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This study proposes a novel gradient‐based neural network model with an activated variable parameter, named as the activated variable parameter gradient‐based neural network (AVPGNN) model, to solve time‐varying constrained quadratic programming (TVCQP) problems. Compared with the existing models, the AVPGNN model has the following advantages: (1) avoids the matrix inverse, which can significantly reduce the computing complexity; (2) introduces the time‐derivative of the time‐varying parameters in the TVCQP problem by adding an activated variable parameter, enabling the AVPGNN model to achieve a predictive calculation that achieves zero residual error in theory; (3) adopts the activation function to accelerate the convergence rate. To solve the TVCQP problem with the AVPGNN model, the TVCQP problem is transformed into a non‐linear equation with a non‐linear compensation problem function based on the Karush Kuhn Tucker conditions. Then, a variable parameter with an activation function is employed to design the AVPGNN model. The accuracy and convergence rate of the AVPGNN model are rigorously analysed in theory. Furthermore, numerical experiments are also executed to demonstrate the effectiveness and superiority of the proposed model. Moreover, to explore the feasibility of the AVPGNN model, applications to the motion planning of a robotic manipulator and the portfolio selection of marketed securities are illustrated. [ABSTRACT FROM AUTHOR]

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users' emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions. [ABSTRACT FROM AUTHOR]

Introduction: Biofilm formation by Staphylococcus aureus is an important virulence determinant mediated by the polysaccharide intercellular adhesin (PIA) encoded by the ica operon or mediated by surface and extracellular proteins. SarX is a 250-residue two-domain SarA homolog that activates spa transcription. Previous studies demonstrated that Staphylococcus epidermidis SarX protein regulated the transcriptional activity of the agr and ica loci and controlled the biofilm phenotype, primarily by regulating icaADBC transcription and PIA production. Results: In this study, biofilm formation and detachment of the clinical isolate S. aureus SA75 were significantly decreased in the sarX mutant strain. The effect of sarX mutation on S. aureus biofilm formation was related to the production of PIA and not to that of eDNA. Deletion of sarX was associated with a 1.8-fold reduction in spa transcription as determined by RT-PCR analysis, and this reduction could be restored by chromosomal complementation of sarX. Expression of Spa protein was also decreased in the S. aureus sarX mutant. Conclusion: sarX promoted biofilm production of S. aureus that may primarily be mediated through increasing ica operon expression and PIA production. Furthermore, deletion of sarX reduced S. aureus biofilm formation by downregulating spa. [ABSTRACT FROM AUTHOR]

Hypervirulent Klebsiella pneumoniae (hvKP) can cause severe invasive infections in healthy and immunocompromised individuals. It is an important clinical pathogen, with the recent identification of highly invasive community-acquired K. pneumoniae that produce purulent liver abscess. Herein, the frequency, clinical characteristics, and molecular epidemiology of K. pneumoniae isolates obtained from January to October 2016 in Wenzhou, China, were examined. In the present study, 33 isolates (68.8%, 33/48) had a positive string test and were identified as hvKP. Both age and sex were associated with a positive string test (p < 0.05). Univariate analysis revealed that liver abscess (odds ratio = 10.154) was a significant risk factor for hvKP. Among the hvKP isolates, K1 was the most common capsular serotype, followed by K2 (p < 0.05). The prevalence of K1 and K2 was significantly higher in hvKP than non-hvKP isolates. The rates of virulence-associated genes, rmpA, iroB, fib, and hib, were significantly higher for hvKP than for non-hvKP (p < 0.05). In this study, kfuB, ybtA, and wcaG were associated with K1 isolates. ST23 was the predominant hvKP type, which belonged to serotype K1. Pulsed-field gel electrophoresis showed that two clusters had >75% similarity and each accounted for >3 isolates. The homology of the 48 clinical isolates was diverse. In conclusion, hvKP isolates had a high frequency of virulence factors and a wide variety of homologs. These results suggest that, because of the toxicity associated with K. pneumoniae, increased clinical understanding of the disease is important to prevent larger, severe outbreaks. [ABSTRACT FROM AUTHOR]

