Impact mechanism of social-related social media use on the job performance of caregivers of older adults.

This study investigated the impact mechanism of social-related social media use on the job performance of caregivers of older adults through mental health and analyzed gender differences. A total of 358 valid questionnaires were collected and analyzed using SPSS and Smart PLS. The results showed that mental health plays an important role in the relationship between social-related social media use and job performance. Such social media use can improve psychological well-being and reduce psychological distress by promoting relaxation experience. Psychological well-being has a significant positive impact on job performance. In addition, significant differences exist in the impact path of social-related social media use on psychological distress among different gender groups. The findings of this study can assist nursing homes in assessing the use of social media within their organizations and provide methodological references for enhancing the job performance of caregivers of older adults.

The willingness to continue using wearable devices among the elderly: SEM and FsQCA analysis.

BACKGROUND: With population aging and the scarcity of resources for elderly individuals, wearable devices pose opportunities and challenges for elderly care institutions. However, few studies have examined the effects of technical characteristics, personal characteristics, and health promotion on the willingness of elderly individuals to continue using wearable devices. OBJECTIVE: This study explored the effects of technical characteristics and personal characteristics on the willingness of elderly individuals to continue using wearable devices through health promotion, drawing on the technology acceptance model and the value attitude behaviour model. METHODS: We obtained 265 valid samples through questionnaire surveys and used structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (FsQCA) to clarify the complex causal patterns of elderly people's willingness to continue using wearable devices. RESULTS: The SEM results showed that perceived usefulness, perceived reliability, self-perceived ageing, and health promotion affected willingness to continue using wearable devices. However, perceived ease of use had no effect. FsQCA showed that elderly individuals are highly willing to continue using wearable devices, yielding five solutions. Perceived ageing was essential in four of these solutions. The impact of perceived ease of use on continued use intention was dynamic and complex. CONCLUSIONS: This study used two methods to provide insight into the willingness of elderly individuals to continue using wearable devices. In addition, this study discussed associated implications, limitations, and future research directions.

Heat shock cognate protein 70 is required for rice stripe tenuivirus accumulation and transmission in small brown planthopper.

Rice stripe tenuivirus (RSV) is mainly transmitted by the insect vector small brown planthopper (SBPH, Laodelphax striatellus) in a persistent-propagative manner. Virus transmission is dependent on the interplay between viral proteins and vector factors. Pc2, a nonstructural protein of RSV, plays an important role in virus transmission. However, the vector proteins that interact with Pc2 are unknown. In this study, we identified three SBPH proteins that interact with the N-terminal 381 amino acids of Pc2 (Pc2N) by using a yeast two-hybrid system (Y2H). The interaction of Pc2N with heat shock protein cognate 70 (HSC70) was studied further. HSC70 was verified to interact with RSV Pc2N by biomolecular fluorescence complementation and co-immunoprecipitation assays. HSC70 colocalized with RSV Pc2N in both Sf9 cells and the hemocytes of SBPHs. Inhibition of HSC70 expression via RNA interference reduced virus levels in hemolymph and salivary glands of SBPHs and resulted in decreased virus transmission efficiency. These data provide evidence that a vector protein, HSC70, is employed by RSV to facilitate virus accumulation in the hemolymph and thereby promote virus transmission. These findings are important for a better understanding of the interactions between plant viruses and insect vectors.

Association of artificial intelligence use and the retention of elderly caregivers: A cross-sectional study based on empowerment theory.

AIM: The purpose of this study is to investigate how the use of artificial intelligence is associated with the retention of elderly caregivers. BACKGROUND: The turnover of elderly caregivers is high and increasing. Elderly care institutions are beginning to use artificial intelligence to support caregivers in their work, and the use of technology is critical to staff retention. Empowerment of elderly caregivers has been neglected by managers and researchers. METHODS: This cross-sectional study involved 511 elderly caregivers in 25 elderly institutions. Six validated standardized scales were used for data collection, and the software SPSS and SmartPLS were used for data analysis. RESULTS: The quality of artificial intelligence has a significant positive effect on empowerment. Artificial intelligence psychological empowerment (ß = .355, p < .001) and artificial intelligence structural empowerment (ß = .375, p < .001) both had positive effects on retention intention, and the jointly explained variance (R2 ) was 42.6%. CONCLUSIONS: The results show that a significant relationship exists between artificial intelligence empowerment and retention intention. Elderly caregivers with more structural empowerment have higher retention intention. IMPLICATIONS FOR NURSING MANAGEMENT: Artificial intelligence suppliers need to pay attention to the role of product quality in elderly care services, continuously improve artificial intelligence quality, and strengthen the application and routine maintenance of artificial intelligence technologies. Elderly care institution managers should pay special attention to artificial intelligence structural empowerment (such as artificial intelligence-related education and training, learning and development opportunities, and resource support).

