Lithium–sulfur batteries (LSBs) are regarded as a new kind of energy storage device due to their remarkable theoretical energy density. However, some issues, such as the low conductivity and the ...large volume variation of sulfur, as well as the formation of polysulfides during cycling, are yet to be addressed before LSBs can become an actual reality. Here, presented is a comprehensive overview illustrating the techniques capable of mitigating these undesirable problems together with the electrochemical performances associated to the different proposed solutions. In particular, the analysis is organized by separately addressing cathode, anode, separator, and electrolyte. Furthermore, to better understand the chemistry and failure mechanisms of LSBs, important characterization techniques applied to energy storage systems are reviewed. Similarly, considerations on the theoretical approaches used in the energy storage field are provided, as they can become the key tool for the design of the next generation LSBs. Afterward, the state of the art of LSBs technology is presented from a geopolitical perspective by comparing the results achieved in this field by the main world actors, namely Asia, North America, and Europe. Finally, this review is concluded with the application status of LSBs technology, and its prospects are offered.
A comprehensive review on lithium‐sulphur batteries is presented. The analysis looks at anodes, cathodes, separators, and electrolytes both from a theoretical and experimental point of view. In this respect, considerations for advanced characterization techniques are also reported. Finally, a geo‐political comparison is presented aiming to identify the most important actors in this field.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
•EMD is applied to reduce the complexity of the prediction subsequence.•The SE of the decomposed sub-sequence is induced to improve the prediction precision of wind speed.•LSTM is applied to predict ...the high frequency sub-sequences.•The ARIMA is employed to predict the low frequency sub-sequences and one residual.•A hybrid EMD-LSTM-ARIMA model is successfully proposed for wind speed prediction.
Wind speed is the key factor of wind power generation. With the increase of the proportion of wind power generation in total power generation, the accurate prediction of wind speeds plays an important role in the stable operations of power grids. However, the strong randomness of wind speeds makes it difficult to accurately predict wind speeds. Thus, a wind speed prediction model combining empirical mode decomposition (EMD) with some novel recurrent neural networks (RNN) and the autoregressive integrated moving average (ARIMA) is proposed to solve the problem. The selected RNNs are long short-term memory network (LSTM) and the gated recurrent unit (GRU) network. In this model, EMD is used to decompose the wind speed sequence to reduce the complexity and non-stationary of the series. The entropy of the samples of the sub-sequences after decomposition is calculated. Consequently, LSTM is applied to predict the high frequency sub-sequences with large entropy while the ARIMA is employed to predict the remaining low frequency sub-sequences and one residual. Finally, the prediction results of each sub series are combined to obtain the final prediction results. To verify the accuracy and stability of the model, four wind speed data sets form Inner Mongolia, China, are used to test the proposed methods. Five models are established in four practical cases and their performances are compared with the performances of the proposed model. The results in this paper show the following: (1) the EMD method can improve the wind speed prediction performance when it is combined with LSTM; (2) after decomposition, LSTM is suitable for predicting high complexity subsequences and the ARIMA is suitable for effectively predicting low complexity subsequences based on the different sample entropies; and (3) the root mean squared errors (RMSEs) of the hybrid model on the four wind speed data sets are 0.4163, 0.2085, 0.1613, and 0.2790, respectively, which are basically lower than those of the five models compared. Therefore, it is feasible to apply the hybrid model to wind speed prediction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZRSKP
Most high-dimensional estimation methods propose to minimize a cost function (empirical risk) that is a sum of losses associated to each data point (each example). In this paper, we focus on the case ...of nonconvex losses. Classical empirical process theory implies uniform convergence of the empirical (or sample) risk to the population risk. While under additional assumptions, uniform convergence implies consistency of the resultingM-estimator, it does not ensure that the latter can be computed efficiently.
