Multifunctional artificial nacre via biomimetic matrix-directed mineralization

Natural nacre, one of the most studied biological structural materials with delicate hierarchical structures and extraordinary performance, has inspired the design and fabrication of artificial structural ceramics with high fracture toughness. However, to meet the diverse requirements of different applications, future structural materials must be multifunctional with superior mechanical properties, such as strength, hardness, and toughness. Herein, based on the matrix-directed mineralization method for producing biomimetic structural materials, we introduce nanoparticles with different inherent functions into the platelets of artificial nacre via the co-mineralization of aragonite and the nanoparticles. Besides their enhanced mechanical properties, the obtained artificial nacre materials also exhibit different functions depending on the type of the nanoparticles. To extend the versatility of this strategy, the effects of nanoparticles of different sizes and zeta potentials on mineralization are also analyzed. This universal strategy can be applied to the fabrication of other types of functionalized biomimetic structural ceramics that have potential applications in various fields, such as biomedical science.     

Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

Strategies Towards Capturing Nitrogenase Substrates and Intermediates via Controlled Alteration of Electron Fluxes

Nitrogenase utilizes an ATP-dependent reductase to deliver electrons to its catalytic component to enable two important reactions: the reduction of N₂ to NH₄⁺, and the reduction of CO to hydrocarbons. The two nitrogenase-based reactions parallel the industrial Haber–Bosch and Fischer–Tropsch processes, yet they occur under ambient conditions. As such, understanding the enzymatic mechanism of nitrogenase is crucial for the future development of biomimetic strategies for energy-efficient production of valuable chemical commodities. Mechanistic investigations of nitrogenase has long been hampered by the difficulty to trap substrates and intermediates relevant to the nitrogenase reactions. Recently, we have successfully captured CO on the Azotobacter vinelandii V-nitrogenase via two approaches that alter the electron fluxes in a controlled manner: one approach utilizes an artificial electron donor to trap CO on the catalytic component of V-nitrogenase in the resting state; whereas the other employs a mismatched reductase component to reduce the electron flux through the system and consequently accumulate CO on the catalytic component of V-nitrogenase. Here we summarize the major outcome of these recent studies, which not only clarified the catalytic relevance of the one-CO (lo-CO) and multi-CO (hi-CO) bound states of nitrogenase, but also pointed to a potential competition between N₂ and CO for binding to the same pair of reactive Fe sites across the sulfur belt of the cofactor. Together, these results highlight the utility of these strategies in poising the cofactor at a well-defined state for substrate- or intermediate-trapping via controlled alteration of electron fluxes, which could prove beneficial for further elucidation of the mechanistic details of nitrogenase-catalyzed reactions.     

Allostatic load as a complex clinical construct: A case‐based computational modeling approach

Allostatic load (AL) is a complex clinical construct, providing a unique window into the cumulative impact of stress. However, due to its inherent complexity, AL presents two major measurement challenges to conventional statistical modeling (the field's dominant methodology): it is comprised of a complex causal network of bioallostatic systems, represented by an even larger set of dynamic biomarkers; and, it is situated within a web of antecedent socioecological systems, linking AL to differences in health outcomes and disparities. To address these challenges, we employed case‐based computational modeling (CBM), which allowed us to make four advances: (1) we developed a multisystem, 7‐factor (20 biomarker) model of AL's network of allostatic systems; (2) used it to create a catalog of nine different clinical AL profiles (causal pathways); (3) linked each clinical profile to a typology of 23 health outcomes; and (4) explored our results (post hoc) as a function of gender, a key socioecological factor. In terms of highlights, (a) the Healthy clinical profile had few health risks; (b) the pro‐inflammatory profile linked to high blood pressure and diabetes; (c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems; and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%); while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles. © 2015 Wiley Periodicals, Inc. Complexity 21: 291–306, 2016     

海堤渗压神经网络分布模型的建立及规律分析

为有效揭示海堤渗压的分布特征,掌握其在临海工作环境下的特殊规律,在利用神经网络建模优点的同时,采用多测点渗压监控信息,并将测点坐标因素加入到输入层,综合前期潮位因子、积分型降雨因子、时效因子,形成海堤渗压神经网络安全监控分布模型结构,以实测信息进行建模训练计算;在获得合理训练结果基础上,根据输入层因子补充插入坐标样本,获得不同位置的渗压模型值及渗压分布曲线.文中以广义回归神经网络为例,结合浦东海堤实测资料,以实例说明以上述方法在神经网络不提供显式的情况下,建立可获得分布曲线的监控模型,并以此对海堤渗压分布规律特色加以分析.     

