NORM CONFORMITY MOTIVATIONS IN HEALTH PREVENTION: ADDING MOTIVATION APPEALS TO ENHANCE NORM-BASED MESSAGE PERSUASIVENESS

Social norms refer to what most people do (i.e., descriptive norms) and what most people (dis)approve of doing (i.e., injunctive norms). The influence of perceived social norms and norm-based messages (i.e., messages presenting descriptive or injunctive norms) on health behaviors has long been a research focus in communication studies. However, the mechanisms that underpin social norm influence have not been fully understood. In addition, researchers have been exploring strategies to enhance the persuasiveness of norm-based messages. Based on social norm theories and the message matching theory, the dissertation focused on understanding norm conformity motivations and testing the effectiveness of norm conformity motivation appeals in changing health-related attitudes and behavioral intentions of getting a coronavirus 2019 (COVID-19) booster vaccine. By focusing on COVID-19 booster vaccine, this study aimed to extend the scope of social norm approach to crisis contexts and provide practical implications to combat the COVID-19 pandemics using norm-based message. Through a literature review, the dissertation provided a framework that synthesized norm conformity motivations identified in the literature. The framework defined five norm conformity motivations and categorized them into motivations to conform to descriptive norms (i.e., accuracy motivation, identification with admired group motivation, and relative benefit motivation) and motivations to conform to injunctive norms (i.e., social award motivation and social punishment motivation). Pilot study 1 developed and validated a 23-item instrument to measure the five motivations. Face validity, construct validity, and reliability were evaluated using Amazon Mechanical Turk (MTurk) samples. And content validity was evaluated by five expert judges. The instrument had adequate validity and reliability. Pilot study 2 designed norm-based messages with motivation appeals (i.e., linking norm (non)conformity with the benefits or costs related to norm conformity motivations). Based on the results of manipulation check, pilot study 2 determined which messages to be used in the main study. The main study compared the influence of norm-based messages and norm-based messages with motivation appeals on U.S. adults’ attitudes and intentions to get a Coronavirus disease 2019 (COVID-19) booster vaccine. The main study also examined the persuasiveness of matching norm conformity motivation appeals with individual characteristics, including norm conformity motivations, perceived uncertainty, need for closure, upward social comparison, fear of missing out, need for approval, and fear of negative evaluation. The results showed that adding norm conformity motivation appeals increased perceived message effectiveness, and in turn, perceived message effectiveness was positively associated with attitudes. However, the total effect of motivation appeals on attitudes and the mediation paths through perceived message relevance were not significant. In addition, matching motivation appeals with individual characteristics did not result in better persuasion outcomes. The study contributes to the social norm literature and health communication practice by providing a conceptual framework and an instrument of norm conformity motivations. The framework helps understand the norm conformity process. And the instrument allows future studies to empirically test the psychological mechanism of norm conformity. Health communication practitioners can use the instrument to gauge recipients’ norm conformity motivations and design tailored messages. The study also contributes to social norm theories and the message matching theory by highlighting the importance of perceived message effectiveness in norm conformity and the importance of motivation salience in message matching

On the Meaning of Berry Force For Unrestricted Systems Treated With Mean-Field Electronic Structure

We show that the Berry force as computed by an approximate, mean-field electronic structure can be meaningful if properly interpreted. In particular, for a model Hamiltonian representing a molecular system with an even number of electrons interacting via a two-body (Hubbard) interaction and a spin-orbit coupling, we show that a meaningful nonzero Berry force emerges whenever there is spin unrestriction--even though the Hamiltonian is real-valued and formally the on-diagonal single-surface Berry force must be zero. Moreover, if properly applied, this mean-field Berry force yields roughly the correct asymptotic motion for scattering through an avoided crossing. That being said, within the context of a ground-state calculation, several nuances do arise as far interpreting the Berry force correctly, and as a practical matter, the Berry force diverges near the Coulson-Fisher point (which can lead to numerical instabilities). We do not address magnetic fields here

A novel gaussian particle swarms optimized particle filter algorithm for the state of charge estimation of lithium-ion batteries.

