Understanding Customer Intention to Use E-Payment for Online Shopping

The emergence of e-commerce has sparked a huge change in consumer behavior, transforming how individuals purchase and conduct transactions. E-payment has become a pillar of this digital transition. However, some customers are still hesitant to adopt e-payment despite its advantages. This paper aims to unravel the factors influencing customer intentions to use e-payment for online shopping. Based on a review of existing literature on the Technology Acceptance Model (TAM Model) and additional external factors, a proposed model was developed to test consumer's intention to use e-payment for online shopping. The result of this study would be useful to understand consumers’ behaviors in employing electronic transactions when making payments for online purchases. This paper also provides valuable insights for e-payment service providers and online retailers on how to promote sustainable online shopping in the future

The role of Candida albicans candidalysin ECE1 gene in oral carcinogenesis

Oral squamous cell carcinoma is associated with many known risk factors including tobacco smoking, chronic alcoholism, poor oral hygiene, unhealthy dietary habits and microbial infection. Previous studies have highlighted Candida albicans host tissue infection as a risk factor in the initiation and progression of oral cancer. C. albicans invasion induces several cancerous hallmarks, such as activation of proto-oncogenes, induction of DNA damage and overexpression of inflammatory signalling pathways. However, the molecular mechanisms behind these responses remain unclear. A recently discovered fungal toxin peptide, candidalysin, has been reported as an essential molecule in epithelial damage and host recognition of C albicans infection. Candidalysin has a clear role in inflammasome activation and induction of cell damage. Several inflammatory molecules such as IL-6, IL-17, NLRP3 and GM-CSF have been linked to carcinogenesis. Candidalysin is encoded by the ECE1 gene, which has been linked to virulence factors of C albicans such as adhesion, biofilm formation and filamentation properties. This review discusses the recent epidemiological burden of oral cancer and highlights the significance of the ECE1 gene and the ECE1 protein breakdown product, candidalysin in oral malignancy. The immunological and molecular mechanisms behind oral malignancy induced by inflammation and the role of the toxic fungal peptide candidalysin in oral carcinogenesis are explored. With increasing evidence associating C albicans with oral carcinoma, identifying the possible fungal pathogenicity factors including the role of candidalysin can assist in efforts to understand the link between C albicans infection and carcinogenesis, and pave the way for research into therapeutic potentials

KappaMask: AI-Based Cloudmask Processor for Sentinel-2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth's land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%