Incorporation of polyfluorenes into poly(lactic acid) films for sensor and optoelectronics applications

Films of neat and plasticized biodegradable poly(lactic acid) (PLA) matrices containing anionic conjugated polyelectrolytes, poly[9,9‐bis(4‐phenoxybutylsulfonate)]fluorene‐2,7‐diyl‐alt‐arylenes, with 1,4‐phenylene and 4,4″‐p‐terphenylene, respectively, as arylene groups or a neutral poly(9,9‐dialkylfluorene) for comparison were prepared by solution casting. These films were characterized using differential scanning calorimetry, thermogravimetry, scanning electron microscopy and fluorescence spectroscopy. In addition, the effects of plasticizer on the thermal properties and the oxygen permeability of the PLA films were measured through the oxygen transmission rate. Results show that it is possible to obtain thin, optically transparent and luminescent films with potential in oxygen sensing, exhibiting good thermal and photochemical stability. At high polyelectrolyte content, evidence is found for phase separation and aggregate formation and it is no longer possible to obtain completely homogeneous films. The possibility of incorporating the cationic metal complex tris(2,2′‐bipyridyl)ruthenium(II) into plasticized PLA films containing conjugated polyelectrolytes for dual‐wavelength ratiometric luminescence sensing is also discussed. Copyright © 2012 Society of Chemical Industry     

Optimal performance assessment for a photo‐Fenton degradation pilot plant driven by solar energy using artificial neural networks

Artificial neural networks (ANN) were proposed as a multivariate experimental design tool for monitoring a photo‐Fenton treatment of wastewaters containing a synthetic mixture of pesticides. ANN and Nelder‐Mead simplex methods were used to find out the optimum operating parameters of a photo‐Fenton pilot plant. ANN was developed to predict the most important operating parameters (e.g., the total organic carbon and the initial mineralization kinetic rate constants of the reactions), which determine the photo‐catalytic degradation efficiency in photo‐Fenton processes. Experimental measurements of temperature, pH, hydrogen peroxide (H₂O₂) consumption, initial concentration of Fe²⁺, and the AE were used as input data for the ANN learning. A feed‐forward with one hidden layer, a Levenberg–Marquardt learning algorithm, a hyperbolic tangent sigmoidal transfer function and a linear transfer function were used to develop the ANN model. The best fitting of the training database was obtained with an ANN architecture constituted by seven neurons in the hidden layer. The simulated results were validated with experimental measurements, showing an acceptable agreement (R² > 0.99). The ANN was subsequently coupled with a Nelder–Mead simplex method to obtain the optimum operating parameters of the photo‐Fenton pilot plant. The H₂O₂ consumption was used as key variable for evaluating the optimization procedure. Errors less than 1% between simulated and experimental data were found. The obtained results showed that the use of ANN provides an excellent predictive performance tool with the additional capability to assess the influence of each operating parameter on the removal process of water pollutants. Copyright © 2011 John Wiley & Sons, Ltd.     

A Dynamic Harmonic Regression Approach to Power System Modal Identification and Prediction

Abstract—Time series models provide a powerful tool to extract nonstationary features from measured data. In this article, a statistical framework based upon a dynamic harmonic regression model for examining modal behavior is provided. In this model, temporal patterns in measured data are modeled within a stochastic state space setting. Estimates of the states or time-varying parameters are then obtained using an optimal estimation method based on the Kalman filter. Techniques to estimate future values of the unobserved signal are also analyzed. The widely applicable technique is illustrated on both simulated and measured data. Factors that affect the performance of the method are discussed, including the effects of non-linear trends, data quality, and sampling design. Connections with other modal identification methods are also investigated.     

Particle breakdown dynamics of heterogeneous foods during mastication: Peanuts embedded inside different food matrices

Heterogeneous foods are widely consumed but little is known about the dynamics of their breakdown during mastication. To investigate these dynamics the particle size and bolus mass at regular stages of the chewing sequence were investigated using a single subject, who was carefully selected according to strict mastication and dental criteria. A standardized volume of both a gelatine gel and chocolate matrix containing roasted peanuts was chewed and expectorated after a specified number of chews or at the point of swallowing. The mass of various components of the bolus was determined along with peanut particle parameters (d₅₀, broadness (b), and specific surface area) at each interval.Results showed that for this subject the type of matrix influenced the pathway that the peanut particles (embedded inside each matrix) were broken down. The d₅₀ of peanut particles within the bolus of the gelatine gel were greater than within the bolus of the chocolate throughout the chewing sequence until the point of swallowing. At the point of swallowing, the d₅₀ of peanut particles inside the bolus of the gelatine gel and chocolate were the same. The broadness value (b) and specific surface area of the peanut particles in the bolus of the chocolate were greater than those in the bolus of the gelatine gel throughout the chewing sequence. The matrices also induced different dynamic trends in the mass of bolus components during the chewing sequence.     

Overcoming the information overload problem in a multiform feedback-based virtual reality system for hand motion rehabilitation: healthy subject case study

The use of composite multiple feedback in a newly proposed virtual reality system enables the patient to perceive similar real-world performance in the virtual world. However, it might cause information overload, which makes the patient feel confused and distracted during training. The aim of this study is to investigate the effectiveness of having separate function-specific feedback pre-training prior to the final multiform feedback task. During the evaluating tests with thirty healthy subjects, it has been found that effective pre-training set could overcome the problem in the main task. Minor modifications on the pre-training set could overcome or aggravate the problem, which indicates the importance of choosing the correct pre-training parameters.     

THE DEVELOPMENT OF COMMERCIAL CRYPTOSYSTEM STANDARDS

There are many requirements for commercial cryptosystems for transmission of intelligence between terminals, between terminals and computers, and between computers. Much commercial information has great value and can warrant the cost of cryptographic protection. This paper claims that the slow rate of implementation of commercial cryptosystems is due to the lack of standardization. This standardization is necessary to achieve general acceptance of cryptosystem products by users. General acceptance is required to permit production volumes which lead to reasonable crypto product prices. This paper discusses the standards efforts that are now ongoing, and projects the types of commercial cryptosystems which will be available in the near future.