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Evolutionary meta-algorithms for pulse shaping of broadband femtosecond duration laser pulses are proposed. The genetic algorithm searching the evolutionary landscape for desired pulse shapes consists of a population of waveforms (genes), each made from two concatenated vectors, specifying phases and magnitudes, respectively, over a range of frequencies. Frequency domain operators such as mutation, twopoint crossover average crossover, polynomial phase mutation, creep and three-point smoothing as well as a time-domain crossover are combined to produce fitter offsprings at each iteration step. The algorithm applies roulette wheel selection; elitists and linear fitness scaling to the gene population. A differential evolution (DE) operator that provides a source of directed mutation and new wavelet operators are proposed. Using properly tuned parameters for DE, the meta-algorithm is used to solve a waveform matching problem. Tuning allows either a greedy directed search near the best known solution or a robust search across the entire parameter space. [PUBLICATION ABSTRACT]

Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.

Radical-polar crossover (RPC) reaction bridges the gap between one- and two-electron reactivities, thus providing an ideal solution to overcome the limitations of both radical and polar chemistry. In this manifold, organic electrochemistry provides a uniquely facile strategy to access a diverse array of radical intermediates, thus broadening the chemical space of the RPC concept. This review highlights the synthetic advances in the field of electrochemical RPC reactions since 2020, with an emphasis on the substrate scope, reaction limitation and mechanistic aspect. The related RPC reactions are categorized as net-oxidative, net-reductive, or redox neutral transformations.

Dense suspensions of swimming bacteria are known to exhibit collective behaviour arising from the interplay of steric and hydrodynamic interactions. Unconfined suspensions exhibit transient, recurring vortices and jets, whereas those confined in circular domains may exhibit order in the form of a spiral vortex. Here we show that confinement into a long and narrow macroscopic 'racetrack' geometry stabilises bacterial motion to form a steady unidirectional circulation. This motion is reproduced in simulations of discrete swimmers that reveal the crucial role that bacteria-driven fluid flows play in the dynamics. In particular, cells close to the channel wall produce strong flows which advect cells in the bulk against their swimming direction. We examine in detail the transition from a disordered state to persistent directed motion as a function of the channel width, and show that the width at the crossover point is comparable to the typical correlation length of swirls seen in the unbounded system. Our results shed light on the mechanisms driving the collective behaviour of bacteria and other active matter systems, and stress the importance of the ubiquitous boundaries found in natural habitats.

Repairing DNA double-strand breaks (DSBs) with homologous chromosomes as templates is the hallmark of meiosis. The critical outcome of meiotic homologous recombination is crossovers, which ensure faithful chromosome segregation and promote genetic diversity of progenies. Crossover patterns are tightly controlled and exhibit three characteristics: obligatory crossover, crossover interference, and crossover homeostasis. Aberrant crossover patterns are the leading cause of infertility, miscarriage, and congenital disease. Crossover recombination occurs in the context of meiotic chromosomes, and it is tightly integrated with and regulated by meiotic chromosome structure both locally and globally. Meiotic chromosomes are organized in a loop-axis architecture. Diverse evidence shows that chromosome axis length determines crossover frequency. Interestingly, short chromosomes show different crossover patterns compared to long chromosomes. A high frequency of human embryos are aneuploid, primarily derived from female meiosis errors. Dramatically increased aneuploidy in older women is the well-known \"maternal age effect.\" However, a high frequency of aneuploidy also occurs in young women, derived from crossover maturation inefficiency in human females. In addition, frequency of human aneuploidy also shows other age-dependent alterations. Here, current advances in the understanding of these issues are reviewed, regulation of crossover patterns by meiotic chromosomes are discussed, and issues that remain to be investigated are suggested.

During meiosis, crossovers (COs) are typically required to ensure faithful chromosomal segregation. Despite the requirement for at least one CO between each pair of chromosomes, closely spaced double COs are usually underrepresented due to a phenomenon called CO interference. Like Mus musculus and Saccharomyces cerevisiae, Arabidopsis thaliana has both interference-sensitive (Class I) and interference-insensitive (Class II) COs. However, the underlying mechanism controlling CO distribution remains largely elusive. Both AtMUS81 and AtFANCD2 promote the formation of Class II CO. Using both AtHEI10 and AtMLH1 immunostaining, two markers of Class I COs, we show that AtFANCD2 but not AtMUS81 is required for normal Class I CO distribution among chromosomes. Depleting AtFANCD2 leads to a CO distribution pattern that is intermediate between that of wild-type and a Poisson distribution. Moreover, in Atfancm, Atfigl1, and Atrmi1 mutants where increased Class II CO frequency has been reported previously, we observe Class I CO distribution patterns that are strikingly similar to Atfancd2. Surprisingly, we found that AtFANCD2 plays opposite roles in regulating CO frequency in Atfancm compared with either in Atfigl1 or Atrmi1. Together, these results reveal that although AtFANCD2, AtFANCM, AtFIGL1, and AtRMI1 regulate Class II CO frequency by distinct mechanisms, they have similar roles in controlling the distribution of Class I COs among chromosomes.

