应力对La0.83Sr0.17MnO3薄膜输运性能的影响

采用溶胶-凝胶方法在Si(111)上制备了LSMO(x=0.17)薄膜.研究了块体材料和不同厚度薄膜R -T曲线、红外光谱和X射线衍射.结果表明，LSMO薄膜属于正交晶体结构，薄膜取向与膜厚度 有关，当膜厚度为450nm或680nm时，主要取向〈200〉，而膜厚度为900nm时取向为〈020〉 ：根据离子对相互作用能和谐振子模型，得到了红外吸收与Mn—O—Mn键长和键角关系式，6 00cm^-1附近红外吸收与晶格常数b的变化有关；块体与薄膜的金属—绝缘体转变 温度(T_MI)存在较大差别，薄膜转变温度显著低于块体，并与厚度有一定关系. 认为是LSMO薄膜中的应力诱导了晶格常数变化，引起键角改变及JT效应是转变温度变化的主 要原因. 关键词： 单晶硅 晶格常数 金属—绝缘体转变温度 应力诱导     

囚禁离子的非线性Jaynes-Cummings模型及其布居数反转演化

从描述囚禁离子与驻波激光场相互作用的Jaynes-Cummings(J-C)模型出发，导出了囚禁离子谐振动单量子共振激发相互作用的非线性J-C模型. 通过求解这一模型的系统状态随时间变化，数值研究了离子布居数反转的演化规律. 结果表明，离子布居数反转演化的崩塌-复原周期与Lamb-Dick参数η和离子在驻波激光场中的位置有关，随着η参数的增大，离子布居数反转的崩塌-复原周期变短，当离子质心的位置从驻波激光场的波节移向波腹时，离子布居数崩塌-复原的周期变长. 关键词： 驻波激光场 囚禁离子 非线性J-C模型 Lamb-Dick参数     

用自适应脉冲微扰引导混沌系统到周期解

用自适应脉冲微扰方法控制的系统的某个系统变量作为驱动,设计了一种自适应控制器方法对两个或多个响应混沌系统进行脉冲微扰,引导这些系统从混沌运动到低周期运动,实现同时控制多个混沌系统到不同的周期态. 当选择相同的自适应控制器输入变量实施脉冲微扰时,还可控制两个或多个混沌系统达到不同的周期态同步. 通过对R?ssler混沌系统的仿真研究证实了方法的有效性. 关键词： 混沌控制 系统参量 自适应控制器 脉冲微扰 周期态同步     

微纳米加工技术在纳米物理与器件研究中的应用

物质在纳米尺度下可能呈现出与体材料不同的物理特件，这正是纳米科技发展的基础之一。要想探索在纳米尺度下材料物埋性质的变化规律及可能的应用领域，离不开相应的技术手段，微纳米加工技术作为当今高技术发展的重要技术领域之一，是实现功能人工纳米结构与器件微纳米化的基础。本文根据几个不同的应用领域，介绍了微纳米加工技术在纳米物理与器件研究领域的应用。     

KrVI离子能级和振子强度的理论计算

利用组态相互作用理论和参数外推法,计算了KrVI离子4s~24p—4s4p~2、4s~24p—4s~24d和4s4p~2—4p~3跃迁的能级、谱线波长和振子强度。与已有实验结果比较表明:波长的理论计算值与观测值在0.7A内很好符合,振子强度的理论计算值较大的跃迁均是实验中观测到的跃迁。     

Drag force on a moving heavy quark with deformed string configuration

To study drag force on a moving heavy quark through a plasma, we use a deformed AdS space-time, in which deformation parameter c describes non-conformality in AdS/QCD. In this case, the quark is mapped to a probe string in the AdS space. Considering the probable contribution of the deformation parameter in the probe string, we apply a general form of c-dependent string ansatz in the drag force computation. Then, we find the acceptable value of this parameter as it satisfies QCD calculations. Using this result, we also discuss the diffusion constant which is in agreement with the phenomenological result for the non-relativistic limit. Also, we show that while in absence of a deformation parameter, the probe string is a strictly increasing function of radial coordinate, the c-dependent probe string has a maximum value versus z.     

