History of depression and smoking cessation outcome: A meta-analysis.

The authors conducted a meta-analysis of published studies to (a) evaluate the premise that a history of major depression is associated with failure to quit smoking and (b) identify factors that moderate the relationship between history of depression and cessation outcome. Fifteen studies met the selection requirements and were coded for various study methodology and treatment characteristics. DSTAT was used to calculate individual study effect sizes, determine the mean effect size across studies. and test for moderator effects. No differences in either short-term (≤ 3 months) or long-term abstinence rates (≥ 6 months) were observed between smokers positive versus negative for history of depression. Lifetime history of major depression does not appear to be an independent risk factor for cessation failure in smoking cessation treatment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Adaptive weighted outer-product learning associative memory

Associative-memory neural networks with adaptive weighted outer-product learning are proposed in this paper. For the correct recall of a fundamental memory (FM), a corresponding learning weight is attached and a parameter called signal-to-noise-ratio-gain (SNRG) is devised. The sufficient conditions for the learning weights and the SNRG's are derived. It is found both empirically and theoretically that the SNRG's have their own threshold values for correct recalls of the corresponding FM's. Based on the gradient-descent approach, several algorithms are constructed to adaptively find the optimal learning weights with reference to global- or local-error measure.     

用奇异模型描述的受限机器人的学习控制

本文了一类受限机器人的迭代学习控制方案，控制器的设计是基于机器人模型的降阶形式。在存在有界未知干扰的情况下，对末端操纵器受线性、无摩擦约束面的受限机器人。本文给出的控制方案保证了机器人系统的完全运动跟踪，同时保证了力跟踪误差有界的，且界的大小是可调节的。     

Transformations for nonideal uniform circular arrays operating in correlated signal environments

The Davies transformation is a method to transform the steering vector of a uniform circular array (UCA) to one with Vandermonde form. As such, it allows techniques such as spatial smoothing for direction-of-arrival (DOA) estimation in a correlated signal environment, developed originally for uniform linear arrays, to be applied to UCAs. However, the Davies transformation can be highly sensitive to perturbations of the underlying array model. This paper presents a method for deriving a more robust transformation using optimization techniques. The effectiveness of the method is illustrated through a number of DOA estimation examples.     

Tg/Tm for unsubstituted polymers

Polymers under discussion can be represented by [CH₂)_nR]_p where R is CH₂, CF₂, O, S or CH = CH; and where n can vary from 0 to ∞. The copolymer, P(ethylene-alt-TFE) is also included. These polymers tend to be highly crystalline with resultant confusion about their Tg and hence their Tg/Tm values and a clarification scheme is proposed in which it is considered that each such polymer has a double glass transition, Tg(L)/f(χ_c), Tg(U) = F(χc); and a sub-glass T<Tg(L) at 0.75 Tg(L), where F(χc) signifies a function of fractional crystallinity, χ_c. T<Tg(L) and Tg(L) increase linearly with Tm. The ratio, Tg(L)/Tm, is therefore, not a constant but is close to 0.5. T<Tg(L)/Tg(L) is also not a constant but is close to 0.75. Tg(U) lies within a wedge whose top and bottom sides increase with Tm. The premelting, intracrystalline transition temperature, Tα_c, is also a linear function of Tm. It is suggested that the scheme outlined above should help to resolve uncertainties in assigning transition temperatures for unsubstituted polymers. The low Tg/Tm values for these polymers is discussed in terms of their small da/dc ratios and small crosssectional areas per chain.