Framework Design of Emergency Management Information System for Cloud Computing in Chemical Park

Collaborative emergency management mode provides a new idea for further reducing system safety risk in the chemical park. In this paper, the cloud computing and the expert system were used for the framework design of the emergency management information system to enhance emergency response capability of collaborative emergency managementmechanisms. The use of middleware integrated with multi-source data achieved the unified data manipulation interface. Intelligent delivery of information system was provided. Using agent-middleware technology to build decision layer could make massive data analysis, data mining and decision from the “cloud computing” platform access for every user. It can provide the necessary theoretical and technical support for enterprises in the chemical park, to give full play to the collaborative emergency management capability.</p

An Efficient Universal Bee Colony Optimization Algorithm

The artificial bee colony algorithm is a global optimization algorithm. The artificial bee colony optimization algorithm is easy to fall into local optimal. We proposed an efficient universal bee colony optimization algorithm (EUBCOA). The algorithm adds the search factor u and the selection strategy of the onlooker bees based on local optimal solution. In order to realize the controllability of algorithm search ability, the search factor u is introduced to improve the global search range and local search range. In the early stage of the iteration, the search scope is expanded and the convergence rate is increased. In the latter part of the iteration, the algorithm uses the selection strategy to improve the algorithm accuracy and convergence rate. We select ten benchmark functions to testify the performance of the algorithm. Experimental results show that the EUBCOA algorithm effectively improves the convergence speed and convergence accuracy of the ABC algorithm

Estimation of PM2.5 concentrations in China using a spatial back propagation neural network

Methods for estimating the spatial distribution of PM2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R2) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R2 values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 μg/m3. The yearly mean PM2.5 concentration in China during the study period was found to be 41.8 μg/m3 and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 μg/m3) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM2.5 concentrations for air quality monitoring

Phase boundary engineering of metal-organic-framework-derived carbonaceous nickel selenides for sodium-ion batteries

Abstract: Sodium-ion batteries (SIBs) are promising power sources due to the low cost and abundance of battery-grade sodium resources, while practical SIBs suffer from intrinsically sluggish diffusion kinetics and severe volume changes of electrode materials. Metal-organic framework (MOFs) derived carbonaceous metal compound offer promising applications in electrode materials due to their tailorable composition, nanostructure, chemical and physical properties. Here, we fabricated hierarchical MOF-derived carbonaceous nickel selenides with bi-phase composition for enhanced sodium storage capability. As MOF formation time increases, the pyrolyzed and selenized products gradually transform from a single-phase Ni3Se4 into bi-phase NiSex then single-phase NiSe2, with concomitant morphological evolution from solid spheres into hierarchical urchin-like yolk-shell structures. As SIBs anodes, bi-phase NiSex@C/CNT-10h (10 h of hydrothermal synthesis time) exhibits a high specific capacity of 387.1 mAh/g at 0.1 A/g, long cycling stability of 306.3 mAh/g at a moderately high current density of 1 A/g after 2,000 cycles. Computational simulation further proves the lattice mismatch at the phase boundary facilitates more interstitial space for sodium storage. Our understanding of the phase boundary engineering of transformed MOFs and their morphological evolution is conducive to fabricate novel composites/hybrids for applications in batteries, catalysis, sensors, and environmental remediation

Diphenyl Difluoroketone: A Potent Chemotherapy Candidate for Human Hepatocellular Carcinoma

Diphenyl difluoroketone (EF24), a molecule having structural similarity to curcumin, was recently reported to inhibit proliferation of various cancer cells significantly. Here we try to determine the effect and mechanism of EF24 on hepatocellular carcinoma. 2 µM EF24 was found to inhibit the proliferation of PLC/PRF/5, Hep3B, HepG2, SK-HEP-1 and Huh 7 cell lines. However, even 8 µM EF24 treatment did not affect the proliferation of normal liver LO2 cells. Accordingly, 20 mg/kg/d EF24 inhibited the growth of the tumor xenografts conspicuously while causing no apparent change in liver, spleen or body weight. In addition, significant apoptosis and G2/M phase cell cycle arrest were found using flow cytometry. Besides, caspases and PARP activation and features typical of apoptosis including fragmented nuclei with condensed chromatin were also observed. Furthermore, the mechanism was targeted at the reduction of nuclear factor kappa b (NF-κB) pathway and the NF-κB–regulated gene products Bcl-2, COX-2, Cyclin B1. Our study has offered a strategy that EF24 being a therapeutic agent for hepatocellular carcinoma

Role of Short-time Acoustic Temporal Fine Structure Cues in Sentence Recognition for Normal-hearing Listeners

© 2018 Acoustical Society of America. Short-time processing was employed to manipulate the amplitude, bandwidth, and temporal fine structure (TFS) in sentences. Fifty-two native-English-speaking, normal-hearing listeners participated in four sentence-recognition experiments. Results showed that recovered envelope (E) played an important role in speech recognition when the bandwidth was \u3e 1 equivalent rectangular bandwidth. Removing TFS drastically reduced sentence recognition. Preserving TFS greatly improved sentence recognition when amplitude information was available at a rate ≥ 10 Hz (i.e., time segment ≤ 100 ms). Therefore, the short-time TFS facilitates speech perception together with the recovered E and works with the coarse amplitude cues to provide useful information for speech recognition

Adaptive Sliding Mode Control With Disturbance Observer for Speed Regulation System of Permanent Magnet Synchronous Motor

In order to improve the robustness and speed tracking performance of a permanent magnetic synchronous motor (PMSM) system, under the framework of PMSM vector control, a nonlinear speed control method based on improved sliding mode control (SMC) and disturbance observer is proposed in this paper. First, a novel fast reaching law is designed to make the system trajectory reach the sliding mode surface within a finite time and to alleviate the chattering phenomenon. Second, a nonlinear integral terminal sliding mode surface is proposed by introducing sign and fractional error integration, which achieves finite-time convergence of the speed tracking errors and integral errors without the singular problem. Then, based on the proposed reaching law and the proposed sliding mode surface, an adaptive sliding mode speed controller with disturbance observer which adaptively compensates the output of the improved sliding mode speed controller, is developed not only to guarantee the finite-time convergence performance of the state trajectories, but also to demonstrate stronger robustness, higher precision and lower chattering compared to conventional sliding mode controller. Finally, the simulation and experimental results confirm the validity of the proposed method for practical applications

Challenges faced by acute care surgeons in China

Abstract The aim of this article is to describe briefly about Chinese ACS surgeons’ work status. It is an undeniable fact that the analysis of ED and ACS resources shows negative tendencies and high work overload, resulting in low patient safety and quality of care. And, there was a substantial shortage of surgeons in the subspecialty. So, a set of strategic measures and state policies should be prioritized