74 results about "Adaptive evolution" patented technology

The invention provides an electrocardiogram signal detection method based on a belief rule base and a deep neural network. The method comprises the following steps: constructing a deep neural networkmodel in accordance with input signals, selecting a network loss function and driving the deep neural network to conduct training in accordance with input data via the network loss function; extracting artificial characteristics via expert knowledge in accordance with the input signals; inputting the artificial characteristics as well as characteristics learned by the deep neural network, so as toconstruct the belief rule base, optimizing parameters of the belief rule base via an improved covariance matrix adaptive evolution strategy, and reducing rules in the belief rule base; and implementing decision fusion on judgment outputs of the deep neural network model and the belief rule base via a fusion method. The electrocardiogram signal detection method provided by the invention, through the full development of advantages of modeling based on expert experience knowledge and discovering complex patterns from mass data based on deep network learning, can automatically judge potential diseases, which may exist, in accordance with electrocardiogram signals of a tested object, so that obtained judgement is more robust and accurate.

The present invention describes the expression cassette for transforming eukaryotic cell which comprises the peptide encoding non-natural sequence of nucleotides with xylose isomerase feature (SEQ ID NO: 1), optionally also comprising other genes of pentose phosphate route. Additionally, it is described the microorganism filed under the number DSM28739, which, in addition to the above-mentioned modifications, also present genetic modifications from adaptive evolution. The described microorganism shows efficient consumption of xylose and conversion of ethanol when compared to its correspondent without said genetic modifications and mutations from evolution. It is also described the process for producing biofuels e biochemicals, preferably ethanol, mainly from the lignocellulosic portion of the vegetal biomass. Biofuels, preferably ethanol, and biochemicals produced by the process of the invention are also described.

The invention discloses a strain in the field of genetic engineering and a method for producing dihydroxyacetone (DHA) by using the strain. The method is characterized in that: modified Gluconobacter oxydans is used to bioconvert glycerin into dihydroxyacetone; sldAB gene expression is added in the modified Gluconobacter oxydans through modification, and mgdh gene and madh gene are removed from the modified Gluconobacter oxydans; and the modified Gluconobacter oxydans is further subject to the adaptive evolution on a medium which uses glucose as the sole carbon source. The genetically engineered strain disclosed herein can grow well on the medium which uses glucose as the sole carbon source, overcomes the defects that wild type Gluconobacter oxydans can only use relatively expensive sorbitol and mannitol as the effective carbon source, and saves the production cost. In addition, compared with a genetically engineered strain GAN cultured on sorbitol, the genetically engineered strain GAN cultured on glucose has higher capability of producing DHA.

The invention provides a complex network cell discovering method under an adaptive evolution bat algorithm for self-media network data. The method includes the following steps that S1, mass data is acquired, a network structure model is established, according to the bat algorithm, a modularity function serves as a fitness function, a coding mode based on characters is adopted, and an initialized population is improved through a label propagation method; S2, the individual speed of the bat algorithm is converted into a mutation probability value, position update is calculated through a cross operator and a mutation operator, therefore, adaptive evolution of the common bat algorithm is achieved, the adaptive evolution bat algorithm is used for dividing a network, and a more accurate network cell division result is obtained. Compared with other intelligent algorithms for cell discovery, the algorithm has the advantages of being high in convergence rate and solution precision, and is more suitable for cell discovery under the large-scale network.

Yeast cell belonging to the genus Saccharomyces having introduced into its genome at least one xylA gene and at least one of each of araA, araB and araD genes and that is capable of consuming a mixed sugar mixture comprising glucose, xylose and arabinose, wherein the cell co-consumes glucose and arabinose, has genetic variations obtained during adaptive evolution and has a specific xylose consumption rate in the presence of glucose that is 0.25 g xylose / h, g DM or more.