A gaussian particle swarm optimized particle filter estimation method, along with the second-order resistance-capacitance model, is proposed for the state of charge estimation of lithium-ion battery in electric vehicles. Based on the particle filter method, it exploits the strong optimality-seeking ability of the particle swarm algorithm, suppressing algorithm degradation and particle impoverishment by improving the importance distribution. This method also introduces normally distributed decay inertia weights to enhance the global search capability of the particle swarm optimization algorithm, which improves the convergence of this estimation method. As can be known from the experimental results that the proposed method has stronger robustness and higher filter efficiency with the estimation error steadily maintained within 0.89% in the constant current discharge experiment. This method is insensitive to the initial amount and distribution of particles, achieving adaptive and stable tracking in the state of charge for lithium-ion batteries

An improved rainflow algorithm combined with linear criterion for the accurate li-ion battery residual life prediction.

Li-ion battery health assessment has been widely used in electric vehicles, unmanned aerial vehicle and other fields. In this paper, a new linear prediction method is proposed. By weakening the sensitivity of the Rainflow algorithm to the peak data, it can be applied to the field of battery, and can accurately count the number of Li-ion battery cycles, and skip the cumbersome link of parameter identification. Then, a linear criterion is proposed based on the idea of proportion, which makes the life prediction of Li-ion battery linear. Under the verification of multiple sets of data, the prediction error of this method is kept within 2.53%. This method has the advantages of high operation efficiency and simple operation, which provides a new idea for battery life prediction in the field of electric vehicles and aerospace

Bisphenol A exposure triggers endoplasmic reticulum stress pathway leading to ocular axial elongation in mice

BackgroundOcular axial elongation is one of the features of myopia progression. Endoplasmic reticulum (ER) stress-associated scleral remodeling plays an important role in ocular axial elongation. Bisphenol A (BPA) is one of the most common environmental pollutants and is known to affect various human organs through ER stress. However, whether BPA exerts an effect on scleral remodeling remains unknown. The purpose of this study was to determine the effect of BPA on the development of myopia and scleral ER stress.MethodsBPA was administered by intraperitoneal injection. 4-PBA was administered as an endoplasmic reticulum stress inhibitor by eye drops. Refraction and axial length were measured by refractometer and SD-OCT system. Western blot was performed to detect the expression level of ER stress-related proteins.ResultsBPA-administered mice exhibit axial elongation and myopic refractive shift with endoplasmic reticulum stress in the sclera. BPA administration activated scleral PERK and ATF6 pathways, and 4-PBA eye drops attenuated ER stress response and suppressed myopia progression.ConclusionBPA controlled axial elongation during myopia development in a mouse model by inducing scleral ER stress and activation of the PERK/ATF6 pathway. 4-PBA eye drops as ER stress inhibitor suppressed BPA-induced myopia development

ATPT: Automate Typhoon Contingency Plan Generation from Text

Artificial intelligence (AI) planning models play an important role in decision support systems for disaster management e.g. typhoon contingency plan development. However, constructing an AI planning model always requires significant amount of manual effort, which becomes a bottleneck to emergency response in a time-critical situation. In this demonstration, we present a framework of automating a domain model of planning domain definition language from natural language input through deep learning techniques. We implement this framework in a typhoon response system and demonstrate automatic generation of typhoon contingency plan from official typhoon plan documents

Keyframe image processing of semantic 3D point clouds based on deep learning

With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make it possible to integrate their large amount of image material efficiently. However, traditional content-based image retrieval techniques are based on images, and there is a “semantic gap” between this and people's understanding of images. To address this “semantic gap,” a keyframe image processing method for 3D point clouds is proposed, and based on this, a U-Net-based binary data stream semantic segmentation network is established for keyframe image processing of 3D point clouds in combination with deep learning techniques

Approaches to enhance electroluminescent efficiency of light-emitting diodes based on quasi-two-dimensional perovskite

Quasi-two-dimensional (quasi-2D) perovskites with (Ai)2(A2)n-iPbnX3n+i multi-quantum well structures are considered as the potential electroluminescence (EL) materials due to their controllable quantum confine effect which would lead a high EL efficiency. However, the quasi-2D perovskite films fabricated with solution processing technologies consist of different n phases and orientated layers, which limits the performance of quasi-2D perovskite light- emitting diodes (PeLEDs). To improve the performance of PeLEDs, it is essential to obtain perovskite thin films with both large exciton binding energy, complete surface coverage and suitable morphology. Here some approaches are developed to improve the performance of quasi-2D PeLEDs