How do self-exempt beliefs affect intentions to quit smoking? An exploration of the mediating role of threat appraisal and coping appraisal

IntroductionNumerous smokers are cognizant of the detrimental effects associated with this habit yet exhibit a persistent reluctance to cease their tobacco consumption. Self-exempt beliefs serve as an obstacle to the cessation of this addictive behavior. This research explored the impact of self-exempt beliefs on the readiness to quit smoking based on the Protection Motivation Theory (PMT) model and the mediating roles of threat appraisal and coping appraisal.MethodsSelf-exempt beliefs, PMT constructs, and the intention to quit smoking constituted the theoretical model. The questionnaires were collected from 488 Chinese adult male smokers based on snowball sampling. Exploratory Factor Analysis (EFA) was used to examine the underlying factor structure of the pre-designed self-exempt beliefs scale. The reliability, validity, path coefficients, and explanatory power of the model were calculated using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results and discussionThe results showed that : (1) three common factors (skeptic beliefs, bulletproof beliefs, and “worth it” beliefs) with a total of 11 items were retained after EFA; (2) skeptic beliefs and “worth it” beliefs had a significantly negative effect on both threat appraisal and coping appraisal, while bulletproof beliefs did not; (3) bulletproof beliefs had a significantly positive direct impact on intention to quit, “worth it” beliefs had a significantly negative direct impact on intention, while skeptic beliefs had no significantly direct impact on intention; (4) threat appraisal and coping appraisal positively and significantly predicted cessation intention; and (5) threat appraisal and coping appraisal, as two main cognitive processes, acted as full mediations between skeptic beliefs and the intention to quit, as complementary partial mediations between “worth it” beliefs and the intention, and as non-mediation between bulletproof beliefs and the intention. Our findings suggest that efforts to undermine or “prevent” these self-exempt beliefs, particularly “worth it” and skeptic beliefs, may be an effective tactic for health communication interventions for quitting smoking

Formation and Characterization of Lactoferrin-Hyaluronic Acid Conjugates and Their Effects on the Storage Stability of Sesamol Emulsions

The purpose of this study was to fabricate biopolymer conjugates from lactoferrin (LF) and hyaluronic acid (HA) and then to investigate their potential as emulsifiers for forming sesamol-loaded emulsions. Initially, LF-HA covalent conjugates were formed using the carbodiimide coupling method in aqueous solutions at pH = 4.5, and then the nature of the conjugates was investigated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, and fluorescence spectroscopy. The results demonstrated the formation of an amide link between the amine groups of LF and the carboxyl groups of HA. Sesamol emulsions were prepared using the LF-HA conjugates as emulsifiers and their stability was determined. The conjugates improved both the physical and chemical stability of the emulsions during storage. Optimum stability of the emulsion was obtained at a LF-to-HA molar ratio of 2:1. Our results suggest that LF-HA conjugates may be effective emulsifiers for use in food stuffs and other applications

An Efficient SDN load balancing scheme based on variance analysis for massive mobile users

In a traditional network, server load balancing is used to satisfy the demand for high data volumes. The technique requires large capital investment while offering poor scalability and flexibility, which difficultly supports highly dynamic workload demands from massive mobile users. To solve these problems, this paper analyses the principle of software-defined networking (SDN) and presents a new probabilistic method of load balancing based on variance analysis. The method can be used to dynamically manage traffic flows for supporting massive mobile users in SDN networks. The paper proposes a solution using the OpenFlow virtual switching technology instead of the traditional hardware switching technology. A SDN controller monitors data traffic of each port by means of variance analysis and provides a probability-based selection algorithm to redirect traffic dynamically with the OpenFlow technology. Compared with the existing load balancing methods which were designed to support traditional networks, this solution has lower cost, higher reliability, and greater scalability which satisfy the needs of mobile users.The work was supported by the National Natural Science Foundation of China (no. 61173188, no. 61572001, and no. 61502008), the Research Fund for the Doctoral Program of Higher Education (no. 20133401110004), the Educational Commission of Anhui Province, China (no. KJ2013A017), the Natural Science Foundation of Anhui Province (no. 1508085QF132), the Tender Project of the Co-Innovation Center for Information Supply & Assurance Technology of Anhui University (no. ADXXBZ2014-7), and the Doctoral Research Startup Funds Project of Anhui University

Factors associated with genital human papillomavirus infection among adult females in the United States, NHANES 2007–2010

BACKGROUND: Patients with human papillomavirus (HPV) infection are at risk of developing cancer later in their life. Current research estimates the prevalence of genital HPV infection and explores the factors that are associated with the infection. FINDINGS: The National Health and Nutrition Examination Survey 2007–2010 was used in this research study. The study population included females in the United States aged 18–59 years. The weighted prevalence of HPV infection was 41.9%. An estimated 59.4% of non-Hispanic black females had HPV infection. In a multivariate analysis, number of sexual partners, race, age, education level, marital status, income, smoking, and insurance status were associated with HPV infection. HPV infection was 5.77 times more likely for women with >11 sexual partners compared to women with 0–1 partners. Non-Hispanic black females were 1.87 times more likely to have HPV infection compared to non-Hispanic white females. Participants with only a high school degree had a 58% increased prevalence compared to college-educated women. Uninsured women had a 39% increased prevalence compared to those with insurance. CONCLUSION: This study found that 41.9% of U.S. females aged 18–59 years tested positive for genital HPV infection. We determined that individuals with more sexual partners, with a lower education level, with non-Hispanic black race, and with no insurance were the populations at greatest risk. It is necessary to continue monitoring the prevalence of this infection in the general population to provide a basis for effective treatment and prevention in the target populations

