Liquid-liquid extraction-thin layer chromatography (LLE-TLC) has been a common and routine combined method for detection of drugs in biological materials. Solid-phase extraction (SPE) is gradually replacing the traditional LLE method. High performance thin layer chromatography (HPTLC) has several advantages over TLC. The present work studied the higher efficiency of a new SPE-HPTLC method over ...

R. M. Shiffrin, R. Ratcliff, K. Murnane, and P. Nobel (1993) claimed that TODAM (a theory of distributed associative memory) is unable simultaneously to predict an absent (or negative) liststrength effect (LSE) and a positive list-length effect (LLE). However, Shiffrin et al. failed to distinguish between situations in which lag (number of items intervening between study and test) is controlled...

The dimension of the population genetics data produced by next-generation sequencing platforms is extremely high. However, the "intrinsic dimensionality" of sequence data, which determines the structure of populations, is much lower. This motivates us to use locally linear embedding (LLE) which projects high dimensional genomic data into low dimensional, neighborhood preserving embedding, as a ...

We show that the thermodynamic entropy density is proportional to the largest Lyapunov exponent (LLE) of a discrete hydrodynamical system, a deterministic two-dimensional lattice gas automaton. The definition of the LLE for cellular automata is based on the concept of Boolean derivatives and is formally equivalent to that of continuous dynamical systems. This relation is justified using a Marko...

Low-loss electron (LLE) imaging in the scanning electron microscope (SEM) is based on the collection of backscattered electrons that have undergone minimal elastic interactions within a sample and therefore carry high-resolution, surface-specific information. Oliver Wells credits the concept of filtering the high-energy emitted electrons in the SEM to a statement made by McMullan [2], that the ...

Locally linear embedding is a popular manifold learning algorithm for nonlinear dimensionality reduction. However, the success of LLE depends greatly on an input parameter neighborhood size, and it is still an open problem how to find the optimal value for it. This paper focuses on this parameter, proposes that it should be self-tuning according to local density not a uniform value for all the ...

Within a quantum molecular dynamics model we calculate the largest Lyapunov exponent (LLE), density fluctuation and mass distribution of fragments for a series of nuclear systems at different initial temperatures. It is found that the LLE peaks at the temperature (”critical temperature”) where the density fluctuation reaches a maximal value and the mass distribution of fragments is best fitted ...

This paper presents a novel method to recognize high density crowd behaviors using micro-behaviors combining with Sparse Representation based on Locally Linear Embedding (named LLE-based Sparse Representation or LLE-SR). We extract micro-behaviors from each frame, respectively named Fountainhead, Bottleneck, Blocking, Lane and Ring/Arch, and construct micro-behaviors histograms to better descri...

Recently, a new method intended to realize conformal mappings has been published. Called Locally Linear Embedding (LLE), this method can map high-dimensional data lying on a manifold to a representation of lower dimensionality that preserves the angles. Although LLE is claimed to solve problems that are usually managed by neural networks like Kohonen’s Self-Organizing Maps (SOMs), the method re...

A number of manifold learning algorithms have been recently proposed, including locally linear embedding (LLE). These algorithms not only merely reduce data dimensionality, but also attempt to discover a true low dimensional structure of the data. The common feature of the most of these algorithms is that they operate in a batch or offline mode. Hence, when new data arrive, one needs to rerun t...