Hopf-type intermediate-scale bifurcation in single-stage power-factor-correction power supplies

Author name used in this publication: Chi K. TseRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Slow-scale instability of single-stage power-factor-correction power supplies

Author name used in this publication: Chi K. Tse2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Fundamental considerations of three-level DC-DC converters : topologies, analyses, and control

Author name used in this publication: Chi K. Tse2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Determinants of carbon emissions cycles in the G7 countries

This paper investigates the relationships between economic growth, energy consumption, exports, tourism, geopolitical risk, and carbon dioxide (hereafter cited as “CO2”) emissions in Group of Seven (hereafter cited as “G7”) countries from 1990 to 2021. Cross-sectional correlation tests, unit root tests, cointegration analysis, regression analysis, panel data estimation, and Granger causality tests are performed. The empirical results show that energy consumption and geopolitical risk negatively affect environmental quality. In addition, globalization exacerbates the problem of ecological degradation. At the same time, the increase in export levels and emerging tourism development are conducive to reducing CO2 emissions. It is recommended that policymakers pay attention to the role of the digital economy and technological innovation in shaping the energy consumption patterns, carbon emissions, and geopolitical risks in G7 countries, encourage digital transformation, use technological innovation in energy efficiency to drive economic growth, use the digital economy to promote sustainable tourism, decouple it from high carbon emissions, challenge the traditional Environmental Kuznets Curve (hereafter cited as “EKC”) framework, and jointly promote dual sustainable development of the economy and environment

Molecular techniques for pathogen identification and fungus detection in the environment

Many species of fungi can cause disease in plants, animals and humans. Accurate and robust detection and quantification of fungi is essential for diagnosis, modeling and surveillance. Also direct detection of fungi enables a deeper understanding of natural microbial communities, particularly as a great many fungi are difficult or impossible to cultivate. In the last decade, effective amplification platforms, probe development and various quantitative PCR technologies have revolutionized research on fungal detection and identification. Examples of the latest technology in fungal detection and differentiation are discussed here

Finding Single Copy Genes Out of Sequenced Genomes for Multilocus Phylogenetics in Non-Model Fungi

Historically, fungal multigene phylogenies have been reconstructed based on a small number of commonly used genes. The availability of complete fungal genomes has given rise to a new wave of model organisms that provide large number of genes potentially useful for building robust gene genealogies. Unfortunately, cross-utilization of these resources to study phylogenetic relationships in the vast majority of non-model fungi (i.e. “orphan” species) remains an unexamined question. To address this problem, we developed a method coupled with a program named “PHYLORPH” (PHYLogenetic markers for ORPHans). The method screens fungal genomic databases (107 fungal genomes fully sequenced) for single copy genes that might be easily transferable and well suited for studies at low taxonomic levels (for example, in species complexes) in non-model fungal species. To maximize the chance to target genes with informative regions, PHYLORPH displays a graphical evaluation system based on the estimation of nucleotide divergence relative to substitution type. The usefulness of this approach was tested by developing markers in four non-model groups of fungal pathogens. For each pathogen considered, 7 to 40% of the 10–15 best candidate genes proposed by PHYLORPH yielded sequencing success. Levels of polymorphism of these genes were compared with those obtained for some genes traditionally used to build fungal phylogenies (e.g. nuclear rDNA, β-tubulin, γ-actin, Elongation factor EF-1α). These genes were ranked among the best-performing ones and resolved accurately taxa relationships in each of the four non-model groups of fungi considered. We envision that PHYLORPH will constitute a useful tool for obtaining new and accurate phylogenetic markers to resolve relationships between closely related non-model fungal species

Toll-like receptor 4 signaling in liver injury and hepatic fibrogenesis

Toll-like receptors (TLRs) are a family of transmembrane pattern recognition receptors (PRR) that play a key role in innate and adaptive immunity by recognizing structural components unique to bacteria, fungi and viruses. TLR4 is the most studied of the TLRs, and its primary exogenous ligand is lipopolysaccharide, a component of Gram-negative bacterial walls. In the absence of exogenous microbes, endogenous ligands including damage-associated molecular pattern molecules from damaged matrix and injured cells can also activate TLR4 signaling. In humans, single nucleotide polymorphisms of the TLR4 gene have an effect on its signal transduction and on associated risks of specific diseases, including cirrhosis. In liver, TLR4 is expressed by all parenchymal and non-parenchymal cell types, and contributes to tissue damage caused by a variety of etiologies. Intact TLR4 signaling was identified in hepatic stellate cells (HSCs), the major fibrogenic cell type in injured liver, and mediates key responses including an inflammatory phenotype, fibrogenesis and anti-apoptotic properties. Further clarification of the function and endogenous ligands of TLR4 signaling in HSCs and other liver cells could uncover novel mechanisms of fibrogenesis and facilitate the development of therapeutic strategies

Transfer-learning-based opinion mining for new-product portfolio configuration over the case-based reasoning cycle

202403 bcvcVersion of RecordSelf-fundedPublishedC