Application of artificial neural network optimization for resilient ceramic parts fabricated by direct ink writing

Geometries of ceramic parts for high-temperature sealing have great influence on their compression-resilience behaviors. In this work, an accurate and large-scale artificial neural network (ANN) was established to match the relationship between structural parameters and mechanical properties of ZrO₂ parts fabricated by 3D printing. Four geometry parameters of the designed ZrO₂ parts were imported as input and apparent Young's modulus and maximum deformation simulated by finite element method (FEM) were imported as output. FEM calculation provided 400 groups of data for the training of ANN, which greatly improved the predicted accuracy of the network. The predicted results show the mechanical performance of the parts with a range of modulus from 9.24 × 10⁻³ GPa to 100.35 × 10⁻³ GPa and a range of maximum deformation from 2.32% to 5.80% can be forecasted with error less than 8%. Based on the optimized structural parameters, the designed ZrO₂ parts were fabricated by Direct Ink Writing (DIW) technique. The experimental compression-rebound property is comparable to that of ANN prediction. It demonstrates that the combined method of ANN and FEM is a preferable way to optimize the structure and guide the fabrication of complex ceramic parts by 3D printing method.     

智能化救援监控系统在煤矿安全生产中的应用

针对煤矿发生事故后传统救援监控系统无法实时对井下人员进行动态定位，导致矿井救援盲目性大、救援效率差、救援难度大等技术难题，为了进一步提高煤矿救援效率，通过技术研究，设计了一套以通信基站为核心的智能化救援监控系统，分析了该系统结构组成、工作原理，通过在担水沟煤矿井下实际应用效果来看，智能化救援监控系统对人员定位精准度达95%，实现人员动态位置三维成像，救援效率提高至80%以上，有效缩短了煤矿事故救援时间，取得了显著应用成效。     

Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

Simplifying the powder metallurgy manufacturing process using soft computing tools

The tools of soft computing will aid the knowledge mining in predicting and classifying the properties of various parameters while designing the composite preforms in the manufacturing of Powder Metallurgy (P/M) Lab. In this paper, an integrated PRNET (PCA-Radial basis functional neural NET) model is proposed in different versions to select the relevant parameters for preparing composite preforms and to predict the deformation and strain hardening properties of Al–Fe composites. It reveals that the predictability of this model has been increased by 67.89% relatively from the conventional models. A new PR-filter is proposed by slightly modifying the conventional filters of RBFNN, which improves the power of PRNET even though raw data are highly non-linear, interrelated and noisy. Moreover, fixing the range of input parameters for classifying the properties of composite preforms can be automated by the Fuzzy logic. These types of models will avoid expensive experimentation and risky environment while preparing sintered composite preforms. Thus the manufacturing process of composites in P/M Lab will be simplified with minimum energy by the support of these soft-computing tools.     

Excited States and Photodebromination of Selected Polybrominated Diphenyl Ethers: Computational and Quantitative Structure—Property Relationship Studies

This paper presents a density functional theory (DFT)/time-dependent DFT (TD-DFT) study on the lowest lying singlet and triplet excited states of 20 selected polybrominateddiphenyl ether (PBDE) congeners, with the solvation effect included in the calculations using the polarized continuum model (PCM). The results obtained showed that for most of the brominated diphenyl ether (BDE) congeners, the lowest singlet excited state was initiated by the electron transfer from HOMO to LUMO, involving a π–σ* excitation. In triplet excited states, structure of the BDE congeners differed notably from that of the BDE ground states with one of the specific C–Br bonds bending off the aromatic plane. In addition, the partial least squares regression (PLSR), principal component analysis-multiple linear regression analysis (PCA-MLR), and back propagation artificial neural network (BP-ANN) approaches were employed for a quantitative structure-property relationship (QSPR) study. Based on the previously reported kinetic data for the debromination by ultraviolet (UV) and sunlight, obtained QSPR models exhibited a reasonable evaluation of the photodebromination reactivity even when the BDE congeners had same degree of bromination, albeit different patterns of bromination.     

适应于自动驾驶的计算结构与平台综述

信息与通信技术领域的不断发展,推动了自动驾驶汽车的巨大进步。自动驾驶汽车有效解决了用户对出行日益提高的安全、便捷、舒适、高效等个性化需求,有着巨大的商业前景和应用价值。首先从自动驾驶的定义出发,对自动驾驶汽车计算任务进行了分类总结,阐述了自动驾驶汽车集移动端、边缘节点和云端一体的计算结构。然后分类讨论了目前自动驾驶汽车移动端计算平台,呈现出异构多核的特点,并对当前主流的底层计算平台进行了对比分析。最后,从性能与成本和信息安全两个方面总结目前自动驾驶汽车计算结构和平台遇到的问题与挑战。     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

Masking the Peroxidase-Like Activity of the Molybdenum Disulfide Nanozyme Enables Label-Free Lipase Detection

Two-dimensional MoS₂ nanoparticles (2D-nps) exhibit artificial enzyme properties that can be regulated at bio-nanointerfaces. We discovered that protein lipase is able to tune the peroxidase-like activity of MoS₂ 2D-nps, offering low-nanomolar, label-free detection and identification in samples with unknown identity. The inhibition of the peroxidase-like activity of the MoS₂ 2D-nps was demonstrated to be concentration dependent, and as low as 5 nm lipase was detected with this approach. The results were compared with those obtained with several other proteins that did not display any significant interference with the nanozyme behavior of the MoS₂ 2D-nps. This unique response of lipase was characterized and exploited for the successful identification of lipase in six unknown samples by using qualitative visual inspection and a quantitative statistical analysis method. The developed methodology in this approach is noteworthy for many aspects; MoS₂ 2D-nps are neither labeled with a signaling moiety nor modified with any ligands for signal readout. Only the intrinsic nanozyme activity of the MoS₂ 2D-nps is exploited for this detection approach. No analytical equipment is necessary for the visual detection of lipase. The synthesis of the water-soluble MoS₂ 2D-nps is low costing and can be performed in bulk scale. Exploring the properties of 2D-nps and their interactions with biological materials reveals highly interesting yet instrumental features that offer the development of novel bioanalytical approaches.     

“人工智能+”背景下的数据安全与隐私保护探析

随着大数据和云计算的技术的深入应用，人工智能时代的机器学习和深度学习更需要日益增长的数据，因此数据安全与隐私保护变得更加迫切。本文介绍人工智能的定义以及特征，探究数据安全和隐私保护现状，分析数据安全和隐私保护面临的诸多问题，并提出在人工智能时代对数据安全和隐私保护的措施。     

Observations of Membrane Domain Reorganization in Mechanically Compressed Artificial Cells

Giant unilamellar vesicles (GUVs) are considered to be the gold standard for assembling artificial cells from the bottom up. In this study, we investigated the behavior of such biomimetic vesicles as they were subjected to mechanical compression. A microfluidic device is presented that comprises a trap to capture GUVs and a microstamp that is deflected downwards to mechanically compress the trapped vesicle. After characterization of the device, we show that single-phase GUVs can be controllably compressed to a high degree of deformation (D=0.40) depending on the pressure applied to the microstamp. A permeation assay was implemented to show that vesicle bursting is prevented by water efflux. Next, we mechanically compressed GUVs with co-existing liquid-ordered and liquid-disordered membrane phases. Upon compression, we observed that the normally stable lipid domains reorganized themselves across the surface and fused into larger domains. This phenomenon, observed here in a model membrane system, not only gives us insights into how the multicomponent membranes of artificial cells behave, but might also have interesting consequences for the role of lipid rafts in biological cells that are subjected to compressive forces in a natural environment.