Repulsive interaction between coplanar cracks in the double–cantilever geometry

Experiments on thin mica sheets are used to demonstrate that coplanar cracks in double-cantilever beam specimens do not universally attract each other, as conventionally portrayed, but, at long range, actually repel. An elasticity analysis explains the repulsion in terms of a compression zone, 0.35 times the beam half-thickness ahead of the crack tip, generated by bending moments from the cantilever arms on the remaining specimen section.

Effects of replication on measured S-N curves of metals

Replication, or repeated tests at the same stress amplitude, is used to provide statistical confidence in life data during the development of S-N curves. This paper discusses the effects of replication on the measurement of S-N curves and presents an alternative to traditional replication methods for the determination of S-N curves, particularly for the development of preliminary S-N curves. Using specimens made out of the extruded bars of a magnesium alloy, it is demonstrated that the S-N curve estimated using the data from non-replication tests is almost same as that from replication tests. The advantage of using non-replication fatigue tests is that it uses fewer specimens, in this instance, only half of that required for 50% replication fatigue test, to achieve the same estimation as that of the replication fatigue tests. Another advantage of using non-replication fatigue tests is that it can detect the non-linearity using limited specimens.

Hydrogen sulfide attenuates carbon tetrachloride-induced hepatotoxicity, liver cirrhosis and portal hypertension in rats

BACKGROUND : Hydrogen sulfide (H(2)S) displays vasodilative, anti-oxidative, anti-inflammatory and cytoprotective activities. Impaired production of H(2)S contributes to the increased intrahepatic resistance in cirrhotic livers. The study aimed to investigate the roles of H(2)S in carbon tetrachloride (CCl(4))-induced hepatotoxicity, cirrhosis and portal hypertension.

METHODS AND FINDINGS : Sodium hydrosulfide (NaHS), a donor of H(2)S, and DL-propargylglycine (PAG), an irreversible inhibitor of cystathionine γ-lyase (CSE), were applied to the rats to investigate the effects of H(2)S on CCl(4)-induced acute hepatotoxicity, cirrhosis and portal hypertension by measuring serum levels of H(2)S, hepatic H(2)S producing activity and CSE expression, liver function, activity of cytochrome P450 (CYP) 2E1, oxidative and inflammatory parameters, liver fibrosis and portal pressure. CCl(4) significantly reduced serum levels of H(2)S, hepatic H(2)S production and CSE expression. NaHS attenuated CCl(4)-induced acute hepatotoxicity by supplementing exogenous H(2)S, which displayed anti-oxidative activities and inhibited the CYP2E1 activity. NaHS protected liver function, attenuated liver fibrosis, inhibited inflammation, and reduced the portal pressure, evidenced by the alterations of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), hyaluronic acid (HA), albumin, tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6 and soluble intercellular adhesion molecule (ICAM)-1, liver histology, hepatic hydroxyproline content and α-smooth muscle actin (SMA) expression. PAG showed opposing effects to NaHS on most of the above parameters.

CONCLUSIONS :  Exogenous H2S attenuates CCl4-induced hepatotoxicity, liver cirrhosis and portal hypertension by its multiple functions including anti-oxidation, anti-inflammation, cytoprotection and anti-fibrosis, indicating that targeting H2S may present a promising approach, particularly for its prophylactic effects, against liver cirrhosis and portal hypertension.

Compression and probing C−H···I hydrogen bonds of iodoform under high pressure by x-ray diffraction and raman scattering

High-pressure methods were applied to investigate the structural stability and hydrogen bonding of polar molecules of iodoform by synchrotron radiation X-ray diffraction and Raman spectra measurements, respectively. Up to a pressure of 40 GPa, no phase transitions were observed. The discontinuous frequency shift of the C−H stretching band is believed to be related to the enhancement of the C−H···I weak hydrogen bonds under high pressures. Ab initio calculations were performed, and the results predict the frequency shift of the C−H stretching vibration as C−H···I interacts via hydrogen bonding. The bulk modulus is 17.3 ± 0.8 GPa, with a pressure derivative of 5.2.

Effect of surface roughness and electrostatic surface potentials on forces between dissimilar surfaces in aqueous solution

The first surface force measurements under electrochemical potential control between a metal and a ceramic surface across a liquid medium (water) are reported. Our experiments also investigate and reveal how increasing levels of surface roughness and dissimilarity between the potentials of the interacting surfaces influence the strength and range of electric double layer, van der Waals, hydration, and steric forces and how this contributes to deviations from DLVO theory at small distances within aqueous solution.

A guided rule reduction system for prioritization of failures in fuzzy FMEA

Traditional Failure Mode and Effect Analysis (FMEA) utilizes the Risk Priority Number (RPN) ranking system to evaluate the risk level of failures, to rank failures, and to prioritize actions. Although this method is simple, it suffers from several shortcomings. In this paper, use of fuzzy inference techniques for RPN determination in an attempt to overcome the weaknesses associated with the traditional RPN ranking system is investigated. However, the fuzzy RPN model, suffers from the combinatorial rule explosion problem. As a result, a generic rule reduction approach, i.e. the Guided Rule Reduction System (GRRS), is proposed to reduce the number of rules that need to be provided by users during the fuzzy RPN modeling process. The proposed approach is evaluated using real-world case studies pertaining to semiconductor manufacturing. The results are analyzed, and implications of the proposed approach are discussed.

A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm

This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.

A mutation-based evolving neural network model and its application to condition monitoring

Data analysis using intelligent systems is a key solution to many industrial problems. In this paper, a mutation-based evolving artificial neural network, which is based on an integration of the Fuzzy ARTMAP (FAM) neural network and evolutionary programming (EP), is proposed. The proposed FAMEP model is applied to detect and classify possible faults from a number of sensory signals of a circulating water system in a power generation plant. The efficiency of FAM-EP is assessed and compared with that of the original FAM network in terms of classification accuracy as well as network complexity. In addition, the bootstrap method is used to quantify the performance statistically. The results positively demonstrate the usefulness of FAM-EP in tackling data classification problems.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.