Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations

Predictive value of PIMREG in the prognosis and response to immune checkpoint blockade of glioma patients

Glioma is the most common primary brain tumor in the human brain. The present study was designed to explore the expression of PIMREG in glioma and its relevance to the clinicopathological features and prognosis of glioma patients. The correlations of PIMREG with the infiltrating levels of immune cells and its relevance to the response to immunotherapy were also investigated. PIMREG expression in glioma was analyzed based on the GEO, TCGA, and HPA databases. Kaplan–Meier survival analysis was used to examine the predictive value of PIMREG for the prognosis of patients with glioma. The correlation between the infiltrating levels of immune cells in glioma and PIMREG was analyzed using the CIBERSORT algorithm and TIMRE database. The correlation between PIMREG and immune checkpoints and its correlation with the patients’ responses to immunotherapy were analyzed using R software and the GEPIA dataset. Cell experiments were conducted to verify the action of PIMREG in glioma cell migration and invasion. We found that PIMREG expression was upregulated in gliomas and positively associated with WHO grade. High PIMREG expression was correlated with poor prognosis of LGG, prognosis of all WHO grade gliomas, and prognosis of recurrent gliomas. PIMREG was related to the infiltration of several immune cell types, such as M1 and M2 macrophages, monocytes and CD8+ T cells. Moreover, PIMREG was correlated with immune checkpoints in glioma and correlated with patients’ responses to immunotherapy. KEGG pathway enrichment and GO functional analysis illustrated that PIMREG was related to multiple tumor- and immune-related pathways. In conclusion, PIMREG overexpression in gliomas is associated with poor prognosis of patients with glioma and is related to immune cell infiltrates and the responses to immunotherapy

Functional micro/nanoreactors for nanospace-confined migrations and diffusions

Diffusion is a natural phenomenon and essential in many fields. Conceptually, diffusion in solid materials, especially in the confined micro/nano-spaces, can boost the development of new functional nanomaterials for applications across a range of areas. In this Minireview, we categorize the diffusion behavior at the nanoscale including atom/ion diffusion and direct migration of nanoparticles, and discuss how these two strategies can be applied for the construction of nanoreactors with unique structures and components. The understanding of diffusion principles in a confined system is summarized in detail. Moreover, the development opportunities of diffusion process in future research are outlined

Analysis of surface-loaded problem of nonhomogeneous elastic half-plane with surface tension

Based on the surface elasticity theory, we investigate the effect of surface tension on miniaturized contact problems of an elastic graded half-plane under plane strain and axisymmetric situations. The Fourier sine and cosine transform method is used to derive the solution for the problem under uniform pressure. The results show some interesting features in the distribution of surface elastic fields, which are different from their homogeneous counterparts. The surface Green’s functions of the nonhomogeneous half-plane are finally obtained which are verified using finite element method. The outcome of this study provides us not only as a potential method for precise nanoindentation-based analysis of elastic graded materials, but also as the investigations of surface effect-induced failure in nanomaterials and nano-devices

Transfer learning to decode brain states reflecting the relationship between cognitive tasks

Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data

An improved sierpinski fractal based network architecture for edge computing datacenters

Edge computing (EC) aims to place partial processing resources at the edge datacenters (EDCs) for terminal devices to improve the delivery of content and applications to end users. Compared with traditional centralized cloud datacenters (CDC), the EDCs are distributed on the edge of the network that closer to terminal devices in geographical location for reducing the delay of data transmission between cloud and terminals, and enhancing the quality of services for the time sensitive applications. Currently, the edge datacenter networks (EDCNs) use the tree-hierarchical architecture which inherits the problems of limited bandwidth capacity and lower server utilization. This requires a new design of scalable and inexpensive EDCN infrastructure which enables high-speed interconnection for exponentially increasing number of terminal devices and provides fault-tolerant and high network capacity. In this paper, we propose a novel architecture call Sierpinski Triangle Based (STB) for EDCN which uses Sierpinski fractal to mitigate throughput bottleneck in aggregate layers as accumulated in tree hierarchical architecture. The results of the experiment show that the STB architecture has higher throughput than both traditional tree-hierarchical and DCell architectures from the scale of 12 to 363 servers without link failure happens.</p

Preparing Co/N-Doped Carbon as Electrocatalyst toward Oxygen Reduction Reaction via the Ancient “Pharaoh’s Snakes” Reaction

