A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

     

Gelling behavior of MgAl2O4 slurries with a common dispersant

MgAl₂O₄ transparent ceramics were shaped by a commonly used polyacrylic acid (PAA), which acted as both dispersant and gelling agent. The spinel slurries were prepared by ball-milling MgAl₂O₄ powder, PAA, and water in an attrition mill. The gelling of slurries happened at room temperature in air atmosphere without any other organic additive. The gelling mechanism was the formation of chelates between Mg²⁺ and carboxyl groups (-COO⁻) of PAA. The frequency-based testing method was applied to investigate the gelling process of the as-prepared slurry. In addition, a novel in situ characterization method based on a modified indentation testing was invented to better understand the strengthening of the wet green body with time and to guide when demolding could be carried out. After sintering, transparent MgAl₂O₄ ceramics with high in-line transmittance were resulted.     

Deep learning-based symbol detection algorithm in IMDD-OOFDM system

In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing ( IMDD-OOFDM ) system, effective channel compensation is a key factor to improve system performance. In order to improve the efficiency of channel compensation, a deep learning-based symbol detection algorithm is proposed in this paper for IMDD-OOFDM system. Firstly, a high-speed data streams symbol synchronization algorithm based on a training sequence is used to ensure accurate symbol synchronization. Then the traditional channel estimation and channel compensation are replaced by an echo state network (ESN) to restore the transmitted signal. Finally, we collect the data from the system experiment and calculate the signal-to-noise ratio (SNR). The analysis of the SNR optimized by the ESN proves that the ESN-based symbol detection algorithm is effective in compensating nonlinear distortion.     

建盏烧制技艺浅析

建盏工艺在我国有着十分悠久的历史,最早可以追溯到宋代,因茶而诞生,在当时是人们进行斗茶最理想的工具,因此有着十分广泛的群众基础,在当时和后世都有着特别广泛的应用。建盏烧制工艺十分独特,其中包含着十分丰富的内涵,也有很多注意事项。基于此,本文着重探讨建盏烧制技艺以及相关内容,希望本文的分析能够为继承和弘扬我国传统文化、传统工艺做出贡献。     

双功能催化剂上四氢萘加氢裂化生成单环芳烃的反应网络研究

Conversion of LCO (light cycle oil) to BTX (benzene, toluene, and xylene) is an economically valuable method for refineries. However, this approach still faces difficulties as the main reactions are not clearly understood. Here we study the detailed hydrocracking pathway of typical reactants, 1-methylnaphthalene and tetralin, through molecular simulations and experiments to improve our understanding of the conversion process of LCO to BTX. Molecular simulations demonstrate that the rate-determining step is the isomerization pathway of six-membered ring to five-membered ring in tetralin as its activation energy (ΔEa) is the highest among all the reactions and the order of ΔEa of reactions is isomerization > ring-opening ≈ side-chain cleavage. The results of experiments show that with the increase in reaction depth, i.e., through a high temperature (350 – 370 °C) and low LHSV (4.5 – 6.0 h−1), isomerization, ring-opening, and side-chain  cleavage reactions occurred, thus improving the selectivity and yield of alkyl aromatics.     

Engineering A-site cation deficiency into LaCoO3 thin sheets for improved microwave absorption performance

Journal of Materials Science - Rationally designing microwave absorption materials with highly efficient and tunable bandwidth is in great demand but remains a huge challenge. In this study,...     

Method of sensitive data mining based on Pan-Bull algebra

Wireless Networks - In order to improve the transmission stability of sensor networks, a sensitive data mining method based on Pan Boolean algebra is proposed. According to the output correctness,...     

An Efficient Approach for Nodal Water Demand Estimation in Large-scale Water Distribution Systems

Water Resources Management - Real-time modeling of a water distribution system (WDS) is a critical step for the control and operation of such systems. The nodal water demand, as the most important...