Data-driven product ranking: A hybrid ranking approach

The sudden COVID-19 epidemic has caused consumers to gradually switch to online shopping, the increasing number of online consumer reviews (OCR) on Web 2.0 sites has made it difficult for consumers and merchants to make decisions by analyzing OCR. Much of the current literature on ranking products based on OCR ignores neutral reviews in OCR, evaluates mostly given criteria and ignores consumers' own purchasing preferences, or ranks based on star ratings alone. This study aims to propose a new decision support framework for the evaluation and selection of alternative products based on OCR. The decision support framework mainly includes three parts: 1) Data preprocessing: using Python to capture online consumer comments for data cleaning and preprocessing, and extracting key features as evaluation criteria;2) Sentiment analysis: using Naive Bayes to analyze the sentiment of OCR, and using intuitionistic fuzzy sets to describe the emotion score;3) Benchmark analysis: a new IFMBWM-DEA model considering the preference of decision makers is proposed to calculate the efficiency score of alternative schemes and rank them according to the efficiency score. Then, the OCR of 15 laptops crawled from JD.com platform is used to prove the usefulness and applicability of the proposed decision support framework in two aspects: on the one hand, the comparison of whether the preference of decision makers is considered, and on the other hand, the comparison with the existing ranking methods. The comparison also proves that the proposed method is more realistic, the recommendations are more scientific and the complexity of the decision is reduced. [ 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.)

The impact of vaccine hesitation on the intentions to get COVID-19 vaccines: The use of the health belief model and the theory of planned behavior model.

Object: During the later period of the COVID-19 pandemic, the public has been at risk of the evolving COVID-19 variants and hesitated to be vaccinated against COVID-19 to a certain extent. In this context, the health belief model (HBM) and the theory of planned behavior model (TPB) were used to compare and summarize the relationship between vaccine hesitation/non-hesitation and the intentions to get COVID-19 vaccines and its influencing factors. Methods: The cross-sectional, population-based online survey was conducted from 14 April to 30 April 2021, and 1757 respondents were recruited to participate in the survey through the Wenjuanxing online survey platform. The HBM and TPB covariate scores were expressed using means and standard deviations and compared between groups using t-tests. Backward multiple linear regression models were used to explore the factors influencing the public's intentions to receive the COVID-19 vaccines. Results: This study found that educational background is one of the factors influencing vaccine hesitation. Most people with high education do not hesitate (65.24%), while a more significant proportion of people with low education have vaccine hesitation (66.00%). According to HBM, for the vaccine hesitation group, self-efficacy, family advice, and doctor's advice were the most critical factors affecting the public's future vaccination intentions; for the vaccine non-hesitation group, self-efficacy, doctor's advice, and perceived benefits are the most important influencing factors. According to the TPB, the subjective norm is the most critical factor affecting the future vaccination intention of the vaccine hesitation group, and the attitude toward behavior is the most critical factor affecting the future vaccination intention of the vaccine non-hesitation group. Conclusions: In the context of COVID-19, the public's hesitation on the "current" vaccines will still affect future vaccination intentions. Using HBM and TPB would help health policymakers and healthcare providers formulate intervention plans.

The Network of Interactions between the Porcine Epidemic Diarrhea Virus Nucleocapsid and Host Cellular Proteins.

Host-virus protein interactions are critical for intracellular viral propagation. Understanding the interactions between cellular and viral proteins may help us develop new antiviral strategies. Porcine epidemic diarrhea virus (PEDV) is a highly contagious coronavirus that causes severe damage to the global swine industry. Here, we employed co-immunoprecipitation and liquid chromatography-mass spectrometry to characterize 426 unique PEDV nucleocapsid (N) protein-binding proteins in infected Vero cells. A protein-protein interaction network (PPI) was created, and gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) database analyses revealed that the PEDV N-bound proteins belong to different cellular pathways, such as nucleic acid binding, ribonucleoprotein complex binding, RNA methyltransferase, and polymerase activities. Interactions of the PEDV N protein with 11 putative proteins: tripartite motif containing 21, DEAD-box RNA helicase 24, G3BP stress granule assembly factor 1, heat shock protein family A member 8, heat shock protein 90 alpha family class B member 1, YTH domain containing 1, nucleolin, Y-box binding protein 1, vimentin, heterogeneous nuclear ribonucleoprotein A2/B1, and karyopherin subunit alpha 1, were further confirmed by in vitro co-immunoprecipitation assay. In summary, studying an interaction network can facilitate the identification of antiviral therapeutic strategies and novel targets for PEDV infection.

Optimization of COVID-19 prevention and control with low building energy consumption.

