Comparison between lipid and protein oxidation progress in the tail and claw muscles of signal crayfish (Pacifastacus leniusculus)

Microbial activity and then oxidation progress are the most important freshness indicators during post-mortem. In this study, we monitored proteomic and microbial changes, as well as biochemical degradation, in the tail and claw muscles of crayfish stored for 12 days at +4 °C. One specified protein band at 107 kDa in the claw muscle and two specified protein bands in the tail muscle at 140 and 36–40 kDa were identified. Western blotting indicated a higher amount of oxidised proteins in the tail compared to the claw muscle. Tail muscle showed higher oxidation progress and calpain activity than claw one. Both muscles were spoiled after 12 days with respect to total viable counts. In the first days, calpain activity is the main reason for protein degradation, while protein oxidation dominates for the rest of the time. Lipid–protein oxidation progress showed probably, protein oxidation started earlier than lipid oxidation in both muscles.     

Large-scale load capacity tests on a geosynthetic encased column

Stone columns have been used to minimize the settlement of embankments on soft soils but their use in very soft soils can become challenging, partly because of the low confinement provided by the surrounding soil. Geosynthetic encased columns (GECs) have been successfully used to enhance to reduce settlements of embankments on soft soils. This paper describes an investigation on the performance of encased columns constructed on a very soft soil using different types of encasement (three woven geotextiles with different values of tensile stiffness) and different column fill materials (sand, gravel and recycled construction and demolition waste, RCDW). The results of load capacity tests conducted on large-scale models constructed to simulate the different types of GECs indicate that the displacement method adopted during column installation can lead to an enhanced shear strength in the smear zone that develops within the very soft soil. In addition, breakage of the column fill material was found to affect the load-settlement response of gravel and RCDW columns. Furthermore, the excess pore water pressure generated in the surrounding soil during installation, was found to remain limited to radial distances smaller than three times the GEC diameter.     

Equivalent linear model for fully self‐centering earthquake‐resisting systems

Modern rocking and stepping cores have been known as the efficient self‐centering earthquake‐resisting systems (SC‐ERSs). The current article proposes an approximate equivalent linear (EL) model for rapid estimation of the SC‐ERS displacement. An equivalent damping ratio and effective stiffness are formulated for flag‐shaped hysteresis of a fully SC‐ERS. The approximate EL model is first established using secant stiffness and Jacobsen's damping model. Nonlinear response history analyses are carried out to compare exact and approximated peak displacements. Findings reveal that EL analysis of the SC‐ERS based on Jacobsen's damping leads to underestimation of the maximum inelastic displacement. Accordingly, a new optimal damping formula is proposed using a genetic algorithm and nonlinear regression analyses. The improved EL model is validated by practical examples, and the results show acceptable accuracy in design‐level displacement estimation.     

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines

This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.     

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.     

Low pressure mercury–argon electrodeless fluorescent lamp simulation using the finite volume method

A finite volume model of a low pressure Ar–Hg electrodeless lamp with external coils is established. The physical configuration is similar to the OSRAM/ENDURA 70 W lamp. A Maxwellian energy distribution is assumed. Two dimensional behaviors of the most important plasma parameters such as the electron density, the electron temperature and the most important mercury states are discussed. Results are in good agreement with former studies and the general understanding in this area. Also, the influence of the argon pressure is studied. Based on the simulation, the population of the main resonance state, Hg (6³P₁ ) depends on the argon pressure and the optimized value is about 300 mTorr.     

The evaluation of the diffuse interface method for phase change simulations using OpenFOAM

In the present study, a new solver named phaseChangeHeatFoam is implemented on the OpenFOAM cfd package to simulate boiling and condensation. The solver is capturing the interface between two immiscible phases with a color function volume of fluid (CF‐VOF) method. The two fluids (vapor and liquid) are assumed Newtonian and incompressible. The surface tension is modeled with continuous surface force (CSF) which is improved with a Lafaurie filter to suppress the spurious current. The mass flux across the interface in the phase change process is determined by either Lee or Tanasawa mass transfer models. Additionally, the slight variation of saturation temperature with local pressure is considered with the simplified Clausius–Clapeyron relation. The coupled velocity pressure equation is solved using the PIMPLE algorithm. The new solver is validated and examined with (i) Stefan problem, (ii) two‐dimensional film boiling, (iii) the film condensation on a horizontal plate, (iv) the laminar film condensation over a vertical plate, and (v) bubble condensation in subcooled boiling. The present study shows the capability of a diffuse interface method in accurate simulation of the phase change process and it is expected to be instructive for further numerical studies in this area.     

Kinetics of zinc sulfide concentrate direct leaching in pilot plant scale and development of semi-empirical model

The direct leaching kinetics of an iron-poor zinc sulfide concentrate in the tubular reactor was examined. All tests were carried out in the pilot plant. To allow the execution of hydrostatic pressure condition, the slurry with ferrous sulfate and sulfuric acid solution was filled into a vertical tube (9 m in height) and air was blown from the bottom of the reactor. The effects of initial acid concentration, temperature, particle size, initial zinc sulfate concentration, pulp density and the concentration of Fe on the leaching kinetics were investigated. Results of the kinetic analysis indicate that direct leaching of zinc sulfide concentrate follows shrinking core model (SCM). This process was controlled by a chemical reaction with the apparent activation energy of 49.7 kJ/mol. Furthermore, a semi-empirical equation is obtained, showing that the order of the iron, sulfuric acid and zinc sulfate concentrations and particle radius are 0.982, 0.189, ?0.097 and ?0.992, respectively. Analysis of the unreacted and reacted sulfide particles by SEM–EDS shows that insensitive agitation in the reactor causes detachment of the sulfur layer from the particles surface in lower than 60% Zn conversion and lixiviant in the face with sphalerite particles.     

CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors

Reliability of the current microprocessor technology is seriously challenged by radiation-induced soft errors. Accurate Vulnerability Factor (VF) modeling of system components is crucial in designing cost-effective protection schemes in high-performance processors. Although Statistical Fault Injection (SFI) techniques can be used to provide relatively accurate VF estimations, they are often very time-consuming. Unlike SFI techniques, recently proposed analytical models can be used to compute VF in a timely fashion. However, VFs computed by such models are inaccurate as the system-level impact of soft errors is overlooked.     

Short-term wind power forecasting using ridgelet neural network

Rapid growth of wind power generation in many countries around the world in recent years has highlighted the importance of wind power prediction. However, wind power is a complex signal for modeling and forecasting. Despite the performed research works in the area, more efficient wind power forecast methods are still demanded. In this paper, a new prediction strategy is proposed for this purpose. The forecast engine of the proposed strategy is a ridgelet neural network (RNN) owning ridge functions as the activation functions of its hidden nodes. Moreover, a new differential evolution algorithm with novel crossover operator and selection mechanism is presented to train the RNN. The efficiency of the proposed prediction strategy is shown for forecasting of both wind power output of wind farms and aggregated wind generation of power systems.