Identification of nitrate sources in Taihu Lake and its major inflow rivers in China, using δ(15)N-NO(3)(-) and δ(18)O-NO(3)(-) values

In the present study, δ(15)N and δ(18)O-NO(3)(-) values, as well as concentrations of some major ion tracers were determined in seasonal water samples from Taihu Lake and major watersheds to investigate the temporal and spatial variations of nitrate sources and assess the underlying nitrogen (N) biogeochemistry process. The results lead to the conclusion that the nitrate concentrations in Taihu Lake are lower in summer than that in winter due to the dilution effect of wet deposition. In winter, sewage and manure were the primary nitrate sources in major inflow rivers and North Taihu Lake (NTL), while nitrate sources in East Taihu Lake (ETL) probably derived from soil organic N. In summer, atmospheric deposition and sewage/manure inputs appear to play an important role in controlling the distribution of nitrates in the whole lake. The δ(18)O-NO(3)(-) values suggest that the nitrate produced from microbial nitrification is another major nitrate source during both winter and summer months. The variations in isotopic values in nitrate suggest denitrification enriched the heavier isotopes of nitrate in NTL in winter and in ETL in summer.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

A variational formulation for fingerprint orientation modeling

Orientation pattern is an important feature for characterizing fingerprint and plays critical roles in fingerprint recognition and fingerprint classification. This paper proposes a framework for modeling the fingerprint orientation field based on the variational principle, where the orientation pattern can be estimated through solving the associated Euler–Lagrange equation. Compared with existing methods, our proposed method has the following features. Firstly, it does not require any prior information about the structure of the acquired fingerprint, such as location of singular point(s). Secondly, it explicitly provides freedom for modeling the singularity in the orientation field. Thirdly, it has less number of parameters. Comparison has been made with respect to state-of-the-arts in fingerprint orientation modeling in terms of modeling accuracy, fingerprint enhancement and singular point detection. Advantages of the proposed method are demonstrated.     

Fault-based test suite prioritization for specification-based testing

Context

Existing test suite prioritization techniques usually rely on code coverage information or historical execution data that serve as indicators for estimating the fault-detecting ability of test cases. Such indicators are primarily empirical in nature and not theoretically driven; hence, they do not necessarily provide sound estimates. Also, these techniques are not applicable when the source code is not available or when the software is tested for the first time.

Objective

We propose and develop the novel notion of fault-based prioritization of test cases which directly utilizes the theoretical knowledge of their fault-detecting ability and the relationships among the test cases and the faults in the prescribed fault model, based on which the test cases are generated.

Method

We demonstrate our approach of fault-based prioritization by applying it to the testing of the implementation of logical expressions against their specifications. We then validate our proposal by an empirical study that evaluates the effectiveness of prioritization techniques using two different metrics.

Results

A theoretically guided fault-based prioritization technique generally outperforms other techniques under study, as assessed by two different metrics. Our empirical results also show that the technique helps to reveal all target faults by executing only about 72% of the prioritized test suite, thereby reducing the effort required in testing.

Conclusions

The fault-based prioritization approach is not only applicable to the instance empirically validated in this paper, but should also be adaptable to other fault-based testing strategies. We also envisage new research directions to be opened up by our work.     

The non-constant CTOD/CTOA in stable crack extension under plane-strain conditions

Contrary to the previous work that successfully applied the constant CTOD/CTOA fracture criteria to relatively thin structures, this paper demonstrates that the initial non-constant portion of the CTOD/CTOA plays an essential role in predicting fracture behavior under plane-strain conditions. Three- and two-dimensional finite element analyses indicate that a severe underestimation of the load would occur as the crack extends if a constant CTOD/CTOA criterion were used. However, the use of a simplified, bilinear CTOD/CTOA criterion to approximate its non-constant portion will closely duplicate the test data. Furthermore, using the experimental data from J-integral tests with various crack length to specimen width ratios (a/W), it is demonstrated that the critical CTOD/CTOA is crack tip constraint dependent. The initial high values of the CTOD/CTOA are in fact a natural consequence of crack growth process that is reflected by, and consistent with, the J-resistance (J-R) curve and its slope (tearing modulus).     

