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Writing artificial logic and dynamic function into complex cellular background to achieve desired phenotypes or improved outputs calls for the development of new genetic tools as well as their innovative use. In this study, we present a sensor-regulator and RNAi-based bifunctional dynamic control network that can provide simultaneous upregulation and downregulation of cellular metabolism for engineered biosynthesis. The promoter-regulator-mediated upregulation function and its transduced downregulation function through RNAi are systematically verified and characterized. We apply this dynamic control network to regulate the phosphoenolpyruvate metabolic node in Escherichia coli and achieve autonomous distribution of carbon flux between its native metabolism and the engineered muconic acid biosynthetic pathway. This allows muconic acid biosynthesis to reach 1.8 g L-1. This study also suggests the circumstances where dynamic control approaches are likely to take effects.

The modification design based on parametric gear model can improve the meshing performance rapidly and effectively. Parametric tooth profile and tooth helix modification of the fully parametric spur gear are carried out in the UG. The finite element simulation is performed by using Hypermesh and Workbench to obtain the gear meshing characteristics under different conditions. The simulation results show that for the same working condition,choosing the proper method of modification and modification parameters can obviously improve the meshing effect. At the same time,through the gear bench test,comparing the gear vibration data before and after modification,it is found that the gear vibration performance of the modified gear are obviously better than that of the unmodified gear. By analyzing the results of simulation and experiments,the modification of tooth axial of drum shape has a better effect on the partial load of tooth flanks and the profile modification has a significant effect on the improvement of load distribution between teeth. The optimum modification method and the amount of modification can quickly obtained by the parametric modification of gear which short the R & D cycle and provide an important theoretical reference for practical engineering.

With the popularization of IoT devices, more and more valuable data is generated. Analyzing and mining big data based on IoT devices has become a hot topic in the academic and industrial circles in recent years. However, due to the lack of necessary detection and protection methods, many IoT devices have serious information security risks. In particular, device hard-coded information is closely related to system encryption and decryption, identity authentication and other functions, which can provide confidentiality protection for core data. Once this information is exploited by malicious attackers, serious consequences such as sensitive information leakage, backdoor attacks, and unauthorized logins will occur. In response to this problem, a multi-type character recognition and positioning scheme was designed and a hard-coded vulnerability detection method in executable files was proposed based on the study of the characteristics of hard-coded vulnerabilities in IoT devices. The proposed method extracted the firmware of IoT devices and filtered all executable files as the source to be analyzed. Then, a solution to identify and locate three types of hard-coded characters was provided. Further, the reachability of the function, where the hard-coded character was located, was analyzed according to the function call relationship. Meanwhile, the instruction heterogeneity was mitigated by an intermediate representation (IR) model. The character and parameter hard-coded values was obtained through a data flow analysis approach. A symbolic execution method was devised to determine the trigger conditions of the hard-coded vulnerabilities, and then the vulnerability detection result was output. On the one hand, the proposed method introduced the method of symbolic execution based on the use of the intermediate representation model, which eliminated the dependency of instruction architecture and reduces the false positive rate of vulnerabilities; On the other hand, this method can integrate characters, files, and cryptographic implementation to realize the different characteristics of three types of hard-coded characters, which increased the coverage of vulnerability detection and improves the versatility of the detection method. The experimental results show that the proposed method can effectively detect three types of hard-coded vulnerabilities of characters, files and cryptographic implementation in various IoT devices, and has good detection accuracy, which can provide certain guidance for the deployment of subsequent security protection technologies. [ABSTRACT FROM AUTHOR]