Quality of manual data collection in Java software: an empirical investigation

Data collection, both automatic and manual, lies at the heart of all empirical studies. The quality of data collected from software informs decisions on maintenance, testing and wider issues such as the need for system re-engineering. While of the two types stated, automatic data collection is preferable, there are numerous occasions when manual data collection is unavoidable. Yet, very little evidence exists to assess the error-proneness of the latter. Herein, we investigate the extent to which manual data collection for Java software compared with its automatic counterpart for the same data. We investigate three hypotheses relating to the difference between automated and manual data collection. Five Java systems were used to support our investigation. Results showed that, as expected, manual data collection was error-prone, but nowhere near the extent we had initially envisaged. Key indicators of mistakes in manual data collection were found to be poor developer coding style, poor adherence to sound OO coding principles, and the existence of relatively large classes in some systems. Some interesting results were found relating to the collection of public class features and the types of error made during manual data collection. The study thus offers an insight into some of the typical problems associated with collecting data manually; more significantly, it highlights the problems that poorly written systems have on the quality of visually extracted data.     

Rapid decolourization and mineralization of the azo dye C.I. Acid Red 14 by heterogeneous Fenton reaction

The decolourization and mineralization of a solution of an azo dye using a catalyst based on Fe(II) supported on Y Zeolite (Fe(II)-Y Zeolite) and adding hydrogen peroxide (heterogeneous Fenton process) have been studied. The catalyst was prepared by ion exchange, starting from a commercial ultra-stable Y Zeolite. All experiments were performed on a laboratory scale set-up. The effects of different parameters such as initial concentration of the dye, initial pH of the solution of the dye, H(2)O(2) concentration, temperature and ratio of amount of catalyst by amount of solution on the decolourization efficiency of the process were investigated. A percentage of colour removal of 99.3±0.2% and a mineralization degree of 84±5% of the solution of the dye were achieved in only 6 min of contact time between the catalyst and the solution, under the following conditions: initial concentration of the dye of 50 ppm, pH 5.96, 8.7 mM of H(2)O(2), T of 80°C and catalyst concentration of 15 g/L. Moreover, the catalyst Fe(II)-Y Zeolite can be easily filtered from the solution, does not leach any iron into the solution (avoiding any secondary contamination due to the metal) and its effectivity can be reproduced after consecutive experiments.     

Reactivity and chemical characterization of effluent organic nitrogen from wastewater treatment plants determined by Fourier transform ion cyclotron resonance mass spectrometry

In advanced wastewater treatment plants that achieve high levels of nitrogen (N) removal, up to one-third of the N in effluent is organic, herein referred to as effluent organic N (EON). While we know that inorganic N is highly labile, it is unclear what fraction of EON is bioavailable. In this study, we demonstrate the utility of a method that can be used to examine the reactivity of EON in natural receiving waters to better understand both the ecosystem response and the potential bioavailability of EON. The technique is suitable for analyzing polar organic matter in natural waters; electrospray ionization coupled with Fourier transform mass spectrometry. Bioassays were performed on samples collected at the end of the biological process from two wastewater treatment plants achieving advanced N removal. The samples were concentrated, and then added to natural water samples collected from the oligohaline James River, a major tributary of the Chesapeake Bay. Our results demonstrate that while the lignin-like fraction of the effluent dissolved organic matter (some of which contains N) was conserved, a large portion of aliphatic and aromatic compounds containing N was removed (79-100%) during incubations, while other compounds were produced. Furthermore, the two effluents exhibited differences in the degree of degradation and type of degradation, which can be related both to the various processes employed in the two WWTPs and the dramatic differences in the type of influent they received. These findings suggest that EON is highly reactive in the natural environment and that simple assays examining net consumption or production of bulk dissolved organic N pools are inadequate for assessing the bioavailability of EON.     

