Multi-objective route recommendation method based on Q-learning algorithm

With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q-value table search mechanism, the proposed algorithm can generate the recommended route almost in real time. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press 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 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 . (Copyright applies to all s.)

Impacts of advisory perception and cultural distance on travel intention: evidence from China

PurposeThis study aims to investigate the impact of travel advisory perception and cultural distance on travel intention based on the push and pull theory, the stimulus–organism–response model and protection motivation theory.Design/methodology/approachThe paper conducts an empirical test with a questionnaire survey. The authors asked respondents to answer questions on a five-point Likert-type scale, which included threat severity, threat susceptibility, advisory perception, perceived cultural distance, travel motivation and travel intention.FindingsIn this paper, through a questionnaire of 424 respondents, the authors found that threat severity has a positive impact on advisory perception and perceived cultural distance, and that advisory perception indirectly affect travel intention through travel motivation.Originality/valuePrevious literature has discussed the influencing factors of travel intention but rarely does it consider the intrinsic relationship and interaction between advisory perception and cultural distance. The results of this study help fill some gaps in the research on advisory perception and perceived cultural distance, guide governments on how to better formulate travel advisories and provide a new perspective for tourism industry practitioners to improve their travel products after the COVID-19 pandemic especially.

Effects of Workplace Ostracism on Burnout among Nursing Staff during the COVID-19 Pandemic, Mediated by Emotional Labor

This study investigated the effects of workplace ostracism on emotional labor and burnout among current nursing staff during the COVID-19 pandemic, as well as the relationship between the surface acting and deep acting of emotional labor as the mediators of workplace ostracism and burnout. The sample for this study consisted of 250 nursing staff recruited from Taiwanese medical institutions, and the questionnaire was divided into two stages. The first stage included questions about ostracism and personal data, and then two months later the same respondents completed part two of the questionnaire regarding emotional labor and burnout, which solved the problem of common-method variance (CMV). The results of this study indicate that ostracism had a positive and significant effect on burnout and surface acting, but its negative effect on deep acting was not supported. While surface acting showed partial mediation between ostracism and burnout, deep acting did not have a significant mediating effect between ostracism and burnout. These results can provide a reference for practice and researchers.

Effects of Workplace Ostracism on Burnout among Nursing Staff during the COVID-19 Pandemic, Mediated by Emotional Labor.

This study investigated the effects of workplace ostracism on emotional labor and burnout among current nursing staff during the COVID-19 pandemic, as well as the relationship between the surface acting and deep acting of emotional labor as the mediators of workplace ostracism and burnout. The sample for this study consisted of 250 nursing staff recruited from Taiwanese medical institutions, and the questionnaire was divided into two stages. The first stage included questions about ostracism and personal data, and then two months later the same respondents completed part two of the questionnaire regarding emotional labor and burnout, which solved the problem of common-method variance (CMV). The results of this study indicate that ostracism had a positive and significant effect on burnout and surface acting, but its negative effect on deep acting was not supported. While surface acting showed partial mediation between ostracism and burnout, deep acting did not have a significant mediating effect between ostracism and burnout. These results can provide a reference for practice and researchers.

Public health crisis and risky road behaviors.

This study investigates how exposure to riskier environments influences risky road behaviors, using the COVID-19 pandemic as a natural experiment. Utilizing administrative individual traffic violation records from Taipei, where neither mandatory lockdown nor mobility restrictions were imposed, we find that pandemic-induced risk decreased speeding violations and that the effect was transitory. However, no significant changes were observed concerning violations with a minimal risk of casualties, such as illegal parking. These findings suggest that experiencing a higher level of life-threatening risk discourages risky behaviors concerning human life but has little spillover effect on those concerning only financial costs.

Examine the relationships between health-related quality of life, achievement motivation and job performance: the case of Taiwan hospitality industry.