In order to capture the complexity of computing M-estimators, we study the landscape of the empirical risk, namely its stationary points and their properties. We establish uniform convergence of the gradient and Hessian of the empirical risk to their population counterparts, as soon as the number of samples becomes larger than the number of unknown parameters (modulo logarithmic factors). Consequently, good properties of the population risk can be carried to the empirical risk, and we are able to establish one-to-one correspondence of their stationary points.We demonstrate that in several problems such as nonconvex binary classification, robust regression and Gaussian mixture model, this result implies a complete characterization of the landscape of the empirical risk, and of the convergence properties of descent algorithms.
We extend our analysis to the very high-dimensional setting in which the number of parameters exceeds the number of samples, and provides a characterization of the empirical risk landscape under a nearly informationtheoretically minimal condition. Namely, if the number of samples exceeds the sparsity of the parameters vector (modulo logarithmic factors), then a suitable uniform convergence result holds. We apply this result to nonconvex binary classification and robust regression in very high-dimension.
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BFBNIB, INZLJ, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK, ZRSKP
The present work focuses on the friction stress and the Hall-Petch relationship in CoCrNi equi-atomic medium entropy alloy (MEA). The CoCrNi equi-atomic MEA and a Ni-40Co alloy were processed by ...high-pressure torsion and subsequent annealing. The specimens with fully-recrystallized microstructure with different grain sizes ranging from 199nm to 111μm were obtained. The Hall-Petch plot of the yield strength of the present specimens indicated that the friction stress of the CoCrNi MEA was much higher than Ni-40Co and pure metals, suggesting that the local lattice distortion in the equi-atomic alloy played an important role for the dislocation activity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
A new antibacterial strategy based on inhibiting bacterial quorum sensing (QS) has emerged as a promising method of attenuating bacterial pathogenicity and preventing bacterial resistance to ...antibiotics. In this study, we screened Echinatin (Ech) with high-efficiency anti-QS from 13 flavonoids through the AI-2 bioluminescence assay. Additionally, crystal violet (CV) staining combined with confocal laser scanning microscopy (CLSM) was used to evaluate the effect of anti-biofilm against
Escherichia coli (E. coli)
. Further, the antibacterial synergistic effect of Ech and marketed antibiotics were measured by broth dilution and Alamar Blue Assay. It was found that Ech interfered with the phenotype of QS, including biofilm formation, exopolysaccharide (
EPS
) production, and motility, without affecting bacterial growth and metabolic activity. Moreover, qRT-PCR exhibited that Ech significantly reduced the expression of QS-regulated genes (
luxS
,
pfs
,
lsrB
,
lsrK
,
lsrR
,
flhC
,
flhD
,
fliC
,
csgD
, and
stx2
). More important, Ech with currently marketed colistin antibiotics (including colistin B and colistin E) showed significantly synergistically increased antibacterial activity in overcoming antibiotic resistance of
E. coli
. In summary, these results suggested the potent anti-QS and novel antibacterial synergist candidate of Ech for treating
E. coli
infections.
Adult mammalian brains have largely lost neuroregeneration capability except for a few niches. Previous studies have converted glial cells into neurons, but the total number of neurons generated is ...limited and the therapeutic potential is unclear. Here, we demonstrate that NeuroD1-mediated in situ astrocyte-to-neuron conversion can regenerate a large number of functional new neurons after ischemic injury. Specifically, using NeuroD1 adeno-associated virus (AAV)-based gene therapy, we were able to regenerate one third of the total lost neurons caused by ischemic injury and simultaneously protect another one third of injured neurons, leading to a significant neuronal recovery. RNA sequencing and immunostaining confirmed neuronal recovery after cell conversion at both the mRNA level and protein level. Brain slice recordings found that the astrocyte-converted neurons showed robust action potentials and synaptic responses at 2 months after NeuroD1 expression. Anterograde and retrograde tracing revealed long-range axonal projections from astrocyte-converted neurons to their target regions in a time-dependent manner. Behavioral analyses showed a significant improvement of both motor and cognitive functions after cell conversion. Together, these results demonstrate that in vivo cell conversion technology through NeuroD1-based gene therapy can regenerate a large number of functional new neurons to restore lost neuronal functions after injury.
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After ischemic brain injury, many neurons die but surviving astrocytes become activated and proliferative. Using NeuroD1 AAV-based gene therapy, Chen and colleagues demonstrate robust neuroregeneration through direct astrocyte-to-neuron conversion and significantly improved functional recovery. This study provides a new paradigm for brain repair using in vivo cell conversion technology.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Surface charge state plays an important role in tuning the catalytic performance of nanocrystals in various reactions. Herein, we report a synthetic approach to unique Pt–Pd–graphene stack structures ...with controllable Pt shell thickness. These unique hybrid structures allow us to correlate the Pt thickness with performance in the hydrogen‐evolution reaction (HER). The HER activity increases with a decrease in the Pt thickness, which is well explained by surface polarization mechanism as suggested by first‐principles simulations. In this hybrid system, the difference in work functions of Pt and Pd results in surface polarization on the Pt surface, tuning its charge state for hydrogen reduction. Meanwhile, the supporting graphene provides two‐dimensional channels for efficient charge transport, improving the HER activities. This work opens up possibilities of reducing Pt usage while achieving high HER performance.
Less is more: Pt–Pd–graphene stack structures (see picture) are prepared by a new method that allows control of the thickness of the Pt shell. This thickness correlates with performance in the hydrogen evolution reaction (HER). As a result of surface polarization, the HER activity actually increases with decreasing Pt thickness, opening possibilities of using less Pt.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due ...to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Abstract
As an emerging technology for harvesting mechanical energy, low surface charge density greatly hinders the practical applications of triboelectric nanogenerators (TENGs). Here, a ...high-performance TENG based on charge shuttling is demonstrated. Unlike conventional TENGs with static charges fully constrained on the dielectric surface, the device works based on the shuttling of charges corralled in conduction domains. Driven by the interaction of two quasi-symmetrical domains, shuttling of two mirror charge carriers can be achieved to double the charge output. Based on the mechanism, an ultrahigh projected charge density of 1.85 mC m
−2
is obtained in ambient conditions. An integrated device for water wave energy harvesting is also presented, confirming its feasibility for practical applications. The device provides insights into new modes of TENGs using unfixed charges in domains, shedding a new light on high-performance mechanical energy harvesting technology.
Steel rebar buckling is an important failure mode in reinforced concrete (RC) columns. Fiber-reinforced polymer (FRP) composites provide an effective mean that can restrain or delay longitudinal ...rebar buckling. To accurately predict the strength and ductility of FRP-strengthened rectangular RC columns, it is necessary to accurately evaluate the interactions between FRP confinement and steel reinforcement buckling behavior at as any loading levels as possible. This paper proposed a composite beam model and a tension-bending beam model to evaluate the lateral support stiffness of FRP-wrapped concrete cover on steel reinforcement in the corner regions and flat sides of rectangular columns. Then, the whole stress-strain curves for reinforcing bar buckling behavior considering the lateral support of FRP jackets can be obtained and validated with the existing test database. For FRP-strengthened rectangular RC columns under monotonic axial compression, the overall load-bearing capacity of RC columns can be determined by summarizing axial loads sustained by FRP-confined corresponding plain concrete (PC) columns and the load contribution of steel rebars considering FRP lateral support. The accuracy of the proposed method is demonstrated by comparing theoretical predictions with test results. This calculation method can accurately predict the strength and ductility of FRP-wrapped rectangular columns within a 20% variation margin, which provides a reliable basis for the design of concrete structures.
•A composite beam model and a tension-bending beam model are developed to evaluate the spring stiffness.•A method is established to predict the overall behavior of FRP-confined rectangular RC columns considering bar buckling.•The factors affecting rebar buckling and the performance of FRP-confined rectangular RC columns are analyzed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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