Research on online monitoring of aircraft skin laser paint removal thickness using standard curve method and PCA-SVR based on LIBS.

High-frequency pulse lasers, applied in the form of rapid scanning, act upon the surface of aircraft skin paint layers, thereby removing the paint layers, exhibiting characteristics of efficiency and eco-friendliness. Real-time monitoring of the paint removal effect and prevention of substrate damage necessitates the continuous monitoring of paint removal thickness. Combining Laser-Induced Breakdown Spectroscopy (LIBS) online monitoring technology enables laser-controlled paint removal under multiple effects coupling, meeting the requirements of airworthiness maintenance. This paper, based on a high-frequency nanosecond infrared pulse laser paint removal LIBS monitoring platform, conducts research on laser paint removal thickness LIBS online monitoring of aluminum alloy plates coated with dual-layer paint. Spectra corresponding to the removal thickness of each group are collected and, respectively, paint removal thickness monitoring models based on LIBS spectra are established using the standard curve method and Principal Component Analysis-Support Vector Regression (PCA-SVR) algorithm. When monitoring paint removal thickness using the standard curve method, the intensity of five Ti element characteristic spectral lines selected is correlated with the paint removal thickness, and segmented curve fitting according to the paint layer structure satisfies the segmented curve fitting of topcoat and topcoat + primer. Among them, the average coefficient of the curve fitting of the Ti II 589.088 nm characteristic spectral line is 0.89, and the root mean square error (RMSE) is 12.28 µm. Its performance is superior in the five standard curves; thus, its fitting equation is used as the criterion for paint removal thickness monitoring. To further improve monitoring accuracy, research on paint removal thickness monitoring models based on PCA-SVR is conducted. Compared to the traditional univariate standard curve method, the PCA-SVR model does not require segmented monitoring. After parameter optimization, the average fitting coefficient reaches 0.97, and the RMSE is 2.92 µm. The results indicate that the PCA-SVR-based paint removal thickness monitoring model has higher accuracy, thereby forming the basis for paint removal thickness monitoring. Through comparative research on paint removal thickness monitoring models, two types of paint removal thickness monitoring criteria are obtained, providing model solutions for high-precision monitoring and automation of aircraft skin laser paint removal thickness.

Research on LIBS online monitoring criteria for aircraft skin laser paint removal based on OPLS-DA.

Online monitoring technology plays a pivotal role in advancing the utilization of laser paint removal in aircraft maintenance and automation. Through the utilization of a high-frequency infrared pulse laser paint removal laser-induced breakdown spectroscopy (LIBS) online monitoring platform, this research conducted data collection encompassing 60 sets of LIBS spectra during the paint removal process. Classification and identification models were established employing principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). These models served as the foundation for creating criteria and rules for the online LIBS monitoring of the controlled paint removal process for aircraft skin. In this research, 12 selected characteristic spectral lines were used to construct the OPLS-DA model, with a predictive root mean square error (RMSEP) of 0.2873. Both full spectrum and feature spectral line data achieved a predictive accuracy of 94.4%. The selection of feature spectral lines maintains predictive performance while significantly reducing the amount of input data. Consequently, this research offers a methodological reference for further advancements in online monitoring technology for laser paint removal in aircraft skin.

A Multifunctional Co-Based Metal-Organic Framework as a Platform for Proton Conduction and Ni trophenols Reduction.

