Visual performance and optical quality with soft lenses in keratoconus patients

Purpose: To assess visual performance and ocular aberrations in keratoconic patients using toric soft contact lenses (SCL), rigid-gas-permeable (RGP) contact lenses and spectacle lens correction. Methods: Twenty-two keratoconus patients (16 RGP lens wearers and six spectacle wearers) were fitted with toric SCL. Ocular aberrations were measured with and without the patient's habitual RGP lenses and with the SCL in place. In the spectacle wearers, aberrations were measured with and without the SCL. Visual performance (high- and low-contrast visual acuity) was evaluated with the patient's habitual correction and with the SCL. Results: In the RGP lens wearers both the habitual lenses and the toric SCL significantly reduced coma, trefoil, 3rd-order, 4th-order cylinder and higher-order root-mean-square (RMS) aberrations (p≤0.015). In the spectacle wearers the toric SCL significantly reduced coma, 3rd-order and higher-order RMS aberrations (p≤0.01). The patients' habitual RGP lenses gave better low-contrast acuity (p≤0.006) compared to the toric SCL; however, no significant difference was found between lens types for high-contrast acuity (p=0.10). In the spectacle wearers no significant differences in visual performance measurements were found between the patients' spectacles and the toric SCL (p≥0.06). Conclusion: The results show that RGP lenses provided superior visual performances and greater reduction of 3rd-order aberrations compared to toric SCL in this group of keratoconic patients. In the spectacle-wearing group, visual performance with the toric SCL was found to be comparable to that measured with spectacles. Nevertheless, with the exception of spherical aberration, the toric SCL were successful in significantly reducing uncorrected higher-order aberrations. Ophthalmic & Physiological Optics © 2012 The College of Optometrists.

A comparison of denoising methods for X-ray fluoroscopic images

Fluoroscopic images exhibit severe signal-dependent quantum noise, due to the reduced X-ray dose involved in image formation, that is generally modelled as Poisson-distributed. However, image gray-level transformations, commonly applied by fluoroscopic device to enhance contrast, modify the noise statistics and the relationship between image noise variance and expected pixel intensity. Image denoising is essential to improve quality of fluoroscopic images and their clinical information content. Simple average filters are commonly employed in real-time processing, but they tend to blur edges and details. An extensive comparison of advanced denoising algorithms specifically designed for both signal-dependent noise (AAS, BM3Dc, HHM, TLS) and independent additive noise (AV, BM3D, K-SVD) was presented. Simulated test images degraded by various levels of Poisson quantum noise and real clinical fluoroscopic images were considered. Typical gray-level transformations (e.g. white compression) were also applied in order to evaluate their effect on the denoising algorithms. Performances of the algorithms were evaluated in terms of peak-signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), mean square error (MSE), structural similarity index (SSIM) and computational time. On average, the filters designed for signal-dependent noise provided better image restorations than those assuming additive white Gaussian noise (AWGN). Collaborative denoising strategy was found to be the most effective in denoising of both simulated and real data, also in the presence of image gray-level transformations. White compression, by inherently reducing the greater noise variance of brighter pixels, appeared to support denoising algorithms in performing more effectively. © 2012 Elsevier Ltd. All rights reserved.

Neuromuscular response to whole body vibration treatment during static and dynamic squat exercises

The impact of whole body vibrations (vibration stimulus mechanically transferred to the body) on muscular activity and neuromuscular response has been widely studied but without standard protocol and by using different kinds of exercises and parameters. In this study, we investigated how whole body vibration treatments affect electromyographic signal of rectus femoris during static and dynamic squat exercises. The aim was the identification of squat exercise characteristics useful to maximize neuromuscular activation and hence progress in training efficacy. Fourteen healthy volunteers performed both static and dynamic squat exercises without and with vibration treatments. Surface electromyographic signals of rectus femoris were recorded during the whole exercise and processed to reduce artifacts and to extract root mean square values. Paired t-test results demonstrated an increase of the root mean square values (p<0.05) in both static and dynamic squat exercises with vibrations respectively of 63% and 108%. For each exercise, subjects gave a rating of the perceived exertion according to the Borg's scale but there were no significant changes in the perceived exertion rate between exercises with and without vibration. Finally, results from analysis of electromyographic signals identified the static squat with WBV treatment as the exercise with higher neuromuscular system response. © 2012 IEEE.