Antimicrobial Activities of Phenolic Extracts Derived from Seed Coats of Selected Soybean Varieties

Soybean hulls or seed coats consist of complex carbohydrates, proteins, lipids, and polyphenols such as anthocyanidins, proanthocyanidins, and isoflavones. The polyphenolics in the seed coats give them various colors such as black, brown, green, yellow, or even a mottled appearance. In this study, the antimicrobial effects of phenolic extracts from the seed coats of different colored soybeans (yellow, dark brown, brown, and black) were evaluated against foodborne pathogens such as Salmonella Typhimurium, Escherichia coli O157:H7, and Campylobacter jejuni in broth‐cultures as well as on chicken skin. The highest total phenolic content was observed for the phenolic extract from soybean variety (R07‐1927) with black colored seed coat (74.1 ± 2.1 mg chlorogenic acid equivalent [CAE]/g extract) and was significantly different (P <0.0001) from the extract of the conventional soybean variety (R08‐4004) with yellow colored seed coat (7.4 ± 1.2 mg CAE/g extract). The extract from black colored soybean produced reductions of 2.10 ± 0.08 to 2.20 ± 0.08‐log CFU/mL for both E. coli O157:H7 and C. jejuni after 3 d when incubated in broth‐culture having 4‐log CFU/mL of bacteria, whereas a 6 d incubation was found to reduce S. Typhimurium and E. coli O157:H7 at 2.03 ± 0.05 and 3.3 ± 0.08‐log CFU/mL, respectively. The extract also reduced S. Typhimurium and E. coli O157:H7 attached to chicken skin by 1.39 ± 0.03 and 1.24 ± 0.06‐log CFU/g, respectively, upon incubation for 6 d. Soybean seed coat extracts may have a potency as antimicrobial agents to reduce foodborne bacteria contaminating poultry products.     

Dynamic demand risk assessment for toll road projects

To bridge the gap between supply of and increasing demand for roads, public–private partnership (PPP) concession contracts in which the investment cost is recovered via payments from the end users have been established. Although this mechanism has been seen as an efficient way for road projects to be completed on time and within budget, the demand risk faced during the operation stage has considerably limited this efficiency. Demand depends on a range of interrelated and dynamic factors such as the demographic and economic conditions. In addition, uncertainty is an inherent aspect of most demand-underlying factors which always make demand estimation inaccurate. However, this uncertainty is largely ignored by modellers where a single demand estimate is often used when evaluating the facility. The aim is to develop a system dynamics model to assess demand risk in road projects. The model captures the factors affecting demand and their relationships and simulates their change over time. By employing Monte Carlo simulation, the model assesses the likelihood and potential effect of an event on the outcome and provides a full picture of the various effects of potential risk. The model can help public, private, and financial stakeholders of PPP facilities make more informed decisions.     

Modeling Blanking Process Using Multiple Regression Analysis and Artificial Neural Networks

The design of blanking processes requires the availability of a procedure able to deal with both tooling and mechanical properties of the workpiece material (blank thickness, hardness, ductility, etc.). This research presents the development and comparison of two models to predict the quality of the blanked edge represented by burrs height, the first model is an artificial neural network (ANN) based, while the second model is a multiple regression analysis (MRA) based. Finite Element modeling of the blanking process was used to generate the data for both models. Both ANN and MRA are able to give good prediction results, however, ANN still more accurate because it deals efficiently with hidden nonlinear relations when compared to MRA. The comparison between experimental and model results shows that average absolute relative error in the case of ANN was <2.20% for carbon steel and 4.85% for corrosion-resistant steel (CRES) compared to 15.18% for carbon steel and 14.22% for CRES obtained from the second order MRA. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost.     

Variability patterns for business processes in BPMN

Many entities, both in academia and the business sector, urge an efficient improvement of business processes. However, when it comes to addressing this point, each slight disparity in the business rules and/or objectives translates into a separate model, which is neither practical nor acceptable as it burdens the host process-aware information system with repetitive and almost verbatim instances. To solve this issue, we propose considering variability. Variability will serve as a business process improvement technique to efficiently design and run a variable business process throughout different business situations that are similar to one another is some ways yet differ in others. First, we define variability within the context of business processes. Second, we present a set of variability patterns and explain how they are used. We validate our approach via the business process improvement patterns known and used by the community. The variability design patterns are a series of business process improvement patterns for building business process with variability and efficiently acting on the improved process performance metrics.