Direct polycondensation of N‐trimellitylimido‐L‐isoleucine with aromatic diamines

N‐Trimellitylimido‐L ‐isoleucine (3) was prepared from the reaction of trimellitic anhydride with L ‐isoleucine [L ‐2‐amino‐3‐methylvalerianic acid or (2S,3S)‐2‐amino‐3‐methyl‐n‐valerinic acid] in an N,N‐dimethylformamide solution at the refluxing temperature. The direct polycondensation reaction of the monomer imide diacid (3) with 1,4‐phenylenediamine, 4,4′‐diaminodiphenylmethane, 4,4′‐diaminodiphenylsulfone, diaminodiphenylether, 1,5‐naphthalendiamine, 2,4‐diaminotoluene, and 1,3‐phenylenediamine was performed in a medium consisting of triphenyl phosphite, N‐methyl‐2‐pyrolidone (NMP), pyridine, and calcium chloride. The polycondensation was performed under two different conditions: in one method, the reaction mixture was heated in an NMP solution at 60, 90, and then 130°C for different periods of time, and in the other method, the reaction mixture was refluxed only for 1 min in the same solvent. The resulting poly(amide imide)s (PAIs), with inherent viscosities of 0.21–0.37 dL/g, were obtained in high yields. All of these compounds were fully characterized by IR spectra, elemental analyses, and specific rotation measurements. Some structural characterizations and physical properties of these new optically active PAIs were examined. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 116–122, 2003     

Simple and rapid spectrophotometric and titrimetric methods for the determination of ascorbic acid in pharmaceutical preparations

Ascorbic acid in pharmaceutical preparations can be determined by either spectrophotometric or titrimetric methods by its reaction with ferricinium trichloroacetate at pH 3. The latter is quantitatively reduced to ferrocene. This is extracted by chloroform and its concentration calculated from the absorbance at 440 nm. Alternatively, an aqueous solution of ascorbic acid is titrated with that of the blue ferricinium salt which acts as an indicator. Vitamins, minerals, hormones, dextrose and coloured additives do not interfere with the reaction. Both methods are simple, rapid and sensitive.     

Introducing a Novel Flexible Conjunction System to Pressure Control in Water Distribution Networks

Pressure management is one of the most significant water demand management methods to reduce leakage in water distribution networks. Leak as an adverse event is directly related to the pressure. Therefore, reducing extra network pressure decreases leakage in water distribution networks. The pressure reducing valves have some disadvantage. For example, they break down quickly. Therefore, in this study, a novel system named Pressure Reducing Flexible Storage (PRFS) was introduced that hasn’t these disadvantages and it could consider a good alternative for pressure reducing valves in water distribution networks. In this system, a spherical tank containing a flexible rubber cover was installed at the network node. By increasing the pressure in the conjunction, the foam was compressed and reduced the pressure. In this study, the presented system was simultaneously modeled by using Flow-3D and ABAQUS softwares, and pressure decrement was estimated in the conjunction. The results show that the proposed system can decrease the pressure in the conjunctions of water distribution network by about 18%. Therefore, it could be considered as a good alternative for pressure reducing valves in water distribution networks.     

Improvement in the mechanical properties of poly(3‐hydroxybutyrate‐co‐3‐hydroxyhexanoate) by preorientation

Preoriented poly(3‐hydroxybutyrate‐co‐3‐hydroxyhexanoate) (Nodax?) films were obtained via the drawing of films to prestretching ratios ranging from 200 to 1000%. As expected, preorientation resulted in substantial improvements in the mechanical properties of the films from the corresponding increases in the amount and nature of the crystallinity present. Stress–strain curves in elongation showed a yield peak, which was relatively broad for the unoriented samples but became sharper, with a well‐defined neck, as the extent of preorientation was increased. In addition, the preoriented films had higher modulus, toughness, yield stress, and tensile strength values. The attribution of these changes in the mechanical properties to changes in the crystallinity was supported by X‐ray diffraction measurements. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 94: 2257–2262, 2004     

Static test case prioritization using topic models

Software development teams use test suites to test changes to their source code. In many situations, the test suites are so large that executing every test for every source code change is infeasible, due to time and resource constraints. Development teams need to prioritize their test suite so that as many distinct faults as possible are detected early in the execution of the test suite. We consider the problem of static black-box test case prioritization (TCP), where test suites are prioritized without the availability of the source code of the system under test (SUT). We propose a new static black-box TCP technique that represents test cases using a previously unused data source in the test suite: the linguistic data of the test cases, i.e., their identifier names, comments, and string literals. Our technique applies a text analysis algorithm called topic modeling to the linguistic data to approximate the functionality of each test case, allowing our technique to give high priority to test cases that test different functionalities of the SUT. We compare our proposed technique with existing static black-box TCP techniques in a case study of multiple real-world open source systems: several versions of Apache Ant and Apache Derby. We find that our static black-box TCP technique outperforms existing static black-box TCP techniques, and has comparable or better performance than two existing execution-based TCP techniques. Static black-box TCP methods are widely applicable because the only input they require is the source code of the test cases themselves. This contrasts with other TCP techniques which require access to the SUT runtime behavior, to the SUT specification models, or to the SUT source code.     

