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Many fish swim using various kinematic patterns of head and tail combinations to change their body posture, which reduces drag while swimming. This study proposes a new method to improve the energy-harvesting performance of a flapping foil by actively controlling the combined movements of the leading- and trailing-edge flaps. These movements allow the foil to deform to a shape with preferable hydrodynamic performance during the flapping motion inspired by fish swimming modes. The study employs two-dimensional (2D) computational fluid dynamics (CFD) simulations to investigate the effect of the forced oscillation of a leading-edge flap on the energy-harvesting performance of a flapping foil. Consequently, the optimal combination of operating parameters of the leading-edge flap leading to greater efficiency and an apparent increase in power production from the flapping foil can be determined. Therefore, the energy-harvesting performance of a flapping foil with dynamic leading- and trailing-edge flaps was investigated numerically. The results suggest that synchronized movement between the leading- and trailing-edge flaps, which mimic the movement of live fish, can increase the camber of the foil and effectively manage the boundary layer by adjusting the frequency and amplitude of the oscillations, thereby generating elevated hydrodynamic forces acting on the foil. Therefore, the proposed flapping foil with swinging leading- and trailing-edge flaps can exhibit superior energy-harvesting performance than its conventional alternative across the full spectrum of operations. • A novel concept of a flapping foil flow energy harvester is proposed in this work. • Actively controlled leading- and trailing-edge flaps were adopted to mimic the head and tail oscillations of a fish. • CFD analysis were performed to investigate the performance of the proposed flapping foil configuration. • The proposed flapping foil energy harvester has better energy harvesting performance than its conventional counterpart. [ABSTRACT FROM AUTHOR]

This paper presents a 2-D real-time modeling approach for a proton-exchange-membrane fuel cell (PEMFC). The proposed model covers multi-physical domains for both fluidic and electrochemical features, which considers in particular the flow field geometric form of fuel cell. The characteristics of reactant gas convection in the serpentine gas pipeline and diffusion phenomenon through the gas diffusion layer (GDL) are thoroughly considered in fluidic domain model. In addition, a three levels iterative solver is developed in order to accurately calculate the implicit spatial physical quantities distribution in electrochemical domain. Moreover, the proposed 2-D real-time modeling approach uses a numerical method to achieve a fast execution time, and can thus be further easily applied to any real-time control implementation or online diagnostic system. After experimental validation under different fuel cell operating conditions, an iterative Least Angle Regression (LAR) method is used to efficiently and accurately perform the global parameters sensitivity analysis based on Sobol definition. The online analysis results give an insight into the influences of modeling parameters on fuel cell performance. The effect of interactions between parameters’ sensitivities is especially investigated, which can provide useful information for degradation understanding, parameters tuning, re-calibration of the parameters and online prognostic. [ABSTRACT FROM AUTHOR]

As an online adaptive optimization algorithm, the extremum seeking method (ESM) can be effectively employed to find an optimal operating point of a static nonlinear system in real-time. This paper presents a comparative study of different ESM schemes for online energy management strategy of fuel cell hybrid electric vehicles (FCHEVs). By applying the extremum seeking controller, the fuel cell system operating points can be maintained in a high efficiency region and thus saving the hydrogen consumption. In addition, battery state of charge (SOC) is considered as the input of penalty function for extremum seeking controller, in order to prevent over-discharging/over-charging of the lithium-ion battery during the FCHEVs operation. Different schemes of ESM presented in this comparative study consist of first-order ESM, high-pass filter based ESM, and band-pass filter based ESC. The main evaluation criteria in this comparative study include the utilization of lithium-ion battery, the fluctuation of fuel cell system output power, the fuel cell system efficiency and the hydrogen consumption. A Hardware-In-the-Loop (HIL) platform is used to experimentally validate the presented comparative study. Experimental comparison results show that, the performance of all the presented ES controllers is close to that of offline benchmark dynamic programming. The band-pass filter based ES controller is preferred to improve both the performance and durability of energy storage system (ESS) in FCHEVs, since this controller is found to have a good ability to limit the fuel cell power dynamics. [ABSTRACT FROM AUTHOR]

