By analyzing four kinds of decision-making problems of remote education, a kind of design and implementation of remote education IDSS based on model database, is discussed. Systems modeling, model algorithm implementation, model storage, management and maintenance of model database technology are discussed.
Abstract Bacterial vaginosis (BV) is a condition with a high short-term cure rate and a high recurrence rate. The formation of Gardnerella vaginalis （GV） biofilm is one of the primary reasons for BV recurrence. This study was the first to explore the impact of Streptococcus agalactiae(group B Streptococcus, GBS), a symbiotic intravaginal bacterium, on G. vaginalis concerning biofilm biomass, biofilm structure, transcriptome, and proteome in a co-culture scenario. The results revealed that the supplementation of GBS to G. vaginalissignificantly increased the biomass in 48-hours dual-species biofilms. As confirmed by qPCR, the differentially expressed gene (DEG) luxS of GBS identified through transcriptome analysis was significantly higher in dual-species biofilm compared to mono-species biofilm. Subsequently, we developed a GBS luxS mutant strain (ΔGBS) and observed significant reductions in ΔGBS biofilms and ΔGBS+GV dual-species biofilms. Upon supplementing with exogenous AI-2 reagents, there was a substantial increase in biofilm biomass noted in GBS, ΔGBS, and GV mono- and dual-biofilms. Furthermore, we found that the expression of two genes related to biofilm formation, murG and GAVG_RS05105, was significantly elevated in GV after receiving AI-2 signals. Collectively, these findings suggest that GBS enhances biofilm formation by luxS/AI-2 in an in vitro co-culture model, potentially offering a new approach for treating RBV.
The traditional microwave excitation with single-pulse-width in microwave-induced thermoacoustic imaging (TAI) technology is difficult to balance the relationship between resolution and imaging depth when imaging biological tissues of different sizes. To alleviate this limitation, we have developed a multi-scale rapid TAI system. By adjusting the microwave pulse width, multi-scale imaging of joints of various sizes is performed, and the joint tissues in the TAI images are analyzed in conjunction with Magnetic Resonance Imaging (MRI) results. The experimental results validate the effectiveness of the multi-pulse-width excitation for multi-scale imaging method.
Remanufacturing cost is a key factor for making decisions on the remanufacturing of used electromechanical devices in the construction sector. Though, remanufacturing costs can vary significantly due to the diversity of quality characteristics, even for the same type of used electromechanical devices. To realize the prediction of the remanufacturing cost for used electromechanical devices relevant to construction, this paper proposes a semi-supervised remanufacturing cost prediction method based on quality characteristics. First, we establish a semi-supervised least squares support vector regression (SLSSVR) model. Then, a novel variable neighborhood search (VNS) algorithm is designed for SLSSVR parameter tuning and optimizing. To verify the performance of the VNS-SLSSVR, we provide three types of simulated examples and conduct a real case study on predicting the remanufacturing cost of used turbine worms. The experimental results show that the proposed methods are of high accuracy and reliability with a limited number of labeled samples and a substantial quantity of unlabeled ones.
Objective To provide an appropriate selection model in purchasing BA detection equipment.Methods Based on integrated weighting method,selection model was established.Integrated weighting method was a combination of AHP and coefficient of variant method.Results Data test by selection model to 10 handheld BA detection equipment showed that weight value and final priority were between the result of subjective selection model and the result of objective model,which was similar to anticipation.Conclusion Integrated selection model reflects subjective requirement as well as objectivity which verifies primarily the rationality of integrated selection model.
Withthe acceleration of the new rural construction, meanwhile, the problem ofbuilding energy consumption is increasingly protruding. As the most importantpart of rural residential construction of retaining structures and its thermalwill play a decisive role on heat preservation andheat insulation of the residence. Therefore, it is imperative to study ruralresidential technology retaining structure in southern Henan province, which toimprove energy consumption structure in rural areas and to improve the ruralresidential energy-saving.
Motivation: While vessel architecture mapping (VAM) is an emerging quantitative MR imaging technique that can characterize cerebral blood vessel microstructure in vivo based on dynamic changes in gradient-echo and spin-echo relaxation rates during contrast agent administration, no study has examined how age-related morphological changes affect VAM parameters. Goal(s): Our goal was to assess region-specific age- and sex-related changes in cerebral microvasculature with VAM. Approach: We applied high-resolution VAM on the healthy contralateral hemisphere of 72 age-matched women and men with stable low-grade brain tumors. Results: We could show that microvascular morphology and aging-related remodeling differ between sexes, particularly in thalamus, insular cortex, and putamen. Impact: This is the first study to characterize age- and sex-specific changes in cerebral microvascular architecture across different anatomical regions using vascular architecture mapping. Results may be of particular importance for future studies on sex-specific diagnostics and prevention of cerebrovascular disease.
Class-imbalanced fault samples are prevalent in real-world industrial scenarios, which makes the need to address class-imbalanced fault diagnosis urgent. The long-tailed distribution and small-sample are the major challenges in this field. To this end, we propose a normalized manifold mixup (NMMU) strategy that not only adapts to fault diagnosis tasks under long-tailed distribution conditions, but also has favorable performance in small-sample scenarios. Specifically, we consider generalization as a key factor to improve network performance. Therefore, we employ hyperbolic tangent function and feature normalization to construct a normalized manifold feature space, and train the network with normalized manifold mixup features to improve the generalization. In addition, due to the high uncertainty of the manifold mixup features in the case of severe underfitting of the network, which introduces massive noise features, we use label smoothing technique to reduce the dependence of the network on the noise features. We perform computational experiments to evaluate the proposed NMMU strategy on bearing fault dataset, charging pile fault dataset and railway track fault dataset, including long-tailed distribution and small-sample conditions. The experimental results show that NMMU outperforms 10 state-of-the-art methods, and achieves more significant performance gains under more challenging conditions. Thus, the NMMU is expected to be an effective strategy to address the weak generalizability of long-tailed distribution and small-sample, and promote the practical application of intelligent fault diagnosis.
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