Neural networks based on memristive devices [1][2][3] have shown potential in substantially improving throughput and energy efficiency for machine learning [4] and artificial intelligence [5], especially in edge applications. [6][7][8][9][10][11][12][13][14][15][16][17][18][19] Because training a neural network model from scratch is very costly, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications (Figure 1a). Some posttuning in memristor conductance to adapt local situations may follow afterward or during applications. Therefore, a critical requirement on memristors for neural network applications is a high-precision programming ability to guarantee uniform and accurate performance across a massive number of memristive networks. [20][21][22][23][24][25][26] That translates into the requirement of many distinguishable conductance levels on each memristive device, not just lab-made devices but more importantly, devices fabricated in foundries. High precision memristors also benefit other neural network applications, such as training and scientific computing. [23,27] Here we report over 2048 conductance levels, the largest number among all types of memories ever reported, achieved with memristors in fully integrated chips with 256 ´ 256 memristor arrays monolithically integrated on CMOS circuits in a standard foundry. We have unearthed the underlying physics that previously limited the number of achievable conductance levels in memristors and developed electrical operation protocols to circumvent such limitations. These results reveal insights into the fundamental understanding of the microscopic picture of memristive switching and provide approaches to enable high-precision memristors for various applications.Memristive switching devices are known for their relatively large dynamical range of conductance, which can potentially lead to a large number of discrete conductance levels. However, the highest number reported to date has been no more than two hundred. [20]

in this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. this study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of floodwaters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristicsflood propagation rate (β), flood incubation rate (α), and recovery rate (µ)-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model is verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision-makers for flood forecast in road networks. Given the essential role transportation plays in emergency response, provision of essential services, and maintenance of economic well-being 1 , the resilience of urban road networks to natural hazards, especially flooding events, has received increasing attention 2, 3. Floodwaters in urban networks propagate over time and space, inducing a great deal of spatial-temporal uncertainty visa -vis protective actions, such as evacuation, and rapid emergency response 4. Developing effective prediction tools to forecast the characteristics of flooding events is critical to the enhancement of urban road network resilience 5. Multiple studies have explored the spatial-temporal properties of floods in urban networks, including impact evaluation of environmental stress 6-8 and cascading effects in road networks 9, 10. In particular, empirical studies adopting remote sensors 11 , hydraulic data 12 , or satellite images 13 have attempted to capture the properties of urban flooding. Temporal evolution of flood status is driven by the time-dependent profile of environmental stress, such as the duration of rainfall in hurricanes 12. This temporal information facilitates identification of the outbreak and inflection points for flooding in affected networks. Flooding also exhibits high spatial correlation 14 in which the co-located road segments are...

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While conceptual definitions have provided a foundation for measuring inequality of access and resilience in urban facilities, the challenge for researchers and practitioners alike has been to develop analytical support for urban system development that reduces inequality and improves resilience. Using 30 million large-scale anonymized smartphone-location data, here, we calibrate models to optimize the distribution of facilities and present insights into the interplay between equality and resilience in the development of urban facilities. Results from ten metropolitan counties in the United States reveal that inequality of access to facilities is due to the inconsistency between population and facility distributions, which can be reduced by minimizing total travel costs for urban populations. Resilience increases with more equitable facility distribution by increasing effective embeddedness ranging from 10% to 30% for different facilities and counties. The results imply that resilience and equality are related and should be considered jointly in urban system development.

Background Hepatocellular carcinoma (HCC) is one of the most common cancers in the digestive system with rapid progression and poor prognosis. Recent studies have shown that RPL27A could be used as a biomarker for a variety of cancers, but its role in HCC is not clear. Method We analyzed the expression of RPL27A in the pan-cancer analysis and analyzed the relationship between the expression of RPL27A and the clinical features and prognosis of patients with HCC. We evaluated the expression difference of RPL27A in HCC tissues and paired normal adjacent tissues using immunohistochemistry. Furthermore, we analyzed the co-expression genes of RPL27A and used them to explore the possible mechanism of RPL27A and screen hub genes effecting HCC. In addition, we studied the role of RPL27A in immune infiltration and mutation. Results We found that the expression level of RPL27A increased in a variety of cancers, including HCC. In HCC patients, the high expression of RPL27A was related to progression and poor prognosis as an independent predictor. We also constructed a protein interaction network through co-expression gene analysis of RPL27A and screened 9 hub genes. Enrichment analysis showed that co-expression genes were associated with ribosome pathway, viral replication, nuclear-transcribed mRNA catabolic process, and nonsense-mediated decay. We found that the expression level of RPL27A was closely related to TP53 mutation and immune infiltration in HCC. Conclusion RPL27A might become a biomarker in the diagnosis, treatment, and follow-up of patients with HCC.