The invention discloses an intelligent factory management and control model and a management and control method thereof, and belongs to the field of intelligent manufacturing. According to the method,establishing a hierarchical and modular micro-service-multi-agent architecture parallel to an actual manufacturing system, carrying out fine-grained division, and establishing a production task as adistributed processing micro-service model and an attribute model; dividing a multi-agent model according to the function distinction of production resources; and establishing a service management model of the parallel system based on data and knowledge hybrid driving. And for different manufacturing systems, analyzing the relationship among the task attributes, the micro-services and the multipleagents, and selecting an optimal agent path by using an adaptive evolution algorithm. The micro-service-multi-agent architecture disclosed by the invention has the capability of finely controlling production resources by a multi-agent system, meanwhile, the support of the micro-service architecture on customized business requirements can be realized, and a model basis is provided for solving a self-adaptive scheduling problem in a production process.

The invention provides a spiral antenna design method based on an adaptive evolution optimization algorithm. The spiral antenna design method comprises the following steps: firstly, establishing a structural model of a spiral antenna and an optimization mathematical model corresponding to the structural model; then, optimizing the antenna structure parameters in the optimization mathematical modelby adopting a reinforcement learning-based adaptive evolution optimization algorithm to obtain final optimized antenna structure parameters; and finally, according to the antenna structure parameters, adjusting the structure model to obtain the designed spiral antenna. The beneficial effects of the method are as follows: the parameters and the operation operator of the evolution algorithm are controlled by utilizing reinforcement learning; therefore, the self-adaptive differential evolution algorithm is realized, and a large amount of intermediate result data generated in the operation process of the self-adaptive differential evolution algorithm is used for guiding subsequent parameter setting of the evolution algorithm, so that the optimal antenna structure parameter of the spiral antenna is obtained, the efficiency is high, and the optimization performance is good.

The invention discloses a high temperature-resistant yeast strain for producing pyruvic acid and a use thereof. Pyruvic acid is an important organic acid in metabolism. The high temperature-resistant yeast strain for producing pyruvic acid has wide purposes. The high temperature-resistant yeast strain is high temperature-resistant Torulopsis glabrata TIB-G90 CGMCC No. 5434 and is screened by a high-temperature adaptive evolution method. The high temperature-resistant Torulopsis glabrata TIB-G90 CGMCC No. 5434 can grow well at a temperature of 40-45 DEG C and does not influence an accumulation amount of pyruvic acid in a fermentation broth. The high temperature-resistant yeast strain can reduce a temperature reduction cost in production and has wide industrial application prospects.

The invention relates to the field of biotechnology, in particular to an SNP marker as well as application and a detection method thereof. A great amount of individuals of Chinese Apis mellifera are subjected to whole genome sequencing, so that 21 specific SNP messages in the Chinese Apis mellifera are obtained and the 21 specific SNP messages serve as marker SNP sites for identifying the Chinese Apis mellifera. The special SNP sites in the Chinese Apis mellifera provided by the invention play an important role in the differentiation of the Chinese Apis mellifera with the Apis mellifera in other regions as well as in the adaptive evolution of the Chinese Apis mellifera to local environment.

The invention provides a method for improving tolerance and increasing the degradation rate of chlorella on phenol. The method comprises the steps that firstly, the chlorella in a logarithmic phase is selected and subjected to shaking culture in a culture medium containing the phenol at certain initial density under the conditions of a certain temperature and illumination, wherein set culture time serves as an adaptive evolution cycle; secondly, the chlorella evolved through one cycle is diluted, the chlorella with the same initial density as that in the last step is taken and subjected to shaking culture in another culture medium under the same conditions for the same time; the culture process is repeated until the growing speed of the chlorella and the degradation rate of the chlorella on the phenol tend to be stable. According to the method, the chlorella is subjected to adaptive evolution experiments, the growing speed of the chlorella and the degradation rate on phenol are increased, and the tolerance on phenol is improved; meanwhile, the initial inoculum density is reduced, the time needed for completely degrading the high-concentration phenol is shortened, wastewater treatment cost is reduced, and superior chlorella is provided for treating industrial wastewater.

