Previous research has found that children can engage in rectification of pre-existing inequality by allocating more resources to individuals and groups of disadvantaged status, but less research has investigated how children address the inequalities using resources of different values, especially when they are linked to group membership (i.e., in-group or out-group member) in the first-party (Study 1) and third-party contexts (Study 2). To address these issues, children aged 5-6 years How children will react to pre-existing inequality, whether to rectify (allocate more to the disadvantaged), distribute equally, or perpetuate (allocate more to the advantaged), is an important issue in children's moral development Olson et al., 2011;. Empirical evidence showed that children as young as 4-year-olds were highly sensitive to the concern of equality and that 6-to 8-year-old children would rather throw away resources than distribute them unequally (Blake & Mcauliffe, 2011;Shaw & Olson, 2012;Wu & Gao, 2018). However, in the context of pre-existing inequality where one person has received more resources than another, the most equitable choice is to rectify inequalities by allocating more resources to the disadvantaged rather than by allocating them strictly equally. It has been found that young children seek to equalize resource distributions between others by allocating more resources to individuals with disadvantaged status, and this rectification of inequality increases with age (
Purpose An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues. Design/methodology/approach A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models. Findings The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity. Research limitations/implications The experimental cases were based on financial data from public companies in North America. Practical implications Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption. Originality/value The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.
A framework combining the Internet of Things (IoT) and blockchain can help achieve system automation and credibility, and the corresponding technologies have been applied in many industries, especially in the area of agricultural product traceability. In particular, IoT devices (radio frequency identification (RFID), geographic information system (GIS), global positioning system (GPS), etc.) can automate the collection of information pertaining to the key aspects of traceability. The data are collected and input to the blockchain system for processing, storage, and query. A distributed, decentralized, and nontamperable blockchain can ensure the security of the data entering the system. However, IoT devices may generate abnormal data in the process of data collection. In this context, it is necessary to ensure the accuracy of the source data of the traceability system. Considering the whole-process traceability chain of agricultural products, this paper analyzes the whole-process information of a tea supply chain from planting to sales, constructs the system architecture and each function, and designs and implements a machine learning- (ML-) blockchain-IoT-based tea credible traceability system (MBITTS). Based on IoT technologies such as radio frequency identification (RFID) sensors, this article proposes a new method that combines blockchain and ML to enhance the accuracy of blockchain source data. In addition, system data storage and indexing methods and scanning and recovery mechanisms are proposed. Compared with the existing agricultural product (tea) traceability system based on blockchain, the introduction of the ML data verification mechanism can ensure the accuracy (up to 99%) of information on the chain. The proposed solution provides a basis to ensure the safety, reliability, and efficiency of agricultural traceability systems.
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