Reliability of Active-Matrix Organic Light-Emitting-Diode Arrays With Amorphous Silicon Thin-Film Transistor Backplanes on Clear Plastic

We have fabricated active-matrix organic light emitting diode (AMOLED) test arrays on an optically clear high-temperature flexible plastic substrate at process temperatures as high as 285 degC using amorphous silicon thin-film transistors (a-Si TFTs). The substrate transparency allows for the operation of AMOLED pixels as bottom-emission devices, and the improved stability of the a-Si TFTs processed at higher temperatures significantly improves the reliability of the light emission over time.     

Ochratoxin A in dried vine fruit: method development and survey

A method is described for the determination of concentrations of the mycotoxin ochratoxin A in dried vine fruits (currants, raisins and sultanas) using acidic methanolic extraction,immunoaffinity chromatography clean-up and HPLC determination. The limit of detection was estimated as 0.2mug/kg, and recoveries of 63-77% were achieved at 5mug/kg. HPLC-mass spectrometric confirmation of the identity of ochratoxin was obtained. Ochratoxin A and aflatoxins were determined in 60 samples of retail dried vine fruits purchased in the United Kingdom. Ochratoxin A was found in excess of 0.2mug/kg in 19 of 20 currant, 17 of 20 sultana and 17 of 20 raisin samples examined, an overall incidence of 88% . The maximum level found was 53.6mug/kg. No aflatoxin was found in any sample analysed, using a method with a detection limit of 0.2mug/kg for each of aflatoxin B1, B2, G1 and G2.     

Highly Accurate Quasi-Static Modeling of Coupled Interconnects in CMOS Technology

A new analytic model is presented (the model is based on the induced current density distribution inside silicon substrate) to calculate frequency dependent mutual inductance and resistance per unit length of coupled on-chip interconnects in CMOS technology. The validity of the proposed model has been checked by a comparison with a quasi-TEM spectral domain numerical simulation and equivalent-circuit modeling procedure. It is found that the silicon semiconducting substrate skin effect must be considered for the accurate prediction of the high-frequency characteristics of VLSI interconnects.     

A 2D non-parabolic six-moments model

In order to describe carrier transport in inversion layers we have developed a two-dimensional non-parabolic macroscopic transport model up to the sixth order. To model the transport parameters with as few simplifying assumptions as possible, we apply an extraction technique from Subband Monte Carlo simulations followed by an interpolation within these Monte Carlo tables through the whole inversion layer. Important effects like surface-roughness scattering as well as quantization are inherently considered in the Subband Monte Carlo data, which are used to model higher-order mobilities as well as the macroscopic relaxation times as a function of the effective field and the carrier temperature. The parameters are compared with the results obtained from models using bulk Monte Carlo data, where neither surface roughness nor quantization are considered. The models are applied to a UTB SOI-MOSFET and their predictions are discussed for different gate lengths.     

Minimization of the Ohmic Loss of Grooved Polarizer Mirrors in High-Power ECRH Systems

A set of two corrugated polarizer mirrors is typically used in high-power electron cyclotron resonance heating (ECRH) systems to provide the required polarization of the ECRH output beam. The ohmic losses of these mirrors can significantly exceed the losses of plane mirrors depending on the polarization of the incident beam with respect to the orientation of the grooves. Since polarizer mirrors incorporated into miter bends of a corrugated waveguide line are limited in size, active water cooling can become critical in high-power cw systems like the one for ITER. The ohmic loss of polarizer mirrors has been investigated experimentally at high power. A strategy to minimize the losses for given mirror geometries has been found.     

Diagnosis of learner dropout based on learning styles for online distance learning

The amount of data generated by computer systems in Online Distance Learning (ODL) contains rich information. One example of this information we define as the Learner Learning Trail (LLT), which is the sequence of interactions between the students and the virtual environment. Another example is the Learner Learning Style (LLS), which is associated with the student behavior and choices during the learning process. This information can be used to identify learner behavior and learning style. We perceived, after the study of related literature, that the research field of learner diagnosis for ODL does not apply the conjoint use of LLT and LLS. In this article, we propose a model capable of integrating data generated from the behavior of students in ODL with cognitive aspects of them, such as their Learning Styles, by crossing LLT with LLS. We also propose the CPAD method (Collect, Preprocessing, Analysis, Diagnosis), which is implemented by collecting the raw data regarding learning activities, preprocessing the data into structured time sequences, analyzing the sequences regarding the learning styles and using this analysis to diagnose the learner behavior. We selected the dropout to investigate, once the dropout rate in ODL is a real problem in universities around the world. In addition, the dropout is a student decision which can be associated with previous students behaviors. We performed a study with 202 learners to evaluate if learning styles are capable of explaining aspects of the student behavior. The results suggest that Sequential/Global learning style dimension is more capable of explaining the dropout than the other dimensions. Also, we performed four classification experiments to verify how the dimensions of Felder-Silverman Learning Style Model influence the learner diagnosis. We perceived that the Sequential/Global dimension could provide a higher accuracy average with lower variation independently of the diagnosis technique.     

