Novel PDMS‐based membranes: Sodium chloride and glucose permeability

Permeation of sodium chloride and glucose through polydimethylsiloxane‐poly(N‐isopropylacrylamide) (PDMS‐PNIPAAm) interpenetrating polymer networks (IPNs) of two different microstructures was investigated. We have successfully developed small‐molecule permeable IPNs, by modifying PDMS film structure. A group of PDMS films was prepared using conventional solvent casting (SC) method and another group produced by introducing oil, followed by SC and leaching the oil out (SCOL method). Scanning electron microscopy (SEM) and attenuated total reflection fourier transformer infrared (ATR‐FTIR) spectroscopy results confirmed the presence of PNIPAAm in the SC and SCOL IPNs. Results obtained from spectra of differential scanning calorimetry (DSC) showed that these IPNs had a phase transition temperature at about 32°C. Permeation measurements showed that the presence of PNIPAAm as the second phase in the IPN, improved the permeability of PDMS film. According to the results, maximum permeation coefficient was related to SCOL IPN containing 15.8% ± 0.3%PNIPAAm, at 23°C (5.98 × 10^?7 ± 7.93 × 10^?9 cm²/s for sodium chloride and 3.6 × 10^?7 ± 7 × 10^?9 cm²/s for glucose). These results suggested that these PDMS‐PNIPAAm IPNs with sodium chloride and glucose permeability may be further developed as ophthalmic biomaterials or corneal replacements. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Mechanical Behavior of Lentil Seeds in Relation to their Physicochemical and Microstructural Characteristics

The mechanical characteristics (rupture force, maximal deformation, and rupture energy) of red and green lentils under compression loading were determined as a function of moisture content ranging from 9.5 to 21.1% (w.b.). Scanning electron microscopy, laser diffraction particle size analysis, and instrumental texture evaluation were successfully applied to relate the microstructure and texture of different lentil seed varieties. Results demonstrated that all of the mechanical parameters of the green lentils, which have smaller starch granules, were significantly (p < 0.05) higher than those of the red lentils. At a loading rate of 4 mm min^?1, the force required for initiating seed rupture decreased with an increase in moisture content, for vertical and horizontal orientations (p < 0.05). The scanning electron microscopy observations also revealed that seeds were more flexible in a horizontal orientation.     

A coupled Thermal‐mechanical FE Model of Flow Localization during the Hot Torsion Test

The hot torsion test (HTT) has been extensively used to analyse and physically model the flow behaviour and microstructure evolution of materials and alloys during hot deformation processes. The geometry of the specimen is a key factor for obtaining reliable results. In the present work, a thermo‐rigid viscoplastic FE code, THORAX.FOR, was developed to describe the interaction of thermal‐mechanical conditions and geometries of the HTT specimens. This was used to recommend the conditions for avoiding flow localization during HTT of API‐X70 microalloyed steel. The simulation results show how an inappropriate choice of both test specimen geometry and twist rate of deformation could lead to a significant temperature raise in the middle of the gauge section and temperature gradient in the radial and longitudinal direction of the specimen. This consequently causes flow localization during the test. Therefore, assumptions of isothermal forming conditions or uniform strain softening may not be valid in many test scenarios. These assumptions could introduce significant errors in the post results of the test such as flow curve and interpretation of microstructure evolution. Recommendations on proper specimen geometry for a specified strain rate will be given to avoid flow localization during the hot torsion test.     

Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods

Neural Processing Letters - Deep learning is an important subcategory of machine learning approaches in which there is a hope of replacing man-made features with fully automatic extracted features....     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

Fuzzy logistic regression based on the least squares approach with application in clinical studies

To model fuzzy binary observations, a new model named “Fuzzy Logistic Regression” is proposed and discussed in this study. In fact, due to the vague nature of binary observations, no probability distribution can be considered for these data. Therefore, the ordinary logistic regression may not be appropriate. This study attempts to construct a fuzzy model based on possibility of success. These possibilities are defined by some linguistic terms such as …, low, medium, high…. Then, by use of the Extension principle, the logarithm transformation of “possibilistic odds” is modeled based on a set of crisp explanatory variables observations. Also, to estimate parameters in the proposed model, the least squares method in fuzzy linear regression is used. For evaluating the model, a criterion named the “capability index” is calculated. At the end, because of widespread applications of logistic regression in clinical studies and also, the abundance of vague observations in clinical diagnosis, the suspected cases to Systematic Lupus Erythematosus (SLE) disease is modeled based on some significant risk factors to detect the application of the model. The results showed that the proposed model could be a rational substituted model of an ordinary one in modeling the clinical vague status.     

Uncertain Classification of Fault-Prone Software Modules

Many development organizations try to minimize faults in software as a means for improving customer satisfaction. Assuring high software quality often entails time-consuming and costly development processes. A software quality model based on software metrics can be used to guide enhancement efforts by predicting which modules are fault-prone. This paper presents statistical techniques to determine which predictions by a classification tree should be considered uncertain. We conducted a case study of a large legacy telecommunications system. One release was the basis for the training dataset, and the subsequent release was the basis for the evaluation dataset. We built a classification tree using the TREEDISC algorithm, which is based on ² tests of contingency tables. The model predicted whether a module was likely to have faults discovered by customers, or not, based on software product, process, and execution metrics. We simulated practical use of the model by classifying the modules in the evaluation dataset. The model achieved useful accuracy, in spite of the very small proportion of fault-prone modules in the system. We assessed whether the classes assigned to the leaves were appropriate by statistical tests, and found sizable subsets of modules with uncertain classification. Discovering which modules have uncertain classifications allows sophisticated enhancement strategies to resolve uncertainties. Moreover, TREEDISC is especially well suited to identifying uncertain classifications.     

Application of neural networks for predicting program faults

Accurately predicting the number of faults in program modules is a major problem in the quality control of large software development efforts. Some software complexity metrics are closely related to the distribution of faults across program modules. Using these relationships, software engineers develop models that provide early estimates of quality metrics that do not become available until late in the development cycle. By considering these early estimates, software engineers can take actions to avoid or prepare for emerging quality problems. Most often, the predictive models are based upon multiple regression analysis. However, measures of software quality and complexity exhibit systematic departures from the assumptions of these analyses. With extreme violations of these assumptions, multiple regression models become unstable and lose most of their predictive quality. Since neural network models carry no data assumptions, these models could be more appropriate than regression models for modeling software faults. In this paper, we explore a neural network methodology for developing models that predict the number of faults in program modules. We apply this methodology to develop neural network models based upon data collected during the development of two commercial software systems. After developing neural network models, we apply multiple linear regression methods to develop regression models on the same data. For the data sets considered, the neural network methodology produced better predictive models in terms of both quality of fit and predictive quality.