Search

Sensor degradation and failure often undermine users’ confidence in adopting a new data-driven decision-making model, especially in risk-sensitive scenarios. A risk assessment framework tailored to classification algorithms is introduced to evaluate the decision-making risks arising from sensor degradation and failures in such scenarios. The framework encompasses various steps, including on-site fault-free data collection, sensor failure data collection, fault data generation, simulated data-driven decision-making, risk identification, quantitative risk assessment, and risk prediction. Leveraging this risk assessment framework, users can evaluate the potential risks of decision errors under the current data collection status. Before model adoption, ranking risk sensitivity to sensor data provides a basis for optimizing data collection. During the use of decision algorithms, considering the expected lifespan of sensors enables the prediction of potential risks the system might face, offering comprehensive information for sensor maintenance. This method has been validated through a case study involving an access control.

Deltamethrin (DM), a Type II pyrethroid, is widely used worldwide in agriculture, household applications, and medicine. Recent studies have shown that DM exerts a variety of toxic effects on organs such as the kidney, heart muscle, and nerves in animals. However, little is known about the effects of high-dose DM on growth and development, and the mechanism of toxicity remains unclear. Using the Caenorhabditis elegans model, we found that high-dose DM caused a delay in nematode development. Our results showed that high-dose DM reduced the activation of the endoplasmic reticulum unfolded protein response (UPRER). Further studies revealed that high-dose DM-induced developmental toxicity and reduced capacity for UPRER activation were associated with the IRE-1/XBP-1 pathway. Our results provide new evidence for the developmental toxicity of DM and new insights into the mechanism of DM toxicity.