Application of a Compaction Simulator to the Design of a High-Dose Tablet Formulation. Part I

The compaction properties of an investigational drug are studied by the use of a compaction simulator. The effects of punch velocity over the range of 30-640 mm^-1 on the compaction properties of the pure drug and a variety of formulas incorporating a high dose of the active compound have been investigated. The data were analyzed by applying the Heckel equation. The pure drug was found to have a high yield pressure at a relatively low punch velocity of 31 mm^-1. As the punch velocity was increased there was a decrease in crushing strength, primarily as a result of increasing yield pressure. These findings indicate that the pure drug predominantly consolidated by fragmentation and elastic deformation, with a slow plastically deforming component. The information obtained on the consolidation mechanism of the pure drug and, subsequently, on model formulas were instrumental in the design and selection of a robust formula and granulation process. The advantages of conducting dosage form design and characterization studies during the early phase of tablet formulation using means such as a compaction simulator are emphasized in this investigation.     

Online learning of task-driven object-based visual attention control

We propose a biologically-motivated computational model for learning task-driven and object-based visual attention control in interactive environments. In this model, top-down attention is learned interactively and is used to search for a desired object in the scene through biasing the bottom-up attention in order to form a need-based and object-driven state representation of the environment. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Top-down attention is learned by the U-TREE algorithm which successively grows an object-based binary tree. Internal nodes in this tree check the existence of a specific object in the scene by biasing the early vision and the object recognition parts. Its leaves point to states in the action value table. Motor actions are associated with the leaves. After performing a motor action, the agent receives a reinforcement signal from the critic. This signal is alternately used for modifying the tree or updating the action selection policy. The proposed model is evaluated on visual navigation tasks, where obtained results lend support to the applicability and usefulness of the developed method for robotics.     

Handwriting style classification

This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method implemented that can predict this legibility. The technique consists of two phases. In the feature-extraction phase, a set of 36 features is extracted from the image contour. In the classification phase, two nonparametric classification techniques are applied to the extracted features in order to compare their effectiveness in classifying words into legible, illegible, and middle classes. In the first method, a multiple discriminant analysis (MDA) is used to transform the space of extracted features (36 dimensions) into an optimal discriminant space for a nearest mean based classifier. In the second method, a probabilistic neural network (PNN) based on the Bayes strategy and nonparametric estimation of probability density function is used. The experimental results show that the PNN method gives superior classification results when compared with the MDA method. For the legible, illegible, and middle handwriting the method provides 86.5% (legible/illegible), 65.5% (legible/middle), and 90.5% (middle/illegible) correct classification for two classes. For the three-class legibility classification the rate of correct classification is 67.33% using a PNN classifier.Received: 6 September 2002, Accepted: 19 September 2002, Published online: 6 June 2003     

Supercritical carbon dioxide extraction of bioactive flavonoid from Strobilanthes crispus (Pecah Kaca)

The bioactive flavonoid compounds of Strobilanthes crispus (Pecah Kaca) leaves obtained by using supercritical carbon dioxide (SC-CO₂) extraction were investigated and the obtained crude extract yields were compared in order to select the best operation parameters. Since carbon dioxide is a non-polar solvent, ethanol was used as co-solvent to increase the polarity of the fluid. The studied parameters were pressure (100, 150 and 200 bar), temperature (40, 50 and 60 °C) and dynamic extraction time (40, 60 and 80 min). The optimum extraction condition occurred at 200 bar, 50 °C and 60 min. Based on the mean value, pressure had dominant effect on the extraction yield. Apart from the optimum SFE conditions two other conditions namely at minimum (100 bar, 40 °C, 40 min) and maximum (200 bar, 60 °C, 80 min) levels of each studied parameters as control runs were analyzed by HPLC to determine the major bioactive flavonoid compounds from S. crispus. Under the optimum conditions eight flavonoid compounds were identified; they were (+)-catechin, (?)-epicatechin, rutin, myricetin, luteolin, apigenin, naringenin and kaempferol.     

Investigation of the Effect of Foaming Agent on the Fabrication of NiTi Foams Using the Self-Propagating,High-Temperature Synthesis Process

Metallurgical and Materials Transactions B - Fabrication of NiTi intermetallic foams using the self-propagating, high-temperature synthesis (SHS) process was investigated. Weighed amounts of...     

Radiation dose to neonates undergoing X-ray imaging in special care baby units in Iran

Radiographic imaging has a significant role in the timely diagnosis of the diseases of neonates in intensive care units. The estimation of the dose received by the infants undergoing radiographic examination is of great importance, due to greater more radiosensitivity and longer life expectancy of the neonates and premature babies. In this study, the values of entrance skin dose (ESD), dose area products (DAPs), energy imparted (EI), whole-body dose, effective dose and risk of childhood cancer were estimated using three methods including direct method [using thermoluminescence dosimetry (TLD) chips], indirect method (using tube output) and Monte Carlo (MC) method (using MCNP4C code). In the first step, the ESD of the neonates was directly measured using TLD-100 chips. Fifty neonates, mostly premature, with different weights and gestational ages in five hospitals mostly suffering from respiratory distress syndrome and pneumonia were involved in this study. In the second step, the values of ESD to neonates were indirectly obtained from the tube output in different imaging techniques. The imaging room, incubator, neonates and other components were then simulated in order to obtain the ESD values using the MCNP4C code. Finally, the values of ESD assessed by the three methods were used for calculation of DAP, EI, whole-body dose, effective dose and risk of childhood cancer. The results indicate that the mean ESD per radiograph estimated by the direct, indirect and MC methods are 56.6±4.1, 50.1±3.1 and 54.5±3.3 μGy, respectively. The mean risk of childhood cancer estimated in this study varied between 4.21×10(-7) and 2.72×10(-6).     

Development of cardiac prescreening device for rural population using ultralow-power embedded system

The invention is inspired by the desire to understand the opportunities and expectations of developing economies in terms of healthcare. The designed system is a point-of-care (POC) device that can deliver heart-care services to the rural population and bridge the rural-urban divide in healthcare delivery. The product design incorporates several innovations including the effective use of adaptive and multiresolution signal-processing techniques for acquisition, denoising, segmentation, and characterization of the heart sounds (HS) and murmurs using an ultralow-power embedded Mixed Signal Processor. The device is able to provide indicative diagnosis of cardiac conditions and classify a subject into either normal, abnormal, ischemic, or valvular abnormalities category. Preliminary results demonstrated by the prototype confirm the applicability of the device as a prescreening tool that can be used by paramedics in rural outreach programs. Feedback from medical professionals also shows that such a device is helpful in early detection of common congenital heart diseases. This letter aims to determine a framework for utilization of automated HS analysis system for community healthcare and healthcare inclusion.