Influence of layered tissue architecture on estimates of tissue optical properties obtained from spatially resolved diffuse reflectometry

Most instruments used to measure tissue optical properties noninvasively employ data-analysis algorithms that rely on the simplifying assumption that the tissue is semi-infinite and homogeneous. The influence of a layered tissue architecture on the determination of the scattering and absorption coefficients has been investigated in this study. Reflectance as a function of distance from a point source for a two-layered tissue architecture that simulates skin overlying fat was calculated by using a Monte Carlocode. These data were analyzed by using a diffusion theory modelfor a homogeneous semi-infinite medium to calculate the scatter and absorption coefficients. Depending on the algorithm and the radial distance, the estimated tissue optical properties were different from those of either layer, and under some circumstances, physically impossible. In addition, the sensitivity and cross talk of the estimated optical properties to changes in input optical properties were calculated for different layered geometries. For typical optical properties of skin, the sensitivity to changes in optical properties is highly dependent on the layered architecture, the measurement distance, and the fitting algorithm. Furthermore, a change in the input absorption coefficient may result in an apparent change in the measured scatter coefficient, and a change in the in put scatter coefficient may result in an apparent change in the measured absorption coefficient.     

AC powder electroluminescent displays

Abstract— The current status of AC powder electroluminescent (ACPEL) displays is reviewed with particular emphasis given to color and lifetime. The printing of the displays in forward and reverse architectures is also discussed, in addition to the fabrication of ACPEL displays with interdigitated electrodes, and different types of ACPEL phosphors and materials for back electrodes, transparent conducting electrodes, binders, and dielectrics are considered. Furthermore, shape conformable and highly flexible ACPEL displays are surveyed.     

Parameter sensitivity study of the Nelder-Mead Simplex Method

This paper presents a parameter sensitivity study of the Nelder-Mead Simplex Method for unconstrained optimization. Nelder-Mead Simplex Method is very easy to implement in practice, because it does not require gradient computation; however, it is very sensitive to the choice of initial points selected. Fan-Zahara conducted a sensitivity study using a select set of test cases and suggested the best values for the parameters based on the highest percentage rate of successful minimization. Begambre-Laier used a strategy to control the Particle Swarm Optimization parameters based on the Nelder Mead Simplex Method in identifying structural damage. The main purpose of the paper is to extend their parameter sensitivity study to better understand the parameter’s behavior. The comprehensive parameter sensitivity study was conducted on seven test functions: B2, Beale, Booth, Wood, Rastrigin, Rosenbrock and Sphere Functions to search for common patterns and relationships each parameter has in producing the optimum solution. The results show important relations of the Nelder-Mead Simplex parameters: reflection, expansion, contraction, and Simplex size and how they impact the optimum solutions. This study is crucial, because better understanding of the parameters behavior can motivate current and future research using Nelder-Mead Simplex in creating an intelligent algorithm, which can be more effective, efficient, and save computational time.     

A poly-hybrid PSO optimization method with intelligent parameter adjustment

This paper presents results on a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within the hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.     

Detecting palindromes,patterns and borders in regular languages

Given a language L and a non-deterministic finite automaton M, we consider whether we can determine efficiently (in the size of M) if M accepts at least one word in L, or infinitely many words. Given that M accepts at least one word in L, we consider how long a shortest word can be. The languages L that we examine include the palindromes, the non-palindromes, the k-powers, the non-k-powers, the powers, the non-powers (also called primitive words), the words matching a general pattern, the bordered words, and the unbordered words.     

Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.     

Rapid scalar value classification and volume clipping for?interactive 3D medical image visualization

In many clinical scenarios, medical data visualization and interaction are important to physicians for exploring inner anatomical structures and extracting meaningful diagnostic information. Real-time high-quality volume rendering, artifact-free clipping, and rapid scalar value classification are important techniques employed in this process. Unfortunately, in practice, it is still difficult to achieve an optimal balance. In this paper, we present some strategies to address this issue, which are based on the calculation of segment-based post color attenuation and dynamic ray–plane intersection (RPI) respectively. When implemented within our visualization system, the new classification algorithm can deliver real-time performance while avoiding the “color over-accumulation” artifacts suffered by the commonly used acceleration algorithms that employ pre-integrated classification. Our new strategy can achieve an optimized balance between image quality and classification speed. Next, the RPI algorithm is used with opacity adjustment technique to effectively remove the “striping” artifacts on the clipping plane caused by the nonuniform integration length. Furthermore, we present techniques for multiple transfer function (TF) based anatomical feature enhancement and “keyhole” based endoscopic inner structure view. Finally, the algorithms are evaluated subjectively by radiologists and quantitatively compared using image power spectrum analysis.     

