Harvesting Green Energy from Blue Ocean in Taiwan: Patent Mapping and Regulation Analyzing

Taiwan is an island with abundant oceanic resources but devoid of resources to significantly utilize ocean power. In fact, the Taiwanese government has initiated several renewable energy policies to transform its energy supply structure from brown (fossil fuel-based) sources of energy to green (renewable-based) energy. In addition, in the 4th National Energy Conference held in 2015, ocean energy was identified as a key contributor to renewable energy source. Therefore, the Taiwanese government proposed the construction of a MW-scale demonstration electricity plant, powered by ocean energy, as promptly as possible. Compared with solar PV, wind, and biomass (waste) energy, the development of ocean energy in Taiwan has lagged behind. Therefore, the aim of this chapter is to boost ocean energy adaptation using analysis from technical and legal perspectives. This chapter first illustrates the ocean energy potential and develop blueprint in Taiwan. Next, through patent research from the Taiwan Patent Search System, this chapter identifies advantageous ocean power technologies innovated by Taiwanese companies, primarily wave and current technologies. Furthermore, through the examination of regulations and competent authorities, this chapter discusses the possible challenges for implementing ocean energy technologies in Taiwan

Towards a full description of MeV dark matter decoupling: a self-consistent determination of relic abundance and NeffN_{\rm eff}

Thermal dark matter at the MeV mass-scale has its abundance set during the highly non-trivial epochs of neutrino decoupling and electron annihilation. The technical obstacles attached to solving Boltzmann equations of multiple interacting sectors being both relativistic and non-relativistic have to-date prevented the full treatment of this problem. Here, for the first time, we calculate the freeze-out of light dark matter, taking into account the energy transfer between the dark sector, neutrinos, and the electromagnetically interacting plasma from annihilation and elastic scattering processes alike. We develop a numerically feasible treatment that allows to track photon and neutrino temperatures across freeze-out and to arrive at a precision prediction of $N_{\rm eff}$ for arbitrary branching ratios of the dark matter annihilation channels. In addition, our treatment resolves for the first time the dark matter temperature evolution across freeze-out involving three sectors. It enters in the efficiency of velocity-dependent annihilation channels and for a flavor-blind $p$-wave annihilation into electron- and neutrino-pairs of all generations, we find the present Planck data excludes a complex scalar dark matter particle of mass of $m_\phi \leq 7$ MeV.Comment: 16 pages, 7 figures, to match the published versio

A Neural Network Decision Method for Software Maintenance Life Cycle Identification

The software maintenance life cycle concept is a powerful model in helping software maintenance planning. The operationalization of the life cycle concept requires a heuristic decision method. Although the heuristic decision method works most of the time, the method requires integration of different tools and sometimes leads to errors. In this paper, we propose a neural network decision method, which combines data smoothing and maintenance stage identification into one unit

Structural study in Highly Compressed BiFeO3 Epitaxial Thin Films on YAlO3

We report a study on the thermodynamic stability and structure analysis of the epitaxial BiFeO3 (BFO) thin films grown on YAlO3 (YAO) substrate. First we observe a phase transition of MC-MA-T occurs in thin sample (<60 nm) with an utter tetragonal-like phase (denoted as MII here) with a large c/a ratio (~1.23). Specifically, MII phase transition process refers to the structural evolution from a monoclinic MC structure at room temperature to a monoclinic MA at higher temperature (150oC) and eventually to a presence of nearly tetragonal structure above 275oC. This phase transition is further confirmed by the piezoforce microscopy measurement, which shows the rotation of polarization axis during the phase transition. A systematic study on structural evolution with thickness to elucidate the impact of strain state is performed. We note that the YAO substrate can serve as a felicitous base for growing T-like BFO because this phase stably exists in very thick film. Thick BFO films grown on YAO substrate exhibit a typical "morphotropic-phase-boundary"-like feature with coexisting multiple phases (MII, MI, and R) and a periodic stripe-like topography. A discrepancy of arrayed stripe morphology in different direction on YAO substrate due to the anisotropic strain suggests a possibility to tune the MPB-like region. Our study provides more insights to understand the strain mediated phase co-existence in multiferroic BFO system.Comment: 18 pages, 6 figures, submitted to Journal of Applied Physic

Increasing fMRI Sampling Rate Improves Granger Causality Estimates

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain