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We employ a time series of Sentinel‐1 differential radar interferometry data from 2018 to detect the variability in grounding line position of the Fisher, Mellor, and Lambert glaciers, which drain about 47 billion tons of ice per year from East Antarctica. We observe kilometer‐scale tidal migration, two orders of magnitude larger than expected for ice flowing over a hard bed. The migration is not in phase with changes in oceanic tide. In two estuaries underlaid by subglacial channels, we observe two states of migration that switch on and off over time scales of several weeks. The range of vertical motion reveals a water column thickness of 2–20 cm. Such intrusions of seawater over wide grounding zones are not accounted for in physical models. Including them will add vigorous melting of grounded ice that will enhance the sensitivity of glaciers to ocean warming and increase projections of mass loss. Plain Language Summary: Grounding lines (GLs) are assumed to migrate back and forth with changes in oceanic tides based on hydrostatic equilibrium, which induces horizontal migration in the range of a few 100 m, similar to the spatial resolution of ice sheet models. Differential radar interferometry data from the Sentinel‐1 mission, however, reveals that for the Fisher, Mellor, and Lambert glaciers, the migration is two orders of magnitude larger. Such widespread intrusions of seawater will melt grounded ice and affect the regime of basal friction of glaciers and their stability. Physical models in charge of projecting sea level rise assume a fixed GL, with zero melt. We report regions of enhanced migration (10–15 km) along estuaries underlaid by subglacial channels where water gets trapped for several weeks before draining out. These irregular, widespread interactions between ice and seawater will have a strong impact on glacier evolution and projections if implemented in physical models. Key Points: The zone of tidal variability of Amery Ice Shelf grounding line (GL) is two orders of magnitude wider than expected from hydrostatic equilibriumWe detect GL migration uncorrelated with tide up to 15 km upstream, along subglacial troughs, with 2–20 cm thick water intrusionsSeawater intrusion in estuaries has high potential for melting ice in grounding zone and enhance sensitivity of glaciers to ocean warming [ABSTRACT FROM AUTHOR]

Purpose: Studies on titanium implants have shown that the mechanical properties of the parts are affected by the microstructure characteristic derived from the manufacturing process. The properties of different orientations of specimens under the same process parameters will be different, which should be considered in the application of bone implants. This paper aims to understand the influence of microstructure on micro-hardness, wear and corrosion resistance in different orientations. Design/methodology/approach: The authors manufactured titanium parts and carried out micro-hardness, wear tests and electrochemical corrosion of different orientations under the same process conditions. Then, finally studied the evolution mechanism of the microstructure in different orientations and its influence mechanism on wear and corrosion mechanism. Findings: The melting method makes the grains on the surface in XY orientation finer. The wear mechanism of XY orientation is abrasive wear, that of XZ and YZ orientations are adhesive wear. During corrosion, XY orientation forms a stable passivation film earlier. Compared with XZ and YZ orientations, XY orientation has higher micro-hardness, better wear and corrosion resistance. Originality/value: In this paper, the microstructure, wear and corrosion resistance of selective laser melted parts were discussed and the differences in different orientations under the same experimental conditions were discussed. The evolution mechanism of the microstructure in different orientations and its influence mechanism on wear mechanism and corrosion mechanism was studied. The mechanical anisotropy of selective laser melted components was discussed. [ABSTRACT FROM AUTHOR]

Simple and scalable manufacturing of flexible energy storage units with high electrochemical performance is essential for wearable electronic devices. Herein, a carbon cloth (CC) coated with Mo ion homo‐dispersed hydrogel ink is used as a precursor for laser scribing, during which laser‐induced graphene/MoO2 (LIG/MoO2) is in‐situ synthesized on the substrate. By regulating the surface topography and nanoparticle distribution, the as‐prepared LIG/MoO2‐CC electrode shows a network structure composed of multiple core‐shell fibers. Among them, the carbon‐fiber core phase provides strong mechanical stability, and the in‐situ growth shell of LIG/MoO2 ensures high electrochemical performance. Experimentally, the MSCs assembled by interdigital electrodes can deliver an areal capacitance of 81.8 mF cm−2 and areal energy density of 113.7 μWh cm−2 at a power density of 2.5 mW cm−2, placing them among the best performing LIG‐based MSCs. Through the series‐parallel structure design of electrodes, the output voltage and total capacitance of MSCs can be adjusted according to the demands of actual applications, presenting a huge potential for integration with other wearable electronics. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion is employed to determine the solutions of last nondominated front into the next generation group. In the evolution process, global general (GG), concerning the margin information and population density, is selected as the suitable optimality criterion of evaluating the performance of solutions. Application of the new p-MORSGA on several multiobjective benchmark functions shows a marked improvement in performance over the modified classical MOEAs with such criterion. Finally, the proposed p-MORSGA is applied to solve two real-world problems, multiobjective portfolio optimization problems (MOPOPs) and multiobjective optimal power flow (OPF) problems. The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems. [ABSTRACT FROM AUTHOR]

The Pareto dominance-based evolutionary algorithms can effectively address multiobjective optimization problems (MOPs). However, when dealing with many-objective optimization problems with more than three objectives (MaOPs), the Pareto dominance relationships cannot effectively distinguish the nondominated solutions in high-dimensional spaces. With the increase of the number of objectives, the proportion of dominance-resistant solutions (DRSs) in the population rapidly increases, which leads to insufficient selection pressure. In this paper, to address the challenges on MaOPs, a knee point-driven many-objective evolutionary algorithm with adaptive switching mechanism (KPEA) is proposed. In KPEA, the knee points determined by an adaptive strategy are introduced for not only mating selection but also environmental selection, which increases the probability of generating excellent offspring. In addition, to remove dominance-resistant solutions (DRSs) in the population, an interquartile range method is adopted, which enhances the selection pressure. Moreover, a novel adaptive switching mechanism between angle-based selection and penalty for selecting solutions is proposed, which is aimed at achieving a balance between convergence and diversity. To validate the performance of KPEA, it is compared with five state-of-the-art many-objective evolutionary algorithms. All algorithms are evaluated on 20 benchmark problems, i.e., WFG1-9, MaF1, and MaF4-13 with 3, 5, 8, and 10 objectives. The experimental results demonstrate that KPEA outperforms the compared algorithms in terms of HV and IGD in most of the test instances. [ABSTRACT FROM AUTHOR]

The effects of the applied field, cell size, and cutting area on the “seed” induced magnetic reversal of a cobalt element array have been studied by a stochastic dynamic micromagnetics code using the Laudau–Lifshitz–Gilbert equation. Three magnetic reversal mechanisms under different magnitudes of the applied field have been investigated by examining the energy profiles. To minimize the effect of the thermal fluctuations on the switching time, an applied field with magnitude around 0.7 or 0.8 T and an element array with cutting area less than 10 nm×10 nm are required. By using the smaller cellsize, the switching time and the storage density of the element array can be improved. A sinusoidal applied field with a period of 0.1 ns was used to generate a single switching event. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Terrestrial plants have evolved remarkable adaptability that enables them to sense environmental stimuli and use this information as a basis for governing their growth orientation and root system development. In this paper, we explain the foraging behaviors of plant root and develop simulation models based on the principles of adaptation processes that view root growing as optimization. This provides us with new methods for global optimization. Accordingly a novel bioinspired optimizer, namely the root system growth algorithm (RSGA), is proposed, which adopts the root foraging, memory and communication and auxin-regulated mechanism of the root system. Then RSGA is benchmarked against several state-of-the-art reference algorithms on a suit of CEC2014 functions. Experimental results show that RSGA can obtain satisfactory performances on several benchmarks in terms of accuracy, robustness and convergence speed. Moreover, a comprehensive simulation is conducted to investigate the explicit adaptability of root system in RSGA. That is, in order to be able to climb noisy gradients in nutrients in soil, the foraging behaviors of root system are social and cooperative that is analogous to animal foraging behaviors. [ABSTRACT FROM AUTHOR]

The Cu65Fe35 (wt.%) composite coatings with a liquid miscibility gap were produced on the different substrates by laser induction hybrid cladding (LIHC) to investigate the effect of substrate types on the microstructure and properties. The microstructure of composite coating on Fe-substrate is characterized by Cu-rich particles embedded in Fe-rich layer and Fe-rich particles dispersed in Cu-rich layer, resulting in Cu p /Fe-Fe p /Cu “duplex-layer structure”. When Cu-substrate is adopted, the interlayer is Fe-rich layer where Cu-rich particles are distributed, and is enveloped by the upper and lower Cu-rich layers where Fe-rich particles are embedded dispersedly, resulting in Fe p /Cu-Cu p /Fe-Fe p /Cu “sandwich-layer structure”. Moreover, the composite coating on Fe-substrate presents the characteristic of “duplex-microhardness”, which is composed of Fe-rich layer with higher microhardness and Cu-rich layer with lower microhardness. The composite coating on Cu-substrate presents the characteristic of “sandwich-microhardness”, which is composed of Fe-rich interlayer with higher microhardness, lower and upper Cu-rich layer with lower microhardness. With increasing distance from the interface of coating/substrate, the microhardness of heat-affected-zone (HAZ) in Fe-substrate decreases, while that of HAZ in Cu-substrate decreases first and then increases. The electrochemical resistance of composite coating on Fe-substrate is higher than that of composite coating on Cu-substrate, Brass, and middle carbon steel (MCS). [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms. [ABSTRACT FROM PUBLISHER]

Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. The survival wisdom of plants, as another species of biology, has been neglected to some extent even though they have evolved for a longer period of time. This paper presents a new plant-inspired algorithm which is called root growth optimizer (RGO). RGO simulates the iterative growth behaviors of plant roots to optimize continuous space search. In growing process, main roots and lateral roots, classified by fitness values, implement different strategies. Main roots carry out exploitation tasks by self-similar propagation in relatively nutrient-rich areas, while lateral roots explore other places to seek for better chance. Inhibition mechanism of plant hormones is applied to main roots in case of explosive propagation in some local optimal areas. Once resources in a location are exhausted, roots would shrink away from infertile conditions to preserve their activity. In order to validate optimization effect of the algorithm, twelve benchmark functions, including eight classic functions and four CEC2005 test functions, are tested in the experiments. We compared RGO with other existing evolutionary algorithms including artificial bee colony, particle swarm optimizer, and differential evolution algorithm. The experimental results show that RGO outperforms other algorithms on most benchmark functions. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-objective OPF model is proposed based on novel M2OBA algorithm. [•] External archive, crowding distance and comprehensive learning are used in M2OBA. [•] Multi-hive cooperative coevolution ensures the diversity of the M2OBA. [•] M2OBA obtains better Pareto solutions than NSGA-II, MOPSO, and MOABC. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel bacterial colony foraging (BCF) algorithm for complex optimization problems. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The cell-to-cell communication enables the historical search experience sharing among the bacterial colony that can significantly improve convergence. With the self-adaptive strategy, each bacterium can be characterized by focused and deeper exploitation of the promising regions and wider exploration of other regions of the search space. A rigorous performance analysis is given where the proposed algorithm is benchmarked against four state-of-the-art reference algorithms using both a classical and a composition test function suites. The individual and collective bacterial foraging behaviors of the proposed algorithmic model are also studied. Statistical analysis of all these tests highlights the significant performance improvement due to the beneficial combination and shows that the proposed algorithm outperforms the reference algorithms. [Copyright &y& Elsevier]

A solidapproach has been developed to ascertain the correlationof electron transfer with molecular vibration in a quantitative manner.Specifically, the reaction coordinate is identified by maximizingthe linear Pearson’s correlation coefficient between atomicdisplacement and the diabatic energy gap. In the limit of fast molecularvibration, the rates of electron transfer driven by multiple vibrationalmodes have been derived respectively under the strong and weak vibroniccoupling conditions. Our functional mode electron-transfer theoryis then justified by investigating the electron transfer of a betaine-30molecule from its first excited state to its ground state when beingsolvated in glycerol triacetate. Among the 210 available vibrationalmodes of betaine-30, only seven are essential to the electron transferby cumulatively accounting for more than 60% of the total reorganizationenergy. Because all essential vibrational modes are significantlyfaster than thermal fluctuation, the electron transfer is primarilydriven by intramolecular quantum tunneling. Interestingly, the calculatedreaction driving force of 1.95 eV is substantially greater than thereorganization energy of 0.58 eV, placing the reaction in the invertedMarcus region. Nevertheless, a sizable Franck–Condon factorof 1.58 × 10–3eV–1is stillachieved due to the large vibronically weighted zero-point energyof the essential vibrational modes. After determining the electroniccoupling strength as 0.14 eV by the constrained density functionaltheory, the overall electron-transfer rate at 300 K is found to be0.30 ps–1, which agrees nearly perfectly with experimentalvalues. [ABSTRACT FROM AUTHOR]

Abstract: A theoretical calculation was performed to evaluate the photo-induced electron transfer (PIET) rate between a gold atom and a gold ion solvated in carbon tetrachloride (CCl4) in the framework of Marcus electron transfer (ET) theory, including both solvent reorganization effects and electronic wavefunction coupling between the ET diabatic states. A novel component of this work involves calculation of the electronic coupling strength using a recently developed constrained real-time time-dependent density-functional-theory (CRT-TDDFT) method. It is found that the PIET rate reaches its maximum value at the electronic resonance wavelength regardless of the inter-particle separation, suggesting a strong correlation between PIET and light absorption. In comparison with thermally activated electron transfer (TAET) at room temperature, light irradiation is demonstrated to be much more efficient than thermal fluctuations in promoting long-range ET, at least for the most common situations, when the light travelling substantially exceeds thermal energy. This work is the first step towards a quantum theory of plasmon enhanced electron transfer, and the theory can also be used to calculate electron transfer rates quite generally for condensed phase problems. [Copyright &y& Elsevier]

Abstract: Prevention of cation permeation in wild-type aquaporin-1 (AQP1) is believed to be associated with the Asn-Pro-Ala (NPA) region and the aromatic/arginine selectivity filter (SF) domain. Previous work has suggested that the NPA region helps to impede proton permeation due to the protein backbone collective macrodipoles that create an environment favoring a directionally discontinuous channel hydrogen-bonded water chain and a large electrostatic barrier. The SF domain contributes to the proton permeation barrier by a spatial restriction mechanism and direct electrostatic interactions. To further explore these various effects, the free-energy barriers and the maximum cation conductance for the permeation of various cations through the AQP1-R195V and AQP1-R195S mutants are predicted computationally. The cations studied included the hydrated excess proton that utilizes the Grotthuss shuttling mechanism, a model “classical” charge localized hydronium cation that exhibits no Grotthuss shuttling, and a sodium cation. The hydrated excess proton was simulated using a specialized multi-state molecular dynamics method including a proper physical treatment of the proton shuttling and charge defect delocalization. Both AQP1 mutants exhibit a surprising cooperative effect leading to a reduction in the free-energy barrier for proton permeation around the NPA region due to altered water configurations in the SF region, with AQP1-R195S having a higher conductance than AQP1-R195V. The theoretical predictions are experimentally confirmed in wild-type AQP1 and the mutants expressed in Xenopus oocytes. The combined results suggest that the SF domain is a specialized structure that has evolved to impede proton permeation in aquaporins. [Copyright &y& Elsevier]