The N-terminal cysteine protease domain of rice stripe tenuivirus Pc1 possesses deubiquitinating enzyme activity.

Virus encoded deubiquitinating enzyme (DUB) plays important roles in viral replication and the regulation of host innate immunity. Bioinformatics-based analysis revealed the presence of an ovarian tumor (OTU) protease domain in the N terminus of rice stripe tenuivirus (RSV) Pc1. Many viral OTU domains have been reported to possess DUB activity, which suggests that RSV OTU probably also have DUB activity. To confirm this prediction, we first expressed and purified RSV OTU domain (the N-terminal 200 amino acids of Pc1) and its three mutants (D42A, C45A and H148A) from Escherichia coli and analyzed its DUB activity in vitro. The purified RSV OTU hydrolyzed both K48-linked and K63-linked polyubiquitin chains, indicating RSV OTU domain has DUB enzyme activity in vitro. The mutations of the predicted catalytic sites (Asp42, Cys45 and His148) resulted in the loss of DUB activity, demonstrating these three residues were required for enzyme activity. Then, RSV OTU and its mutants were expressed in insect cells and assayed their DUB activities in vivo by co-transfection with HA-tagged ubiquitin. RSV OTU dramatically reduced ubiquitin-conjugated cellular proteins compared to control and the mutants, showing that RSV OTU also displays DUB activity in vivo. Characterization of RSV OTU DUB enzyme activity and its key catalytic residues will facilitate the development of novel antiviral reagents against RSV.

How Social Media Use at Work Affects Improvement of Older People's Willingness to Delay Retirement During Transfer From Demographic Bonus to Health Bonus: Causal Relationship Empirical Study.

BACKGROUND: With the increased older population in China and the subsequent reduced labor force, the "demographic bonus" is disappearing. The Chinese government proposed a Healthy China strategy in 2017. The transfer of the demographic bonus to a "health bonus" extended the working life of people and reduced the negative impact of the population's aging on the labor force structure. OBJECTIVE: This research focuses on the effect of older workers' social media usage at work on their work ability (related to both physical and mental health) and thus their willingness to delay retirement. METHODS: The questionnaire respondents were older than 55 years, and they obtained the questionnaire from social media, from June to July 2018. A total of 1020 valid questionnaires were collected, and SmartPLS 3.28 (SmartPLS GmbH) was used to analyze the data. Effects were analyzed using 2-tailed t tests. RESULTS: (1) Use of social media at work can improve information support (t14=13.318, P<.001), emotional support (t14=13.184, P<.001), and self-efficacy (t14=6.364, P<.001) for older people; (2) information support is the main factor affecting the self-efficacy of older workers (t14=23.304, P<.001), as compared with emotional support (t14=1.799, P=0.07); (3) the impacts of emotional support on work ability (t14=8.876, P<.001) and work stress (t14=9.545, P<.001) are generally higher than those of information support (t14=4.394, P<.001; t14=5.002, P<.001); (4) self-efficacy has an impact on work ability (t14=5.658, P<.001) and work stress (t14=4.717, P<.001); and (5) the impacts of work ability (t14=8.586, P<.001) and work stress (t14=8.579, P<.001) on retirement willingness are greater than those of emotional support (t14=2.112, P=.04) and information support (t14=4.314, P<.001). CONCLUSIONS: Our study confirms that the use of social media at work has a positive impact on older workers. Based on the findings, we have put forward proposals to extend people's working lives and help governments implement health bonus policies. In the future, we will compare the different values of willingness to delay retirement among older people in different occupations and different cultures.

Mobile Social Media Use and Trailing Parents' Life Satisfaction: Social Capital and Social Integration Perspective.