Staphylococcus epidermidis is a commensal bacterium which widely colonizes in human skin and mucous membrane and rarely causes clinically manifested infections. S. epidermidis surface protein I (SesI) is considered to be the major virulence factor of S. epidermidis infection, but its pathogenesis is not clear. Here, we demonstrated that the prevalence of sesI among S. epidermidis invasive isolates (20.8%, 26/125) was significantly higher than that among colonizing isolates (3.8%, 4/106). The positive rates of biofilm-associated genes (aap, icaA, IS256) and resistance-associated genes mupA among the sesI-positive isolates were significantly higher than those among sesI-negative isolates (p < 0.05). And antimicrobial susceptibility testing showed that the resistance rates of sesI-positive isolates to ciprofloxacin, gentamicin and trimethoprim/sulfamethoxazole were significantly higher than those among sesI-negative isolates. Interestingly, 80.8% (21/26) of sesI-positive isolates belong to ST2 determined by MLST, while ST2 was not found among any of the 99 sesI-negative invasive isolates, indicating that there is a strong association between carriage of sesI and ST2 clone. In order to further study the role of sesI gene in pathogenesis, the sesI gene mutant (S. epidermidis RP62AΔsesI) and complementary expression strain (S. epidermidis RP62AΔsesI-C) were successfully constructed. All experimental data indicated that sesI may promote S. epidermidis to adhere and aggregate, but it had no obvious effect on the mature stage of biofilm formation. Taken together, these results suggest that sesI, along with antimicrobial and other biofilm-associated genes enables S. epidermidis easier for colonization and adhesion and contributes to the spread of S. epidermidis, especially ST2 clone. [ABSTRACT FROM AUTHOR]

As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers' reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Retrieving 3D models by adopting hand-drawn sketches to be the input has turned out to be a popular study topic. Most current methods are based on manually selected features and the best view produced for 3D model calculations. However, there are many problems with these methods such as distortion. For the purpose of dealing with such issues, this paper proposes a novel feature representation method to select the projection view and adapt the maxout network to the extended Siamese network architecture. In addition, the strategy is able to handle the over-fitting issue of convolutional neural networks (CNN) and mitigate the discrepancies between the 3D shape domain and the sketch. A pre-trained AlexNet was used to sketch the extract features. For 3D shapes, multiple 2D views were compiled into compact feature vectors using pre-trained multi-view CNNs. Then the Siamese convolutional neural networks were learnt for transforming the two domains' original characteristics into nonlinear feature space, which mitigated the domain discrepancy and kept the discriminations. Two large data sets were used for experiments, and the experimental results show that the method is superior to the prior art methods in accuracy. [ABSTRACT FROM AUTHOR]

With the rapid development of sharing bicycles, unreasonable dispatching methods are likely to cause a series of issues, such as resource waste and traffic congestion in the city. In this paper, a new dynamic scheduling method is proposed, named Tri-G, so as to solve the above problems. First of all, the whole visualization information of bike stations was built based on a Spatio-Temporal Graph (STG), then Gaussian Mixture Mode (GMM) was used to group individual stations into clusters according to their geographical locations and transition patterns, and the Gradient Boosting Regression Tree (GBRT) algorithm was adopted to predict the number of bikes inflow/outflow at each station in real time. This paper used New York's bicycle commute data to build global STG visualization information to evaluate Tri-G. Finally, it is concluded that Tri-G is superior to the methods in control groups, which can be applied to various geographical scenarios. In addition, this paper also discovered some human mobility patterns as well as some rules, which are helpful for governments to improve urban planning. [ABSTRACT FROM AUTHOR]

Agri-food trade has a profound impact on social stability and sustainable economic development. However, there are several technological problems in current agricultural product transactions. For example, it is almost impossible to improve the efficiency of transactions and maintain market stability. This paper designs a novel Food Trading System with COnsortium blockchaiN (FTSCON) to eliminate information asymmetry in the food trade, in order to establish a sustainable and credible trading environment, the system uses consortium blockchain technology to meet the challenge of different authentications and permissions for different roles in food trade. Meanwhile, we have used the online double auction mechanism to eliminate competition. We also have designed a improved Practical Byzantine Fault Tolerance (iPBFT) algorithm to improve efficiency. In addition, a case study based on a series of data from Shandong Province, China indicate that the FTSCON can achieve profit improvement of merchants. Therefore, the proposed system proved to have high commercial value. [ABSTRACT FROM AUTHOR]

The emergence of carbapenem-resistant Klebsiella pneumoniae (CRKP) has posed a great threat to public health. Among 133 nonduplicated CRKP isolates collected between September 2016 and November 2017 in a tertiary hospital in China, 89 (89/133, 66.9%) and 31 (31/133, 23.3%) were positive for blaNDM-5 and blaKPC-2. Multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE) revealed that ST290 represented the majority of NDM-5 producers (67/89, 75.3%) and PFGE cluster E accounted for 50 (50/67, 74.6%) ST290 isolates from the burn ward, suggesting that K. pneumoniae ST290 clone carrying blaNDM-5 resulted in an outbreak in this hospital. Whole genome sequencing of the plasmid carrying blaNDM-5 showed that the resistance gene blaNDM-5 was located in a ∼49 kb multireplicon plasmid with a peculiar insertion of ISKpn19 of the IncX3-type plasmid. To the best of our knowledge, this is the first report of outbreak of K. pneumoniae ST290 clone carrying blaNDM-5. [ABSTRACT FROM AUTHOR]