Capability Matrix : A Framework for Analyzing Capabilities in Value Chains

This paper develops a Capability Matrix for analyzing capabilities of developing country firms that participate in global and national value chains. This is a generic framework to capture firm-level knowledge accumulation in the context of global and local industrial constellations, by integrating key elements of the global value chain (GVC) and technological capabilities (TC) approaches. The framework can visually portray characteristics of firms’ capabilities, and highlight a relatively overlooked factor in the GVC approach: local firms’ endogenous learning efforts in varieties of relationship with lead firms.Developing Countries, Industrial Management, Business Enterprises, Capability Matrix, Capabilities, Value Chains, Lead Firms, Local Firms

Instrumental Resolution of the Chopper Spectrometer 4SEASONS Evaluated by Monte Carlo Simulation

We performed simulations of the resolution function of the 4SEASONS spectrometer at J-PARC by using the Monte Carlo simulation package McStas. The simulations showed reasonably good agreement with analytical calculations of energy and momentum resolutions by using a simplified description. We implemented new functionalities in Utsusemi, the standard data analysis tool used in 4SEASONS, to enable visualization of the simulated resolution function and predict its shape for specific experimental configurations.Comment: 8 pages, 5 figure

Discriminating different classes of biological networks by analyzing the graphs spectra distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them on (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed

Capability Matrix : A Framework for Analyzing Capabilities in Value Chains

This paper develops a Capability Matrix for analyzing capabilities of developingcountry firms that participate in global and national value chains. This is a genericframework to capture firm-level knowledge accumulation in the context of globaland local industrial constellations, by integrating key elements of the global valuechain (GVC) and technological capabilities (TC) approaches. The framework canvisually portray characteristics of firms’ capabilities, and highlight a relativelyoverlooked factor in the GVC approach: local firms’ endogenous learning efforts invarieties of relationship with lead firms

Direct Observation of Non-Monotonic dx2-y2-Wave Superconducting Gap in Electron-Doped High-Tc Superconductor Pr0.89LaCe0.11CuO4

We performed high-resolution angle-resolved photoemission spectroscopy on electron-doped high-Tc superconductor Pr0.89LaCe0.11CuO4 to study the anisotropy of the superconducting gap. The observed momentum dependence is basically consistent with the dx2-y2-wave symmetry, but obviously deviates from the monotonic dx2-y2 gap function. The maximum gap is observed not at the zone boundary, but at the hot spot where the antiferromagnetic spin fluctuation strongly couples to the electrons on the Fermi surface. The present experimental results unambiguously indicate the spin-mediated pairing mechanism in electron-doped high-Tc superconductors.Comment: 4 pages, 4 figure