The design and development of proton conduction materials for clean energy-related applications is obviously important and highly desired but challenging. An ultrastable cobalt-based metal-organic framework Co-MOF, formulated as [Co2(btzip)2(µ2-OH2)] (namely, LCUH-103, H2btzip = 4, 6-bis(triazol-1-yl)-isophthalic acid) had been successfully synthesized via the hydrothermal method. LCUH-103 exhibits a three-dimensional framework and a one-dimensional microporous channel structure with scu topology based on the binuclear metallic cluster {Co2}. LCUH-103 indicated excellent chemical and thermal stability; peculiarly, it can retain its entire framework in acid and alkali solutions with different pH values for 24 h. The excellent stability is a prerequisite for studying its proton conductivity, and its proton conductivity σ can reach up to 1.25 × 10-3 S·cm-1 at 80 °C and 100% relative humidity (RH). In order to enhance its proton conductivity, the proton-conducting material Im@LCUH-103 had been prepared by encapsulating imidazole molecules into the channels of LCUH-103. Im@LCUH-103 indicated an excellent proton conductivity of 3.18 × 10-2 S·cm-1 at 80 °C and 100% RH, which is 1 order of magnitude higher than that of original LCUH-103. The proton conduction mechanism was systematically studied by various detection means and theoretical calculations. Meanwhile, LCUH-103 is also an excellent carrier for palladium nanoparticles (Pd NPs) via a wetness impregnation strategy, and the nitrophenols (4/3/2-NP) reduction in aqueous solution by Pd@LCUH-103 indicated an outstanding conversion efficiency, high rate constant (k), and exceptional cycling stability. Specifically, the k value of 4-NP reduction by Pd@LCUH-103 is superior to many other reported catalysts, and its k value is as high as 1.34 min-1 and the cycling stability can reach up to 6 cycles. Notably, its turnover frequency (TOF) value is nearly 196.88 times more than that of Pd/C (wt 5%) in the reaction, indicating its excellent stability and catalytic activity.

Seizure detection with reduced electroencephalogram channels: research trends and outlook.

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

Self-Assembly 2D Ti3C2/g-C3N4 MXene Heterojunction for Highly Efficient Photocatalytic Degradation of Tetracycline in Visible Wavelength Range.

An ultrathin 2D Ti3C2/g-C3N4 MXene (2D-TC/CN) heterojunction was synthesized, using a facile self-assembly method; the perfect microscopic-morphology and the lattice structure presented in the sample with a 2 wt% content of Ti3C2 were observed by the field-emission scanning electron microscopy (SEM) and transmission electron microscope (TEM). The optimized sample (2-TC/CN) exhibited excellent performance in degrading the tetracycline (TC), and the degradation rate reached 93.93% in the conditions of 20 mg/L, 50 mL of tetracycline within 60 min. Except for the increased specific-surface area, investigated by UV-vis diffuse reflectance spectra (UV-vis DRS) and X-ray photoelectron microscopy (XPS) valence spectra, the significantly enhanced photocatalytic activity of the 2-TC/CN could also be ascribed to the formation of Ti-N bonds between Ti3C2 and g-C3N4 nanosheets, which reduced the width of the band gap through adjusting the position of the valence band, thus resulting in the broadened light-absorption. Furthermore, the facilitated electron transmission was also proved by time-resolved photoluminescence (TRPL) and electrochemical impedance spectroscopy (EIS), which is effective in improving the quantum efficiency of photo-generated electrons. In addition, the resulting radical-capture experiment suggested that superoxide radicals have the greatest influence on photodegradation performance, with the photodegradation rate of TC reducing from 93.16% to 32.08% after the capture of superoxide radicals, which can be attributed to the production of superoxide radicals only, by the 2-TC/CN composites with a high conduction-band value (-0.62 eV). These facilely designed 2D Ti3C2/g-C3N4 composites possess great application potential for the photodegradation of tetracycline and other antibiotics.

Weak self-supervised learning for seizure forecasting: a feasibility study.

This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.

A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification.

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) presents a simpler and more wearable alternative for long-term ambulatory monitoring. To assess the performance of EEG and ECG signals, AI systems offer a promising way forward for developing high performing models in securing both a reasonable sensitivity and specificity. There are crucial needs for these AI systems to be developed with more clinical relevance and inference generalization. In this work, we implement an ECG-specific convolutional neural network (CNN) model with residual layers and an EEG-specific convolutional long short-term memory (ConvLSTM) model. We trained, validated, and tested these models on a publicly accessible Temple University Hospital (TUH) dataset for reproducibility and performed a non-patient-specific inference-only test on patient EEG and ECG data of The Royal Prince Alfred Hospital (RPAH) in Sydney, Australia. We selected 31 adult patients to balance groups with the following seizure types: generalized, frontal, frontotemporal, temporal, parietal, and unspecific focal epilepsy. Our tests on both EEG and ECG of these patients achieve an AUC score of 0.75. Our results show ECG outperforms EEG with an average improvement of 0.21 and 0.11 AUC score in patients with frontal and parietal focal seizures, respectively.Clinical relevance-Prior research has demonstrated the value of using ECG for seizure documentation. It is believed that specific epileptic foci (seizure origin) may involve network inputs to the autonomic nervous system. Our result indicates that ECG could outperform EEG for individuals with specific seizure origin, particularly in the frontal and parietal lobes.