This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multi-swarm to converge to the global optimum. The PS2O algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With a set of 17 mathematical benchmark functions (including both continuous and discrete cases), PS2O is proved to have significantly better performance than four other successful variants of PSO. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a general optimization model gleaned ideas from the coevolution of symbiotic species in natural ecosystems. Species extinction and speciation events are also considered in this model to tie it closer to natural evolution, as well as improve the algorithm robustness. This model is instantiated as a novel multi-species optimizer, namely PS2O, which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic coevolution between species. When tested against benchmark functions, the PS2O markedly outperforms the canonical PSO algorithm in terms of accuracy, robustness and convergence speed. [Copyright &y& Elsevier]

The design of flexible wearable electronic devices that can shield electromagnetic waves and work in all weather conditions remains a challenge. We present in this work a low-cost technology to prepare an ultra-thin carbon fabric–graphene (CFG) composite film with outstanding electromagnetic interference shielding effectiveness (EMI SE) and electro-photo-thermal effect. The compatibility between flexible carbon fabric skeleton and brittle pure graphene matrix empowers this CFG film with adequate flexibility. The reticular fibers and porous structures play a vital role in multiple scattering and absorption of electromagnetic waves. In the frequency range of 30–1500 MHz, the CFG film can achieve a significantly high EMI SE of about 46 dB at tiny thickness (0.182 mm) and density (1.4 g cm−3) predominantly by absorption. At low safe voltages or only in sunlight, the film can self-heat to its saturation value rapidly in 40 s. Once the electricity or light supply is stopped, it can quickly dissipate heat in tens of seconds. A combination of the EMI SE and the prominent electro-photo-thermal effect further enables such a remarkable EMI shielding film to have more potential applications for communication devices in extreme zones. [ABSTRACT FROM AUTHOR]

Ordered mesoporous Fe2O3 supported gold nanoparticles with a desired specific surface area and porous structure (Au/meso-Fe2O3) was successfully fabricated with a hard templating method by using KIT-6 as the template. The morphology and physico-chemical properties of Au/meso-Fe2O3 were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM) and transmission electron microscope (TEM), etc. The gold nanoparticles are highly dispersed on the surface of the mesoporous Fe2O3. The catalytic performance of the synthesized catalyst was studied for the aerobic oxidation of benzylic alcohols in β–O–4 linked lignin model dimers to the corresponding carbonyl products under atmosphere pressure. Au/meso-Fe2O3 shows an enhanced activity for the aerobic oxidation of 1-phenylethanol in comparison with that of Au/bulk-Fe2O3. The promoted catalytic activity is related to the confined porous structure of mesoporous Fe2O3 and more boundaries contact between gold and meso-Fe2O3, which shows that the porous structure of the support has a significant influence on the activity of gold catalysts. [ABSTRACT FROM AUTHOR]

Monte Carlo simulations using a three-dimensional lattice model studied the Ruderman-KittelKasuya-Yosida (RKKY) indirect exchange interaction of doped magnetic Co ions in ZnO films. The results of the calculations show that the RKKY interaction in Co-doped ZnO is long ranged and its magnitude is proportional to R[sup -1] (inverse of the distance R from a central Co[sup 2+] ion). The sign oscillates with a frequency that depends on the concentration of the carrier. The long-distance sum of the RKKY indirect exchange energies is positive indicating that these materials are ferromagnetic, in direct correlation with previously reported results. [ABSTRACT FROM AUTHOR]

The equilibrium thermodynamic properties of a heteropolymer chain of 4 hydrophobic and 26 hydrophilic segments with Lennard-Jones interaction among segments were studied by multicanonical Monte Carlo methods in a three-dimensional off-lattice model. It is approved that the multicanonical algorithms provide us a practical way to accurately calculate thermodynamic properties of the chain at lower temperature. The collapse transition of a heteropolymer chain cooled from higher temperature is able to be separated in three steps: First, the hydrophobic segments associate together to form a hydrophobic core; then, the hydrophilic segments collapse on the surface of the core; finally, the heteropolymer chain reorganizes into its lowest energy state. © 2001 American Institute of Physics. [ABSTRACT FROM AUTHOR]

The annular coaxial nozzle has good convergence and is widely used in laser melting deposition (LMD). In this paper, a new-type annular coaxial nozzle is developed, which can transform four-channel powder flow into annular distribution through the internal channel to obtain a more uniform distribution of powder in the circumferential direction. A three-dimensional computational fluid dynamics (CFD) numerical model is established. The dynamic behavior of Fe-based powder is described by discrete phase model (DPM). The influence of gas on powder particles is described by two-way turbulence coupling model. The convergence and transport characteristics of the nozzle are revealed. The accuracy of CFD model is verified by powder feeding experiment, and the deposition performance of powder nozzle is verified by single-track deposition experiment. The results show that the nozzle can provide stable powder feeding and excellent convergence characteristics. The powder flow is converged in a "Y" shape on the longitudinal section. The higher the powder concentration at the focal plane of the nozzle, the smaller the aspect ratio of single-track coating. The powder utilization rate of the nozzle can reach 76.97%. [ABSTRACT FROM AUTHOR]

The ant colony optimization algorithm is an effective way to solve the problem of unmanned vehicle path planning. First, establish the environment model of the unmanned vehicle path planning, process and describe the environmental information, and finally realize the division of the problem space. Next, the biomimetic behavior of the ant colony algorithm is described. The ant colony algorithm has been improved by adding a penalty strategy. This penalty strategy can enhance the utilization of resources and guide the ants to explore other unknown areas by using the worse value in the search history to enhance the volatility of the pheromone. [ABSTRACT FROM AUTHOR]

Large-scale radio frequency identification (RFID) network planning (RNP) problem has been proven to be a NP-hard issue, which can be formulated as a high-dimensional nonlinear optimization problem with a mixture of discrete and continuous variables and uncertain parameters. First, a two-level optimization model for RFID network planning based on distributed decision making (DDM) is presented in this paper. In this model, the mixed discrete and continuous planning variables, namely the number, location, and radiate power of RFID readers are optimized. In each level of the optimization model, the different objectives to determine optimal values for these planning variables are as follows: (i) minimization of total installation cost of RFID network in the top-level; (ii) maximization of tag coverage and network reliability, and minimization of reader interference in the lower-level. In order to solve the proposed model effectively, this work proposes an efficient approach for RNP problem, namely the hybrid artificial bee colony optimizer (HABC), which employs the natural life-cycle mechanism to cast the original ABC framework to a cooperative and population varying fashion. In the proposed HABC, individuals can dynamically shift their survival states and population size varies dynamically according to the local fitness landscape during the executions of algorithm. These new characteristics of HABC help to avoid redundant search and maintain diversity of population in complex environments. Experiments are conducted on a set of CEC2005 and discrete benchmarks for evaluating the proposed algorithm. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness. [ABSTRACT FROM AUTHOR]

Singlet fission has the great potential to overcome the Shockley–Queisser thermodynamic limit and thus promotes solar power conversion efficiency. However, the current limited understandings of detailed singlet fission mechanisms hinder a further improved design of versatile singlet fission materials. In the present study, we combined ultrafast transient infrared spectroscopy with ab initio calculations to elucidate the roles played by the vibrational normal modes in the process of singlet fission for hexacene. Our transient infrared experiments revealed three groups of vibrational modes that are prominent in vibronic coupling upon photoexcitation. Through our computational study, those normal modes with notable Franck-Condon shifts have been classified as ring-twisting modes near 1300.0 cm−1, ring-stretching modes near 1600.0 cm−1, and ring-scissoring modes near 1700.0 cm−1. Experimentally, a ring-stretching mode near 1620.0 cm−1 exhibits a significant blue-shift of 4.0 cm−1 during singlet fission, which reaction rate turns out to be 0.59 ± 0.07 ps. More interestingly, the blue-shifted mode was also identified by our functional mode singlet fission theory as the primary driving mode for singlet fission, suggesting the importance of vibronic coupling when a correlated triplet pair of hexacene is directly converted from its first excited state singlet exciton. Our findings indicate that the ultrafast transient infrared spectroscopy, in conjunction with the nonadiabatic transition theory, is a powerful tool to probe the vibronic fingerprint of singlet fission. [ABSTRACT FROM AUTHOR]

• A laser melting deposition technology with circular oscillating laser beam was proposed, and the multilayer deposition layer of GH3536 nickel-based superalloy was successfully fabricated. • Compared with Gaussian distribution laser, the dilution ratio and heat affected zone of circular oscillating laser are smaller. • Increasing the oscillating frequency of the laser beam can reduce the grain size of the deposited layer and promote the transformation from columnar dendrite to equiaxed crystal. • The GH3536 alloy coating deposited by circular oscillating laser has higher solid solubility, and the volume fraction and size of Laves phase are reduced. • The GH3536 alloy deposited by the oscillating laser has stronger toughness and smaller difference in mechanical properties in the vertical and horizontal directions. GH3536 nickel-based superalloy was fabricated by circular oscillating laser melting deposition and traditional Gaussian laser melting deposition in the present investigation. The effects of circular oscillating laser beam on the macro morphology, microstructure evolution and mechanical properties of the GH3536 alloy were studied. The results show that the dilution ratio and heat affected zone of circular oscillating laser are smaller than those of Gaussian laser. The oscillating laser beam drives the melt flow, destroys the growth of columnar dendrites, promotes the diffusion of elements and inhibits the precipitation of Laves phase. The volume fraction of Laves phase is about 30.03% lower than that of Gaussian laser. Increasing the oscillating frequency or amplitude can refine the grain. The ultimate tensile strength of GH3536 alloy deposited by circular oscillating laser in parallel and vertical directions is 683.64 ± 17.02 MPa and 687.23 ± 5.89 MPa respectively, which is 6.57% and 9.96% lower than that of Gaussian laser, respectively; The elongation in parallel and vertical directions is 35.86% and 38.1%, which is 91.36% and 41.22% higher than that of Gaussian laser, respectively. The circular oscillating laser can significantly improve the toughness of GH3536 alloy and improve the difference of mechanical properties in different sampling directions. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is proposed. First, the population diversity is enhanced by the initialization of the chaotic method. Second, the cosine function is used to better simulate the characteristics of the periodic change of the energy of the prey in the repeated contests with the group of hawks, to better balance the exploration and exploitation of the algorithm. Third, Cauchy mutation on the optimal individual in the exploration phase is performed, and the characteristics of the Cauchy distribution to enhance the diversity of the population are used, which can effectively prevent the algorithm from falling into the local optimum. Fourth, the local optimization ability of the algorithm by using the ergodicity of the chaotic system in the exploitation phase to perform a chaotic local search for the optimal individual is enhanced, which can effectively jump out after the algorithm falls into the local optimum. Finally, we use the elite individuals of the population to guide the position update of the population's individuals, fully communicate with the dominant individuals, and speed up the convergence speed of the algorithm. Through the simulation experiments on CCCHHO with 11 different benchmark functions, CCCHHO is better than the gray wolf optimization algorithm (GWO), the Salp swarm algorithm (SSA), the ant lion optimization algorithm (ALO), and three improved HHO algorithms in terms of convergence speed and optimization accuracy, whether it is a unimodal benchmark function or a multimodal benchmark function. The experimental results show that CCCHHO has excellent algorithm efficiency and robustness. [ABSTRACT FROM AUTHOR]