In this paper, an innovative robust prediction algorithm for performance degradation of proton exchange membrane fuel cell (PEMFC) is proposed based on a combination of model-based and data-driven prognostic method. A novel approach using the moving window method is applied, in order to 1) train the developed models; 2) update the weight factors of each method and 3) further fuse the predicted results iteratively. In the proposed approach, both model-based and data-driven methods are simultaneously used to achieve a better accuracy. During the prediction process, each dataset in the proposed moving window are divided into three sections respectively: training, evaluation and prediction. The training data are used first to identify the models parameters. The evaluation data are then used to measure the weight of each method, which represents the degree of confidence of each method in the actual state. Based on these dynamically adjusting weight factors, the prediction results from different methods are then fused using weighted average methodology to calculate the final prediction results. In order to verify the proposed method, three experimental validations with different aging testing profiles have been performed. The results demonstrate that the proposed hybrid prognostic approach can achieve a higher accuracy than conventional prediction methods. In addition, in order to find the satisfactory trade-off between the prediction accuracy and forecast time for optimizing on-line prognostic, the performance variation of proposed approach with different moving window length is further showed and discussed. [ABSTRACT FROM AUTHOR]

In November 2021, Omicron BA.1, containing a raft of new spike mutations, emerged and quickly spread globally. Intense selection pressure to escape the antibody response produced by vaccines or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection then led to a rapid succession of Omicron sub-lineages with waves of BA.2 and then BA.4/5 infection. Recently, many variants have emerged such as BQ.1 and XBB, which carry up to 8 additional receptor-binding domain (RBD) amino acid substitutions compared with BA.2. We describe a panel of 25 potent monoclonal antibodies (mAbs) generated from vaccinees suffering BA.2 breakthrough infections. Epitope mapping shows potent mAb binding shifting to 3 clusters, 2 corresponding to early-pandemic binding hotspots. The RBD mutations in recent variants map close to these binding sites and knock out or severely knock down neutralization activity of all but 1 potent mAb. This recent mAb escape corresponds with large falls in neutralization titer of vaccine or BA.1, BA.2, or BA.4/5 immune serum. [Display omitted] • 25 potent RBD mAbs generated from vaccinees suffering BA.2 breakthrough infections • Potent mAbs bind 3 clusters, 2 correspond to early-pandemic binding hotspots • RBD mutations in recent variants map close to these binding sites • Neutralization activity of all but 1 potent mAb is reduced Dijokaite-Guraliuc et al. analyze potently neutralizing antibodies from vaccinated individuals with BA.2 breakthrough infections. The antibodies bind 3 sites on the receptor-binding domain, 2 of which are in common with early-pandemic antibodies. Mutations in more recent variants map closely to these sites, leading to reduced neutralization in all but one mAb. [ABSTRACT FROM AUTHOR]

Variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have caused successive global waves of infection. These variants, with multiple mutations in the spike protein, are thought to facilitate escape from natural and vaccine-induced immunity and often increase in affinity for ACE2. The latest variant to cause concern is BA.2.75, identified in India where it is now the dominant strain, with evidence of wider dissemination. BA.2.75 is derived from BA.2 and contains four additional mutations in the receptor-binding domain (RBD). Here, we perform an antigenic and biophysical characterization of BA.2.75, revealing an interesting balance between humoral evasion and ACE2 receptor affinity. ACE2 affinity for BA.2.75 is increased 9-fold compared with BA.2; there is also evidence of escape of BA.2.75 from immune serum, particularly that induced by Delta infection, which may explain the rapid spread in India, where where there is a high background of Delta infection. ACE2 affinity appears to be prioritized over greater escape. [Display omitted] • BA.2.75 affinity for ACE2 is increased 9-fold compared with BA.2 • N460K increases neutralization escape and likely increases ACE2 affinity • The revertant R493Q decreases neutralization escape but increases ACE2 affinity • Affinity to ACE2 appears to be prioritized over neutralization escape Huo et al. characterize the SARS-CoV-2 variant BA.2.75 (originally identified in India). Its affinity for ACE2 is increased 9-fold over BA.2, and there is evidence of escape of BA.2.75 from immune serum, particularly from Delta infection. ACE2 affinity appears to be prioritized over greater escape via the R493Q reversion mutation. [ABSTRACT FROM AUTHOR]

