Large strain nonlinear viscoelastic modeling of polymer nanocomposites

Polymer nanocomposites, where nanofillers (carbon black, silica, etc.) are incorporated into rubberlike polymeric matrix, provides high-performance material properties that combine the matrix’s high ductility, high damping, and good abrasion resistance and fillers’ high stiffness and strength. A linear viscoelastic modeling tool has earlier been developed by the group incorporating an interphase regime between matrix and fillers with properties different from the bulk matrix. However, the linear viscoelastic model is inadequate for capturing the strain-dependent and rate-dependent viscoelastic behavior of the nanocomposites under large deformation. In this study, the constitutive modeling of the incompressible, isotropic polymer matrix consists of two parts: pure hyperelasticity and nonlinear viscoelasticity. The Marlow model was chosen to describe the hyperelastic behavior. Different nonlinear viscoelastic models, including the Adaptive Quasi-linear Viscoelastic (AQLV) model and the Bergstrom–Boyce model, were compared for predicting the relaxation behavior of both the pure rubber and the nanocomposites at different strain levels. The AQLV model was selected for detailed study and calibrated. A three-phase (matrix, fillers, and interphase) composite model was implemented in Abaqus using a Scanning Electron Microscope images based reconstruction as the geometric input. Simulations on the strain-dependent viscoelastic properties of the nanocomposites are performed with assumption for the interphase properties based on AFM testing. The simulated results are compared with composite experimental data in frequency domain to provide further information on the properties of interfacial polymer in the composites

Electoral Accountability and Selection with Personalized Information Aggregation

We study a model of electoral accountability and selection (EAS) in which heterogeneous voters can aggregate the incumbent's performance data into personalized signals through paying limited attention. Extreme voters' signals exhibit an own-party bias, which hampers their abilities to discern good and bad performances. While this effect alone would undermine EAS, there is a countervailing effect stemming from partisan disagreements, which make the centrist voter pivotal and could potentially enhance EAS. Overall, increasing mass polarization and shrinking attention spans have ambiguous effects on EAS, whereas correlating voters' signals unambiguously improves EAS and voter welfare

A Rational Inattention Theory of Echo Chamber

Finite players allocate limited attention capacities across biased primary sources and other players in order to gather information about an uncertain state. The resulting Poisson attention network transmits information from primary sources to a player either directly or indirectly through the other players. We study when and why rational inattention leads players with similar preferences to form echo chambers, and why mandatorily exposing players to all biased sources could dissolve echo chambers but undermine welfare. We characterize the opinion distribution within an echo chamber, establishing the law of the few and the controversy of policy interventions that augment source visibility

Learning News Bias: Misspecifications and Consequences

We study how a decision maker (DM) learns about the bias of unfamiliar news sources. Absent any frictions, a rational DM uses known sources as a yardstick to discern the true bias of a source. If a DM has misspecified beliefs, this process fails. We derive long-run beliefs, behavior, welfare, and corresponding comparative statics, when the DM has dogmatic, incorrect beliefs about the bias of known sources. The distortion due to misspecified learning is succinctly captured by a single-dimensional metric we introduce. Our model generates the hostile media effect and false polarization, and has implications for fact-checking and misperception recalibration

Examining Effects of Badge Repeatability and Level on Users’ Knowledge Sharing in Online Q&A Communities

This study investigates the differential effects of badge repeatability and level on users’ knowledge sharing behaviors in an online Q&A (Question & Answer) community. Drawing on reinforcement theory and attribution theory of motivation, we conjecture that nonrepeatable badges reinforce individuals’ behaviors primarily by promoting internal attributions that strengthen their self-determination motivation, while repeatable badges reinforce people’s behaviors mainly via external attributions that undermine their self-determination motivation. By using fixed-effects models to analyze a panel data, we observe that nonrepeatable badges can better motivate users to share their knowledge than repeatable badges. In addition, the results show that attaining a higher level of nonrepeatable badges is associated with an increased effect for knowledge sharing, and that attaining a higher level of repeated badges leads to a decreased effect. These findings can contribute to extant literature by offering a probable explanation regarding why some gamified awards can motivate people better than others

The Impact of Beneficiary Facial Expressions on Donation Intention in Medical Crowdfunding

In recent years, medical crowdfunding has become an emerging and effective way to raise funds for patients with severe illness and their families, and has solved huge economic problems for many families. This study studies the information expression of medical crowdfunding projects. This study combines the S-O-R model, considered the model of altruistic and egoistic motives for helping, adopted laboratory research methods, studied the effect of the facial expressions of beneficiary on individual donate intention. The results showed that individual altruism and guilt can positively influence individual donate intention. The facial expressions of beneficiaries affected both egoistic motivation and altruism motivation at the same time, and there were significant differences in the two types of motivation. In addition, research has found that individual guilt has a moderating effect on altruism. This study enriched the research of the SOR model and the altruistic and self-interest motivation model in the context of medical crowdfunding, at the same time studied the impact of facial expressions on personal motivation to provide recommendations for medical crowdfunding content writing

The influence of Big Five personality traits on college students’ key competencies: the mediating effect of psychological capital

BackgroundIn recent years, both society and employers have put forward higher requirements for the comprehensive quality of college students in the new era. Based on the conservation of resources theory and life-cycle approach, this study aimed to examine the relationship between the Big Five personality traits, the psychological capital, and the key competencies among college students and analyzed the mediating role of the psychological capital in this link.MethodsA total of 1,132 Chinese undergraduates (67.40% girls; 48.67% from key universities) participated. Participants completed self-report questionnaires that evaluated the five key characteristics of personality, psychological capital, and key competencies.ResultsThere were extremely significant university-type differences in key competencies of college students. And the mediating role of psychological capital in the link between Big Five personality traits and key competencies was validated according to PROCESS model 4. Psychological capital serves as a partial mediator in the relationships between neuroticism and critical thinking, openness and creativity, conscientiousness and creativity, openness and communication, conscientiousness and communication, extraversion and collaboration, as well as openness and collaboration. The proportion of mediating effects for the above models was 5.97, 10.89, 11.82, 12.24, 11.98, 12.39, and 22.72%, respectively.DiscussionThe findings provide a better understanding of the key competencies of college students from the perspectives of the Big Five personality traits and psychological capital and suggest a greater emphasis to focusing on personality and improving psychological capital

Campaign-style governance of air pollution in China? A comprehensive analysis of the central environmental protection inspection

Central environmental protection inspection (CEPI) is a major institutional innovation in China's environmental governance, but its effectiveness in improving air quality is still unclear. However, the effectiveness of CEPI is of great significance and can be regarded as an important reference for deepening the reform of environmental governance system in China. This article takes the CEPI as a quasi-natural experiment and uses the regression discontinuity design (RDD) and the difference-in-differences (DID) methods to examine the effectiveness of this policy. The study found that the first round of CEPI reduced the air pollution of cities in the inspected provinces in a short time. Moreover, this positive policy effect persisted in the aftermath of the inspection, but this long-term effect is mainly reflected in PM10 and SO2. Heterogeneity analysis showed that CEPI was only effective in reducing air pollutants of industry-oriented cities, cities in Central and Eastern China, and cities with large or small population size. The moderating effect analysis indicated that a healthy relationship (close and clean) between the local governments and businesses was conducive to reducing air pollution. The research confirmed the presence of “selective” reduction of air pollutants in the long run caused by CEPI, thereby providing new inspiration for the improvement of campaign-style environmental governance and the follow-up CEPI work