Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. ...However, real designs of materials systems involve both qualitative and quantitative design variables representing material compositions, microstructure morphology, and processing conditions. For mixed-variable problems, existing Bayesian Optimization (BO) approaches represent qualitative factors by dummy variables first and then fit a standard Gaussian process (GP) model with numerical variables as the surrogate model. This approach is restrictive theoretically and fails to capture complex correlations between qualitative levels. We present in this paper the integration of a novel latent-variable (LV) approach for mixed-variable GP modeling with the BO framework for materials design. LVGP is a fundamentally different approach that maps qualitative design variables to underlying numerical LV in GP, which has strong physical justification. It provides flexible parameterization and representation of qualitative factors and shows superior modeling accuracy compared to the existing methods. We demonstrate our approach through testing with numerical examples and materials design examples. The chosen materials design examples represent two different scenarios, one on concurrent materials selection and microstructure optimization for optimizing the light absorption of a quasi-random solar cell, and another on combinatorial search of material constitutes for optimal Hybrid Organic-Inorganic Perovskite (HOIP) design. It is found that in all test examples the mapped LVs provide intuitive visualization and substantial insight into the nature and effects of the qualitative factors. Though materials designs are used as examples, the method presented is generic and can be utilized for other mixed variable design optimization problems that involve expensive physics-based simulations.
This article examines the nature of urbanism in British concessions in China from the mid-nineteenth century to the present by investigating the evolution of Tianjin's Victoria Park, the centre of ...the largest British concession in China. While existing works often subsume the urban development of concessions under the hegemony of British arrivals, this research explores how residents of the British concession and locals in Tianjin interacted to frame their urbanism, contributing to our understanding of the urban formation of the British concession in China and beyond. It reveals that initially Victoria Park was primarily a place of entertainment, and then, through interaction between the local Chinese and British residents, evolved firstly into the very symbol of British pride in the concession; then into a neglected pocket of parkland - representing the dark side of British settlers; and finally, into an important part of the precious heritage of the city.
Piezoelectric materials convert mechanical energy into electric energy, and
vice versa
. They have been applied in many critical fields, such as motor vehicles, medical devices, the military and ...aerospace. Recently, the development of lead-free piezoelectrics due to environmental concerns has attracted enormous attention in both scientific and industrial fields. This review summarises the effect of chemical modification on the enhancement of potassium sodium niobate lead-free piezoelectric materials. The origin of the high piezoelectricity is attributed to the construction of phase coexistence and local heterogeneities. The choice of dopants is discussed from the aspects of ionic radius, valency, electronegativity and hybridisation, as well as their influence on thermal stability and fatigue behaviour. An assessment of heterogeneity at different length scales on the piezoelectric performance is provided.
Heterogeneity at different length scales.
Well-preserved ostracods are documented for the first time from the upper Shihtzupu Formation (late Darriwilian to early Sandbian) of northern Guizhou Province, SW China. A total of 10 species are ...identified, including Pilla reedi (Wolfart,
2001a
), Laccochilina (Prochilina) yanpingensis sp. nov., Ulrichia chekiangensis? Hou, 1956, Euprimitia compta Sun,
1987
, Aechmina sp., Quadritia sp., Longiscula? sp. 1, Longiscula? sp. 2, Longiscula? sp. 3 and Silenis? sp., with the first two most common. Such an ostracod assemblage shows some similarities to ostracods documented from the Miaopo Formation of the same age in the Yichang area, Hubei Province, southern China, as well as to contemporaneous faunas found in Baltica, Australia, India, Sibumasu and the Argentine Precordillera. A global review of the temporal and spatial distribution of the biogeographically significant genus Pilla suggests that it might have originated in low-latitude areas and then migrated to other peri-Gondwana terranes like South China during the Ordovician.
Yichi Zhang
zhangyc@nigpas.ac.cn
, School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, PR China; State Key Laboratory of Palaeobiology and Stratigraphy, Center for Excellence in Life and Palaeoenvironment, Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences, Nanjing 210008, PR China.
The three types of oil reservoirs in water drive have entered the later stage of ultra-high water cut development 1 after three times of well pattern infilling adjustment and years of oil ...stabilization and water control. With the gradual transfer of high quality reserves to chemical flooding, the development objects of water flooding are mainly three types of oil reservoirs, and the exploitation objects are getting worse and worse, and the difficulty of controlling decline, exploiting potential and benefit development is increasing year by year. To this end, the blocks that have been tested for water control and efficiency improvement are selected to conduct precise potential-tapping tests, to carry out technological breakthroughs of “water control and efficiency improvement”, to explore and form a series of high-efficiency development technologies of water flooding, and to guide the deep potential-tapping in the ultra-high water-cut stage of water flooding 2. This paper mainly focuses on the comprehensive utilization of injection and production well pattern, as well as the principle and effect of injection well comprehensive adjustment program and tracking adjustment program.