Unlike traditional rotary turbines, the flapping hydrofoil energy harvesters can capture kinetic energy from flowing fluids by undergoing combined heaving and pitching motions. In this paper, a new type of flapping hydrofoil energy harvester whose trailing-edge can deflect upwards during downstroke flapping cycle and downwards during the upstroke has been proposed. The energy-extraction performance of the proposed flapping hydrofoil then was investigated by numerical simulations based on the two-dimensional incompressible Navier-Stokes equations at a Reynolds number of 4.7 × 10 5. Influences of various characteristic parameters including deflection angle amplitude, flap length, gap width on the energy-extraction efficiency of the proposed flapping hydrofoil were analyzed. The present results show that the heaving force is the most dominant contributor to total power output of the flapping hydrofoil and it can be greatly increased due to the deflection of TEF. It is also found that there exist optimal deflection angle amplitude and TEF length which can significantly improve the energy-extraction efficiency at moderate to high values of reduced frequency. Moreover, the smaller the gap width, the higher the efficiency can be achieved. As a result, the maximum energy-extraction efficiency of the flapping hydrofoil with optimized TEF can be increased by approximately 23.5% compared to the conventional flapping hydrofoil-based current energy harvester. • A novel flapping hydrofoil energy harvester with trailing-edge flap (TEF) is proposed. • Energy extraction performance of the proposed flapping hydrofoil is numerically studied. • Effects of various parameters of TEF on the power extraction efficiency are analyzed. • An increment in power extraction efficiency is achieved by using the optimized TEF. [ABSTRACT FROM AUTHOR]

• Monolayer WS 2 crystal with edge lengths ranged from 10 to 460 μm can be readily synthesized on SiO 2 /Si substrate. • The WS 2 crystals exhibit homogeneous distributions of the crystallinity, electron doping and residual strain. • The amount of WO 3 used in this study is three orders lower than the commonly reported one and the semi-sealed quartz tube can be reused more than 50 times after mildly cleaning. Synthesis of monolayer WS 2 crystals on SiO 2 /Si substrate has attracted interests due to the advantage of fabrication of field effect transistor without WS 2 transfer process. Although substantial efforts have been achieved in recent years, controllable synthesis of uniform and large-area monolayer WS 2 crystals on SiO 2 /Si substrate is still challenging. Herein, we report an elegant method to synthesize monolayer WS 2 crystals on SiO 2 /Si substrate by using NaCl as a growth promoter in one semi-sealed quartz tube. It is found that triangular monolayer WS 2 with edge lengths ranged from 10 to 460 μm can be readily synthesized within 5 min of growth by adjusting the growth temperature and weight ratio of NaCl and WO 3. The Raman mapping results indicate that the as-synthesized WS 2 crystals exhibit homogeneous distributions of the crystallinity, electron doping and residual strain across the entire triangular domains regardless of their dimensions. However, the smaller WS 2 crystals exhibit a higher electron doping and less residual strain compared to the larger one obtained under the same growth conditions. Importantly, the amount of WO 3 used in this study is three orders lower than the commonly reported one and the semi-sealed quartz tube can be reused more than 50 times after mildly cleaning. [ABSTRACT FROM AUTHOR]

