The Time Preference of Chinese Tend to be Less Affected by Positive Emotions: As Proved by an Experimental Study

This paper aims at testing whether positive emotions have a different impact on Chinese participants’ time preference choices from American respondents on average, considering that the Chinese people own a different culture background and more inward-oriented national characteristic. The researcher conducted a controlled experiment based on random assignments, and the experiment is specifically adapted for Chinese participants. Further, in order to approach to a more accurate result, the research also determines such effect is influenced, if any, by personality factors such as risk preference. The BDM (Becker-DeGroot-Marschak) and an MPL（Multiple Price List）methods were utilized to gather sufficient data and to ensure accurate measures. This paper indicates that, on average, for a Chinese participant, positive emotion will still reduce their time preference over intertemporal decision regarding to cash payment, but in a smaller amount on average, compared to an American respondent. Also, the result shows that, risk preference does play a role and tend to risk neutral persons have a weaker time-preference, compared to risk-takers and risk-avoiders. Moreover, several other factors, such as the health state, family income, and gender may also have correlation with time preferences. Alternative explanations are proposed at the end. This research may contribute to explain the differences of credit card usages preferences between the Chinese and American consumers and to explicate the reasoning of the Chinese economic miracles in the recent decades

EFFECTS OF LABEL USAGE ON QUESTION LIFECYCLE IN Q&A COMMUNITY

Community question answering (CQA) sites have developed into vast collections of valuable knowledge. Questions, as CQA’s central component, go through several phases after they are posted, which are often referred to as the questions’ lifecycle or questions’ lifespan. Different questions have different lifecycles, which are closely linked to the topics of the questions that can be determined by their attached labels. We conduct an empirical analysis based on the dynamic panel data of a Q&A website and propose a framework for explaining the time sensitivity of topic labels. By applying a Discrete Fourier Transform and a Knee point detection method, we demonstrate the existence of three broad label clusters based on their recurring features and four common question lifecycle patterns. We further prove that the lifecycles of questions in disparate clusters vary significantly. The findings support our hypothesis that questions with more time-sensitive labels are more likely to hit their saturation point sooner than questions with less time-sensitive labels. The research results could be applied for better CQA interface design and more efficient digital resources management

Knowledge pricing structures on MOOC platform – A use case analysis on edX

University courses provided online in form of MOOCs (Massive Open Online Courses) are gaining increased attention, yet their pricing structure is rarely studied. MOOCs can be treated as knowledge products, and MOOC platforms, therefore, become the marketplace for market-participants to trade those products. A functional knowledge market cannot be established without an appropriate and reliable pricing model, but so far, there have only been a very limited number of studies focusing on the pricing strategies in MOOCs. This study fills this gap by providing a systematic price analysis on one of the largest non-for-profit MOOC platforms, edx.org. In doing so, we establish a model to explain the price differences among different courses. This study can act as a well-grounded starting-point for future MOOC-pricing studies and knowledge products\u27 valuation research

Interpreting Distributional Reinforcement Learning: A Regularization Perspective

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we attribute the superiority of distributional RL to its regularization effect in terms of the value distribution information regardless of its expectation. Firstly, by leverage of a variant of the gross error model in robust statistics, we decompose the value distribution into its expectation and the remaining distribution part. As such, the extra benefit of distributional RL compared with expectation-based RL is mainly interpreted as the impact of a \textit{risk-sensitive entropy regularization} within the Neural Fitted Z-Iteration framework. Meanwhile, we establish a bridge between the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL, focusing specifically on actor-critic algorithms. It reveals that distributional RL induces a corrected reward function and thus promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments corroborate the role of the regularization effect of distributional RL and uncover mutual impacts of different entropy regularization. Our research paves a way towards better interpreting the efficacy of distributional RL algorithms, especially through the lens of regularization

Farmers' preferences for sustainable farmland construction — Insights from a discrete choice experiment in China

peer reviewedSustainable farmland construction (SFC) is a priority development strategy used to address the integrated goals of “efficiency output, resource conservation, and environmental friendliness” in agricultural systems. Introducing farmer participation to optimize SFC institutions can improve farmland construction efficiency and address limited construction funding. This study analyzed farmer preferences for participating in SFC through a discrete choice experiment survey of farmers in the project area. This study also evaluated farmers' willingness to pay for different SFC schemes. The findings indicate that farmers prefer constructing mechanized production roads (MPR), leveling farmland and transforming the contiguous farmland (LF and CF), integrated irrigation and fertilizer facilities (IIFF), and moderate improvement in ecological protection facilities. On the basis of the heterogeneity of the farmer preferences, they can be classified as benefits-driven and ecology-driven. In addition, factors such as age, educational level, risk proneness, land transfer, and cultivated land quality can influence the classification of farmer preferences. Farmers' willingness to pay for MPR, LF and CF, ED, IIFF, and moderate improvement in ecological facilities has reached 50–80 % of construction costs, essentially bridging the investment gap under the SF standards set by the central government. Based on the aforementioned, SFC schemes should be designed to consider farmers' needs and regional development requirements. Allocating SF construction costs according to farmers' willingness to pay for various facilities, formulating diverse investment ratios, and forming a coherent government-farmer cooperation mode are recommended. This study introduces policy tools to establish a farmers' participation mechanism in farmland construction, offering valuable insights into institutional reforms in land consolidation projects across other developing countries

Predictors of metabolic monitoring among schizophrenia patients with a new episode of second-generation antipsychotic use in the Veterans Health Administration