Research on Modeling and Experiment of Glass Substrate Peeling Based on Adhesion Theory

In this paper, the peeling of glass substrates is modeled, in a setting of automatic polishing and grinding for flat panel display glass substrates. The mechanical model of glass substrates-adhesive pad structure is established. The vacuum adsorbing force between them is regarded as adhesive force. The model is simplified as a distributed spring group which can describe the desorption and shear behavior of the glass substrates-adhesive pad structure. The corresponding analytical solution method is proposed. Finally, experiment is conducted to verify the accuracy and feasibility of the proposed mechanical model

How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting Governance

Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the original Steem community to leave the blockchain that they maintained for years. This is a historical event in the evolution of blockchains and Web 3.0. Despite its significant disruptive impact, little is known about how vulnerable DPoS blockchains are in general to takeovers and the ways in which we can improve their resistance to takeovers. In this paper, we demonstrate that the resistance of a DPoS blockchain to takeovers is governed by both the theoretical design and the actual use of its underlying coin-based voting governance system. When voters actively cooperate to resist potential takeovers, our theoretical analysis reveals that the current active resistance of DPoS blockchains is far below the theoretical upper bound. However in practice, voter preferences could be significantly different. This paper presents the first large-scale empirical study of the passive takeover resistance of EOSIO, Steem and TRON. Our study identifies the diversity in voter preferences and characterizes the impact of this diversity on takeover resistance. Through both theoretical and empirical analyses, our study provides novel insights into the security of coin-based voting governance and suggests potential ways to improve the takeover resistance of any blockchain that implements this governance model.Comment: This work has been accepted by ACM CCS 202

Creative design inspired by biological knowledge: Technologies and methods

Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market. To identify the technologies and methods that can facilitate the development of biologically inspired creative designs, this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields. Afterward, this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design (BID) process. This research then discusses the future development trends of the BID process before presenting the conclusions

Acute Effects of Particulate Air Pollution on Ischemic Stroke and Hemorrhagic Stroke Mortality

Background and Purpose: A large body of literature reported the association of particulate matter (PM) with stroke in high-income countries. Few studies have examined the association between PM and stroke in middle- and low-income countries and considered the types of stroke. In this study, we examined the short-term effects of particulate matter <2.5 μm in diameter (PM2.5) and particulate matter <10 μm in diameter (PM10) on ischemic stroke mortality and hemorrhagic stroke mortality in Beijing, China.Methods: We used an ecological study design and quasi-Poisson generalized additive models to evaluate the association of PM2.5 and PM10 and cerebrovascular diseases mortality, as well as ischemic- and hemorrhagic stroke mortality. In the model, we controlled long-term and season trends, temperature, and relative humidity, the day of the week and air pollution. For cerebrovascular diseases mortality, we examined the effects stratified by sex and age with different lag days.Results: A total of 48,122 deaths for cerebrovascular disease (32,799 deaths for ischemic stroke and 13,051 deaths for hemorrhagic stroke) were included in the study. PM2.5 was associated with stroke mortality. The 10 μg/m3 increase of PM2.5 was associated with the increase of mortality, 0.27% (95% CI, 0.12–0.43%) for cerebrovascular diseases, 0.23% (95% CI, 0.04–0.42%) for ischemic stroke and 0.37% (95% CI, 0.07–0.67%) for hemorrhagic stroke -. The associations between PM10 and mortality were also detected for cerebrovascular diseases and ischemic stroke, but not in hemorrhagic stroke. The stratified analysis suggested age and gender did not modify the effects of PM on mortality significantly.Conclusions: Our study suggested that short-term exposure to ambient PM was associated with the risk of stroke mortality

Application Progress of Deep Learning in Imaging Examination of Breast Cancer

Breast cancer is the most common malignant tumor in women and its early detection is decisive. Breast imaging plays an important role in early detection of breast cancer as well as monitoring and evaluation during treatment, but manual detection of medical images is usually time-consuming and labor-intensive. Recently, deep learning algorithms have made significant progress in early breast cancer diagnosis. By combing the relevant literature in recent years, a systematic review of the application of deep learning techniques in breast cancer diagnosis with different imaging modalities is conducted, aiming to provide a reference for in-depth research on deep learning-based breast cancer diagnosis. Firstly, four breast cancer imaging modalities, namely mammography, ultrasonography, magnetic resonance imaging and positron emission tomography, are outlined and briefly compared, and the public datasets corresponding to multiple imaging modalities are listed. Focusing on the different tasks (lesion detection, segmentation and classification) of deep learning architectures based on the above four different imaging modalities, a systematic review of the algorithms is conducted, and the performance of each algorithm, improvement ideas, and their advantages and disadvantages are compared and analyzed. Finally, the problems of the existing techniques are analyzed and the future development direction is prospected with respect to the limitations of the current work