Bandwidth Maximization Approach for Displaced Left-Turn Crossovers Coordination under Heterogeneous Traffic Conditions

As one of the UAIDs(unconventional alternative intersection designs),DLTs(displaced left-turn crossovers)have been presented to mitigate traffic congestion.Although,qualitatively and quantitatively isolated UAIDs outperform their conventional counterparts,there is no simplified procedure to consider the DLTs coordination.Hence,this research investigates the coordination of consecutive DLTs under heterogeneous traffic conditions.To achieve the optimal coordination and provide an efficient coordination control,a bandwidth maximization progression approach was used.Seeking the optimal offset for each pair of consecutive intersections to guarantee the green bandwidth waves along the coordinated corridor,a mixed-integer linear program was adopted.The optimization problem was formulated and solved based on the standard branch-and-bound technique.As a real-world study case,data of three typical intersections located in an arterial corridor in Cairo,Egypt was used.PTV-VISSIM as a microsimulation platform was employed to simulate and evaluate the different signal timing plans.However,to represent the heterogeneous traffic characteristics as close as possible to the reality,different simulation parameters were tuned and validated carefully.The results emphasized the undoubted improvement of coordinated DLTs by different operational performance indices.The total travel time,average delay,the number of stops per vehicle were obviously improved.

Genomic features of meiotic crossovers in diploid potato

Meiotic recombination plays an important role in genome evolution and crop improvement.Potato(Solanum tuberosum L.)is the most important tuber crop in the world,but research about meiotic recombination in potato is limited.Here,we resequenced 2163 F2 clones derived from five different genetic backgrounds and identified 41945 meiotic crossovers.Some recombination suppression in euchromatin regions was associated with large structural variants.We also detected five shared crossover hotspots.The number of crossovers in each F2 individual from the accession Upotato 1 varied from 9 to 27,with an average of 15.5,78.25%of which were mapped within 5 kb of their presumed location.We show that 57.1%of the crossovers occurred in gene regions,with poly-A/T,poly-AG,AT-rich,and CCN repeats enriched in the crossover intervals.The recombination rate is positively related with gene density,SNP density,Class II transposon,and negatively related with GC density,repeat sequence density and Class I transposon.This study deepens our understanding of meiotic crossovers in potato and provides useful information for diploid potato breeding.

Temperature regulates negative supercoils to modulate meiotic crossovers and chromosome organization

Crossover recombination is a hallmark of meiosis that holds the paternal and maternal chromosomes(homologs)together for their faithful segregation,while promoting genetic diversity of the progeny.The pattern of crossover is mainly controlled by the architecture of the meiotic chromosomes.Environmental factors,especially temperature,also play an important role in modulating crossovers.However,it is unclear how temperature affects crossovers.Here,we examined the distribution of budding yeast axis components(Red1,Hop1,and Rec8)and the crossover-associated Zip3 foci in detail at different temperatures,and found that both increased and decreased temperatures result in shorter meiotic chromosome axes and more crossovers.Further investigations showed that temperature changes coordinately enhanced the hyperabundant accumulation of Hop1 and Red1 on chromosomes and the number of Zip3 foci.Most importantly,temperature-induced changes in the distribution of axis proteins and Zip3 foci depend on changes in DNA negative supercoils.These results suggest that yeast meiosis senses temperature changes by increasing the level of negative supercoils to increase crossovers and modulate chromosome organization.These findings provide a new perspective on understanding the effect and mechanism of temperature on meiotic recombination and chromosome organization,with important implications for evolution and breeding.

Geostrophic current estimation using altimeter data at ground track crossovers in the northwest Pacific Ocean

