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With improved quality of life, types of waste have become increasingly complex, and waste classification has become a global issue. As a result, the world is facing a waste-classified collection problem. However, the existing research on waste collection has paid little attention to waste classification. In this paper, we consider the pretreatment and classification of waste transfer stations. In recent years, global warming caused by carbon emissions has become a serious problem. Therefore, this work proposes the first dual-objective multi-depot two-echelon green vehicle routing system with various pickups to optimize waste-classified collection based on a mixed-integer programming model. To ensure the efficiency of our developed model, we designed a multiobjective brainstorming optimization algorithm with a novel clustering strategy based on the rank-clustering method and differential mutation. Compared with two classical multiobjective optimization algorithms in various generated test instances and a real-world case, the experimental results showed that the proposed model can help sanitation departments improve the economic and environmental benefits of waste-classified collection, and the proposed algorithm is an excellent optimizer for solving associated problems. [ABSTRACT FROM AUTHOR]

Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model's features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem. [ABSTRACT FROM AUTHOR]

Recently, the solution algorithm for multiobjective scheduling problems has gained more and more concerns from the community of operational research since many real-world scheduling problems usually involve multiple objectives. In this paper, a new evolutionary multiobjective optimization (EMO) algorithm, which is termed as decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy (DMOGA-AMP), is proposed to addressmultiobjective permutation flowshop scheduling problems (PFSPs). In the proposed DMOGA-AMP algorithm, a subproblem decomposition scheme is employed to decompose a multiobjective PFSP into a number of scalar optimization subproblems and then introduce the decomposed subproblems into the running course of algorithm in an adaptive fashion, while a subpopulation construction method is employed to construct multiple subpopulations adaptively to optimize their corresponding subproblems in parallel. In addition, several special strategies on genetic operations, i.e., selection, crossover, mutation and elitism, are also designed to improve the performance of DMOGA-AMP for the investigated problem. Based on a set of test instances of multiobjective PFSP, experiments are carried out to investigate the performance of DMOGA-AMP in comparison with several state-of-the-art EMO algorithms. The experimental results show the better performance of the proposed DMOGA-AMP algorithm in multiobjective flowshop scheduling. [ABSTRACT FROM AUTHOR]

Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-objective evolutionary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the framework of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploitation of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respectively. Multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease 9 (CRISPR/Cas9) system has emerged as a promising technology for specific genome editing in many species. Here we constructed one vector targeting eight agronomic genes in rice using the CRISPR/Cas9 multiplex genome editing system. By subsequent genetic transformation and DNA sequencing, we found that the eight target genes have high mutation efficiencies in the T generation. Both heterozygous and homozygous mutations of all editing genes were obtained in T plants. In addition, homozygous sextuple, septuple, and octuple mutants were identified. As the abundant genotypes in T transgenic plants, various phenotypes related to the editing genes were observed. The findings demonstrate the potential of the CRISPR/Cas9 system for rapid introduction of genetic diversity during crop breeding. [ABSTRACT FROM AUTHOR]

Key message: Moderate overexpression of CYP734A4 improves grain number per main panicle and seed setting rate. Abstract: Brassinosteroid (BR) homeostasis and signaling are crucial for plant growth and development. CYP734A genes encode cytochrome P450 monooxygenases that control the level of bioactive BRs by degrading BRs. However, fertile plants overexpressing CYP734As have not been reported in rice. Here, we isolated a novel semi-dominant mutant brd3-D, in which T-DNA was inserted approximately 4 kb upstream of the CYP734A4 gene (GenBank Accession AB488667), causing its overexpression. The mutant is characterized by dwarfism, small grains, and erect leaves and is less sensitive to brassinolide-induced lamina joint inclination and primary root elongation. However, increased grain number per main panicle and improved seed setting rate were also found in heterozygous brd3-D. To our knowledge, these traits have not been reported in other BR deficient mutants. Quantitative real-time PCR analysis indicated that phenotypic severity of the brd3-D mutant is positively correlated with the CYP734A4 transcription level. In accordance with the increased expression of CYP734A4, a lower castasterone (a rice BR) content was detected in the brd3-D mutants. Knockout of brd3-D by using the CRISPR/Cas9 system rescued the mutation. In addition, transgenic plants overexpressing CYP734A4 with the 35S enhancer mimicked the brd3-D phenotypes, confirming that moderate overexpression of the CYP734A4 gene can improve grain number per main panicle and the seed setting rate in rice. Further studies showed that overexpression of CYP734A4 influences the expressions of multiple genes involved in the BR pathway, and the expression of CYP734A4 is induced by exogenous brassinolide, confirming the negative regulatory role of CYP734A4 in the BR pathway. CYP734A4 might provide a useful gene resource for developing new high-yielding rice varieties. [ABSTRACT FROM AUTHOR]

The same figure was misused for the PCR/RE assay results of Gn1a and GW2 fragments in Figure 3, and the arrows in the graphicsal result of GW2 were not on the tape. The corrected Figure 3 is as follows. [ABSTRACT FROM AUTHOR]

Presents a genetic analysis and identification of a large leaf angles mutant found in rice. Establishment of a large number of T-DNA insertional rice mutant pools; Isolation of the genes from the rice; Participation of WRKY gene in plant developmental processes.