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It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

Whilst most bioorthogonal reactions focus on targeting binding-site cysteine residues, proximity-induced reactivity effect ensures that reaction also occurs at nucleophilic lysine residues. We report one example here that the propargylated-sulfonium center undergoes a nucleophilic reaction with lysine residue via proximity-induced conjugation. This propargylated-sulfonium tethered peptide resulting from a facile propargylation of thiolethers, enables amino-yne reaction at the selected lysine on MDM4 protein. This strategy represents a viable approach of lysine-targeted covalent inhibition in proximity.

The behavior of room temperature phosphorescence quenching and fluorescence enhancing of gadolinium labeled sinoporphyrin sodium (Gd-DVDMS) in the aqueous solution were studied. Gd-DVDMS was characterized by various techniques including mass spectra, Fourier transform infrared spectra and transmission electron microscopy. The optical behavior was monitored through UV–vis absorption spectra and photoluminescence spectra. The result shows that the phosphorescence intensity gradually quenches, almost disappears after dissolved 120 min, meanwhile the fluorescence intensity enhances 5 times. The above behavior is attributed to the decreased population of Gd-DVDMS as first excited triplet state (T1). Based on the time-invariant of both absorption spectra and phosphorescence decay curves at 710 nm of Gd-DVDMS in different dissolution time, the reason for the decreased population of Gd-DVDMS in T1 state is that the intersystem crossing from first excited state (S1) to T1 is prevented. In addition, the fluorescent decay curves of Gd-DVDMS initially dissolved in the aqueous solution and dissolved for 120 min were also measured for comparison. The big change in fluorescent lifetime further proves the conclusion that intersystem crossing is prevented, meanwhile, the increase in fluorescence intensity is demonstrated. In this work, the possibility of decomposition of Gd-DVDMS is excluded because the phosphorescence decay curve in Gd-DVDMS aqueous solution is sensible to DVDMS.

As a lead-free material, AgNbO3 has received a lot of attention for its potential application in the field of energy storage. Herein, AgxNbO2.5+x/2 ceramics were successfully synthesized via solid state reaction with the x ranged from 1 to 0.92. The effect of silver deficiency on structure, dielectric properties and energy storage performance was investigated. As x value decreased from 1 to 0.94, the unit volume of AgxNbO2.5+x/2 ceramics contracted. Meanwhile, all the compositions displayed double hysteresis loops with reduced Pmax, Pr and enhanced transformation fields (EF and EB) as functions of silver deficiency. Silver deficiency stabilized the antiferroelectric state in the FIE (-like) phase and improved the energy storage performance of AgNbO3 ceramics. Hence, an enhanced energy storage performance (Wrec = 3.1 J/cm3, η = 49%) were achieved in Ag0.94NbO2.97 ceramic.

The combustion characteristics of a hydrogen-fueled scramjet combustor were investigated experimentally and numerically. One nonreacting case (case 1), and two different equivalence ratio (ER) reacting cases (cases 2 and 3) were compared. The combustion process of each reacting case was divided into three phases. In the first phase, the monitor pressure in case 2 (ER = 0.1) reached a higher level due to the fuel injected before the hydrogen was ignited, whereas the change in case 3 (ER = 0.3) was the opposite, being less than that in the nonreacting flow. Almost all of the hydrogen in case 2 was in the front of the cavity, and that in case 3 was both throughout the whole cavity and near the top wall behind the cavity. In the second phase, the ignition times were about 0.010 s in case 2 and about 0.022 in case 3; a larger ER of hydrogen might be difficult to ignite. Finally, in the last phase, the hydrogen combustion was stable. The shock train in case 3 was pushed into the isolator, and the disturbing distance was about 0.08 m, in accordance with the wall pressure distribution. The higher static temperature in case 2 was mainly in the back of the cavity and that in case 3 was in the cavity shear layer, in line with the hydroxyl planner laser-induced fluorescence (OH-PLIF) results. The combustion mode in case 2 was supersonic combustion and that in case 3 was subsonic combustion.

Diversity preservation is a crucial technique in multiobjective evolutionary algorithms (MOEAs), which aims at evolving the population toward the Pareto front (PF) with a uniform distribution and a good extensity. In spite of many diversity preservation approaches in existing MOEAs, most of them encounter difficulties in tackling complex PFs. This article gives a detail introduction to existing diversity preservation approaches, as well as a revelation of the limitations of them. To address the limitations of existing diversity preservation approaches, this article proposes a multistage MOEA for better diversity performance. The proposed MOEA divides the optimization process into multiple stages according to the population in each generation, and updates the population by different steady-state selection schemes in different stages. According to the experimental results on 21 benchmark problems, the proposed MOEA exhibits better diversity performance than 11 existing MOEAs.

Herein, we report the high De-NOx performance of the A-site defective perovskite-based Pd/La0.5Sr0.3MnO3 catalyst. The formation of the defective perovskite structure can be proved by both the increased Mn4+/Mn3+ ratio and serious lattice contraction due to cationic nonstoichiometry. It promotes the Sr doping into perovskite lattice and reduces the formation of the SrCO3 phase. Our results demonstrate that below 300 °C the A-site defective perovskite can be more efficiently regenerated than the SrCO3 phase as NOx storage sites due to the latter's stronger basicity, and also exhibits the higher NO oxidation ability than the A-site stoichiometric and excessive catalysts. Both factors promote the low-temperature De-NOx activity of the Pd/La0.5Sr0.3MnO3 catalyst through improving its NOx trapping efficiency. Nevertheless, above 300 °C, the NOx reduction becomes the determinant of the De-NOx activity of the perovskite-based catalysts. A-site defects can weaken the interactions between perovskite and Pd, inducing activation of Pd sites by in-situ transformation from PdO to metallic Pd in the alternative lean-burn/fuel-rich atmospheric alternations, which boosts the De-NOx activity of the Pd/La0.5Sr0.3MnO3 catalyst. The Pd/La0.5Sr0.3MnO3 catalyst exhibits the high sulfur tolerance as well. These findings provide insight into optimizing the structural properties and catalytic activities of the perovskite-based catalysts via tuning formulation, and have potential to be applied for various related catalyst systems.