Classically activated M1 macrophages and alternatively activated M2 macrophages are two polarized subsets of macrophages at the extreme ends of a constructed continuum. In the field of cancer ...research, M2 macrophage reprogramming is defined as the repolarization of pro-tumoral M2 to anti-tumoral M1 macrophages. It is known that colony-stimulating factor 1 (CSF1)/CSF1 receptor (CSF1R) and CSF2/CSF2R signaling play important roles in macrophage polarization. Targeting CSF1/CSF1R for M2 macrophage reprogramming has been widely performed in clinical trials for cancer therapy. Other targets for M2 macrophage reprogramming include Toll-like receptor 7 (TLR7), TLR8, TLR9, CD40, histone deacetylase (HDAC), and PI3Kγ. Although macrophages are involved in innate and adaptive immune responses, M1 macrophages are less effective at phagocytosis and antigen presenting, which are required properties for the activation of T cells and eradication of cancer cells. Similar to T and dendritic cells, the “functionally exhausted” status might be attributed to the high expression of programmed death-ligand 1 (PD-L1) or programmed cell death protein 1 (PD-1). PD-L1 is expressed on both M1 and M2 macrophages. Macrophage reprogramming from M2 to M1 might increase the expression of PD-L1, which can be transcriptionally activated by STAT3. Macrophage reprogramming or PD-L1/PD-1 blockade alone is less effective in the treatment of most cancers. Since PD-L1/PD-1 blockade could make up for the defect in macrophage reprogramming, the combination of macrophage reprogramming and PD-L1/PD-1 blockade might be a novel treatment strategy for cancer therapy.
•Spatiotemporal variation in vegetation on the Loess Plateau was investigated.•Precipitation was the greatest driving factor.•There was a bivariate or nonlinear enhancement between all factors.•Key ...factors' adaptation range or type suitable for vegetation cover was identified.
Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVIymax) and growing season mean NDVI (NDVIgsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVIymax and NDVIgsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVIymax and NDVIgsmean. However, precipitation was the most critical factor both on NDVIymax and NDVIgsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies.
By exploiting the vulnerabilities in cyber components, an attacker could intrude in the wind farm supervisory control and data acquisition (SCADA) system and energy management system (EMS), and ...maliciously trip one or multiple wind turbines. The reliability of the overall power system could thus be impacted by the performance of wind farms. In this paper, cyber attack scenarios concerning cyber components or networks are considered in the integrated wind farm SCADA/EMS system architecture. Two Bayesian attack graph models are adopted to represent the procedures of successful cyber attacks, and a mean time-to-compromise model is used by considering different attack levels and various vulnerabilities. Frequencies of successful cyber attacks on the wind farm SCADA/EMS system are estimated. A procedure for evaluating the power system reliability is proposed by considering wind turbine trips caused by various cyber attacks. Simulations are conducted based on a typical IEEE reliability test system. Simulation results indicate that the overall system reliability decreases when the frequency of successful attacks on the wind farm SCADA/EMS system and skill levels of attackers increase.
Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we ...address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials.
Building sensible processing-structure-property (PSP) links to gain fundamental insights and understanding of materials behavior has been the focus of many works in computational materials science. ...Microstructure characterization and reconstruction (MCR), coupled with machine learning techniques and materials modeling and simulation, is an important component of discovering PSP relations and inverse material design in the era of high-throughput computational materials science. In this article, we provide a comprehensive review of representative approaches for MCR and elaborate on their algorithmic details, computational costs, and how they fit into the PSP mapping problems. Multiple categories of MCR methods relying on statistical functions (such as n-point correlation functions), physical descriptors, spectral density function, texture synthesis, and supervised/unsupervised learning are reviewed. As no MCR method is applicable to the analysis and (inverse) design of all material systems, our goal is to provide the scientific community with a close examination of the state-of-the-art techniques for MCR, as well as useful guidance on which MCR method to choose and how to systematically apply it to a problem at hand. We illustrate applications of MCR on materials modeling and building structure-property relations via two examples: One on learning the materials law of a class of composite microstructures, and the second on relating the permittivity and dielectric loss to a structural parameter in nanodielectrics.
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