Muscle movement and electrodes motion artifact during vibration treatment

Vibration treatment by oscillating platforms is more and more employed in the fields of exercise physiology and bone research. The rationale of this treatment is based on the neuromuscular system response elicited by vibration loads. surface Electromyography (EMG) is largely utilized to assess muscular response elicited by vibrations and Root Mean Square of the electromyography signals is often used as a concise quantitative index of muscle activity; in general, EMG envelope or RMS is expected to increase during vibration. However, it is well known that during surface bio-potential recording, motion artifacts may arise from relative motion between electrodes and skin and between skin layers. Also the only skin stretch, modifying the internal charge distribution, results in a variation of electrode potential. The aim of this study is to highlight the movements of muscles, and the succeeding relevance of motion artifacts on electrodes, in subjects undergoing vibration treatments. EMGs from quadriceps of fifteen subjects were recorded during vibration at different frequencies (15-40 Hz); Triaxial accelerometers were placed onto quadriceps, as close as possible to muscle belly, to monitor motion. The computed muscle belly displacements showed a peculiar behavior reflecting the mechanical properties of the structures involved. Motion artifact related to the impressed vibration have been recognized and related to movement of the soft tissues. In fact large artifacts are visible on EMGs and patellar electrodes recordings during vibration. Signals spectra also revealed sharp peaks corresponding to vibration frequency and its harmonics, in accordance with accelerometers data. © 2008 Springer-Verlag.

Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip°s width, and chip°s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed. © ASM International.

The effects of entrance pupil centration and coma aberrations on myopic progression following orthokeratology

Background: The aim was to assess the potential association between entrance pupil location relative to the coaxially sighted corneal light reflex (CSCLR) and the progression of myopia in children fitted with orthokeratology (OK) contact lenses. Additionally, whether coma aberration induced by decentration of the entrance pupil centre relative to the CSCLR, as well as following OK treatment, is correlated with the progression of myopia, was also investigated. Methods: Twenty-nine subjects aged six to 12years and with myopia of -0.75 to -4.00 DS and astigmatism up to 1.00DC were fitted with OK contact lenses. Measurements of axial length and corneal topography were taken at six-month intervals over a two-year period. Additionally, baseline and three-month topographic outputs were taken as representative of the pre- and post-orthokeratology treatment status. Pupil centration relative to the CSCLR and magnitude of associated corneal coma were derived from corneal topographic data at baseline and after three months of lens wear. Results: The centre of the entrance pupil was located superio-temporally to the CSCLR both pre- (0.09±0.14 and -0.10±0.15mm, respectively) and post-orthokeratology (0.12±0.18 and -0.09±0.15mm, respectively) (p>0.05). Entrance pupil location pre- and post-orthokeratology lens wear was not significantly associated with the two-year change in axial length (p>0.05). Significantly greater coma was found at the entrance pupil centre compared with CSCLR both pre- and post-orthokeratology lens wear (both p<0.05). A significant increase in vertical coma was found with OK lens wear compared to baseline (p<0.001) but total root mean square (RMS) coma was not associated with the change in axial length (all p>0.05). Conclusion: Entrance pupil location relative to the CSCLR was not significantly affected by either OK lens wear or an increase in axial length. Greater magnitude coma aberrations found at the entrance pupil centre in comparison to the CSCLR might be attributed to centration of orthokeratological treatments at the CSCLR.