The invention discloses a dynamic recommendation method based on user interest adaptive evolution, which comprises the following steps: firstly establishing a user interest model of each user, then segmenting user interest vectors according to a time sequence, calculating the similarity among the users, screening out a plurality of to-be-recommended users, and fully considering the characteristicthat the user interest is changed along with time; Based on historical comment records of the target user and the to-be-recommended user, obtaining a recommendation result; constructing a historical interest sequence matrix His; and performing interest evolution iteration for preset times through a PSO (Particle Swarm Optimization) algorithm to obtain a recommendation priority sequence of the target user and each user in the to-be-recommended user set, and finally recommending the users which are screened out from the to-be-recommended user set and ranked in front to the target user. By the adoption of the technical scheme, the stability of the recommendation result is controlled, meanwhile, the interest change of the user is reasonably simulated, and the recommendation accuracy, predictability and comprehensiveness are improved.

The invention discloses a micro-service adaptive evolution method based on RMAE in a cloud computing environment. The micro-service adaptive evolution method comprises the following steps: step 1, constructing a demand interaction model under a micro-service architecture; step 2, providing a key component module required by the RMAE framework, particularly designing RMAE lange for describing the micro-service, and supporting a system to adaptively understand user requirements; step 3, providing an RMAE overall architecture and an operation process; step 4, introducing a DYNAMICO reference model proposed by the Villegas into an RMAE framework, wherein the DYNAMICO provides the structure and behavior characteristics of components required for realizing the SAS system; step 5, further providing a routing delegation method of the RMAE framework for the user demand; and step 6, based on the previous five steps, giving an RMAE cooperation algorithm. A self-adaptive evolution capability of the software system is improved so that the dynamic diversified user requirements are met.

A method for detecting adaptive evolution and structural modeling of rbcL genes of tamaricaceae plants comprises steps as follows: acquisition of rbcL gene sequences of the tamaricaceae plants; pedigree analysis and adaptive site detection of 17 species of the tamaricaceae plants; Rubisco structural simulation and selective site analysis of the tamaricaceae plants. The rbcl genes of tamaricaceae covering three genera and 17 species are taken as objects, a branch-site model is taken as a model, the genes have three positive selection sites in the amino acid level according to detection, and the selective sits are remarkably related with hydrophobicity and entropy for maintaining protein and are also related with the concentration of global CO2. Besides, the adaptability of tamaricaceae plants to the drought environment is related with adaptive evolution of the rbcL genes.

The invention discloses a saccharomyces cerevisiae strain enduring ferulic acid. The saccharomyces cerevisiae strain is a strain which is obtained after adaptive evolution of saccharomyces cerevisiaewith a ferulic acid inhibitor culture medium and has remarkably improved endurance to ferulic acid, the strain is classified and named as Saccharomyces cerevisiae PAT02 and is preserved in the China General Microbiological Culture Collection Center, and the preservation number is CGMCC No.18022. The saccharomyces cerevisiae strain is capable of enduring ferulic acid, and when the strain is appliedto a fermentation system with the ferulic acid, the growth performance and the ferulic acid degradation velocity of the strain are both higher than those of an original strain.

The invention proposes beer yeast capable of producing beer with the low acetaldehyde content, and a domestication method of the beer yeast. The beer yeast is DY-01, a preservation number of the beeryeast is CGMCC No. 19840, and the beer yeast is preserved in the China General Microbiological Culture Collection Center on May 19, 2020. The domestication method mainly comprises the steps such as adaptive evolution treatment, domestication treatment, enzyme labeling determination, and fermentation result evaluation and screening. The domestication method of the invention does not perform drasticmutagenesis on the strain, and does not produce drastic changes to the strain's genome, but an adaptive evolution method is adopted, and an alcohol dehydrogenase repressor and an acetaldehyde dehydrogenase repressor are added into a culture medium, so that activities of both alcohol dehydrogenase (ADH2) and acetaldehyde dehydrogenase (ALDH) of the beer yeast are improved, and the ability that acetaldehyde is metabolized into ethanol and acetic acid is further improved. The beer yeast and domestication method have broad application prospects in the field of beer yeast and a domestication method thereof.

