation, which participate in the photocatalytic reaction. For example, photo-induced electrons can transform protons into H₂, as well as CO₂ into methane, methanol, or formic acid. Furthermore, photo-induced holes can convert organic waste into H₂O and CO₂. For photoelectrocatalytic and electrocatalytic applications, SiC is used as a catalyst under high temperatures and highly acidic or basic environments because of its remarkable physicochemical characteristics, including low thermal expansion, superior thermal conductivity, and high oxidation and corrosion resistance. The last section of the review will reveal the major obstacles impeding the industrial application of nanostructured SiC photocatalysts, such as insufficient visible absorption, slow reaction kinetics, and hard fabrication, as well as provide some ideas on how to overcome these obstacles.      

钾改性氧化铝基羰基硫水解催化剂及其失活机理

天然气、油田伴生气、高炉煤气等化工生产过程中伴生COS气体,不仅会腐蚀管道和毒害催化剂,还会严重污染环境并危害人类健康。COS催化水解反应可在温和条件下高效的将COS脱除,是最具应用前景的COS脱除技术之一。碱金属元素因其具有独特的电子供体性质、表面碱性和静电吸附等特性,常被用作助催化剂以提高Al₂O₃的COS催化水解性能。近年来,以钾为助剂改性的Al₂O₃催化剂(K₂CO₃/Al₂O₃)在COS催化水解反应中得到广泛的应用,但由于负载在Al₂O₃上的K物种的组成复杂,目前研究者对K₂CO₃/Al₂O₃催化剂上COS水解机理的理解仍存在一定的困惑和争议。本论文通过湿法浸渍法合成出一系列钾盐和钠盐改性的Al₂O₃催化剂,并利用各类先进的表征技术对这些催化剂进行分析。活性测试表明,以K₂CO₃、K₂C₂O₄、NaHCO₃、Na₂CO₃和NaC₂O₄改性Al₂O₃催化剂均有助于COS的水解。其中K₂CO₃/Al₂O₃拥有最佳的COS水解性能,连续运行20 h后其COS转化率仍高于~93%,远远优于未改性的Al₂O₃ (~58%)。我们利用原位红外光谱和X射线光电子能谱探明了反应过程中催化剂的化学结构特征,阐明了H₂O分子在K₂CO₃/Al₂O₃上的水解作用机制。原位红外表明COS在K₂CO₃/Al₂O₃上的水解过程中形成了硫代碳酸氢盐中间产物。X射线光电子能谱表征证明催化剂的失活主要是因为催化剂表面积累了硫酸盐和单质硫。此外,我们还研究了水蒸气含量对COS水解性能的影响,研究发现,由于H₂O和COS分子在催化剂表面存在竞争吸附,过量的H₂O会引起催化活性的下降。上述研究表明,K₂CO₃/Al₂O₃催化剂上COS水解性能的提高主要是形成了HO-Al-O-K界面活性位。更为重要的是,所制备的催化剂都是在模拟工业工况条件下进行的,这为后续的工业应用提供了宝贵理论指导。本工作为理解助剂钾在Al₂O₃催化剂上COS水解活性的增强提供了新的见解,这为未来设计稳定高效的COS水解催化剂打开了新的发展方向。     

Overall Carbon-neutral Electrochemical Reduction of CO2 in Molten Salts using a Liquid Metal Sn Cathode

An overall carbon-neutral CO₂ electroreduction requires enhanced conversion efficiency and intensified functionality of CO₂-derived products to balance the carbon footprint from CO₂ electroreduction against fixed CO₂. A liquid Sn cathode is herein introduced into electrochemical reduction of CO₂ in molten salts to fabricate core–shell Sn−C spheres (Sn@C). An in situ generated Li₂SnO₃/C directs a self-template formation of Sn@C. Benefitting from the accelerated reaction kinetics from the liquid Sn cathode and the core–shell structure of Sn@C, a CO₂-fixation current efficiency higher than 84 % and a high reversible lithium-storage capacity of Sn@C are achieved. The versatility of this strategy is demonstrated by other low melting point metals, such as Zn and Bi. This process integrates energy-efficient CO₂ conversion and template-free fabrication of value-added metal-carbon, achieving an overall carbon-neutral electrochemical reduction of CO₂.     

Understanding Synergistic Catalysis on Cu-Se Dual Atom Sites via Operando X-ray Absorption Spectroscopy in Oxygen Reduction Reaction

The construction and understanding of synergy in well-defined dual-atom active sites is an available avenue to promote multistep tandem catalytic reactions. Herein, we construct a dual-hetero-atom catalyst that comprises adjacent Cu-N₄ and Se-C₃ active sites for efficient oxygen reduction reaction (ORR) activity. Operando X-ray absorption spectroscopy coupled with theoretical calculations provide in-depth insights into this dual-atom synergy mechanism for ORR under realistic device operation conditions. The heteroatom Se modulator can efficiently polarize the charge distribution around symmetrical Cu-N₄ moieties, and serve as synergistic site to facilitate the second oxygen reduction step simultaneously, in which the key OOH*-(Cu₁-N₄) transforms to O*-(Se₁-C₂) intermediate on the dual-atom sites. Therefore, this designed catalyst achieves satisfied alkaline ORR activity with a half-wave potential of 0.905 V vs. RHE and a maximum power density of 206.5 mW cm⁻² in Zn-air battery.