Country Image, e-WOM and Purchase Intention of Korean Products in China——With Korean Cosmetic Products as an Example

Country image is viewed as the overall perception of consumers from a particular country, based on their prior perception of the country’s production and marketing strengths and weaknesses and this image affect customer attitudes like purchase intention. The purpose of our study was to develop and validate the relationship among country image, e-WOM and purchase intention of foreign products. Based on literatures, a comprehensive set of constructs and hypotheses was compiled with a methodology for testing them. A questionnaire was constructed and data were collected from 255 customers in Beijing and Shanghai. The results indicated that country image affect purchase intention of Korean Cosmetic products through e-WOM

Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT makes increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park, CA, US

A novel variant of DNM1L expanding the clinical phenotypic spectrum: a case report and literature review

Abstract Background Mitochondrial diseases are heterogeneous in terms of clinical manifestations and genetic characteristics. The dynamin 1-like gene (DNM1L) encodes dynamin-related protein 1 (DRP1), a member of the GTPases dynamin superfamily responsible for mitochondrial and peroxisomal fission. DNM1L variants can lead to mitochondrial fission dysfunction. Case presentation Herein, we report a distinctive clinical phenotype associated with a novel variant of DNM1L and review the relevant literature. A 5-year-old girl presented with paroxysmal hemiplegia, astigmatism, and strabismus. Levocarnitine and coenzyme Q10 supplement showed good efficacy. Based on the patient’s clinical data, trio whole-exome sequencing (trio-WES) and mtDNA sequencing were performed to identify the potential causative genes, and Sanger sequencing was used to validate the specific variation in the proband and her family members. The results showed a novel de novo heterozygous nonsense variant in exon 20 of the DNM1L gene, c.2161C>T, p.Gln721Ter, which is predicted to be a pathogenic variant according to the ACMG guidelines. The proband has a previously undescribed clinical manifestation, namely hemiparesis, which may be an additional feature of the growing phenotypic spectrum of DNM1L-related diseases. Conclusion Our findings elucidate a novel variant in DNM1L-related disease and reveal an expanding phenotypic spectrum associated with DNM1L variants. This report highlights the necessity of next generation sequencing for early diagnosis of patients, and that further clinical phenotypic and genotypic analysis may help to improve the understanding of DNM1L-related diseases

Additional file 1 of A novel variant of DNM1L expanding the clinical phenotypic spectrum: a case report and literature review

Supplementary Material

Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification, and U-RadIT optimization