Robotic moment: The influence mechanism of frequency of socializing on willingness to interact with ai robots

Past research has discovered that the shape design and interaction process design of AI robots, as well as the users' constant features, are the major factors that affect users' willingness to interact with AI robots. Currently, AI robots that play a vital part in the daily activities of our society are becoming increasingly prevalent, thus things about AI robots have gone from mythic to prosaic. But when and where people are more likely to adopt AI robots remains an important research topic. With the development of online technology and the long-term impact of COVID-19, there has been a recent trend in the lower frequency of socializing. To investigate whether a state of low socializing frequency is a robotic moment and whether it affects people's willingness to interact with AI robots, we conducted two-wave questionnaire surveys to collect data from 300 participants from 23 provinces in China. The results showed that the frequency of socializing had a significant negative correlation with the willingness to interact with the AI robots via the mediation role of social compensation. Furthermore, the relationship between social compensation and willingness to interact with the AI robots was demonstrated to be stronger, when participants had a lower anthropomorphic tendency. These findings have theoretical implications for the human-computer interaction literature and managerial implications for the robotics industry. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

NAG-Net: Nested attention-guided learning for segmentation of carotid lumen-intima interface and media-adventitia interface.

Cardiovascular diseases (CVD), as the leading cause of death in the world, poses a serious threat to human health. The segmentation of carotid Lumen-intima interface (LII) and Media-adventitia interface (MAI) is a prerequisite for measuring intima-media thickness (IMT), which is of great significance for early screening and prevention of CVD. Despite recent advances, existing methods still fail to incorporate task-related clinical domain knowledge and require complex post-processing steps to obtain fine contours of LII and MAI. In this paper, a nested attention-guided deep learning model (named NAG-Net) is proposed for accurate segmentation of LII and MAI. The NAG-Net consists of two nested sub-networks, the Intima-Media Region Segmentation Network (IMRSN) and the LII and MAI Segmentation Network (LII-MAISN). It innovatively incorporates task-related clinical domain knowledge through the visual attention map generated by IMRSN, enabling LII-MAISN to focus more on the clinician's visual focus region under the same task during segmentation. Moreover, the segmentation results can directly obtain fine contours of LII and MAI through simple refinement without complicated post-processing steps. To further improve the feature extraction ability of the model and reduce the impact of data scarcity, the strategy of transfer learning is also adopted to apply the pretrained weights of VGG-16. In addition, a channel attention-based encoder feature fusion block (EFFB-ATT) is specially designed to achieve efficient representation of useful features extracted by two parallel encoders in LII-MAISN. Extensive experimental results have demonstrated that our proposed NAG-Net outperformed other state-of-the-art methods and achieved the highest performance on all evaluation metrics.

Longitudinal relationships among career adaptability, resilience, and career commitment in chinese nursing undergraduates: testing differences in career interest between cross-lagged models.

PURPOSE: Various physiological and psychological negative situations experienced by nurses as a result of COVID-19 pandemic have been shown to increase their perception of organizational difficulty and decrease their career commitment, thereby accelerating the turnover rate of nurses. Resilience and career adaptability have important influences on career commitment, so there is a need to evaluate the relationships between them and the underlying mechanisms. PATIENTS AND METHODS: Using a cross-lagged design, the Career Adaptability Scale, the Chinese version of career commitment, and Davidson's Resilience Scale as research methods, we studied 692 nursing students for two consecutive years to evaluate the relationship among career adaptability, resilience, and career commitment. RESULTS: Career adaptability at T1 substantially and positively predicts the career commitment at T2. Career adaptability and resilience are mutually predictive. No interaction is found between resilience and career commitment over time. There is a substantial difference in the cross-lagged relationship among career adaptability, resilience, and career commitment for low- and high-career interest. CONCLUSION: Our results show the importance of developing career commitment early on. Developing career adaptability, enhancing resilience, and increasing career interest in nursing students might help to increase career commitment.

Critical review on quality of methodology and recommendations of clinical practice guidelines for peri-implantitis.

