Phase-Field Modeling of Nugget Zone for a AZ31-Mg-Alloy Friction Stir Weld

This work presents simulation of microstructure evolution in the nugget zone (NZ) of a AZ31-Mg-alloy friction stir weld. The process parameters (tool geometrical characteristics, rotational speed, travel speed, applied load) have been correlated with the resulting microstructural features in the NZ of the weld (grain size and population) with the aid of the MICRESS software, which provides the ability to simulate both nucleation and grain growth during dynamic recrystallization phenomena evolving in the NZ during the weld thermal cycle. The input parameters of the developed model include the tool geometry, the welding conditions as well as the recrystallization energy, the grain boundary mobility and specific material properties. NZ microstructure obtained by simulation shows good agreement with experimental measurements for both grain population and size.     

Towards Diastereomeric Pure Salts with Antisolvent Methods

Chiral molecules, especially enantiomers and diastereomers of purity > 99 %, present a significant market share within the chemical, pharmaceutical, and flavor industries. Antisolvent precipitations, both batch and semicontinuous operations to serve the current trends in flow chemistry were demonstrated to be environmentally benign and efficient tools in achieving high optical purities. Although salts are known to be insoluble in supercritical CO₂, instabilities of the nascent salts were detected and applied for increasing efficiency. Diastereomeric excess values of the crystalline products exceeded 99 % in maximum of three consecutive steps both by repeated resolution with half molar equivalent of the amine to the acid and by direct recrystallization of the salts.     

Milk from cows fed a diet with a high forage:concentrate ratio improves inflammatory state,oxidative stress,and mitochondrial function in rats

Excessive energy intake may evoke complex biochemical processes characterized by inflammation, oxidative stress, and impairment of mitochondrial function that represent the main factors underlying noncommunicable diseases. Because cow milk is widely used for human nutrition and in food industry processing, the nutritional quality of milk is of special interest with respect to human health. In our study, we analyzed milk produced by dairy cows fed a diet characterized by a high forage:concentrate ratio (high forage milk, HFM). In view of the low n-6:n-3 ratio and high content of conjugated linoleic acid of HFM, we studied the effects of this milk on lipid metabolism, inflammation, mitochondrial function, and oxidative stress in a rat model. To this end, we supplemented for 4 wk the diet of male Wistar rats with HFM and with an isocaloric amount (82 kJ, 22 mL/d) of milk obtained from cows fed a diet with low forage:concentrate ratio, and analyzed the metabolic parameters of the animals. Our results indicate that HFM may positively affect lipid metabolism, leptin:adiponectin ratio, inflammation, mitochondrial function, and oxidative stress, providing the first evidence of the beneficial effects of HFM on rat metabolism.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.