Tectonic features,genetic mechanisms and basin evolution of the eastern Doseo Basin,Chad

The features of the unconformity,fault and tectonic inversion in the eastern Doseo Basin,Chad,were analyzed,and the genetic mechanisms and basin evolution were discussed using seismic and drilling data.The following results are obtained.First,four stratigraphic unconformities,i.e.basement(Tg),Mangara Group(T10),lower Upper Cretaceous(T5)and Cretaceous(T4),four faulting stages,i.e.Barremian extensional faults,Aptian–Coniacian strike-slip faults,Campanian strike-slip faults,and Eocene strike-slip faults,and two tectonic inversions,i.e.Santonian and end of Cretaceous,were developed in the Doseo Basin.Second,the Doseo Basin was an early failed intracontinental passive rift basin transformed by the strike-slip movement and tectonic inversion.The initial rifting between the African and South American plates induced the nearly N-S stretching of the Doseo Basin,giving rise to the formation of the embryonic Doseo rift basin.The nearly E-W strike-slip movement of Borogop(F1)in the western section of the Central African Shear Zone resulted in the gradual cease of the near north-south rifting and long-term strike-slip transformation,forming a dextral transtension fault system with inherited activity but gradually weakened in intensity(interrupted by two tectonic inversions).This fault system was composed of the main shear(F1),R-type shear(F2-F3)and P-type shear(F4-F5)faults,with the strike-slip associated faults as branches.The strike-slip movements of F1 in Cretaceous and Eocene were controlled by the dextral shear opening of the equatorial south Atlantic and rapid expanding of the Indian Ocean,respectively.The combined function of the strike-slip movement of F1 and the convergence between Africa and Eurasia made the Doseo Basin underwent the Santonian dextral transpressional inversion characterized by intensive folding deformation leading to the echelon NE-SW and NNE-SSW nose-shaped uplifts and unconformity(T5)on high parts of the uplifts.The convergence between Africa and Eurasia caused the intensive tectonic inversion of Doseo Basin at the end of Cretaceous manifesting as intensive uplift,denudation and folding deformation,forming the regional unconformity(T4)and superposing a nearly E-W structural configuration on the Santonian structures.Third,the Doseo Basin experienced four evolutional stages with the features of short rifting and long depression,i.e.Barremian rifting,Aptian rifting–depression transition,Albian–Late Cretaceous depression,and Cenozoic extinction,under the control of the tectonic movements between Africa and its peripheral plates.

Genetic Variation of Mitochondrial D-loop Region and Evolution Analysis in Some Chinese Cattle Breeds

The complete mitochondrial D-loop region from 123 individuals in 12 Chinese cattle breeds and two individuals in Germany Yellow cattle breed was sequenced and analyzed. The results were shown as follows： 93 variations and 57 haplotypes were detected, and the average number of nucleotide difference was 22.708, nucleotide diversity （π） was 0.0251 ± 0.00479, and haplotype diversity （Hd） was 0.888 ± 0.026, indicating very high genetic diversity in Chinese cattle breeds. In the Neighbor-Joining tree, 13 cattle breeds were divided into two main clades, Bos taurus and Bos indicus; new Clade Ill had only one sequence from Apeijiaza cattle breed in Tibet, which was similar to that of yak at a higher level than other cattle breeds, proving the introgression of genes from the yak. The proportions of Bos taurus and Bos indicus were 64.3 % and 35.7 % respectively in Xigaze Humped cattle breed, and 50.0% and 50.0% respectively in Apeijiaza cattle breed, which revealed that Tibet cattle included Bos indicus haplotypes. The importance of Yunnan cattle in the origin of Chinese cattle was also confirmed based on their abundant haplotypes. Then, a very special haplotype il discovered in 27 Chinese cattle breeds, including 11 breeds in this study and 16 breeds in the GenBank, played the role of a nucleus in Chinese zebu and was further discussed. At the same time, the construction of Chinese zebu core group based on haplotype il validated the distinct origin of Bos indicus in Tibet, which was different from that of the other cattle breeds with zebu haplotypes in China.