BACKGROUND: Peri-implantitis is of high prevalence with the popularity of dental implants nowadays. Guidelines or consensus have been developed in succession, and we are little-known about their quality. The objective of this study is to evaluate the methodological quality of these guidelines and analyze the consistency of the clinical recommendations. METHODS: We searched for guidelines or consensus on prevention, diagnosis, and/or treatment of peri-implantitis through PubMed, Web of Science, Cochrane Library until January 15th, 2022. In addition, we also searched the websites of the American Dental Association, International Team for Implantology, FDI World Dental Federation, and some guideline collection databases. Appraisal of Guidelines for Research & Evaluation II methodological quality instrument was used to assess the selected guidelines. Furthermore, we described the consistency of recommendations across the included guidelines. RESULTS: In total, 15 guidelines were included. The mean values of the six domains score all below 50%. The mean scores of Applicability were lowest (mean:15%, range:4-29%). As to the overall quality, eleven (73%) were recommended after being modified, and four (27%) were not recommended. Among the clinical recommendations, 53 (67.09%) are for treatment of peri-implantitis, 13 (16.46%) for monitoring issue, 7 (8.86%) for diagnosis, 3 (3.80%) for the disease prevention. CONCLUSIONS: Improving methodology quality and strengthening clinical evidence is essential in the future guideline development in a range of disciplines for improving the treatment effectiveness of people with peri-implantitis. And there is a lack of integrated guidelines in the case of the COVID-19 pandemic.

Construction of rice supply chain supervision model driven by blockchain smart contract.

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.

Research on the Cross-Chain Model of Rice Supply Chain Supervision Based on Parallel Blockchain and Smart Contracts

Rice is one of the three major staple foods in the world, and the quality and safety of rice are related to the development of human beings. The new crown epidemic, pesticide residues, insect pests, and heavy metal pollution have a certain security impact on the food supply chain. The rice supply chain is characterized by a long life cycle;complex roles in the main links;many types of hazards;and multidimensional, multisource, and heterogeneous information. To strengthen the rice supply chain’s supervision ability under the epidemic situation, a supervision cross-chain model suitable for the complicated data of the rice supply chain based on parallel blockchain theory and smart contract technology was built. Firstly, the data collected in the rice supply chain and different types of data stored in different parallel blockchains were analyzed. Secondly, based on data analysis, a collection/supervision cross-chain mechanism based on “hash lock + smart contract + relay chain”, a concurrency mechanism based on the K-means algorithm and a Bloom filter, and a consensus mechanism suitable for multichain consensus named the Supervision Practical Byzantine Fault Tolerance (SPBFT) were proposed. Furthermore, a cross-chain model of rice supply chain supervision was constructed. Finally, theoretical verification and simulation experiments were used to analyze the operation process, safety, cross-chain efficiency, and scalability of the model. The results showed that the application of parallel blockchains and smart contracts to supervision of rice supply chain information improved the convenience and security of information interaction between various links in the rice supply chain, the storage cost of supply chain data and the high latency of interaction was reduced, and the refined management of the rice supply chain data and personnel was realized. This research applied new information technology to the coordination and resource sharing of the food supply chain, and provides ideas for the digital transformation of the food industry.

Cellular and molecular atlas of the placenta from a COVID-19 pregnant woman infected at midgestation highlights the defective impacts on foetal health.

OBJECTIVES: The impacts of the current COVID-19 pandemic on maternal and foetal health are enormous and of serious concern. However, the influence of SARS-CoV-2 infection at early-to-mid gestation on maternal and foetal health remains unclear. MATERIALS AND METHODS: Here, we report the follow-up study of a pregnant woman of her whole infective course of SARS-CoV-2, from asymptomatic infection at gestational week 20 to mild and then severe illness state, and finally cured at Week 24. Following caesarean section due to incomplete uterine rupture at Week 28, histological examinations on the placenta and foetal tissues as well as single-cell RNA sequencing (scRNA-seq) for the placenta were performed. RESULTS: Compared with the gestational age-matched control placentas, the placenta from this COVID-19 case exhibited more syncytial knots and lowered expression of syncytiotrophoblast-related genes. The scRNA-seq analysis demonstrated impaired trophoblast differentiation, activation of antiviral and inflammatory CD8 T cells, as well as the tight association of increased inflammatory responses in the placenta with complement over-activation in macrophages. In addition, levels of several inflammatory factors increased in the placenta and foetal blood. CONCLUSION: These findings illustrate a systematic cellular and molecular signature of placental insufficiency and immune activation at the maternal-foetal interface that may be attributed to SARS-CoV-2 infection at the midgestation stage, which highly suggests the extensive care for maternal and foetal outcomes in pregnant women suffering from COVID-19.

