A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

Magnetorheological elastomer and smartphone enable microfluidic biosensing of foodborne pathogen

Rapid detection of foodborne pathogens is crucial to prevent the outbreaks of foodborne diseases.In this work,we proposed a novel microfluidic biosensor based on magnetorheological elastomer(MRE)and smartphone.First,micropump and microvalves were constructed by deforming the MRE under magnetic actuation and integrated into the microfluidic biosensor for fluidic control.Then,the micropump was used to deliver immune porous gold@platinum nanocatalysts(Au@PtNCs),bacterial sample,and immunomagnetic nanoparticles(MNPs)into a micromixer,where they were mixed,incubated and magnetically separated to obtain the Au@PtNC-bacteria-MNP complexes.After 3,3',5,5'-tetramethylbenzidine and hydrogen peroxide were injected and catalyzed by the Au@PtNCs,smartphone was used to measure the color of the catalysate for quantitative analysis of target bacteria.Under optimal conditions,this biosensor could detect Salmonella typhimurium quantitatively and automatically in 1 h with a linear detection range of 8.0×10^(1) CFU/mL to 8.0×10^(4) CFU/mL and a detection limit of 62 CFU/mL.The microfluidic biosensor was compact in size,simple to use,and efficient for detection,and might be used for in-field screening of foodborne pathogens to prevent food poisoning.

Development of automatic and efficient immuno-separator of foodborne pathogenic bacteria using magnetophoresis and magnetic mixing

In recent years,the outbreaks of foodborne diseases caused by pathogenic bacteria have made considerable economic losses and shown a threat to public health.The key to prevent and control these diseases is fast screening of pathogenic bacteria,which is usually performed with three procedures:sample collection,bacteria separation and bacteria detection.For sample collection,the national standard methods are often employed.For bacteria detection,currently available methods such as Polymerase Chain Reaction and Enzyme Linked Immuno-Sorbent Assay are often used.For bacteria separation,traditional methods such as filtration and centrifugation are not capable to specifically separate the target bacteria.However,food samples are very complicated and require efficient pretreatment for bacteria separation and concentration to achieve accurate and reliable results.The conventional immune magnetic separation method can be used to specifically separate the bacteria,but it still cannot meet the requirements for food sample pretreatment due to very low concentration of target bacteria in food.Therefore,this study developed an automatic and efficient immuno-separator of foodborne bacteria based on magnetophoresis and magnetic mixing,and E.coli O157:H7 was used as research model.A magnetic mixer was applied to facilitate the immunoreaction between the immune magnetic nanoparticles and the target bacteria cells,and a magnetophoretic separation tubing was utilized for magnetophoretic separation of the magnetic bacteria.Under the optimal mixing time of 20 min and the optimal flow rate of 50μL/min,the separation efficiency of E.coli O157:H7 could be more than 90%,showing that the developed immuno-separator is promising to be applied for efficient separation of foodborne bacteria and can be easily extended for separation of other biological targets by using their specific antibodies.