The oxygen reduction reaction (ORR) is of great importance for clean energy storage and conversion techniques such as fuel cells and metal–air batteries (MABs). However, the ORR is kinetically sluggish, and expensive noble metal catalysts are required. The high price and limited preservation of noble metal catalysts has largely hindered the wide application of clean power sources such as fuel cells and MABs. Therefore, it is important to prepare non-expensive metal catalysts (NPMC) to cut the price of the fuel cells and MABs for wide application. Here, we report the preparation of a Co3O4 carried on the N-doped carbon (Co/N-C) as the ORR NPMC with a facile Pharaoh’s Snakes reaction. The gas generated during the reaction is able to fabricate the porous structure of the resultant carbon doped with heteroatoms such as Co and N. The catalyst provides a high electrocatalytic activity towards ORR via the 4-e pathway with an onset and half-wave potential of 0.98 and 0.79 V (vs. RHE), respectively, in an electrolyte of 0.1 M KOH. The onset and half-wave potentials are close to those of the commercial Pt/C. This work demonstrates the promising potential of an ancient technology for preparing NPMCs toward the ORR

Preparing Co/N-Doped Carbon as Electrocatalyst toward Oxygen Reduction Reaction via the Ancient &ldquo;Pharaoh&rsquo;s Snakes&rdquo; Reaction

The oxygen reduction reaction (ORR) is of great importance for clean energy storage and conversion techniques such as fuel cells and metal&ndash;air batteries (MABs). However, the ORR is kinetically sluggish, and expensive noble metal catalysts are required. The high price and limited preservation of noble metal catalysts has largely hindered the wide application of clean power sources such as fuel cells and MABs. Therefore, it is important to prepare non-expensive metal catalysts (NPMC) to cut the price of the fuel cells and MABs for wide application. Here, we report the preparation of a Co3O4 carried on the N-doped carbon (Co/N-C) as the ORR NPMC with a facile Pharaoh&rsquo;s Snakes reaction. The gas generated during the reaction is able to fabricate the porous structure of the resultant carbon doped with heteroatoms such as Co and N. The catalyst provides a high electrocatalytic activity towards ORR via the 4-e pathway with an onset and half-wave potential of 0.98 and 0.79 V (vs. RHE), respectively, in an electrolyte of 0.1 M KOH. The onset and half-wave potentials are close to those of the commercial Pt/C. This work demonstrates the promising potential of an ancient technology for preparing NPMCs toward the ORR

Lightning Protection Performance Assessment of Transmission Line Based on ATP model Automatic Generation

This paper presents a novel method to solve the initial lightning breakdown current by combing ATP and MATLAB simulation software effectively, with the aims to evaluate the lightning protection performance of transmission line. Firstly, the executable ATP simulation model is generated automatically according to the required information such as power source parameters, tower parameters, overhead line parameters, grounding resistance and lightning current parameters, etc. through an interface program coded by MATLAB. Then, the data are extracted from the generated LIS files which can be obtained by executing the ATP simulation model, the occurrence of transmission lie breakdown can be determined by the relative data in LIS file. The lightning current amplitude should be reduced when the breakdown occurs, and vice the verse. Thus the initial lightning breakdown current of a transmission line with given parameters can be determined accurately by continuously changing the lightning current amplitude, which is realized by a loop computing algorithm that is coded by MATLAB software. The method proposed in this paper can generate the ATP simulation program automatically, and facilitates the lightning protection performance assessment of transmission line

Lightning Protection Performance Assessment of Transmission Line Based on ATP model Automatic Generation

This paper presents a novel method to solve the initial lightning breakdown current by combing ATP and MATLAB simulation software effectively, with the aims to evaluate the lightning protection performance of transmission line. Firstly, the executable ATP simulation model is generated automatically according to the required information such as power source parameters, tower parameters, overhead line parameters, grounding resistance and lightning current parameters, etc. through an interface program coded by MATLAB. Then, the data are extracted from the generated LIS files which can be obtained by executing the ATP simulation model, the occurrence of transmission lie breakdown can be determined by the relative data in LIS file. The lightning current amplitude should be reduced when the breakdown occurs, and vice the verse. Thus the initial lightning breakdown current of a transmission line with given parameters can be determined accurately by continuously changing the lightning current amplitude, which is realized by a loop computing algorithm that is coded by MATLAB software. The method proposed in this paper can generate the ATP simulation program automatically, and facilitates the lightning protection performance assessment of transmission line