A Cellular Automata Based Rainfall-Runoff Model for Urban Inundation Analysis Under Different Land Uses

Recently many runoff models based on cellular automaton (CA) have been developed to simulate floods; however, the existing models cannot be readily applied to complex urban environments. This study proposes a novel rainfall-runoff model based on CA (RRCA) to simulate inundation. Its main contributions include a fine runoff generation process that considers 12 urban scenarios rather than a single land use type and the confluence process determined by the new transition rules considering water supply and demand (WS-WD transition rules). RRCA was compared with another CA based flood model (E2DCA). With the benchmark model, the results showed that there was good agreement, with an R-squared greater than 0.9, and that RRCA was more sensitive to waterlogging levels than E2DCA. Furthermore, the simulated vegetation interception, infiltration and drainage processes had varying degrees of impact on waterlogging. Corresponding measures can be taken in urban flood management according to the identification of areas experiencing drainage difficulties.

     

Hot corrosion ofα-SiC ceramics by V2O5 melt

The corrosion behaviour of-SiC in V₂O₅ melt has been investigated at elevated temperatures. The corrosion products on the surface of the specimen are removed using HF. The morphologies are also examined. From the observations of bubble formation in the scale and the temperature dependence of the corrosion rate, a kinetic mechanism is proposed. Based on the consistency of the plotted data with the proposed equation and high values of surface reaction rate constant, a diffusion controlling process has been developed.     

肠道杆菌的扫描电镜观察

我们用扫描电镜观察肠道杆菌种(Enterobacteriaceae)中的四种细菌,大肠埃希氏杆菌(Escherichiacoli),普通变形杆菌(Proteus vulgaris),伤寒沙门氏菌(Salmonella typhi)和福氏志贺氏菌(Shigella Flexneri)。未处理的菌落标本,菌落表面都可形成一层厚薄不同的表膜(Surface film)。四种细菌菌落的表膜,形状不一,千姿百态。适当处理后的菌落标本,则可显示菌体的本来面目。四种细菌菌体在菌落表面的分布和排列,也是各不相同,千差万别。本文根据扫描电镜的观察,对四种不同菌落进行了讨论。     

Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models

Engineering with Computers - The indirect and accurate determination of blast-induced rock movement has important significance in the reduction of ore loss and dilution and in the protection of...     

Maximizing learning without sacrificing the fun: Stealth assessment,adaptivity and learning supports in educational games

In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game-level delivery methods and various in-game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth- to eleventh-grade students into four groups: adaptive, linear, free choice and no-treatment control. Each condition had access to the same in-game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game-level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in-game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions.     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Protect privacy of deep classification networks by exploiting their generative power

Machine Learning - Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a...     

OWL2Vec*: embedding of OWL ontologies

Machine Learning - Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing...     

A traffic prediction model based on multiple factors

The Journal of Supercomputing - Traffic prediction is a vital part of intelligent transportation systems. The ability of traffic risk prediction is of great significance to prevent traffic...