Paleoenvironmental Evolution and Organic Matter Enrichment Genesis of the Late Turonian Black Shale in the Southern Songliao Basin,NE China

The Upper Cretaceous Qingshankou Formation black shales,deposited in the late Turonian(LTB shales),are the main source rocks of the Songliao Basin.The origins of organic matter enrichment of the shales is a contentious subject fuelling many ongoing debates.This study investigates the genesis of the organic matter-rich shale by using molecular geochemistry.The LTB shales can be divided into three sections.The SectionⅠshales were deposited in saline,stratified and anoxic water conditions,which are related to seawater incursion events.At least three episodic and periodic seawater incursion events were recognized during SectionⅠshale deposition.The SectionⅡshales deposited in brackish to fresh and deep lake-level conditions with high primary productivity,which are related to lake-level transgression.The SectionⅢshales were deposited under fresh and slightly oxidized water conditions,which are related to lake-level regression.Two organic matter enrichment models for the LTB shales are identified,that is,the seawater incursion model and the maximum lake-level transgression sedimentation model,which act on different shale sections,both playing significant roles in the enrichment of organic matter.

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

A Genetic-Algorithm-Based Information Evolution Model for Social Networks

the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ‘new' and ‘comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content.A series of experiments tested on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; 2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.

Genetic Evolution Analysis of Actinobacillus pleuropneumoniae

In order to explore the genetic evolution of Actinobacillus pleuropneumoniae(App) in different countries and clarify the relationships among different App in each region, the 16 S rRNA gene of App in the NCBI nucleotide database was analyzed and compared by the bioinformatics method. The phylogenetic tree was constructed after tailoring alignment. The results showed that a stable genetic phenomenon was indicated in the evolutionary process of App. The isolates derived from China were clustered and showed a high degree of conservation. They had a certain genetic relationship with the British and American strains, but had far relationship with the strains from Japan which was a neighboring country of China. The isolates from different countries in the Eurasian continent shared high homology. The isolates of the two regions originated from common ancestors.

Allopolyploidization increases genetic recombination in the ancestral diploid D genome during wheat evolution

Genetic recombination produces new allelic combinations,thereby introducing variation for domestication.Allopolyploidization has increased the evolutionary potential of hexaploid common wheat by conferring the advantages of heterosis and gene redundancy,but whether a relationship exists between allopolyploidization and genetic recombination is currently unknown.To study the impact of allopolyploid ization on genetic recombination in the ancestral D genome of wheat,we generated new synthetic hexaploid wheats by crossing tetraploid Triticum turgidum with multiple diploid Aegilops tauschii accessions,with subsequent chromosome doubling,to simulate the evolutionary hexaploidization process.Using the DArT-Seq approach,we determined the genotypes of two new synthetic hexaploid wheats with their parents,F;plants in a diploid population(2 x,D_(1)D_(1)×D_(2)D_(2))and its new synthetic hexaploid wheatderived population(6 x,AABBD_(1)D_(1)×AABBD_(2)D_(2)).About 11%of detected SNP loci spanning the D genome of Ae.tauschii were eliminated after allohexaploidization,and the degree of segregation distortion was increased in their hexaploid offspring from the F_(1) generation.Based on codominant genotypes,the mean genetic interval length and recombination frequency between pairs of adjacent and linked SNPs on D genome of the hexaploid F;population were 2.3 fold greater than those in the diploid F_(2) population,and the recombination frequency of Ae.tauschii was increased by their hexaploidization with T.turgidum.In conclusion,allopolyploidization increases genetic recombination of the ancestral diploid D genome of wheat,and DNA elimination and increased segregation distortion also occur after allopolyploidization.Increased genetic recombination could have produced more new allelic combinations subject to natural or artificial selection,helping wheat to spread rapidly to become a major global crop and thereby accelerating the evolution of wheat via hexaploidization.

