糖心视频

A study in the International Journal of Critical Infrastructures a financial early warning system based on an artificial jellyfish algorithm that combines machine learning with principles of the circular economy.

The system could help manufacturers detect financial stress before it escalates into a crisis. Such a system could also offer industrial stability as economic models shift toward sustainability and resource efficiency.

The algorithm, as the headline suggests, is inspired by the adaptive movements of jellyfish and how they respond to changing environments. It is underpinned by a random forest model, which first detects complex patterns in data with high accuracy. The jellyfish algorithm then takes in the data and alternates between exploring possible solutions broadly, like drifting with the oceanic currents, and intensively refining promising areas, which is akin to a jellyfish actively moving within a swarm.

In this way, the jellyfish algorithm can efficiently optimize the predictions of the random forest model and so differentiate between stable and high-risk companies with close to 90% accuracy.

The data fed into the system on which these predictions are based is not limited to standard financial metrics such as profits or debt ratios. It also takes into account financial and operational indicators, such as inventory turnover rate, accounts receivable turnover rate, and the liquidity ratio. The inclusion of the Herfindahl index, a measure of market concentration commonly used in competition policy, also adds a useful perspective to the predictions.

The researchers explain the significance of their new predictive system in the context of China's changing manufacturing sector. The sector is undergoing a transition from a strategy focused on rapid output growth toward one rooted in the circular economy, the researchers point out.

The new economic early warning system emphasizes resource efficiency, waste reduction, and long-term resilience for manufacturers operating on thin margins. Such companies are dangerously exposed to global supply chain fluctuations and debilitating financial crises. The early warning system could help them avert the worst problems by allowing them to adapt in a timely manner and so avoid the cascade of effects on the company, employment, investment, and regional development.