Ultrasound-assisted of alkali chloride separation using bulk ionic liquid membrane

An ultrasonic-assisted separation of alkali chloride (LiCl, NaCl, and KCl) salts have been carried out using of an hydrophobic ionic liquid membrane (ILM). The ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate and tributyl phosphate mixture have been used as ILM. An ultrasonic probe with different frequencies (25, 100, and 250) kHz have been applied as source of ultrasound generator with different times of sonication (2, 5, and 10) min in three phases system containing feed, ILM, and receiver in osmotic U-shaped tube. Also, 250, 500, and 1000 ppm of the feed (alkali chloride) concentration have been used to separate. The frequency of 250 kHz with higher sonication time provides optimum condition for separation of LiCl with lower feed concentration. The thermodynamic properties such as density and speed of sound and the related thermodynamic properties have been calculated to optimize ILM composition (x_IL = 0.45) for ultrasound-separation.     

Machine Learning Algorithms for Prediction of the Quality of Transmission in Optical Networks

Increasing demand in the backbone Dense Wavelength Division (DWDM) Multiplexing network traffic prompts an introduction of new solutions that allow increasing the transmission speed without significant increase of the service cost. In order to achieve this objective simpler and faster, DWDM network reconfiguration procedures are needed. A key problem that is intrinsically related to network reconfiguration is that of the quality of transmission assessment. Thus, in this contribution a Machine Learning (ML) based method for an assessment of the quality of transmission is proposed. The proposed ML methods use a database, which was created only on the basis of information that is available to a DWDM network operator via the DWDM network control plane. Several types of ML classifiers are proposed and their performance is tested and compared for two real DWDM network topologies. The results obtained are promising and motivate further research.     

A Traditional Scientific Perspective on the Integrated Information Theory of Consciousness

This paper assesses two different theories for explaining consciousness, a phenomenon that is widely considered amenable to scientific investigation despite its puzzling subjective aspects. I focus on Integrated Information Theory (IIT), which says that consciousness is integrated information (as ϕ^Max) and says even simple systems with interacting parts possess some consciousness. First, I evaluate IIT on its own merits. Second, I compare it to a more traditionally derived theory called Neurobiological Naturalism (NN), which says consciousness is an evolved, emergent feature of complex brains. Comparing these theories is informative because it reveals strengths and weaknesses of each, thereby suggesting better ways to study consciousness in the future. IIT’s strengths are the reasonable axioms at its core; its strong logic and mathematical formalism; its creative “experience-first” approach to studying consciousness; the way it avoids the mind-body (“hard”) problem; its consistency with evolutionary theory; and its many scientifically testable predictions. The potential weakness of IIT is that it contains stretches of logic-based reasoning that were not checked against hard evidence when the theory was being constructed, whereas scientific arguments require such supporting evidence to keep the reasoning on course. This is less of a concern for the other theory, NN, because it incorporated evidence much earlier in its construction process. NN is a less mature theory than IIT, less formalized and quantitative, and less well tested. However, it has identified its own neural correlates of consciousness (NCC) and offers a roadmap through which these NNCs may answer the questions of consciousness using the hypothesize-test-hypothesize-test steps of the scientific method.     

Knowledge Discovery from Medical Data and Development of an Expert System in Immunology

Artificial intelligence is one of the fastest-developing areas of science that covers a remarkably wide range of problems to be solved. It has found practical application in many areas of human activity, also in medicine. One of the directions of cooperation between computer science and medicine is to assist in diagnosing and proposing treatment methods with the use of IT tools. This study is the result of collaboration with the Children’s Memorial Health Institute in Warsaw, from where a database containing information about patients suffering from Bruton’s disease was made available. This is a rare disorder, difficult to detect in the first months of life. It is estimated that one in 70,000 to 90,000 children will develop Bruton’s disease. But even these few cases need detailed attention from doctors. Based on the data contained in the database, data mining was performed. During this process, knowledge was discovered that was presented in a way that is understandable to the user, in the form of decision trees. The best models obtained were used for the implementation of expert systems. Based on the data introduced by the user, the system conducts expertise and determines the severity of the course of the disease or the severity of the mutation. The CLIPS language was used for developing the expert system. Then, using this language, software was developed producing six expert systems. In the next step, experimental verification was performed, which confirmed the correctness of the developed systems.