Lipid enhancement in microalgae by temporal phase separation:Use of indigenous sources of nutrients

Microalgae have been recommended as superior candidate for fuel production because of their advantages of higher photosynthetic efficiency, biomass & lipid productivity, and faster growth rate as compared to other energy crops. To meet up all these criteria, we have developed a continuous outdoor micro-algal raceway pond reactor(RPR) and a lab scale indoor tubular photo bioreactor(PBR) for biofuel production. An attempt to utilise indigenous sources of nutrients to improve the economics also revealed that micro-algal culturing can also be used as a mode of nutrient removal and water treatment. The photosynthetic rate and lipid production were enhanced by arresting daytime cell division and promoting night-time cell division. A 50% lipid improvement was observed for the particular algal consortia. Microscopic studies revealed that temporal phase separation could be achieved by adjusting nutrient distribution pattern. To monitor temporal phase separation, it is required to know DNA multiplication model. Quantification of g DNA in RPR confirmed that cell division happens during the night which positively affects the photosynthetic efficiency and lipid productivity of microalgae.

Multi-Stream Temporally Enhanced Network for Video Salient Object Detection

Video salient object detection(VSOD)aims at locating the most attractive objects in a video by exploring the spatial and temporal features.VSOD poses a challenging task in computer vision,as it involves processing complex spatial data that is also influenced by temporal dynamics.Despite the progress made in existing VSOD models,they still struggle in scenes of great background diversity within and between frames.Additionally,they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration.We propose a multi-stream temporal enhanced network(MSTENet)to address these problems.It investigates saliency cues collaboration in the spatial domain with a multi-stream structure to deal with the great background diversity challenge.A straightforward,yet efficient approach for temporal feature extraction is developed to avoid the accumulative noises and reduce time consumption.The distinction between MSTENet and other VSOD methods stems from its incorporation of both foreground supervision and background supervision,facilitating enhanced extraction of collaborative saliency cues.Another notable differentiation is the innovative integration of spatial and temporal features,wherein the temporal module is integrated into the multi-stream structure,enabling comprehensive spatial-temporal interactions within an end-to-end framework.Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on five benchmark datasets while maintaining a real-time speed of 27 fps(Titan XP).Our code and models are available at https://github.com/RuJiaLe/MSTENet.

Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.