Analysis of Genetic Evolution of Escherichia fergusonii

[Objectives]This study was conducted to explore the genetic evolution of Escherichia fergusonii in different countries and regions,and to clarify the genetic relationship of E.fergusonii in different countries and regions.[Methods]Bioinformatics method and bacterial 16 S rRNA sequencing technology were used to sort out and prune 16 S rRNA genes isolated in laboratory and searched in NCBI database to construct a molecular evolutionary tree for analysis and comparison.[Results]The direction of evolution of E.fergusonii has broken through regions,and there was cross evolution among continents.The origin of E.fergusonii was the Asian continent,and its adaptability to arid climate was not strong.[Conclusions]This study revealed the genetic evolution laws of E.fergusonii in the spread and mutation of livestock and poultry diseases,and provides a theoretical reference for the prevention and treatment of the disease.

Use of Context Blocks in Genetic Programming for Evolution of Robot Morphology

The paper explores applications of genetic programming to co-evolution of morphology and low-level control. In most reasonably difficult tasks, facilitation provided by modularity has proved to be vital for successful application of genetic programming. However, the need for sharing data among nodes in the syntactic tree becomes especially acute when evolving modular programs. It has been shown before that it may be beneficial that modules themselves be node-attached. The paper presents extensions to standard genetic programming （the so-called contexts and context blocks） that allow for straight-forward storage, retrieval, transfer, and modification of data stored in the context of a syntactic tree, and shared by multiple nodes. Framework is thus provided for both： data sharing and node-attached modules. Finally, using context blocks, a genetic algorithm has been embedded within genetic programming to evolve values of constants. In genetic programming evolution of constants has been a long-standing problem. The paper shows how context blocks can be utilized to provide a more granular and flexible approach to their evolution. As shown in previous works, node-attached modules perform favorably when compared with existing approaches. Concerning evolution of context block constants, it is shown here that they too perform favorably when compared with ephemeral constants.

Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

Evolution and conservation genetics of pangolins

Pangolins(Pholidota,Manidae)are classified as an evolutionarily distinct and globally endangered mammal due to their unique morphology(nail-like scales and a myrmecophagous diet)and being the victim of heavy poaching and worldwide trafficking.As such,pangolins serve as a textbook example for studying the special phenotypic evolutionary adaptations and conservation genetics of an endangered species.Recent years have demonstrated significant advancements in the fields of molecular genetics and genomics,which have translated to a series of important research achievements and breakthroughs concerning the evolution and conservation genetics of pangolins.This review comprehensively presents the hitherto advances in phylogeny,adaptive evolution,conservation genetics,and conservation genomics that are related to pangolins,which will provide an ample understanding of their diversity,molecular adaptation mechanisms,and evolutionary potentials.In addition,we highlight the priority of investigating species/population diversity among pangolins and suggest several avenues of research that are highly relevant for future pangolin conservation.

Multi-strategy Differential Evolution Algorithm for QoS Multicast Routing

This paper studies the difference algorithm parameters characteristic of the multicast routing problem, and to compare it with genetic algorithms. The algorithm uses the path of individual coding, combined with the differential cross-choice strategy and operations optimization. Finally, we simulated 30 node networks, and compared the performance of genetic algorithm and differential evolution algorithm. Experimental results show that multi-strategy Differential Evolution algorithm converges faster and better global search ability and stability.

