It has long been known that Easterners exhibit more conservative attitudes, cautiousness behaviors, and self-control ability than Westerners; people in Eastern countries show stronger defensive reactions to societal threats than Western people. Are East Asians really risk averters or do some richer underlying preferences drive their behaviors in their decision-making under uncertainty? To answer this question, we examined the risk and ambiguity attitudes of East Asian populations in both gain and loss domains using classical behavioral economic experimental methods. Based on our sample of university students, we found that Easterners are more risk intolerant but more willing to accept ambiguous conditions than their Westerner counterparts in the gain domain. Perhaps surprisingly, Eastern people and Western people have a similar attitude toward risk and ambiguity in the loss domain. The higher level of risk aversion observed among East Asians may be due to the cultural difference between Western countries and Eastern countries. Historically, such risk aversion may make sense, because it would minimize the influence of numerous ecological and historical threats and socio-political practices. Our findings suggest that models that were designed to analyze and predict aggregate behaviors and markets may be ineffective for Eastern populations, and, in the future, it is of significance to develop appropriate representative agent models from the eastern perspective.

A dynamic uncertain causality graph (DUCG) is a probabilistic graphical model for knowledge representation and reasoning, which has been widely used in many areas, such as probabilistic safety assessment, medical diagnosis, and fault diagnosis. However, the convention DUCG model fails to model experts’ knowledge precisely because knowledge parameters were crisp numbers or fuzzy numbers. In reality, domain experts tend to use linguistic terms to express their judgements due to professional limitations and information deficiency. To overcome the shortcomings of DUCGs, this article proposes a new type of DUCG model by integrating Pythagorean uncertain linguistic sets (PULSs) and the evaluation based on the distance from average solution (EDAS) method. In particular, experts express knowledge parameters in the form of the PULSs, which can depict the uncertainty and vagueness of expert knowledge. Furthermore, this model gathers the evaluations of experts on knowledge parameters and handles conflicting opinions among them. Moreover, a reasoning algorithm based on the EDAS method is proposed to improve the reliability and intelligence of expert systems. Lastly, an industrial example concerning the root cause analysis of abnormal aluminum electrolysis cell condition is provided to demonstrate the proposed DUCG model.

Stress-induced hypertension (SIH), driven by reactive oxygen/nitrogen species (ROS/RNS) accumulation and neuroinflammation in the rostral ventrolateral medulla (RVLM), lacks effective targeted therapies. Here, we synthesize ultrasmall manganese-doped carbon dots (Mn@CDs) (~ 2.5 nm) with multienzyme-mimetic activities, including superoxide dismutase, catalase, and peroxidase. Mn@CDs enable effective scavenging of diverse types of ROS/RNS, thereby protecting rat neuroblastoma B104 and microglial GMI-R1 cells from H₂O₂-induced damage. Mn@CDs treatment also suppresses the cell apoptosis (via regulating Bcl-2/Bax and cleaved caspase-3) and reduce pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) of GMI-R1 cells. In SIH rats, injection of Mn@CDs into RVLM normalizes arterial/systolic/diastolic blood pressure and renal sympathetic nerve activity. Mechanistically, Mn@CDs downregulate NOX2/NOX4 within RVLM region and alleviate oxidative stress, thus reversing microglial activation and reducing neuronal apoptosis. This work presents a non-pharmaceutical catalytic strategy based on biocompatible Mn@CDs nanozymes targeting the RVLM ROS-neuroinflammation axis for SIH.