An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II), the multiobjective particle swarm optimizer (MOPSO), and the multiobjective ABC (MOABC). Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy. [ABSTRACT FROM AUTHOR]

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm intelligence algorithms which inspired by the foraging behavior of honey bee swarms. It has been widely used in numerical and engineering optimization problems. This paper presents a hybrid artificial bee colony (HABC) model to improve the canonical ABC algorithm. The main idea of HABC is to enhance the information exchange between bees by introducing the crossover operator of genetic algorithm to ABC. With suitable crossover operation, valuable information is fully utilized and it is expected that the algorithm can converge faster and more accurate. Eight versions of HABC algorithm combined by different selection and crossover methods under the model were proposed and tested on several benchmark functions. Then, the settings of the new parameter crossover rate for two well performed HABC versions are tested to verify their best values. Finally, four rotated functions and four shifted functions are used to test the performance of the two algorithms on complex functions and asymmetric functions. Experiment results showed that these two versions of HABC algorithm offer significant improvement over the original ABC and are superior to other two state of the art algorithms on some functions. [ABSTRACT FROM AUTHOR]

This paper presents a general optimization model gleaned ideas from root growth behaviours in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for optimization of higher-dimensional numerical function. For this study, a mathematical framework and architecture are designed to model root growth patterns of plant. Under this architecture, the interactions between the soil and root growth are investigated. A novel approach called 'root growth algorithm' (RGA) is derived in the framework and simulation studies are undertaken to evaluate this algorithm. The simulation results show that the proposed model can reflect the root growth behaviours of plant in the soil and the numerical results also demonstrate RGA is a powerful search and optimization technique for higher-dimensional numerical function optimization. [ABSTRACT FROM AUTHOR]

Summary A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods. [ABSTRACT FROM AUTHOR]

Currently, artificial intelligence is being used in automatic programming by producing snippets of code. NPI (neural programmer-interpreter) is the most used technology that uses machine learning to implement automatic programming. This paper is aimed to improve the performance of traditional NPI and improve the speed of NPI training without loss of precision. To achieve this goal, we changed the core structure of NPI by adopting the GRU (gated recurrent unit) to replace LSTM (long short-term memory) in NPI. GRU has a control unit that regulates the flow of information within the hidden unit while without single memory unit. Numerical results have been presented to demonstrate the performance of the proposed methodology. That is, GRU-based NPI improved the performance of the original LSTM-based NPI by nearly 33% under the premise of ensuring equal accuracy. [ABSTRACT FROM AUTHOR]

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed. [ABSTRACT FROM AUTHOR]

For profit maximization, the model-based stock price prediction can give valuable guidance to the investors. However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. The data preprocessing with the soft/hard threshold method can obviously restrain noise, and a new multioptimal combination wavelet transform (MOCWT) method is proposed. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction. The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of the vibration data collected by the regulation system during the unstable regulation process and the deficiency of the traditional wavelet anomaly detection method, an on-line anomaly detection method combining the autoregressive and the wavelet analysis is proposed to detect the abnormal data of the regulation system. By introducing the improved robust AR model, this method can overcome the problem that the time and frequency of traditional anomaly detection using wavelet analysis method cannot be well balanced, and ensure the rationality of abnormal value detection of process data. Considering the general parameters of the regulation system is time-varying and has strong dynamic characteristics, the method proposed in this paper has the ability of online detection and real-time updating of parameters to ensure that the control parameters of time-varying control system; In order to avoid the problem that the traditional anomaly detection method needs to set the detection threshold in advance, HMM is introduced to analyze the wavelet coefficients and update the HMM parameters online, which can ensure that the HMM can well reflect the actual distribution of process data anomalies. It is proved that the method of anomaly data detection proposed in this paper is more suitable for the unstable regulation process data and has certain practicability through experiment and application. [ABSTRACT FROM AUTHOR]