The invention discloses an adaptive evolution method for gluconobacter oxydans efficiently utilizing non-glucose carbon sources and application of the adaptive evolution method, and belongs to the biotechnology and microbial technology field. The method comprises the concrete steps that firstly, the gluconobacter oxydans is cultured in a hydrolysate containing inhibitor lignocellulose, then the cultured gluconobacter oxydans is transferred into a hydrolysate without the inhibitor for further culture, and the gluconobacter oxydans is alternately transferred in such a way until the fermentationperformance of a strain reaches stable. The new strain obtained by acclimatization has obviously improved utilization efficiency of the non-glucose carbon sources, and the strain is named as gluconobacter oxydans RM7, and the collection number of the strain is CGMCC No.14801; and the method is simple and efficient, and has important referential significance in breeding of strains containing an inhibitor lignocellulose system.

The present invention describes a method to generate new chemical entities (NCEs) that have well-defined activities such as, but not limited to, anti-bacterial, antifungal and anthelmintic effects. The NCEs are generated through adaptive evolution of one microbe (the producer) against another organism or cell type (the target). The producer is made to compete against the target over time by co-culturing the two together and serially passing the producer organism until the producer adaptively evolves by synthesizing an NCE(s) that inhibits growth of or kills the target. The molecular structure of the chemical entity (or entities) is then clucidated using tools from genomics, molecular biology, computational biology, analytical chemistry, organic chemistry and related fields.

The invention provides an efficient breeding method of an ultrahigh-concentration beer yeast strain, and belongs to the field of strain breeding. The breeding method comprises the steps of screening original strains, screening primarily screened strains, culturing the adaptability of the primarily screened strains, evaluating and testing the strains and the like. The ultrahigh-concentration beer yeast strain TG-01 with the preservation number of CGMCC (China General Microbiological Culture Collection Center) No.19839 is screened by utilizing the efficient breeding method provided by the invention, and is preserved in China General Microbiological Culture Collection Center on May 26, 2020. According to the method disclosed by the invention, by establishing a method of combining specific stress condition efficient screening and adaptive evolution screening, 2-deoxy-D-glucose, sorbitol and alcohol are combined together to simulate a stress condition higher than an ultrahigh-concentrationfermentation environment of mass production, and the target strain which is insensitive to glucose, resistant to hypertonic and resistant to alcohol can be further screened out directionally and efficiently. The method has a very wide application prospect in the field of strain breeding.

The invention belongs to the technical field of microalgae organisms, and in particular relates to an algae species breeding method of microalgae capable of tolerating high-concentration flue gas. The algae species breeding method refers to the highest emission flue gas concentration allowed by the national standard as an environmental stress condition, adopts a strategy of gradually increasing the flue gas concentration, and enhances the tolerance of the microalgae to the flue gas by activating adaptive evolution, so as to carry out biological fixation on the flue gas. Before the adaptive evolution, the best growth flue gas concentration of the microalgae is about CO2 2%+NOx 0ppm+SO2 0ppm; when the flue gas concentration is CO2 10%+NOx 200ppm+SO2 100ppm, the microalgae cannot survive obviously. By means of the adaptive evolution of the breeding method, the obtained microalgae can tolerate CO2 10%+NOx 200ppm+SO2 100ppm and maintain a more biomass accumulation, and is high in sugar content. Compared with large-scale screening, the breeding method provided by the invention has the advantages of less amount of work, definite purpose and high efficiency.