Geostrophic current comprises a large portion of the ocean current, which plays an important role in global climate change. Based on classic oceanography, geostrophic current can be derived from pressure gradient. Assuming water density to be constant, we can estimate geostrophic current from Absolute Dynamic Topography （ADT）. In this paper, we use ADT data obtained from multi-satellite altimeters to extract sea surface tilts along- track at crossover points. The calculated tilts along these two tracks can be converted into orthogonal directions and are used to estimate geostrophic current. In northwest Pacific, computed geostrophic current velocities are evaluated with Argos data. In total, 771 pairs of temporally and spatially consistent Argos measurements along with estimated geostrophic velocity datasets are used for validation. In this study, the effect of different cut-off wavelengths of the low pass filter applied to ADT is discussed. Our results show that a cut-offwavelength of 75 km is the most suitable choice for the study area. The estimated geostrophic velocity and the Argos measurement are in good agreement with each other, with a correlation coefficient of 0.867 for zonal component, and 0.734 for meridional one. Furthermore, an empirical relationship between the estimated geostrophic velocity and Argos measurement is derived, providing us a favorable and convenient approach to estimate sea surface flow velocity from the geostrophic velocity derived from altimeter data. The experimental application of the derived method on Kuroshio reveals reasonable results compared with previous studies.

Effect of valence and spin state on ethane dehydrogenation in Fe-S-1 catalyst

Light alkanes non-oxidative dehydrogenation is an attractive non-oil route for olefins production.The alkane dehydrogenation reaction is limited by thermodynamic equilibrium,and the C-H bond cleavage is commonly considered as the rate-determined step.The valence state of metal sites in catalysts will influence the stabilization of the vital intermediate(i.e.,C_(x)H_(y)...M^(δ+)...H)during the C-H bond cleavage process,which in turn affects the catalytic reactivity.Herein,we explicitly investigated the effect of different valence states of framework-Fe in silicate-1 zeolite on ethane dehydrogenation reaction through the combination of experimental and theoretical study.Fe(Ⅱ)-S-1 and Fe(Ⅲ)-S-1 catalysts are successfully synthesized by ligand-assisted in situ crystallization method,In-situ C_(2)H_6-FTIR shows the higher coverage of hydrocarbon intermediates on Fe(Ⅱ)-S-1,Under the same evaluation co nditio n,Fe(Ⅱ)-S-1 exhibits a higher space time yield of ethylene.Density functional theory(DFT)results reveal that the more coordinate-unsaturated and electron-enriched Fe(Ⅱ)sites boost the first C-H bond activation by slight deformation and efficient electron donation with C_(2)H_(5)^(*)species.Remarkably,the second C-H bond cleavage on Fe(Ⅱ)-S-1 undergoes a spin-crossing process from quintet state to triplet state,which involves a two-electro n-two-orbital interaction,further promoting the formation of ethylene.Microkinetic analysis is consistent with the experimental and DFT results.This work could provide methodology for elucidating the effect of metal valence states on catalytic performance as well as offer guidance for designing more efficient Fe-zeolite catalysts.

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Suppression of current-induced membrane discharge of bipolar membranes by regulating ion crossover transport

Bipolar membranes(BPMs)exhibit the unique capability to regulate the operating environment of electrochemical system through the water dissociation-combination processes.However,the industrial utilization of BPMs is limited by instability and serious energy consumption.The current-induced membrane discharge(CIMD)at high-current conditions has a negative influence on the performance of anion-exchange membranes,but the underlying ion transport mechanisms in the BPMs remain unclear.Here,the CIMD-coupled Poisson-Nernst-Planck(PNP)equations are used to explore the ion transport mechanisms in the BPMs for both reverse bias and forward bias at neutral and acid-base conditions.It is demonstrated that the CIMD effect in the reverse-bias mode can be suppressed by enhancing the diffusive transport of salt counter-ions(Na^(+)and Cl^(−))into the BPMs,and that in the forward-bias mode with acid-base electrolytes can be suppressed by matching the transport rate of water counter-ions(H_(3)O^(+)and OH^(−)).Suppressing the CIMD can promote the water dissociation in the reverse-bias mode,as well as overcome the plateau of limiting current density and reduce the interfacial blockage of salt co-ions(Cl^(−))in the anion-exchange layer in the forward-bias mode with acid-base electrolytes.Our work highlights the importance of regulating ion crossover transport on improving the performance of BPMs.