Dynamic Network User Equilibrium with State-Dependent Time Lags

This paper recasts the Friesz et al. (1993) measure theoretic model of dynamic network user equibrium as a controlled variational inequality problem involving Riemann integrals. This restatement is done to make the model and its foundations accessible to a wider audience by removing the need to have a background in functional analysis. Our exposition is dependent on previously unavailable necessary conditions for optimal control problems with state-dependent time lags. These necessary conditions, derived in an Appendix, are employed to show that a particular variational inequality control problem has solutions that are dynamic network user equilibria. Our analysis also shows that use of proper flow propagation constraints obviates the need to explicitly employ the arc exit time functions that have complicated numerical implementations of the Friesz et al. (1993) model heretofore. We close by describing the computational implications of numerically determining dynamic user equilibria from formulations based on state-dependent time lags.     

The BIOPHILE Sample Process Management System - Automated Sample Access at Ultra Low Temperatures

The genomics, proteomics, clinical, and drug discovery laboratories have a growing need to maintain valuable samples at ultra-low (−80°C) temperatures in a validated, secure environment. Automated sample processing systems have until now required manual (off-line) storage of samples at −80°C, reducing system reliability and speed. Both of these important needs are addressed by the Sample Process Management System being introduced by BIOPHILE Inc. Conventional sample management processes, such as storage, retrieval, and cataloging, are increasingly strained by the growing sample populations. There are variable sample types, access requirements and storage requirements. Security and inventory procedures are implemented manually. The evolving technologies present in the laboratory cannot interface with conventional manual storage techniques. Addressing these limitations, the primary benefits of BIOPHILE's solutions are:

• Fully validated sample management process that coordinates the life-cycles of samples and their related data.

• Robotic technology to securely store and retrieve samples, improving their accessibility and stability. Thermal shock is reduced, improving sample longevity and quality. The robotic technology allows integration with larger automation systems.

• A process program to develop a Sample Management Strategy. This strategy is developed by analyzing long-term research goals, current baseline processes, and identification of current sample life cycles. A full validation documentation package can be generated, providing a high level of quality assurance.

• Improved sample visibility and quality assurance - automated sample population cataloging; controlled sample management access and security.

     

Cross-talk induces bifurcations in nonlinear models of synaptic plasticity

Linear models of synaptic plasticity provide a useful starting-point for examining the dynamics of neuronal development and learning, but their inherent problems are well known. Models of synaptic plasticity that embrace the demands of biological realism are therefore typically nonlinear. Viewed from a more abstract perspective, nonlinear models of synaptic plasticity are a subset of nonlinear dynamical systems. As such, they may therefore exhibit bifurcations under the variation of control parameters, including noise and errors in synaptic updates. One source of noise or error is the cross-talk that occurs during otherwise Hebbian plasticity. Under cross-talk, stimulation of a set of synapses can induce or modify plasticity in adjacent, unstimulated synapses. Here, we analyze two nonlinear models of developmental synaptic plasticity and a model of independent component analysis in the presence of a simple model of cross-talk. We show that cross-talk does indeed induce bifurcations in these models, entirely destroying their ability to acquire either developmentally or learning-related patterns of fixed points. Importantly, the critical level of cross-talk required to induce bifurcations in these models is very sensitive to the statistics of the afferents' activities and the number of afferents synapsing on a postsynaptic cell. In particular, the critical level can be made arbitrarily small. Because bifurcations are inevitable in nonlinear models, our results likely apply to many nonlinear models of synaptic plasticity, although the precise details vary by model. Hence, many nonlinear models of synaptic plasticity are potentially fatally compromised by the toxic influence of cross-talk and other sources of noise and errors more generally. We conclude by arguing that biologically realistic models of synaptic plasticity must be robust against noise-induced bifurcations and that biological systems may have evolved strategies to circumvent their possible dangers.