For supported metal catalyst systems, the impact on catalysis originates from the interaction between metal nanoparticles and their support. Metal-support interactions (MSI) can change electronic properties, geometric morphologies, or chemical compositions of metal nanoparticles to make active sites have specific properties and catalytic activities. Fischer-Tropsch synthesis (FTS) is one of the most effective ways to convert cheap non-petroleum-based carbon sources into high value-added chemicals or ultra-clean liquid fuels. In this review, we summarize and classify the impact of MSI on the catalytic activity, selectivity and stability of FTS catalysts. The strategies to tune MSI are introduced in detail, and the recent development of high-efficiency FTS catalysts through the manipulation of SMI strategies has been highlighted. It is emphasized that the active metal sites, which are endowed with special functions by MSI, can change the strength of adsorption bond of adsorbates, consequently controlling the product distribution.

Imbalanced data sets are very common in online credit scoring. Their imbalanced structures may cause statistical bias and poor performance of those traditional models. Hence, how to properly deal with them and dig useful information from them are very crucial for the risk management. This paper proposes a new approach to address the imbalanced data classification in this area by directly generating two quadratic surfaces in the original space. In this way, it avoids the time-consuming task for searching a proper kernel function and its corresponding parameters in the traditional support vector machine (SVM) models, hence significantly improve the total efficiency of the approach. Moreover, the homocentric structure and maximization margin principle are applied to obtain a good performance of our model on the issue. Besides, fuzzy weight is also incorporated to further increase the classification accuracy and robustness. It is worth noting that the linear programming structure of our model not only guarantees the global optimality of its solution, but also leads to a much higher efficiency than those benchmark models. In addition, the fewer parameters in our model further saves more time in the tuning process. Hence it is quite suitable for handling those huge-sized problems in this big data era. Finally, we conduct a comprehensive experiment to compare the performances and efficiencies of different methods. The numerical results on various data sets strongly verify the superiority of our method to some benchmark methods in handling the imbalanced data classification in online credit scoring.

Describing and processing complex as well as ambiguous and uncertain information has always been an inescapable and challenging topic in multi-attribute decision analysis (MADA) problems. As an extension of Dempster-Shafer (D-S) evidence theory, D numbers breaks through the constraints of the constraint framework and is a new way of expressing uncertainty. The soft likelihood function based on POWA operator is one of the most useful tools recently developed for dealing with uncertain information, since it provides a more excellent performance for the aggregation of multiple compatible evidence. Recently, a new MADA model based on D numbers has been proposed, called DMADA. In this paper, inspired by the above mentioned theories, based on soft likelihood functions, POWA aggregation and D numbers we design a novel model to improve the performance of representing and processing uncertain information in MADA problems as an improvement of the DMADA approach. In contrast, our advantages include mainly the following. Firstly, the proposed method considers the reliability characteristics of each initial D number information. Secondly, the proposed method empowers decision makers with the possibility to express their perceptions through attitudinal features. In addition, an interesting finding is that the preference parameter in the proposed method can clearly distinguish the variability between candidates by adjusting the space values between adjacent alternatives, making the decision results clearer. Finally, the effectiveness and superiority of this model are proved through analysis and testing.

In this article, a new harmonic mitigation system using double balanced impedance filtering power transformer (DBI-FPT) is proposed for improving the power quality (PQ). Designed for multistage distribution network with severe harmonic pollution, the proposed system enhances multiport filtering performance, mitigates resonance along with reduction of construction and maintenance costs compared to other alternative system. The simplified model of the proposed system is established by combining the short-circuit impedance calculation method and the characteristics of dual-balanced winding. By analyzing the influence of manufacturing errors on the dual-balanced winding in detail, the optimization method for filtering performance is given, which in turn guides the practical application of DBI-FPT effectively. After that, through comprehensively comparative analysis, the multiport harmonic mitigation characteristics, resonance avoidance, and amplification weaken mechanism of the proposed method are revealed. The experimental results validate that the proposed system is suitable for multiterminal PQ improvement and industrial application in multistage regional distribution network.

Constrained multiobjective optimization problems (CMOPs) widely exist in real-world applications, and they are challenging for conventional evolutionary algorithms (EAs) due to the existence of multiple constraints and objectives. When the number of objectives or decision variables is scaled up in CMOPs, the performance of EAs may degenerate dramatically and may fail to obtain any feasible solutions. To address this issue, we propose a paired offspring generation-based multiobjective EA for constrained large-scale optimization. The general idea is to emphasize the role of offspring generation in reproducing some promising feasible or useful infeasible offspring solutions. We first adopt a small set of reference vectors for constructing several subpopulations with a fixed number of neighborhood solutions. Then, a pairing strategy is adopted to determine some pairwise parent solutions for offspring generation. Consequently, the pairwise parent solutions, which could be infeasible, may guide the generation of well-converged solutions to cross the infeasible region(s) effectively. The proposed algorithm is evaluated on CMOPs with up to 1000 decision variables and ten objectives. Moreover, each component in the proposed algorithm is examined in terms of its effect on the overall algorithmic performance. Experimental results on a variety of existing and our tailored test problems demonstrate the effectiveness of the proposed algorithm in constrained large-scale multiobjective optimization.