Injectable Collagen–Genipin Gel for the Treatment of Spinal Cord Injury: In Vitro Studies

Spinal cord injury (SCI) often results in a cavitary lesion, contained within the dura, which involves only a portion of the cord. Injectable biopolymers are an attractive treatment option for SCI to re‐establish cell migratory pathways within the lesion while minimizing the collateral damage attendant to an open surgical procedure. In this study we evaluate a thermoresponsive soluble collagen gel incorporating genipin, an amine reactive covalent cross‐linker with low cytotoxicity and fluorogenic attributes. Unlike previous studies, genipin is being investigated as an in situ covalent cross‐linker that will continue to act on the gel after injection. Physical characterization studies show that the addition of genipin provides control over the mechanical and degradative behavior of the gel, to meet design specifications of an injectable material for neural tissues. Additionally, an improved in situ assay to predict the extent of cross‐linking reaction is investigated. Encapsulation of mesenchymal stem cells (MSCs) in collagen–genipin gels show the gels support cell viability and proliferation, and thus serve as a cell delivery vehicle. Neural stem cells are found to be more sensitive to genipin, with respect to toxicity, as compared to MSCs. From our studies, 0.25‐0.5 mM is an appropriate genipin concentration to use for an in situ forming scaffold capable of delivering cells and therapeutic agents.     

Learning With Weblogs: Enhancing Cognitive and Social Knowledge Construction

This study investigated the impact of weblog use on individual learning in the context of university senior-level business education. As an emergent form of personal communication, weblogs enable people to publish their thoughts as webpages, and to share information and knowledge. Recognizing the potential impact of weblogs on knowledge expression and sharing, this research sought to empirically examine whether the continuous use of weblogs as online learning logs would affect student learning performance. The assumption was that effective use of weblogs promoted the constructivist models of learning by supporting both cognitive and social knowledge construction, and by reinforcing individual accountability in learning. Results from an Information Systems undergraduate course with 31 participants indicated that the performance of students' weblogs was a significant predictor of the learning outcome (while traditional coursework was not). Moreover, individuals' cognitive construction effort to build their own mental models and social construction effort to further enrich/expand knowledge resources appeared to be two key aspects of the constructivist learning with weblogs. Our results imply the potential benefit of using weblogs as a knowledge construction tool and a social learning medium     

Highly Stretchable Conducting SIBS‐P3HT Fibers

Poly(styrene‐β‐isobutylene‐β‐styrene)‐poly(3‐hexylthiophene) (SIBS‐P3HT) conducting composite fibers are successfully produced using a continuous flow approach. Composite fibers are stiffer than SIBS fibers and able to withstand strains of up 975% before breaking. These composite fibers exhibit interesting reversible mechanical and electrical characteristics, which are applied to demonstrate their strain gauging capabilities. This will facilitate their potential applications in strain sensing or elastic electrodes. Here, the fabrication and characterization of highly stretchable electrically conducting SIBS‐P3HT fibers using a solvent/non‐solvent wet‐spinning technique is reported. This fabrication method combines the processability of conducting SIBS‐P3HT blends with wet‐spinning, resulting in fibers that could be easily spun up to several meters long. The resulting composite fiber materials exhibit an increased stiffness (higher Young’s modulus) but lower ductility compared to SIBS fibers. The fibers’ reversible mechanical and electrical characteristics are applied to demonstrate their strain gauging capabilities.     

A new approximate likelihood estimator for ARMA-filtered hiddenMarkov models

Hidden Markov models (HMMs) are successfully applied in various fields of time series analysis. Colored noise, e.g., due to filtering, violates basic assumptions of the model. Although it is well known how to consider autoregressive (AR) filtering, there is no algorithm to take into account moving-average (MA) filtering in parameter estimation exactly. We present an approximate likelihood estimator for MA-filtered HMM that is generalized to deal with an autoregressive moving-average (ARMA) filtered HMM. The approximation order of the likelihood calculation can be chosen. Therefore, we obtain a sequence of estimators for the HMM parameters as well as for the filter coefficients. The recursion equations for an efficient algorithm are derived from exact expressions for the forward iterations. By simulations, we show that the derived estimators are unbiased in filter situations where standard HMM's are not able to recover the true dynamics. Special implementation strategies together with small approximations yield further acceleration of the algorithm