并联式混合动力发动机神经网络法转矩预测与闭环控制

利用实际发动机的标定数据搭建了GT-Suite及Matlab/Simulink联合仿真模型，建立了基于进气和发动机状态参数的预测转矩反馈协同控制模块。对比了ANN法和现有发动机的标定脉谱插值预测（MAP）法2种方法下发动机稳态及瞬态转矩变化、升降挡等工况预测的结果误差，结果表明：稳态工况下MAP法较为可靠，低、中、高3种发动机转速下转矩预测波动小，误差比ANN法低1.31%、1.09%和1.52%；实际瞬态转矩跃变及阶跃工况下，ANN法较MAP法误差低5.62%和1.32%，升降挡工况下低1.93%和0.84%。     

A new characteristic approach for incompressible thermo‐flow in Cartesian and non‐Cartesian grids

A virtual‐characteristic approach is developed for thermo‐flow with finite‐volume methodology in which a multidimensional characteristic (MC) scheme is applied along with artificial compressibility. To obtain compatibility equations and pseudo‐characteristics, energy equation is taken into account in the MC scheme. With this inherent upwinding of convective fluxes, no artificial viscosity is required even at high Reynolds numbers. Another remarkable advantage of the MC scheme lies in its faster convergence rate with respect to the averaging scheme that is found to exhibit substantial delays in convergence. As benchmarks, forced and mixed convections in a cavity and in flow over cylinder and between parallel plates are examined for a wide range of Reynolds, Grashof, and Prandtl numbers. The MC and averaging schemes are applied for simulation purposes. Results show the better performance of the MC scheme in forced and mixed convections. Results confirm the robustness of the MC scheme in terms of accuracy and convergence. Copyright © 2015 John Wiley & Sons, Ltd.     

Probing the Catalytically Active Species in POM-Catalysed DNA-Model Hydrolysis**

Phosphoester hydrolysis is an important chemical step in DNA repair. One archetypal molecular model of phosphoesters is para-nitrophenylphosphate (pNPP). It has been shown previously that the presence of molecular metal oxide [Mo₇O₂₄]⁶⁻ may catalyse the hydrolysis of pNPP through the partial decomposition of polyoxomolybdate framework resulting in a [(PO₄)₂Mo₅O₁₅]⁶⁻ product. Real-time monitoring of the catalytic system using electrospray ionisation mass spectrometry (ESI-MS) provided a glance into the species present in the reaction mixture and identification of potential catalytic candidates. Following up on the obtained spectrometric data, Density Functional Theory (DFT) calculations were carried out to characterise the hypothetical intermediate [Mo₅O₁₅(pNPP)₂(H₂O)₆]⁶⁻ that would be required to form under the hypothesised transformation. Surprisingly, our results point to the dimeric [Mo₂O₈]⁴⁻ anion resulting from the decomposition of [Mo₇O₂₄]⁶⁻ as the active catalytic species involved in the hydrolysis of pNPP rather than the originally assumed {Mo₅O₁₅} species. A similar study was carried out involving the same species but substituting Mo by W. The mechanism involving W species showed a higher barrier and less stable products in agreement with the non-catalytic effect found in experimental results.     

连铸坯质量判定模糊专家系统

鉴于连铸过程中存在众多不确定性,考虑某中厚板厂的实际生产状况与中厚板热送热装的控制要求,以表面纵裂为例,按照模糊专家系统的开发思路,建立起连铸坯纵裂知识库,开发推理机制.采用纵裂指数预测和神经网络预测相结合的方法对连铸坯质量进行离线判断、推理和决策,并给出相应的缺陷产生原因解释.采用VC++6.0软件建立人机界面,数据库采用Oracle,调试结果表明该系统可以达到较高的判定准确率(92.8%),为在线应用奠定了基础.