COVID-19 is a global threat. Non-pharmaceutical interventions were commonly adopted for COVID-19 prevention and control. However, during stable periods of the pandemic, energy would be inevitably wasted if all interventions were implemented. The study aims to reduce the building energy consumption when meet the demands of epidemic prevention and control under the stable period of COVID-19. Based on the improved Wells-Riley model considering dynamic quanta generation and pulmonary ventilation rate, we established the infection risk - equivalent fresh air volume - energy consumption model to analyze the infection risk and building energy consumption during different seasons and optimized the urban building energy consumption according to the spatio-temporal population distribution. Shopping centers and restaurants contributed the most in urban energy consumption, and if they are closed during the pandemic, the total infection risk would be reduced by 25%-40% and 15%-25% respectively and the urban energy consumption would be reduced by 30%-40% and 13%-20% respectively. If people wore masks in all public indoor environments (exclude restaurants and KTV), the infection risk could be reduced by 60%-70% and the energy consumption could be reduced by 20%-60%. Gyms pose the highest risk for COVID-19 transmission. If the energy consumption kept the same with the current value, after the optimization, infection risk in winter, summer and the transition season could be reduced by 65%, 53% and 60%, respectively. After the optimization, under the condition of R t  < 1, the energy consumption in winter, summer, and the transition season could be reduced by 72%, 64%, and 68% respectively.

Posttranslational modification of microtubules by the MATCAP detyrosinase.

The detyrosination-tyrosination cycle involves the removal and religation of the C-terminal tyrosine of α-tubulin and is implicated in cognitive, cardiac, and mitotic defects. The vasohibin-small vasohibin-binding protein (SVBP) complex underlies much, but not all, detyrosination. We used haploid genetic screens to identify an unannotated protein, microtubule associated tyrosine carboxypeptidase (MATCAP), as a remaining detyrosinating enzyme. X-ray crystallography and cryo-electron microscopy structures established MATCAP's cleaving mechanism, substrate specificity, and microtubule recognition. Paradoxically, whereas abrogation of tyrosine religation is lethal in mice, codeletion of MATCAP and SVBP is not. Although viable, defective detyrosination caused microcephaly, associated with proliferative defects during neurogenesis, and abnormal behavior. Thus, MATCAP is a missing component of the detyrosination-tyrosination cycle, revealing the importance of this modification in brain formation.

Risk-Averse Two-Stage Stochastic Minimum Cost Consensus Models with Asymmetric Adjustment Cost.

In the process of reaching consensus, it is necessary to coordinate different views to form a general group opinion. However, there are many uncertain factors in this process, which has brought different degrees of influence in group decision-making. Besides, these uncertain elements bring the risk of loss to the whole process of consensus building. Currently available models not account for these two aspects. To deal with these issues, three different modeling methods for constructing the two-stage mean-risk stochastic minimum cost consensus models (MCCMs) with asymmetric adjustment cost are investigated. Due to the complexity of the resulting models, the L-shaped algorithm is applied to achieve an optimal solution. In addition, a numerical example of a peer-to-peer online lending platform demonstrated the utility of the proposed modeling approach. To verify the result obtained by the L-shaped algorithm, it is compared with the CPLEX solver. Moreover, the comparison results show the accuracy and efficiency of the given method. Sensitivity analyses are undertaken to assess the impact of risk on results. And in the presence of asymmetric cost, the comparisons between the new proposed risk-averse MCCMs and the two-stage stochastic MCCMs and robust consensus models are also given.

Inactivated Vaccines Against SARS-CoV-2: Neutralizing Antibody Titers in Vaccine Recipients.

Background: Although effective vaccines have been developed against coronavirus disease 2019 (COVID-19), the level of neutralizing antibodies (NAbs) induced after vaccination in the real world is still unknown. The aim of this work was to evaluate the level and persistence of NAbs induced by two inactivated COVID-19 vaccines in China. Methods: Serum samples were collected from 1,335 people aged 18 years and over who were vaccinated with an inactivated COVID-19 vaccine at Peking University People's Hospital from January 19 to June 23, 2021, for the detection of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies. Results: The positive rate for NAbs against SARS-CoV-2 was 79-91% from the first month to the second month after the second vaccine dose. The gradual decline in positivity rate for NAb response was observed from 78% at 3 months post-vaccination to 0% at 12 months post-vaccination. When there was a 21-day interval between the two doses of vaccine, the NAb positivity rate was 0% 6 months after the second dose. NAb levels were significantly higher when the interval between two doses were 3-8 weeks than when it was 0-3 weeks (χ2 = 14.04, p < 0.001). There was a linear correlation between NAbs and IgG antibodies in 1,335 vaccinated patients. NAb levels decreased in 31 patients (81.6%) and increased in 7 patients (18.4%) over time in the series of 38 patients after the second vaccination. The NAb positivity rate was significantly higher in 18- to 40-year-old subjects than in 41- to 60-year-old subjects (t = -1.959, p < 0.01; t = 0.839, p < 0.01). Conclusion: The NAb positivity rate was the highest at the first and second month after the second dose of vaccine, and gradually decreased over time. With a 21-day interval between two doses of vaccine, neutralizing antibody levels persisted for only 6 months after the second dose of vaccine. Therefore, a third vaccine dose is recommended. Our results suggest that in cases in which NAbs cannot be detected, IgM/IgG antibodies can be detected instead. The level of NAbs produced after vaccination was affected by age but not by sex. Our results suggest that an interval of 21 to 56 days between shots is suitable for vaccination.