BACKGROUND: Employees are considered as one of the most important assets in many organizations, and their health well-being is critical to help achieve a sustainable and motivated workforce that is committed to delivering quality hospitality services through enhanced performance and productivity. Given the extent of the challenges and impact presented by the COVID-19 pandemic to the hospitality industry, it is timely to gain further insights on employees' health well-being. The key purpose of this study is to examine the relationships between health-related quality of life, achievement motivation and job performance in the Taiwan hospitality industry, to acquire a better understanding of their relationships through the job performance pathway models. METHODS: This study has used a purposeful sampling technique to select the 10 highest-earning hospitality companies in Taiwan. A total of 292 questionnaires were collected from the employees of these hospitality companies. Based on the multi-dimensional concept of health-related quality of life (HRQoL), the relationships between the five key dimensions (i.e. psychological health, physical health, social health, achievement motivation, and job performance) were examined. To measure these dimensions, the survey questions were adapted from previous research such as the World Health Organization's WHOQOL-BREF scale, Minnesota Satisfaction Questionnaire. Partial least squares - Structural Equation Modeling method was used to explore these dimensions, and two job performance pathway models (for manager and staff) were subsequently developed. RESULTS AND CONCLUSIONS: Findings showed that psychological health directly affected the manager's job performance and physical health had a similar effect through social health. While psychological health had not affected the staff's job performance, but it could affect achievement motivation through both direct and indirect effects of social health. The pathway models that were developed indicated that the manager's job performance was mainly affected by psychological health and social health, whereas the key dimension that had affected the staff's job performance was achievement motivation.

Multi-objective route recommendation method based on Q-learning algorithm

With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q-value table search mechanism, the proposed algorithm can generate the recommended route almost in real time. [ FROM AUTHOR]

The impact of COVID-19 on the tourism and hospitality Industry: Evidence from international stock markets

COVID-19 seriously affects the tourism and hospitality industry. In this study, we investigate the behavior of 40 tourism and hospitality stock market indices worldwide from two perspectives. First, empowered by the Granger causality test and network analysis, we test the spillover effects among these stock markets and find that the dynamics of interconnectedness network structures differ significantly in the pre-pandemic and in-pandemic periods. Second, we employ econometric models to explore how the influence of COVID-19 on these stock markets varies by considering the interconnectedness structure, the government response stringency index, and other country-level characteristics. We find that the interconnectedness structure significantly and robustly affects stock returns in the tourism and hospitality markets. Our investigation provides a better understanding of the impact of COVID-19 on tourism and hospitality industry.

Antecedents and consequences of shared decision making for patients with chronic kidney diseases: A cross-sectional survey.

BACKGROUND: Chronic kidney disease (CKD) has a strong negative impact on patients. Finding ways to improve CKD patients' conditions by shared decision-making is receiving much attention. However, little attention has been paid to influencing antecedents and effects of shared decision-making. Meanwhile, as advanced technologies bring in new communication devices, effects of different types of communications used in shared decision-making need to be addressed. OBJECTIVE: This study proposes a research framework to determine the influencing antecedents of shared decision-making, and to evaluate the effects of shared decision-making on patient outcomes when they are computer-mediated and when the decision-makers communicate face-to-face. METHODS: A cross-section survey was conducted and a total of 48 valid samples were obtained. The participants were CKD Stage III, IV, or V patients who had received medical treatment in a hospital in Taiwan. The collected data were subjected to an independent t-test and partial least squares analysis to validate the research framework. RESULTS: Doctor-patient communication (DPC) and doctor-patient relationship (DPR) have no significant direct impact on patient outcomes. Nevertheless, both DPC and DPR significantly impact shared decision-making which in turn impacts patient outcomes. Moreover, patients who use computer-mediated communication were found to have significantly higher perceptions of shared decision-making than those who did not. CONCLUSIONS: The incidence and prevalence of end-stage renal disease in Taiwan are among the highest in the world. The results of this study can serve as a reference for hospitals to improve CKD patients' outcomes. Meanwhile, during the COVID-19 pandemic, this study suggested hospitals should encourage shared decision-making with computer-mediated communication to ensure that patients receive proper treatment and have the desired outcomes.

Impact of the Digital Transformation of Small- and Medium-Sized Listed Companies on Performance: Based on a Cost-Benefit Analysis Framework

The ultimate goals of digital transformation, which is the only means of realizing the sustainable development of current enterprises, are to achieve cost reduction, efficiency improvement, and innovation for enterprises. However, there are limited empirical quantitative studies on the investment costs and impact of organizational performance related to the digital transformation of small- and medium-sized listed companies. This study first uses Excel VBA to sort the digital transformation panel data of 319 small- and medium-sized listed companies in China’s Shanghai and Shenzhen A-share markets from 2007 to 2020. Thereafter, Stata, a general-purpose statistical software package, is used to analyze the impact of digital transformation on the performance of small- and medium-sized listed companies. The results show that the digital transformation of small- and medium-sized listed companies has a positive correlation with operational performance, and an inverted U-shaped relationship with innovation performance;however, the U-shaped relationship with financial performance is not significant. For small- and medium-sized listed companies to promote digital transformation, first, it must help the organization to improve its internal operational efficiency;second, it can optimize and improve the organization’s financial efficiency;and finally, through innovative efficiency, the organization can continue to develop steadily and improve its organizational resilience.

