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...     

一种集中式水下无线传感器网跨层调度协议

分析了水下无线传感器网络（WSNs）中单链路发射功率和距离与频率的关系,提出一种跨层的集中式调度方法,AP（access point）在网络中通过广播信标的形式收集估计各节点的时延和距离信息,在MAC层考虑水下链路延迟特点,对各链路进行调度减少冲突;节点在物理层通过自适应地改变节点发射功率和发射频率,减少能耗.仿真结果表明：所提方案在节省系统能量消耗的同时,能够减少碰撞,提高系统吞吐量.     

稻草强化生物膜浮床脱除工业尾水中氮素及COD的效果

工业污水处理厂排放的尾水中总氮含量偏高,容易引起地表水体的富营养化。为了提高尾水中总氮的去除效率,通过模拟稳定塘静态和动态试验,对比了不同稻草添加量(0,0.18,0.54 g/L)强化生物膜浮床脱除工业尾水中总氮(TN)的效果。静态试验结果表明:随着稻草量的增加,TN的去除率显著提高;10 d后,添加0(CK)、0.18 g/L(T1)和0.54 g/L(T2)稻草的尾水中,TN去除率分别为18%,40%,93%。动态试验结果显示,水力停留时间(HRT)为 4 d时,CK、T1和T2的平均TN去除率分别为10%,22%,49%,且3种情况下的进出水COD浓度无显著差异。这主要是由于稻草通过缓慢释放碳源为生物膜中异养反硝化细菌提供可利用的有机物。研究成果可为工业尾水深度处理脱氮技术提供参考。     

基于Andorid的酒店点菜管理系统设计

利用Android系统平台的开放性,开发一个在手机上使用的无线点餐系统。系统采用基于Java语言的WEB技术、Android平台、Eclipse和MyEclipse开发工具相结合的方式,实现了基于B/S模式的酒店点菜管理,能使酒店点菜服务质量及网上订餐效率得到提高。     

一种新的等高线质量评价方法

分析了传统等高线质量评价相关模型,提出了基于信息量的等高线精度评价方法和基于形状指数的等高线光滑度评价方法,并给出了具体的量化计算过程;最后通过实验验证了该评价方法的可行性。