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Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) require extensive and costly calibration to achieve high performance. Using transfer learning to re-use existing calibration data from old stimuli is a promising strategy, but finding commonalities in the SSVEP signals across different stimuli remains a challenge.This study presents a new perspective, namely time-frequency-joint representation, in which SSVEP signals corresponding to different stimuli can be synchronized, and thus can emphasize common components. According to this time-frequency-joint representation, an adaptive decomposition technique based on the multi-channel adaptive Fourier decomposition (MAFD) is proposed to adaptively decompose SSVEP signals of different stimuli simultaneously. Then, common components can be identified and transformed across stimuli.A simulation study on public SSVEP datasets demonstrates that the proposed stimulus-stimulus transfer method has the ability to extract and transfer these common components across stimuli. By using calibration data from eight source stimuli, the proposed stimulus-stimulus transfer method can generate SSVEP templates of other 32 target stimuli. It boosts the stimulus-stimulus transfer based recognition method's ITR from 95.966 bits/min to 123.684 bits/min.By extracting and transfer common components across stimuli in the proposed time-frequency-joint representation, the proposed stimulus-stimulus transfer method produces good classification performance without requiring calibration data of target stimuli.This study provides a synchronization standpoint to analyze and model SSVEP signals. In addition, the proposed stimulus-stimulus method shortens the calibration time and thus improve comfort, which could facilitate real-world applications of SSVEP-based BCIs.

Carbon neutrality is one of the central topics of not only the scientific community but also the majority of human society. The development of highly efficient carbon dioxide (CO

The aim is to study the benefits of using advanced medical services for coronary CT angiography. From August 2019 to August 2020, 50 patients who underwent CT angiography were selected and divided into control groups and study groups, with 25 patients in each group. The monitoring team provides basic support, and the training team provides maximum support. Experimental results showed that the positive microscopic image of the study group after the intervention was better than that of the control group, and the stress score was lower than that of the control group ( P < 0.05 s and 30.73 ± 9.57 min) and are short ( 58.32 ± 13.15 seconds and 53.17 ± 11.84 minutes) between control groups, with significant differences. The significance is ( P < 0.05 . Patient care is relevant to patients with coronary CT angiography, which has been shown to improve heart rate, reduce stress, improve microscopic imaging, and provide relevant liver function tests. It is recommended to promote the show.

The ongoing quest for transportation electrification with the massive proliferation of EV charging stations (EVCSs) will deepen the interaction and require the further coordination of coupled power and transportation networks (PTN). The individually-owned EVCSs located in an urban transportation network (UTN) will compete using price signals to maximize their respective payoffs. In this paper, a multi-agent deep reinforcement learning (MA-DRL) method is proposed to model the pricing game in UTN and determine the optimal charging prices for a single EVCS. The EVCS charging demand is first analyzed using a modified user equilibrium traffic assignment problem (UE-TAP) with elastic traveling demands and different charging prices. The price competition problem is then formulated as a game with incomplete information in which the market environment is very complex due to nonlinear traffic assignments. Thus, the MA-DRL approach is proposed to learn the charging pricing strategies of multiple EVCSs and approximate the Nash Equilibrium (NE) of the pricing game using the incomplete information. The proposed solution will determine the optimal pricing strategies for an EVCS in UTN. The case studies on a 24-node Sioux-Falls network and real-world Xian city are conducted to verify the effectiveness of the proposed approach.

Background: Basal-like breast cancer may originate from luminal epithelial or cancerous cells. Inadequately repaired DNA damage impairs luminal differentiation and promotes aberrant luminal to basal trans-differentiation in mammary epithelial cells. USP11, a deubiquitinase, plays a critical role in DNA damage repair. The role of USP11 in controlling mammary cell differentiation and tumorigenesis remains poorly understood. Methods: We generated Usp11 knock-out mice and breast cancer cell lines expressing wild type and mutant form of USP11. By using these mutant mice, cell lines, and human USP11 deficient and proficient breast cancer tissues, we tested how USP11 controls mammary cell fate. Results: We generated Usp11 knock-out mice and found that deletion of Usp11 impaired luminal differentiation and promoted DNA damage in mammary epithelial cells. Over-expression of wild-type (WT) USP11, not a deubiquitinase-inactive mutant form of USP11, promoted luminal and epithelial differentiation, enhanced DNA damage repair, and suppressed tumorigenesis in mice. Mechanistically, we found that Usp11 enhanced the protein expression of E-cadherin dependent on its deubiquitinase activity. We discovered that USP11 bound to E-cadherin through its C-terminal region, and that USP11 was a deubiquitinase of E-cadherin. In human breast cancers, expression of USP11 was positively correlated with that of E-cadherin and high USP11 predicted a better recurrence-free survival. Conclusions: Our findings provide compelling genetic and biochemical evidence that USP11 not only promotes DNA damage repair, but also deubiquitinates E-cadherin and maintains the luminal feature of mammary epithelial and cancerous cells, to suppress breast cancer.