The thermodynamics of a homopolymer chain with the Lennard-Jones (LJ) potential was studied by the multicanonical Monte Carlo method. The results confirm there indeed exists a liquid-solid-like first-order transition at lower temperatures for a free-joint chain, revealing that the transition is a characteristic of a homopolymer chain, independent of the algorithms and potential used in simulation. © 2000 American Institute of Physics. [ABSTRACT FROM AUTHOR]

The simulations on equilibrium thermodynamic properties of short homopolymer chains, 3-, 4-, and 5-mer, by multicanonical Monte Carlo in a three-dimensional off-lattice model are quantitatively coincident with the exact analytical results of recently reported by Taylor [J. Chem. Phys. 114, 6472 (2001)], indicating that multicanonical algorithm is a permitted method in overcoming nonergodicity in observation of freezing transitions of single macromolecular chains. The investigation of freezing transition behaviors of 5-mer chains at ultralow temperatures reveals that four isomers coexist in the liquid state, then freeze into one of four isomers in the solid state depending on the bond length and bond fluctuation of the chain model after the occurrence of a liquid-to-solid-like first-order transition. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the cost-efficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multiobjective problem. In order to optimize this model, this paper proposes a specific multiobjective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • ROS and 3g-C 3 N 4 * generated by undoped and C-doped g-C 3 N 4 were systematically quantified. • The contribution of ROS and 3g-C 3 N 4 * was negligible to initial contaminant degradation. • The critical role of holes for contaminant degradation was elucidated by MD and DFT simulations. • C-dopant on g-C 3 N 4 could affect the hole oxidation of contaminants by changing the binding affinity and CPCET rates. • The outcome will promote the smart design of g-C 3 N 4 for applications with enhanced photocatalytic selectivity. Abstract Solar-energy-enabled photocatalysis is a sustainable process to destruct persistent environmental pollutants via the attack of photogenerated reactive species (e.g., holes, reactive oxygen species such as OH, O 2 −/HO 2 , H 2 O 2 , and 1O 2). Graphitic carbon nitride (g-C 3 N 4) has emerged as a promising polymeric photocatalyst, and its highly tunable properties allow the material with an enhanced photocatalytic activity for environmental remediation. In this study, by taking advantage of simulation and experimental tools, we systematically evaluated the production of reactive species of undoped and carbon (C)-doped g-C 3 N 4 samples, and identify the role of these species in contaminant oxidation under simulated visible sunlight irradiation (xenon lamp, λ > 400 nm). Both g-C 3 N 4 samples produced negligible OH or triplet-excited states (3g-C 3 N 4 *), but the C-doped g-C 3 N 4 sample generated more 1O 2 , O 2 −, and H 2 O 2 compared to the undoped counterpart. Surprisingly, all these oxidative species did not contribute substantially for the degradation kinetics of contaminant phenol and atrazine, at least for the initial oxidation of the parent compounds; and the results otherwise highlighted the important role of the holes for contaminant transformation. The surface-mediated hole oxidation of the contaminants, including the adsorption of the contaminants on the photocatalyst surface (quantified by the binding free energy) and electron transfer kinetics from the contaminants to the excited photocatalysts (quantified by the concerted proton-coupled electron transfer (CPCET) rate) were investigated by molecular dynamics (MD) and density functional theory (DFT) simulations. The simulation results indicated that C-doping could favor the binding of atrazine but not of phenol on g-C 3 N 4. The C-doping also significantly decreased the CPCET rate of phenol on g-C 3 N 4 but could have little to no adverse impact for the CPCET rate of atrazine. These simulation and experimental results could explain the selective oxidation of phenol and atrazine on undoped and C-doped g-C 3 N 4 in our previous study, and the results also highlight the dominant role of the holes for contaminant transformation that was largely overlooked before. This study generates mechanistic insights of photocatalytic oxidation, and will provide guidelines for the rational design of g-C 3 N 4 as an effective visible-light-responsive photocatalyst for many applications, including contaminant degradation, chemical synthesis, and beyond. [ABSTRACT FROM AUTHOR]

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests. [ABSTRACT FROM AUTHOR]

Graphitic carbon nitride (g-C3N4) has recently emerged as a promising visible-light-responsive polymeric photocatalyst; however, a molecular-level understanding of material properties and its application for water purification were underexplored. In this study, we rationally designed nonmetal doped, supramolecule-based g-C3N4 with improved surface area and charge separation. Density functional theory (DFT) simulations indicated that carbon-doped g-C3N4 showed a thermodynamically stable structure, promoted charge separation, and had suitable energy levels of conduction and valence bands for photocatalytic oxidation compared to phosphorus-doped g-C3N4. The optimized carbon-doped, supramolecule-based g-C3N4 showed a reaction rate enhancement of 2.3-10.5-fold for the degradation of phenol and persistent organic micropollutants compared to that of conventional, melamine-based g-C3N4 in a model buffer system under the irradiation of simulated visible sunlight. Carbon-doping but not phosphorus-doping improved reactivity for contaminant degradation in agreement with DFT simulation results. Selective contaminant degradation was observed on g-C3N4, likely due to differences in reactive oxygen species production and/or contaminant-photocatalyst interfacial interactions on different g-C3N4 samples. Moreover, g-C3N4 is a robust photocatalyst for contaminant degradation in raw natural water and (partially) treated water and wastewater. In summary, DFT simulations are a viable tool to predict photocatalyst properties and oxidation performance for contaminant removal, and they guide the rational design, fabrication, and implementation of visible-light-responsive g-C3N4 for efficient, robust, and sustainable water treatment. [ABSTRACT FROM AUTHOR]