In recent years, a special group of elderly migrants emerged in some developing countries, namely "trailing parents," who migrated with adult children in their later years. Lots of developing countries such as China have an increasing number of old people using mobile social media for social interaction, information, and entertainment. Based on social capital theory, we developed a research model and investigated the relationship between mobile social media use (MSMU) and social capital, social integration, and life satisfaction of trailing parents. We completed an empirical study by collecting 244 valid samples. The results show that MSMU has a positive impact on trailing parents' life satisfaction, and bonding social capital, bridging social capital and social integration are all important predictors of trailing parents' life satisfaction. Furthermore, social capital and social integration play an important chain mediation role in the relationship between MSMU and trailing parents' life satisfaction.

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis.

BACKGROUND: With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. OBJECTIVE: Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. METHODS: A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research. RESULTS: The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field. CONCLUSIONS: Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud-based mobile health, cloud-based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.

Exploring emerging IoT technologies in smart health research: a knowledge graph analysis.

BACKGROUND: At present, Internet of Things technology has been widely used in various fields, and smart health is also one of its important application areas. METHODS: We use the core collection of Web of Science as a data source, using tools such as CiteSpace and bibliometric methods to visually analyze 9561 articles published in the field of smart health research based on the Internet of things (IoT) in 2003-2019, including time distribution, spatial distribution, and literature co-citation analysis and keyword analysis. RESULTS: The field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. In addition, the knowledge base in this field has been initially formed, and most of the published literatures are multi-theme research. CONCLUSIONS: This study discusses the research status, research hotspots and future development trends in this field, and provides important knowledge support for subsequent research.

How transformational leadership and clan culture influence nursing staff's willingness to stay.

AIM: To explore the impact mechanism of transformational leadership and clan culture on the willingness to stay of nursing staff in the aged care industry. BACKGROUND: Previous researchers had paid much attention to the leadership and culture, yet it was unclear how the transformational leadership and clan culture influence the willingness to stay of nursing staff, especially those in the aged care industry. METHODS: A questionnaire was conducted in an aged care agency in Anhui Province, with a total of 217 valid questionnaires collected for empirical analysis. RESULTS: (a) Transformational leadership indirectly affects willingness to stay by affecting organisational commitment and job satisfaction. (b) Clan culture also affects the willingness to stay by affecting their organisational commitment and job satisfaction. (c) Professional identity also has a significant positive impact on the willingness to stay. CONCLUSIONS: The findings of the study emphasize the importance of transformational leadership and clan culture in enhancing the willingness to stay of nursing staff in the aged care industry, and clarify the specific impact mechanism. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse leaders in the aged care agencies should improve transformational leadership, and strive to build clan culture to help retain nursing staff better.

A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots.

Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

The Ac124 protein is not essential for the propagation of Autographa californica multiple nucleopolyhedrovirus, but it is a viral pathogenicity factor.

orf124 (ac124) of AcMNPV is one of the highly conserved unique genes in group I lepidopteran nucleopolyhedroviruses. So far, its function remains unknown. In this study, infection with a virus expressing an ac124-gfp fusion showed that Ac124 localized to the cytoplasm throughout the infection. In addition, an ac124 knockout virus was generated to determine the role of ac124 in the baculovirus life cycle. Our results showed that an ac124 knockout AcMNPV could produce infectious budded viruses (BVs) and occlusion bodies (OBs) like those produced by the wild virus and ac124 repair virus. These three viruses had similar growth kinetics during the infection phase. There was no significant difference in nucleocapsids, occlusion-derived viruses and OBs visualized by electron microscopy. The ac124 deletion mutant did not reduce AcMNPV infectivity for S. exigua in an LD50 bioassay. However, it took 20 h longer for the ac124 deletion mutant to kill S. exigua than wild-type virus in the LT50 bioassay. Altogether, these results demonstrate that ac124 is not required for viral replication, but it accelerates the killing of infected larvae.

Intracellular localization of rice stripe virus RNA-dependent RNA polymerase and its interaction with nucleocapsid protein.