<p>Abstract</p> <p>Background</p> <p>To examine the baseline metabolic monitoring (MetMon) for second generation antipsychotics (SGA) among patients with schizophrenia in the Veterans Integrated Service Network (VISN) 16 of the Veterans Health Administration (VHA).</p> <p>Methods</p> <p>VISN16 electronic medical records for 10/2002-08/2005 were used to identify patients with schizophrenia who received a new episode of SGA treatment after 10/2003, in which the VISN 16 baseline MetMon program was implemented. Patients who underwent MetMon (MetMon+: either blood glucose or lipid testing records) were compared with patients who did not (MetMon-), on patient characteristics and resource utilization in the year prior to index treatment episode. A parsimonious logistic regression was used to identify predictors for MetMon+ with adjusted odds ratios (OR) and 95% confidence intervals (CI).</p> <p>Results</p> <p>Out of 4,709 patients, 3,568 (75.8%) underwent the baseline MetMon. Compared with the MetMon- group, the MetMon+ patients were found more likely to have baseline diagnoses or mediations for diabetes (OR [CI]: 2.336 [1.846-2.955]), dyslipidemia (2.439 [2.029-2.932]), and hypertension (1.497 [1.287-1.743]), substance use disorders (1.460 [1.257-1.696]), or to be recorded as obesity (2.052 [1.724-2.443]). Increased likelihood for monitoring were positively associated with number of antipsychotics during the previous year (FGA: 1.434 [1.129-1.821]; SGA: 1.503 [1.290-1.751]). Other significant predictors for monitoring were more augmentation episodes (1.580 [1.145-2.179]), more outpatient visits (1.007 [1.002-1.013])), hospitalization days (1.011 [1.007-1.015]), and longer duration of antipsychotic use (1.001 [1.001-1.001]). Among the MetMon+ group, approximately 38.9% patient had metabolic syndrome.</p> <p>Discussion</p> <p>This wide time window of 180 days, although congruent with the VHA guidelines for the baseline MetMon process, needs to be re-evaluated and narrowed down, so that optimally the monitoring event occurs at the time of receiving a new episode of SGA treatment. Future research will examine whether or not patients prescribed an SGA are assessed for metabolic syndrome following the index episode of antipsychotic therapy, and whether or not such baseline and follow-up monitoring programs in routine care are cost-effective.</p> <p>Conclusion</p> <p>The baseline MetMon has been performed for a majority of the VISN 16 patients with schizophrenia prior to index SGA over the study period. Compared with MetMon- group, MetMon+ patients were more likely to be obese and manifest a more severe illness profile.</p

Nanoparticles insert a three dimensional cavity structure of proteins for function inhibition: The Case of CeO2 and SARS-CoV-2

The selective interaction of nanomaterials with proteins for protein function suppression has been reported. However, whether the nanomaterials could be used to target a three-dimensional (3D) structure of proteins for the consequent function inhibition is not defined. When SARS-CoV-2 binds to the host cell surface ACE2 receptor, the spike protein trimer changes to an "Open State" which forms a 5 nm cavity structure, consequently exposing the receptor binding domain (RBD) for the following viral infection. We found that the 3 nm cerium oxide nanoparticles (CeO2@3) showed a better anti-SARS-CoV-2 effect than 30 nm cerium oxide nanoparticles (CeO2@30). We performed a series of experiments and demonstrated that the CeO2@3 could target the 5 nm spike protein trimer cavity and tightly bind with the RBD, thus effectively blocking the following virus-cell interaction and rendering CeO2@3 as an effective anti-viral agent. As all coronaviruses possess similar spike protein structures as homologous proteins, CeO2@3 can be used as a broad-sperm anti-coronavirus nanodrug candidate by targeting the spike protein 3D structure. This work, for the first time, demonstrated that rationally engineered inorganic nanomaterials can be used to specifically target a 3D structure of a certain protein for function inhibition, thus providing a novel methodological approach and paving the way for future molecular targeting nanodrug candidate design.This study was supported by the National Key R&D Program of China (2021YFE0113000, 2022YFC2303700), the National Natural Science Foundation of China (82261138630, 32171390, 32201154, 51872318, 32371469, 31971322), the Natural Science Foundation of Guangdong Province (2023A0505050123, 2023B1515020104, 2022A1515010549), the International Partnership Program (IPP) of CAS (172644KYSB20210011), Key Collaborative Research Program of the Alliance of International Science Organizations (ANSO-CR-KP-2022-01), the CAS President's International Fellowship Initiative (2020VBA0022), the NanoProCov project of the Austrian Academic Exchange Service (OeAD, grant CN06/2021), and the SmartCERIALS project of the Austrian Research promotion Agency (FFG, grant 890610).Peer reviewe

How Question Features Influence Page Traffic? A Comparative Study on General and Domain-specific Q&As

Online Question and Answering services (Q&As) are becoming increasingly popular among information seekers. Users on these platforms identify their information needs by asking questions and interacting with others. Frequent user activities have led to a significant increase in traffic on Q&As, which motivates researchers to study the driving factors behind page traffic. The differences in the impacts of question quality features on the page traffic of domain-general and domain-specific Q&As remain unclear. To address this research gap, this study compares the traffic-driven effects of question features on general and domain-specific Q&A communities based on a database with more than 160,000 questions and their related 20 textual and non-textual features. Grey Relational Analysis is used to generate ranking lists for the two communities. The results indicate that review features drive the traffic of general Q&As the most, while user features are more significant in driving traffic for domain-specific Q&As