An LSPR Sensor Integrated with VCSEL and Microfluidic Chip.

The work introduces a localized surface plasmon resonance (LSPR) sensor chip integrated with vertical-cavity surface-emitting lasers (VCSELs). Using VCSEL as the light source, the hexagonal gold nanoparticle array was integrated with anodic aluminum oxide (AAO) as the mask on the light-emitting end face. The sensitivity sensing test of the refractive index solution was realized, combined with microfluidic technology. At the same time, the finite-difference time- domain (FDTD) algorithm was applied to model and simulate the gold nanostructures. The experimental results showed that the output power of the sensor was related to the refractive index of the sucrose solution. The maximum sensitivity of the sensor was 1.65 × 106 nW/RIU, which gives it great application potential in the field of biomolecular detection.

Health-Related Rumor Control through Social Collaboration Models: Lessons from Cases in China during the COVID-19 Pandemic.

Widely spread health-related rumors may mislead the public, escalate social panic, compromise government credibility, and threaten public health. Social collaboration models that maximize the functions and advantages of various agents of socialization can be a promising way to control health-related rumors. Existing research on health-related rumors, however, is limited in studying how various agents collaborate with each other to debunk rumors. This study utilizes content analysis to code the text data of health-related rumor cases in China during the COVID-19 pandemic. The study found that socialized rumor-debunking models could be divided into the following five categories: the government-led model, the media-led model, the scientific community-led model, the rumor-debunking platform-led model, and the multi-agent collaborative model. In addition, since rumors in public health crises often involve different objects, rumor refutation requires various information sources; therefore, different rumor-debunking models apply. This study verifies the value of socialized collaborative rumor debunking, advocates and encourages the participation of multiple agents of socialization and provides guidance for establishing a collaborative rumor-debunking model, thereby promoting efficient rumor-debunking methods and improving the healthcare of society.

Annotations of Lung Abnormalities in Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases.

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People's Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state-of-the-art for image segmentation methods toward improving the performance of fine-grained segmentation of TB-consistent findings in digital Chest X-ray images. The annotation collection comprises the following: 1) annotation files in JSON (JavaScript Object Notation) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; 2) mask files saved in PNG format for each abnormality per TB patient; 3) a CSV (comma-separated values) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs. Dataset: https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html.

Impact of COVID-19 Pandemic on Elevated Anxiety Symptoms of Maintenance Hemodialysis Patients in China: A One-Year Follow-Up Study.

Recent studies have shown that coronavirus disease 2019 (COVID-19) aggravates anxiety in patients with maintenance hemodialysis (MHD), but it is still unclear how long this adverse effect will last. This study aims to investigate the impact of COVID-19 on the elevated anxiety symptoms of MHD patients 1 year after the outbreak. Assessment of elevated anxiety symptoms was performed on patients with MHD during early COVID-19 (February 17-February 29, 2020) and 1-year follow-up (March 1-March 13, 2021), and a total of 100 patients had completed face-to-face questionnaires at the first and 1-year follow-up. At the beginning of the outbreak, 40% of the patients with MHD had anxiety symptoms [self-rating anxiety scale (SAS) score ≥ 50], and 11% (SAS score: 60-69) and 2% (SAS score ≥ 70) of the patients had moderate and severe anxiety symptoms, respectively. Multivariate analysis shows that possibility of unaccompanied transfer, possibility of family members or themselves being infected in a hospital, added body temperature monitoring during dialysis, and increased medical procedures are the risk factors in elevated anxiety symptoms during early COVID-19. At the 1-year follow-up, the incidence of anxiety symptoms in the same group of patients declined to 28%, and all the patients had mild anxiety symptoms (SAS score: 50-59), which is significantly lower than that of the early COVID-19 pandemic with statistically significant difference (p = 0.003). Increased protective measures taken by the medical staves were the only risk factor in elevated anxiety symptoms during the 1-year follow-up. This study shows that COVID-19 has a direct impact on the deterioration of anxiety symptoms in patients with MHD. With the changes of the requirements for COVID-19 prevention and control, as well as the enhancement of propaganda and education of the pandemic and psychological care, the severity and risk factors of anxiety symptoms in the patients with MHD are changing. Thus, targeted interventions are suggested to improve the psychological endurance of the patients with MHD.

