Analysis of factors contributing to success in a program of micromanipulation-assisted fertilization

OBJECTIVE: To determine factors important to clinical success in micromanipulation-assisted in vitro fertilization (IVF). DESIGN: Procedures invoked in two separate series of micromanipulation-assisted IVF cycles, one unsuccessful (series I) and the other successful (series II), were compared in an effort to identify changes that led to clinical success. SETTING: University-based IVF clinic. PATIENTS: In both IVF series involving micromanipulation, patients consisted of infertile couples who fit any of five categories of male-factor related infertility. The female patients underwent controlled hyperstimulation for oocyte retrieval and the oocytes were inseminated normally or were subjected either to partial zona dissection or subzonal sperm insertion to assist fertilization. Results in all groups were compared between the two patient series. RESULTS: In the successful series II, a noticeable improvement in fertilization rate and embryo quality was observed compared with series I. A significant increase in the percentage of patients reaching embryo transfer, the pregnancy rate per transfer, and the pregnancy rate per retrieval were noted in series II; a 25% ongoing pregnancy rate per retrieval was observed overall in this successful group, with "ongoing" defined as manifestation of at least a fetal sac on ultrasound with no detectable problems. Patients with a mixed transfer of embryos derived from manipulated and normally inseminated oocytes had a 75% rate of pregnancy per transfer in series II. Differences between the two series could not be attributed to patient selection or biases in selection of oocytes relegated to micromanipulation. However, oocyte handling, micromanipulation, and culture protocols differed significantly between the two series in that temperature and pH of oocytes was better controlled, and micromanipulation time was minimized in series II. CONCLUSION: Success in micromanipulation depends on maintenance of the oocyte in a stable and supportive environment throughout the micromanipulation procedure. It is also important to minimize trauma to the eggs by performing micromanipulation rapidly and with minimal distortion of the egg. Patients with a poor fertilization rate in standard IVF may experience a substantial increase in the likelihood of pregnancy when micromanipulation-assisted fertilization is performed on some eggs.     

Exploiting spatial indexes for semijoin-based join processing indistributed spatial databases

In a distributed spatial database system, a user may issue a query that relates two spatial relations not stored at the same site. Because of the sheer volume and complexity of spatial data, spatial joins between two spatial relations at different sites are expensive in terms of computational and transmission costs. In this paper, we address the problems of processing spatial joins in a distributed environment. We propose a semijoin-like operator, called the spatial semijoin, to prune away objects that do not contribute to the join result. This operator also reduces both the transmission and local processing costs for a later join operation. However, the cost of the elimination process must be taken into account, and we consider approaches to minimize these overheads. We also study and compare two families of distributed join algorithms that are based on the spatial semijoin operator. The first is based on multi-dimensional approximations obtained from an index such as the R-tree, and the second is based on single-dimensional approximations obtained from object mapping. We have conducted experiments on real data sets and report the results in this paper     

Simultaneous nonlinear absorption and index effects in the propagation of intense TEA CO2 laser pulses through CDF3

Changes in the beam profile of the CO2 laser 10R(26) line, caused by transmission through, and absorption by, CDF3 were studied using an array of pyroelectric detectors. During the propagation of the laser beam through CDF3, nonlinear absorption and self-defocusing of the beam have both been determined from measurements of the effect on the exit beam of fluence, radiant energy, CDF3 pressure, transmission cell length, and distance from the exit of the cell to the detector array.     

Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Accelerated hypothesis generation for multistructure data via preference analysis

Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points having arisen from the same model, and is obtained without recourse to domain knowledge (e.g., keypoint matching scores) typically used in previous sampling enhancement methods. More crucially, our approach encourages sampling within coherent structures and thus can very rapidly generate all-inlier minimal subsets that maximize the robust criterion. Sampling within coherent structures also affords a natural ability to handle multistructure data, a condition that is usually detrimental to other methods. The result is a sampling scheme that offers substantial speed-ups on common computer vision tasks such as homography and fundamental matrix estimation. We show on many computer vision data, especially those with multiple structures, that ours is the only method capable of retrieving satisfactory results within realistic time budgets.     

Acousto-optical PD detection for transformers

Partial discharge (PD) is one of the factors that could lead to failure of power transformers, leading to power outage and expensive repairs. The acoustic wave induced by PD can be measured and used for monitoring, diagnosing, and locating potential failures in the transformers. Fiber optic sensors have been shown to be attractive devices for PD detection because of a number of inherent advantages including small size, high sensitivity, electrical nonconductivity, and immunity to electromagnetic interference. A fiber optic sensor based on a Fabry-Perot interferometry is constructed by a simple micromachining process compatible with microelectromechanical system technology. The sensors are used in a transformer to measure PD acoustic waves. The experimental results show the sensor not only has an inherent high signal to noise capability, but is able to accurately localize the PD sources inside the transformer.     

Image reconstruction of a buried perfectly conducting cylinder illuminated by transverse electric waves

This article presents a computational approach to the image reconstruction of a perfectly conducting cylinder illuminated by transverse electric waves. A perfectly conducting cylinder of unknown shape buried in one half‐space and scatters the incident wave from another half‐space where the scattered field is recorded. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived, and the imaging problem is reformulated into an optimization problem. The steady state genetic algorithm is then employed to find out the global extreme solution of the cost function. Numerical results demonstrated that, even when the initial guess is far away from the exact one, good reconstruction can be obtained. In such a case, the gradient‐based methods often get trapped in a local extreme. In addition, the effect of different noise on the reconstruction is investigated. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 261–265, 2005     

Fast image retrieval using color-spatial information

In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism. Received August 1, 1997 / Accepted December 9, 1997     

Calculating the elastic moduli of steel-fiber reinforced concrete using a dedicated empirical formula

The equivalent inclusion method (EIM) is adopted to study the characteristics of the equivalent material properties of steel-fiber reinforced concrete as a function of the volume fraction and the length to diameter ratio of the fibers. It is found that the equivalent material moduli of concrete reinforce with randomly orientated and distributed fibers are insensitive to the length to diameter ratio of the steel fibers. A set of empirical formulae is then proposed for the purposes of engineering applications. The proposed empirical model can simplify the calculation of the equivalent material moduli. Verifications of the proposed empirical formulae with the EIM model and with experimental data are performed with two examples. The first is a compression test. The second is 4 point bending test. The empirical formulae, based on the equivalent inclusion method proposed in this study, represent an alternative means of quickly calculating the effective elastic modulus of steel-fiber reinforced concrete materials.