An on-line microcomputer-based respiratory monitor for manual materials handling

The main objective of the present study is to develop a low cost microcomputer-based respiratory system that is capable of computing the oxygen consumption and minute ventilation of individuals engaged in manual materials handling on an on-line basis. The design, specifications, and merits of the system are given. The software package developed was written in assembly language for the IBM AT personal computer.     

Diffusion of saturated hydrocarbons in low density polyethylene (LDPE) films

The diffusion coefficients at zero penetrant concentration, D₀, of n-heptane, n-heptane, n-octane, n-decane, and 2,2,4-trimethylpentane (TMP) in LDPE were obtained in the range of 25–50°C, using the desorption method. The dependence of D₀ on the size and shape of the penetrant is reported. It was found that D₀ decreases with increasing penetrant molecule size. The activation energies of diffusion in the temperature range of 25–50°C increase with increasing penetrant molecule size and are independent of temperature. The results are interpreted in terms of the free volume theory and semiquantitative estimates of the free volume parameters are reported.     

Diffusion of organic penetrants through low density polyethylene (LDPE) films: Effect of size and shape of the penetrant molecules

The diffusion coefficient at zero penetrant concentration D₀ of dichloromethane, chloroform, carbon tetrachloride, cyclohexane, benzene, o-xylene, m-xylene, and p-xylene, and n-hexane in LDPE were measured at 25°C, using the desorption method. The D₀ values obtained in this way are correlated with the size, shape, and chemical nature of the penetrant molecules. The temperature dependence of the diffusion coefficients of toluene and n-hexane in LDPE are also reported in the limited temperature range of 25–45°C. It indicates that, in spite of a size larger than that of toluene, n-hexane has a lower activation energy of diffusion.     

Studying high impact fix-inducing changes

As software systems continue to play an important role in our daily lives, their quality is of paramount importance. Therefore, a plethora of prior research has focused on predicting components of software that are defect-prone. One aspect of this research focuses on predicting software changes that are fix-inducing. Although the prior research on fix-inducing changes has many advantages in terms of highly accurate results, it has one main drawback: It gives the same level of impact to all fix-inducing changes. We argue that treating all fix-inducing changes the same is not ideal, since a small typo in a change is easier to address by a developer than a thread synchronization issue. Therefore, in this paper, we study high impact fix-inducing changes (HIFCs). Since the impact of a change can be measured in different ways, we first propose a measure of impact of the fix-inducing changes, which takes into account the implementation work that needs to be done by developers in later (fixing) changes. Our measure of impact for a fix-inducing change uses the amount of churn, the number of files and the number of subsystems modified by developers during an associated fix of the fix-inducing change. We perform our study using six large open source projects to build specialized models that identify HIFCs, determine the best indicators of HIFCs and examine the benefits of prioritizing HIFCs. Using change factors, we are able to predict 56 % to 77 % of HIFCs with an average false alarm (misclassification) rate of 16 %. We find that the lines of code added, the number of developers who worked on a change, and the number of prior modifications on the files modified during a change are the best indicators of HIFCs. Lastly, we observe that a specialized model for HIFCs can provide inspection effort savings of 4 % over the state-of-the-art models. We believe our results would help practitioners prioritize their efforts towards the most impactful fix-inducing changes and save inspection effort.     

Studying re-opened bugs in open source software

Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects—namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1–78.6 % and a recall in the range of 70.5–94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs.     

Web-based algorithm for cylindricity evaluation using support vector machine learning

This paper introduces a cylindricity evaluation algorithm based on support vector machine learning with a specific kernel function, referred to as SVR, as a viable alternative to traditional least square method (LSQ) and non-linear programming algorithm (NLP). Using the theory of support vector machine regression, the proposed algorithm in this paper provides more robust evaluation in terms of CPU time and accuracy than NLP and this is supported by computational experiments. Interestingly, it has been shown that the SVR significantly outperforms LSQ in terms of the accuracy while it can evaluate the cylindricity in a more robust fashion than NLP when the variance of the data points increases. The robust nature of the proposed algorithm is expected because it converts the original nonlinear problem with nonlinear constraints into other nonlinear problem with linear constraints. In addition, the proposed algorithm is programmed using Java Runtime Environment to provide users with a Web based open source environment. In a real-world setting, this would provide manufacturers with an algorithm that can be trusted to give the correct answer rather than making a good part rejected because of inaccurate computational results.     

Incorporating Software Visualization in the Design of Intelligent Diagnosis Systems for User Programming

Program diagnosis systems were developed to help users solve programming problems. By providing guidence on errors and misconceptions, these systems can help the users in writing programs and understanding their dynamic behavior. Features of software visualization which aim at providing visual and concrete depictions to the abstractions and operations of programs have also shown to be making programs more understandable. The main theme of this paper is to asses the usefulness of incorporating features of software visualization into the design of program diagnosis systems intended for novices. We report an empirical evaluation to assess the effectiveness of supporting visualization features during problem solving. The system used in the evaluation integrates visualzation and immediacy features and supports a model-tracing based approach to program diagnosis. Unlike other similar systems, our prototype system supports a more flexible style of interaction by increasing the grain size of diagnosis to a complete programming statement. The evaluation reported here seems to suggest that when supported with visualization features, systems for program diagnosis tend to be more effective in helping the users during problem solving.     

Mutual effects of chemical reaction and thermal radiation on MHD peristaltic transport of Jeffrey nanofluids: Tumor tissues treatment

As thermal radiation is one of the fundamental means of heat transfer, therefore, this study analyzes the impacts of thermal radiation and magnetic field on the peristaltic transport of a Jeffrey nanofluid in a nonuniform asymmetric channel. Further, Two models of viscosity are debated: Model (I), in which all parameters dependent on viscosity behave as a constant (as treated before in nanofluid research); Model (II), in which these known parameters are considered to vary with the temperature of the flow. Under the condition of long wavelength and low Reynolds number, the problem is rearranged. The resulting system of partial differential equations (PNE) is solved with aid of Mathematica 11. Furthermore, the streamline graphs are presented by significance of trapping bolus phenomenon. To emphasize the quality of solutions, comparisons between the previous results and recent published results by Reddy et al. have been made and signified. The comparisons are shown in Table 1 and are found to be in good agreement. As the thermal radiation increases, the diameter of nanoparticles rises (thermal radiation is a diminishing function of temperature, and with a decrease in the temperature, the diameter of the nanoparticles increases, that is, the size of nanoparticles increases and they become more active near malignant tumor tissues). Therefore, its work as agents for radiation remedy, produce limited radiation quantities, and selectively target malignant tumor for controlled mutilation (radiotherapy of oncology). Such a model is appropriate for the transportation of physiological flows in the arteries with heat and mass transfer (blood flow models).