Impact of the Arts on Public Perception, Comprehension, and Retention of Scientific Research

Communicating research to broad audiences is a fundamental task for scientists. Art is hypothesized to be an effective medium for improving public perception of research, as well as student comprehension and retention of scientific discoveries. To test these hypotheses, we first created an interactive art exhibit with 20 original pieces aimed at communicating findings from two recently published papers. Next, to test whether art improves public perception of research, we asked visitors of the art show to fill out surveys about their perception of research before and after visiting the exhibit. Next, using content quizzes, we tested whether interacting with art allowed ecology students to better retain and comprehend scientific findings compared to reading scientific abstracts. Participation in the art exhibit caused a 20% improvement in perception of research for individuals with non-scientific backgrounds. However, participation in the art exhibit was less effective for participants with scientific backgrounds (10% improvement). Next, contrary to our hypothesis, participation in the art exhibit did not improve ecology student comprehension and retention of scientific material. In contrast, students scored the highest when reading abstracts. Collectively, this suggests that the use of art can facilitate scientific appreciation but is most influential with individuals with non-scientific backgrounds

Giant Lamb shift in photonic crystals

We obtain a general result for the Lamb shift of excited states of multilevel atoms in inhomogeneous electromagnetic structures and apply it to study atomic hydrogen in inverse-opal photonic crystals. We find that the photonic-crystal environment can lead to very large values of the Lamb shift, as compared to the case of vacuum. We also suggest that the position-dependent Lamb shift should extend from a single level to a miniband for an assembly of atoms with random distribution in space, similar to the velocity-dependent Doppler effect in atomic/molecular gases

SUSTAINABILITYAND GROWTH OFONLINE KNOWLEDGE COMMUNITIES: EXAMINING THE IMPORTANCE OFPERCEIVED COMMUNITYSUPPORTAND PERCEIVED LEADER SUPPORT

Voluntary behaviors (i.e., knowledge contribution and word of mouth) are important to the sustainability and growth of online knowledge communities. Although previous studies have identified various factors leading to knowledge contribution and related behaviors, the underlying psychological processes have rarely been examined. In particular, previous studies have not examined how characteristics of online knowledge communities influence voluntary behaviors through support perception. This study aims to fill the gap in the literature by developing and testing a model to explain voluntary behaviors in online knowledge communities. To develop the research model, we drew on theories of justice, organizational support, and citizenship behavior to explain the influence of characteristics of online knowledge communities on individuals\u27 voluntary behaviors through their perceptions of support from the community and the leader. The research model was tested on survey data collected from 214 online knowledge community users. The results largely supported our model. In particular, we found that pro-sharing norm and information need fulfillment affect perceived community support. Perceived recognition from leader and perceived co-presence of leader affect perceived leader support. Additionally, perceived community support was found to be important in shaping knowledge contribution and word of mouth. Perceived leader support was found to influence individuals\u27 knowledge contribution behavior. Theoretical and Practical implications are discussed

Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control

Driven by the critical needs of biomanufacturing 4.0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of bioprocess mechanisms. It can faithfully capture the important properties, including nonlinear reactions, partially observed state, and nonstationary dynamics. Given very limited real process observations, we derive a posterior distribution quantifying model estimation uncertainty. To avoid the evaluation of intractable likelihoods, Approximate Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC) is utilized to approximate the posterior distribution. Under high stochastic and model uncertainties, it is computationally expensive to match output trajectories. Therefore, we create a linear Gaussian dynamic Bayesian network (LG-DBN) auxiliary likelihood-based ABC-SMC approach. Through matching the summary statistics driven through LG-DBN likelihood that can capture critical interactions and variations, the proposed algorithm can accelerate hybrid model inference, support process monitoring, and facilitate mechanism learning and robust control.Comment: 11 pages, 2 figure

Classification of the conditionally observable spectra exhibiting central symmetry

We show how in PT-symmetric 2J-level quantum systems the assumption of an upside-down symmetry (or duality) of their spectra simplifies their classification based on the non-equivalent pairwise mergers of the energy levels.Comment: 10 pp. 3 figure