Organic fluorophores/photosensitizers have been widely used in biological imaging and photodynamic and photothermal combination therapy in the first near-infrared (NIR-I) window. However, their applications in the second near-infrared (NIR-II) window are still limited primarily due to low fluorescence quantum yields (QYs). Here, a boron dipyrromethene (BDP) is created as a molecularly engineered thiophene donor unit with high QYs to the redshift. Thiophene insertion initiates substantial redshifts of the absorbance as compared to its counterparts in which iodine is introduced. The fluorescent molecule can be triggered by an NIR laser with a single wavelength, thereby producing emission in the NIR-II windows. Single NIR laser-triggered phototherapeutic nanoparticles (NPs) are developed by encapsulating the BDP and the chemotherapeutic drug docetaxel (DTX) by using a synthetic amphiphilic poly(styrene-co-chloromethyl styrene)-graft-poly(ethylene glycol) functionalized with folic acid (FA). These BDP-T-N-DTX-FA NPs not only show superior solubility and high singlet oxygen QY (ΦΔ=62%) but also demonstrate single NIR laser-triggered multifunctional characteristics. After intravenous administration of the NPs into 4T1 tumor-bearing mice, the accumulation of the NPs in the tumor showed a high signal-to-background ratio (11.8). Furthermore, 4T1 tumors in mice were almost eradicated by DTX released from the BDP-T-N-DTX-FA NPs under single NIR laser excitation and the combination of photodynamic therapy (PDT) and photothermic therapy (PTT). Statement of significance The application of organic photosensitizers is still limited primarily due to low fluorescence quantum yields (QYs) in the second near-infrared (NIR-II) window. Here, a boron dipyrromethene (BDP) as a molecularly engineered thiophene donor unit with high QYs to the redshift is created. Phototherapeutic nanoparticles (NPs) are developed by encapsulating the BDP and docetaxel (DTX) using a synthetic amphiphilic poly(styrene-co-chloromethyl styrene)-graft-poly(ethylene glycol) functionalized with folic acid (FA). These BDP-T-N-DTX-FA NPs not only show high singlet oxygen QY (ΦΔ=62%) but also demonstrate single NIR laser-triggered multifunctional characteristics and a high signal-to-background ratio (11.8). Furthermore, 4T1 tumors in mice were almost eradicated by DTX released from the BDP-T-N-DTX-FA NPs under single NIR laser excitation and the PDT/PTT combination therapy.

Multimodal multiobjective optimization problems (MMOPs) widely exist in real-world applications, which have multiple equivalent Pareto-optimal solutions that are similar in the objective space but totally different in the decision space. While some evolutionary algorithms (EAs) have been developed to find the equivalent Pareto-optimal solutions in recent years, they are ineffective to handle large-scale MMOPs having a large number of variables. This article thus proposes an EA for solving large-scale MMOPs with sparse Pareto-optimal solutions, i.e., most variables in the optimal solutions are 0. The proposed algorithm explores different regions of the decision space via multiple subpopulations and guides the search behavior of the subpopulations via adaptively updated guiding vectors. The guiding vector for each subpopulation not only provides efficient convergence in the huge search space but also differentiates its search direction from others to handle the multimodality. While most existing EAs solve MMOPs with 2-7 decision variables, the proposed algorithm is shown to be effective for benchmark MMOPs with up to 500 decision variables. Moreover, the proposed algorithm also produces a better result than state-of-the-art methods for the neural architecture search.

Constrained multi-objective optimization problems (CMOPs) are difficult to handle because objectives and constraints need to be considered simultaneously, especially when the constraints are extremely complex. Some recent algorithms work well when dealing with CMOPs with a simple feasible region; however, the effectiveness of most algorithms degrades considerably for CMOPs with complex feasible regions. To address this issue, this paper proposes a multi-stage evolutionary algorithm, where constraints are added one after the other and handled in different stages of evolution. Specifically, in the early stages, the algorithm only considers a small number of constraints, which can make the population efficiently converge to the potential feasible region with good diversity. As the algorithm moves to the later stages, more constraints are considered to search the optimal solutions based on the solutions obtained in the previous stages. Furthermore, a strategy for sorting the constraint-handling priority according to the impact on the unconstrained Pareto front is proposed, which can accelerate the convergence of the algorithm. Experimental results on five benchmark suites and three real-world applications showed that the proposed algorithm outperforms several state-of-the-art constraint multi-objective evolutionary algorithms when dealing with CMOPs, especially for problems with complex constraints.

Background The study of plant phenotype by deep learning has received increased interest in recent years, which impressive progress has been made in the fields of plant breeding. Deep learning extremely relies on a large amount of training data to extract and recognize target features in the field of plant phenotype classification and recognition tasks. However, for some flower cultivars identification tasks with a huge number of cultivars, it is difficult for traditional deep learning methods to achieve better recognition results with limited sample data. Thus, a method based on metric learning for flower cultivars identification is proposed to solve this problem. Results We added center loss to the classification network to make inter-class samples disperse and intra-class samples compact, the script of ResNet18, ResNet50, and DenseNet121 were used for feature extraction. To evaluate the effectiveness of the proposed method, a public dataset Oxford 102 Flowers dataset and two novel datasets constructed by us are chosen. For the method of joint supervision of center loss and L2-softmax loss, the test accuracy rate is 91.88%, 97.34%, and 99.82% across three datasets, respectively. Feature distribution observed by T-distributed stochastic neighbor embedding (T-SNE) verifies the effectiveness of the method presented above. Conclusions An efficient metric learning method has been described for flower cultivars identification task, which not only provides high recognition rates but also makes the feature extracted from the recognition network interpretable. This study demonstrated that the proposed method provides new ideas for the application of a small amount of data in the field of identification, and has important reference significance for the flower cultivars identification research.

Summary Most simulators currently use the advection/diffusion model (ADM), where the total flux comprises Darcian advection and Fickian diffusion. However, significant errors can arise, especially for modeling diffusion processes in fractured unconventional reservoirs, if diffusion is modeled by the conventional Fick’s law using molar concentration. Hence, we propose an improved multicomponent diffusion model for fractured reservoirs to better quantify the multiphase multicomponent transport across the fracture/matrix interface. We first give a modified formulation of the Maxwell-Stefan (MS) equation to model the multicomponent diffusion driven by the chemical potential gradients. A physics-based modification is proposed for the ADM in fractured reservoirs, where fracture, matrix, and their interface are represented by three different yet interconnected flow domains to honor the flux continuity at the fracture/matrix interface. The added interface using a more representative fluid saturation and composition of the interface can hence better capture the transient mass fluxes between fracture and matrix. The proposed approach is also implemented in an in-house compositional simulator. The multicomponent diffusion model is validated with both intraphase and interphase diffusion experiments. Then, the improved model for fracture/matrix interaction is compared with a fine-grid model. The proposed multiple interacting continua (MINC) model with three continua (MINC3) can better match the fine-grid model’s result than the double-porosity (DP) model, which only obtains a fair match at an early time. Then, we simulate a gas huff ‘n’ puff (HnP) well in the Permian Basin to investigate the effect of diffusion within the fractured tight oil reservoir. The simulation reveals that diffusion has a minor effect on the performance of depletion when oil is the dominant phase. For gas HnP, the simulation neglecting diffusion will underestimate the oil recovery factor (RF) but overestimate the gas rate. The DP approach tends to overestimate the RF of heavy components but leads to a similar cumulative oil RF compared with MINC3. With the diffusion included in the simulation, gas HnP performance becomes more sensitive to the soaking time than the model without diffusion. Although increasing the soaking time will lead to a higher RF after considering diffusion, the incremental oil is not sufficiently large to justify a prolonged soaking time.

