Evaluation of antioxidant level and protein oxidation of rainbow trout (Oncorhynchus mykiss) fillets during rigor and post-rigor

In this study, the relationship between stunning techniques and protein oxidation which are accepted as the main cause of food spoilage was investigated. For this purpose, the antioxidant status, in vivo myofibrillary protein (MP) oxidation and sensitivity, and postmortem oxidation (inducted with hydroxyl radical system) of rainbow trout (Oncorhynchus mykiss) fillets killed by hitting to head (T1), neck crushing (T2), and convulsion (T3) methods, were investigated. Statistically significant differences (p < .05) were found among all parameters examined and it was observed that the most stressing technique was the convulsion method. It was determined that in protein profiles myosin were influenced too much from stunning technique and in actin observed oxidation-induced reductions. Reductions in S-S and S-H were also found to be increased in carbonyl concentrations, but the most effective values in both processes were determined by the convulsive technique. Our results show that short-term stunning techniques (hitting to head, neck crushing) give better meat quality results in terms of O. mykiss welfare and low MP oxidation rates. In general, we can say that T3 group fillets are more sensitive to oxidative damage, while T1 and T2 groups give better results in maintaining meat quality with low MP oxidation rates.     

Structural Characterization of Different Asphaltenes of Kuwaiti Origin

Asphaltenes obtained by precipitation from crude Kuwaiti oils have been analyzed by proton magnetic resonance (¹H-NMR), carbon-13 nuclear magnetic resonance (¹³C-NMR) and Infrared (IR) spectral techniques. The molecular weight and elemental analysis were also determined. These combined analytical data were used for the characterization of these Kuwaiti oils. The asphaltenes molecular weights range from approximately 4200-6500 with an H/C ratio of 0.91-1.1 with an average 45-71% aromatic carbons. The average side chain length was of 4-6 carbons. It can also be concluded that the asphaltenes under investigation contain 5-9 sets of condensed aromatic rings joined together by bridges of alkyl chains or other hetero atoms and the average number of each of these sets of condensed aromatic rings is nearly 7. There are a number of alicyclic rings and condensed alicyclic rings in asphaltene. The IR spectra showed main molecular groups including OH, NH, SH, C=O and aliphatic and aromatic C-H's.     

New correlation for predicting the viscosity of heavy petroleum fractions

A new, simple and reliable empirical correlation for predicting the viscosities of heavy petroleum fractions is proposed, applicable to fractions with mid-boiling points from 80 to 550°C over a wide range of viscosities (0.4 to 260 mm² s⁻¹) and measurement temperatures (40 to 200°C). This correlation showed better accuracy in the prediction of heavy petroleum fraction viscosities than that of the most accurate and much more complicated predictive methods in the literature, since it gave an overall average absolute deviation of 6.5% when tested on 296 data for medium and heavy petroleum fractions.     

Stress and strain state of concrete during freezing and thawing cycles

The objective of this work is to calculate the pressures, stresses, and strains induced into moist concrete during freezing and thawing. The applied theory is based on thermodynamics and the linear theory of elasticity. If no additional salts are dissolved in the pore water the inputs needed in the theory are relative humidity and temperature measured in the sample chamber and inside concrete and evaporable water amount in the pore structure. Theoretical results were compared with the test results made with two concretes cured under water or at 96% relative humidity. One of the concretes was air entrained and in the comparison concrete no air-entraining agents were used. In the test cylinders cured under water the largest tensional stresses in freezing occurred on the surface of the test cylinders both in the axial and tangential direction. The largest tensional stress was 2.2 MPa, both in air-entrained and in non air-entrained concretes. The largest tensional stresses in the warming phase took place at the end of the thawing period when the chamber temperature was around +5 °C. Then the maximum tension occurred in the middle of the concrete cylinder in the axial direction of the cylinder. This maximum tensional stress was over 2.5 MPa in the air-entrained concrete cured in the relative humidity of 96%. The thermodynamic pumping effect at the end of the thawing phase in every cycle can increase the pore water amount remarkably if free water or moisture is available on the surface of the structure or in the environment vapor. The thermodynamic pumping effect seems to be remarkably greater and more dangerous in air-entrained concretes.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Silver Nanoflower Decorated Graphene Oxide Sponges for Highly Sensitive Variable Stiffness Stress Sensors

Soft conductive materials should enable large deformation while keeping high electrical conductivity and elasticity. The graphene oxide (GO)‐based sponge is a potential candidate to endow large deformation. However, it typically exhibits low conductivity and elasticity. Here, the highly conductive and elastic sponge composed of GO, flower‐shaped silver nanoparticles (AgNFs), and polyimide (GO‐AgNF‐PI sponge) are demonstrated. The average pore size and porosity are 114 µm and 94.7%, respectively. Ag NFs have thin petals (8–20 nm) protruding out of the surface of a spherical bud (300–350 nm) significantly enhancing the specific surface area (2.83 m² g^?1). The electrical conductivity (0.306 S m^?1 at 0% strain) of the GO‐AgNF‐PI sponge is increased by more than an order of magnitude with the addition of Ag NFs. A nearly perfect elasticity is obtained over a wide compressive strain range (0–90%). The strain‐dependent, nonlinear variation of Young's modulus of the sponge provides a unique opportunity as a variable stiffness stress sensor that operates over a wide stress range (0–10 kPa) with a high maximum sensitivity (0.572 kPa^?1). It allows grasping of a soft rose and a hard bottle, with the minimal object deformation, when attached on the finger of a robot gripper.     

A recursive state estimator in the presence of state inequality constraints

This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.