Effect of strain rate on the dynamic compressive mechanical behaviors of rock material subjected to high temperatures

Strain rate is not only an important measure to characterize the deformation property, but also an important parameter to analyze the dynamic mechanical properties of rock materials. In this paper, by using the SHPB test system improved with high temperature device, the dynamic compressive tests of sandstone at seven temperatures in the range of room temperature to 1000 °C and five impact velocities in the range of 11.0–15.0 m/s were conducted. Investigations were carried out on the influences of strain rate on dynamic compressive mechanical behaviors of sandstone. The results of the study indicate that the enhancement effects of strain rates on dynamic compressive strength, peak strain, energy absorption ratio of sandstone under high temperatures still exist. However, the increase ratios of dynamic compressive strength, peak strain, and energy absorption ratio of rock under high temperature compared to room temperature have no obvious strain rate effects. The temperatures at which the strain rates affect dynamic compressive strength and peak strain most, are 800, and 1000 °C, respectively. The temperatures at which the strain rates affect dynamic compressive strength and peak strain weakest, are 1000 °C, and room temperature, respectively. At 200 and 800 °C, the strain rate effect on energy absorption ratio are most significant, while at 1000 °C, it is weakest. There are no obvious strain rate effects on elastic modulus and increase ratio of elastic modulus under high temperatures. According to test results, the relationship formula of strain rate with high temperature and impact load was derived by internalizing fitting parameters. Compared with the strain rate effect at room temperature condition, essential differences have occurred in the strain rate effect of rock material under the influence of high temperature.     

Control of the particle microstructure during the synthesis of bulk-polymerized transparent methacrylate-acrylonitrile-butadiene-styrene resin

A series of methacrylate-acrylonitrile-butadiene-styrene (MABS) resins was prepared using bulk polymerization. The polarity of the continuous phase and the compatibility of two phases were changed by adjusting the methyl methacrylate (MMA) content, choosing values that were close to styrene-butadiene rubber solubility value. The possibility of controlling the microstructure of the MABS resin by changing the polarity of the components and the compatibility of two phases was assessed. The dynamic mechanical analysis shows that the compatibility of two phases varies with the MMA content. The morphological analysis shows that increasing MMA contents results in a gradual decrease in the sub-inclusion structure with a network skeleton of rubber particles, and that all the particles become solid rubber when the MMA content reaches 75%. The sub-inclusion structure reappears but does not have a network skeleton when the MMA content is 90%. The impact strength and morphological analysis indicate that the solid rubber particles and the sub-inclusion structure with a network skeleton provide excellent toughness, while the sub-inclusion structure without a network skeleton does not. In contrast, the transmittance of the ABS resin first increased and then decreased with increasing MMA content.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

Electroactive polymers containing 3-arylcarbazolyl units as hole transporting materials for OLEDs

Monomers and their polymers containing 3-arylcarbazolyl electrophores have been synthesized by the multi-step synthetic route. The materials were characterized by thermo-gravimetric analysis, differential scanning calorimetry and electron photoemission technique. The polymers represent materials of high thermal stability having initial thermal degradation temperatures in the range of 331–411 °C. The glass transition temperatures of the amorphous polymeric materials were in the rage of 148–175 °C. The electron photoemission spectra of thin layers of monomers showed ionization potentials in the range of 5.6–5.65 eV. Hole-transporting properties of the polymers were tested in the structures of organic light emitting diodes with Alq₃ as the green emitter. The device containing hole-transporting layers of polyether with 3-naphthylcarbazolyl groups exhibited the best overall performance with a maximum current efficiency of 3.3 cd/A and maximum brightness of about 1000 cd/m².     

Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed.     

In situ characterization of LiY(WO4)2 nanotubes for electrochemical energy storage

Here, LiY(WO₄)₂ nanotubes are prepared via a feasible electrospinning technique. This new anode material shows excellent electrochemical properties. The capacity loss of LiY(WO₄)₂ nanotubes is as low as 6.9% after 156 cycles, while bulk LiY(WO₄)₂ presents the capacity loss higher than 55.0%. Even after 600 long-life cycles, the capacity loss of the nanotubes is only 9%. It can be seen that the hollow structure with a rough surface and a porous morphology contributes to the improvement of electrochemical performance. Furthermore, online X-ray diffraction (XRD) method is firstly applied to understand the lithium ions insertion/extraction mechanism of LiY(WO₄)₂ nanotubes. It can be concluded that it is an asymmetrical two-phase reaction. A phase transformation from LiY(WO₄)₂ to Li₃Y(WO₄)₂ can be obviously seen from the in situ XRD during discharge process. While Li₂Y(WO₄)₂ appears as an intermediate phase with a reverse charge reaction. In addition, in situ XRD also demonstrates that LiY(WO₄)₂ nanotubes have surprised electrochemical reversibility. All the above results indicate that LiY(WO₄)₂ nanotubes can be expected to be anode candidate for rechargeable lithium ion batteries (LIBs).     

基于滑模的运载器主动段俯仰通道姿控系统设计

与传统比例-积分-微分（PID）控制方法相比，滑模控制（SMC）方法可以比较容易地将不确定性纳入控制器设计中，从而增强系统的鲁棒性。探索了SMC技术在运载器主动段姿态控制中的工程应用，首先通过分析基于趋近律的SMC系统，提出了降低不连续切换项系数的需求，然后研究了基于干扰上界的SMC方法。三通道小偏差仿真结果验证了两种方法的控制效果，表明第2种控制器的鲁棒性更好，稳态误差小，同时发动机喷管摆角需求较小。     

泛在电力物联网在北京经济技术开发区的应用探索

泛在电力物联网是物联网在电力领域的具体体现，建设泛在电力物联网是推进"三型两网"建设的关键环节，也是实现能源转型的必要手段。阐述了泛在电力物联网的定义，从供电公司角度出发，根据北京经济技术开发区实际情况，讨论了依托地区特点的泛在电力物联网实施策略和实际应用场景，分析了可能遇到的问题。得出结论：随着泛在电力物联网的深入建设，供电公司能够为电力客户提供多元化服务，提高客户满意度；地市供电公司能够依托泛在电力物联网开展更多电力相关业务；泛在电力物联网的安全性、实用性、有效性是发展面临的首要问题。