碳纤维增强环氧树脂复合材料在切向气流和激光作用下的损伤

实验研究了靶板表面有0.4 Mach(1 Mach=340 m/s)切向空气气流、 0.4 Mach切向氮气气流和无气流时, 976 nm连续激光对碳纤维增强E-51环氧树脂复合材料的辐照效应, 得到了该材料在不同功率密度下的烧蚀规律。实验结果表明: 当样品发生剧烈热分解时, 随热分解气体流出边界的固体颗粒对入射激光有屏蔽作用; 切向气流一方面可以减弱这种屏蔽作用, 有利于辐照区的烧蚀, 另一方面对样品有冷却作用, 不利于烧蚀; 相比于氮气流, 空气流有助于产物的燃烧, 对下游附近区域产生明显的加热作用; 切向空气气流的加载会明显提高扩散到样品表面的氧气浓度, 导致碳纤维发生氧化烧蚀; 三种气流状态下, 当入射激光功率密度在100～800 W/cm²范围内, 随着功率密度的增大, 激光能量的利用效率逐渐降低。     

飞秒脉冲激光入射单层光学薄膜的光场特性数值模拟

飞秒脉冲激光入射光学薄膜形成瞬态光场分布是一个非稳态过程,该过程不同于纳秒脉冲或连续波入射的情形,不能直接采用求解薄膜特征矩阵的方法进行处理。采用多光束叠加的方法建立了超短脉冲入射单层膜的反射率和内部光强分布的理论模型,并根据ZnS薄膜材料的参数和单层增反膜的特点进行了数值计算。结果表明,对单层增反膜,薄膜反射率与脉宽成正比,并随脉宽增加逐渐趋近于连续波入射时的情形。在同一脉宽下,膜层厚度增加,反射率下降,且反射脉冲形状也发生改变。膜层中的光强分布计算结果也明显不同于连续波辐照情形,且薄膜厚度越大,差异越显著,表现为连续波入射时,膜层内的光强分布呈等振幅的波动,而超短脉冲入射时,波动的振幅逐渐增大,在膜层和玻璃分界处达到最大值。     

A new prediction model of electricity load based on hybrid forecast engine

In this paper, a new prediction model is introduced based on hybrid forecast engine and new feature selection. In this model, the load signal is filtered by feature selection to filter out the best candidates. Then, the proposed forecast engine is predicted the output of feature selection. In this model, the weights of proposed forecast engine are optimised by an intelligent algorithm to increase its accuracy. Effectiveness of the proposed method is applied over real-world engineering test case and compared with other different well-known methods. Obtained results proof the validity of the proposed method.     

Applying LSSVM algorithm as a novel and accurate method for estimation of interfacial tension of brine and hydrocarbons

One of the critical parameters in petroleum and chemical engineering is the interfacial tension between brine and hydrocarbon which has major effects on trapping and residual oil in reservoir pore throat so it becomes one of the interesting topics in enhancement of oil recovery in this work Least squares support vector machine (LSSVM) algorithm was applied as a novel predicting machine for prediction of interfacial tension of brine and hydrocarbons in terms of hydrocarbon carbon number, temperature, pressure and ionic strength of brine. A total number of 175 interfacial tensions were collected from literature in the purpose of training and testing of the model. The root mean squared error (RMSE), average absolute relative deviation (AARD) and the coefficient of determination (R²) were calculated overall datasets as 0.23964, 0.27444 and 0.98509 respectively. The results of study showed that predicting LSSVM machine can be applicable for estimation of interfacial tension and EOR processes.     

Applying Fuzzy c-means approach as a novel method for prediction of interfacial tension between carbon dioxide and hydrocarbons

The worldwide demand for energy increases and also the price of crude oil increases so these reasons have been caused the searchers have motivated to investigate enhanced of oil recovery (EOR) processes. The carbon dioxide injection is recognized as one of the favorable approaches of EOR because of high displacement efficiency, environmentally aspects and lower cost. The Interfacial tension between crude oil and carbon dioxide is known as one of the critical factors which affect the performance of injection. The main objective of the present investigation is development of Fuzzy c-means (FCM) approach as novel method to estimate interfacial tension between carbon dioxide and hydrocarbons as function of pressure, temperature, liquid and gas densities and molecular weight of alkane. The performance of predicting model was evaluated statistically and graphically and the results confirmed the ability of the model to predict interfacial tension between carbon dioxide and hydrocarbons.     

Dynamics of epileptic phenomena determined from statistics of ictal transitions

In this paper, we investigate the dynamical scenarios of transitions between normal and paroxysmal state in epilepsy. We assume that some epileptic neural network are bistable i.e., they feature two operational states, ictal and interictal that co-exist. The transitions between these two states may occur according to a Poisson process, a random walk process or as a result of deterministic time-dependent mechanisms. We analyze data from animal models of absence epilepsy, human epilepsies and in vitro models. The distributions of durations of ictal and interictal epochs are fitted with a gamma distribution. On the basis of qualitative features of the fits, we identify the dynamical processes that may have generated the underlying data. The analysis showed that the following hold. 1) The dynamics of ictal epochs differ from those of interictal states. 2) Seizure initiation can be accounted for by a random walk process while seizure termination is often mediated by deterministic mechanisms. 3) In certain cases, the transitions between ictal and interictal states can be modeled by a Poisson process operating in a bistable network. These results imply that exact prediction of seizure occurrence is not possible but termination of an ictal state by appropriate counter stimulation might be feasible.     

Big Data Based Self-optimization Networking: A Novel Approach Beyond Cognition

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation wireless networks. A self-optimization technique is introduced as the only viable solution for controlling and managing this type of huge data networks. This technique allows control of resources and key performance indicators without human intervention, based solely on the network intelligence. The present study proposes a big data based self optimization networking (BD-SON) model for wireless networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate the required resources to each part of the network based on made decision in SON engine, which can satisfy QoS constraints of a multicast session in which satisfying interference constraints is the main challenge. A load-balanced gradient power allocation (L-GPA) scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. Experimental results confirm that the proposed power allocation techniques considerably increase the chances of finding an optimal solution. Also, results confirm that proposed model achieves significant gain in terms of quality of service and capacity along with low complexity and load balancing optimality in the network.     

The Embeddedness of Selfish Routines: How Routines are Replicated in Business Networks

The notion of routines as a powerful metaphor for explaining organizational capabilities and the evolution of organizations has been widely accepted as a central issue in evolutionary economics. The configuration and management of routines across organizational, and ownership, boundaries involves a range of specific managerial and strategic issues which are not addressed in the current literature on routines. However, as new less-hierarchical and more fluent organizational forms evolve, where boundaries between the organization and the environment are increasingly arbitrary, the role of routines beyond the organizational perimeter becomes increasingly relevant. It is argued here that social networks sustain interpersonal routines, which not only transgress organizational boundaries but on occasion also incubate new organizations in order to ensure their continued existence. This paper presents an overview of the routine concept and an argument for applying it in a network embeddedness context. Two case studies are presented, covering examples from the Danish dairy and the Danish machinery industry.