Biphasic self-stratified batteries (BSBs) provide a new direction in battery philosophy for large-scale energy storage, which successfully reduces the cost and simplifies the architecture of redox flow batteries. However, current aqueous BSBs have intrinsic limits on the selection range of electrode materials and energy density due to the narrow electrochemical window of water. Thus, herein, we develop nonaqueous BSBs based on Li-S chemistry, which deliver an almost quadruple increase in energy density of 88.5 Wh L−1 as compared with the existing aqueous BSBs systems. In situ spectral characterization and molecular dynamics simulations jointly elucidate that while ensuring the mass transfer of Li+, the positive redox species are strictly confined to the bottom-phase electrolyte. This proof-of-concept of Li-S BSBs pushes the energy densities of BSBs and provides an idea to realize massive-scale energy storage with large capacitance.

The proliferation of distributed renewable energy resources and plug-in electric vehicles (EVs) have helped residential electricity consumers evolve into prosumers as they participate in the local energy market (LEM) by engaging in transactions of surplus electricity. In this system, the budgetbalance problem is a frequent issue, particularly when Vickrey-Clarke-Groves (VCG)-based mechanisms are applied to managing the two-sided nature of LEM. Although this issue could be partially addressed by manually modifying the LEM, the variance in the LEM environment needs to be better understood. This paper proposes a deep learning-based automatic mechanism design (AMD) method to improve VCG for tackling the budget-balanced two-sided LEM, as a way to avoid tedious manual adjustments. A convolutional neural network (CNN) with self-attention mechanism is constructed to extract features from biddings and to provide robust generalization capabilities for participating prosumers. The gated recurrent units (GRUs) are utilized to extend the proposed approach to the non-stationary bidding environment. This improved mechanism is targeted as efficient and incentive compatible, with the ability to keep the balance between the budget-balance and individual rationality. Case studies are conducted to demonstrate effectiveness of the proposed automatically improved mechanism and adaptive ability to various bidding environments.

The abnormal growth of cells in the breast is called malignancy or breast cancer; it is a life-threatening and dangerous cancer in women around the world. In the treatment of cancer, the doctors apply different techniques to stop cancer cell development, remove cancer cells through surgery, or kill cancer cells. In chemotherapy treatment, powerful drugs are used to kill abnormal cells; however, it has adverse reactions on the patient heart which is called cardiotoxicity. In this paper, we formulate the dynamics of cancer in the breast with adverse reactions of chemotherapy treatment on the heart of a patient in the fractional framework to visualize its dynamical behaviour. We listed the fundamental results of the fractional calculus for the analysis of our model. The model is then analyzed for the basic properties, and the existence and uniqueness of the proposed breast cancer system are investigated through fixed point theory. Furthermore, the Adams-Bashforth numerical technique is presented for the solution of fractional-order system to illustrate the time series of breast cancer model. The dynamical behaviour of different stages of breast cancer is then highlighted numerically to show the effect of fractional-order ϑ and to visualize the role of input parameter on the dynamics of breast cancer.

We explore the effect of organic ammonium cations with different Lewis acidity on the tip effect. Tetraethylammonium cations with medium Lewis acidity can form the most robust protecting shield, thus inhibiting the growth of dendrites.

Oxygen-vacancy-rich nickel hydroxide nanosheets as a multifunctional layer between Ir and Si toward enhanced solar hydrogen production in alkaline media.

This review summarizes and discusses the recent efforts devoted to the material design and reactor engineering for electrocatalytic nitrogen reduction to ammonia under ambient conditions.