Estimation of computer waste quantities using forecasting techniques

Technology changes rapidly over years providing continuously more options for computer alternatives and making life easier for economic, intra-relation or any other transactions. However, the introduction of new technology “pushes” old Information and Communication Technology (ICT) products to non-use. E-waste is defined as the quantities of ICT products which are not in use and is bivariate function of the sold quantities, and the probability that specific computers quantity will be regarded as obsolete. In this paper, an e-waste generation model is presented, which is applied to the following regions: Western and Eastern Europe, Asia/Pacific, Japan/Australia/New Zealand, North and South America. Furthermore, cumulative computer sales were retrieved for selected countries of the regions so as to compute obsolete computer quantities. In order to provide robust results for the forecasted quantities, a selection of forecasting models, namely (i) Bass, (ii) Gompertz, (iii) Logistic, (iv) Trend model, (v) Level model, (vi) AutoRegressive Moving Average (ARMA), and (vii) Exponential Smoothing were applied, depicting for each country that model which would provide better results in terms of minimum error indices (Mean Absolute Error and Mean Square Error) for the in-sample estimation. As new technology does not diffuse in all the regions of the world with the same speed due to different socio-economic factors, the lifespan distribution, which provides the probability of a certain quantity of computers to be considered as obsolete, is not adequately modeled in the literature. The time horizon for the forecasted quantities is 2014-2030, while the results show a very sharp increase in the USA and United Kingdom, due to the fact of decreasing computer lifespan and increasing sales.

Measurement and correlation of the solubility of telmisartan (form A) in nine different solvents from 277.85 to 338.35 K

The solubility of telmisartan (form A) in nine organic solvents (chloroform, dichloromethane, ethanol, toluene, benzene, 2-propanol, ethyl acetate, methanol and acetone) was determined by a laser monitoring technique at temperatures from 277.85 to 338.35 K. The solubility of telmisartan (form A) in all of the nine solvents increased with temperature as did the rates at which the solubility increased except in chloroform and dichloromethane. The mole fraction solubility in chloroform is higher than that in dichloromethane, which are both one order of magnitude higher than those in the other seven solvents at the experimental temperatures. The solubility data were correlated with the modified Apelblat equation and λh equations. The results show that the λh equation is in better agreement with the experimental data than the Apelblat equation. The relative root mean square deviations (σ) of the λh equation are in the range from 0.004 to 0.45 %. The dissolution enthalpies, entropies and Gibbs energies of telmisartan in these solvents were estimated by the Van’t Hoff equation and the Gibbs equation. The melting point and the fusion enthalpy of telmisartan were determined by differential scanning calorimetry.

Surrogate-based analysis and optimization for the design of heat sinks with jet impingement

Heat sinks are widely used for cooling electronic devices and systems. Their thermal performance is usually determined by the material, shape, and size of the heat sink. With the assistance of computational fluid dynamics (CFD) and surrogate-based optimization, heat sinks can be designed and optimized to achieve a high level of performance. In this paper, the design and optimization of a plate-fin-type heat sink cooled by impingement jet is presented. The flow and thermal fields are simulated using the CFD simulation; the thermal resistance of the heat sink is then estimated. A Kriging surrogate model is developed to approximate the objective function (thermal resistance) as a function of design variables. Surrogate-based optimization is implemented by adaptively adding infill points based on an integrated strategy of the minimum value, the maximum mean square error approach, and the expected improvement approaches. The results show the influence of design variables on the thermal resistance and give the optimal heat sink with lowest thermal resistance for given jet impingement conditions.

Short- and long-term changes in corneal aberrations and axial length induced by orthokeratology in children are not correlated

