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Zooplankton size is a crucial indicator in marine ecosystems, reflecting demographic structure, species diversity and trophic status. Traditional methods for measuring zooplankton size, which involve direct sampling and microscopic analysis, are laborious and time-consuming. In situ imaging systems are useful sampling tools; however, the variation in angles, orientations, and image qualities presented considerable challenges to early machine learning models tasked with measuring sizes.. Our study introduces a novel, efficient, and precise deep learning-based method for zooplankton size measurement. This method employs a deep residual network with an adaptation: replacing the fully connected layer with a convolutional layer. This modification allows for the generation of an accurate predictive heat map for size determination. We validated this automated approach against manual sizing using ImageJ, employing in-situ images from the PlanktonScope. The focus was on three zooplankton groups: copepods, appendicularians, and shrimps. An analysis was conducted on 200 individuals from each of the three groups. Our automated method's performance was closely aligned with the manual process, demonstrating a minimal average discrepancy of just 1.84%. This significant advancement presents a rapid and reliable tool for zooplankton size measurement. By enhancing the capacity for immediate and informed ecosystem-based management decisions, our deep learning-based method addresses previous challenges and opens new avenues for research and monitoring in zooplankton.

Summary: Haematococcus pluvialis, the richest bioresource for natural astaxanthin, encounters a challenge of achieving high growth rate when it comes to mass biomass production. Based on the substrate consumption model and Redfield ratio, rapid algae growth benefits from a proper carbon supply. However, the conventional cultivation schemes with limited carbon dioxide (CO2) supply and inefficient carbon mass transfer could have constrained the carbon capture and growing ability of H. pluvialis. We hypothesize that optimal H. pluvialis growth improvement may be achieved by efficient CO2 supply. Here, in this study, we first identified the carbon consumption of H. pluvialis during exponential growth. Then, a novel microbubble-driven photobioreactor (MDPBR) was designed to satisfy the carbon demand. The novel microbubble photobioreactor improves the CO2 supply by reducing bubble size, significantly elevating the CO2 mass transfer. With only 0.05 L min−1 of gas flow rate, higher cell growth rate (0.49 d−1) has been achieved in MDPBR.

The unicellular microalgae Haematococcus lacustris is an astaxanthin-rich organism that is widely used for commercial cultivation, but its main limitation is its relatively low biomass yield. It is widely accepted that the use of appropriate high light intensity could promote algal growth; however, H. lacustris is very sensitive to high-intensity light, and its growth can be readily arrested by inappropriate illumination. To exploit the growth-promoting benefit of higher light intensities while avoiding growth arrestment, we examined the growth of H. lacustris under high light intensities using various light profiles, including different light colors and light/dark cycles. The results show that light color treatments could not alleviate cellular stress under high light intensities; however, it was interesting to find that red light was favored the most by cells out of all the colors. In terms of the light/dark cycle, the 2/2 h light/dark cycle treatment was shown to lead to the highest specific growth rate, which was 46% higher than that achieved in the control treatment (18 μmol/m2·s light intensity, white light). Therefore, in further experiments, the 2/2 h light/dark cycle with red-light treatment was examined. The results show that this combination enabled a significantly higher specific growth rate, which was 66.5% higher than that achieved in the control treatment (18 μmol/m2·s light intensity, white light). These findings offer new strategies for the optimization of illumination for the growth of H. lacustris and lay the foundations for more reasonable lighting utilization for the cultivation of commercially valuable algal species.