The high degree of crystallinity of discharging intermediates of Li-S batteries (Li2S2/Li2S) causes a severe capacity attenuation at low temperatures. Herein, a sulfur-rich polymer is fabricated, which enables all the discharging intermediates to exist in an amorphous state without long-range order, promoting the substantial conversion of discharging intermediates and enhancing Li-S batteries' performance at low temperatures greatly. This cathode material exhibits excellent performance both at room and low temperatures. Even under an extremely low temperature (–40 ℃), the discharge capacity can remain 67% of that at room temperature. Besides, in-situ UV/Vis spectroscopy and density functional theory calculations reveal that this organosulfur cathode undergoes a new mechanism during discharge. Li2S6 and Li2S3 are the primary discharging intermediates that are quite different from conventional Li-S batteries. These results provide a new direction for a broader range of applications of Li-S batteries.

Preparation of Ag-M-NSCDs for the fluorometric detection of formaldehyde and lysosome-targeted bioimaging.

This investigation determined the effectiveness of an exterior magnetic field on bacteria enclosed by thousands of magnetite nanoparticles. Variable thermal conductivity and Joule heating were used in the interstitial nano liquid in which artificial bacteria were swimming in a biotic cell. The unsteady motions of a Powell–Eyring fluid in two dimensions were assumed. The porous extending wall was used as a bent surface shape. To convert the governing non-linear PDEs into non-linear ODEs, suitable transformations were exploited. The homotopy analysis technique (HAM) was utilized to resolve the semi-analytical results of non-linear ODEs. Plots were utilized to investigate the impact of significant parameters of velocity distribution, temperature profile, bacterial density field, nutrient concentration field, skin friction, Nusselt number, and nutrient concentration density. Clinical disease has shown that daring tumors have reduced blood flow. The results of this study showed that augmenting the values of unsteady parameters improved the blood velocity profile. The velocity distribution decreased for higher magnetite volume fraction values, as well as porosity and magnetic parameters. As the concentration of magnetite nanoparticles increased, so did the blood temperature distribution. As a result, the immersion of magnetite nanoparticles improved the physical characteristics of the blood. These findings also demonstrated that magnetic parameters and Eckert number play an essential role in increasing heat transfer rates.

In this paper, we propose an applicable algorithm of the so-called adaptive Fourier decomposition in [Formula: see text] the Hardy [Formula: see text] space on the upper half-plane [Formula: see text], which provides a new method for decomposing real-valued signals and analytic signals on the real line. As a by-product, a new approach for computing the Hilbert transform on the real line is also given. Numerical experiments are used to demonstrate the performance of the proposed algorithm.

Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original intrinsic or artificially established graph structure which may not accurately reflect the "true" correlation among data and are not optimal for semi-supervised node classification in the downstream graph neural networks. Besides, while existing graph-based methods mostly utilize the explicit graph structure, some implicit information, for example, the density information, can also provide latent information that can be further exploited. To address these limitations, this paper proposes the Dual Hypergraph Neural Network (DualHGNN), a new dual connection model integrating both hypergraph structure learning and hypergraph representation learning simultaneously in a unified architecture. The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization. Then, DualHGNN employs a density-aware hypergraph attention network to explore the high-order semantic correlation among data points based on the density-aware attention mechanism. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach., Comment: This work has been accepted by 2023 International Joint Conference on Neural Networks (IJCNN 2023). arXiv admin note: text overlap with arXiv:2201.11511

In radiological practice, multi-sequence MRI is routinely acquired to characterize anatomy and tissue. However, due to the heterogeneity of imaging protocols and contra-indications to contrast agents, some MRI sequences, e.g. contrast-enhanced T1-weighted image (T1ce), may not be acquired. This creates difficulties for large-scale clinical studies for which heterogeneous datasets are aggregated. Modern deep learning techniques have demonstrated the capability of synthesizing missing sequences from existing sequences, through learning from an extensive multi-sequence MRI dataset. In this paper, we propose a novel MR image translation solution based on local implicit neural representations. We split the available MRI sequences into local patches and assign to each patch a local multi-layer perceptron (MLP) that represents a patch in the T1ce. The parameters of these local MLPs are generated by a hypernetwork based on image features. Experimental results and ablation studies on the BraTS challenge dataset showed that the local MLPs are critical for recovering fine image and tumor details, as they allow for local specialization that is highly important for accurate image translation. Compared to a classical pix2pix model, the proposed method demonstrated visual improvement and significantly improved quantitative scores (MSE 0.86 x 10^-3 vs. 1.02 x 10^-3 and SSIM 94.9 vs 94.3)