Amphiphilic Polymer Electrolyte Blocking Lattice Oxygen Evolution from High-Voltage Nickel-rich Cathodes for Ultra-Thermal Stabile Batteries.

Ni-rich cathodes have been intensively adopted in Li-ion batteries to pursuit high energy density, which still suffering irreversible degradation at high voltage. Some unstable lattice O2- species in Ni-rich cathodes would be oxidized to singlet oxygen 1O2 and released at high volt, which lead to irreversible phase transfer from the layered rhombohedral (R) phase to a spinel-like (S) phase. To overcome the issue, the amphiphilic copolymers (UMA-Fx) electrolyte were prepared by linking hydrophobic C-F side chains with hydrophilic subunits, which could self-assemble on Ni-rich cathode surface and convert to stable cathode-electrolyte interphase layer. Thereafter, the oxygen releasing of polymer coated cathode was obviously depressed and substituted by the Co oxidation (Co3+→Co4+) at high volt (>4.2V), which could suppressed irreversible phase transfer and improve cycling stability. Moreover, the amphiphilic polymer electrolyte was also stable with Li anode and had high ion conductivity. Therefore, the NCM811//UMA-F6//Li pouch cell exhibited outstanding energy density (362.97 Wh/kg) and durability (cycled 200 times at 4.7V), which could be stalely cycled even at 120℃ without short circuits or explosions.

Epileptic Seizure Prediction Using Spatiotemporal Feature Fusion on EEG.

Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure prediction has significant value for clinical treatment of epilepsy. Currently, prediction methods using Convolutional Neural Network (CNN) primarily focus on local features of EEG, making it challenging to simultaneously capture the spatial and temporal features from multi-channel EEGs to identify the preictal state effectively. In order to extract inherent spatial relationships among multi-channel EEGs while obtaining their temporal correlations, this study proposed an end-to-end model for the prediction of epileptic seizures by incorporating Graph Attention Network (GAT) and Temporal Convolutional Network (TCN). Low-pass filtered EEG signals were fed into the GAT module for EEG spatial feature extraction, and followed by TCN to capture temporal features, allowing the end-to-end model to acquire the spatiotemporal correlations of multi-channel EEGs. The system was evaluated on the publicly available CHB-MIT database, yielding segment-based accuracy of 98.71%, specificity of 98.35%, sensitivity of 99.07%, and F1-score of 98.71%, respectively. Event-based sensitivity of 97.03% and False Positive Rate (FPR) of 0.03/h was also achieved. Experimental results demonstrated this system can achieve superior performance for seizure prediction by leveraging the fusion of EEG spatiotemporal features without the need of feature engineering.

Study on Phenol-Formaldehyde Resin-Montmorillonite Impregnation and Compression Modification of Chinese Fir.

In this study, a phenol-formaldehyde resin-montmorillonite intercalation composite solution was used as a modifier to treat Chinese fir via impregnation and compression. The basic characteristics and wettability of the PF (phenol-formaldehyde)-montmorillonite impregnation solution were analyzed. The effects of the solid content of PF, the quantity of montmorillonite, and the impregnation time on the impregnation weight gain of Chinese fir were studied through orthogonal experiments. The results showed that when the amount of montmorillonite was 1%, the wettability of the PF-montmorillonite impregnation solution performed the best, the curing time was short, and the curing strength was high. The optimal impregnation process consists of a PF solid content of 25%, an impregnation time of 120 min, and a montmorillonite ratio of 1%. Under these conditions, the modified Chinese fir was prepared via hot pressing. The effects of the addition of montmorillonite and different levels of compressibility on the physical and mechanical properties of modified wood were studied. The physical and mechanical properties were found to be better when the compression ratio was 33%: the density increased from 0.33 g/cm3 to 0.58 g/cm3; the surface hardness increased from 33.6 HD to 70.9 HD; the static bending strength increased from 60.4 MPa to 98.7 MPa; and the elastic modulus increased from 6 390 MPa to 11 498 MPa. After 30 days of release, the compression rebound rate was 3.97%. Meanwhile, the micromorphology and heat resistance of the impregnated compressed Chinese fir showed that the PF-montmorillonite impregnation solution entered into the cell cavity and intercellular space of the Chinese fir and formed a good composite, thus improving the water resistance, heat resistance, and physical and mechanical properties.

A novel dynamic outlier-robust Kalman filter with Moving Horizon Estimation.

The existence of dynamic outliers poses a significant challenge to the Kalman filter (KF). In addressing this challenge, this paper presents an innovative solution: Firstly, by analyzing a period of measurement information to more accurately identify state and measurement dynamic outliers, the system's capacity to adapt to dynamic changes is significantly improved. Next, noise is modeled as a Gaussian-Student's t mixture distribution (GSTM), with mixed model parameters inferred using the variational Bayesian (VB) method based on measurement information, cleverly integrated into the Moving Horizon Estimation (MHE) framework, thus enhancing the flexibility and accuracy of the noise model. Lastly, the optimal window size was identified through simulation experiment analysis to further increase the estimation accuracy. Simulation results demonstrate that the proposed filter exhibits stronger robustness in resisting dynamic outliers compared to existing filters.

CK1 and PP1 regulate Rift Valley fever virus genome replication through L protein phosphorylation.

Rift Valley fever virus (RVFV) is an arbovirus in the Phenuiviridae family identified initially by the large 'abortion storms' observed among ruminants; RVFV can also infect humans. In humans, there is a wide variation of clinical symptoms ranging from subclinical to mild febrile illness to hepatitis, retinitis, delayed-onset encephalitis, or even hemorrhagic fever. The RVFV is a tri-segmented negative-sense RNA virus consisting of S, M, and L segments. The L segment encodes the RNA-dependent RNA polymerase (RdRp), termed the L protein, which is responsible for both viral mRNA synthesis and genome replication. Phosphorylation of viral RdRps is known to regulate viral replication. This study shows that RVFV L protein is serine phosphorylated and identified Casein Kinase 1 alpha (CK1α) and protein phosphatase 1 alpha (PP1α) as L protein binding partners. Inhibition of CK1 and PP1 through small molecule inhibitor treatment, D4476 and 1E7-03, respectively, caused a change in the phosphorylated status of the L protein. Inhibition of PP1α resulted in increased L protein phosphorylation whereas inhibition of CK1α decreased L protein phosphorylation. It was also found that in RVFV infected cells, PP1α localized to the cytoplasmic compartment. Treatment of RVFV infected cells with CK1 inhibitors reduced virus production in both mammalian and mosquito cells. Lastly, inhibition of either CK1 or PP1 reduced viral genomic RNA levels. These data indicate that L protein is phosphorylated and that CK1 and PP1 play a crucial role in regulating the L protein phosphorylation cycle, which is critical to viral RNA production and viral replication.

Analysis of the clinical profile and treatment efficiency of hyperlipidemic acute pancreatitis.

BACKGROUND: The incidence of hyperlipidemic acute pancreatitis (HLAP) has been increasing annually. However, population-based morbidity assessments need to be updated. Early, rapid, and effective lipid-lowering may minimize pancreatic injury and improve clinical prognosis. It is essential to choose the proper treatment. However, treatment options for HLAP are controversial, and there is no uniform treatment protocol. METHODS: In this retrospective study, 127 patients with hyperlipidemic severe acute pancreatitis (HL-SAP) were registered from January 2018 to December 2022 at the General Hospital of Ningxia Medical University. Medical and radiological records of hospitalized patients were collected to determine clinical features, severity, complications, mortality, recurrence rate, and treatment. Risk factors for HL-SAP were analyzed using multifactorial logistic regression. A propensity score matching method was used to compare the clinical outcomes of standard and plasma exchange therapies. RESULTS: In this research, the prevalence of HLAP increased about 1.6 times, and the prevalence of HL-SAP was 50.60%. HL-SAP occurs most often in people between the ages of 30 and 39. Amylase exceeded 110 U/L in 84.3% of patients and 330 U/L in only 47.2%. 83.5% of HL-SAP patients had fatty livers and high body mass index (BMI). A total of 48.0% of patients experienced organ failure, ICU treatment (55.1%), recurrence (33.1%), and death (21.3%). Between the hyperlipidemic group and the biliary group in terms of age, gender, BMI, fatty liver, pleural effusion, abdominal constriction syndrome (ACS), multiple organ dysfunction syndrome (MODS), length of hospital, medical costs, morbidity and mortality, triglyceride, cholesterol, creatinine, blood glucose, D-dimer, amylase, albumin, lactate dehydrogenase, serum phosphorus, serum calcium, oxygenation index, and recurrence rate were statistically significant (P < 0.05). High BMI (P = 0.0038, odds ratio (OR) = 1.336, 95%CI: 0.99-1.804), high C-reactive protein (CRP) (P = 0.022, OR = 1.011, 95%CI: 1.003-1.019), low calcium (P = 0.003, OR = 0.016, 95%CI. 0.001-0.239), low albumin (P = 0.012, OR = 0.045, 95%CI: -0.062-0.192), and high D-dimer (P = 0.041, OR = 0.619, 95%CI: 0.053-2.510) were risk factors for HL-SAP, according to multifactorial logistic regression analysis. Adjusted for propensity score matching (PSM), Serum triglyceride (TG) was significantly lower in both the standard treatment (P < 0.001) and plasma exchange (P < 0.001) groups at 48 h compared with the initial test after the attack. Clearance (83.20% ± 0.0% vs. 84.4% ± 0.0%, P = 0.531), length of hospital stay (19.9 ± 4.9 vs. 19.8 ± 11.1, P = 0.092), and death (26.3% vs. 23.6%, P = 0.791) showed no difference between the two groups. However, the difference in medical costs(P = 0.039)between the two groups was statistically significant. CONCLUSION: The incidence of HLAP exhibited a significant increase, remarkable severity, recurrent trend, and mortality. High BMI, high CRP, low calcium, low albumin, and high D-dimer are risk factors for HL-SAP. Compared with standardized treatment, plasma exchange does not improve the prognosis of HL-SAP patients, and standardized treatment is equally effective, safe, and low-cost in early treatment.

Cosine convolutional neural network and its application for seizure detection.

Traditional convolutional neural networks (CNNs) often suffer from high memory consumption and redundancy in their kernel representations, leading to overfitting problems and limiting their application in real-time, low-power scenarios such as seizure detection systems. In this work, a novel cosine convolutional neural network (CosCNN), which replaces traditional kernels with the robust cosine kernel modulated by only two learnable factors, is presented, and its effectiveness is validated on the tasks of seizure detection. Meanwhile, based on the cosine lookup table and KL-divergence, an effective post-training quantization algorithm is proposed for CosCNN hardware implementation. With quantization, CosCNN can achieve a nearly 75% reduction in the memory cost with almost no accuracy loss. Moreover, we design a configurable cosine convolution accelerator on Field Programmable Gate Array (FPGA) and deploy the quantized CosCNN on Zedboard, proving the proposed seizure detection system can operate in real-time and low-power scenarios. Extensive experiments and comparisons were conducted using two publicly available epileptic EEG databases, the Bonn database and the CHB-MIT database. The results highlight the performance superiority of the CosCNN over traditional CNNs as well as other seizure detection methods.

Rational Design of F-Modified Polyester Electrolytes for Sustainable All-Solid-State Lithium Metal Batteries.

Solid polymer electrolytes (SPEs) are one of the most practical candidates for solid-state batteries owing to their high flexibility and low production cost, but their application is limited by low Li+ conductivity and a narrow electrochemical window. To improve performance, it is necessary to reveal the structure-property relationship of SPEs. Here, 23 fluorinated linear polyesters were prepared by editing the coordination units, flexible linkage segments, and interface passivating groups. Besides the traditionally demonstrated coordinating capability and flexibility of polymer chains, the molecular asymmetry and resulting interchain aggregation are observed critical for Li+ conductivity. By tailoring the molecular asymmetry and coordination ability of polyesters, the Li+ conductivity can be raised by 10 times. Among these polyesters, solvent-free poly(pentanediol adipate) delivers the highest room-temperature Li+ conductivity of 0.59 × 10-4 S cm-1. The chelating coordination of oxalate and Li+ leads to an electron delocalization of alkoxy oxygen, enhancing the antioxidation capability of SPEs. To lower the cost, high-value LiTFSI in SPEs is recycled at 90%, and polyesters can be regenerated at 86%. This work elucidates the structure-property relationship of polyester-based SPEs, displays the design principles of SPEs, and provides a way for the development of sustainable solid-state batteries.

Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model.

Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5-45 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures.

Identification of Unambiguous Borrelia Peptides in Human Urine Using Affinity Capture and Mass Spectrometry.

The combination of advanced mass spectrometry and enrichment-based sample preparation methods has enhanced analytical capabilities in clinical proteomics. In this chapter, we describe a method of proteome analysis to identify Borrelia-derived peptides in urine that includes a sample affinity enrichment method coupled with liquid chromatography tandem mass spectrometry analysis and a bioinformatic peptide authentication algorithm.

The relationship between decline rate of residual renal function in the first year and mortality in peritoneal dialysis patients.

INTRODUCTION: To assess the relationship between the rate of residual renal function (RRF) decline in the first year and all-cause and cardiovascular mortality in peritoneal dialysis (PD) patients. METHODS: Incident PD patients were divided into two groups by the corresponding RRF decline value, when hazard ratio (HR) = 1 was found by the restricted cubic spline. The associations of rate of decline of RRF in the first year with mortality were evaluated. RESULTS: Of 497 PD patients, 122 patients died. After adjusting for confounding factors, patients in fast-decline group had a significant increase risk of all-cause and cardiovascular mortality (HR: 1.97 and 2.09, respectively). Each 0.1-mL/min/1.73 m2 /month decrease in RRF in the first year of PD was associated with a 19% and 20% higher risk of all-cause and cardiovascular mortality, respectively. CONCLUSIONS: Faster decline of RRF in the first year was independently associated with all-cause and cardiovascular mortality in PD patients.

Grain-growth Inhibitor with Three-section-sintering for Highly Dispersed Single-crystal NCM90 Cubes.

Single crystallization of LiNix Coy Mn1-x-y O2 (NCM) is currently an effective strategy to improve the cycling life of NCM cathode, owing to the reduced surface area and enhanced mechanical strength, but the application of single crystal NCM(SC-NCM) is being hindered by severe particle agglomeration and poor C-rate performance. Here, a strategy of three-section-sintering(TSS) with the presence of grain-growth inhibitor was proposed, in which, the TSS includes three sections of phase-formation, grain-growth and phase-preservation. While, the addition of MoO3 inhibits the grain growth and restrains the particle agglomeration. With the help of TSS and 1 mol % of MoO3 , highly dispersed surface Mo-doped SC-NCM(MSC-NCM) cubes are obtained with the average diameter of 1.3 µm. Benefiting from the surface Mo-doping and the reduced surface energy, the Li+ -migration preferred (1 0 4) crystalline facet is exposed as the dominant plane in MSC-NCM cubes, because of which, C-rate performance is significantly improved compared with the regular SC-NCM. Furthermore, this preparation strategy is compatible well with the current industrial production line, providing an easy way for the large-scale production of SC-NCM.

Gold-Nanorod-Assisted Live Cell Nuclear Imaging Based on Near-Infrared II Dark-Field Microscopy.

Dark-field microscopy offers several advantages, including high image contrast, minimal cell damage, and the absence of photobleaching of nanoprobes, which make it highly advantageous for cell imaging. The NIR-II window has emerged as a prominent research focus in optical imaging in recent years, with its low autofluorescence background in biological samples and high imaging SBR. In this study, we initially compared dark-field imaging results of colorectal cancer cells in both visible and NIR-II wavelengths, confirming the superior performance of NIR-II imaging. Subsequently, we synthesized gold nanorods with localized surface plasmon resonance (LSPR) absorption peaks in the NIR-II window. After bio-compatible modification, we non-specifically labeled colorectal cancer cells for NIR-II dark-field scattering imaging. The imaging results revealed a sixfold increase in SBR, especially in the 1425-1475 nm wavelength range. Finally, we applied this imaging system to perform dark-field imaging of cell nuclei in the NIR-II region and used GNRs for specific nuclear labeling in colorectal cancer cells. The resulting images exhibited higher SBR than non-specifically-labeled cell imaging, and the probe's labeling was precise, confirming the potential application of this system in photothermal therapy and drug delivery for cancer cells.

Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.

Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.

m1A Regulatory gene signatures are associated with certain immune cell compositions of the tumor microenvironment and predict survival in kidney renal clear cell carcinoma.

