The effect of elevated temperature and silicon addition on a cobalt-based wear resistant superalloy

The effects of an elevated temperature and a 5 wt% silicon addition on the resultant microstructure and inherent phases of Stellite 6 were investigated by using room and high temperature optical microscopy, X-ray diffraction, scanning electron microscopy (SEM) and also bulk hardness and microhardness measurements. It has been observed that exposing Stellite 6 to heat treatments at 1000°C results in a characteristic textured structure and coarsening of interdendritic regions due to bulk diffusion. In addition, both dendritic and interdendritic hardness values increase due to texture formation and increased amounts of carbide and intermetallic phases, respectively. On the other hand, silicon addition to Stellite 6 causes the transformation of the original spongy dendritic microstructure in as-cast Stellite 6 to a eutectic dendritic and skeleton interdendritic structure. Also, when silicon added Stellite 6 was heat treated at 1000°C, particulates emanating from the interdendritic skeleton become irregularly dispersed in the dendritic region. In addition, similarly to Stellite 6; a high temperature heat treatment results in an increase in hardness values of silicon added Stellite 6 due to the presence of an Co₂ Si intermetallic phase.     

General solution of one-dimensional,time dependent coupled heat flow systems

A new, powerful method of analysis, involving the combined use of finite integral transform and finite element techniques, is presented for the solution of time dependent heat flow systems composed of many one-dimensional elements connected through the nodes. This method leads to an eigenvalue problem which is not of the conventional Sturm-Liouville type. A procedure for the determination of the eigenvalues is described. The solution obtained is in the form of an infinite series and contains quasi-steady and transient terms. The general solution obtained can be applied in the mathematical modelling of many engineering applications such as the determination of the penetration of the daily temperature cycle into buildings, the analysis of heat transfer in array of extended surfaces in compact heat exchangers, and many others.     

Local mobility within villin 14T probed via heteronuclear relaxation measurements and a reduced spectral density mapping

Villin 14T, a representative domain from the actin severing and bundling protein villin, binds calcium ions and actin monomers. To begin to understand the contributions of mobility to the villin-calcium and villin-actin interactions, relaxation rates for magnetization involving the amide nitrogens and protons have been measured for 15N-labeled villin 14T in solution. Although we have measured the complete set of rates required for a full spectral density map, difficulties in the accurate measurement of relaxation rates for antiphase coherence and two-spin order led us to consider a reduced mapping formalism. From the reduced spectral density map, a model-free analysis, or directly from the measured Nx,y relaxation rates, local variations in mobility along the backbone of villin 14T have been revealed. Fast motions are evident not only at the amino and carboxyl termini but also in the turn between strands beta 4 and beta 5 of the central beta-sheet and in the turn between helix alpha 3 and strand beta 7. Slower motions are suggested for the turn between strands beta 2 and beta 3. Motions on the microsecond to millisecond time scale have been probed directly by examining the dependence of the proton transverse relaxation rate on the spin-locking field strength. Leu11 shows a strong dependence on field strength, implying conformational exchange with a time constant of 125 +/- 69 microseconds. The backbone at the actin-binding interface appears to be rather rigid.     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

The role of visual complexity and prototypicality regarding first impression of websites: Working towards understanding aesthetic judgments

This paper experimentally investigates the role of visual complexity (VC) and prototypicality (PT) as design factors of websites, shaping users' first impressions by means of two studies. In the first study, 119 screenshots of real websites varying in VC (low vs. medium vs. high) and PT (low vs. high) were rated on perceived aesthetics. Screenshot presentation time was varied as a between-subject factor (50 ms vs. 500 ms vs. 1000 ms). Results reveal that VC and PT affect participants' aesthetics ratings within the first 50 ms of exposure. In the second study presentation times were shortened to 17, 33 and 50 ms. Results suggest that VC and PT affect aesthetic perception even within 17 ms, though the effect of PT is less pronounced than the one of VC. With increasing presentation time the effect of PT becomes as influential as the VC effect. This supports the reasoning of the information-processing stage model of aesthetic processing (Leder et al., 2004), where VC is processed at an earlier stage than PT. Overall, websites with low VC and high PT were perceived as highly appealing.     

Electrical properties of monoazacrown ether-substituted phthalocyanines

D.c. conductivities of polycrystalline monoazacrown ether-substituted phthalocyanines (M=2H, Ni, Zn, Pb, Cu) and diphthalocyanine (M=Lu) are measured as Au-MPc-Au sandwiches to be of the order 10^–10–10^–12 S m^–1. Chemical doping with oxidants (e.g. NOBF₄) and enhancing the stacking of planar phthalocyanine moieties through the formation of alkali metal adducts with sodium and potassium ions leads to increase in conductivity of the order 10¹–10². The low conductivity and the diamagneticity of the bis(phthalocyaninato)-lutetium can be ascribed to the lack of radical nature in LuH(Pc)₂. For the a.c. conductivities, lead and lutetium complexes form a group with higher conductivities and the rest show lower conductivity. The conduction activation energies calculated from Arrhenius plots exhibit the lowest value (0.40 eV) for the lutetium compound.Part of this work was presented at NATO-ASI on Semiconductor Materials and Processing Technologies, Erice, Sicily, 1–13 July 1991.     

Is LRP2 Involved in Leptin Transport over the Blood-Brain Barrier and Development of Obesity?

The mechanisms underlying the transport of leptin into the brain are still largely unclear. While the leptin receptor has been implicated in the transport process, recent evidence has suggested an additional role of LRP2 (megalin). To evaluate the function of LRP2 for leptin transport across the blood-brain barrier (BBB), we developed a novel leptin-luciferase fusion protein (pLG), which stimulated leptin signaling and was transported in an in vitro BBB model based on porcine endothelial cells. The LRP inhibitor RAP did not affect leptin transport, arguing against a role of LRP2. In line with this, the selective deletion of LRP2 in brain endothelial cells and epithelial cells of the choroid plexus did not influence bodyweight, body composition, food intake, or energy expenditure of mice. These findings suggest that LRP2 at the BBB is not involved in the transport of leptin into the brain, nor in the development of obesity as has previously been described.     

Jet bundle formulation of infinite-dimensional port-Hamiltonian systems using differential operators

We consider infinite-dimensional port-Hamiltonian systems described on jet bundles. Based on a power balance relation we introduce the port-Hamiltonian system representation using differential operators regarding the structural mapping, the dissipation mapping and the input mapping. In contrast to the well-known representation on the basis of the underlying Stokes–Dirac structure our approach is not necessarily based on using energy-variables which leads to a different port-Hamiltonian representation of the analyzed partial differential equations. The presented constructions will be specialized to mechanical systems to which class also the presented examples belong.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.