A regression method for EEG-based cross-dataset fatigue detection

Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model.Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information.Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods.Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices

A Rapid Synchronous Determination Method for Soil Inorganic Carbon Content and its Carbon Isotope Ratio

The accumulation and leaching of soil inorganic carbon (SIC) play crucial roles in the global carbon balance and represent a key research focus in carbon cycling studies. Accurate quantification of SIC content and its stable isotope ratio is critical for identifying the current "missing" carbon sink in terrestrial ecosystems. This study developed a rapid，high-throughput method for synchronous measurement of soil inorganic carbon (IC) content and its carbon isotope ratios using cavity ring-down spectroscopy(CRDS) combined with an automated small-volume gas sampler. A synchronous analysis method for inorganic carbon content and isotope ratios in different types of soils was established by analyzing certified reference materials. Results demonstrated that this method has a measurement range of 0.050−0.500 mg (as carbonate)，with a correlation coefficient ≥0.999. The accuracy of SIC analysis was better than 1 g/kg，and the accuracy of carbon isotope analysis was better than 0.5 ‰，with no observed isotope fractionation. The newly developed method was applied to determine inorganic carbon content and isotope ratios in soils with different types and SIC contents. The results showed that all samples achieved good repeatability，and the results were consistent with those measured using the original method. Moreover，the accuracy of SIC content and isotope ratios in soils of 100 mesh is better than that in soils of 60 mesh. The optimized method is simple to operate，offers a low detection limit，requires minimal processing time，and exhibits excellent repeatability，making it highly suitable for rapid and batch analysis of SIC content and its stable carbon isotope ratio

Effects of culture and social cynicism on anxious attachment transference from mother to partner

We examined the role of culture and social cynicism beliefs in the transference of an anxious attachment style from mother to romantic partner among a group of undergraduates from the US (n = 200) and Hong Kong (n = 147). The results showed that anxious attachment to mother and to partner was moderately correlated among both cultural groups. However, social cynicism beliefs were found to moderate the relationship between anxious attachment to mother and attachment to partner among U.S. but not Hong Kong Chinese participants. This observed differential effect of social cynicism beliefs could be explained by differences in self-direction values across the 2 cultural groups. The findings in the study are of theoretical significance as they provide insights for further research on the influences of cultural variables and personal beliefs on attachment transference.</jats:p

Diagnostic value of serum human epididymal secretory protein 4 for endometrial cancer: a systematic review and meta analysis

Abstract Objective The diagnostic value of serum human epididymal secretory protein 4 (HE4) for endometrial cancer (EC) was assessed via evidence-based medicine (EBM) and systematic review (SR) methodologies. Methods The Cochrane Library, PubMed, Web of Science, Embase, CBM, CNKI, and Wan Fang databases were searched up to April 1st, 2024, to identify relevant literature. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was utilized to evaluate the quality of the selected studies. The meta-analysis was conducted via RevMan 5.3, STATA 16.1, and Meta-disc software. Results A total of 22 studies comprising 9036 cases (3776 cases in the case group and 5260 cases in the control group) were included. The results revealed that HE4 exhibited a pooled sensitivity of 0.59 [95% CI (0.53, 0.64)], specificity of 0.93 [95% CI (0.88, 0.96)], positive likelihood ratio (PLR) of 6.87 [95% CI (4.57, 10.33)], negative likelihood ratio (NLR) of 0.46 [95% CI (0.39, 0.54)], diagnostic odds ratio (DOR) of 14.36 [95% CI (9.37, 21.17)], and area under the receiver operating characteristic curve (AUC) of 0.78 [95% CI (0.75, 0.82)]. Conclusions Serum HE4 demonstrates high specificity and moderate sensitivity for diagnosing EC, thus serving as a valuable biomarker for clinicians either alone or in conjunction with other tumour markers

Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN) model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS) dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction