Experimental Study on Fracture Energy of Low-Heat Concrete by Three-Point Bend Tets

To examine the fracture characteristics of low-heat concrete which is used to prevent the thermal crack caused by hydration heat, static loading test was performed by Determination of the Fracture Energy of Mortar and Concrete by Means of Three-Point Bend Tests on Notched Beam (suggested by RILEM 50-FMC Committee). Fracture energy of Mode I (opening mode) for the Ordinary and Low-Heat Concrete is compared and examined according to experimental variables, such as water-cement ratio, compressive strength and age. Test results show that the deflections at the fracture was decreased as the fracture strength were increased and the reduction of water-cement ratio caused to increase fracture energy in both specimens. The fracture energy of low-heat concrete turned out to be similar to that of ordinary portland concrete as the increases.     

Cluster randomized trials with treatment noncompliance.

Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Pulsed Electromagnetic Field (PEMF) Treatment Reduces Lipopolysaccharide-Induced Septic Shock in Mice

Despite advances in medicine, mortality due to sepsis has not decreased. Pulsed electromagnetic field (PEMF) therapy is emerging as an alternative treatment in many inflammation-related diseases. However, there are few studies on the application of PEMF therapy to sepsis. In the current study, we examined the effect of PEMF therapy on a mouse model of lipopolysaccharide (LPS)-induced septic shock. Mice injected with LPS and treated with PEMF showed higher survival rates compared with the LPS group. The increased survival was correlated with decreased levels of pro-inflammatory cytokine mRNA expression and lower serum nitric oxide levels and nitric oxide synthase 2 mRNA expression in the liver compared with the LPS group. In the PEMF + LPS group, there was less organ damage in the liver, lungs, spleen, and kidneys compared to the LPS group. To identify potential gene targets of PEMF treatment, microarray analysis was performed, and the results showed that 136 genes were up-regulated, and 267 genes were down-regulated in the PEMF + LPS group compared to the LPS group. These results suggest that PEMF treatment can dramatically decrease septic shock through the reduction of pro-inflammatory cytokine gene expression. In a clinical setting, PEMF may provide a beneficial effect for patients with bacteria-induced sepsis and reduce septic shock-induced mortality.     

Venom Peptide Toxins Targeting the Outer Pore Region of Transient Receptor Potential Vanilloid 1 in Pain: Implications for Analgesic Drug Development

The transient receptor potential vanilloid 1 (TRPV1) ion channel plays an important role in the peripheral nociceptive pathway. TRPV1 is a polymodal receptor that can be activated by multiple types of ligands and painful stimuli, such as noxious heat and protons, and contributes to various acute and chronic pain conditions. Therefore, TRPV1 is emerging as a novel therapeutic target for the treatment of various pain conditions. Notably, various peptides isolated from venomous animals potently and selectively control the activation and inhibition of TRPV1 by binding to its outer pore region. This review will focus on the mechanisms by which venom-derived peptides interact with this portion of TRPV1 to control receptor functions and how these mechanisms can drive the development of new types of analgesics.     

New approach to immobilization and specific-sequence detection of nucleic acids based on poly(4-hydroxyphenylacetic acid)

Immobilization and hybridization of oligonucleotides or specific-gene PCR product (DENV-1), a conserved genomic sequence of the dengue virus, onto graphite electrode modified with poly(4-hydroxyphenylacetic acid), were carried out with success using both direct electrochemical oxidation of guanine or redox electroactive indicator ethidium bromide.Studies of oligonucleotides hybridization with the complementary target showed a decrease of both guanosine and adenosine current peaks, when compared with the peak previously obtained before the hybridization. Immobilized ssDNA, DENV-1, was hybridized with various concentrations of target DNA. The interaction between DENV-1 hybridized onto the modified graphite electrodes surface and the intercalator, ethidium bromide, was observed by differential pulse voltammetry, monitoring the current change generated to the DNA intercalator accumulated onto the modified electrode after DNA hybridization. For the determination of complementary target, the proposed method exhibited a good dynamic range (12–42 nmol L^? 1) and a low detection limit (7.12 nmol L^? 1).AFM images showed that the oligonucleotides or single-stranded DNA, DENV-1, before hybridization, had roughness values lower than the double stranded obtained after hybridization.The new surface obtained in these work, as well as the possibility of utilization of the same to monitor hybridization events is a promising strategy for the development of DNA electrochemical biosensors.     

Application of visible-light photocatalysis with nitrogen-doped or unmodified titanium dioxide for control of indoor-level volatile organic compounds

The present study evaluated visible-light photocatalysis, applying an annular reactor coated with unmodified or nitrogen (N)-doped titanium dioxide (TiO(2)), to cleanse gaseous volatile organic compounds (VOCs) at indoor levels. The surface chemistry investigation of N-doped TiO(2) suggested that there was no significant residual of sulfate ions or urea species on the surface of the N-doped TiO(2). Under visible-light irradiation, the photocatalytic technique using N-doped TiO(2) was much superior to that for unmodified TiO(2) for the degradation of VOCs. Moreover, the degradation efficiency by a reactor coated with N-doped TiO(2) was well above 90% for four target compounds (ethyl benzene, o,m,p-xylenes), suggesting that this photocatalytic system can be effectively employed to cleanse these pollutants at indoor air quality (IAQ) levels. The degradation efficiency of all target compounds increased as the stream flow rate (SFR) decreased. For most target compounds, a reactor with a lower hydraulic diameter (HD) exhibited elevated degradation efficiency. The result on humidity effect suggested that the N-doped photocatalyst could be employed effectively to remove four target compounds (ethyl benzene, o,m,p-xylenes) under conditions of less humidified environments, including a typical indoor comfort range (50-60%). Consequently, it is suggested that with appropriate photocatalytic conditions, a visible-light-assisted N-doped photocatalytic system is clearly an important tool for improving IAQ.     

P2P computing for intelligence of things

Peer-to-Peer Networking and Applications -     

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.     

Input output linearization approach to state observer design fornonlinear system

Presents a state observer for a class of nonlinear systems based on the input output linearization. While the previous result presented state observers for nonlinear systems of full relative degree, we proposed a procedure fur the design of nonlinear state observers which do not require the hypothesis of full relative degree. Assuming that there exists a global state observer for internal dynamics and that some functions are globally Lipschitz, we can design a globally convergent state observer. It is also shown that if the zero dynamics are locally exponentially stable, then there exists a local state observer. An example is given to illustrate the proposed design of nonlinear state observers