Effect of Compressive Load on Uplift Capacity of Model Piles

Thirty six tests on model tubular steel piles embedded in sand were carried out in the laboratory to assess the effects of compressive load on uplift capacity of piles considering various parameters. The model piles were of 25 mm outside diameter and 2 mm wall thickness. The soil–pile friction angles were 21 and 29° in loose and dense conditions of sand. The piles were embedded in sand for embedment length/diameter ratios of 8,16, and 24 inside a model tank. They were subjected to a static compressive load of 0, 25, 50, 75, and 100% of their ultimate capacity in compression and subjected to pull out loading tests. The experimental results indicated that the presence of the compressive load on the pile decreases the net uplift capacity of a pile and the decrease depends on the magnitude of the compressive load. A logical approach, based on the experimental results, has been suggested to predict the net uplift capacity of a pile considering the presence of compressive load.     

Investigations of surface characterization of silicone rubber due to tracking phenomena under a.c. and d.c. voltages

In the present work, tracking phenomena has been studied with silicone rubber material under the a.c. and d.c. voltages following IEC-587 standards. The surface condition of the tracked zone was analysed using wide angle X-ray diffraction (WAXD) and thermogravimetric differential thermal analysis (TG-DTA) studies. The tracking time was different for a.c. and d.c. voltages.     

Accommodation of a D-Phe residue into a right-handed 3(10)-helix: structure of Boc-D-Phe-(Aib)4-Gly-L-Leu-(Aib)2-OMe, an analogue of the amino terminal segment of antiamoebins and emerimicins

Recently an unstable trinucleotide CTG repeat, located within the 3' untranslated region of a gene on 19q13.3 was discovered in kindreds with myotonic dystrophy (DM). The age-of-onset/severity of DM shows a good correlation with CTG repeat size, and pedigrees and data reported to date have shown a striking trend toward amplification of the size of the CTG repeat during transmission from parent to child. The amplification has been accepted as the biological explanation for anticipation in the clinical severity observed in many families with DM. In this paper we report on 3 families where CTG amplification decreased during transmission from parent to child. In one case there was a gene conversion event, while in the remaining 2 there was a simpler reduction in the size of the repeat length. The changes appear to have been accompanied by a reduction in clinical severity in the child when compared to the parent. These observations are discussed in terms of their clinical implications and the biases that may exist in much of the reported data.     

One-step oxidation of cyclohexane to adipic acid

Liquid-phase oxidation of cyclohexane with Co(III) catalyst and gaseous oxygen was found to be influenced by reaction temperature, catalyst concentration and the duration. Maximum adipic acid product selectivity (77%) with about 85% cyclohexane conversion was attained at 100°C using catalyst: cyclohexane molar ratio 0·08. Under these conditions more than 80% cyclohexane was converted in the first hour, although selectivity to adipic acid continued to increase for the next 5 h. Cyclohexyl acetate and cyclohexyl monoadipate were identified as important intermediates. This study supports the mechanism proposed by Schultz, J. G. D. and Opchenko, A., J. Org. Chem., 38 (21) (1973) 3729.     

Transport properties in a mixed inorganic ion exchanger—zirconium phosphomolybdate—in comparison to its single salt counterparts

Amorphous inorganic ion exchangers zirconium phosphate, zirconium molybdate and zirconium phosphomolybdate have been prepared and characterized by elemental analysis, TGA, FTIR and surface area measurements. The protonic conductivity in these materials were determined at various temperatures and compared. It was found that the mixed material zirconium phosphomolybdate showed maximum conductivity at all temperatures.     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

Functionalized Reduced Graphene Oxide as a Versatile Tool for Cancer Therapy

Cancer is one of the deadliest diseases in human history with extremely poor prognosis. Although many traditional therapeutic modalities—such as surgery, chemotherapy, and radiation therapy—have proved to be successful in inhibiting the growth of tumor cells, their side effects may vastly limited the actual benefits and patient acceptance. In this context, a nanomedicine approach for cancer therapy using functionalized nanomaterial has been gaining ground recently. Considering the ability to carry various anticancer drugs and to act as a photothermal agent, the use of carbon-based nanomaterials for cancer therapy has advanced rapidly. Within those nanomaterials, reduced graphene oxide (rGO), a graphene family 2D carbon nanomaterial, emerged as a good candidate for cancer photothermal therapy due to its excellent photothermal conversion in the near infrared range, large specific surface area for drug loading, as well as functional groups for functionalization with molecules such as photosensitizers, siRNA, ligands, etc. By unique design, multifunctional nanosystems could be designed based on rGO, which are endowed with promising temperature/pH-dependent drug/gene delivery abilities for multimodal cancer therapy. This could be further augmented by additional advantages offered by functionalized rGO, such as high biocompatibility, targeted delivery, and enhanced photothermal effects. Herewith, we first provide an overview of the most effective reducing agents for rGO synthesis via chemical reduction. This was followed by in-depth review of application of functionalized rGO in different cancer treatment modalities such as chemotherapy, photothermal therapy and/or photodynamic therapy, gene therapy, chemotherapy/phototherapy, and photothermal/immunotherapy.