98047 research outputs found
Sort by
Big data meets sustainable marketing: A new integrated curriculum for hospitality education
No full textThis study presents a novel educational intervention, the Sustainable Marketing with Big Data (SMBD) module, grounded in the theory of change. The SMBD module integrated data literacy, sustainability knowledge, and marketing skills through problem-based learning, creative problem solving, computer-based learning, and project-based pedagogical approaches. Employing a mixed-methods research design, this investigation evaluated the effectiveness of the proposed SMBD curriculum through a quasiexperimental design and compared learning outcomes between an experimental group (n = 44) and a control group (n = 39) taught with traditional methods. Pre- and posttests were used to assess students' data literacy, sustainability attitudes and behaviors, and marketing competency. ANCOVA revealed that the experimental group exhibited significantly greater improvements across all dimensions than did the control group. Regression analysis confirmed the positive influence of gains in big data literacy and sustainability knowledge on marketing skill demonstration. Qualitative analysis of focus group data provided further insights into students' learning experiences and engagement with the SMBD curriculum. The results support the efficacy of the theory of change framework and active learning pedagogies in enhancing undergraduate hospitality students' competencies in three critical domains. The SMBD model offers educators a comprehensive, empirically validated curriculum for preparing future hospitality leaders to leverage big data for sustainable marketing in a rapidly evolving industry.補正完畢TW
Approaching precision public health by automated syndromic surveillance in communities
No full textBackground: Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named "Sentinel plus" which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians.
Methods: Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019.
Results: The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC.
Conclusions: This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.補正完畢US
Beyond the chaos: how crowded exhibitions shape customer politeness and service interactions
No full text補正完畢GB
Modeling social capital, training transfer, and occupational commitment: moderation of learning value of the job
No full textTraining significantly provides a valuable avenue for career development, influencing employees’ occupational commitment. This study delves into the influence of social capital on occupational commitment indirectly through training transfer, considering the moderating role of the learning value of the job (LVJ). Empirical findings derived from a field survey of high-tech professionals indicate that social interaction, trust, and shared vision indirectly affect occupational commitment through the mediation of training transfer. Additionally, the relationship between shared vision and training transfer is moderated by LVJ. The paper discusses on-the-job training and education implications for research and practices based on these empirical insights.補正完畢GB
Efficient License Plate Alignment and Recognition Using FPGA‑Based Edge Computing
No full textEfficient and accurate license plate recognition (LPR) in unconstrained environments remains a critical challenge, particularly when confronted with skewed imaging angles and the limited computational capabilities of edge devices. In this study, we propose a high-performance, FPGA-based license plate alignment and recognition (LPAR) system to address these issues. Our LPAR system integrates lightweight deep learning models, including YOLOv4-tiny for license plate detection, a refined convolutional pose machine (CPM) for pose estimation and alignment, and a modified LPRNet for character recognition. By restructuring the pose estimation and alignment architectures to enhance the geometric correction of license plates and adding channel and spatial attention mechanisms to LPRNet for better character recognition, the proposed LPAR system improves recognition accuracy from 88.33% to 95.00%. The complete pipeline achieved a processing speed of 2.00 frames per second (FPS) on a resource-constrained FPGA platform, demonstrating its practical viability for real-time deployment in edge computing scenarios.國科會(NSTC)補正完畢CH
Chitosan/carboxymethyl cellulose nanocomposites prepared via electrolyte gelation–spray drying for controlled ampicillin delivery and enhanced antibacterial activity
No full textThis study reports the fabrication of chitosan/carboxymethyl cellulose (C/M) nanocomposites by electrolyte gelation-spray drying and the evaluation of their antibacterial performance as carriers for the antibiotic ampicillin. Chitosan (C), a cationic biopolymer derived from chitin, was combined with the anionic polysaccharide carboxymethyl cellulose (M) at different mass ratios to form stable nanocomposites via electrostatic interactions and then collected by a spraying dryer. The resulting particles exhibited mean diameters ranging from 800 to 1500 nm and zeta potentials varying from +90 to −40 mV, depending on the C:M ratio. The optimal formulation (C:M = 2:1 ratio) achieved a high recovery yield (71.1%) and ampicillin encapsulation efficiency EE (82.4%). Fourier transform infrared spectroscopy (FTIR) confirmed the presence of hydrogen bonding and ionic interactions among C:M, and ampicillin within the nanocomposite matrix. The nano-microcomposites demonstrated controlled ampicillin release and pronounced antibacterial activity against Staphylococcus aureus, with minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values of 3.2 µg/mL and 5.3 µg/mL, respectively, which were lower than those of free ampicillin. These results indicate that the chitosan/carboxymethyl cellulose nano-microcomposites are promising, eco-friendly carriers for antibiotic delivery and antibacterial applications.補正完畢CH