The RNA-dependent RNA polymerase (RdRp) of rice stripe virus (RSV) is critical for both the transcription and replication of the viral genome. Despite its importance, little is known about how it functions in cells. In the present study, RSV RdRp was split into three pieces, since expression of the full protein could not be achieved. Then, the intracellular localization of these three RdRp fragments and their interactions with nucleocapsid protein (NP) were investigated, which is another viral protein required for viral RNA synthesis. The data showed that all three RdRp fragments displayed punctuate staining patterns in the cytoplasm, and the C-terminal fragment co-localized with NP in the perinuclear region. Both bimolecular fluorescence complementation and co-immunoprecipitation experiments demonstrated that of the three RdRp fragments, only the C-terminal fragment could interact with NP. Further analysis using a series of truncated NPs identified the N-terminal 50-amino-acid region within NP as the determinant for its interaction with the C-terminus of RdRp.

The RNA-binding properties and domain of Rice stripe virus nucleocapsid protein.

The nucleocapsid protein (NP) of rice stripe virus (RSV) encapsidates viral genomic RNAs to form virion. The binding of NP with RNA is essential for the formation of virus particle. In this study, the binding specificity of RSV NP to RNA and the domains within the NP that mediate this interaction were investigated by gel electrophoretic mobility shift assays and Northwestern blot analysis. The results demonstrated that RSV NP was able to bind to all synthetic RNAs and DNAs without sequence specificity. Using a series of truncated NPs expressed in E. coli and synthetic peptides, we mapped the RNA-binding domain of NP to the central region from amino acid residues 201-232. Further alanine substitution analysis revealed that Lys(206), Lys(207), Lys(220), and Tyr(221) in the RNA-binding domain were essential for NP to bind with RNA.

An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship.

In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship. First, the supremum and infimum incentives-based rule driven by trust relationship is defined. Based on the assumption that if incentive conditions are met, then experts will be willing to adjust their preferences, the incentive behavior-driven minimum adjustment consensus model is developed to generate optimal incentive-based recommendation preferences. Thus, the proposed incentive mechanism can effectively reduce the preference adjustment cost and promote group consensus reaching. Third, the updated trust relationships between experts are shown to be strengthen by the proposed incentive-driven preference revision. Consequently, the optimization model based on trust interaction relationship is constructed to obtain the final group preference matrix. Finally, a supplier selection case of high-end medical equipment is provided to illustrate the proposed method and show the rationality and advantages of the proposed methodology with both a sensitivity analysis and a comparison analysis.

The effect of digitalization on the career intentions of nursing students: A cross-sectional study.

AIM: We investigate how the confirmation of expectations about digital technology in the workplace affects the career intentions of nursing students. We also explore the role of task fit in mediating (1) digital technology satisfaction and job satisfaction and (2) digital technology satisfaction and career intentions. BACKGROUND: The turnover of graduating geriatric nursing students is very high and rising. To support the work of nursing staff, elderly care institutions are beginning to adopt digital technologies that aid in nursing tasks. However, it is unclear whether students' perceptions of those digital technologies have an impact on their career intentions. DESIGN: This is a cross-sectional study. METHODS: We recruited 549 geriatric nursing students. Data were collected from December 2022 to March 2023. The questionnaire included seven sections: expectation confirmation, perceived usefulness, perceived safety, digital technology satisfaction, task fit, job satisfaction and career intentions. The validity and reliability of the model were confirmed. RESULTS: The results show that the confirmation of students' expectations for the digital technology used in elderly care services has a positive impact on their career intentions. However, the results do not confirm the impact of perceived security on digital technology satisfaction, or the effect of job satisfaction on career intentions. CONCLUSION: Elderly care institutions and educators should monitor the current state of the digital work environment to ensure that it can adequately support students in their work. They should ensure the use of advanced and appropriate technology tools in teaching and clinical environments to provide a richer and more vivid learning experience. These initiatives can support nursing students in their transition from school to practice and increase their willingness to stay in the profession.

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak.

During major infectious disease outbreak, such as COVID-19, the goods and parcels supply and distribution for the isolated personnel has become a key issue worthy of attention. In this study, we propose a delivery problem that arises in the last-mile delivery during major infectious disease outbreak. The problem is to construct a Hamiltonian tour over a subset of candidate parking nodes, and each customer is assigned to the nearest parking node on the tour to pick up goods or parcels. The aim is to minimize the total cost, including the routing, allocation, and parking costs. We propose three models to formulate the problem, which are node-based, flow-based and bilevel programing formulations. Moreover, we develop a variable neighborhood search algorithm based on the ideas from the bilevel programing formulations to solve the problem. Finally, the proposed algorithm is tested on a set of randomly generated instances, and the results indicate the effectiveness of the proposed approach.