Epidemiological and clinical characteristics of discharged patients infected with SARS-CoV-2 on the Qinghai plateau. (Special issue on new coronavirus (2019-nCoV or SARS-CoV-2) and the outbreak of the respiratory illness (COVID-19): part-VII.)

Since the outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, a series of confirmed cases of COVID-19 were found on the Qinghai-Tibet plateau. We aimed to describe the epidemiological, clinical characteristics, and outcomes of all confirmed cases in Qinghai, a province at high altitude. The region had no sustained local transmission. Of all 18 patients with confirmed SARS-CoV-2 infection, 15 patients comprising four transmission clusters were identified. Three patients were infected by direct contact without travel history to Wuhan. Of 18 patients, 10 patients showed bilateral pneumonia and two patients showed no abnormalities. Three patients with comorbidities such as hypertension, liver diseases, or diabetes developed severe illness. High C-reactive protein levels and elevations of both alanine aminotransferase and aspartate aminotransferase were observed in three severely ill patients on admission. All 18 patients were eventually discharged, including the three severe patients who recovered after treatment with noninvasive mechanical ventilation, convalescent plasma, and other therapies. Our findings confirmed human-to-human transmission of SARS-CoV-2 in clusters. Patients with comorbidities are more likely to develop severe illness.

Single-cell analysis of SARS-CoV-2 receptor ACE2 and spike protein priming expression of proteases in the human heart.

AIMS: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) directly binds to ACE2 (angiotensin-converting enzyme 2) to facilitate cellular entry. Compared with the lung or respiratory tract, the human heart exhibits greater ACE2 expression. However, little substantial damage was found in the heart tissue, and no viral particles were observed in the cardiac myocytes. This study aims to analyse ACE2 and SARS-CoV-2 spike (S) protein proteases at the single-cell level, to explore the cardiac involvement in COVID-19 and improve our understanding of the potential cardiovascular implications of COVID-19. METHODS AND RESULTS: With meta-analysis, the prevalence of cardiac injury in COVID-19 patients varies from 2% [95% confidence interval (CI) 0-5%, I2 = 0%] in non-ICU patients to 59% (95% CI 48-71%, I2 = 85%) in non-survivors. With public single-cell sequence data analysis, ACE2 expression in the adult human heart is higher than that in the lung (adjusted P < 0.0001). Inversely, the most important S protein cleavage protease TMPRSS2 (transmembrane protease serine protease-2) in the heart exhibits an extremely lower expression than that in the lung (adjusted P < 0.0001), which may restrict entry of SARS-CoV-2 into cardiac cells. Furthermore, we discovered that other S protein proteases, CTSL (cathepsin L) and FURIN (furin, paired basic amino acid cleaving enzyme), were expressed in the adult heart at a similar level to that in the lung, which may compensate for TMPRSS2, mediating cardiac involvement in COVID-19. CONCLUSION: Compared with the lung, ACE2 is relatively more highly expressed in the human heart, while the key S protein priming protease, TMPRSS2, is rarely expressed. The low percentage of ACE2+/TMPRSS2+ cells reduced heart vulnerability to SARS-CoV-2 to some degree. CTSL and FURIN may compensate for S protein priming to mediate SARS-CoV-2 infection of the heart.

Epidemiological and clinical characteristics of discharged patients infected with SARS-CoV-2 on the Qinghai plateau

Since the outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, a series of confirmed cases of COVID-19 were found on the Qinghai-Tibet plateau. We aimed to describe the epidemiological, clinical characteristics, and outcomes of all confirmed cases in Qinghai, a province at high altitude. The region had no sustained local transmission. Of all 18 patients with confirmed SARS-CoV-2 infection, 15 patients comprising 4 transmission clusters were identified. Three patients were infected by direct contact without travel history to Wuhan. Of 18 patients, 10 patients showed bilateral pneumonia and 2 patients showed no abnormalities. Three patients with comorbidities such as hypertension, liver diseases or diabetes developed severe illness. High C-reactive protein levels and elevations of both ALT and AST were observed in 3 severely ill patients on admission. All 18 patients were eventually discharged, including the 3 severe patients who recovered after treatment with non-invasive mechanical ventilation, convalescent plasma and other therapies. Our findings confirmed human-to-human transmission of SARS-CoV-2 in clusters. Patients with comorbidities are more likely to develop severe illness. This article is protected by copyright. All rights reserved.