A stimulus-organism-response perspective to examine green hotel patronage intention

PurposeThis study aims to investigate the relationships among monetary cost (stimulus), perceived greenwash fear, attitude and perceived behavioural control (organism-related factors) and green hotel patronage intention (response) using the stimulus-organism-response (S-O-R) model.Design/methodology/approachA total of 262 valid questionnaires were collected. Data were collected using the purposive sampling method and tested using the partial least squares (PLS) approach.FindingsMonetary cost is positively related to only one organism-related factor which is perceived greenwash fear. All organism-related factors are positively related to response, which is green hotel patronage intention. Attitude mediates the relationship between perceived greenwash fear and green hotel patronage intention, as well as perceived behavioural control and green hotel patronage intention.Research limitations/implicationsA longitudinal study can be performed in the future to observe the actual green hotel patronage behaviour of customers.Practical implicationsGreen hoteliers should focus on the development of communication strategies to enhance their corporate reputation. Green hoteliers also need to build trust by showing their green initiatives are genuine, identify consumers who are willing to pay more for green hotels and offer promotions with price incentives such as frequency discounts, coupons and rebates to increase interest and trialability.Originality/valueFew studies have focused on the use of monetary cost as a stimulus in the S-O-R model to predict green hotel patronage intention. This study also tested the mediating effect of attitude, one of the organism-related factors, in the model.

Zinc status in public health: exploring emerging research trends through bibliometric analysis of the historical context from 1978 to 2022.

The current study aims to provide a roadmap for future research by analyzing the research structures and trends in scholarly publications related to the status of zinc in public health. Only journal articles published between 1978 and 2022 are included in the refined bibliographical outputs retrieved from the Web of Science (WoS) database. The first section announces findings based on WoS categories, such as discipline heterogeneity, times cited and publications over time, and citation reports. The second section then employs VoSViewer software for bibliometric analysis, which includes a thorough examination of co-authorship among researchers, organizations, and countries and a count of all bibliographic databases among documents. The final section discusses the research's weaknesses and strengths in zinc status, public health, and potential future directions; 7158 authors contributed to 1730 papers (including 339 with publications, more than three times). "Keen, C.L." is a researcher with the most publications and a better understanding of zinc status in public health. Meanwhile, the USA has been the epicenter of research on the status of zinc in public health due to the highest percentage of publications with the most citations and collaboration with the rest of the world, with the top institution being the University of California, Davis. Future research can be organized collaboratively based on hot topics from co-occurrence network mapping and bibliographic couplings to improve zinc status and protect public health.

Optical reflectometric measurement of SARS-CoV-2 (COVID-19) RNA based on cationic cysteamine-capped gold nanoparticles.

The coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a major public health outbreak in late 2019 and was proclaimed a global pandemic in March 2020. A reflectometric-based RNA biosensor was developed by using cysteamine-stabilized gold nanoparticles (cysAuNPs) as the colorimetric probe for bioassay of COVID-19 RNA (SARS-CoV-2 RNA) sequence. The cysAuNPs aggregated in the presence of DNA probes via cationic and anionic electrostatic attraction between the positively charged cysteamine ligands and the negatively charged sugar-phosphate backbone of DNA, whilst in the presence of target RNAs, the specific recognition between DNA probes and targets depleted the electrostatic interaction between the DNA probes and cysAuNPs signal probe, leading to dispersed particles. This has rendered a remarkable shifting in the surface plasmon resonance (SPR) on the basis of visual color change of the RNA biosensor from red to purplish hue at the wavelength of 765 nm. Optical evaluation of SARS-CoV-2 RNA by means on reflectance transduction of the RNA biosensor based on cysAuNPs optical sensing probes demonstrated rapid response time of 30 min with high sensitivity, good linearity and high reproducibility across a COVID-19 RNA concentration range of 25 nM to 200 nM, and limit of detection (LOD) at 0.12 nM. qPCR amplification of SARS-CoV-2 viral RNA showed good agreement with the proposed RNA biosensor by using spiked RNA samples of the oropharyngeal swab from COVID-19 patients. Therefore, this assay is useful for rapid and early diagnosis of COVID-19 disease including asymptomatic carriers with low viral load even in the presence of co-infection with other viruses that manifest similar respiratory symptoms.