Seizure Susceptibility Prediction in Uncontrolled Epilepsy.

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of "What are the true biomarkers for seizure prediction" or even "Is there a valid biomarker for seizure prediction?" are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.

Superconductivity with High Upper Critical Field in the Cubic Centrosymmetric η-Carbide Nb4Rh2C1-δ.

The upper critical field is a fundamental measure of the strength of superconductivity in a material. It is also a cornerstone for the realization of superconducting magnet applications. The critical field arises because of the Cooper pair breaking at a limiting field, which is due to the Pauli paramagnetism of the electrons. The maximal possible magnetic field strength for this effect is commonly known as the Pauli paramagnetic limit given as µ0 H Pauli ≈ 1.86[T/K]·T c for a weak-coupling Bardeen-Schrieffer-Cooper (BCS) superconductor. The violation of this limit is only rarely observed. Exceptions include some low-temperature heavy Fermion and some strongly anisotropic superconductors. Here, we report on the superconductivity at 9.75 K in the centrosymmetric, cubic η-carbide-type compound Nb4Rh2C1-δ, with a normalized specific heat jump of ΔC/γT c = 1.64. We find that this material has a remarkably high upper critical field of µ0 H c2(0) = 28.5 T, which is exceeding by far its weak-coupling BCS Pauli paramagnetic limit of µ0 H Pauli = 18.1 T. Determination of the origin and consequences of this effect will represent a significant new direction in the study of critical fields in superconductors.

Consumption Trends During the COVID-19 Crisis: How Awe, Coping, and Social Norms Drive Utilitarian Purchases.

As the coronavirus disease 2019 (COVID-19) crisis continues to worsen globally, there exists a widespread enthusiasm for buying utilitarian products in the retail market, irrespective of culture or nationality. However, the questions of whether and why being involved in a public health emergency like the COVID-19 crisis could modify consumer behaviors have been underexplored by previous literature. Drawing on the theory of awe that highlights the important role in influencing human behaviors when they are facing unexpected events that transcends the frame of existing references, the present research aims to clarify the relationship between COVID-19 involvement and consumer preference for utilitarian versus hedonic products. We collected data from 512 Chinese participants (319 women; average age 29.11 years; SD = 11.89) during the outbreak of COVID-19. The results of structural equation modeling showed that COVID-19 involvement was positively related to the preference for utilitarian products (vs. hedonic products). More importantly, this relationship could be explained via the mediated effects of awe, problem-focused coping, and social norm compliance. The present research emphasizes the non-negligible role of public health emergencies in modifying consumer behavior and the role of awe in explaining the psychological influence of public health emergencies.

Mediating effect of social support on the associations between health literacy, productive aging, and self-rated health among elderly Chinese adults in a newly urbanized community.