Absence of BCS–BEC crossover in FeSe_(0.45)Te_(0.55) superconductor

In iron-based superconductor Fe(Se,Te), a flat band-like feature near the Fermi level was observed around the Brillouin zone center in the superconducting state. It is under debate whether this is the evidence on the presence of the BCS–BEC[Bardeen–Cooper–Schrieffer(BCS), Bose–Einstein condensation(BEC)] crossover in the superconductor. High-resolution laser-based angle-resolved photoemission measurements are carried out on high quality single crystals of FeSe_(0.45)Te_(0.55) superconductor to address the issue. By employing different polarization geometries, we have resolved and isolated the dyz band and the topological surface band, making it possible to study their superconducting behaviors separately. The dyz band alone does not form a flat band-like feature in the superconducting state and the measured dispersion can be well described by the BCS picture. We find that the flat band-like feature is formed from the combination of the dyz band and the topological surface state band in the superconducting state. These results reveal the origin of the flat band-like feature and rule out the presence of BCS-BEC crossover in Fe(Se,Te) superconductor.

Structures and magnetism of dinuclear Co complexes based on imine derivatives

This study presents the synthesis of three dinuclear cobalt complexes based on three imine derivatives:bis-[4-(2-pyridylmethyleneamino)-phenyl]thioether(L1),bis-[4-(2-pyridylmethyleneamino)-phenyl]ether(L2),and bis-[4-(2-pyridylmethyleneamino)-phenyl]methane(L3).Single-crystal X-ray diffraction analysis reveals that the complexes[Co_(2)(L1)3](ClO_(4))4·2CH_(3)CN(1),[Co_(2)(L2)3](ClO_(4))4·2CH_(3)OH(2),and[Co_(2)(L3)3](ClO_(4))4·2CH_(3)OH(3)all exhibit a dinuclear structure.Magnetic test results show that complex 3 exhibited irreversible SCO behavior induced by loss of solvent at 300 K,with the average Co-N bond length increasing from 0.2139(3)to 0.2153(3)nm.Meanwhile,the desolvated complex 3 exhibited paramagnetic behavior similar to that of complexes 1 and 2.Variable-temperature UV-Vis spectroscopic studies also indicate that complex 3 undergoes a solvent-loss-induced spin-state transition.CCDC:2347354,1(120 K);2347355,2(120 K);2347356,3(120 K);2347357,3(400 K).

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.

Solving the Generalized Traveling Salesman Problem Using Sequential Constructive Crossover Operator in Genetic Algorithm

The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is highly expensive,we will develop genetic algorithms(GAs)to obtain heuristic solutions to the problem.In GAs,as the crossover is a very important process,the crossovermethods proposed for the traditional TSP could be adapted for the GTSP.The sequential constructive crossover(SCX)and three other operators are adapted to use in GAs to solve the GTSP.The effectiveness of GA using SCX is verified on some GTSP Library(GTSPLIB)instances first and then compared against GAs using the other crossover methods.The computational results show the success of the GA using SCX for this problem.Our proposed GA using SCX,and swap mutation could find average solutions whose average percentage of excesses fromthe best-known solutions is between 0.00 and 14.07 for our investigated instances.

Game Theory Optimization via Diverse Genetic Crossover Intelligence

Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.

Genetic Optimization via Diverse Crossover Intelligence

An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.

Genome-wide recombination variation in biparental segregating and reciprocal backcross populations provides information for introgression breeding in Brassica napus

Variation in patterns of recombination in plant genomes provides information about species evolution,genetic diversity and crop improvement. We investigated meiotic crossovers generated in biparental segregating and reciprocal backcross populations of the allopolyploid genome of rapeseed(Brassica napus)(AACC, 2n = 38). A structured set of 1445 intercrossed lines was derived from two homozygous de novo genome-assembled parents that represented the major genetic clusters of semi-winter Chinese and winter European rapeseeds, and was used to increase QTL resolution and achieve genomic reciprocal introgression. A high-density genetic map constructed with 6161 genetic bins and anchored centromere regions was used to establish the pattern of recombination variation in each chromosome. Around 93%of the genome contained crossovers at a mean rate of 3.8 c M Mb^(-1), with the remaining 7% attributed to centromeres or low marker density. Recombination hotspots predominated in the A genome, including two-thirds of those associated with breeding introgression from B. rapa. Genetic background might affect recombination variation. Introgression of genetic diversity from European winter to Chinese semi-winter rapeseed showed an increase in crossover rate under the semi-winter environment. Evidence for an elevated recombination rate having historically contributed to selective trait improvement includes accumulation of favorable alleles for seed oil content on hotspots of chromosome A10. Conversely, strong artificial selection may affect recombination rate variation, as appears to be the case with a coldspot resulting from strong selection for glucosinolate alleles on A09. But the cold region would be promptly reactivated by crossing design indicated by the pedigree analysis. Knowledge of recombination hotspots and coldspots associated with QTL for 22 traits can guide selection strategies for introgression breeding between the two gene pools. These results and rich genomic resources broaden our understanding of recombination behavior in allopolyploids and may advance rapeseed genetic improvement.