Cloud computing promises elasticity, flexibility and cost-effectiveness to satisfy service level agreement conditions. The cloud service providers should plan and provision the computing resources rapidly to ensure the availability of infrastructure to match the demands with closed proximity. The workload prediction has become critical as it can be helpful in managing the infrastructure effectively. In this paper, we present a workload forecasting framework based on neural network model with supervised learning technique. An improved and adaptive differential evolution algorithm is developed to improve the learning efficiency of predictive model. The algorithm is capable of optimizing the best suitable mutation operator and crossover operator. The prediction accuracy and convergence rate of the learning are observed to be improved due to its adaptive behavior in pattern learning from sampled data. The predictive model’s performance is evaluated on four real-world data traces including Google cluster trace and NASA Kennedy Space Center logs. The results are compared with state-of-the-art methods, and improvements up to 91%, 97% and 97.2% are observed over self-adaptive differential evolution, backpropagation and average-based workload prediction techniques, respectively.

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning. •Binary Horse herd Optimization Algorithm (BHOA) is proposed for Feature selection.•Three transfer functions were investigated in BHOA: S-shape, V-shape, and U-shape.•To improve exploitation, three types of crossover operators are studied.•BHOA with S-shape transfer function using a one-point crossover is the best alternative.•Comparative evaluation against 21 methods reveals the viability of BHOA.

Moiré superlattices 1 , 2 have recently emerged as a platform upon which correlated physics and superconductivity can be studied with unprecedented tunability 3 – 6 . Although correlated effects have been observed in several other moiré systems 7 – 17 , magic-angle twisted bilayer graphene remains the only one in which robust superconductivity has been reproducibly measured 4 – 6 . Here we realize a moiré superconductor in magic-angle twisted trilayer graphene (MATTG) 18 , which has better tunability of its electronic structure and superconducting properties than magic-angle twisted bilayer graphene. Measurements of the Hall effect and quantum oscillations as a function of density and electric field enable us to determine the tunable phase boundaries of the system in the normal metallic state. Zero-magnetic-field resistivity measurements reveal that the existence of superconductivity is intimately connected to the broken-symmetry phase that emerges from two carriers per moiré unit cell. We find that the superconducting phase is suppressed and bounded at the Van Hove singularities that partially surround the broken-symmetry phase, which is difficult to reconcile with weak-coupling Bardeen–Cooper–Schrieffer theory. Moreover, the extensive in situ tunability of our system allows us to reach the ultrastrong-coupling regime, characterized by a Ginzburg–Landau coherence length that reaches the average inter-particle distance, and very large T BKT / T F values, in excess of 0.1 (where T BKT and T F are the Berezinskii–Kosterlitz–Thouless transition and Fermi temperatures, respectively). These observations suggest that MATTG can be electrically tuned close to the crossover to a two-dimensional Bose–Einstein condensate. Our results establish a family of tunable moiré superconductors that have the potential to revolutionize our fundamental understanding of and the applications for strongly coupled superconductivity. Highly tunable moiré superconductivity is observed in magic-angle twisted trilayer graphene, and observations suggest that this superconductor can be tuned close to the crossover to a two-dimensional Bose–Einstein condensate.

Control of the interlayer twist angle in two-dimensional van der Waals (vdW) heterostructures enables one to engineer a quasiperiodic moiré superlattice of tunable length scale1–8. In twisted bilayer graphene, the simple moiré superlattice band description suggests that the electronic bandwidth can be tuned to be comparable to the vdW interlayer interaction at a ‘magic angle’9, exhibiting strongly correlated behaviour. However, the vdW interlayer interaction can also cause significant structural reconstruction at the interface by favouring interlayer commensurability, which competes with the intralayer lattice distortion10–16. Here we report atomic-scale reconstruction in twisted bilayer graphene and its effect on the electronic structure. We find a gradual transition from an incommensurate moiré structure to an array of commensurate domains with soliton boundaries as we decrease the twist angle across the characteristic crossover angle, θc ≈ 1°. In the solitonic regime (θ < θc) where the atomic and electronic reconstruction become significant, a simple moiré band description breaks down and the secondary Dirac bands appear. On applying a transverse electric field, we observe electronic transport along the network of one-dimensional topological channels that surround the alternating triangular gapped domains. Atomic and electronic reconstruction at the vdW interface provide a new pathway to engineer the system with continuous tunability.An investigation of the structural and transport properties of bilayer graphene as a function of the twist angle between the layers reveals atomic-scale reconstruction for twist angles smaller than a critical value.