Research on the Influencing Factors of the Continuous Use of Online Health Information —Health Literacy as a Moderator

Studying the influencing factors of users\u27 willingness to continue using online health information will help the online health platform to provide health information that meets users\u27 needs and improve the users\u27 experience, and can promote the popularization of health information. Based on the theory of expectation confirmation, this study explores the influencing factors of users\u27 willingness to continue to use online health information from the perspectives of information quality and health literacy theory, and uses PLS to verify the conceptual model. The research results show that: (1) Both users\u27 satisfaction and perceived usefulness positively significantly affect the willingness to use online health information continuously and the perceived usefulness has a decisive influence.(2) Perceived usefulness plays a part intermediary role between the quality of information content and the users\u27 continued willingness to use online health information, and it partially plays a intermediary role between the credibility of information sources and the users\u27 willingness to use online health information continuously. (3) Users\u27 health literacy negatively regulates the relationship between information source credibility and perceived usefulness, and positively regulates the relationship between information content quality and perceived usefulness

Research on Medical Overtreatment Based on LDA and Structural Equation Model

Medical overtreatment has caused a lot of waste of medical resources. In the face of the increasingly serious medical overtreatment phenomenon, it is of great significance to clarify the factors of medical overtreatment to help solve this problem in China. In this study, we use the medical overtreatment text as a corpus and use latent Dirichlet allocation (LDA) topic model for topic extraction. Based on the extracted topics, a path model is established and the structural equation model(SEM) is used to test the path model. Finally, the influence factors of medical overtreatment are obtained. The results show that this study has extracted three main reasons that affect medical overtreatment, namely doctors, hospitals and patients. The factors influencing doctors\u27 medical overtreatment are the institutions, benefits, and induced demand. The factors that affect patients\u27 overtreatment are health and medical insurance. The factors that influence hospitals\u27 medical overtreatment are monopoly, economics, and management. These factors significantly affect the occurrence of medical overtreatment. Therefore, public health organizations should proceed from these three aspects and formulate effective measures to solve the problem of medical overtreatment

Twisting the Toll: Electric Vehicles and Information Provision

The transportation sector generates the largest share of greenhouse gas emissions, which concerns governments and communities worldwide. Electric vehicles (EVs) are believed to be the future. Various incentives have been provided to further broaden their acceptance and accelerate their adoption, including toll-exemption programs for EVs. At the same time, an individual’s driving behaviors are largely shaped by navigation applications that provide real-time traffic conditions. In this paper, we aim to understand how information provision affects the optimal structure of the EV-exempt toll. By analyzing a Bayesian routing game, we illustrate the optimality of a non-monotonic tolling strategy as a function of the EV adoption rate. For policymakers, our finding reveals the importance of understanding how the IT-enabled information provision has altered individual drivers\u27 behavior. In addition, the results uncover the general impact of IT, which expands the action space of individuals and the effective regimes of policies

STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction

As the development of cities, traffic congestion becomes an increasingly pressing issue, and traffic prediction is a classic method to relieve that issue. Traffic prediction is one specific application of spatio-temporal prediction learning, like taxi scheduling, weather prediction, and ship trajectory prediction. Against these problems, classical spatio-temporal prediction learning methods including deep learning, require large amounts of training data. In reality, some newly developed cities with insufficient sensors would not hold that assumption, and the data scarcity makes predictive performance worse. In such situation, the learning method on insufficient data is known as few-shot learning (FSL), and the FSL of traffic prediction remains challenges. On the one hand, graph structures' irregularity and dynamic nature of graphs cannot hold the performance of spatio-temporal learning method. On the other hand, conventional domain adaptation methods cannot work well on insufficient training data, when transferring knowledge from different domains to the intended target domain.To address these challenges, we propose a novel spatio-temporal domain adaptation (STDA) method that learns transferable spatio-temporal meta-knowledge from data-sufficient cities in an adversarial manner. This learned meta-knowledge can improve the prediction performance of data-scarce cities. Specifically, we train the STDA model using a Model-Agnostic Meta-Learning (MAML) based episode learning process, which is a model-agnostic meta-learning framework that enables the model to solve new learning tasks using only a small number of training samples. We conduct numerous experiments on four traffic prediction datasets, and our results show that the prediction performance of our model has improved by 7\% compared to baseline models on the two metrics of MAE and RMSE

Microstructural and Mechanical Performance of Al2O3 Nanoparticle Reinforced 17-4 PH Stainless Steel Bulk Composite Parts Fabricated by Laser Engineered Net Shaping Process

