Applicability of Enchytraeus bulbosus as a model species in ecotoxicology and risk assessment.

Enchytraeus bulbosus is listed in the ISO and OECD standard guidelines as a possible test species of enchytraeid. However, in contrast to other listed species, its applicability in ecotoxicological studies as well as its sensitivity is widely unknown. Therefore, copper, pentachlorophenol(PCP), carbendazim, and chloroacetamide have been investigated by performing two-generation studies with multiple endpoints. Comparable toxicity trends to the existing studies were shown for copper and PCP in the two-generation studies of E. bulbosus. Dose-related abnormal swelling of clitella were found for the first time with PCP and chloroacetamide treatments. Sensitivity comparisons of E. bulbosus to other terrestrial test species were also conducted. E. bulbosus showed high sensitivity, it has comparable sensitivity as other sensitive species of genus Enchytraeus ( E. albidus or E. luxuriosus)to different chemicals, and was more sensitive than E. crypticus and earthworm species ( Eisenia fetida or Eisenia andrei). Combined with the phylogenetic and biological characterization, the results lead to the conclusion that E.bulbosus is a suitable model species in ecotoxicology and the chemical risk assessment (especially in multi-generation) because it has a short generation time, comparatively moderate fecundity, ideal and reasonable sensitivity.

Adaptive Memory Broad Learning System for Unsupervised Time Series Anomaly Detection.

Time series anomaly detection is the process of identifying anomalies within time series data. The primary challenge of this task lies in the necessity for the model to comprehend the characteristics of time-independent and abnormal data patterns. In this study, a novel algorithm called adaptive memory broad learning system (AdaMemBLS) is proposed for time series anomaly detection. This algorithm leverages the rapid inference capabilities of the broad learning algorithm and the memory bank's capacity to differentiate between normal and abnormal data. Furthermore, an incremental algorithm based on multiple data augmentation techniques is introduced and applied to multiple ensemble learners, thereby enhancing the model's effectiveness in learning the characteristics of time series data. To bolster the model's anomaly detection capabilities, a more diverse ensemble approach and a discriminative anomaly score are recommended. Extensive experiments conducted on various real-world datasets demonstrate that the proposed method exhibits superior inference speed and more accurate anomaly detection compared to the existing competitors. A detailed experimental investigation is presented to elucidate the effectiveness of the proposed method and the underlying reasons for its efficacy.