[Clinical observation of Xuanfei Baidu Decoction in treatment of severe coronavirus disease 2019 (COVID-19)].

The present study evaluated the effectiveness and safety of Xuanfei Baidu Decoction(XFBD) for severe cases with coronavirus disease 2019(COVID-19).Forty-one patients(diagnosed as severe or critical type) admitted to Hubei Provincial Hospital of Integrated Chinese and Western Medicine and Wuhan Hospital of Traditional Chinese Medicine from February 1 to March 1, 2020, were included.All patients were treated with XFBD based on conventional therapies.Clinical outcomes, length of hospital stay, and lung CT images of patients were observed.Laboratory indicators were compared between admission and the 14 th day of treatment.Traditional Chinese medicine(TCM) symptoms and signs on the 7 th and 14 th days of treatment were also compared with baseline.The differences in clinical characteristics and clinical outcomes between XFBD and western medicine or conventional therapies were analyzed with the published trials on severe COVID-19 cases during the same period as external controls.According to the results, among the 41 cases, 40 were cured and discharged, and 1 died; the median length of hospital stay was 22 days, and the improvement rate of lung CT was 87.2%(34/39).Compared with the conditions on admission, the levels of white blood cells(WBC), C-reactive protein(CRP), fibrinogen(FIB), and lactate dehydrogenase(LDH) were reduced(P<0.05, P<0.01), and levels of procalcitonin(PCT), prothrombin time(PT), creatine kinase(CK), alanine aminotransferase(AST), total bilirubin(TBiL), and other indicators showed a downward trend.Moreover, symptoms like fever, cough, chest tightness/shortness of breath, dyspnea, head and body pain, anorexia, and greasy tongue coating were significantly improved on the 7 th and 14 th days of treatment(P<0.05, P<0.01), and fatigue was improved on the 14 th day of treatment(P<0.01).The mortality, adverse reactions, and major events of the XFBD group were significantly lower than those of the western medicine and conventional treatment groups in the same period, and the usage of antibiotics, hormones, vasopressin, and invasive mechanical ventilation during treatment were generally less than other groups.In conclusion, XFBD has good efficacy and safety in the treatment of severe COVID-19 cases by improving inflammation and clinical symptoms, promoting the absorption of lung inflammation, and reducing mortality.

Reveal the Mechanisms of Yi-Fei-Jian-Pi-Tang on Covid-19 through Network Pharmacology Approach.

Objectives: The Traditional Chinese Medicine (TCM) formula Yi-Fei-Jian-Pi-Tang (YFJPT) has been demonstrated effective against Corona Virus Disease 2019 (Covid-19). The aim of this article is to make a thorough inquiry about its active constituent as well as mechanisms against Covid-19 via TCM network pharmacology. Methods: All the ingredients of YFJPT are obtained from the pharmacology database of the TCM system. The genes which are associated with the targets are obtained by utilizing UniProt. The herb-target network is built up by utilizing Cytoscape. The target protein-protein interaction network is built by utilizing the STRING database and Cytoscape. The critical targets of YFJPT are explored by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Results: The outcomes show that YFJPT might has 33 therapeutic targets on Covid-19, namely, interleukin 2 (IL2), heme oxygenase 1 (HMOX1), interleukin 4 (IL4), interferon gamma (FNG), α nuclear factor of kappa light polypeptide gene enhancer in Bcells inhibitor, alpha (NFKBIA), nuclear factor-k-gene binding (NFKB), nitric oxide synthase 3 (NOS3), intercellular adhesion molecule 1 (ICAM1), hypoxia inducible factor 1 subunit alpha (HIF1A), mitogen-activated protein kinase 3 (MAPK3), epidermal growth factor receptor (EGFR), interleukin 10 (IL10), jun proto-oncogene (JUN), C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 8 (CXCL8), tumor protein p53 (TP53), interleukin 1 beta (IL1B), AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor (TNF), interleukin 6 (IL6), erb-b2 receptor tyrosine kinase 2 (ERBB2), RELA proto-oncogene (RELA), NF-κB subunit, caspase 8 (CASP8), peroxisome proliferator activated receptor alpha (PPARA), TIMP metallopeptidase inhibitor 1 (TIMP1), transforming growth factor beta 1 (TGFB1), interleukin 1 alpha (IL1A), signal transducer and activator of transcription 1 (STAT1), mitogen-activated protein kinase 8 (MAPK8), myeloperoxidase (MPO), matrix metallopeptidase 3 (MMP3), matrix metallopeptidase 1 (MMP1), and NFE2 like bZIP transcription factor 2 (NFE2L2). The gene enrichment analysis prompts that YFJPT most likely contributes to patients related to Covid-19 by regulating the pathways of cancers. Conclusions: That will lay a foundation for the clinical rational application and further experimental research of YFJPT.