Adenosine N1 methylation (m1A) of RNA, a type of post-transcriptional modification, has been shown to play a significant role in the progression of cancer. The objective of the current research was to analyze the genetic alteration and prognostic significance of m1A regulators in kidney renal clear cell carcinoma (KIRC). Genomic and clinicopathological characteristics were obtained from 558 KIRC patients in the Cancer Genome Atlas (TCGA) and Gene Omnibus Expression (GEO) databases. Alterations in the gene expression of ten m1A-regulators were analyzed and survival analysis was performed using the Cox regression method. We also identified three clusters of patients based on their distinct m1A alteration patterns, using integrated analysis of the ten m1A-related regulators, which were significantly related to overall survival (OS), disease-free survival (DFS) and tumor microenvironment (TME) immune cell infiltration cells in KIRC. Our findings showed that m1A alteration patterns have critical roles in determining TME complexity and its immune cell composition. Furthermore, different m1A expression patterns were significantly associated with DFS and OS rates in KIRC patients. In conclusion, the identified m1A RNA modification patterns offer a potentially effective way to classify KIRC patients based on their TME immune cell infiltration, enabling the development of more personalized and successful treatment strategies for these patients.

Identification of cuproptosis-related genes for predicting the development of prostate cancer.

Copper can be toxic at very high intracellular concentrations and can inhibit prostate cancer (PCa) progression. Recently, a study reported the mechanism of cuproptosis and the potentially associated genes. However, the function of these cuproptosis-related genes in PCa remains unknown. Based on the RNA sequence and clinical data from public databases, we analyzed the clinical value of cuproptosis-related genes in PCa. DLD, DLAT, PDHA1, and CDKN2A were expressed differently between normal and PCa tissues. The FDX1, LIAS, DLAT, GLS, and CDKN2A genes can affect PCa progression, while PDHA1 and CDKN2A influence the patients' disease-free survival (DFS) status. The expression of LIAS, LIPT1, DLAT, and PDHB did not alter upon the incidence of PCa in Chinese patients. A constructed regression model showed that FDX1, PDHA1, MTF1, and CDKN2A can be risk factors leading to PCa in both Western and Chinese patients with PCa. The lasso regression model reflected that these genes can affect the patients' DFS status. Additionally, the cuproptosis-related genes were associated with immune cell infiltration. We also verified the high expression of PDHA1 and CDKN2A, in clinical samples. In conclusion, we identified a novel cuproptosis-related gene signature for predicting the development of PCa.

Combination of sequencing batch reactor activated sludge process with sludge lysis using thermophilic bacterial community for minimizing excess sludge.

Sludge reduction is a major challenge in biological wastewater treatment. Hydrolytic enzymes secreted by thermophilic bacteria can lyse sludge and thus achieve sludge reduction, and the indigenous thermophilic community in sludge can lyse sludge more effectively. In this study, the feasibility of combining a sludge lysis reactor based on thermophilic bacteria community (LTBC reactor, 75 °C) with a conventional sequencing batch activated sludge reactor (SBR) for sludge reduction (i.e., LTBC-SBR process) was systematically investigated first time. The effect of lysed sludge returning to the biochemical tank on pollutant removal efficiency, sludge flocculation, sludge settling, and microbial community and function of the LTBC-SBR process was studied. In the LTBC1-SBR process, a sludge growth rate of 0.71 g TSS/day was observed when the lysed sludge reflux ratio (LRR) was 1, and the sludge generation was reduced by 81.5% compared to the conventional SBR reactor. In the LTBC1-SBR process, the removal efficiencies of chemical oxygen demand and total nitrogen were 94.0% and 80.5%, respectively. There was no significant difference in the sludge volume index from the SBR to the LTBC1-SBR stage, however, the effluent suspended solids concentration increased from 35.2 ± 2.1 mg/L to 80.1 ± 5.3 mg/L. This was attributed to the reflux of sludge lysate. In addition, the changes in extracellular polymers content and composition resulted in poor sludge flocculation performance. Heterotrophic bacteria associated with Actinobacteria and Patescibacteria enriched in LTBC1-SBR with relative abundance of 28.51 ± 1.25% and 20.01 ± 1.21%, respectively, which decomposed the macromolecules in the refluxed lysed sludge and contributed to the sludge reduction. Furthermore, due to the inhibition of nitrite-oxidizing bacteria, the nitrite concentration in the effluent of the LTBC1-SBR system reached 4.7 ± 1.1 mg/L, and part of the denitrification process was achieved by short-cut nitrification and simultaneous denitrification. These results indicate that in-situ sludge reduction technology based on lyse sludge lysing by thermophilic community has considerable potential to be widely used in wastewater treatment.

Photodissociation dynamics of the ethyl radical via the Ã2A'(3s) state: H-atom product channels and ethylene product vibrational state distribution.

The photodissociation dynamics of jet-cooled ethyl radical (C2H5) via the Ã2A'(3s) states are studied in the wavelength region of 230-260 nm using the high-n Rydberg H-atom time-of-flight (TOF) technique. The H + C2H4 product channels are reexamined using the H-atom TOF spectra and photofragment translational spectroscopy. A prompt H + C2H4(X̃1Ag) product channel is characterized by a repulsive translational energy release, anisotropic product angular distribution, and partially resolved vibrational state distribution of the C2H4(X̃1Ag) product. This fast dissociation is initiated from the 3s Rydberg state and proceeds via a H-bridged configuration directly to the H + C2H4(X̃1Ag) products. A statistical-like H + C2H4(X̃1Ag) product channel via unimolecular dissociation of the hot electronic ground-state ethyl (X̃2A') after internal conversion from the 3s Rydberg state is also examined, showing a modest translational energy release and isotropic angular distribution. An adiabatic H + excited triplet C2H4(ã3B1u) product channel (a minor channel) is identified by energy-dependent product angular distribution, showing a small translational energy release, anisotropic angular distribution, and significant internal excitation in the C2H4(ã3B1u) product. The dissociation times of the different product channels are evaluated using energy-dependent product angular distribution and pump-probe delay measurements. The prompt H + C2H4(X̃1Ag) product channel has a dissociation time scale of <10 ps, and the upper bound of the dissociation time scale of the statistical-like H + C2H4(X̃1Ag) product channel is <5 ns.

Self-attention-based convolutional neural network and time-frequency common spatial pattern for enhanced motor imagery classification.

BACKGROUND: Motor imagery (MI) based brain-computer interfaces (BCIs) have promising potentials in the field of neuro-rehabilitation. However, due to individual variations in active brain regions during MI tasks, the challenge of decoding MI EEG signals necessitates improved classification performance for practical application. NEW METHOD: This study proposes a self-attention-based Convolutional Neural Network (CNN) in conjunction with a time-frequency common spatial pattern (TFCSP) for enhanced MI classification. Due to the limited availability of training data, a data augmentation strategy is employed to expand the scale of MI EEG datasets. The self-attention-based CNN is trained to automatically extract the temporal and spatial information from EEG signals, allowing the self-attention module to select active channels by calculating EEG channel weights. TFCSP is further implemented to extract multiscale time-frequency-space features from EEG data. Finally, the EEG features derived from TFCSP are concatenated with those from the self-attention-based CNN for MI classification. RESULTS: The proposed method is evaluated on two publicly accessible datasets, BCI Competition IV IIa and BCI Competition III IIIa, yielding mean accuracies of 79.28 % and 86.39 %, respectively. CONCLUSIONS: Compared with state-of-the-art methods, our approach achieves superior classification results in accuracy. Self-attention-based CNN combining with TFCSP can make full use of the time-frequency-space information of EEG, and enhance the classification performance.

Efficiencies of Various in situ Polymerizations of Liquid Electrolytes and the Practical Implications for Quasi Solid-state Batteries.

In situ polymerization of liquid electrolytes is currently the most feasible way for constructing solid-state batteries, which, however, is affected by various interfering factors of reactions and so the electrochemical performance of cells. To disclose the effects from polymerization conditions, two types of generally used in situ polymerizing reactions of ring-opening polymerization (ROP) and double bond radical polymerization (DBRP) were investigated on the aspects of monomer conversion and electrochemical properties (Li+ -conductivity and interfacial stability). The ROP generated poly-ester and poly-carbonate show a high monomer conversion of ≈90 %, but suffer a poor Li+ -conductivity of lower than 2×10-5  S cm-1 at room temperature (RT). Additionally, the terminal alkoxy anion derived from the ROP is not resistant to high-voltage cathodes. While, the DBRP produced poly-VEC(vinyl ethylene carbonate) and poly-VC(vinylene carbonate) show lower monomer conversions of 50-80 %, delivering relatively higher Li+ -conductivities of 2×10-4  S cm-1 at RT. Compared two polymerizing reactions and four monomers, the VEC-based F-containing copolymer possesses advantages in Li+ -conductivity and antioxidant capacity, which also shows simultaneous stability towards Li-metal with the help of LiF-based passivating layer, allowing a long-term stable cycling of high-voltage quasi solid-state cells.