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Every nation celebrates their Independence Day as a grand celebration by the whole country together. The purpose of this study is to identify the people’s emotions and sentiments about their country at the time of Independence Day celebration through the analysis of social media messages.

To collect data from Twitter now which is known as “X”, this study used python computer programming language. Total 119,151 tweets were collected, and sentiment analysis was done based on the polarity score and Latent Dirichlet Allocation to understand the sentiments of public. The topic modelling was also done in this study, where topics and categories were created based on the words found through the Latent Dirichlet Allocation.

The result of this study revealed that people have the respect towards their country and are always ready to come together for the nation. They also feel proud to be a citizen of an independent nation. Therefore, they celebrate their country’s Independent Day every year with joy. Every nation needs a dynamic leader to lead the nation, whom people can trust easily and follow blindly.

The result of this study can be generalised as everyone supports and respects their nation and the national flag. This study is practically useful to other researchers to conduct similar study of sentiment analysis and topic modelling on other counties’ patriotism or other campaigns at national or global level.

As a result, this study contributes to understand people’s respect towards the nation. This study also showed people’s positive response at their leader’s request. There are many studies done on topic modelling and content analysis, but this is the first and original work attempted on the special subject on national patriotism campaign to the best of the authors’ knowledge.

Owing to the limitations of traditional infectious disease dynamic systems in accurately encapsulating the nuances of China’s dynamic epidemic prevention policies and considering the varying sensitivity of local governments to the unfolding of public health emergencies (PHEs), this paper introduces a novel infectious disease dynamic system.

This system, rooted in the distinct characteristics of infectious diseases and nuanced prevention and control measures, leverages a learning model for enhanced precision. It intricately incorporates factors such as the infectivity in sealed and controlled areas and the role of asymptomatic patients, thereby refining the dynamics of isolation, sealing, control and the transition from asymptomatic to confirmed cases. Employing the Markov Chain Monte Carlo (MCMC) parameter estimation approach significantly augments the accuracy in pinpointing the valid parameters of disease spread. Empirical analysis was meticulously carried out, using data from the Shanghai epidemic from 1 Mar 2022 to 1 Jul 2022.

This analysis not only illuminates the profound impact of control efforts on the trajectory of the epidemic but also underscores the pivotal role of social distancing in curbing the rapid transmission of infectious diseases. Furthermore, it reveals that an accelerated detection rate during the swift spread and peak of the epidemic paradoxically leads to a surge in confirmed cases and a consequent strain on medical resources, thereby impeding the pace of medical intervention.

A stage-wise dissection of the Shanghai epidemic and comparative analyses against the evolution profiles in ASEAN countries elucidates the five stages of PHE risk evolution in alignment with the crisis lifecycle theory. These stages encompass hidden transmission, multi-point dissemination, multi-chain parallelism, rapid spread, fluctuation rebound and multi-community spread, each presenting unique challenges and dynamics in the control and management of the epidemic.

The need to match highly variable demand for healthcare services with existing capacity has been a significant health system challenge experienced by the COVID-19 pandemic. Despite mounting research on various COVID-19 responses in the recent literature, there has not been a structured review examining key determinants of health system volume flexibility. The focus of the present research is to review recent literature based on a conceptual framework developed based on the volume flexibility literature on COVID-19. The review also provides a meaningful way to guide practice and future research in the area.

A literature review was performed guided by the theoretical frameworks developed in the previous volume of flexibility literature. Selected research papers were reviewed and analyzed to identify key determinants of health system volume flexibility.

Seven determinants of health system volume flexibility under three domains (demand management, capacity management, and performance) included demand management strategies, integration, capacity management strategies, workforce management, technology, quality of disease outcomes, and health system knowledge.

The research provides both health systems researchers as well as practitioners with a foundational conceptual framework and knowledge related to health system volume flexibility. In addition, this research identifies future research areas to expand knowledge relevant to health system volume flexibility.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

In the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling.

Data collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data.

The obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration.

This finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way.

This study investigates the direct impact of app attachment on service recovery and customer advocacy and their combined impact on recommendation and purchase intention. The mediating mechanisms of service recovery and customer advocacy between app attachment and customer responses are also tested in the context of food delivery apps (FDAs).

Utilizing a quantitative approach, the authors surveyed 207 responses from users of FDAs who had experienced service failures. Structural equation modeling in Smart PLS 3.0 was used to analyze the data.

The results supported direct effects among all constructs in the model. The main contribution of the study confirms the mediating mechanisms of service recovery and customer advocacy between app attachment and customer responses.

Previous studies have mostly relied on the technology acceptance model (TAM). This theory posits that perceived usefulness and ease of use influence the decision of individuals to use a new technology. Although this theory is valuable in terms of accepting new technologies, it neglects psychological phenomena involving the individual and the technological entity – in this case, the FDA. Thus, our study is unique in applying attachment theory and putting emphasis on the importance of building trust in the relationship between FDAs and their customers. Social exchange theory is applied to explain the importance of overcoming the cost of experiencing a failure through service recovery. Thus, we extend the knowledge regarding psychological individual reactions to mobile technologies in the food context, an important sector within the hospitality market.

FDA managers should invest in developing emotional ties with customers. Specific actions include messaging customers on their birthdays or other festive dates. Short testimonial videos on TikTok or other social media with customers advocating in favor of the company could help spread recommendations and the intentions of other customers using the FDAs. To use these practical recommendations properly, we recommend that FDA managers consider the level of quality service recovery delivered and individuals’ cultures, beliefs and values regarding where the company operates to avoid misunderstandings.

This study is original in proposing a model to FDA operators considering app attachment, service recovery, customer advocacy, recommendation and purchase intention. It further supports the mediating effect of service recovery and customer advocacy between app attachment, recommendation and purchase intention on mobile phones.