Microstructure evolution of TI-SN-NB alloy prepared by mechanical alloying

In the present study, Ti-16Sn-4Nb alloy was prepared by mechanical alloying (MA). Optical microscopy, scanning electron microscopy combined with energy dispersive X-ray analysis (SEM-EDX), and X-ray diffraction analysis (XRD) were used to characterise the phase transformation and the microstructure evolution. Results indicated that ball milling to 8 h led to the formation of a supersaturated hcp &alpha;-Ti and partial amorphous phase due to the solid solution of Sn and Nb into Ti lattice. The microstructure of the bulk sintered Ti-16Sn-4Nb alloy samples made from the powders at shorter ball milling times, i.e. 20 min- 2 h, exhibited a primary &alpha; surrounded by a Widmanst&auml;tten structure (transformed &beta;); while in the samples made from the powders at longer ball milling times, i.e. 5- 10 h, the alloy evolved to a microstructure with a disordered and fine &beta; phase dispersed homogeneously within the &alpha; matrix. These results contribute to the understanding of the microstructure evolution in alloys of this type prepared by powder metallurgy.<br /

Exploring Green Innovation Practices: Content Analysis of the Fortune Global 500 Companies

Green innovation has been attracting increasing attention due to its contributions to the conservation of resources and environmental protection. However, in the process of exploring green innovation, the allocation of resources and the direction of innovation are often inaccurate, which leads to a low efficiency of green innovation. If we can learn the green innovation practices from successful companies, we can certainly provide reference strategies for those companies that are exploring green innovation. Therefore, taking the Fortune Global 500 companies as the analysis object, this research develops the criteria of green innovation practices and conducts a cluster analysis of these companies by using a content analysis method. Finally, this paper summarizes the green innovation practices of the six types of industries and provides corresponding countermeasures and suggestions, which provide a strong reference for relevant companies to carry out green innovation

Automated Action Model Acquisition from Narrative Texts

Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narrative texts in an automated way is essential, but challenging because of the inherent complexities of such texts. We present NaRuto, a system that extracts structured events from narrative text and subsequently generates planning-language-style action models based on predictions of commonsense event relations, as well as textual contradictions and similarities, in an unsupervised manner. Experimental results in classical narrative planning domains show that NaRuto can generate action models of significantly better quality than existing fully automated methods, and even on par with those of semi-automated methods.Comment: 10 pages, 3 figure

Poly[bis­[μ-1,3-bis­(diphenyl­phosphan­yl)propane-κ2 P:P′]-di-μ-thio­cyanato-κ2 S:N;κ2 N:S-disilver(I)]

In the title coordination polymer, [Ag2(NCS)2(C27H26P2)2]n, two centrosymmetrically related Ag+ cations are linked by two thio­cyanate anions into binuclear eight-membered macrocycles. The Ag⋯Ag separation within the macrocycle is 5.4400 (6) Å. The distorted tetra­hedral coordination about each metal atom is completed by the P atoms of two bridging 1,3-bis­(diphenyl­phosphan­yl)propane ligands, forming polymeric ribbons parallel to the a axis

Single Image Texture Translation for Data Augmentation

Recent advances in image synthesis enables one to translate images by learning the mapping between a source domain and a target domain. Existing methods tend to learn the distributions by training a model on a variety of datasets, with results evaluated largely in a subjective manner. Relatively few works in this area, however, study the potential use of semantic image translation methods for image recognition tasks. In this paper, we explore the use of Single Image Texture Translation (SITT) for data augmentation. We first propose a lightweight model for translating texture to images based on a single input of source texture, allowing for fast training and testing. Based on SITT, we then explore the use of augmented data in long-tailed and few-shot image classification tasks. We find the proposed method is capable of translating input data into a target domain, leading to consistent improved image recognition performance. Finally, we examine how SITT and related image translation methods can provide a basis for a data-efficient, augmentation engineering approach to model training