应变纤锌矿ZnO/MgxZn1-xO量子点中离子施主束缚激子的光学性质

Within the framework of the effective-mass approximation and the dipole approximation, considering the three-dimensional confinement of the electron and hole and the strong built-in electric field(BEF) in strained wurtzite Zn O/Mg0:25Zn0:75O quantum dots(QDs), the optical properties of ionized donor-bound excitons(D+, X)are investigated theoretically using a variational method. The computations are performed in the case of finite band offset. Numerical results indicate that the optical properties of(D+, X) complexes sensitively depend on the donor position, the QD size and the BEF. The binding energy of(D+, X) complexes is larger when the donor is located in the vicinity of the left interface of the QDs, and it decreases with increasing QD size. The oscillator strength reduces with an increase in the dot height and increases with an increase in the dot radius. Furthermore, when the QD size decreases, the absorption peak intensity shows a marked increment, and the absorption coefficient peak has a blueshift. The strong BEF causes a redshift of the absorption coefficient peak and causes the absorption peak intensity to decrease remarkably. The physical reasons for these relationships have been analyzed in depth.     

Coil‐Type Asymmetric Supercapacitor Electrical Cables

Cable‐shaped supercapacitors (SCs) have recently aroused significant attention due to their attractive properties such as small size, lightweight, and bendability. Current cable‐shaped SCs have symmetric device configuration. However, if an asymmetric design is used in cable‐shaped supercapacitors, they would become more attractive due to broader cell operation voltages, which results in higher energy densities. Here, a novel coil‐type asymmetric supercapacitor electrical cable (CASEC) is reported with enhanced cell operation voltage and extraordinary mechanical‐electrochemical stability. The CASECs show excellent charge–discharge profiles, extraordinary rate capability (95.4%), high energy density (0.85 mWh cm⁻³), remarkable flexibility and bendability, and superior bending cycle stability (≈93.0% after 4000 cycles at different bending states). In addition, the CASECs not only exhibit the capability to store energy but also to transmit electricity simultaneously and independently. The integrated electrical conduction and storage capability of CASECS offer many potential applications in solar energy storage and electronic gadgets.     

Rethinking Enterprise Architecture for Sustainable Energy System Development

The development of a sustainable energy system throughout an enterprise is a complex task, which requires an agile holistic approach. Such an approach needs to include a variety of objectives including energy strategy formation and strategic decision-making, which are directly related to the analysis and management of the main areas of sustainable development:The economic, technological, environmental, and social. These multidimensional requirements of sustainability are often difficult to achieve within the enterprise, because these aspects are interrelated and influenced by various internal and external environment factors. This paper first reviews the main challenges for an energy system, and then demonstrates how a strategic agile enterprise architecture driven approach could effectively guide the sustainable energy system development. The study presented in this paper provides a holistic approach that contributes to the advancement and usage of literature dealing with issues of sustainable energy system development and agile enterprise architecture, which has not been discussed before to any great extent.     

综放高瓦斯矿井煤层瓦斯涌出量综合预测

为了成功预测竹林山煤矿综放高瓦斯矿井大采高工作面煤层瓦斯涌出量,以主采3号煤层为主要研究对象,针对3号煤层以往开采情况,通过布设测点测量其煤层瓦斯含量和了解相邻矿井瓦斯含量,采用分源预测法、回归法及统计法等预测方法得到了3号煤层瓦斯含量的分布规律,并绘制了3号煤层的瓦斯含量等值线图。对矿井不同生产时期的瓦斯含量进行预测,得到了生产前期、中期及后期采区的最大绝对瓦斯涌出量和最大相对瓦斯涌出量,说明了竹林山煤矿各个时期均属于高瓦斯矿井。     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Charge transport and recombination in heterostructure organic light emitting transistors

Light-emitting field effect transistors (LEFETs) are a class of organic optoelectronic device capable of simultaneously delivering the electrical switching characteristics of a transistor and the light emission of a diode. We report on the temperature dependence of the charge transport and emissive properties in a model organic heterostructure LEFET system from 300 K to 135 K. We study parameters such as carrier mobility, brightness, and external quantum efficiency (EQE), and observe clear thermally activated behaviour for transport and injection. Overall, the EQE increases with decreasing temperature and conversely the brightness decreases. These contrary effects can be explained by a higher recombination efficiency occurring at lower temperatures, and this insight delivers new knowledge concerning the optimisation of both the transport and emissive properties in LEFETs.     

Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。     

储层泥浆侵入深度预测方法研究

泥浆侵入半径的确定尚缺乏严格的验证标准.以油水两相渗流理论和离子扩散方程为基础,结合储集层特点,研究了不同储层参数下泥浆滤液对地层的侵入特性.数值模拟侵入时间选取10 d和20 d.数值模拟结果表明,侵入半径在渗透率不变的情况下随孔隙度的增大而减小,在孔隙度不变的情况下随渗透率的增大而增大;当渗透率和孔隙度都发生变化时,泥浆侵入半径一般随孔隙度的增加呈幂函数增加.依据这种关系对测井资料约束处理,得到的泥浆侵入半径较客观地反映了地层的真实情况.