An enzymeimmunoassay for total thyroxine using avidin-biotin separation system and thyroxine-peroxidase conjugate

An avidin-biotin enzymeimmunoassay for total thyroxine in serum is described. Avidin was adsorbed to biotinylated bovine serum albumin coated tubes prepared with glutaraldehyde as coupling agent. In the enzymeimmunoassay, affinity purified biotinylated anti-thyroxine IgG, sample or standards, and thyroxine-horseradish peroxidase were simultaneously added to the avidin coated tubes. The bound enzymatic activity was then measured with o-phenylenediamine and H2O2. Results showed that the assay has good precision (within-assay CV% less than 10% and between assay 11.7% in hypo- and 6.9% in hyperthyroid range), good assay range (0-800 nmol/L), good sensitivity (4 nmol/L), and can be performed in 2.5 hours. The results obtained correlate well (r = 0.93) with those of an RIA.     

Friction and adsorption properties of normal and high-oleic soybean oils

The steel/steel boundary friction properties of soybean oil (SBO) and high-oleic soybean oil (HOSBO) are compared. HOSBO is significantly more saturated than SBO and more oxidatively stable. Changes in degree of unsaturation affect lateral interactions of adsorbate molecules, which in turn affects their adsorption and, hence, their boundary lubrication properties. To investigate this possibility, the free energies of adsorption (ΔG _ads) of SBO, HOSBO, and methyl laurate (ML) were determined from the analysis of friction-derived adsorption isotherms using the Langmuir and Temkin adsorption models. The results showed a stronger adsorption for the vegetable oils than for ML, an indication of multiple interactions between the ester groups of the triglycerides and the steel surface. The result also showed no difference in the ΔG _ads values of SBO and HOSBO obtained using either the Langmuir or Temkin models. This was interpreted as an indication of the lack of appreciable net lateral interaction between triglyceride adsorbates.     

Effects of composition and phase relations on mechanical properties and crystallization of silicate glasses

Crystallization, mechanical properties, and workability are all important for the commercialization and optimization of silicate glass compositions. However, the inter-relations of these properties as a function of glass composition have received little investigation. Soda-lime-silica glasses with Na₂O-MgO-CaO-Al₂O₃-SiO₂ compositions relevant to commercial glass manufacture were experimentally studied and multiple liquidus temperature and viscosity models were used to complement the experimental results. Liquidus temperatures of the fabricated glasses were measured by the temperature gradient technique, and Rietveld refinements were applied to X-Ray powder diffraction (XRD) data for devitrified glasses, enabling quantitative determination of the crystalline and amorphous fractions and the nature of the crystals. Structural properties were investigated by Raman spectroscopy. Acoustic echography, micro-Vicker's indentation, and single-edge-notched bend testing methods were used to measure Young's moduli, hardness, and fracture toughness, respectively. It is shown that it is possible to design lower-melting soda-lime-silica glass compositions without compromising their mechanical and crystallization properties. Unlike Young's modulus, brittleness is highly responsive to the composition in soda-lime-silica glasses, and notably low brittleness values can be obtained in glasses with compositions in the wollastonite primary phase field: an effect that is more pronounced in the silica primary phase field. The measured bulk crystal fractions of the glasses subjected to devitrification at the lowest possible industrial conditioning temperatures indicate that soda-lime-silica glass melts can be conditioned close to their liquidus temperatures within the compositional ranges of the primary phase fields of cristobalite, wollastonite, or their combinations.     

Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects

This article aims to explore the impact of the integration of risk factors into delayed milestones for construction projects. A simulation model was developed to determine the impact of schedule variability on cost estimation. To generate random scenarios a Monte Carlo Simulation (MCS) technique was applied. The developed model computes the cost impact of delayed milestone in the expected budget. Using a risk integration approach revealed the critical time frame that may lead to a budget deficit for a project. As a result, a number of cost-sensitive risk factors and schedule delays were identified for the critical time period where the risk of budget deficit increases. The method of integration proposed in this article highlights the priority of risk factors and schedule delays for construction contracts involving Payments at Event Occurrences (PEO). Consequently, the developed method can be useful for practitioners in anticipation of potential increase of costs, hence, prevention of failure due to budget deficit.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

An upper bound on the minimum number of monomials required to separate dichotomies of {-1, 1}n

It is known that any dichotomy of {-1, 1}n can be learned (separated) with a higher-order neuron (polynomial function) with 2n inputs (monomials). In general, less than 2n monomials are sufficient to solve a given dichotomy. In spite of the efforts to develop algorithms for finding solutions with fewer monomials, there have been relatively fewer studies investigating maximum density (Pi(n)), the minimum number of monomials that would suffice to separate an arbitrary dichotomy of {-1, 1}n . This article derives a theoretical (upper) bound for this quantity, superseding previously known bounds. The main theorem here states that for any binary classification problem in {-1, 1}n (n > 1), one can always find a polynomial function solution with 2n -2n/4 or fewer monomials. In particular, any dichotomy of {-1, 1}n can be learned by a higher-order neuron with a fan-in of 2n -2n/4 or less. With this result, for the first time, a deterministic ratio bound independent of n is established as Pi(n)/2n < or = 0 75. The main theorem is constructive, so it provides a deterministic algorithm for achieving the theoretical result. The study presented provides the basic mathematical tools and forms the basis for further analyses that may have implications for neural computation mechanisms employed in the cerebral cortex.     

Equilibrium distributions of key components of spearmint oil in sub/supercritical carbon dioxide

Effects of temperature (at 35, 45 or 55°C) and pressure (10–110 atm) on the relative distribution coefficients of the twelve key components of spearmint oil (essential oil ofMentha cardiaca; Scotch spearmint) at equilibrium in dense CO₂ were investigated under conditions ranging from subcritical to supercritical regions. Effects of vapor pressure, molecular weight and polarity of the key components on their equilibrium distributions in sub/supercritical CO₂ are discussed. At 35°C, all key components of spearmint oil are equally soluble in dense CO₂ within the 12–102 atm pressure region. At 45 and 55°C, the key components are equally soluble for pressures greater than about 60 atm. However, around either 45°C/27 atm or 55°C/35 atm conditions, the relative distribution coefficients of all monoterpene hydrocarbons and of isomenthone (an oxygenated monoterpene) exhibit maxima, which are due to significantly higher vapor pressures of these components and significantly lower solvating power of the dense-gas solvent at these particular temperatures and pressures. Vapor-pressure effects, coupled with the decrease in solvating power, dominate the effects of polarity and molecular mass of the key components. Deterpenation of spearmint oil with dense CO₂ is possible around either 45°C/27 atm or 55°C/35 atm, where the monoterpene hydrocarbons tend to concentrate in the CO₂-rich phase.     

Robotic grasping and manipulation through human visuomotor learning

A major goal of robotics research is to develop techniques that allow non-experts to teach robots dexterous skills. In this paper, we report our progress on the development of a framework which exploits human sensorimotor learning capability to address this aim. The idea is to place the human operator in the robot control loop where he/she can intuitively control the robot, and by practice, learn to perform the target task with the robot. Subsequently, by analyzing the robot control obtained by the human, it is possible to design a controller that allows the robot to autonomously perform the task. First, we introduce this framework with the ball-swapping task where a robot hand has to swap the position of the balls without dropping them, and present new analyses investigating the intrinsic dimension of the ball-swapping skill obtained through this framework. Then, we present new experiments toward obtaining an autonomous grasp controller on an anthropomorphic robot. In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot. The data collected is then analyzed for inferring the grasping strategy discovered by the human operator. Finally, a method to generalize grasping actions using the collected data is presented, which allows the robot to autonomously generate grasping actions for different orientations of the target object.     

Reinforcement learning to adjust parametrized motor primitives to new situations

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR?6.     

An improved counting method for motile protozoan cultures

A simple counting method is described for routine cultures of highly motile organisms. It is based on dyeing the organisms with a suitable stain, filtering them through a Millipore filter and counting single organisms directly under a microscope after rendering the filter transparent.