The invention provides a method for improving tolerance and increasing the degradation rate of chlorella on phenol. The method comprises the steps that firstly, the chlorella in a logarithmic phase is selected and subjected to shaking culture in a culture medium containing the phenol at certain initial density under the conditions of a certain temperature and illumination, wherein set culture time serves as an adaptive evolution cycle; secondly, the chlorella evolved through one cycle is diluted, the chlorella with the same initial density as that in the last step is taken and subjected to shaking culture in another culture medium under the same conditions for the same time; the culture process is repeated until the growing speed of the chlorella and the degradation rate of the chlorella on the phenol tend to be stable. According to the method, the chlorella is subjected to adaptive evolution experiments, the growing speed of the chlorella and the degradation rate on phenol are increased, and the tolerance on phenol is improved; meanwhile, the initial inoculum density is reduced, the time needed for completely degrading the high-concentration phenol is shortened, wastewater treatment cost is reduced, and superior chlorella is provided for treating industrial wastewater.

The invention discloses yeast capable of enduring high-concentration pyruvic acid and low pH and a breeding method thereof, and belongs to the field of strain breeding. In the invention, adaptive evolution technology is adopted, Torulopsis glabrata CCTCC M202019 is subjected to progressive increase of stress intensity of high-concentration sodium pyruvate by a chemostat culture system under the condition of low pH of 4.5, and an evolved strain capable of enduring high-concentration pyruvic acid and low pH stress is obtained. Under the condition that the pH is 5.5, 4.7 and 4.3 respectively, the pyruvic acid yield is 55.8g.l<-1>, 35.97g.l<-1> and 18.5g.l<-1> respectively, which is improved by 15.5 percent (48.3g.l<-1>), 51.8 percent (23.7g.l<-1>) and 83.2 percent (10.1g.l<-1>) respectively compared with that of a starting strain. The invention overcomes the sensitivity of the yeast to the low pH value and the high-concentration pyruvic acid, and has important significance for producing pyruvic acid.

The invention discloses a breeding method of a high-concentration CO2-tolerant microalgae species. The breeding method comprises the following steps: 1) cultivating inoculated microalgae with the presence of less than 40% of CO2 and 20-500 [micro]moles of photons m-2s-1 at 15-40 DEG C under the conditions of a light-dark cycle of (1-24h) / (0-23h) and an apparent ventilation rate of 0.01-1.0cm / s for 2-10 days as an adaptive evolution culture cycle; 2) inoculating the microalgae which is cultivated in the step 1) once again, and cultivating by a new cycle under conditions same with that of the step 1) for 2-10 days; and 3) turning to a next culture cycle as methods prescribed within the step 2) by taking operations prescribed within the step 1) as a culture cycle, and repeating the process by 10-200 culture cycles, so that the CO2-tolerant microalgae species is obtained. The breeding method disclosed by the invention is applicable to the breeding of microalgae bio-fuel species; and meanwhile, the breeding method can also provide a basis for microalgae biological studies.

The invention discloses a scheduling method based on a multi-strategy water wave optimization algorithm. According to an operation mechanism of a water wave optimization algorithm and problem characteristics of three operations in the algorithm, the method mainly comprises the steps that for a continuous optimization problem, the water wave algorithm based on reverse learning and a covariance matrix adaptive evolution method is proposed, and refraction operation of the original water wave algorithm is replaced by updating a population with a covariance matrix to improve population diversity. In the zero idle flow shop scheduling problem, a skewness and variable coefficient combined method is provided for an initialized population to generate an initial sequence population to improve population stability, and a neighborhood search method based on different three operation operation attributes is provided. According to a distributed zero-idle flow shop scheduling problem with an assembly process, machine learning and variable neighborhood search are introduced, so that a proposed algorithm obtains a high-quality solution within limited time through training and reward in an iterative updating process. The method has the advantages of being simple in frame, easy to implement and clear in logic.

Xylitol is an important functional food additive and chemical raw material, and has extensive applications. According to the invention, a high-temperature adaptive evolution method is utilized to screen TIB-Y1020 of high-temperature resistant Candida maltose which is CGMCC (china general microbiological culture collection center) No. 5521 and can grow well at the temperature ranging from 38 DEG C to 42 DEG C without influencing the transformation production of the xylitol. The high-temperature resistance strain can reduce the manufacturing cost increase brought by temperature reduction, thereby having an extensive industrial application prospect.