This paper presents a novel multi-objective model of active distribution network planning based on stochastic programming and uncertain random network (URN) theory. The planning model is proposed to find the final scheme with optimal alternative, location, size and operational strategy for the candidate distribution lines, transformer substations (TSs), distribution generations (DGs), static var compensators (SVCs) and on-load tap changers (OLTCs). Firstly, a scenario-based approach is developed to analyse the uncertainties in network system, such as the demand and intermittency of renewable sources. Since the impact of multiple uncertain factors on network cannot be ignored, a network frame is then modelled by uncertain and random weights of spanning tree (ST) instead of fixed value. In order to achieve the minimization of total cost, and further the selection of a minimum spanning tree (MST) with the uncertain and random weight, a 3-dimensional uncertain space is constructed based on the combination of the previous two targets. In addition, a second-order cone programming (SOCP) is applied to cope with the multi-objective, mixed-integer nonlinear nature of the proposed planning model. Simulation is performed on a modified Pacific Gas and Electric Company (PG&E) 69-bus distribution system, and the results demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimized algorithm of two-impulse Earth–Moon transfers in the circular restricted three-body problem (CR3BP) is proposed based on differential correction approach. In this innovative approach, the initial and final states of transfer orbit are estimated reasonably by fully considering the periapsis map (PM) condition. Then the Newton–Raphson method is utilized iteratively to deduce the differential equations of transfer orbit and further to yield the accurate initial state. Thus, the planar two-impulse Earth–Moon transfer can be accomplished. In addition, a simple design procedure is developed to solve the problem that arises from more unknown parameters in the spatial CR3BP. Therefore, the proposed method can be applied not only for the planar CR3BP but also the spatial CR3BP. Numerical results show the two-impulse Earth–Moon transfers need less energy than the Hohmann transfers and shorter flight time than the Weak Stability Boundary (WSB) transfers. Moreover, the proposed method can effectively enable a large set of two-impulse Earth–Moon transfers to be computed numerically. [ABSTRACT FROM AUTHOR]

The polarization characteristic (V I curve) describes the basic working performance of proton exchange membrane (PEM) fuel cell. The variations of internal parameters including physical and empirical ones have great impact on the polarization characteristic. This paper investigates the parametric variations effect on the fuel cell voltage during the full range current. Some important parameters in the electrochemical domain and fluidic domain are selected and their influences on the mass transfer and electrochemical reaction are studied. The analytic results show that the positive- and negative-deviations of the selected parameters have very different effect on the cell voltage. Multi-parameter sensitivity analysis (MPSA) based on Monte-Carlo method is used to identify the sensitivity index of each parameter. The study results could provide a guidance for the parameter identification during the fuel cell modeling or manufacturing. [ABSTRACT FROM AUTHOR]

Free-standing nanocrystalline graphite (NCG) membranes directly grew on SiN x substrates with the aid of the catalyst-free chemical vapor deposition method. A new type of NCG nanopore was formed in the NCG membrane via controlled dielectric breakdown in KCl electrolyte. The electrical properties, especially the ionic conductance of the NCG nanoporewere examined under different physical parameters (pore size, ion strength, and pH condition). Due to their impressive transport properties, we first realized DNA sensing using the NCG nanopores. [Display omitted] • A nanopore was formed in a free-standing nanocrystalline graphite 2D membrane via controlled dielectric breakdown. • Nonlinear but symmetric I–V curves of the NCG nanopore indicate the existence of internal electrostatic potential. • DNA translocation caused stochastic upward current pulses. • The dsDNA translocation speed was slowed down compared to other nanopores. [ABSTRACT FROM AUTHOR]

• Long duration dynamic load durability test is performed on a single PEMFC. • The attention mechanism is applied to combine with the RNN model (LSTM and GRU) to perform the prediction work. • Two datasets are processed to assess the efficiency of different prediction models. Currently, the larger-scaled commercialization of fuel cell technology is considerably impeded by the limited durability of fuel cells. Prognostics and health management (PHM) is one of the most widely researched technologies used to improve the durability of fuel cell devices. More recently, the combination of deep neural network approaches and PHM techniques shows a broad research prospect. Attention mechanisms can enhance their data processing ability, which helps to extract useful features more efficiently. Herein, we propose an attention-based Recurrent neural network (RNN) model to improve the prognostics of PHM, which enables a more accurate prediction of the output voltage degradation of proton exchange membrane fuel cell (PEMFC) based on the original long-term dynamic loading cycle durability test data. In particular, the prediction results with different prediction models, namely, long short-term memory (LSTM), gated recurrent unit (GRU), attention-based LSTM, and attention-based GRU are obtained and compared. For dynamic test data (dataset 1), the root mean square error results for the attention-based LSTM and GRU models are 0.016409 and 0.015518, respectively, whereas for the LSTM and GRU model the corresponding error results are 0.017637 and 0.018206, respectively. The same effects are demonstrated and proved for the pseudo–steady dataset (dataset 2). The attention-based RNN model achieves a high prediction accuracy, proving that it can help improve the prediction accuracy and may further help the implementation of PHM in the fuel cell system. [ABSTRACT FROM AUTHOR]

