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This study aimed to assess the feasibility of rumen microorganisms inoculated in a modified pilot-scale system for enhancing biogas production of (1) solely corn straw (CS) and (2) CS with livestock manure under different solid contents and mixture ratios. The biogas liquid was proven to pretreat CS at this scale. The digestion system was started up within 32 days at a retention time of 20 days. The rumen culture was found to have a positive response to the impact on temperature and pH. The optimal solid content of CS was detected to be 3%, resulting in a stable biogas yield of 395 L kg −1 •total solid (TS) −1. A higher biogas yield of 400 L kg −1 •TS −1-420 L kg −1 •TS −1 was achieved at a solid content of 10% organic loading rate (OLR, 4.42 kg volatile solid (VS) m −3 •d −1) in co-digestion systems with CS and livestock manure. The methane content could be maintained at about 60%. Hydrogenotrophic methanogens were dominated by Methanobacterium in the solely CS digestion system, and two methanogenetic pathways, including hydrogenotrophic and acetoclastic methanogens by Methanosarcina and Methanobacterium, co-occurred for methane production during the co-digestion of CS with pig manure (PM). This study indicates that rumen microbes could be utilized in a pilot-scale digestion system and that they greatly promoted the biogas yield.

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Traditional neural objection detection methods use multi-scale features that allow multiple detectors to perform detecting tasks independently and in parallel. At the same time, with the handling of the prior box, the algorithm's ability to deal with scale invariance is enhanced. However, too many prior boxes and independent detectors will increase the computational redundancy of the detection algorithm. In this study, we introduce Dubox, a new one-stage approach that detects the objects without prior box. Working with multi-scale features, the designed dual scale residual unit makes dual scale detectors no longer run independently. The second scale detector learns the residual of the first. Dubox has enhanced the capacity of heuristic-guided that can further enable the first scale detector to maximize the detection of small targets and the second to detect objects that cannot be identified by the first one. Besides, for each scale detector, with the new classification-regression progressive strapped loss makes our process not based on prior boxes. Integrating these strategies, our detection algorithm has achieved excellent performance in terms of speed and accuracy. Extensive experiments on the VOC, COCO object detection benchmark have confirmed the effectiveness of this algorithm.

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