Optical reflectometric measurement of SARS-CoV-2 (COVID-19) RNA based on cationic cysteamine-capped gold nanoparticles

Graphical The coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a major public health outbreak in late 2019 and was proclaimed a global pandemic in March 2020. A reflectometric-based RNA biosensor was developed by using cysteamine-stabilized gold nanoparticles (cysAuNPs) as the colorimetric probe for bioassay of COVID-19 RNA (SARS-CoV-2 RNA) sequence. The cysAuNPs aggregated in the presence of DNA probes via cationic and anionic electrostatic attraction between the positively charged cysteamine ligands and the negatively charged sugar-phosphate backbone of DNA, whilst in the presence of target RNAs, the specific recognition between DNA probes and targets depleted the electrostatic interaction between the DNA probes and cysAuNPs signal probe, leading to dispersed particles. This has rendered a remarkable shifting in the surface plasmon resonance (SPR) on the basis of visual color change of the RNA biosensor from red to purplish hue at the wavelength of 765 nm. Optical evaluation of SARS-CoV-2 RNA by means on reflectance transduction of the RNA biosensor based on cysAuNPs optical sensing probes demonstrated rapid response time of 30 min with high sensitivity, good linearity and high reproducibility across a COVID-19 RNA concentration range of 25 nM to 200 nM, and limit of detection (LOD) at 0.12 nM. qPCR amplification of SARS-CoV-2 viral RNA showed good agreement with the proposed RNA biosensor by using spiked RNA samples of the oropharyngeal swab from COVID-19 patients. Therefore, this assay is useful for rapid and early diagnosis of COVID-19 disease including asymptomatic carriers with low viral load even in the presence of co-infection with other viruses that manifest similar respiratory symptoms.

Visualization and analysis of mapping knowledge domains for coronavirus research.

BACKGROUND: In recent years, many countries around the world have been threatened by COVs. The aim of this study was to better grasp developments and trends in research on coronavirus around the world and to promote theoretical research into their prevention and control. METHODS: Research on coronavirus was reviewed and analyzed using bibliometrics based on a total of 4860 publications collected from the Web of Science Core Collection database. Yearly quantitative distribution of literature, country/region distribution, organization distribution, main source journal distribution, subject category distribution, research knowledge bases, and research hotspots and frontiers were all analyzed, and CiteSpace and VOSviewer were used to plot knowledge domain maps, Excel was used to plot keyword strategy diagram. RESULTS: Coronavirus research could be roughly divided into 4 stages: preliminary development stage (before 2000), rapid growth stage (2000-2005), slow decline stage (2006-2011) and sustained growth stage (since 2012). America had taken the leading position in this field. The study of COVs involves many subject categories, mainly includes virology, veterinary sciences, biology, and immunology. At present, the key words in the field of coronavirus research were mainly divided into 6 major hot clusters, namely, the introduction and structure analysis of coronavirus, the research on the outbreak source and transmission of coronavirus, the research on the infection pathway of coronavirus in human body, the research on the pathogenesis of coronavirus, the research on the diagnosis and symptoms of coronavirus infection, and the research on the treatment of coronavirus. CONCLUSION: Coronavirus, which occurs all over the world, often causes huge casualties and economic losses, and poses a serious threat to the safe and stable operation of the social and economic system. Objective literature review and analysis can help scholars in related fields to deepen their overall understanding. And, there are several key issues that should be further explored in future research.

Assessing the Impacts of COVID-19 on the Industrial Sectors and Economy of China.

Since December 2019, the COVID-19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative-based approach of assessing the potential impact of the COVID-19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision-making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4-8% in the absence of COVID-19, GDP growth in 2020 would be -8.77 to -12.77% after the COVID-19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.

Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks

The novel coronavirus 2019 (COVID-19) rapidly spreading around the world and turns into a pandemic situation, consequently, detecting the coronavirus (COVID-19) affected patients are now the most critical task for medical specialists. The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide, resulting in the number of infected cases is expanding. Therefore, a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method, which hinders the spreading of coronavirus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification framework (COVMCNet) using eight different pre-trained architectures such as VGG16, VGG19, ResNet50V2, DenseNet201, InceptionV3, MobileNet, InceptionResNetV2, Xception which are trained and tested on the X-ray images of COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The results from 4-class (Normal vs. COVID-19 vs. Viral Pneumonia vs. Bacterial Pneumonia) demonstrated that the pre-trained model DenseNet201 provides the highest classification performance (accuracy: 92.54%, precision: 93.05%, recall: 92.81%, F1-score: 92.83%, specificity: 97.47%). Notably, the DenseNet201 (4-class classification) pre-trained model in the proposed COV-MCNet framework showed higher accuracy compared to the rest seven models. Important to mention that the proposed COV-MCNet model showed comparatively higher classification accuracy based on the small number of pre-processed datasets that specifies the designed system can produce superior results when more data become available. The proposed multi-classification network (COV-MCNet) significantly speeds up the existing radiology based method which will be helpful for the medical community and clinical specialists to early diagnosis the COVID-19 cases during this pandemic.

A Review on the Development of Gold and Silver Nanoparticles-Based Biosensor as a Detection Strategy of Emerging and Pathogenic RNA Virus.

