The Charisma of Online Group-Buying: The Moderating Role of Social Motivation

Group buying can spread worldwide because the growth of the online shopping market has been considerable. In addition to deal popularity and discount rate, social motivation was included in this study. If a consumer cannot achieve an economic exchange benefit, a social exchange benefit might provide another function for the group member to stay in the community. This study adopted convenience sampling and an online questionnaire to conduct a survey. Among 240 questionnaires collected, 204 were valid. According to ANOVA analysis, the results demonstrated that social motivation has a positive influence on the relationship between the discount of a product and customers 'purchasing intention but not on the relationship between the popularity of a product and customers' purchasing intention. Therefore, we concluded that strengthening social networking can have a positive effect on customers' purchasing intention and thus encouraging the development of group purchasing retailers and related industries

Access to Isothiazolones from Simple Acrylamides by Pd-Catalyzed C–H Bond Activation

International audienc

Detection of Tooth Position by YOLOv4 and Various Dental Problems Based on CNN With Bitewing Radiograph

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding extensive experiential knowledge, such as dentistry. By harnessing AI, the potential arises to amplify process efficiency and velocity. In this study, bitewing radiographs are used as the image source, and there are two major steps to detect the dental symptoms including 1) tooth position identification; and 2) symptom identification. The study combines image enhancement techniques and tooth position identification using Gaussian filtering and adaptive binarization for data preprocessing, facilitated by the YOLOv4 model to precisely mark tooth positions. The subsequent step enhances symptom area visibility via contrast enhancement, utilizing a CNN model, particularly the AlexNet model, with significant improvements in caries recognition accuracy (92.85%) and restorations recognition accuracy (96.55%) compared to prior research. Moreover, the inclusion of periodontal disease symptoms achieves an accuracy of 91.13%. By harnessing deep learning techniques based on CNN models, this research enhances diagnostic precision, reduces errors, and increases efficiency for dentists, thereby providing meticulous and swift patient care. This innovation not only saves time but also has the potential for widespread implementation in remote and preventive medicine, aligning with the aspiration of universal health care accessibility

Coarse-Grain Model for Lipid Bilayer Self-Assembly and Dynamics: Multiparticle Collision Description of the Solvent

A mesoscopic coarse-grain model for computationally-efficient simulations of biomembranes is presented. It combines molecular dynamics simulations for the lipids, modeled as elastic chains of beads, with multiparticle collision dynamics for the solvent. Self-assembly of a membrane from a uniform mixture of lipids is observed. Simulations at different temperatures demonstrate that it reproduces the gel and liquid phases of lipid bilayers. Investigations of lipid diffusion in different phases reveals a crossover from subdiffusion to normal diffusion at long times. Macroscopic membrane properties, such as stretching and bending elastic moduli, are determined directly from the mesoscopic simulations. Velocity correlation functions for membrane flows are determined and analyzed

Repurposing Metformin for Lung Cancer Management

In this article, we introduced the background knowledge of lung cancer management and considered repurposing old drugs to overcome therapy bottleneck. We chose metformin to prove both its antihyperglycemia and antitumor formation effects. Based on the metformin-related AMPK-dependent pathway, we tried to explore the AMPK-independent pathway in inhibition of lung tumorigenesis by metformin. Using preclinical data mining from clinical settings with a literature review, we attempted to clarify the role of metformin in lung cancer management. Additional objective and strong evidence are needed using randomized control studies to verify the benefit of metformin in clinical practice. Furthermore, we proposed two lung cancer animal models and showed the establishment processes thoroughly. We hope that these two lung cancer animal models provide a useful platform for furthering old drug repurposing as well as new drug investigations in the future

Rhodamine-triazine based probes for Cu²⁺ in aqueous media and living cells

The performance of a number of rhodamine-triazine derivatives（probe R1~R4) which utilize rhodamine as the fluorophore with cyanuric chloride as the molecular platform have been evaluated. Spectroscopic analysis revealed that differing structural substitution patterns of the probe resulted in different sensitivity and selectivity for specific metal ions. The probes R1 and R2 were fluorescent/colorimetric probes for Cu²⁺, whilst R3 and R4 were probes for Al³⁺, Cr³⁺ and Fe³⁺. The probe R2 exhibited superior recognition for Cu²⁺ in neutral aqueous medium, and the optical switching behavior of R2 for Cu²⁺ and S²⁻ could be used to construct a molecular logic gate. In addition, fluorescence imaging of probe R2 for Cu²⁺ in living cells was demonstrated