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.     

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.     

同步语言的时间可预测多线程代码生成方法

能够提供更强计算能力的多核处理器将在安全关键系统中得到广泛应用.但是,由于现代处理器所使用的流水线、乱序执行、动态分支预测、Cache等性能提高机制以及多核之间的资源共享,使得系统的最坏执行时间分析变得非常困难.为此,国际学术界提出时间可预测系统设计的思想,以降低系统的最坏执行时间分析难度.已有研究主要关注硬件层次及其编译方法的调整和优化,而较少关注软件层次,即时间可预测多线程代码的构造方法以及到多核硬件平台的映射.本文提出一种基于同步语言模型驱动的时间可预测多线程代码生成方法,并对代码生成器的语义保持进行证明;提出一种基于AADL(Architecture Analysis and Design Language)的时间可预测多核体系结构模型,作为本文研究的目标平台;最后,给出多线程代码到多核体系结构模型的映射方法,并给出系统性质的分析框架.     

Experimental study of a micro-scale sloped solar chimney power plant

Journal of Mechanical Science and Technology - Solar chimney power plants (SCPP) are structures that have the potential to generate a significant amount of electrical energy without harming the...     

Anomaly localization in regular textures based on deep convolutional generative adversarial networks

Applied Intelligence - Pixel-level anomaly localization is a challenging problem due to the lack of abnormal training samples. The existing adversarial network methods attempt to segment anomalies...     

Global and Local Mechanical Properties of Autogenously Laser Welded Ti-6Al-4V

Ti-6Al-4V sheets, 3.2-mm in thickness, were butt welded using a continuous wave 4 kW Nd:YAG laser welding system. The effect of two main process parameters, laser power and welding speed, on the joint integrity was characterized in terms of the joint geometry, defects, microstructure, hardness, and tensile properties. In particular, a digital image correlation technique was used to determine the local tensile properties of the welds. It was determined that a wide range of heat inputs can be used to fully penetrate the Ti-6Al-4V butt joints during laser welding. At high laser power levels, however, significant defects such as underfill and porosity, can occur and cause marked degradation in the joint integrity and performance. At low welding speeds, however, significant porosity occurs due to its growth and the potential collapse of instable keyholes. Intermediate to relatively high levels of heat input allow maximization of the joint integrity and performance by limiting the underfill and porosity defects. In considering the effect of the two main defects on the joint integrity, the underfill defect was found to be more damaging to the mechanical performance of the weldment than the porosity. Specifically, it was determined that the maximum tolerable underfill depth for Ti-6Al-4V is approximately 6 pct of the workpiece thickness, which is slightly stricter than the value of 7 pct specified in AWS D17.1 for fusion welding in aerospace applications. Hence, employing optimized laser process parameters allows the underfill depth to be maintained within the tolerable limit (6 pct), which in turn prevents degradation in both the weld strength and ductility. To this end, the ability to maintain weld ductility in Ti-6Al-4V by means of applying a high energy density laser welding process presents a significant advantage over conventional arc welding for the assembly of aerospace components.