The invention discloses an adaptive dynamic evolution calculation method for a rough machining process. The method is used for processing complicated mould cavity type parts. According to the method,local inscribed circle movement representation models are firstly established according to the corresponding relationship between the central axis of a feature and the inscribed circle arc centers ofa machining area to guide calculation of a primary rough machining area and a residual area of the feature; then, the type is identified based on the central axis topological structure of the residualarea; then, according to the primary rough machining area and the residual area of the feature, a dynamic evolution model of the rough machining area and the residual area of a part based on the initial process plan is established; finally, an adaptive evolution mechanism of the part rough machining process driven by a process design idea is proposed to eliminate the interference and undercut problems in the rough machining process and ensure the continuity of the rough machining process. According to the method, the adaptive evolution method of the rough machining process is decided according to the type of residuals and the moment of forming the residuals to make up for the deficiencies of an existing method of analysis from the geometrical point of view, the burden of process designersis reduced, and the efficiency of numerical control programming is improved.

The invention discloses an adaptive evolution method of a cloud service system based on an improved wolf pack algorithm. The method can be used for quickly and efficiently completing the adaptive evolution of the cloud service system, and includes the following steps: S1, constructing a candidate subset of the cloud service system; S2, performing artificial wolf coding on the cloud service system;S3, defining a fitness function of the adaptive evolution of the cloud service system; S4, initializing a wolf pack by using information entropy; S5, carrying out migration on the cloud service system; S6, calling the cloud service system; S7, conducting a siege of the cloud service system; S8, updating the position of the leader wolf; S9, updating the wolf pack; S10, judging termination conditions; and S11, outputting a target cloud service system corresponding to the position of the leader wolf.

The invention discloses a self-adaptive evolutionary algorithm for supply and demand matching of reservation type shared parking under an AVP condition, which utilizes the empty driving characteristic of an AVP vehicle, parks the vehicle at different parking spaces at different time periods of shared parking demands, maximally meets the shared parking demands with the least AVP shifting times, and improves the utilization rate of the parking spaces. The method specifically comprises the following steps: on the basis of dividing shared parking supply and demand time, defining pairing, time period pairing and pairing scheme concepts; designing an adaptive optimization strategy of the AVP vehicle for berth adjustment in all time periods by analyzing the condition of berth adjustment of the vehicle in a given time period and the influence of berth change on the number of times of vehicle moving in the pairing scheme; a brand-new algorithm is designed, the calculation time is shorter than 1 second when the algorithm is used for solving a medium-scale matching problem, and the algorithm can be used for actual online parking space distribution of the shared parking platform.

The invention discloses an adaptive evolution method of a cloud service system based on an improved wolf pack algorithm. The method can be used to quickly and efficiently complete the adaptive evolution of a cloud service system, and includes the following steps: S1. Constructing a candidate subset of a cloud service system; S2 .The artificial wolf coding of the cloud service system; S3.Defining the fitness function of the self-adaptive evolution of the cloud service system;S4.Using the information entropy to initialize the wolves;S5.Wandering of the cloud service system;S6.The call of the cloud service system;S7 Siege of the cloud service system; S8. Update the position of the wolf leader; S9. Update the pack of wolves; S10. Judgment of the termination condition; S11. Output the target cloud service system corresponding to the location of the wolf leader.

The invention relates to the biotechnology field, in particular to a gene and application thereof. Whole genome sequencing is adopted for carrying out whole genome re-sequencing on a large number of western black bee individuals, and the special gene of western black bees is obtained. The gene has the important role in differentiation of the western black bees and bees in other regions and the adaptive evolution of the western black bees for the local environment. The Foxo gene or the Ebony gene can be used for identifying the western black bees and other species, and can also be used for researching the species resource diversity of bees and researching cold-resist genes. The research gap of researchers in China in the western black bee field can be filled.