ERTNet: an interpretable transformer-based framework for EEG emotion recognition

BackgroundEmotion recognition using EEG signals enables clinicians to assess patients’ emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.MethodsWe developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state.ResultsExperiments’ results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data.DiscussionGiven its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface

Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection

IntroductionSleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients’ suffering.MethodsTo this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.ResultsThe experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.DiscussionAll of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor’s diagnostic process and provide a better medical experience for patients

MASH Explorer: A Universal Software Environment for Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research

CHANGE PATTERN DISCOVERY IN MULTISTAGE STATISTICAL PROCESS CONTROL

Change pattern discovery and identification is one of the main challenges in multistage statistical process control, since it can provide operation engineers the information to diagnose the root cause once an abnormal operation occurs. This paper establishes a model for multistage processes in which the inertia of the process is emphasized. The analytical expressions for the change patterns are obtained by Z-transform. The properties of the change patterns are analyzed by partitioning into two parts: a steady state and a transient period. The final value can characterize the steady state and the relative information entropy can characterize the transient period. Significance: The discovery and analysis of change patterns will help in the quick identification of out-of-control sources by linking the current stage signal to information about earlier stages in the process

Zircon U–Pb chronology, geochemistry, and petrogenesis of the high Nb–Ta alkaline rhyolites at the Tuohe Tree Farm, northern Volcanic Belt, Great Xing’an Range, China

We studied newly found high Nb–Ta alkaline rhyolites in the northern volcanic belt of the Great Xing’an Range, China. The LA–ICP–MS U–Pb weighted mean age is 114.07 ± 0.55 Ma, indicating that the rocks formed during the late Early Cretaceous and were the product of the late eruption of a Mesozoic volcano. The major element contents are characterized by high Si, rich K, low Fe, and poor Ca and Mg. In the total alkaline–silicon diagram, the sample points are in the alkaline rhyolite region. Meanwhile, rare earth elements show obvious Ce/Ce* positive anomalies and Eu/Eu* negative anomalies. In addition, trace elements are characterized by high Nb, Ta, and Yb, and low Sr. The two-stage Nd isotopic model age T2DM of the depleted mantle is between 799–813 Ma, indicating that the diagenetic material originated from the depleted mantle or partial melting of newly formed young crustal materials. The source rocks melted at a relative shallow depth (<30 km), under lower pressure (<0.5 Gpa) and high oxygen fugacity; moreover, the residues in the source region were Ca-rich mafic plagioclase + amphibole + orthopyroxene. In the Nb–Y–3Ga and Nb–Y–Ce diagrams, the sample points are in the A1 type region. It can be concluded that the mantle-derived basaltic magma underplated and supplied the heat sources for partial melting of the metamorphic crustal rocks in an intraplate extensional tectonic environment related to a rift, mantle plume, and hot spot.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Reliability Evaluation of Photovoltaic System Considering Inverter Thermal Characteristics

The reliable operation of photovoltaic (PV) power generation systems is related to the security and stability of the power grid and is the focus of current research. At present, the reliability evaluation of PV power generation systems is mostly calculated by applying the standard failure rate of each component, ignoring the impact of thermal environment changes on the failure rate. This paper will use the fault tree theory to establish the reliability assessment method of PV power plants, model the PV power plants working in the variable environment through the hardware-in-the-loop simulation system, and analyze the influence of the thermal characteristics of the inverter’s key components on the reliability of the PV power plant. Studies have shown that the overall reliability of bus capacitors, inverters, and PV power plants is reduced by 18.4%, 30%, and 18.7%, respectively, compared to when the thermal characteristics of bus capacitors are not considered. It can be seen that thermal attenuation has a great influence on the reliability of the PV power generation system

Characterization of Destrins with Different Dextrose Equivalents

Dextrins are widely used for their functional properties and prepared by partial hydrolysis of starch using acid, enzymes, or combinations of both. The physiochemical properties of dextrins are dependent on their molecular distribution and oligosaccharide profiles. In this study, scanning electron microscopy (SEM), X-ray diffractometry (XRD), rapid viscoanalysis (RVA), high-performance Liquid Chromatography (HPLC) and gel permeation chromatography (GPC) were used to characterize dextrins prepared by common neutral and thermostable α-amylase hydrolysis. The dextrin granules displayed irregular surfaces and were badly damaged by the enzyme treatment. They displayed A-type X-ray diffraction patterns with a decrease of intensity of the characteristic diffraction peaks. The RVA profiles showed that the viscosity of dextrin decreased with the increase of its Dextrose Equivalent (DE) value. According to HPLC analysis, the molecular weight, degree of polymerization and the composition of oligosaccharides in dextrins were different