A survey and tutorial of wireless relay network protocols based on network coding

Ever since the wireless relay network (RN) was introduced, it has led to the discovery of several new protocols, which have been consistently improved by researchers to overcome many constraints such as throughput, capacity bound, symbol error rate, outage probability, and power allocations. Meanwhile, network coding (NC) was invented as an efficient technique to address these problems. To the best of our knowledge, there has been no comprehensive discussion about wireless-RN modes in the literature. We therefore provide a tutorial concerning the basic concepts of NC, dealing with the classification of particular relay modes, and presenting new wireless-RN protocols with/without NC in this paper. Under appropriate categories (i.e., fixed and dynamic), much attention has been given to various proposed wireless-RN protocols because they may differ individually depending on their processing at relay nodes and network topology. We compare the available wireless-RN protocols with/without NC, and conclude that the wireless-RN based on NC outperforms the traditional wireless-RN in certain scenarios.     

Studying the impact of social interactions on software quality

Correcting software defects accounts for a significant amount of resources in a software project. To make best use of testing efforts, researchers have studied statistical models to predict in which parts of a software system future defects are likely to occur. By studying the mathematical relations between predictor variables used in these models, researchers can form an increased understanding of the important connections between development activities and software quality. Predictor variables used in past top-performing models are largely based on source code-oriented metrics, such as lines of code or number of changes. However, source code is the end product of numerous interlaced and collaborative activities carried out by developers. Traces of such activities can be found in the various repositories used to manage development efforts. In this paper, we develop statistical models to study the impact of social interactions in a software project on software quality. These models use predictor variables based on social information mined from the issue tracking and version control repositories of two large open-source software projects. The results of our case studies demonstrate the impact of metrics from four different dimensions of social interaction on post-release defects. Our findings show that statistical models based on social information have a similar degree of explanatory power as traditional models. Furthermore, our results demonstrate that social information does not substitute, but rather augments traditional source code-based metrics used in defect prediction models.     

A direct-forcing immersed boundary method for the thermal lattice Boltzmann method

In this study, a direct-forcing immersed boundary method (IBM) for thermal lattice Boltzmann method (TLBM) is proposed to simulate the non-isothermal flows. The direct-forcing IBM formulas for thermal equations are derived based on two TLBM models: a double-population model with a simplified thermal lattice Boltzmann equation (Model 1) and a hybrid model with an advection–diffusion equation of temperature (Model 2). As an interface scheme, which is required due to a mismatch between boundary and computational grids in the IBM, the sharp interface scheme based on second-order bilinear and linear interpolations (instead of the diffuse interface scheme, which uses discrete delta functions) is adopted to obtain the more accurate results. The proposed methods are validated through convective heat transfer problems with not only stationary but also moving boundaries – the natural convection in a square cavity with an eccentrically located cylinder and a cold particle sedimentation in an infinite channel. In terms of accuracy, the results from the IBM based on both models are comparable and show a good agreement with those from other numerical methods. In contrast, the IBM based on Model 2 is more numerically efficient than the IBM based on Model 1.     

Single pass text classification by direct feature weighting

The Feature Weighting Classifier (FWC) is an efficient multi-class classification algorithm for text data that uses Information Gain to directly estimate per-class feature weights in the classifier. This classifier requires only a single pass over the dataset to compute the feature frequencies per class, is easy to implement, and has memory usage that is linear in the number of features. Results of experiments performed on 128 binary and multi-class text and web datasets show that FWC??s performance is at least comparable to, and often better than that of Naive Bayes, TWCNB, Winnow, Balanced Winnow and linear SVM. On a large-scale web dataset with 12,294 classes and 135,973 training instances, FWC trained in 13?s and yielded comparable classification performance to a state of the art multi-class SVM implementation, which took over 15?min to train.     

Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Utilization of data mining in software engineering has been the subject of several research papers. Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation. The ability to predict software fault modules and the ability to correlate relations between faulty modules and product attributes using statistics is the subject of this paper. Correlations and relations between the attributes and the categorical variable or the class are studied through generating a pool of records from each dataset and then select two samples every time from the dataset and compare them. The correlation between the two selected records is studied in terms of changing from faulty to non-faulty or the opposite for the module defect attribute and the value change between the two records in each evaluated attribute (e.g. equal, larger or smaller). The goal was to study if there are certain attributes that are consistently affecting changing the state of the module from faulty to none, or the opposite. Results indicated that such technique can be very useful in studying the correlations between each attribute and the defect status attribute. Another prediction algorithm is developed based on statistics of the module and the overall dataset. The algorithm gave each attribute true class and faulty class predictions. We found that dividing prediction capability for each attribute into those two (i.e. correct and faulty module prediction) facilitate understanding the impact of attribute values on the class and hence improve the overall prediction relative to previous studies and data mining algorithms. Results were evaluated and compared with other algorithms and previous studies. ROC metrics were used to evaluate the performance of the developed metrics. Results from those metrics showed that accuracy or prediction performance calculated traditionally using accurately predicted records divided by the total number of records in the dataset does not necessarily give the best indicator of a good metric or algorithm predictability. Those predictions may give wrong implication if other metrics are not considered with them. The ROC metrics were able to show some other important aspects of performance or accuracy.