Ultrathin polypyrrole films on self-assembled monolayers as an efficient ultramicroelectrode assay

In this work, we investigated the effect of electrodeposition of polypyrrole (PPy) films on two different self-assembled monolayers (SAMs) modified gold electrode for the electrochemical construction of ultramicroelectrode (UME). In order to obtain SAM modified surfaces, 4-mercapto-1-butanol and 11-mercaptoundecanoic acid were used. The effect of these two chains on pyrrole electropolymerization was compared. Electropolymerization of pyrrole on SAM modified Au electrode was carried out by using cyclic voltammetry (CV) and constant potential electrolysis. To investigate the UME formation, the obtained surfaces were tested in Fe(CN)₆^3−/4− redox system. UMEs were characterized using scanning electron microscopy energy-dispersive X-ray spectroscopy, attenuated total reflectance fourier transform infrared spectroscopy and electrochemical impedance spectroscopy. The designed UME was applied as an immobilization matrix to entrap a redox protein, Hemoglobin (Hb), as a model. Direct electron transfer between this protein and the fabricated thiol modified PPy based UME was achieved, which is very challenging on bare electrode surfaces. With this study, a simple, low-cost and reproducible UME production way has been successfully accomplished and Hb modified UME is promising for different bioanalytical applications, for instance; cellular hydrogen peroxide or nitrite sensing.     

A Flexible Classifier Design Framework Based on Multiobjective Programming

We propose a multiobjective programming (MOP) framework for finding compromise solutions that are satisfactory for each of multiple competing performance criteria in a pattern classification task. The fundamental idea for our formulation of classifier learning, which we refer to as iterative constrained optimization (ICO), evolves around improving one objective while allowing the rest to degrade. This is achieved by the optimization of individual objectives with proper constraints on the remaining competing objectives. The constraint bounds are adjusted based on the objective functions obtained in the most recent iteration. An aggregated utility function is used to evaluate the acceptability of local changes in competing criteria, i.e., changes from one iteration to the next. Although many MOP approaches developed so far are formal and extensible to large number of competing objectives, their capabilities are examined only with two or three objectives. This is mainly because practical problems become significantly harder to manage when the number of objectives gets larger. We, however, illustrate the proposed framework in the context of automatic language identification (LID) of 12 languages and three dialects. This LID task requires the simultaneous minimization of the false-acceptance and false-rejection rates for each of the 15 languages/dialects, and, hence, is an MOP problem with a total of 30 competing objectives. In our experiments, we observed that the ICO-trained classifiers result in not only reduced error rates but also a good balance among the many competing objectives when compared to those classifiers that minimize an overall objective. We interpret our experimental findings as evidence for ICO offering a greater degree of freedom for classifier design.     

Evaluation of some of the physical properties of atmospheric plasma treated polypropylene fabric

In this study, atmospheric pressure plasma (APP) treatment was applied to polypropylene (PP) fabric with non‐polymerizing gases, such as air, argon, and nitrogen. Properties of the APP‐treated samples including low‐stress mechanical behavior, air permeability, water vapor permeability, and thermal characteristics were evaluated. Tensile and friction coefficient of the specimens were also measured. The changes in these properties are believed to be closely related to the inter‐fiber and inter‐yarn frictional force induced by the APP. The decrease in the air permeability of the APP‐treated PP fabric was probably because of the plasma action effect on change in fabric surface morphology, which was confirmed by scanning electron microscopy micrographs. The change in the thermal properties of the APP‐treated PP fabric was in good agreement with the earlier findings and can be attributed to the amount of air trapped between the yarns and fibers. This study suggests that the APP treatment can influence the final properties of the PP fabric, and also provides information for developing APP‐treated PP fabric for industrial use.     

基于流域水文模拟的径流变化原因研究

利用黄河中游三川河流域天然时期的降水径流资料,对SIMHYD降水径流模型进行了率定,并延展了人类活动影响期间的天然径流过程.采用水文模拟途径分析了三川河流域径流量的变化原因.结果表明,SlMHYD模型对三川河流域月径流过程具有良好的模拟效果,人类活动和气候变化对径流的绝对影响量呈增加趋势,人类活动是三川河流域径流量减少的主要原因.     