Data-driven methods have been widely applied to fault diagnosis and aging predictions to assist fuel cell Prognostic and Health Management (PHM) system, in order to achieve early maintenance management and corrective measures for fuel cell systems. This paper proposes a novel fuel cell aging prediction method considering the applicability of data and algorithm. This method first adopts empirical mode decomposition (EMD) to split the aging data into several intrinsic mode functions (IMFs), and each IMF represents a different characteristic. Then the sample entropy (SE) is used as the quantitative criterion for complexity threshold. Furthermore, the nonlinear autoregressive neural network (NARNN) and the Long Short-Term Memory (LSTM) recurrent neural network are combined to ensure the applicability of data and algorithm. The results show that EMD can split the various data types of the aging data and weaken or even eliminate the excessive mutation phenomenon that occurs at the beginning of each experimental fuel cell. In addition, the targeted selection of data-driven methods can ensure the applicability of the data and algorithm. Finally, by comparing different prediction methods, the proposed method shows higher accuracy in the prediction of each experimental dataset, and good generality for different fuel cell types. • The aging data is adaptively split into IMFs reflecting different characteristics. • Sample entropy is used as the complexity threshold quantitative criterion for IMFs. • The NARNN and LSTM are combined to ensure the applicability of data and algorithm. • Four datasets are used to validate the advantages of the proposed method. • The proposed method has generality for different fuel cell types aging predictions. [ABSTRACT FROM AUTHOR]

• PDE is used to describe the coupling effects of multi-physical domains in fuel cell. • The PDE-TMET framework achieves rapid prototyping and online implementation. • SSOR increases convergence probability and thus improve calculation efficiency. • The calculation time can be significantly shortened from hours even days to seconds. • MAPE of different physical distribution characteristics can remain within 10%. Fuel cell model provides state observation and quantitative analysis for Hardware-In-the-Loop (HIL) test platform. For a high-dimensional multi-physics model, the observed distribution of physical properties is meaningful for fuel cell stack optimization, manufacturing process and model-based control development. However, traditional modeling approach is difficult to consider both the calculation accuracy and efficiency. To solve this problem, this paper proposes a high-dimensional multi-physics real-time modeling approach for fuel cells based on partial differential equations (PDEs)-tridiagonal matrix equation transformation (TMET) and symmetric successive overrelaxation (SSOR) algorithm. In the proposed method, a generalized framework of TMET and a novel staggered grid technique is developed. In this case, both the convection and diffusion phenomena can be accurately described. For different fuel cells or parameters change, supplementary model modification or development is not required, only the global variables need to be re-assigned. Moreover, SSOR with bi-directional iteration mechanism is used to increase the probability of computational convergence and thus improve the calculation efficiency. Compared with commercial COMSOL model, by using the proposed modeling approach, the mean absolute percentage error (MAPE) of the simulated polarization output characteristics is 1.77%, the MAPE of different distribution characteristics can remain within 10%, and the calculation time can be shortened from hours even days to seconds. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) have been applied to many fields including image classification. Multi-focus image fusion can be regarded as the classification of focused areas and unfocused areas. Therefore, CNN has been widely used in multi-focus image fusion. However, most methods only use information from the last convolutional layer to complete the fusion task, which leads to a suboptimal fusion result. Aiming to solve this problem, we propose a novel convolutional neural network based on the Dempster-Shafer theory (DST) for multi-focus image fusion. Firstly, as a theoretical method for dealing with uncertain issues, the DST is introduced to fuse the results from different branch layers, thus increasing the reliability of the results. Also, a gradient residual block is designed to boost the utilization of edge information by the network while reducing the dimension from feature maps in the branch layers, thereby improving the performance of the network and reducing the number of training parameters. Compared with other state-of-the-art fusion methods, the decision map of the proposed method is more precise. And objectively, the average metrics of our proposed method for the 20 images from the ''Lytro" and ''Nature" datasets perform best in terms of information entropy, mutual information, structural similarity metric, and visual perception metric. [ABSTRACT FROM AUTHOR]

