Space-variant optical logic operations based on operation-dependent encoding method

We propose performing space-variant optical logic operations in a space-invariant optical system by selectively assigning encoding states that are operation dependent. With this method, encoders using liquid-crystal cells and liquid-crystal light valves to perform space-variant encoding for all 16 Boolean functions are designed. Multiple-instruction-multiple-data processing can then be realized in optical logic systems.     

含泥砂河流水轮机增容改造的探讨─刘家峡2~＃水轮机增容改造

本文简要叙述了刘家峡电厂水轮机增容改造中的水力研究、结构设计、中间试验、材料选择以及制造工艺等方面内容,并探讨了减轻泥砂磨蚀的措施。该水轮机的增容改造经验对含泥砂河流水轮机的设计与改造是有益的。     

Stabilizing Redox-Active Hexaazatriphenylene in a 2D Conductive Metal–Organic Framework for Improved Lithium Storage Performance

Organic redox-active materials are promising electrode candidates for lithium-ion batteries by virtue of their designable structure and cost-effectiveness. However, their poor electrical conductivity and high solubility in organic electrolytes limit the device's performance and practical applications. Herein, the π-conjugated nitrogen-containing heteroaromatic molecule hexaazatriphenylene (HATN) is strategically embedded with redox-active centers in the skeleton of a Cu-based 2D conductive metal–organic framework (2D c-MOF) to optimize the lithium (Li) storage performance of organic electrodes, which delivers improved specific capacity (763 mAh g⁻¹ at 300 mA g⁻¹), long-term cycling stability (≈90% capacity retention after 600 cycles at 300 mA g⁻¹), and excellent rate performance. The correlation of experimental and computational results confirms that this high Li storage performance derives from the maximum number of active sites (CN sites in the HATN unit and CO sites in the CuO₄ unit), favorable electrical conductivity, and efficient mass transfer channels. This strategy of integrating multiple redox-active moieties into the 2D c-MOF opens up a new avenue for the design of high-performance electrode materials.     

Voltage Decay of Li-Rich Layered Oxides: Mechanism,Modification Strategies,and Perspectives

Li-rich layered oxides (LLOs) have been considered as the most promising cathode materials for achieving high energy density Li-ion batteries. However, they suffer from continuous voltage decay during cycling, which seriously shortens the lifespan of the battery in practical applications. This review comprehensively elaborates and summarizes the state-of-the-art of the research in this field. It is started from the proposed mechanism of voltage decay that refers to the phase transition, microscopic defects, and oxygen redox or release. Furthermore, several strategies to mitigate the voltage decay of LLOs from different scales, such as surface modification, elemental doping, regulation of components, control of defect, and morphology design are summarized. Finally, a systematic outlook on the real root of voltage decay is provided, and more importantly, a potential solution to voltage recovery from electrochemistry. Based on this progress, some effective strategies with multiple scales will be feasible to create the conditions for their commercialization in the future.     

ACTSSD: social spammer detection based on active learning and co-training

The rumors, advertisements and malicious links are spread in social networks by social spammers, which affect users’ normal access to social networks and cause security problems. Most methods aim to detect social spammers by various features, such as content features, behavior features and relationship graph features, which rely on a large-scale labeled data. However, labeled data are lacking for training in real world, and manual annotating is time-consuming and labor-intensive. To solve this problem, we propose a novel method which combines active learning algorithm with co-training algorithm to make full use of unlabeled data. In co-training, user features are divided into two views without overlap. Classifiers are trained iteratively with labeled instances and the most confident unlabeled instances with pseudo-labels. In active learning, the most representative and uncertain instances are selected and annotated with real labels to extend labeled dataset. Experimental results on the Twitter and Apontador datasets show that our method can effectively detect social spammers in the case of limited labeled data.