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

In this study, the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised. Silica fume was used at concentrations of 0%, 5%, 10%, and 20%. Cube specimens (100 mm × 100 mm × 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20°C±2°C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20°C, 200°C, 400°C, 600°C, and 800°C. Two well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures. The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectively. Various statistical measures were used to validate the prediction performances of both the approaches. This study found that the LSTM network achieved better results than the stacked autoencoders. In addition, this study found that deep learning, which has a very good prediction ability with little experimental data, was a convenient method for civil engineering.     

Is there any interaction of resistin and adiponectin levels with protein‐energy wasting among patients with chronic kidney disease

The aim of this study was to evaluate the effects of adipocytokines including adiponectin, leptin, resistin, neuropeptide Y and ghrelin in chronic kidney disease (CKD) patients on appearance of protein‐energy wasting (PEW). One hundred fifty patients with mean age of 45.4 ± 15.9 years, without active infections or chronic inflammatory conditions were recruited into the study. Study groups were control group (consisting of 30 healthy volunteers with normal kidney functions), hemodialysis group, predialysis group, peritoneal dialysis group and kidney transplant group. Fasting morning serum leptin, ghrelin, acylated ghrelin, neuropeptide Y, adiponectin, resistin levels of all of the groups were measured. Anthropometric and nutritional assessments of all patients were obtained. Diagnosis of PEW was made according to definition recommended by the International Society of Renal Nutrition and Metabolism. Presence of PEW in hemodialysis (23.3%) and peritoneal dialysis (26.7%) groups were significantly higher than those of predialysis (3.3%), and transplantation (0%) groups. Adiponectin and resistin levels in predialysis, peritoneal dialysis and hemodialysis patients were significantly higher than control group (p: 0.0001). This study had given significant positive correlations between presence of PEW and serum resistin (r: 0.267, p: 0.001), and serum adiponectin levels (r: 0.349, p: 0.0001). There were no relationship between presence of PEW and ghrelin, acylated‐ghrelin, neuropeptide Y, and leptin levels of the groups. CKD patients except transplant patients had higher adiponectin and resistin levels than control group. PEW was found to be linearly correlated with resistin and adiponectin. High serum resistin and adiponectin levels might have a role in development of PEW among dialysis patients.     

中国商飞大型客机制造工程技术大楼结构设计

中国商飞大型客机制造工程技术大楼为复杂连体结构,下部4个主体塔楼(1#～4#楼)采用框架-剪力墙体系,上部屋盖采用网架及桁架体系将下部塔楼连为一体。介绍结构体系、地基基础等设计要点的同时,重点介绍了该复杂连体结构设计中风荷载、温度作用等主要问题及解决方案。根据工程抗震超限情况,提出了抗震性能化设计目标和方法。进行了小震、中震下的整体计算和罕遇地震下的弹塑性分析,分析结果表明,针对本工程提出的性能化设计目标是合理的,结构安全性能够得到保证。     

Combinations of synergistic interactions and additive behavior during the co-oxidation of chars from lignite and biomass

The aim of this study is to investigate the co-combustion behavior of two different pyrolytic chars. For this purpose, Elbistan lignite and woody shells of hazelnut were pyrolysed in a tube furnace by heating to 900 °C with a heating rate of 40 °C min^− 1 under dynamic nitrogen flow of 400 mL min^− 1 to obtain pyrolytic char. These chars were mixed to obtain blends having the biomass char in the ratios of 5, 10, and 20 wt.%. Non-isothermal DTA and TGA profiles of the chars were obtained from ambient to 900 °C with a heating rate of 40 °C min^− 1 under the static ambient atmosphere. DTA and TGA profiles of the blend chars were interpreted considering the thermal characteristics such as ignition point, burnout at a given temperature, maximum burning rate, the end of combustion etc. Relations between the fraction of the biomass char in the blends and the thermal behavior of the blends were evaluated according to the synergistic approach. It was found that addition of biomass char led to important variations in some thermal properties which can not be explained by the additive behavior. However it can be concluded in general that the combinations of synergistic interactions and additive behavior govern the thermal properties of the blend chars during co-oxidation.