Globalization and Knowledge Spillover: International Direct Investment, Exports and Patents

This paper examines the impact of the three main channels of international trade on domestic innovation, namely outward direct investment, inward direct investment (IDI) and exports. The number of Triadic patents serves as a proxy for innovation. The data set contains 37 countries that are considered to be highly competitive in the world market, covering the period 1994 to 2005. The empirical results show that increased exports and outward direct investment are able to stimulate an increase in patent output. In contrast, IDI exhibits a negative relationship with domestic patents. The paper shows that the impact of IDI on domestic innovation is characterized by two forces, and the positive effect of cross-border mergers and acquisitions by foreigners is less than the negative effect of the remaining IDI.International direct investment; Export; Triadic Patent; Outward Direct Investment; Inward Direct Investment; R&D; negative binomial model

Globalization and Knowledge Spillover: International Direct Investment, Exports and Patents

This paper examines the impact of the three main channels of international trade on domestic innovation, namely outward direct investment, inward direct investment (IDI) and exports. The number of Triadic patents serves as a proxy for innovation. The data set contains 37 countries that are considered to be highly competitive in the world market, covering the period 1994 to 2005. The empirical results show that increased exports and outward direct investment are able to stimulate an increase in patent output. In contrast, IDI exhibits a negative relationship with domestic patents. The paper shows that the impact of IDI on domestic innovation is characterized by two forces, and the positive effect of cross-border mergers and acquisitions by foreigners is less than the negative effect of the remaining IDI.International direct investment, Export, Triadic Patent, Outward Direct Investment, Inward Direct Investment, R&D, negative binomial model

Phase and Amplitude Responses of Narrow-Band Optical Filter Measured by Microwave Network Analyzer

The phase and amplitude responses of a narrow-band optical filter are measured simultaneously using a microwave network analyzer. The measurement is based on an interferometric arrangement to split light into two paths and then combine them. In one of the two paths, a Mach-Zehnder modulator generates two tones without carrier and the narrow-band optical filter just passes through one of the tones. The temperature and environmental variations are removed by separated phase and amplitude averaging. The amplitude and phase responses of the optical filter are measured to the resolution and accuracy of the network analyzer

Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1

The Information-Leveling Role of Voluntary Disclosure Quality in Facilitating Investment Efficiency

This study examines whether and under what conditions voluntary disclosure quality plays an information-leveling role in facilitating investment efficiency. Measuring voluntary disclosure quality as the (inverse) standard deviation of managers’ prior earnings forecast errors (i.e., management forecast consistency), we document a positive association between management forecast consistency and investment efficiency that strengthens when the information environment becomes more constrained and when there are negative shocks to financial reporting quality. We also find that the management forecast consistency/investment efficiency association strengthens when firms are younger, faster growing, and financially constrained, but not when firms are weakly governed and financially unconstrained, which suggests that voluntary disclosure quality facilitates investment efficiency by mitigating adverse selection (but not moral hazard) frictions. Last, when we employ a changes-based model, we find that increases in management forecast consistency are associated with increases in investment efficiency, which mitigates concerns that voluntary disclosure quality’s empirical link to investment efficiency is purely driven by managers’ inherent forecasting abilities. Overall, we show that voluntary disclosure quality can facilitate investment efficiency when financial reporting and other elements of the information environment are constrained in their ability to mitigate market frictions that impede efficiency

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Applying the Resilience to the Community Development in Taiwan

In 1999, the Shishui community in Nantou County, Taiwan was severely damaged by the 921 earthquakes, with many houses collapsing and community facilities suffering unprecedented damage. In late 2001, a community development association was established, and with the efforts of residents, the community was awarded the title of Classic Rural Area by the government in 2007, becoming a nationally recognized leisure agriculture demonstration base. However, with the subsequent reduction of external resources, continuous loss of internal talents, and operational dysfunction of community organizations, the Shishui community eventually stagnated. How can we evaluate the community\u27s resilience in this situation? This study takes the Shishui community as its research field, integrates expert and scholar research on community resilience, constructs the elements of Shishui community development resilience through in-depth interviews with community residents, and analyzes Shishui community resilience indicators using the Fuzzy Delphi Method (FDM), with five main dimensions: sustainable development capacity, organizational leadership capacity, financial management capacity, community cohesion, and network resource capacity. Then, using the Similarity-based Importance-Performance Analysis (SBIPA) method, the study analyzes the evaluations of Shishui community residents on the satisfaction and importance of each resilience indicator. Finally, a total of 18 issues that residents consider important but have not yet reached the expected level of performance are identified, such as community resources and mechanisms to support young people returning to their hometowns and fair distribution of community resources by community organizations, which will become important directions for future community governance improvement