An improved genetic algorithm for searching for pollution sources

As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristics of high-speed computer calculation and conditions of the known relationship between the objective function and independent variables. There are several hundred generations of evolvement, but the functional relationship is unknown in pollution source searches. Therefore, the genetic algorithm cannot be used directly. Certain improvements need to be made based on the actual situation, so that the genetic algorithm can adapt to the actual conditions of environmental problems, and can be used in environmental monitoring and environmental quality assessment. Therefore, a series of methods are proposed for the improvement of the genetic algorithm： （1） the initial generation of individual groups should be artificially set and move from lightly polluted areas to heavily polluted areas; （2） intervention measures should be introduced in the competition between individuals; （3） guide individuals should be added; and （4） specific improvement programs should be put forward. Finally, the scientific rigor and rationality of the improved genetic algorithm are proven through an example.

Analysis of the meiosis in the F_1 hybrids of Longiflorum × Asiatic(LA) of lilies(Lilium) using genomic in situ hybridization

Longiflorum and Asiatic lilies of the genus Lilium of the family Liliaceae are two important groups of modem lily cultivars. One of the main trends of lily breeding is to realize introgression between these groups. With cut style pollination and embryo rescue, distant hybrids between the two groups have been obtained. However, the FI hybrids are highly sterile or some of them could produce a small number of 2n gametes, and their BC1 progenies are usually triploids. Dutch lily breeders have selected many cultivars from these BC1 progenies based on their variation. It is presumably suggested that such variation could be caused by intergenomic recombination and abnormal meiosis during gamete formation in F1 hybrids of Longiflorum × Asiatic （LA） hybrids in Lilium. Therefore, the meiotic process of ten F1 LA hybrids was cytologically investigated using genomic in situ hybridization and traditional cytological methods in the present research. The results showed that： at metaphase I, the homoeologous chromosome pairing among different F1 hybrids ranged from 2.0 to 11.4 bivalents formed by homoeologous chromosomes per pollen mother cell （PMC）, and very few multivalents, and even very few bivalents were formed by two chromosomes within one genome rather than homoeologous chromosomes in some PMCs; at anaphase I, all biva- lents were disjoined and most univalents were divided. Both the disjoined bivalents （half-bivalents） and the divided univalents （sister chromatids） moved to the opposite poles, and then formed two groups of chromosomes; because the two resulting half-bivalents retained their axes in the cell undisturbed, many crossover types, including single crossovers, three strand double crossovers, four strand double crossovers, four strand triple crossovers, and four strand multiple crossovers between the non-sister chromatids in the tetrads of bivalents, were clearly inferred by analyzing the breakpoints on the disjoined bivalents. The present investigation not only explained the reason for sterility of the Fl LA hybrids and the variation of their BCx progenies, but also provided a new method to analyze crossover types in other F1 interspecific hybrids as well.

A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Pickup and Delivery

In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.

Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.

A Novel Genetic Algorithm for Stable Multicast Routing in Mobile Ad Hoc Networks

Data transmission among multicast trees is an efficient routing method in mobile ad hoc networks(MANETs). Genetic algorithms(GAs) have found widespread applications in designing multicast trees. This paper proposes a stable quality-of-service(QoS) multicast model for MANETs. The new model ensures the duration time of a link in a multicast tree is always longer than the delay time from the source node. A novel GA is designed to solve our QoS multicast model by introducing a new crossover mechanism called leaf crossover(LC), which outperforms the existing crossover mechanisms in requiring neither global network link information, additional encoding/decoding nor repair procedures. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA. Specifically, the simulation study indicates that our algorithm can obtain a better QoS route with a considerable reduction of execution time as compared with existing GAs.