• High sensitive and selective vanillin sensor. • Nanopore fabrication using controlled dielectric breakdown. • Interaction between the vanillin-binding aptamer and vanillin molecules. • Vanillin molecules detection in food samples. Dielectric breakdown technique was utlised to fabricate 5–6 nm nanopores for vanillin detection in various food samples. A highly selective aptamer (Van_74) with high binding affinity towards vanillin was used as capture probe. Under optimal conditions, aptamer/vanillin complex translocation induced deeper events than the bare aptamer. As a result, the proposed nanopore aptasensor exhibits a linear range from 0.5 to 5 nM (R2 = 0.972) and a low detection limit of 500 pM, which is significantly better than conventional platforms. Furthermore, our aptasensor showed excellent immunity against different interferons and was used to detect vanillin in different food samples. The food sample measurements were confirmed with an additional UV–Vis assay, the results of the two techniques were statistically evaluated and showed no statistically significant difference. Hence, this work represents a proof-of-concept involving the design and testing of aptamer/nanopore sensors for small molecules detection, which plays a critical role in food safety. [ABSTRACT FROM AUTHOR]

• Different frequency components of voltage are adaptively separated to obtain IMFs. • EEMD is used to solve mode mixing problem in order to improve diagnostic accuracy. • Norm-entropy reasonably describes the statistical characteristics of fault signals. • The proposed EEMD-IMF-NE can be fast implemented without parameter optimization. • This method has robustness to wind speed, sensor noise and training/testing ratio. In order to decrease the downtime, enhance maintenance efficiency, and reduce the management cost of the wind power system, fault diagnosis technologies are considered as powerful tools for its good operation and maintenance. For example, high fault diagnosis accuracy can ensure effective fault-tolerant control and improve the durability of wind turbines under fault conditions. However, fault diagnosis accuracy can be easily affected by nonlinear and noise of measured signals under different working conditions. In this paper, a novel fault diagnostic method of power converters is proposed for the wind power generation system. In the proposed method, the measured output voltage is firstly processed by ensemble empirical mode decomposition (EEMD), and a series of intrinsic mode functions (IMF) can be obtained. Norm entropy (NE) is then calculated based on the statistical characteristics of the IMFs, and the extracted IMF-NE information is used to describe the diagnostic features. The effectiveness and reliability of the proposed method are then validated in a simulated 1.5 MW doubly-fed wind power system. The results show that the final diagnostic accuracy of 22 fault modes is 99.57% for wind speed variation, and the diagnosis accuracy can be maintained above around 70% for different signal to noise ratio. Compared with the other advanced fault diagnosis methods, the proposed method shows outstanding performance in terms of robustness, high accuracy, and simple implementation without complex parameter tuning. [ABSTRACT FROM AUTHOR]

Accuracy cognition in the complex and dynamic environment plays a pivotal part in artificial intelligence. Accurate classification of acoustic events is one of the foundations of environment acoustic awareness that has a strong correlation with the selected features. In this paper, the objective is to present a performance analysis of the of different acoustic features aggregation schemes on environment sound classification (ESC) tasks to find the best feature aggregate strategies to overcome the challenging problem of elevating the classification accuracy of environment sounds. With a considerable number of experiments, the feature combination including MFCC, Log-mel Spectrogram, Chroma, Spectral Contrast and Tonnetz achieves the state-of-art classification accuracy on the ESC dataset (85.6%) and 93.4% on the UrbanSound8K dataset. [ABSTRACT FROM AUTHOR]