At present, the study of upper-limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper-limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine- (FSM-) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification.

With the increasing interest in exploring the deep-space environment, the problems of health and safety of astronauts' bone induced by microgravity warrants an investigation. Recent studies have discovered that several microRNAs (miRNAs) have played a vital role in osteoblast differentiation and bone formation under microgravity, whereas the in-depth signaling pathway mechanisms are not yet completely understood. Here, we performed the hind limb unloading (HLU) mice model and 3D clinostat-random position machine (RPM) to simulate the effects of microgravity on bone at animal and cellular level, respectively. Firstly, we screened the different expressed miRNAs under simulated microgravity by miRNA sequencing, we then identified the highest different expressed miRNA (miR-138–5p) that was up-regulated under simulated microgravity and inhibited osteoblast differentiation in MC3T3-E1 cells. Moreover, we analyzed miR-138's targets during osteogenic differentiation and we found that these targets regulated osteogenic differentiation through Wnt/β-catenin signaling, a key signaling pathway in controlling osteoblast differentiation. The association between miR-138–5p and β-catenin activity was determined by a luciferase reporter assay. Further experiments confirmed that miR-138–5p suppressed β-catenin expression and β-catenin activity. Moreover, β-catenin overexpression attenuated osteoblast differentiation reduction induced by increased miR-138–5p level in miR-138–5p overexpression osteoblastic cells. In addition, inhibition of miR-138–5p counteracted the negative effects of simulated microgravity on osteoblast differentiation and β-catenin expression in MC3T3-E1 cells. Our findings demonstrated that a signaling pathway mechanism of miR-138–5p in regulating osteoblast differentiation under simulated microgravity, which may reveal a novel mechanism for astronauts' bone loss induced by microgravity and provide a potential therapeutic target for disused osteoprosis.

DNA origami technology has proven to be an excellent tool for precisely manipulating molecules and colloidal elements in a three-dimensional manner. However, fabrication of single crystals with well-defined facets from highly programmable, complex DNA origami units is a great challenge. Here, we report the successful fabrication of DNA origami single crystals with Wulff shapes and high yield. By regulating the symmetries and binding modes of the DNA origami building blocks, the crystalline shapes can be designed and well-controlled. The single crystals are then used to induce precise growth of an ultrathin layer of silica on the edges, resulting in mechanically reinforced silica-DNA hybrid structures that preserve the details of the single crystals without distortion. The silica-infused microcrystals can be directly observed in the dry state, which allows meticulous analysis of the crystal facets and tomographic 3D reconstruction of the single crystals by high-resolution electron microscopy., DNA origami is a valuable tool for precise manipulation of molecules in a three dimensional manner, but the design and assembly of origami units into single crystals is challenging. Here, the authors report successful fabrication of DNA origami single crystals with Wulff shapes, and regulation of their shapes by changing the symmetry and binding modes of DNA origami building blocks.

Incentive-Based Traffic Demand Management (IBTDM) provides monetary incentives to encourage commuters to alter their departures spatially or temporary with the goal of alleviating congestion. With the proliferation of smartphone technology, mobility apps have become ideal platforms for carrying out IBTDM. Tremendous amounts of empirical app usage data have been collected, but research into the behavioral insights of IBTDM remains limited. It is unclear who IBTDM's target users should be, and which users are the most likely to be stable (actively use the app) and behaviorally sustainable (willing to contribute to congestion alleviation). This study aims to profile the socio-demographics of such favored users based on behavioral and socio-demographic data collected by the Metropia app. The Ensemble Empirical Mode Decomposition (EEMD) method was used for usage trend detection. The detected usage trends were then used in pattern classification to identify stable and sustainable users. Next, binary logistic regression was adopted to explore the socio-demographic characteristics of each category of users. It was found that factors including home work days, household annual income, household size and schedule flexibility played important roles in users' usage patterns and departure time decisions. Specifically, home work days and household annual income co-influenced app usage patterns. Household size and schedule flexibility were the main determinants of departure time behavior. The findings of this research can be used to guide administrators of budget-constrained IBTDM programs who need to wisely allocate their marketing budget to increase penetration among favored users as to maximize the utility of the program.

Herein, we report the excellent De-NOx performance of La0.7Sr0.3MnO3 (LSM) perovskite-supported Pd catalysts (Pd-LSM) in alternating lean-burn/fuel-rich atmospheres using C3H6 as reductant and describe the in situ activation of the Pd catalysts via metal-support interaction (MSI) tuning. The NOx reduction conversion of the Pd-LSM catalyst increased significantly from 56.1% to 90.1% and the production of N2O was suppressed. Our results demonstrated that this behavior was mainly attributed to the in situ transformation of Pd2+ into Pd0 during the reaction. The generated Pd0 species could readily activate the C3H6 reductant and achieve an eight-fold higher turnover frequency than Pd2+ for the reduction of NOx. Moreover, excessive MSIs inhibited the in situ generation of Pd0, and thereby, lowered the De-NOx activity of the catalyst even at high Pd dispersion. In addition, the Pd-LSM catalysts exhibited much higher S tolerance than conventional Al2O3-supported catalysts. Our study provides a new approach for analyzing and designing highly active metal catalysts operated under dynamic alternating oxidizing/reducing atmospheric conditions.