PURPOSE: To assess the correlation between changes in corneal aberrations and the 2-year change in axial length in children fitted with orthokeratology (OK) contact lenses. METHODS: Thirty-one subjects 6 to 12 years of age and with myopia −0.75 to −4.00DS and astigmatism ≤1.00DC were fitted with OK. Measurements of axial length and corneal topography were taken at regular intervals over a 2-year period. Corneal topography at baseline and after 3 and 24 months of OK lens wear was used to derive higher-order corneal aberrations (HOA) that were correlated with OK-induced axial length changes at 2 years. RESULTS: Significant changes in C3, C4, C4, root mean square (RMS) secondary astigmatism and fourth and total HOA were found with both 3 and 24 months of OK lens wear in comparison with baseline (all P0.05). Coma angle of orientation changed significantly pre-OK in comparison with 3 and 24 months post-OK as well as secondary astigmatism angle of orientation pre-OK in comparison with 24 months post-OK (all P0.05). DISCUSSION: Short-term and long-term OK lens wear induces significant changes in corneal aberrations that are not significantly correlated with changes in axial elongation after 2-years.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Thermodynamic description and phase transformation of highly symmetrical monoatomic structures

Based on theoretical considerations an explanation for the temperature dependence of the thermal expansion and the bulk modulus is proposed. A new equation state is also derived. Additionally a physical explanation for the latent heat of fusion is presented. These theoretical predictions are tested against experiments on highly symmetrical monatomic structures. ^ The volume is not an independent variable and must be broken down into its fundamental components when the relationships to the pressure and temperature are defined. Using zero pressure and temperature reference frame, the initial parameters, volume at zero pressure and temperature[V°], bulk modulus at zero temperature [K°] and volume coefficient of thermal expansion at zero pressure[α°] are defined. ^ The new derived EoS is tested against the experiments on perovskite and epsilon iron. The Root-mean-square-deviations (RMSD) of the residuals of the molar volume, pressure, and temperature are in the range of the uncertainty of the experiments. ^ Separating the experiments into 200 K ranges, the new EoS was compared to the most widely used finite strain, interatomic potential, and empirical isothermal EoSs such as the Burch-Murnaghan, the Vinet, and the Roy-Roy respectively. Correlation coefficients, RMSD's of the residuals, and Akaike Information Criteria were used for evaluating the fitting. Based on these fitting parameters, the new p-V-T EoS is superior in every temperature range relative to the investigated conventional isothermal EoS. ^ The new EoS for epsilon iron reproduces the preliminary-reference earth-model (PREM) densities at 6100-7400 K indicating that the presence of light elements might not be necessary to explain the Earth's inner core densities. ^ It is suggested that the latent heat of fusion supplies the energy required for overcoming on the viscous drag resistance of the atoms. The calculated energies for melts formed from highly symmetrical packing arrangements correlate very well with experimentally determined latent heat values. ^ The optical investigation of carhonado-diamond is also part of the dissertation. The collected first complete infrared FTIR absorption spectra for carhonado-diamond confirm the interstellar origin for the most enigmatic diamonds known as carbonado. ^

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Mitigating congestion by integrating time forecasting and realtime information aggregation in cellular networks

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

Electro and Magneto-Electropolished Surface Micro-Patterning on Binary and Ternary Nitinol

In this study, an Atomic Force Microscopy (AFM) roughness analysis was performed on non-commercial Nitinol alloys with Electropolished (EP) and Magneto-Electropolished (MEP) surface treatments and commercially available stents by measuring Root-Mean-Square (RMS) , Average Roughness (Ra), and Surface Area (SA) values at various dimensional areas on the alloy surfaces, ranging from (800 x 800 nm) to (115 x 115µm), and (800 x 800 nm) to (40 x 40 µm) on the commercial stents. Results showed that NiTi-Ta 10 wt% with an EP surface treatment yielded the highest overall roughness, while the NiTi-Cu 10 wt% alloy had the lowest roughness when analyzed over (115 x 115 µm). Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analysis revealed unique surface morphologies for surface treated alloys, as well as an aggregation of ternary elements Cr and Cu at grain boundaries in MEP and EP surface treated alloys, and non-surface treated alloys. Such surface micro-patterning on ternary Nitinol alloys could increase cellular adhesion and accelerate surface endothelialization of endovascular stents, thus reducing the likelihood of in-stent restenosis and provide insight into hemodynamic flow regimes and the corrosion behavior of an implantable device influenced from such surface micro-patterns.