How Safety Climate Influences the Willingness to Stay of Nursing Staff during the COVID-19 Outbreak.

The outbreak of COVID-19 in China at the beginning of 2020 has made the problems that the aged care agency face with large mobility and high turnover of aged nursing staff become more serious. Aiming at this problem, this paper incorporates psychological capital and social panic into the model from the perspective of the organizational safety climate and constructs a theoretical model of the mechanism of the effect on nursing staff's willingness to stay in the context of the outbreak. Through a questionnaire survey in an aged care agency in Anhui Province, a total of 321 valid questionnaires were collected for empirical analysis. The results show that: (1) the safety climate of the organization has a significant positive impact on the transactional psychological capital and interpersonal psychological capital of nursing staff in the aged care industry and their willingness to stay; (2) transactional psychological capital and social panic have a significant positive impact on the willingness to stay of nursing staff, while interpersonal psychological capital has no significant impact on the willingness to stay; (3) the mediating role of transactional psychological capital and interpersonal psychological capital between the safety climate and the willingness to stay is established, and the moderating role of social panic between psychological capital and willingness to stay is also established. Finally, based on the research conclusions, corresponding countermeasures and suggestions are put forward to deal with the problems that occur in special periods.

The Effect of Social Media Use on Depressive Symptoms in Older Adults with Self-Reported Hearing Impairment: An Empirical Study.

Older people with hearing impairment are more likely to develop depressive symptoms due to physical disability and loss of social communication. This study investigated the effects of social media on social relations, subjective aging, and depressive symptoms in these older adults based on the stimulus-organism-response (S-O-R) framework. It provides new empirical evidence to support improving the mental health and rebuilding the social relations of older people. A formal questionnaire was designed using the Wenjuanxing platform and distributed online through WeChat; 643 valid questionnaires were received from older people with self-reported hearing impairments, and SmartPLS 3.28 was used to analyze the data. The results show that (1) social media significantly impacts the social relations of older people with hearing impairment (social networks, ß = 0.132, T = 3.444; social support, ß = 0.129, T = 2.95; social isolation, ß = 0.107, T = 2.505). (2) For these older people, social isolation has the biggest impact on their psychosocial loss (ß = 0.456, T = 10.458), followed by the impact of social support (ß = 0.103, T = 2.014); a hypothesis about social network size was not confirmed (ß = 0.007, T = 0.182). Both social media (ß = 0.096, T = 2.249) and social support (ß = 0.174, T = 4.434) significantly affect the self-efficacy of hearing-impaired older people. (3) Both subjective aging (psychosocial loss, ß = 0.260, T = 6.036; self-efficacy, ß = 0.106, T = 3.15) and social isolation (ß = 0.268, T = 6.307) significantly affect depressive symptoms in older people with hearing impairment. This study expands the theories of social media aging cognition, social support, and social networks and can provide practical contributions to the social media use and mental health of special persons 60 years and older.

Impact of Internet Use on Elderly Health: Empirical Study Based on Chinese General Social Survey (CGSS) Data.

In the current era, the rapid spread of Internet technology has combined with various traditional industries; this provides new research perspectives and solutions for current problems, such as those in the elderly care industry. Elderly health is an important social problem in various countries, and governments have turned to the internet for new methods and better solutions. However, internet-use behavior has a certain influence on the elderly's health status. This study investigates the effects of internet use on the elderly's physical health, mental health, and self-rated health, along with the moderating role of individual cognitive ability in the above relationship. This study uses data from the Chinese General Social Survey (CGSS) in 2012 and 2015 as samples for analysis via the hierarchical regression method. The sample is from China and had 2821 and 3185 valid respondents in 2012 and 2015, respectively. Results show that internet use significantly affects the physical and mental health of the elderly and does not significantly affect self-rated health. In addition, individual cognitive ability plays a negative moderating role between internet use and physical and mental health. Finally, on the basis of results analysis and discussion, this study provides new recommendations to achieve targeted health improvements.