Haematococcus lacustris (H. lacustris), a promising source for astaxanthin production, is a light-sensitive microalga that is prone to sluggish growth when subjected to high levels of irradiance. A challenge in H. lacustris culture is to find a way to efficiently use illumination to maintain vigorous growth and harvest dense biomass, which is essential for further exploiting the potential for astaxanthin production. Previous studies have shown that in addition to illumination, carbon supply in culture is a key limitation for algae growth. Here, we investigated a combined culture approach involving high light intensity (110 μmol m−2s-1) and injection of a 1% (v/v) CO2 air-gas mixture which provided an effective method for H. lacustris culture to achieve both a high growth rate and high cell density. The cell number in the group with high light exposure combined with CO2 enrichment was increased almost four-fold compared with a high light group (110 μmol m−2s-1 without CO2 injection). Additional experiments suggested a possible mechanism in which elevated CO2 increases the electron sink capacity, thus alleviating photoinhibition and oxidative damage. The scaled-up photobioreactor demonstrated much better performance, with growth rates improved by 50–350 %, providing further evidence that this new method can improve algal cell production. Overall, our work provides an efficient way for H. lacustris culture and manufacture, with potential industrial applications.

Barrier coverage in wireless sensor networks plays a key role in intrusion detection system.How to build a robust barrier is a key problem in this research area.In order to fix the weak points caused by nodes’ energy consumption,a reinforcing method was proposed to strengthen the barrier by re-deploying some mobile nodes,in which set-based max-flow algorithm was employed to calculate the number of weak points.The vulnerability degree of each weak point was analyzed so that the mobile nodes can move to these spots to reinforce the whole barrier.Experiment results show that the algorithm can effectively strengthen the barrier,extend the survival time of the barrier and the algorithm complexity is relatively lower.

A periodic CO2 dosing strategy for D. salina 19/30 batch culture is proposed. A model of periodic CO2 dosing including dosing time calculation, dosing interval estimation and final chlorophyll yield prediction was established. In experiments, 5% CO2/95% N2 gas was periodically dosed into D. salina culture. Two different gas dosing flow rates were tested. The corresponding dosing time for each flow rate was estimated via the model (10 min·d−1 for 0.7 L·min−1 and 36 min·d−1 for 0.3 L·min−1). Daily pH measurements showed that the pH of these cultures dosed periodically was always kept between 7.5 and 9.5, which highlights that periodic gas supply can maintain a suitable range of pH for microalgal growth without expensive buffers. Notably the culture dosed for set daily intervals was seen to have similar growth to the culture supplied constantly, but with much higher CO2 capture efficiency (11%–18%) compared to continuous dosing (0.25%). It shows great potential for using periodic gas supply to reduce cost, wasted gas and energy use.

Localization is one of the most important technologies in wireless sensor networks. A lightweight distributed node localization scheme is proposed by considering the limited computational capacity of WSNs. The proposed scheme introduces the virtual force model to determine the location by incremental refinement. Aiming at solving the drifting problem and malicious anchor problem, a location verification algorithm based on the virtual force mode is presented. In addition, an anchor promotion algorithm using the localization reliability model is proposed to re-locate the drifted nodes. Extended simulation experiments indicate that the localization algorithm has relatively high precision and the location verification algorithm has relatively high accuracy. The communication overhead of these algorithms is relative low, and the whole set of reliable localization methods is practical as well as comprehensive.

A periodic CO2 dosing strategy for D. salina 19/30 batch culture is proposed. A model of periodic CO2 dosing including dosing time calculation, dosing interval estimation and final chlorophyll yield prediction was established. In experiments, 5% CO2/95% N2 gas was periodically dosed into D. salina culture. Two different gas dosing flow rates were tested. The corresponding dosing time for each flow rate was estimated via the model (10 min·d-1 for 0.7 L·min-1 and 36 min·d-1 for 0.3 L·min-1). Daily pH measurements showed that the pH of these cultures dosed periodically was always kept between 7.5 and 9.5, which highlights that periodic gas supply can maintain a suitable range of pH for microalgal growth without expensive buffers. Notably the culture dosed for set daily intervals was seen to have similar growth to the culture supplied constantly, but with much higher CO2 capture efficiency (11%-18%) compared to continuous dosing (0.25%). It shows great potential for using periodic gas supply to reduce cost, wasted gas and energy use. [ABSTRACT FROM AUTHOR]