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.     

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.     

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.     

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.     

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.     

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.     

A simple model for the photocurrent density of a graded band gap CIGS thin film solar cell

A simple model for the photocurrent density of a linearly graded band gap Cu(In,Ga)Se₂ solar cell is presented. Both generation and recombination mechanisms in the space charge region and absorber region of the cell are considered. The carrier collection function and effective absorption coefficient are introduced in the calculations to obtain a more realistic model. The results show that photocurrent density of the graded band-gap solar cell is higher than that with a constant averaged band gap. There is an optimum for grading strength or band gap widening of the absorber region. Recombination current reduces the photocurrent density with a lower reduction in the absorber material than in the depletion region. For longer diffusion lengths (or greater values of carrier collection factor), a higher photocurrent density is obtained except where collection probability is already unity everywhere in the absorber.     

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.     

Cobalt Ferrite in YSZ for Use as Reactive Material in Solar Thermochemical Water and Carbon Dioxide Splitting, Part II: Kinetic Modeling

The kinetics of 10 wt.% cobalt ferrite (CoFe₂O₄) in 8 mol.% yttria-stabilized zirconia, synthesized via the co-precipitation method and formed into a porous structure, are investigated in support of simulating the performance of a solar thermochemical reactor. Kinetic parameters for the thermal reduction (T-R) of CoFe₂O₄ at temperatures of 1325–1500°C were investigated by thermogravimetry. A nonlinear best fit of a uniform conversion model was used to determine kinetic parameters from experimental data. In the temperature range of 1375–1450°C, the activation energy and preexponential term were found to be 386 ± 13 kJ mol^?1 and 8.8 × 10⁹ ± 2.0 × 10⁸ min^?1, respectively, while increasing at higher temperatures. Simultaneous thermogravimetric analysis and differential scanning calorimetry studies showed an increase in the reaction rate of T-R upon the onset of melting (1440°C). Oxidation studies of the material using CO₂ yield an activation energy and preexponential term of 52.1 ± 6.8 kJ mol^?1 and 2.86 ± 0.2 min^?1, respectively, which is in good agreement with past work. The reaction order for CO₂ was determined to be 0.750 ± 0.08. The reaction kinetics for oxidation using CO₂ were best described by a 3-D diffusion Jander model.     

Speed regulation in steering-based source seeking

The simplest strategy for extremum seeking-based source localization, for sources with unknown spatial distributions and nonholonomic unicycle vehicles without position measurement, employs a constant positive forward speed. Steering of the vehicle in the plane is performed using only the variation of the angular velocity. While keeping the forward speed constant is a reasonable strategy motivated by implementation with aerial vehicles, it leads to complexities in the asymptotic behavior of the vehicle, since the vehicle cannot settle—at best it can converge to a small-size attractor around the source. In this paper we regulate the forward velocity, with the intent of bringing the vehicle to a stop, or as close to a stop as possible. The vehicle speed is controlled using simple derivative-like feedback of the sensor measurement (the derivative is approximated with a washout filter) to which a speed bias parameter V_c is added. The angular velocity is tuned using standard extremum seeking. We prove two results. For V_c in a certain range around zero, we show that the vehicle converges to a ring around the source and on average the limit of the vehicle’s heading is either directly away or towards the source. For other values of V_c>0, the vehicle converges to a ring around the source and it revolves around the source. Interestingly, the average heading of this revolution around the source is more outward than inward—this is possible because the vehicle’s speed is not constant, it is lower during the outward steering intervals and higher during the inward steering intervals. The theoretical results are illustrated with simulations.