Alloy 17-4 PH (AISI 630) is a precipitation-hardening martensitic stainless steel that has been extensively employed in the industries of aerospace, marine, and chemical. In this study, bulk parts of both 17-4 PH and Al2O3 reinforced 17-4 PH composites were fabricated on a steel substrate by laser engineered net shaping (LENS) process to investigate the effects of Al2O3 reinforcements on the part performance. The 17-4 PH powders were pre-mixed with Al2O3 nanoparticles by ball milling. The microstructures of both parts were observed using scanning electron microscopy and mechanical properties including microhardness and compressive properties were evaluated by means of a Vickers microhardness tester and a universal tester, respectively. The results indicate that Al2O3 reinforced 17-4 PH composite parts fabricated by LENS process exhibited superior microhardness and compressive properties as compared to pure 17-4 PH parts.Mechanical Engineerin

FDDM1 and FDDM2, Two SGS3-like Proteins, Function as a Complex to Affect DNA Methylation in Arabidopsis

DNA methylation is an important epigenetic modification required for the specific regulation of gene expression and the maintenance of genome stability in plants and animals. However, the mechanism of DNA demethylation remains largely unknown. Here, we show that two SGS3-like proteins, FACTOR OF DNA DEMETHYLATION 1 (FDDM1) and FDDM2, negatively affect the DNA methylation levels at ROS1-dependend DNA loci in Arabidopsis. FDDM1 binds dsRNAs with 50 overhangs through its XS (rice gene X and SGS3) domain and forms a heterodimer with FDDM2 through its XH (rice gene X Homology) domain. A lack of FDDM1 or FDDM2 increased DNA methylation levels at several ROS1-dependent DNA loci. However, FDDM1 and FDDM2 may not have an additive effect on DNA methylation levels. Moreover, the XS and XH domains are required for the function of FDDM1. Taken together, these results suggest that FDDM1 and FDDM2 act as a heterodimer to positively modulate DNA demethylation. Our finding extends the function of plant-specific SGS3-like proteins

Large Language Model Alignment: A Survey

Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various concerns. The potential of these models is undeniably vast; however, they may yield texts that are imprecise, misleading, or even detrimental. Consequently, it becomes paramount to employ alignment techniques to ensure these models to exhibit behaviors consistent with human values. This survey endeavors to furnish an extensive exploration of alignment methodologies designed for LLMs, in conjunction with the extant capability research in this domain. Adopting the lens of AI alignment, we categorize the prevailing methods and emergent proposals for the alignment of LLMs into outer and inner alignment. We also probe into salient issues including the models' interpretability, and potential vulnerabilities to adversarial attacks. To assess LLM alignment, we present a wide variety of benchmarks and evaluation methodologies. After discussing the state of alignment research for LLMs, we finally cast a vision toward the future, contemplating the promising avenues of research that lie ahead. Our aspiration for this survey extends beyond merely spurring research interests in this realm. We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.Comment: 76 page

Quantitative Evaluation of Chinese Herb Medicine in the Treatment of Sialorrhea and Frequent Nighttime Urination in Patients with Parkinson’s Disease

Aims. To evaluate the efficacy of Lian-Se formula (LSF), one Chinese herb formulation for treating sialorrhea and frequent overnight urination in patients with Parkinson’s disease (PD). Methods. 96 PD patients suffering from sialorrhea and/or frequent nighttime urination were divided into two groups: an LSF group (n = 48) treated with LSF for 6 weeks and a placebo group (n = 48) treated with a placebo formula whose appearance and taste were the same as LSF for 6 weeks. All patients were treated by standard antiparkinsonism medicine according to the PD guideline of China. The changes of the quantity of saliva (QS) (mL), frequency of nighttime urination (FNU) and early sleep activity (ESA), and nocturnal activity (NA) by analyzing actigraphic records as the primary results and the total score of unified Parkinson’s disease rating scale (UPDRS) and the Epworth Sleepiness Scale (ESS) as the secondary results were used to evaluate the clinical efficacy in both groups. Results. There were no significant differences in the baseline values of QS, FNU, NA, ESA, UPDRS total score, and ESS between the two groups. At the end of week 6, the QS, FNU, NA, and ESA in the LSF group showed superior results to those of the placebo group with no differences in the total UPDRS score between the two groups during the investigation. The ESS was significantly improved at the end of week 6 compared with the baseline and the placebo group. Laboratory test results indicated there were no side effects in either group. Conclusion. The findings of LSF treatment have clear clinical effects in patients with sialorrhea and frequent overnight urination. LSF thus appears to be a potential choice as an additional drug that can improve the sialorrhea and frequent overnight urination symptoms of PD patients