Light-enhanced electro-optic spectral tuning in annealed proton-exchanged periodically poled lithium niobate channel waveguides

[[abstract]]We report the observation of light-enhanced electro-optic spectral tuning in annealed proton-exchanged, asymmetric domain-duty-cycle periodically poled lithium niobate (PPLN) channel waveguides for second-harmonic generation. The spectral tuning rate was increased rapidly from 0.07 nm/(kV/mm) to a saturated value of 0.32 nm/(kV/mm) in a 30%/70% domain-duty-cycle PPLN waveguide when the fundamental pump power near 1534 nm was increased from 0.6 to 46 mW. The second-harmonic laser power at 767 nm was identified to be the source enhancing he spectral tuning.[[fileno]]2030114010039[[department]]電機工程學

Electro-optic periodically poled lithium niobate Bragg modulator as a laser Q-switch

[[abstract]]We report an electro-optic Bragg modulator using a periodically poled lithium niobate (PPLN) crystal. We measured a half-wave voltage of 160 V when transmitting a 1064 nm laser through a 14.2 ram long, 780 mu m thick, 20.13 mu m period PPLN crystal at the Bragg angle. We also demonstrated a Q-switched Nd:YVO4 laser using such a PPLN Bragg modulator as its Q-switch, producing 7.8 us, 201 mu J pulses at a 10 kHz repetition rate when pumped by a 19.35 W diode laser at 808 run.[[fileno]]2030114010044[[department]]電機工程學

Languaging in Content and Language Integrated Learning (CLIL) Classrooms: implications for English across the curriculum

Invited colloquiumLanguage is a primary semiotic (meaning-making) resource in construing the world, and the world (or content) is grasped mainly through language (Halliday, 1993). Hence, it has been argued that successful learning or knowledge construction depends on “guidance through interaction in the context of shared experience” (Rose & Martin, 2012, p. 58), or through the process of languaging, where language is used to mediate formulation of concepts (Swain & Lapkin, 2013). These highlight the importance of ‘dialogue’ or ‘dialogic discourse’. However, what actually constitutes ‘dialogic discourse’ and how this can be achieved by teachers and students in classrooms are still being explored, especially in Content and Language Integrated Learning (CLIL) classrooms, where such languaging processes and dialogic discourses take place through students’ (and often teachers’) ...postprin

Floral syndrome and breeding system of Senna (Cassia) corymbosa

Senna (Cassia) corymbosa is an ornamental plant with asymmetric flower in which petals and stamens are also involved in floral asymmetry. The pollen number of abaxial lateral stamen (AL), abaxial median stamen (AM) and middle stamen (MI) are descended in sequence. In field, the insects of visiting flowers are available and pollinators are essential to the pollination success of S. corymbosa. Bombidae was presumably the effective pollinators by buzzing pollination and wasp may be the potential pollinators. Pollen number and germination rate per type of stamen experiments supported the hypothesis of “division-of-labour” among stamens by Darwin. Both AL, AM and MI may afford food to visiting insects, while long stamens (including AL and AM) function as the “pollinating” stamens and the brownish yellow is presumably the effective color attractants to pollinators.Key words: Senna, pollen, pollination, breeding system

Extracellular Protease Inhibition Alters the Phenotype of Chondrogenically Differentiating Human Mesenchymal Stem Cells (MSCs) in 3D Collagen Microspheres

Matrix remodeling of cells is highly regulated by proteases and their inhibitors. Nevertheless, how would the chondrogenesis of mesenchymal stem cells (MSCs) be affected, when the balance of the matrix remodeling is disturbed by inhibiting matrix proteases, is incompletely known. Using a previously developed collagen microencapsulation platform, we investigated whether exposing chondrogenically differentiating MSCs to intracellular and extracellular protease inhibitors will affect the extracellular matrix remodeling and hence the outcomes of chondrogenesis. Results showed that inhibition of matrix proteases particularly the extracellular ones favors the phenotype of fibrocartilage rather than hyaline cartilage in chondrogenically differentiating hMSCs by upregulating type I collagen protein deposition and type II collagen gene expression without significantly altering the hypertrophic markers at gene level. This study suggests the potential of manipulating extracellular proteases to alter the outcomes of hMSC chondrogenesis, contributing to future development of differentiation protocols for fibrocartilage tissues for intervertebral disc and meniscus tissue engineering.published_or_final_versio

A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism

© 2015 Elsevier B.V. In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems

Bounded Verification of Higher-Order Stateful Programs

In this thesis we explore bounded verification techniques for higher-order stateful programs. We consider two settings: open and closed higher-order, which are defined by the type-order of free variables present in each. Closed higher-order programs allow free variables only if they are of ground type, whereas open higher-order programs generalise this by allowing free variables of arbitrary order. We elaborate on the challenges involved in reasoning within said settings, and define a higher-order stateful language—an ML-like -calculus with recursion and higher-order global state—as our vehicle of study. We define a Bounded Model Checking technique for closed higher-order programs via defunctionalization using nominal techniques, and a Symbolic Execution Game Semantics to perform Bounded Symbolic Execution of open higher-order programs. Contributions presented in this thesis involve theoretical and experimental results. On the theoretical side, all approaches defined herein are sound and bounded-complete in the sense that they report errors if and only if errors are reachable up to the given bound—all results necessary to show this are included. For the experimental side, we implemented prototype tools for each technique, collected and created benchmarks to test each higher-order setting, and measured the performance of our tools to compare them to other relevant existing tools. Results presented herein for closed and open higher-order programs have been published in SETTA 2019 and FSCD 2020 respectively

Fuzzy Integral with Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface

© 2016 IEEE. A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications

A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

© 2016 IEEE. A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications

Evidence for non-self-similarity of microearthquakes recorded at a Taiwan borehole seismometer array

We investigate the relationship between seismic moment M0 and source duration tw of microearthquakes by using high-quality seismic data recorded with a vertical borehole array installed in central Taiwan. We apply a waveform cross-correlation method to the three-component records and identify several event clusters with high waveform similarity, with event magnitudes ranging from 0.3 to 2.0. Three clusters—Clusters A, B and C—contain 11, 8 and 6 events with similar waveforms, respectively. To determine how M0 scales with tw, we remove path effects by using a path-averaged Q. The results indicate a nearly constant tw for events within each cluster, regardless of M0, with mean values of tw being 0.058, 0.056 and 0.034 s for Clusters A, B and C, respectively. Constant tw, independent of M0, violates the commonly used scaling relation tw∝M1/30tw∝M01/3. This constant duration may arise either because all events in a cluster are hosted on the same isolated seismogenic patch, or because the events are driven by external factors of constant duration, such as fluid injections into the fault zone. It may also be related to the earthquake nucleation size