Mechanisms of strength loss in underwater concrete

In-situ hardened properties of underwater concrete (UWC) are affected by washout loss and water infiltration occurring during the casting and post-casting stages. This paper evaluates the suitability of the falling-head method determined using a permeameter cell to assess water infiltration or, permeability (k), in freshly mixed UWC. Correlations between k and washout loss determined using the CRD C61 test method are established. The paper also seeks to quantify the coupled effect of washout loss and water infiltration on the drop in UWC compressive strength. Test results showed that Darcy’s law is valid to evaluate water permeability in UWC. The k and washout loss values are found to be well correlated within each other. UWC mixtures subjected to washout or water infiltration exhibited lower compressive strengths, as compared to reference concrete sampled in dry conditions. The drop in UWC strength was attributed to a combination of factors including washout loss of cementitious phase together with relative increases in aggregate concentration and specified water-to-cement ratio.     

Morphology Controlled Magnesium Ethoxide: Influence of Reaction Parameters and Magnesium Characteristics

Controlled reaction of magnesium with ethanol was found to produce morphological magnesium ethoxide. The scanning electron microscopy studies indicate that particles of magnesium ethoxide grow on the surface of magnesium granules and are detached from the surface as a result of shearing and collisions during the reaction. Particle characteristics of magnesium show influence on the kinetics of the reaction as well as morphological characteristics of magnesium ethoxide. Impact of other process parameter variations on the magnesium ethoxide particle characteristics is also studied. The reaction rate, agitation speed, and type of agitator show influence on the product characteristics, whereas no influence of change in reaction pressure is observed.     

Composites with needle-like inclusions exhibiting negative thermal expansion: A preliminary investigation

In this work a simple cylindrical structure with a stiff needle-like inclusion embedded within a much softer matrix is presented and analysed with the aim of obtaining a system with tunable thermal expansion properties. It is shown that by the correct combination of the thermal and mechanical properties of the matrix and inclusion, it is possible to design a system which can be tailor-made to exhibit particular values of the coefficient of thermal expansion (CTE) in the radial direction and also negative thermal expansion (NTE). In particular an analytical model to quantify the radial strain with changes in temperature is derived and verified through finite element analysis. The model is used to find correct property combinations which lead to particular values of thermal expansion which could also be negative or zero.     

Volumetric colon wall unfolding using harmonic differentials

Volumetric colon wall unfolding is a novel method for virtual colon analysis and visualization with valuable applications in virtual colonoscopy (VC) and computer-aided detection (CAD) systems. A volumetrically unfolded colon enables doctors to visualize the entire colon structure without occlusions due to haustral folds, and is critical for performing efficient and accurate texture analysis on the volumetric colon wall. Though conventional colon surface flattening has been employed for these uses, volumetric colon unfolding offers the advantages of providing the needed quantities of information with needed accuracy. This work presents an efficient and effective volumetric colon unfolding method based on harmonic differentials. The colon volumes are reconstructed from CT images and are represented as tetrahedral meshes. Three harmonic 1-forms, which are linearly independent everywhere, are computed on the tetrahedral mesh. Through integration of the harmonic 1-forms, the colon volume is mapped periodically to a canonical cuboid. The method presented is automatic, simple, and practical. Experimental results are reported to show the performance of the algorithm on real medical datasets. Though applied here specifically to the colon, the method is general and can be generalized for other volumes.     

Pain, nicotine, and smoking: Research findings and mechanistic considerations.

Tobacco addiction and chronic pain represent 2 highly prevalent and comorbid conditions that engender substantial burdens upon individuals and systems. Interrelations between pain and smoking have been of clinical and empirical interest for decades, and research in this area has increased dramatically over the past 5 years. We conceptualize the interaction of pain and smoking as a prototypical example of the biopsychosocial model. Accordingly, we extrapolated from behavioral, cognitive, affective, biomedical, and social perspectives to propose causal mechanisms that may contribute to the observed comorbidity between these 2 conditions. The extant literature was 1st dichotomized into investigations of either effects of smoking on pain or effects of pain on smoking. We then integrated these findings to present a reciprocal model of pain and smoking that is hypothesized to interact in the manner of a positive feedback loop, resulting in greater pain and increased smoking. Finally, we proposed directions for future research and discussed clinical implications for smokers with comorbid pain disorders. We observed modest evidence that smoking may be a risk factor in the multifactorial etiology of some chronically painful conditions and that pain may come to serve as a potent motivator of smoking. We also found that whereas animal studies yielded consistent support for direct pain-inhibitory effects of nicotine and tobacco, results from human studies were much less consistent. Future research in the emerging area of pain and smoking has the potential to inform theoretical and clinical applications with respect to tobacco smoking, chronic pain, and their comorbid presentation. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A computational model for signaling pathways in bounded small-world networks corresponding to brain size

A computational model, the bounded composite inverse-d architecture (BCIA), was developed to characterize signaling in small-world networks with large but bounded numbers of nodes, as in human brains. The model is based upon an N-dimensional symmetrical grid with borders, with complete local connections from each node and relatively fewer long-range connections. The length of the signaling pathway generated by a greedy algorithm between two nodes exhibited polylogarithmic behavior when the grid distance between the nodes was less than m, the maximal length of a long-range connection for that network. The simulated length of signaling pathway became linear with internode distance when the grid distance between the two nodes was greater than m. The intensity of long-range connections among nodes was found to be negatively related to the simulated length of signaling pathway. For a constant grid distance between nodes, the average length of a simulated signaling pathway increased with dimension of the BCIA graph. Most strikingly, BCIA simulations of networks with large but bounded numbers (10⁹–10¹³) of nodes, approximating the number of neurons in the human brain, found that the length of simulated signaling pathway can be substantially shorter than that predicted by the best known asymptotic theoretical bound in small-world networks of infinite size.