Ennoblement of stainless steel in natural seawater – A new explanation

The ennoblement of corrosion potential (E_corr) of passive metal immersed in seawater was investigated with electrochemical technology and epifluorescence microscopy. The in situ observation showed that the bacteria number increased on the metal surface according to an exponential law which was in the same way with the ennoblement of E_corr. At the same time, the anodic polarization current of high‐Mo stainless steel decreased in the initial days. According to the mix‐potential theory and the characteristics of polarization curves of high‐Mo steel in natural seawater, the ennoblement of corrosion potential may be induced by the decrease of the passive current density.     

Novel tiO2 nanocatalysts for wastewater purification: tapping energy from the sun.

Water treatment using TiO2 semiconductor as a durable heterogeneous photocatalyst has been the focus of environmentalists in recent years. Currently, we developed an inexpensive and highly efficient approach for synthesizing nitrogen-doped TiO2 with lower band-gap energy that can respond to visible light. Doping on the molecular scale led to an enhanced nitrogen concentration of up to 21.8%. Reflectance measurements showed the synthesized N-doped TiO2 nanoparticles are catalytically active with the absorbance that extends into the visible region up to 600 nm. The water purification potential of this new class of compound was evaluated by studying the photodegradation of Acid Orange 7 (AO7) and E. coli. Experiments were conducted to compare the photocatalytic activities of N-doped TiO2 nanocatalysts and commercially available Degussa P25 power under identical solar light exposure. N-doped TiO2 demonstrated superior photocatalytic activities in both chemical compound degradation and bactericidal reactions. The result of this study shows the potential of applying new generations of catalyst for wastewater purification and disinfection.     

Entropy of multidimensional cellular automata

Since the topological entropy of a vast class of two-dimensional cellular automata (CA) is infinite, of interest is the possibility to renormalize it so that to obtain a positive finite value. We find the asymptotics of the information function of a multidimensional CA and, accordingly, introduce the renormalized topological entropy as a coefficient of this asymptotics. We describe some properties of the introduced quantity, in particular, its positivity for CA of the type of “The Game of Life.” Also, we give an example of an explicit evaluation of this parameter for a particular cellular automaton.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

Evaluating software reuse alternatives: a model and its application to an industrial case study

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.     

Second-order stresses and strains in heterogeneous steels: Self-consistent modeling and X-ray diffraction analysis

Theoretical and experimetal methods have been developed to characterize the effect of mechanical loading on the mesoscopic and macroscopic mechanical state of polycrystalline materials. Ferritic and austenitic single-phase materials were first analyzed, then phase interaction was studied in a multiductile phase material (austeno-ferritic duplex steel) and a natural reinforced composite (pearlitic steel). The theoretical method is based on the self-consistent approach in which elastic and plastic characteristics of the phases have been applied through the micromechanical behavior of single-crystal-using slip systems and microscopic hardening. The effects of a crystallographic texture and phase interaction during loading and after unloading were studied. The elastic and plastic anisotropy of the grains having the same crystallographic orientation were assessed by diffraction strain analysis. The simulation was compared with the experiments performed using the X-ray diffraction technique. In the considered duplex and pearlitic steels, it was observed that the ferrite stress state is much lower than the austenite and cementite ones. The results of diffraction strain distribution have showed the pertinence of the models and give valuable information, for example, for the yield stress and the hardening parameters of each phase in a two-phase material.     

Sequential Bayesian bit error rate measurement

As bit error rates decrease, the time required to measure a bit error rate (BER) or perform a BER test (i.e., to determine that a particular communications device's BER is less than some acceptable limit) increases dramatically. One cause of long measurement times is the difficulty of deciding a priori how many bits to measure to establish the BER to within a predetermined confidence interval width. This paper explores a new approach to deciding how many bits to measure, namely a sequential Bayesian approach. As measurement proceeds, the posterior distribution of BER is checked to see if the conclusion can be made that the BER rate is known to be within the desired range with high enough probability. Desired properties of the posterior distribution such as the maximum a postiori estimate and confidence limits can be computed quickly using off-the-shelf numerical software. Examples are given of using this method on bit error data measured with an Agilent 81250 parallel BER tester.