Exploring CRM through Technology-enabled Experience in Virtual Environment: The Era of COVID-19

Purpose: The purpose of the study is to explore the application of Augmented Reality (AR) technology to enhance interactivity and decision making via technology-enabled experience particularly, in the context of COVID-19. This study investigated effects of perceived utilitarian value, hedonic value, social value, and perceived risk on customer satisfaction with AR technology that are rarely examined in previous studies. Research design, data and methodology: Online survey data was used in the study. This study applied factor analysis and regression analysis to test the hypotheses and employed ANOVA and mediation effect analysis to explore additional findings. Results: The results suggested that customers’ perceived usefulness, arousal, social preference, innovativeness, financial risk, and performance risk have statistically significant effect on customer satisfaction. Conclusions: The findings of the study provided managerial and policy implications to develop and advertise the introduction of AR technology with the emphasis on the practical and utilitarian benefits of the technology. The result of this study highlighted the importance of customer relationship management by providing advanced services to customers through AR technology. This study contributes to technology-enabled CRM literature by providing the empirical result to verify the assumption that AR technology can be an effective tool of firms’ CRM strategy2

Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation

In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation. While the mesh-free nature of PINNs offers significant advantages in handling high-dimensional partial differential equations (PDEs), challenges arise when applying quadrature rules for accurate integral evaluation in the BGK operator, which can compromise the mesh-free benefit and increase computational costs. To address this, we leverage the canonical polyadic decomposition structure of SPINNs and the linear nature of moment calculation, achieving a substantial reduction in computational expense for quadrature rule application. The multi-scale nature of the particle density function poses difficulties in precisely approximating macroscopic moments using neural networks. To improve SPINN training, we introduce the integration of Gaussian functions into SPINNs, coupled with a relative loss approach. This modification enables SPINNs to decay as rapidly as Maxwellian distributions, thereby enhancing the accuracy of macroscopic moment approximations. The relative loss design further ensures that both large and small-scale features are effectively captured by the SPINNs. The efficacy of our approach is demonstrated through a series of five numerical experiments, including the solution to a challenging 3D Riemann problem. These results highlight the potential of our novel method in efficiently and accurately addressing complex challenges in computational physics

Toxicity Studies on Secretio Bufonis: A Traditional Supplement in Asia

AbstractObjectivesThis study was performed to investigate the toxicity of Secretio Bufonis (SB) on male mice and assess its no-observed-adverse-effect-level (NOAEL).Materials and MethodsAfter feeding an aqueous solution of SB extracts to mice for either 1 or 8 weeks, their blood and urine were assayed and their liver and kidney morphology examined. The numerical data was analyzed by the Mann-Whitney U-test and analysis of variance test.ResultsMice administered SB in 50 mg/kg/day for 1 week had higher heart weights and higher aspartate transaminase activities; those administered SB in 0.01 and 0.05 mg/kg/day for 8 weeks had lower creatinine concentrations; and those administered SB in 0.5 mg/kg/day for 8 weeks had higher brain weights and higher blood urea nitrogen.ConclusionsThe extracts of SB had cardiac toxicity in the short term and hepatotoxicity in the long term. The NOAEL of the extract was under 5 mg/kg/day for 1 week and under 0.25 mg/kg/day for 8 weeks

Enhancement on Radon Adsorption Property of GAC using Nano-size Carbon Colloids

Granular activated carbon (GAC) is well-known as an efficient adsorbent against a number of gaseous pollutants. Radon is one of those pollutants, and radon has been classified as the second leading cause of lung cancer in USA. This study was to enhance the radon removal efficiency with applying nano-technology. Nano-size carbon colloids (NCC) was produced through electrolysis which is simple and cheap. NCC was used for impregnation with activated carbon. Surface areas of both NCC-treated and non-treated activated carbon did not show a significant difference. However, the results of radon removal efficiency show that impregnated carbon with NCC could capture about 1.3 ~ 2 times of more radon gas compared to non-treated activated carbon. It is assumed that nano-size carbon colloids might have filled up meso-pores, and meso-pores turned into micro-pores eventually. Because meso-pores initially accounted for large portion of whole pores, more radon could be captured to NCC-impregnated activated carbon. Keywords: Radon, Nano-Size Carbon Collid, Activated Carbo