Herein, Cu-modified SAPO-34 catalysts with various copper loadings are synthesized via a pH-controlled ion-exchange method. Cu exists predominantly as isolated cations at lower loadings, located at octahedrally coordinated sites in the ellipsoidal cavity of SAPO-34 zeolites, while CuO is gradually generated on the external surface at higher loadings. Our results reveal that the isolated Cu cations are the active sites for C₃H₆-SCR. The presence of CuO species enhances the C₃H₆ activation ability of the catalysts, which thus, improves the C₃H₆-SCR performance at medium and low temperatures (below 400 °C). At high temperatures, however, the formation of CuO accelerates the nonselective oxidation of C₃H₆ and causes activity decline. These findings suggest that the active temperature window for hydrocarbons-selective catalytic reduction (HC-SCR) reactions over Cu/SAPO-34 catalysts can be tailored by tuning the proportion of various copper species to fit the need in real applications, which provide new insights into understanding and designing highly efficient Cu-based zeolite catalysts.

The crystallization methodology of DNA origami frames has found salient utility in large-scalely integrating multifarious functional components following organized arrangements, thus opening up the possibilities for optical, biological, and other interdisciplinary applications. However, the single strand-dominated spacing region between adjacent DNA origami units has extremely restricted the adjustment of DNA origami separations, leading the soft crystals susceptible to environmental influences. Herein, we developed a cocrystallization pathway by incorporating rigid DNA rods into a DNA origami assembly system to achieve mutually ordered bridging on a three-dimensional scale. The intervention of DNA rods significantly improved the rigidity and crushing resistance of entire cocrystals and rendered DNA origami units exhibiting different spacing distances within the obtained crystal phase when varying DNA rod structures artificially. Such a tuning strategy that uses DNA rods as allosteric factors would provide a rational method for accessing diverse crystalline states and even modulating the tailorable properties of materials on demand.

The self-assembly of block copolymers in a confined space has been proven to be a facile and robust strategy for fabricating assembled structures with various potential applications. Herein, we employed a new pH-responsive polymer self-assembly method to regulate ion transport inside artificial nanochannels. The track-etched asymmetric nanochannels were functionalized with PS22k-b-P4VP17k/hPS4k blend polymers, and the ionic conductance and rectification properties of the proposed system were investigated. The pH-actuated changes in the surface charge and wettability resulted in the selective pH-gated ionic transport behavior. The designed system showed a good switching property to the pH stimulus and could recover during the repetitive experiments. The gating ability of the polymer-nanochannel system increased with increasing the weight of the homopolymer, and the proposed platform demonstrated robust stability and reusability. Numerical and the dissipative particle dynamics simulations were implemented to emulate the pH-dependent self-assembling behavior of diblock copolymers in a confined space, which were consistent with the experimental observations. As an example of the self-assembly of polymers in nanoconfinements, this work provides a facile and robust strategy for the regulation of ion transport in synthetic nanochannels. Meanwhile, this work can be further extended to design artificial smart nanogates for various applications such as mass delivery and energy harvesting.

Dispersions of magnetic nanoparticles (MNPs) can exhibit paramagnetic ferrofluid or ferromagnetic liquid behavior. By modifying the surface functionality of MNPs, ferrofluids have been used to fabricate novel magnetically actuated devices. If the surface-functionalized MNPs interact with complementary ligands at a fluid-fluid interface, MNP surfactants form and in situ assemble at the interface. When jammed interfacially, MNP surfactants give rise to ferromagnetic behavior of the liquid (droplet), which is endowed with permanent magnetic dipoles while maintaining all of the characteristics of a fluid system. Here, we give a brief overview of the developments in the dispersion of MNPs in liquids from ferrofluids to ferromagnetic liquid droplets, their responses to external fields, and the manipulation of these responses for end uses. The reversible room-temperature para-to-ferro transformation of magnetic liquids is highlighted. We discuss challenges in the synthesis and characterization of these unusual liquids along with potential technological applications.

Zero-dimensional lead-free organic-inorganic hybrid metal halides have drawn attention as a result of their local metal ion confinement structure and photoelectric properties. Herein, a lead-free compound of (Gua)3Cu2I5 (Gua = guanidine) with a different metal ion confinement has been discovered, which possesses a unique [Cu2I5]3- face-sharing tetrahedral dimer structure. First-principles calculation demonstrates the inherent nature of a direct band gap for (Gua)3Cu2I5, and its band gap of ∼2.98 eV was determined by experiments. Worthy of note is that (Gua)3Cu2I5 exhibits a highly efficient cool-white emission peaking at 481 nm, a full-width at half-maximum of 125 nm, a large Stokes shift, and a photoluminescence quantum efficiency of 96%, originating from self-trapped exciton emission. More importantly, (Gua)3Cu2I5 single crystals have a reversible thermoinduced luminescence characteristic due to a structural transition scaled by the electron-phonon coupling coefficients, which can be converted back and forth between cool-white and yellow color emission by heating or cooling treatment within a short time. In brief, as-synthesized (Gua)3Cu2I5 shows great potential for application both in single-component white solid-state lighting and sensitive temperature scaling.

The ability to manipulate liquids in a loss-free and refined manner has long been anticipated, with applications in fields such as analytical chemistry, medical diagnosis, and droplet-based manufacturing. The challenge derives from the liquid/solid contacts, which incur spontaneous spreading, strong pinning, and substantial retention. The retention-proof interfaces, including superhydrophobic surfaces, lubricant-infused surfaces, and liquid marbles, mitigate these issues by respectively introducing air, lubricant, and particulate layers to isolate liquids from underlying solids. Assisted by these interfaces, contrastive physical/chemical designs and engineering methods are leveraged to unlock unparalleled liquid-control methods that are otherwise inaccessible. In this review, we focus on the application of retention-proof interfaces in three facets of manipulation: the aliquoting, grip, and transport of fluids. We discuss the key features, strategies and implementations, highlighting the fundamental physics and operation principles. For aliquoting, the cooperation between diminutive geometries and interfaces in partition droplets are examined. For the grip, we discuss the impact of micro-/nanotextures on adhesion behavior and highlight the mechanisms of switching the adhesive forces. For the transport, we review various engineering and functionalizing forms through which subtle driving forces are dexterously imposed on mobile droplets. The performance of different techniques is evaluated, and potential directions are proposed.