The emergence of highly pathogenic and deadly human coronaviruses, namely SARS-CoV and MERS-CoV within the past two decades and currently SARS-CoV-2, have resulted in millions of human death across the world. In addition, other human viral diseases, such as mosquito borne-viral diseases and blood-borne viruses, also contribute to a higher risk of death in severe cases. To date, there is no specific drug or medicine available to cure these human viral diseases. Therefore, the early and rapid detection without compromising the test accuracy is required in order to provide a suitable treatment for the containment of the diseases. Recently, nanomaterials-based biosensors have attracted enormous interest due to their biological activities and unique sensing properties, which enable the detection of analytes such as nucleic acid (DNA or RNA), aptamers, and proteins in clinical samples. In addition, the advances of nanotechnologies also enable the development of miniaturized detection systems for point-of-care (POC) biosensors, which could be a new strategy for detecting human viral diseases. The detection of virus-specific genes by using single-stranded DNA (ssDNA) probes has become a particular interest due to their higher sensitivity and specificity compared to immunological methods based on antibody or antigen for early diagnosis of viral infection. Hence, this review has been developed to provide an overview of the current development of nanoparticles-based biosensors that target pathogenic RNA viruses, toward a robust and effective detection strategy of the existing or newly emerging human viral diseases such as SARS-CoV-2. This review emphasizes the nanoparticles-based biosensors developed using noble metals such as gold (Au) and silver (Ag) by virtue of their powerful characteristics as a signal amplifier or enhancer in the detection of nucleic acid. In addition, this review provides a broad knowledge with respect to several analytical methods involved in the development of nanoparticles-based biosensors for the detection of viral nucleic acid using both optical and electrochemical techniques.

Impact of COVID-19 on health profession education in Singapore: Adoption of innovative strategies and contingencies across the educational continuum

Introduction: The Coronavirus-19 pandemic has had profound effects on health professions education (HPE) posing serious challenges to the continued provision and implementation of undergraduate, postgraduate and continuing medical education (CME). Across these HPE domains, the major disruptions included the exclusion of undergraduate learners from clinical learning environments, restricted intra-, inter-institutional and overseas movement of medical professionals, termination of face-to-face learner-educator interactions, deployment of postgraduate learners into non-scope service settings, and CME postponement. Methods: In this review we report on how in Singapore various adaptive measures were instituted across the 3 HPE domains at institutional and national level to maintain adequate resources at the frontline to meet service exigencies, promote healthcare professionals' wellbeing and safety as well as mitigate the spread of the pandemic. Results: We identified several strategies and contingencies developed to address these challenges. These involved the use of online learning platforms, distributed and asynchronous learning, an undergraduate Pathway Programme, and use of innovative hands-on technology like simulation. Robust, well pre-planned pandemic preparedness, effective communication, as well as provision of psychological support resources ensured maintenance of service and academic continuity, trust and resilience within HPE. However, several challenges remain, namely the timing and manner of conducting formative and summative assessments, cybersecurity, and the indispensable hands-on, in-person experiential learning for surgical training. Conclusion: Strong leadership with vision and planning, good communication, prioritising learners' and educators' wellbeing and safety, and harnessing existing and emerging online learning technologies are crucial elements for effective contingencies for HPE disruption during pandemics. [ABSTRACT FROM AUTHOR] Copyright of Asia Pacific Scholar is the property of Centre for Medical Education (CenMed) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Type A Aortic Dissection During COVID-19 Pandemic: Report From Tertiary Aortic Centers in the United States and China.

Coronavirus disease 2019 (COVID-19) has substantially disrupted many processes of care related to emergency cardiac conditions, while there has been no clinical guidance regarding the management of type A aortic dissection. A retrospective multicenter study involving 52 consecutive patients (mean age 52.3, 28.9% women) with type A aortic dissection during COVID-19 pandemic was conducted at tertiary aortic centers in Michigan, Wuhan and Changsha (China). Twenty-four (46.2%) were considered clinically suspicious for COVID-19 based on radiographic lung lesions (70.8%) followed by dyspnea (25.0%), cough (12.5%), and fever (12.5%). Overall, 47 (90.4%) underwent an operation and 5 (9.6%) managed nonoperatively. All suspected patients underwent a reverse-transcriptase-polymerase-chain-reaction at arrival, whereas 82.1% in the nonsuspected (P = 0.054). Among the 24 patients either nonoperatively managed or whose operation was delayed for >24 hours, only 1 (4.2%) died. A total of 3 (6.4%) operated patients had a positive reverse-transcriptase-polymerase-chain-reaction at various timings, including 1 nonsuspected patient preoperatively and 2 with very recent COVID-19 infection. The first patient died of respiratory failure despite uneventful surgical repair and maximal medical management. The postoperative course of both patients with recent COVID-19 was characterized by severe coagulopathy requiring massive transfusions and prolonged ICU stay. However, both survived to hospital discharge. In light of the possible dismal outcomes associated with dual diagnoses of type A aortic dissection/COVID-19 and the higher-than-expected number of asymptomatic carriers, all type A dissection patients should be immediately tested for COVID-19. Surgical interventions in patients recovered from recent COVID-19 may be safe.