Research on the Karyotype and Evolution of Drosophila melanogaster Species Group

Mitotic metaphase chromosomes of 34 species of Drosophila melanogaster species group were examined. Certain new karyotypes were described for the first time, and their evolutionary and interspecific genetic relationships among 8 subgroups of D. melanogaster species group were analyzed systematically. The results were as follows. The basic karyotype of elegans subgroup was type A. The karyotypes of eugracilis subgroup, melanogaster subgroup, and ficusphila subgroup were all type C. The karyotypes of takahashii subgroup and suzukii subgroup were both type C and type D. The montium subgroup had six kinds of karyotypes types B, C, C＇, D, D＇, and E. The ananassae subgroup had three kinds of karyotypes： types F, G, and H. Thus, the melanogaster species group was classified into five pedigrees based on the diversity of these karyotypes： 1） elegans; 2） eugracilis-melanogasterficusphila; 3） takkahashii-suzukii; 4） montium; 5） ananassae. The above-mentioned results in karyotypic evolution were consistent with those of DNA sequence analysis reported by Yang except for the elegans subgroup and this subgroup was considered as the ancestral subgroup. Karyotype analysis of the same drosophila from different isofemale lines indicated that the same Drosophila from different places showed karyotypic variation which might be due to different geographical environment and evolutionary degree or interaction between the two factors.

Genetic Differentiation of A and B Chromosomes Between Common Wheat and Wild Emmer as Revealed by RFLP Analysis

To investigate chromosome differentiation of genome A and B between common wheat and wild emmer wheat (Triticum turgidum var. dicoccoides (Koern.) Bowden), the authors conducted a RFLP analysis of the two species using 153 genomic, cDNA and chromosome_specific probes. 75.8% of the probes had detected hybridization polymorphism in at least one of the five restriction enzymes. However, the polymorphic probes were unevenly distributed among different homoeologous groups, between different genomes and in different regions of a single chromosome. Homoeologous group 1 possessed the highest level of polymorphism (96.2%), followed by group 6 and 2 (84.6% and 82.1% respectively). In contrast, only 60%-67% of probes of the other four groups was polymorphic. In most groups the number of probes capable of detecting B chromosome polymorphism was slightly higher than that revealing A chromosome difference (totally 51.8% vs 43.1%). In a single chromosome, RFLP was predominant in the distal region (65.1%) and showed a decreasing trend from the proximal (46.2%) to the pericentric (42.4%) regions. The results suggest that there exists a substantial amount of DNA polymorphism between the A and B chromosomes of common wheat and those of wild emmer wheat, indicating that a considerable degree of genetic differentiation has taken place in the A and B genoms of two species during evolution from wild emmer to common wheat. The extent of the genetic differentiation may vary among different homoeologous groups, between A and B chromosomes and in different regions of individual chromosome.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

Evolutionary Trajectory Planning for an Industrial Robot

This paper presents a novel general method for computing optimal motions of an industrial robot manipulator （AdeptOne XL robot） in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm （NSGA-II） and multi-objective differential evolution （MODE）, have been used for the optimization. Two methods （normalized weighting objective functions and average fitness factor） are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.

A New Cultivation-Evolution Technology of Accident Immune Function for a Petrochemical Enterprise

For the purpose of developing an immune function on production accidents in a petrochemical enterprise, a new cultivation-evolution approach of preventive mechanism is suggested by analyzing various factors relating to immune deficiency syndrome and by referring to immunity genetic algorithm and relevant concepts applied in medicine science. Accident-immunity system for highly hazardous petrochemical enterprise, which is made up of its productive system＇s Safety Organ and Safety Organization, is typically an evolution-cultivation progress for immune function, The new B immune cell is generated after several layers＇ screening, clone expanding, receptor editing, organizing in immune system of work accident in petrochemical enterprise. There is a B immune cell with high appetency and a manipulative function chain for accident-immunity. Taking the antigen of accidents in industry as the target function and the immune antibody as the solution, the authors carried out a computation diagram for prediction of appetency between the antigen and antibody.

Niche Genetic Algorithm with Accurate Optimization Performance

Based on crowding mechanism, a novel niche genetic algorithm was proposed which can record evolution- ary direction dynamically during evolution. After evolution, the solutions’s precision can be greatly improved by means of the local searching along the recorded direction. Simulation shows that this algorithm can not only keep population diversity but also find accurate solutions. Although using this method has to take more time compared with the standard GA, it is really worth applying to some cases that have to meet a demand for high solution precision.