Optimization of photomask design for reducing aberration-induced placement error

In semiconductor manufacturing, the accurate placement of circuit components ensures the proper functioning of microelectronic circuits. This is often subject to photolithography, an optical technique that transfers circuit patterns from photomasks to silicon wafers. Sources of placement error include aberration and misalignment between different levels, and we focus on the former. Aberration is an optical phenomenon that often degrades imaging system performance. Since aberration differs from one imaging system to another, a photomask design that minimizes the aberration-induced placement error is desired. In this paper, we discuss the optimization process of a general one-dimensional mask pattern under a general illumination condition. The constraint is a known population mean of the root mean square aberrations for the imaging systems under consideration. To apply the theory, we search for the optimal parameters for two common mask designs: alternating phase-shifting masks (PSMs) and attenuated PSMs. The theoretical results are compared with those from a Monte Carlo analysis on a large set of imaging systems. These results are indicative to mask manufacturers and circuit designers of increasing manufacturability of circuits.     

Competitive Effects of Trichloroethylene on Cr(VI) Removal by Zero-Valent Iron

Eleven columns were set up under various groundwater geochemistry conditions to investigate the competitive effect of trichloroethylene (TCE) on hexavalent chromium [Cr(VI)] removal by zero-valent iron (Fe0). They were found to be electron competitors in the redox reactions. In the presence of TCE, the Cr(VI) removal capacities of Fe0 were decreased by about 40% when compared with their respective Cr(VI) removal capacities with identical groundwater geochemistry but without TCE. The specific reaction rate constant (kSA) of TCE was decreased by about 50% when Cr(VI) was singly applied. The kSA of TCE was further decreased by 75% in the presence of both Cr(VI) and carbonate. However, there was no apparent effect on the kSA of TCE when Cr(VI), hardness and carbonate were all present. It revealed that TCE was a stronger electron competitor of Cr(VI) and the degradation of TCE became more favorable when both hardness and carbonate were present. This suggests that the passivated precipitates formed on the Fe0 surface in the presence of both hardness and carbonate may significantly affect the Cr(VI) removal by Fe0 but has insignificant effect on the TCE removal.     

Web Discovery and Filtering Based on Textual Relevance Feedback Learning

We develop a new approach for Web information discovery and filtering. Our system, called WID, allows the user to specify long-term information needs by means of various topic profile specifications. An entire example page or an index page can be accepted as input for the discovery. It makes use of a simulated annealing algorithm to automatically explore new Web pages. Simulated annealing algorithms possess some favorable properties to fulfill the discovery objectives. Information retrieval techniques are adopted to evaluate the content-based relevance of each page being explored. The hyperlink information, in addition to the textual context, is considered in the relevance score evaluation of a Web page. WID allows users to provide three forms of the relevance feedback model, namely, the positive page feedback, the negative page feedback, and the positive keyword feedback. The system is domain independent and does not rely on any prior knowledge or information about the Web content. Extensive experiments have been conducted to demonstrate the effectiveness of the discovery performance achieved by WID.     

An LMI approach to guaranteed cost control for uncertain linear neutral delay systems

This paper considers the problem of guaranteed cost control for uncertain neutral delay systems with a quadratic cost function. The system under consideration is subject to norm‐bounded time‐varying parametric uncertainty appearing in all the matrices of the state‐space model. The problem we address is the design of a state feedback controller such that the closed‐loop system is not only stable but also guarantees an adequate level of performance for all admissible uncertainties. A sufficient condition for the existence of guaranteed cost controllers is given in terms of a linear matrix inequality (LMI). When this condition is feasible, the desired state feedback controller gain matrices can be obtained via convex optimization. An illustrative example is provided to demonstrate the effectiveness of the proposed approach. Copyright © 2002 John Wiley & Sons, Ltd.     

HIGH‐DIMENSIONAL LEARNING FRAMEWORK FOR ADAPTIVE DOCUMENT FILTERING*

We investigate the unique requirements of the adaptive textual document filtering problem and propose a new high‐dimensional on‐line learning framework, known as the REPGER (relevant feature pool with good training example retrieval rule) algorithm to tackle this problem. Our algorithm possesses three characteristics. First, it maintains a pool of selective features with potentially high predictive power to predict document relevance. Second, besides retrieving documents according to their predicted relevance, it also retrieves incoming documents that are considered good training examples. Third, it can dynamically adjust the dissemination threshold throughout the filtering process so as to maintain a good filtering performance in a fully interactive environment. We have conducted experiments on three document corpora, namely, Associated Press, Foreign Broadcast Information Service, and Wall Street Journal to compare the performance of our REPGER algorithm with two existing on‐line learning algorithms. The results demonstrate that our REPGER algorithm gives better performance most of the time. Comparison with the TREC (Text Retrieval Conference) adaptive text filtering track participants was also made. The result shows that our REPGER algorithm is comparable to them.