Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites

Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR) based composites is a typical and crucial property in practical applications. Previous studies show that the abrasion resistance can be calculated by the multiple linear regression model. In our study, considering this relationship can also be described into the non-linear conditions, a Multilayer Feed-forward Neural Networks model with 3 nodes (MLFN-3) was successfully established to describe the relationship between the abrasion resistance and other properties, using 23 groups of data, with the RMS error 0.07. Our studies have proved that Artificial Neural Networks (ANN) model can be used to predict the SSBR-based composites, which is an accurate and robust process

Statistical Methods of Two-Stage Sampling on Simmons Model for Sensitive Question Survey with and Its Application

To explore scientific survey methods and corresponding formulas for sensitive question survey on two-stage sampling. We use Simmons model for dichotomous sensitive questions, two-stage sampling, total probability formulas and properties of variance to deduce corresponding formulas. Then  the formulas and its variance on Simmons model for dichotomous sensitive questions on two-stage sampling were designed and applied for the survey of the using rate of condoms among the Men who have sex with men in Beijing, the rate is 78.65% and its 95% confidence limit is 71.10% to 82.60%

Feasible Policy Iteration

Safe reinforcement learning (RL) aims to solve an optimal control problem under safety constraints. Existing $\textit{direct}$ safe RL methods use the original constraint throughout the learning process. They either lack theoretical guarantees of the policy during iteration or suffer from infeasibility problems. To address this issue, we propose an $\textit{indirect}$ safe RL method called feasible policy iteration (FPI) that iteratively uses the feasible region of the last policy to constrain the current policy. The feasible region is represented by a feasibility function called constraint decay function (CDF). The core of FPI is a region-wise policy update rule called feasible policy improvement, which maximizes the return under the constraint of the CDF inside the feasible region and minimizes the CDF outside the feasible region. This update rule is always feasible and ensures that the feasible region monotonically expands and the state-value function monotonically increases inside the feasible region. Using the feasible Bellman equation, we prove that FPI converges to the maximum feasible region and the optimal state-value function. Experiments on classic control tasks and Safety Gym show that our algorithms achieve lower constraint violations and comparable or higher performance than the baselines

The Research Progress of Oil Sand Separation Technology in China

From 2007 to 2008, Research Institute of Petroleum Exploration & Development, Langfang Branch launched oil sand resource exploration and the study of hot water separation technology in Fengcheng area, Northwest of Junggar Basin, and the recoverable oil-sand oil resource is 54.98 million tons with the oil content in 7.1-10%, which is distributed in Cretaceous and Jurassic with the thickness of 80-140 meters, the cover depth of oil sand is 50-90 meters. Combining with the characteristics of the oil sand in this area and based on the research of hot water separation mechanism in oil sand, the hot water separation reagent for the oil sand in this area has been successfully developed, and its separation rate reaches 90%, provided that the concentrations of the agent is 4% and the separation temperature is 85 °C. Based on series of study, the construction of testing site, which is capable of processing 10,000 tons oil sand in this area, is completed, and the on-site separation tests of oil sand are launched with the recovery rate of 90% in normal operation, and the hot water separation technology and equipment research & development are successful.Key words: Oil sand; Hot water separation technology; Separation reagent; Test

A CNN based system for predicting the implied volatility and option prices.

The evaluations of option prices and implied volatility are critical for option risk management and trading. Common strategies in existing studies relied on the parametric models. However, these models are based on several idealistic assumptions. In addition, previous research of option pricing mainly depends on the historical transaction records without considering the performance of other concurrent options. To address these challenges, we proposed a convolutional neural network (CNN) based system for predicting the implied volatility and the option prices. Specifically, the customized non-parametric learning approach is first used to estimate the implied volatility. Second, several traditional parametric models are also implemented to estimate these prices as well. The convolutional neural network is utilized to obtain the predictions based on the estimation of the implied volatility. Our experiments based on Chinese SSE 50ETF options demonstrate that the proposed framework outperforms the traditional methods with at least 40.12% performance enhancement in terms of RMSE

Varying Levels of the Dian Lakes and the Dian Lakes Culture

Historical records state that the Dian kingdom was based on thousands of square li of rich flat land around Dianchi Lake. However, through use of a digital elevation model of the area, it is found that this area was about 800 li2—substantially less. Even if the other major Dian Lakes—Fuxian, Xinyung and Qi Lu—are included, the area increases to only about 1,000 li2. In the process of checking the area stated in the historical records, some issues warranting further exploration have been brought to light: the possibility of a human role in the recurring floods of Dianchi Lake from the 13th c CE; the idea that the settlement site found near Wangjiadun village and tentatively assigned to the early Bronze Age, could be dated to at least 4,500 BP, well before; and that the Shizhaishan and Lijiashan elite cemeteries may have looked out over water to their east. The seemingly limited area of fertile land also suggests that other sources of wealth such as trade and minerals played a greater role, and that the population was relatively small. This calls into question the nature of the socio-political structure within the Dian lakes culture

Patch Is Not All You Need

Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks. However, their inherent reliance on sequential input enforces the manual partitioning of images into patch sequences, which disrupts the image's inherent structural and semantic continuity. To handle this, we propose a novel Pattern Transformer (Patternformer) to adaptively convert images to pattern sequences for Transformer input. Specifically, we employ the Convolutional Neural Network to extract various patterns from the input image, with each channel representing a unique pattern that is fed into the succeeding Transformer as a visual token. By enabling the network to optimize these patterns, each pattern concentrates on its local region of interest, thereby preserving its intrinsic structural and semantic information. Only employing the vanilla ResNet and Transformer, we have accomplished state-of-the-art performance on CIFAR-10 and CIFAR-100, and have achieved competitive results on ImageNet

Two-stage Sampling on Additive Model for Quantitative Sensitive Question Survey and Its Application

Objective To explore scientific sampling methods and corresponding formulas for quantitative sensitive question survey on two-stage random sampling. To provide scientific data for the prevention and control of high risk AIDS population in Beijing. Methods Additive model for quantitative sensitive question survey, two-stage random sampling, properties of variance and mean were used. Results Formulas for the esti¬mation of the population proportions and its variance on additive model for quantitative sensitive question survey were deduced. The survey methods and formulas were employed successfully in the survey of the age of the first time when MSM having sex with men and the result was 21.9747. Conclusion The methods and corresponding formulas for two-stage sampling on additive model for quantitative sensitive question survey are feasible. Key words: Sensitive questions; Additive model for randomized response technique; Two-stage sampling; MS