With the growing urbanization of China, which has changed older adults' living conditions, lifestyle, and the source of support, coupled with rapid population aging, the health status of the elderly should be paid attention to. In addition to objective indicators such as the decline of function, specific factors, such as social support, health literacy, and productive aging, also have an impact on the health outcomes of the elderly. However, the interrelationships among these factors and their potential mechanisms in the context of urbanization remain unclear. Thus, this study was the first to explore the interrelationships among social support, health literacy, productive aging, and self-rated health in older adults living in a newly urbanized community in China.We aimed to investigate the mediating effect of social support on the associations between health literacy, productive aging, and self-rated health among elderly Chinese adults in a newly urbanized community to provide reference data for future health interventions for the elderly.This cross-sectional study was conducted between June and August 2013. Questionnaires on social support, health literacy, productive aging, and self-rated health were administered to 992 elderly residents. Structural equation models were used to examine the relationships among these 4 variables. Statistical analyses were performed using IBM SPSS Statistics 21.0 and Mplus 7.0.The mean scores for social support, health literacy, productive aging, and self-rated health were 34.5 ±â€Š5.8, 13.6 ±â€Š4.4, 11.3 ±â€Š3.0, and 3.4 ±â€Š0.7, respectively. Social support was directly related to self-rated health (ß=0.119, 95% confidence interval [CI]: 0.041-0.198), while productive aging and health literacy had indirect associations with self-rated health via social support (ß=0.071, 95% CI: 0.054-0.216; ß=0.049, 95% CI: 0.066-0.183). Both productive aging (ß=0.214, 95% CI: 0.047-0.381) and health literacy had direct associations with social support (ß=0.327, 95% CI: 0.175-0.479), while health literacy had a direct association with productive aging (ß=0.676, 95% CI: 0.604-0.748). Productive aging mediated the relationship between health literacy and social support.Overall, improving health outcomes among older adults requires enhancement of social support, along with consideration of productive aging and health literacy.

Direct and Indirect Inhibition Effects of Resveratrol against Toxoplasma gondii Tachyzoites In Vitro.

Toxoplasma gondii is one of the most widespread obligatory parasitic protozoa and infects nearly all warm-blooded animals, leading to toxoplasmosis. The therapeutic drugs currently administered, like the combination of pyrimethamine and sulfadiazine, show high rates of toxic side effects, and drug resistance is encountered in some cases. Resveratrol is a natural plant extract with multiple functions, such as antibacterial, anticancer, and antiparasite activities. In this study, we evaluated the inhibitory effects of resveratrol on tachyzoites of the Toxoplasma gondii RH strain extracellularly and intracellularly. We demonstrate that resveratrol possesses direct antitoxoplasma activity by reducing the population of extracellularly grown tachyzoites, probably by disturbing the redox homeostasis of the parasites. Moreover, resveratrol was also able to release the burden of cellular stress, promote apoptosis, and maintain the autophagic status of macrophages, which turned out to be regulated by intracellular parasites, thereby functioning indirectly in eliminating T. gondii In conclusion, resveratrol has both direct and indirect antitoxoplasma effects against RH tachyzoites and may possess the potential to be further evaluated and employed for toxoplasmosis treatment.

Light-Induced Bidirectional Metal-to-Metal Charge Transfer in a Linear Fe2 Co Complex.

It is a challenge to reversibly switch both magnetism and polarity using light irradiation. Herein we report a linear Fe2 Co complex, whereby interconversion between FeIIILS (µ-CN)CoIIHS (µ-NC)FeIIILS (LS=low-spin, HS=high-spin) and FeIIILS (µ-CN)CoIIILS (µ-NC)FeIILS linkages could be achieved upon heating and cooling, or alternating laser irradiation at 808 and 532 nm. The electron spin arrangement and charge distribution were simultaneously tuned accompanying bidirectional metal-to-metal charge transfer, providing switchable polarity and magnetism in the complex.

Spin-dependent Transport Properties of CrO2 Micro Rod.

The CrO2 micro rod powder was synthesized by decomposing the CrO3 flakes at a specific temperature to yield precursor and annealing such a precursor in a sealed glass tube. The magneto-transport properties have been measured by a direct current four-probe method using a Cu/CrO2 rods/colloidal silver liquid electrode sandwich device. The largest magnetoresistance (MR) around ~72 % was observed at 77 K with applied current of 0.05 µA. The non-linear I-V curve indicates a tunneling type transport properties and the tunneling barrier height is around 2.2 ± 0.04 eV at 77 K, which is obtained with fitting the non-linear I-V curves using Simmons' equation. A mixing of Cr oxides on the surface of CrO2 rod observed by X-ray photoemission spectroscopy provides a tunneling barrier rather than a single phase of Cr2O3 insulating barrier. The MR shows strong bias voltage dependence and is ascribed to the two-step tunneling process.