Microneedle-based two-step transdermal delivery of Langerhans cell-targeting immunoliposomes induces a Th1-biased immune response.

Novel Coronavirus is affecting human's life globally and vaccines are one of the most effective ways to combat the epidemic. Transcutaneous immunization based on microneedle (MN) has attracted much attention because of its painlessness, rapidity, high efficiency and good compliance. In this study, CD11c monoclonal antibody-immunoliposomes (OVA@CD11c-ILP) actively targeting to Langerhans cells (LCs) were successfully prepared and were delivered by the microchannels of skin produced by MN to induce an immune response in vivo. OVA@CD11c-ILP could be targeted to LCs by conjugating CD11c monoclonal antibody to the surface of the ILP. OVA@CD11c-ILP promoted the maturation of dendritic cells (DCs) and the uptake and endocytosis of antigen by LCs. Moreover, OVA@CD11c-ILP immunization can significantly inhibit tumor growth and prolong overall survival. Furthermore, a higher antibody's titer ratio of IgG1/IgG2a indicated that the immune response stimulated by this immunization method was Th1-biased and the liposomes showed Th1-type adjuvant effect. In conclusion, the combination delivery system of immunoliposomes and microneedle can significantly improve the efficiency of antigen presentation and effectively activate cellular immune responses in the body, which is expected to be a promising transdermal immune strategy.

Research on the Relationship between Digital Transformation and Performance of SMEs

Objective: Through an empirical analysis of the performance of SMEs undergoing digital transformation, this study attempts to identify the influencing factors that determine their sustainable development to provide reference for academic researchers and industrial decision makers. Method: This study first uses an interview method to investigate the impact of SMEs’ three main resources on digital transformation: digital technology, employee digital skills, and digital transformation strategy. Second, we assess the impact of digital transformation on financial performance. Using the structural equation model, 335 valid questionnaires were recovered through the questionnaire method, and the key factors were identified using SPSS and SPSSAU tools. Results: In the Chinese context, digital transformation affects SME performance, and the three resources mentioned above are positively correlated with SMEs’ digital transformation. Digital transformation is positively correlated with performance, and it is the mediator of the impact of digital transformation strategies on performance. Conclusion: For SMEs, focusing on investing in digital technologies, employee digital skills, and digital transformation strategies are three key factors that are beneficial for digital transformation, thus helping to improve performance and maintain their sustainable development.

Prevalence and prognosis of increased pancreatic enzymes in patients with COVID-19: A systematic review and meta-analysis.

INTRODUCTION: The prevalence of increased pancreatic enzymes (elevated serum amylase and/or lipase) and its relationship to clinical outcomes in patients with coronavirus disease 2019 (COVID-19) infection is not known. METHODS: A systematic review and meta-analysis of relevant studies reporting prevalence and impact of increased pancreatic enzymes (defined as an elevation in amylase and/or lipase levels above the upper limit of normal [ULN] value) in COVID-19 was undertaken. RESULTS: A total of 36,496 patients from 21 studies were included for this meta-analysis. The overall prevalence and mortality for increased pancreatic enzymes (>ULN) in COVID-19 were 25.4% (95% CI, 15.8%-36.2%) and 34.6% (95% CI, 25.5%-44.4%), respectively. The overall prevalence and mortality for increased pancreatic enzymes (>3 × ULN) were 6.1% (95% CI, 3.6%-9.2%) and 39.2% (95% CI, 18.7%-61.6%), respectively. Patients with increased pancreatic enzymes, including elevated serum lipase or amylase of either type, had worse clinical outcomes, including need for ICU admission, mechanical ventilation and mortality. DISCUSSION: Increased pancreatic enzymes is frequent and may exacerbate the consequences of COVID-19 infection.