Background: Controlling excess biomass accumulation and clogging is important for maintaining the performance of gas biofilters and reducing energy consumption. Interruption of bacterial communication (quorum quenching) can modulate gene expression and alter biofilm properties. However, whether the problem of excess biomass accumulation in gas biofilters can be addressed by interrupting bacterial communication remains unknown. Results: In this study, parallel laboratory-scale gas biofilters were operated with Rhodococcus sp. BH4 (QQBF) and without Rhodococcus sp. BH4 (BF) to explore the effects of quorum quenching (QQ) bacteria on biomass accumulation and clogging. QQBF showed lower biomass accumulation (109 kg/m³) and superior operational stability (85–96%) than BF (170 kg/m³; 63–92%) at the end of the operation. Compared to BF, the QQBF biofilm had lower adhesion strength and decreased extracellular polymeric substance production, leading to easier detachment of biomass from filler surface into the leachate. Meanwhile, the relative abundance of quorum sensing (QS)-related species was found to decrease from 67 (BF) to 56% (QQBF). The QS function genes were also found a lower relative abundance in QQBF, compared with BF. Moreover, although both biofilters presented aromatic compounds removal performance, the keystone species in QQBF played an important role in maintaining biofilm stability, while the keystone species in BF exhibited great potential for biofilm formation. Finally, the possible influencing mechanism of Rhodococcus sp. BH4 on biofilm adhesion was demonstrated. Overall, the results of this study achieved excess biomass control while maintaining stable biofiltration performance (without interrupting operation) and greatly promoted the use of QQ technology in bioreactors. Graphical

In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. Air combat engagement database (ACED) is a dedicated database for researching WVR air combat. Utilizing the data in ACED, a Transformer-based BFM decision support scheme is developed to enhance the pilot’s BFM decision making in WVR air combat. The proposed Transformer-based model significantly outperforms the baseline long short-term memory (LSTM)-based model in accuracy. To augment the interpretability of this approach, Shapley Additive Explanation (SHAP) analysis is employed, exhibiting the rationality of the Transformer-based model’s decisions. Furthermore, this study establishes a comprehensive framework for evaluating air combat performance, validated through the utilization of data from ACED. The application of the framework in WVR air combat experiments shows that the Transformer-based model increases the winning rate in combat from 30% to 70%, the average percentage of tactical advantage time from 4.81% to 14.73%, and the average situational advantage time share from 17.83% to 25.19%, which substantially improves air combat performance, thereby validating its effectiveness and applicability in WVR air combat scenarios.