Impacts of a firm's technological diversification on product diversification and performance

Master'sMASTER OF SCIENCE (BUSINESS

Optimization of Superplastic Forming Process of AA7075 Alloy for the Best Wall Thickness Distribution

This work aims to optimize the process parameters for improving the wall thickness distribution of the sheet superplastic forming process of AA7075 alloy. The considered factors include forming pressure p (MPa), deformation temperature T (°C), and forming time t (minutes), while the responses are the thinning degree of the wall thickness ε (%) and the relative height of the product h*. First, a series of experiments are conducted in conjunction with response surface method (RSM) to render the relationship between inputs and outputs. Subsequently, an analysis of variance (ANOVA) is conducted to verify the response significance and parameter effects. Finally, a numerical optimization algorithm is used to determine the best forming conditions. The results indicate that the thinning degree of 13.121% is achieved at the forming pressure of 0.7 MPa, the deformation temperature of 500°C, and the forming time of 31 minutes

Hexagonal RMnO3: a model system for two-dimensional triangular lattice antiferromagnets

The hexagonal RMnO3(h-RMnO3) are multiferroic materials, which exhibit the coexistence of a magnetic order and ferroelectricity. Their distinction is in their geometry that both results in an unusual mechanism to break inversion symmetry and also produces a two-dimensional triangular lattice of Mn spins, which is subject to geometrical magnetic frustration due to the antiferromagnetic interactions between nearest-neighbor Mn ions. This unique combination makes the h-RMnO3 a model system to test ideas of spin-lattice coupling, particularly when both the improper ferroelectricity and the Mn trimerization that appears to determine the symmetry of the magnetic structure arise from the same structure distortion. In this review we demonstrate how the use of both neutron and X-ray diffraction and inelastic neutron scattering techniques have been essential to paint this comprehensive and coherent picture of h-RMnO3. (c) 2016 International Union of Crystallography110111scopu

VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph

The availability of vast amounts of visual data with heterogeneous features is a key factor for developing, testing, and benchmarking of new computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited image data distribution for very specific situations, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that ours is knowledge-based rather than metadatabased. It enhances the enrichment of the semantics at both image and instance levels and offers various data retrieval and exploratory services via SPARQL. VisionKG currently contains 519 million RDF triples that describe approximately 40 million entities, and are accessible at https://vision.semkg.org and through APIs. With the integration of 30 datasets and four popular CV tasks, we demonstrate its usefulness across various scenarios when working with CV pipelines