With the rapid development of aquaculture and the gradual decrease in fishery resources, aquatic animal welfare (AAW) has received increasing attention from animal protection organisations, government departments, biologists, fish culturists and consumers. Although environmental enrichment (EE) can potentially benefit AAW, the relevant results are sharply mixed, and the drivers for these discrepancies are largely unclear. In this study, we conducted a series of meta‐analyses to overcome this knowledge gap. We firstly compiled a full data set, consisting of 1171 effect sizes from 147 studies across 82 species and then performed a multilevel mixed‐effects model to evaluate the overall effect size and conduct the subsequent meta‐regression analyses. Overall, our results showed that aquatic animals from a physically enriched environment had significantly improved AAW compared with their counterparts from barren environments. For moderators, specific welfare traits, animal taxa, animal stages, enrichment types and enrichment colours showed strong positive EE effects on AAW. Subsequently, we constructed a fish data set and reran the meta‐analysis, yielding results substantially similar to those obtained using the full data set. Finally, we performed a multi‐model inference to identify the importance ranking of potential moderators. Results showed that enrichment type, animal taxon and welfare category are the best moderators of the direction and magnitude of EE effects on AAW. These results provide insights into the possible drivers of EE effects on aquatic animals with important implications for aquaculture, fisheries, conservation, research and aquarium, providing evidence‐based guidance for future animal welfare theories and practices.

No abstract

For years great emphasis has been placed on Intensive Reading (IR) course. IR has dominated English language curriculum in the teacher-dominated class, students do nothing but just read, listen, write, translate, imitate, memorize. IR has incurred criticisms which point out disadvantages stemming from IR approach. A number of learning strategies, presumably relevant to IR course in Chinese context, are suggested for Chinese teachers of English. They may integrate instruction on the use of the suggested learning strategies with regular classroom activities.

No abstract

As the flight envelope is widening continuously and operational capability is improving sequentially, gas turbine engines are faced with new challenges of increased operation and maintenance requirements for efficiency, reliability, and safety. The measures for security and safety and the need for reducing the life cycle cost make it necessary to develop more accurate and efficient monitoring and diagnostic schemes for the health management of gas turbine components. Sensors along the gas path are one of the components in gas turbines that play a crucial role in turbofan engines owing to their safety criticality. Failures in sensor measurements often result in serious problems affecting flight safety and performance. Therefore, this study aims to develop an online diagnosis system for gas path sensor faults in a turbofan engine. The fault diagnosis system is designed and implemented using a genetic algorithm optimized recursive reduced least squares support vector regression algorithm. This method uses a reduction technique and recursion strategy to obtain a better generalization performance and sparseness, and exploits an improved genetic algorithm to choose the optimal model parameters for improving the training precision. The effectiveness of the sensor fault diagnosis system is then validated through typical fault modes of single and dual sensors.