Georgia College & State University

About: Georgia College & State University is a education organization based out in Milledgeville, Georgia, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 950 authors who have published 1591 publications receiving 37027 citations.

Effects of Silver Nanoparticle on Soil-Nitrification Processes

Abstract: The release of silver (Ag) nanoparticles (NPs) from the use of consumer products to the environment has raised concern about the risk to ecosystems because of its unpredictable toxicological impact to microorganisms in terrestrial environment. In this study, the impact of Ag chemical speciation (Ag+ and Ag-NPs [50-nm uncoated and 15-nm polyvinylpyrrolidone (PVP)-coated Ag-NPs]) to soil nitrification kinetics was investigated using a batch soil-slurry nitrification method along with sorption isotherm and dissolution experiments. The results of nitrification potential (i.e., kinetic rate) suggest that Ag+/Ag-NPs, which strongly sorb in soils, suppressed the nitrification processes. Among each chemical species, the degree of suppression increased with increasing [Ag]total. Although ionic Ag (Ag+) species is known to exhibit greater antimicrobial effects than the solid state Ag species, such as Ag-NPs, in most studies, PVP-coated 15-nm Ag-NPs, however, more effectively suppressed the soil nitrification process than did Ag+ under the same dose. Although several physicochemical-based toxicity mechanisms of dispersed Ag-NPs were discussed in pure culture and aquatic systems, it is not clearly understood how PVP-coated Ag-NPs could exhibit greater toxicity to nitrifying bacteria than Ag+ in soils. In assessing the impact of Ag-NPs to microbial mediated processes (e.g., N cycles) in the terrestrial environment, it might be critical to understand the interactions and reactivity of Ag-NPs at the soil–water interface.

Effects of non-nucleonic degrees of freedom in the D(p

Abstract: Measurements of the D(p-->, gamma )3He and p(d-->, gamma )3He reactions below Ep,d = 80 keV are compared to the results of calculations based oncorrelated hyperspherical harmonic wave functions obtained from realistic interactions with full inclusion of Coulomb distortion in the initial continuum state, and anuclear current operator with one- and two-body components. Dramatic effects due to the tensor force and the associated two-body (meson-exchange) interactioncurrents are observed in the vector and, to some extent, tensor analyzing powers for the first time. The extrapolation to zero energy leads to an S-factor value of S(E= 0) = 0.165z0.014 eV b, in reasonable agreement with theory.

A Multi-Objective Software Quality Classification Model Using Genetic Programming

Abstract: A key factor in the success of a software project is achieving the best-possible software reliability within the allotted time & budget. Classification models which provide a risk-based software quality prediction, such as fault-prone & not fault-prone, are effective in providing a focused software quality assurance endeavor. However, their usefulness largely depends on whether all the predicted fault-prone modules can be inspected or improved by the allocated software quality-improvement resources, and on the project-specific costs of misclassifications. Therefore, a practical goal of calibrating classification models is to lower the expected cost of misclassification while providing a cost-effective use of the available software quality-improvement resources. This paper presents a genetic programming-based decision tree model which facilitates a multi-objective optimization in the context of the software quality classification problem. The first objective is to minimize the "Modified Expected Cost of Misclassification", which is our recently proposed goal-oriented measure for selecting & evaluating classification models. The second objective is to optimize the number of predicted fault-prone modules such that it is equal to the number of modules which can be inspected by the allocated resources. Some commonly used classification techniques, such as logistic regression, decision trees, and analogy-based reasoning, are not suited for directly optimizing multi-objective criteria. In contrast, genetic programming is particularly suited for the multi-objective optimization problem. An empirical case study of a real-world industrial software system demonstrates the promising results, and the usefulness of the proposed model

Reducing overfitting in genetic programming models for software quality classification

Abstract: A high-assurance system is largely dependent on the quality of its underlying software. Software quality models can provide timely estimations of software quality, allowing the detection and correction of faults prior to operations. A software metrics-based quality prediction model may depict overfitting, which occurs when a prediction model has good accuracy on the training data but relatively poor accuracy on the test data. We present an approach to address the overfitting problem in the context of software quality classification models based on genetic programming (GP). The problem has not been addressed in depth for GP-based models. The presence of overfitting in a software quality classification model affects its practical usefulness, because management is interested in good performance of the model when applied to unseen software modules, i.e., generalization performance. In the process of building GP-based software quality classification models for a high-assurance telecommunications system, we observed that the GP models were prone to overfitting. We utilize a random sampling technique to reduce overfitting in our GP models. The approach has been found by many researchers as an effective method for reducing the time of a GP run. However, in our study we utilize random to reduce overfitting with the aim of improving the generalization capability of our GP models.