Image segmentation of activated sludge phase contrast images using phase stretch transform

Real-time monitoring of activated sludge flocs via enhanced mask region-based Convolutional Neural networks

The functionality of activated sludge in wastewater treatment processes depends largely on the structural and microbial composition of its flocs, which are complex assemblages of microorganisms and their secretions. However, monitoring these flocs in real-time and consistently has been challenging due to the lack of suitable technologies and analytical methods. Here we present a laboratory setup capable of capturing instantaneous microscopic images of activated sludge, along with algorithms to interpret these images. To improve floc identification, an advanced Mask R-CNN-based segmentation that integrates a Dual Attention Network (DANet) with an enhanced Feature Pyramid Network (FPN) was used to enhance feature extraction and segmentation accuracy. Additionally, our novel PointRend module meticulously refines the contours of boundaries, significantly minimising pixel inaccuracies. Impressively, our approach achieved a floc detection accuracy of >95%. This development marks a significant advancement in real-time sludge monitoring, offering essential insights for optimising wastewater treatment operations proactively.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Single-Cell Techniques in Environmental Microbiology

Environmental microbiology has been an essential part of environmental research because it provides effective solutions to most pollutants. Hence, there is an interest in investigating microorganism behavior, such as observation, identification, isolation of pollutant degraders, and interactions between microbial species. To comprehensively understand cell heterogeneity, diverse approaches at the single-cell level are demanded. Thus far, the traditional bulk biological tools such as petri dishes are technically challenging for single cells, which could mask the heterogeneity. Single-cell technologies can reveal complex and rare cell populations by detecting heterogeneity among individual cells, which offers advantages of higher resolution, higher throughput, more accurate analysis, etc. Here, we overviewed several single-cell techniques on observation, isolation, and identification from aspects of methods and applications. Microscopic observation, sequencing identification, flow cytometric identification and isolation, Raman spectroscopy-based identification and isolation, and their applications are mainly discussed. Further development on multi-technique integrations at the single-cell level may highly advance the research progress of environmental microbiology, thereby giving more indication in the environmental microbial ecology.

Influence of Aerobic Exercise Load Intensity on Children's Mental Health

As people have become more aware in recent years, aerobic physical exercise plays an important role in alleviating people's mental health problems. However, traditionally, it is believed that young children do not have mental health problems. To help people change this fixed idea, and to study how to correctly adjust the load intensity of aerobic physical exercise under the condition of limited physical fitness of young children, and accurately help children's mental health development, this paper studies the influence of aerobic physical exercise load intensity on children's mental health. In this paper, the detection and tracking technology of video moving objects is used to analyze the data of the research object. This technique includes several commonly used and improved video analysis algorithms. The use of moving target and tracking technology and algorithms can completely extract moving targets, eliminate the phenomenon of void and nothingness, and improve data acquisition and analysis capabilities. The results show that taking part in aerobic exercise with appropriate intensity is beneficial to regulating children's emotional state, reducing their psychological burden, enhancing their negative energy resistance, and arousing their positive participation. Compared with before aerobic exercise, the learning efficiency was improved by 6.36%.

Application of Deep Learning Architectures for Accurate Detection of Olive Tree Flowering Phenophase

The importance of monitoring and modelling the impact of climate change on crop phenology in a given ecosystem is ever-growing. For example, these procedures are useful when planning various processes that are important for plant protection. In order to proactively monitor the olive (Olea europaea)’s phenological response to changing environmental conditions, it is proposed to monitor the olive orchard with moving or stationary cameras, and to apply deep learning algorithms to track the timing of particular phenophases. The experiment conducted for this research showed that hardly perceivable transitions in phenophases can be accurately observed and detected, which is a presupposition for the effective implementation of integrated pest management (IPM). A number of different architectures and feature extraction approaches were compared. Ultimately, using a custom deep network and data augmentation technique during the deployment phase resulted in a fivefold cross-validation classification accuracy of 0.9720 ± 0.0057. This leads to the conclusion that a relatively simple custom network can prove to be the best solution for a specific problem, compared to more complex and very deep architectures.