M2M energy saving strategy in 5G millimeter wave system

Telecommunication Systems - Machine to Machine technology has a broad application prospect in the 5G network, but there is a bottleneck in the energy consumption of intelligent devices powered by...     

中国废钢资源发展战略研究

 为提高中国钢铁工业的废钢比,推动钢铁工业的绿色发展,分析了中国废钢资源的概况、废钢行业技术发展现状和钢铁工业废钢利用的现状,并对中国未来废钢资源量进行预测。在此基础上,针对中国废钢资源的发展提出了3条战略判断和6条政策建议。     

Unique CNTs-chained Li4Ti5O12 nanoparticles as excellent high rate anode materials for Li-ion capacitors

Composite anode materials with a unique architecture of carbon nanotubes (CNTs)-chained spinel lithium titanate (Li₄Ti₅O₁₂, LTO) nanoparticles are prepared for lithium ion capacitors (LICs). The CNTs networks derived from commercial conductive slurry not only bring out a steric hindrance effect to restrict the growth of Li₄Ti₅O₁₂ particles but greatly enhance the electronic conductivity of the CNTs/LTO composites, both have contributed to the excellent rate capability and cycle stability. The capacity retention at 30 C (1 C = 175 mA g^?1) is as high as 89.7% of that at 0.2 C with a CNTs content of 11 wt%. Meanwhile, there is not any capacity degradation after 500 cycles at 5 C. The LIC assembled with activated carbon (AC) cathode and such a CNTs/LTO composite anode displays excellent energy storage properties, including a high energy density of 35 Wh kg^?1 at 7434 W kg^?1, and a high capacity retention of 87.8% after 2200 cycles at 1 A g^?1. These electrochemical performances outperform the reported data achieved on other LTO anode-based LICs. Considering the facile and scalable preparation process proposed herein, the CNTs/LTO composites can be very potential anode materials for hybrid capacitors towards high power-energy outputs.     

Influence of surface topography evolution of grinding wheel on the optimal material removal rate in grinding process of cemented carbide

Most of the reported studies on the optimization of grinding parameters do not consider the evolution of the surface topography of grinding wheels, and the established empirical models will no longer apply when the surface conditions of the grinding wheel changes. In this paper, an integrated model based on the surface topography of grinding wheel is established. The grinding process of cemented carbide is simulated using the established model, and the simulation results are analyzed to obtain the surface roughness model and the specific grinding energy model based on the undeformed chip thickness distribution. Subsequently, the grinding constraint models are defined according to the two grinding constraints—surface roughness and specific grinding energy. Through inversion analysis, the maximum material removal rate of the given grinding wheel surface conditions satisfying the defined grinding constraints are obtained, and the influence rules of the grinding wheel surface conditions on the maximum material removal rate are analyzed. Then the grinding wheel surface conditions are adjusted by changing the radial dressed height of the grinding wheel and the arrangement distance of the grains in wheel circumferential direction to improve the maximum material removal rate of the grinding wheel. Finally, the optimization results are verified through grinding tests of cemented carbide.     

Restructuring bank networks after mergers and acquisitions: A capacitated delocation model for closing and resizing branches

During restructuring processes, due to mergers and acquisitions, banks frequently face the problem of having redundant branches competing in the same market. In this work, we introduce a new Capacitated Branch Restructuring Model which extends the available literature in delocation models. It considers both closing down and long term operations׳ costs, and addresses the problem of resizing open branches in order to maintain a constant service level. We consider, as well, the presence of competitors and allow for ceding market share whenever the restructuring costs are prohibitively expensive.We test our model in a real life scenario, obtaining a reduction of about 40% of the network size, and annual savings over 45% in operation costs from the second year on. We finally perform a sensitivity analysis on critical parameters. This analysis shows that the final design of the network depends on certain strategic decisions concerning the redundancy of the branches, as well as their proximity to the demand nodes and to the competitor׳s branches. At the same time, this design is quite robust to changes in the parameters associated with the adjustments on service capacity and with the market reaction.     

Primary energy demand in Japan: an empirical analysis of long-term trends and future CO2 emissions

This paper examines the long-run relationship between energy demand, GNP and the real energy price in Japan using data covering 1887–2001. It is found that, if an Underlying Energy Demand Trend is appropriately incorporated, the resulting econometric model produces a long-run income elasticity of about unity and a long-run price elasticity of about–0.2. The estimated model is utilised to forecast energy consumption and CO₂ emissions up to 2012. It is shown that given current economic conditions and policies there is considerable uncertainty about whether Japan will be able to meet its Kyoto target by reducing CO₂ emissions in 2008–2012 to the 1990 level. It is shown that this uncertainty depends on the strength of the economy and leaves the Japanese government with a difficult policy dilemma. If there is a resurgence in growth to something near the annual average growth rate since the early 1980s a considerable effort will be required in order to meet its Kyoto target; requiring not only using the Kyoto Mechanisms, but also additional tougher domestic policies and measures such as emissions capping, R&D incentives, and education for energy conservation in addition to a pricing and tax policy.     

Analysis of early warning of corporate financial risk via deep learning artificial neural network

To forecast the financial crisis of manufacturing corporations more accurately, a risk warning model of corporate finance is constructed based on back propagation (BP) neural network to forecast the financial crisis. Firstly, based on the principle of index selection, the forecast indexes are selected and the index system of financial risk early warning is constructed. Then the index system is optimized by factor analysis. Finally, the BP neural network algorithm model is adopted to forecast the financial crisis of 200 manufacturing corporations in 2018 and 2019, and the forecasting results are compared with the traditional method. The results show that the prediction accuracy of the enterprise financial risk early warning model based on the BP neural network for 2018 is above 85%, and the prediction accuracy for 2019 is above 95%, or even 100%. Through comparison with other traditional methods, the prediction accuracy of the BP neural network in 2018 (above 88%) is higher than that of other algorithms (below 87%). In 2019, the prediction accuracy of BP neural network (above 90%) is higher than other algorithms (less than 88%). The accuracy of the proposed financial risk warning model is 95%, and the accuracy is at least 2% higher than traditional method, which prove that the risk early warning model constructed in this study can accurately forecast the financial crisis of the corporation. This study is of important reference value for the establishment of efficient financial crisis forecasting model under deep learning.     

Bibliometric analysis on tendency and topics of artificial intelligence over last decade

Microsystem Technologies - Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and...