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

Health-care waste (HCW) management is an important issue, especially in developing countries. How to choose the best management technology is a challenging and open subject in this issue. Limited work has been done, but there is still a lack of a technical approach that not only takes into account multi-granular linguistic terminology, but also considers the attitude characters of decision makers (DMs). To adress the above problem, in this paper a hybrid multi-criteria decision making scheme is proposed based on soft likelihood function and D-numbers. First, the D-numbers is used to characterize complex multi-grained decision information. Secondly, a novel soft likelihood function based on power ordered weighted averaging operator (POWA) is designed to effectively take into account the DMs’ preferences, which is then integrated into the proposed HCW management approach. Eventually, the effectiveness and superiority of the proposed approach is demonstrated through an application example. In particular, an intuitive advantage has been confirmed that the proposed method can adjust the gap between adjacent alternatives through decision preference to distinguish differences. This is expected to provide a reliable fault-tolerant interval for decision-making in HCW management, and further improve the reliability of the algorithm. In addition, the analysis and evaluation also confirm the reliability and practicality of the proposed technology.

To accurately assess the combustion fluctuation for a scramjet, a new method based on indium tin oxide (ITO) thin-film thermocouples (TFTCs) and time–frequency analysis (TFA) was developed in this study. ITO TFTCs were used to measure the transient wall-temperature change of the combustor, and the combustion fluctuation features were extracted from the temperature signal via TFA. The combustion fluctuation-resolving abilities of different TFA methods, including the short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert–Huang transform (HHT), were investigated using a reconstruction signal. The results indicated that the CWT can provide an excellent global view and that the HHT is useful for localizing the frequency features in the time domain. Then, the effect of the equivalence ratio (ER) on the supersonic combustion fluctuation was researched. The total energy of the fluctuation was strongly correlated with the increase in the ER. Meanwhile, the frequency of main fluctuation was always concentrated at approximately 395 Hz, just like an intrinsic characteristic of the combustor. Additionally, the fuel type affected the energy of the combustion fluctuation, rather than the frequency.

Developing low-priced, yet effective and robust catalysts for the nitrogen reduction reaction (NRR) is of vital importance for scalable and renewable electrochemical NH3 synthesis. Herein, we provide the first demonstration of MoS3 as an efficient and durable NRR catalyst in neutral media. The prepared amorphous MoS3 naturally possessed enriched S vacancies and delivered an NH3 yield of 51.7 μg h−1 mg−1 and a Faradaic efficiency of 12.8% at −0.3 V (RHE) in 0.5 M LiClO4, considerably exceeding those of MoS2 and most reported NRR catalysts. Density functional theory calculations unraveled that S vacancies involved in MoS3 played a crucial role in activating the NRR via a consecutive mechanism with a low energetics barrier and simultaneously suppressing the hydrogen evolution reaction.

In this study, the effects of five different ionic and nonionic surfactants on the physical properties and microstructures of a metakaolin-based geopolymer (MKG) were investigated. It is the first comprehensive comparative study of the effects of sodium lauryl sulfonate (SLS), alkyl polyglycoside (APG), benzalkonium chloride (BAC), sucrose fatty acid esters (SE), and stearic acid (STA) on MKG. Viscosity, densities, apparent water absorption, and compressive strength were measured, and pore structures, micro-defects, and gels observed through scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP). In the MKG slurry, a high affinity of surfactants to liquid-air interfaces increased viscosity and promoted the generation of bubbles. Based on both the ionic types and molecular configurations of the surfactants, stronger adsorption of a surfactant on the surface of the metakaolin resulted in better dispersion of metakaolin particles and a denser microstructure of the MKG. The surfactants with weaker adsorption (SLS and APG) caused higher porosity, a larger pore size, and more micro-defects, while those with stronger adsorption (BAC, SE, and STA) led to relatively lower porosity and denser microstructures. Density, water absorption, and compressive strength were closely related to the total intrusion porosity of the MKG. The mechanisms underlying surfactant adsorption to the surface of metakaolin are proposed.

Aided by advancements in smartphone technology, many apps have been developed to provide personalized incentives to trigger changes in travel behavior, targeted at relieving road congestion. Typical personalized incentives include information, travel feedback, and monetary rewards. Understanding how different types of personalized incentives jointly help alleviate peak-hour traffic and save time is critical to the success of mobility management. Data from Metropia, a mobility management app, was used for this analysis. By employing a panel binomial logistic model and a panel zero-inflated Poisson model to examine the effects of such incentives, this study found that: based on real-time information, users rely more on expected time-savings to adjust travel plans to eventually save time. In terms of feedback, previous user experiences do have an impact on future travel plans. Economic incentives encourage peak avoidance and help save travel time, but the dependence on rewards to avoid peak-hour traffic increases over time. Users who plan travel tend to flee from peak-hour traffic. These findings provide evidence to support incentive-based mobility management. To promote a larger scale of peak avoidance and save more drivers' travel time, local agencies and app developers should collaborate and provide users detailed real-time information and recommend personalized departure time. App developers can further improve the prediction accuracy of driving time, which, in return, helps obtain users' positive feedback and gain more users. Although it is costly to provide monetary rewards, continual provision of a greater amount of money can help trigger departure time changes. Sending reminders to urge travelers to make a travel plan can promote peak avoidance and help save drivers’ travel time.

This paper proposes a second-order cone programming (SOCP) relaxation for the generalized trust-region problem by exploiting the property that any symmetric matrix and identity matrix can be simultaneously diagonalizable. We show that our proposed SOCP relaxation can provide a lower bound as tight as that of the standard semidefinite programming (SDP) relaxation. Moreover, we provide a sufficient condition under which the proposed SOCP relaxation is exact. Since the standard SDP relaxation suffers from a much heavier computing burden, the proposed SOCP relaxation has a much higher efficiency in solving process. Then we design a branch-and-bound algorithm based on this SOCP relaxation to obtain the global optimal solution for a general problem. Three types of numerical experiments are carried out to demonstrate the effectiveness and efficiency of our proposed SOCP relaxation.

Knowledge distillation has proven to be an effective model compression method that exploits the knowledge from a teacher model for supervising a student model by minimizing the distribution difference between the knowledge and the prediction produced by the student model. In this work, we focus on improving its performance from the perspective of divergence measures. A general form representing a family of divergence measures is introduced and a novel learning paradigm that jointly optimizes multiple measures is proposed by formalizing it as a multi-objective learning problem. Conditioned on Pareto optimality, the weights of different divergences are tuned in an automated way during training. Extensive experiments on multiple datasets show the proposed method can significantly improve the performance of student networks compared to the state-of-the-art methods for knowledge distillation. Codes are available at https://github.com/CML00/MoDiv .

As a promising cathode material for high performance lithium ion batteries, olivine LiMnxFe1−xPO4 (LMFP) combines the high safety of LiFePO4 and the high energy density of LiMnPO4. However, there are still obstacles to overcome for achieving higher rate performance, especially its inherent low electronic conductivity and Li+ diffusion coefficient. Here, the restricting factors for realizing high rate performance LMFP cathode materials are reviewed systematically. The bulk properties that affect the internal ion transport and electronic conduction are thoroughly expounded, particularly the phase transition mechanism, lattice distortion, point defects, element doping, Fe/Mn ratio and particle morphology. Moreover, the effect of utilizing a carbon-based/non-carbon material coating for improving the interface structure on rate performance is discussed comprehensively. A particular emphasis is placed on the design of LMFP-based batteries with high rate capability as well from the aspect of cell preparation technology, including the electrolyte selection and electrode design. Finally, on the basis of state-of-the-art understanding of bulk properties, the interface structure and cell preparation engineering, several technical challenges and research trends in improving the rate performance of LMFP cathode materials are proposed.

A chirp compensation method of fiber Bragg grating (FBG) is proposed to improve the residual stress measurement accuracy in the hole-drilling method. The effects of the radii of the hole, the distance between the FBG and the hole, the Young’s modulus of the material, and the residual stress on the FBG chirped spectra are analyzed. The relationship between radii of the hole, the distance between the FBG and the hole, the full-width at half maximum (FWHM), and the peak wavelength are established. The feasibility of the method is verified through experiments, which shows that the residual stress measurement error can be reduced by the compensation method.

The electrochemical nitrogen reduction reaction (NRR) provides a promising route for energy-effective and sustainable ammonia synthesis, while developing high-efficiency NRR electrocatalysts is extremely desirable. Here we first exploited foordite SnNb2O6 nanosheets as an effective and durable NRR catalyst, which presented an NH3 yield of 53.1 μg h−1 mg−1 and a faradaic efficiency of 17.6% at −0.3 V (vs. reversible hydrogen electrode). Theoretical computations uncovered the dual-active-center mechanism of Nb3c (3c means three-fold coordinated bond) and Sn4c–Nb5c dimer on SnNb2O6 (311) to synergistically enhance the N2 adsorption and activation, consequently lowering the energy barrier for the rate-determining step and boosting the electrocatalytic NRR.

Due to the proposal and evolution of the DNA origami technique over the past decade, DNA molecules have been utilized as building blocks for the precise construction of nanoscale architectures. Benefiting from the superior programmability of DNA molecules, the sequence-dependent recognition mechanism and robust complementation among DNA strands make it possible to customize almost arbitrary structures. Such an assembly strategy bypasses some of the limits of conventional fabrication methods; the fabrication accuracy and complexity of the target product are unprecedentedly promoted as well. Furthermore, due to the spatial addressability of the final products, nanostructures assembled through the DNA origami technique can also serve as a versatile platform for the spatial positioning of functional elements, represented by colloidal nanoparticles (NPs). The subsequent fabrication of heterogeneous functional nanoarchitectures is realized via modifying colloidal NPs with DNA strands and manipulating them to anchor into DNA origami templates. This has given rise to investigations of their novel properties in nanophotonics and therapeutic effects towards some diseases. In this review, we survey the crucial progress in the development of DNA origami design, assembly and structural analysis and summarize available applications in nanophotonics and cancer therapy based on the object-dressed DNA origami complex. Moreover, we elucidate the development of this field and discuss the potential directions of this kind of application-oriented nanomanufacturing.

Pathological angiogenesis is a critical contributor to atherosclerotic plaque rupture. However, there are few effective theranostic strategies to stabilize plaques by suppressing neovascularization. In this study, we fabricated a polymeric nanosystem using 3 nm manganese ferrite (MnFe2O4) and perfluorohexane (PFH) stabilized by polylactic acid-glycolic acid (PLGA) shells and conjugated to the surface of an anti-vascular endothelial growth factor receptor 2 (VEGFR2) antibody [ramucirumab (Ram)]. The PFH@PLGA/MnFe2O4-Ram nanoparticles (NPs) were used as atherosclerotic plaque angiogenesis theranostics for multimodal imaging-guided photothermal therapy (PTT). Three-nanometer MnFe2O4 is an excellent magnetic resonance imaging T1 and photoacoustic imaging contrast agent. Upon exposure to near-infrared (NIR) light, MnFe2O4 in the NPs could transform NIR light into thermal energy for the photothermal elimination of plaque angiogenesis. Additionally, optical droplet vaporization of PFH in the NPs triggered by the thermal effect to form gas bubbles enhanced ultrasound imaging. Our in vitro experiments revealed that PFH@PLGA/MnFe2O4-Ram NPs actively accumulated in rabbit aortic endothelial cells, and NP-mediated PTT promoted endothelial cell apoptosis while inhibiting their proliferation, migration, and tubulogenesis. Notably, the PFH@PLGA/MnFe2O4-Ram NPs possessed excellent photostability and biocompatibility. In the rabbit advanced atherosclerotic plaque model, PFH@PLGA/MnFe2O4-Ram NP-guided PTT significantly induced apoptosis of neovascular endothelial cells and improved the hypoxia status in the plaque 3 days after treatment. On day 28, PTT significantly reduced the density of neovessels and subsequently stabilized rabbit plaques by inhibiting plaque hemorrhage and macrophage infiltration. Collectively, these results suggest that PFH@PLGA/MnFe2O4-Ram NP-guided PTT is a safe and effective theranostic strategy for inhibiting atherosclerotic plaque angiogenesis.

Oxidative stress has been proven to play a critical role in the pathogenesis of neuronal injury. As a novel adipocytokine, omentin is produced by visceral adipose with insulin sensitizing effects and has been revealed to possess anti-inflammatory effects. However, the possible effect of omentin on oxidative stress remains unknown. The present study aimed to detect the potential protective effect of omentin against hydrogen peroxide (H

This paper proposes a novel quadratic convex reformulation (QCR) for the nonconvex quadratic program with convex quadratic constraints. This new QCR is based on the technique of simultaneous diagon...