Solar-driven photocatalytic generation of H 2 O 2 over metal-free catalysts is a sustainable approach for value-added chemical production. Here, we synthesized chlorine-doped graphitic carbon nitride (Cl-doped g-C 3 N 4) through a solvothermal method to effectively produce H 2 O 2 with a rate of 1.19 ± 0.06 µM min−1 under visible light irradiation, which was improved by 104 times compared to pristine g-C 3 N 4. Continuous net production of H 2 O 2 was realized at a rate of 2.78 ± 0.10 µM min−1 up to 54 h with isopropanol as the hole scavenger, whereas H 2 O 2 production was only sustained for ~ 6 h without scavengers. Both molecular simulations and advanced spectroscopic characterizations elucidated that the Cl dopant increased the charge transfer rate, decreased the bandgap, and reduced the activation energy of the rate-limiting step of O 2 reduction, all of which favored H 2 O 2 production. This work implemented a novel metal-free photocatalyst for sustainable H 2 O 2 production and elucidated the mechanism for promoting H 2 O 2 production that can guide future photoreactive nanomaterial design. [Display omitted] • Cl-doped g-C 3 N 4 was synthesized via a solvothermal method. • Cl dopant decreased the g-C 3 N 4 bandgap and promoted charge transfer. • Cl dopant lowered the activation energy of the rate-determining step in H 2 O 2 formation. • In the presence of isopropanol H 2 O 2 was continuously produced on Cl-doped g-C 3 N 4. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid artificial bee colony optimizer (HABC) by restructuring the artificial bee colony system with life-cycle, Powell’s search and social learning. The proposed HABC based on life-cycle is a cooperative and varying-population model where the bee can switch its state periodically according to the local environmental landscape. Through this new characteristic, two significant merits of reducing redundant search and maintaining diversity of population can be obtained. In addition, with the social learning, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powell’s method enables the bees to deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. Then, eight basic benchmarks, seven CEC 2005 composite functions, and a real-world problem of RFID networks optimization are solved by HABC, successively. The experimental results validate the incorporated combinatorial strategies and demonstrate the performance superiority of HABC. [ABSTRACT FROM AUTHOR]

We prepared a single-atom Fe catalyst supported on an oxygen-doped, nitrogen-rich carbon support (SAFe-OCN) for degrading a broad spectrum of contaminants of emerging concern (CECs) by activating peroxides such as peroxymonosulfate (PMS). In the SAFe-OCN/PMS system, most selected CECs were amenable to degradation and high-valent Fe species were present for oxidation. Moreover, SAFe-OCN showed excellent performance for contaminant degradation in complex water matrices and high stability in oxidation. Specifically, SAFe-OCN, with a catalytic center of Fe coordinated with both nitrogen and oxygen (FeN x O 4−x), showed 5.13-times increased phenol degradation kinetics upon activating PMS compared to the catalyst where Fe was only coordinated with nitrogen (FeN 4). Molecular simulations suggested that FeN x O 4−x , compared to FeN 4 , was an excellent multiple-electron donor and it could potential-readily form high-valent Fe species upon oxidation. In summary, the single-atom Fe catalyst enables efficient, robust, and sustainable water and wastewater treatment, and molecular simulations highlight that the electronic nature of Fe could play a key role in determining the activity of the single-atom catalyst. [Display omitted] • The heterogeneity of the local coordination environment of Fe atoms was recognized. • The single-atom Fe catalyst (SAFe-OCN) activated peroxides and degraded CECs. • High-valent Fe was demonstrated as a key species for CEC removal. • SAFe-OCN was robust for CEC degradation in complex water matrices. • O-coordination could promote the electron transfer of Fe and contaminant degradation. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) algorithm has been widely used in solving complex optimization due to its few control parameters and outstanding global search capability. However, ABC suffers from slow convergence rate, which limits its real-world applications. To overcome such disadvantage, this paper proposes a surrogate-assisted multi-swarm artificial bee colony (SAMSABC). The multiple swarm strategy is employed to further keep the diversity. To enhance the local exploitation capability, the orthogonal method is utilized to provide a guide vector. Moreover, to avoid wasting the computation resources, the fitness estimation strategy for artificial bee colony algorithm, as a surrogate-assistance technology, is designed. Finally, the experimental results of 20 benchmark functions verify its outstanding performance on solving complex numerical optimization problems. [ABSTRACT FROM AUTHOR]

A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]