Preparation conditions of immobilization activated sludge were studied with polyvinyl alcohol-sodium alginate as carrier and calcium nitrate as cross linking agent. Physical properties and removal effect of immobilization activated sludge were also investigated, where cross linking agent was boric acid, calcium chloride, calcium nitrate, respectively. The effect of sewage treatment was compared with activated sludge cross optimum linker calcium nitrate and pure activated sludge. The results indicated that the optimum preparation conditions were as following: calcium nitrate concentration was 2%, cross link time was 12 h, cross link solution pH valve was 7.5. The properties of immobilization activated sludge was optimum, where cross linking agent was calcium nitrate; Immobilization activated sludge treat domestic sewage were better than dissociation sludge.
Before modeling basic hypotheses were determined firstly according to realistic status of network information diffusion. Then based on above hypotheses the mode of network information diffusion was analyzed, including its information amount, which provided the quantitative basis to describe the effect of network dimension-force. Then the cycle of network information diffusion was analyzed, and emphasis was put on cycle structure, information attainable stage, information adoptable stage and information secondary transfer stage. On that basis, we determined the principle and method of using the D-value (difference value) of diffusion stage to describe the effect of network dimension-force, and established network information diffusion model. Simulation was performed with practical data, and confirmed the effectiveness of the established model.
Submerged fermentation of green tea with the basidiomycete Mycetinis scorodonius resulted in a pleasant chocolate-like and malty aroma, which could be a promising chocolate flavor alternative to current synthetic aroma mixtures in demand of consumer preferences towards healthy natural and 'clean label' ingredients. To understand the sensorial molecular base on the chocolate-like aroma formation, key aroma compounds of the fermented green tea were elucidated using a direct immersion stir bar sorptive extraction combined with gas chromatography-mass spectrometry-olfactometry (DI-SBSE-GC-MS-O) followed by semi-quantification with internal standard. Fifteen key aroma compounds were determined, the most important of which were dihydroactinidiolide (odor activity value OAV 345), isovaleraldehyde (OAV 79), and coumarin (OAV 24), which were also confirmed by a recombination study. Furthermore, effects of the fermentation parameters (medium volume, light protection, agitation rate, pH, temperature, and aeration) on the aroma profile were investigated in a lab-scale bioreactor at batch fermentation. Variation of the fermentation parameters resulted in similar sensory perception of the broth, where up-scaling in volume evoked longer growth cycles and aeration significantly boosted the concentrations yet added a green note to the overall flavor impression. All findings prove the robustness of the established fermentation process with M. scorodonius for natural chocolate-like flavor production.
Complex objects are usually with multiple labels, and can be represented by multiple modal representations, e.g., the complex articles contain text and image information as well as multiple annotations. Previous methods assume that the homogeneous multi-modal data are consistent, while in real applications, the raw data are disordered, e.g., the article constitutes with variable number of inconsistent text and image instances. Therefore, Multi-modal Multi-instance Multi-label (M3) learning provides a framework for handling such task and has exhibited excellent performance. However, M3 learning is facing two main challenges: 1) how to effectively utilize label correlation and 2) how to take advantage of multi-modal learning to process unlabeled instances. To solve these problems, we first propose a novel Multi-modal Multi-instance Multi-label Deep Network (M3DN), which considers M3 learning in an end-to-end multi-modal deep network and utilizes consistency principle among different modal bag-level predictions. Based on the M3DN, we learn the latent ground label metric with the optimal transport. Moreover, we introduce the extrinsic unlabeled multi-modal multi-instance data, and propose the M3DNS, which considers the instance-level auto-encoder for single modality and modified bag-level optimal transport to strengthen the consistency among modalities. Thereby M3DNS can better predict label and exploit label correlation simultaneously. Experiments on benchmark datasets and real world WKG Game-Hub dataset validate the effectiveness of the proposed methods.
At present, lactic acid bacteria (LAB) fermentation is commonly considered as an effective strategy to remarkably drive the improvement of flavor and nutritional value, and extend shelf-life of fermented foods. In this study, the by-product of tea manufacture, including broken tea segments and tea stalk, was used to produce fermented tea beverages. In addition, the residual components of matrices and bacterial metabolites were measured, as well as the sensory quality of the beverage was evaluated. Subsequently, the determination of monosaccharides, volatile aroma profile, free amino acids, biogenic amines and organic acids, and several functional substances involving γ-aminobutyric acid (GABA), polyphenols, caffeine and L-theanine were carried out. The results revealed that glucose, fructose, mannose and xylose are principal carbon source of Lactobacillus plantarum RLL68 during the fermentation; moreover, the abundance of aromatic substances is varied dramatically and the characteristic flavors of the beverages, particularly fermentation for 48 h and 72 h, are imparted with sweet and fruity odor on the basis of initial nutty and floral odor; Meanwhile, the organoleptic qualities of fermented beverages is also enhanced. Furthermore, the levels of organic acids and GABA are elevated, while the bitter amino acids, as well as some bioactive substances including tea polyphenols and L-theanine are declined; Besides, the caffeine level almost remains constant, and quite low levels of various biogenic amines are also observed. The results of this study will provide the theoretical basis to steer and control the flavor and quality of the fermented tea beverages in the future.
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