公共建筑水系统监测体系的构建

我国公共建筑用水现状动态变化范围大,供水设备综合能效低,并存在漏损难发现、设备选型性能过剩等问题。构建公共建筑用水精细化计量监测体系,可通过收集详尽的供用水数据,形成针对不同工况的供水策略,实现节能降耗,以及对建筑供水系统设计及设备选型优化的数据支撑。阐述公共建筑水系统监测体系,包括监测指标、监测实现方法、监测位置、监测条件的设置等,探讨监测数据的应用价值,指出公共建筑水系统监测体系是实现建筑全面精细化节水的重要技术路径。     

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.

     

Optimized decomposition and two-step nonlinear integration model with error correction strategy coupled interval prediction for digital currency price forecast

Digital currency price prediction is vital to both sellers and purchasers. Over these years, decomposition and integration models have been applied more and more to realize the goal of precise prediction, however, many of them tend to neglect the reconstruction of features or the residual series. Altogether, one of the biggest drawbacks of the decomposition and integration framework is the method applied requires manual parameter setting whether it is for decomposition or integration. Still, for the results, they are merely satisfied with the point prediction which brings high uncertainty. In this paper, an optimized feature reconstruction decomposition and two-step nonlinear integration method is proposed which gives consideration to feature reconstruction, nonlinear integration, optimization and interval prediction. The original data series is decomposed through improved variational mode decomposition based approximate entropy feature reconstruction system. Then, improved particle swarm optimization-gated recurrent unit (iPSO-GRU) is utilized in the first and second nonlinear integration part separately. Meanwhile, the residual series is given attention, if it is not a white noise series, the residual will be the input of iPSO-GRU whose result will be added back to the second integration result to form the point prediction result. Based on the point prediction result, interval prediction estimate will be generated as well via maximum likelihood function. This study chooses three kinds of digital currency as cases and the results show that the MAPE values of point prediction are all below 3.5%, and CP values of interval prediction are all 1 with suitable MWP. In addition, compared with other benchmark models, the proposed model shows better performance.

     

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.     

Diffusion and perfusion MRI parameters in the evaluation of placenta accreta spectrum disorders in patients with placenta previa

Magnetic Resonance Materials in Physics, Biology and Medicine - To evaluate the placental function by monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in patients with...     

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.