     

Greenhouse gases mitigation by CO<Subscript>2</Subscript> reforming of methane to hydrogen-rich syngas using praseodymium oxide supported cobalt catalyst

This study focuses on the potential of hydrogen-rich syngas production by CO₂ reforming of methane over Co/Pr₂O₃ catalyst. The Co/Pr₂O₃ catalyst was synthesized via wet-impregnation method and characterized for physicochemical properties by TGA, XRD, BET, H₂-TPR, FESEM, EDX, and FTIR. The CO₂ reforming of methane over the as-synthesized catalyst was studied in a tubular stainless steel fixed-bed reactor at feed ratio ranged 0.1–1.0, temperature ranged 923–1023 K, and gas hourly space velocity (GHSV) of 30,000 h^?1 under atmospheric pressure condition. The catalyst activity studies showed that the increase in the reaction temperature from 923 to 1023 K and feed ratio from 0.1 to 1.0 resulted in a corresponding increase in the reactant’s conversion and the product’s yields. At 1023 K and feed ratio of 1.0, the activity of the Co/Pr₂O₃ catalyst climaxed with CH₄ and CO₂ conversions of 41.49 and 42.36 %. Moreover, the catalyst activity at 1023 K and feed ratio of 1.0 resulted in the production of H₂ and CO yields of 40.7 and 40.90 %, respectively. The syngas produced was estimated to have H₂:CO ratio of 0.995, making it suitable as chemical building blocks for the production of oxygenated fuel and other value-added chemicals. The used Co/Pr₂O₃ catalyst which was characterized by TPO, XRD, and SEM-EDX show some evidence of carbon formation and deposition on its surface.     

数控机床操控器人机界面优化设计研究

目的控制器和显示器共同组成数控机床控制面板的整体界面,是机床接受与执行命令的交互平台。从人机交互角度,聚焦于控制器的界面布局进行优化设计。方法基于人机工程学理论的优化流程,采用现场访谈、现场观察等方法,将人机工程学的理论转化为具体考核指标。结论从人机工程学的研究角度出发,结合工业设计、用户体验设计学等内容,从功能区域分区、操纵器色彩设计等方面优化数控机床控制器的界面,提高系统整体效能及使用者满意度,为数控机床系统开发设计提供参考。     

Composite bending-dominated hollow nanolattices: A stiff,cyclable mechanical metamaterial

Manufacturing ultralight and mechanical reliable materials has been a long-time challenge. Ceramic-based mechanical metamaterials provide significant opportunities to reverse their brittle nature and unstable mechanical properties and have great potential as strong, ultralight, and ultrastiff materials. However, the failure of ceramics nanolattice and degradation of strength/modulus with decreasing density are caused by buckling of the struts and failure of the nodes within the nanolattices, especially during cyclic loading. Here, we explore a new class of 3D ceramic-based metamaterials with a high strength–density ratio, stiffness, recoverability, cyclability, and optimal scaling factor. Deformation mode of the fabricated nanolattices has been engineered through the unique material design and architecture tailoring. Bending-dominated hollow nanolattice (B-H-Lattice) structure is employed to take advantages of its flexibility, while a few nanometers of carbonized mussel-inspired bio-polymer (C-PDA) is coherently deposited on ceramics’ nanolayer to enable non-buckling struts and bendable nodes during deformation, resulting in reliable mechanical properties and outperforming the current bending-dominated lattices (B-Lattices) and carbon-based cellulose materials. Meanwhile, the structure has comparable stiffness to stretching-dominated lattices (S-Lattices) while with better cyclability and reliability. The B-H-Lattices exhibit high specific stiffness (>10⁶?Pa·kg^?1·m^?3), low-density (～30?kg/m³), buckling-free recovery at 55% strain, and stable cyclic loading behavior under up to 15% strain. As one of the B-Lattices, the modulus scaling factor reaches 1.27, which is lowest among current B-Lattices. This study suggests that non-buckling behavior and reliable nodes are the key factors that contribute to the outstanding mechanical performance of nanolattice materials. A new concept of engineering the internal deformation behavior of mechanical metamaterial is provided to optimize their mechanical properties in real service conditions.     

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress–strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes’ rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro–macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.