Matrix Transformation in Boron Containing High-Temperature Co–Re–Cr Alloys

An addition of boron largely increases the ductility in polycrystalline high-temperature Co–Re alloys. Therefore, the effect of boron on the alloy structural characteristics is of high importance for the stability of the matrix at operational temperatures. Volume fractions of ε (hexagonal close-packed—hcp), γ (face-centered cubic—fcc) and σ (Cr₂Re₃ type) phases were measured at ambient and high temperatures (up to 1500 °C) for a boron-containing Co–17Re–23Cr alloy using neutron diffraction. The matrix phase undergoes an allotropic transformation from ε to γ structure at high temperatures, similar to pure cobalt and to the previously investigated, more complex Co–17Re–23Cr–1.2Ta–2.6C alloy. It was determined in this study that the transformation temperature depends on the boron content (0–1000 wt. ppm). Nevertheless, the transformation temperature did not change monotonically with the increase in the boron content but reached a minimum at approximately 200 ppm of boron. A probable reason is the interplay between the amount of boron in the matrix and the amount of σ phase, which binds hcp-stabilizing elements (Cr and Re). Moreover, borides were identified in alloys with high boron content.     

Stimuli-Responsive Nanocomposite Hydrogels for Biomedical Applications

The complex tissue-specific physiology that is orchestrated from the nano- to the macroscale, in conjugation with the dynamic biophysical/biochemical stimuli underlying biological processes, has inspired the design of sophisticated hydrogels and nanoparticle systems exhibiting stimuli-responsive features. Recently, hydrogels and nanoparticles have been combined in advanced nanocomposite hybrid platforms expanding their range of biomedical applications. The ease and flexibility of attaining modular nanocomposite hydrogel constructs by selecting different classes of nanomaterials/hydrogels, or tuning nanoparticle-hydrogel physicochemical interactions widely expands the range of attainable properties to levels beyond those of traditional platforms. This review showcases the intrinsic ability of hybrid constructs to react to external or internal/physiological stimuli in the scope of developing sophisticated and intelligent systems with application-oriented features. Moreover, nanoparticle-hydrogel platforms are overviewed in the context of encoding stimuli-responsive cascades that recapitulate signaling interplays present in native biosystems. Collectively, recent breakthroughs in the design of stimuli-responsive nanocomposite hydrogels improve their potential for operating as advanced systems in different biomedical applications that benefit from tailored single or multi-responsiveness.     

Thermodynamic and Kinetic Studies of Uranium and REEs Leaching by Oxalic Acid from Abu-Tartur Phosphate Rock,Western Desert,Egypt

Radiochemistry - Leaching of uranium and REEs with oxalic acid from the Egyptian Abu-Tartur phosphate rock (PR) was studied. The effect exerted on the leaching process by oxalic acid concentration,...     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.