This article studies multiagent (convex and nonconvex ) optimization over static digraphs. We propose a general distributed asynchronous algorithmic framework whereby 1) agents can update their local variables as well as communicate with their neighbors at any time, without any form of coordination; and 2) they can perform their local computations using (possibly) delayed, out-of-sync information from the other agents. Delays need not be known to the agent or obey any specific profile, and can also be time-varying (but bounded). The algorithm builds on a tracking mechanism that is robust against asynchrony (in the above sense), whose goal is to estimate locally the average of agents’ gradients. When applied to strongly convex functions, we prove that it converges at an R-linear (geometric) rate as long as the step-size is sufficiently small. A sublinear convergence rate is proved, when nonconvex problems and/or diminishing, uncoordinated step-sizes are considered. To the best of our knowledge, this is the first distributed algorithm with provable geometric convergence rate in such a general asynchronous setting. Preliminary numerical results demonstrate the efficacy of the proposed algorithm and validate our theoretical findings.

The paper presents the results of an experimental and numerical study on the combustion characteristics of a supersonic combustor operation enhanced by pilot hydrogen and air throttling, including ...

The shelter location is very important for evacuation planning in natural disasters, and evolutionary algorithms (EAs) have demonstrated their effectiveness in solving this challenging problem. However, few EAs have been reported focusing on the shelter location problem under uncertainty of road networks due to the expensive cost of calculating the evacuation distance for individual evaluation. To address this issue, in this article, we propose a clustering-based surrogate-assisted multiobjective EA, termed AR-MOEA+SA, in the framework of a recently developed EA AR-MOEA. In AR-MOEA+SA, a surrogate model, the radial basis function (RBF), is adopted to approximately calculate the evacuation distance under uncertainty of road networks. Due to the fact that there often exist a large number of communities needing to be considered in shelter location, a clustering strategy is suggested to convert the surrogate of high-dimensional problem into the one of low-dimensional problem in the proposed AR-MOEA+SA for efficiently building the RBF network. A population initialization strategy is also suggested in AR-MOEA+SA to enhance the quality of training data in the early stages of evolution. Experimental results on a variety of test instances demonstrate the superiority of the proposed AR-MOEA+SA over the original version of AR-MOEA in terms of both computational efficiency and solution quality.

3-dimensional (3D) bioprinting technology provides promising strategy in the fabrication of artificial tissues and organs. As the fundamental element in bioprinting process, preparation of bioink with ideal mechanical properties without sacrifice of biocompatibility is a great challenge. In this study, a supramolecular hydrogel-based bioink is prepared by polyethylene glycol (PEG) grafted chitosan, α-cyclodextrin (α-CD) and gelatin. It has a primary crosslinking structure through the aggregation of the pseudo-polyrotaxane-like side chains, which are formed from the host-guest interactions between α-CD and PEG side chain. Apparent viscosity measurement shows the shear-shinning property of this bioink, which might be due to the reversibility of the physical crosslinking. Moreover, with β-glycerophosphate at different concentrations as the secondary crosslinking agent, the printed constructs demonstrate different Young's modulus (p, Graphical abstract Image 1, Highlights • Supramolecular bioink demonstrates good shear-shinning property. • Dual crosslinking provides the bioink with tunable strength. • Tunable strength facilitates stem cells lineage differentiation.

The subcycle interaction of light and electrons has been one of the key frontiers in free-electron lasers, attosecond science and dynamical investigation of matter. Capturing the underlying subcycle dynamics of electrons with an optical field promises fascinating vistas with unprecedented temporal resolution. Yet the rigorous synchronization requirement has kept its realization out of reach. Here, by direct spatial observation of periodic electron bunch fringes, we demonstrate a laser streaking concept for revealing the dynamics of free electrons emitted from a plasma mirror under sub-relativistic laser intensity. Field-induced electron beam deflection demonstrates subcycle charge dynamics with a streaking speed of ~60 μrad as−1. This provides us with an attosecond-resolution metrology to obtain more direct evidence about the light-field-induced electron dynamics in the plasma mirror. Our results offer unprecedented characterization of attosecond dynamics and open the way to extensive experimental investigations of the interaction of attosecond electrons with intense lasers. By focusing a sub-relativistic infrared laser pulse onto a silica target, a periodic deflection pattern of attosecond electron pulse trains is observed. It reveals these subcycle charge dynamics with a streaking speed of ~60 μrad as−1.

The travel impedance skim matrix is one of the most essential intermediate products within transportation forecasting models and is a fundamental input for activity-based transportation forecasting models. It reflects interzonal travel time, travel time reliability, travel costs, etc. by time of day. The traditional method to obtain skim matrices is to execute multiple times of time-dependent, shortest-path calculations. However, the computational and memory use burden can easily increase to an intractable level when dealing with mega-scale networks, such as those with thousands of traffic-analysis zones. This research proposes two new approaches to extract the interzonal travel impedance information from the already existing vehicle trajectory data. Vehicle trajectories store travel impedance information in a more compact format when compared to time-dependent link performance profiles. The numerical experiments highlight huge potential advantages of the proposed approaches in terms of saving both memory and CPU time.

In the last decade, the detection of overlapping communities has received increasing attention in network science. Among various clustering techniques, the fuzzy clustering has been widely adopted in overlapping community detection, since the soft assignment provided by it naturally meets the overlapping between multiple communities. The crucial step of fuzzy-clustering-based overlapping community detection is to find the optimal community centers, so that the overlapping communities can be obtained according to the membership degrees between nodes and community centers. In this article, we propose an evolutionary multiobjective optimization-based fuzzy method for overlapping community detection. In contrast to traditional fuzzy clustering methods, the proposed method optimizes the community centers by using a specially tailored multiobjective evolutionary algorithm. Moreover, it can also find an appropriate fuzzy threshold for each node, so that diverse overlapping community structures can be uncovered. In the experiments, we compare the proposed method with six state-of-the-art overlapping community detection approaches on synthetic and real-world networks with different scales and characteristics. The statistical results demonstrate that the proposed method can obtain the best results on most test instances.