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Signal preprocessing and feature extraction are decisive factors in determining the frequency of bearing faults. The presence of noise interference in the status signal of rolling bearings often hampers accurate fault detection. Although there are various methods for preprocessing vibration signals in rolling bearings, they need further improvement in terms of enhancing fault feature expression and localizing fault frequency bands. This limitation significantly hinders the accuracy of fault frequency determination. In order to enhance the representation of fault information on the frequency spectrum, this study proposes a combined approach that incorporates sparse stacked autoencoder (SSAE), wavelet packet decomposition (WPD), and adaptive spectrum amplitude modulation (ASAM). The resulting method is referred to as SSAE-WPD-ASAM. Firstly, the bearing vibration signal is decomposed by wavelet packet according to the scale and frequency band of the signal. On this basis, the signal reconstruction is realized based on the wavelet packet coefficient and energy distribution in different frequency bands. Secondly, for the whole life cycle signal, the reconstructed signal is self-encoded by sparse stacked autoencoder to achieve dimensionality reduction of the reconstructed signal. Then, the spare reconstructed signal is subjected to ASAM. Finally, through envelope demodulation, peak detection of fault frequency and empirical fault frequency comparison, the specific fault types of rolling bearings are determined. The proposed method is verified by theoretical simulation and three groups of practical experiments. The results show that the proposed method has a significant improvement in diagnostic efficiency and accuracy compared with traditional diagnostic methods. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF) is associated with pronounced systemic inflammation, and neutrophil extracellular traps (NETs) are key components of this response. The primary objective of this study was to establish an NET-related scoring system for patients with HBV-ACLF. A prospective training cohort of 81 patients from the Beijing Ditan Hospital was included. The concentrations of NET markers (cell-free DNA, myeloperoxidase DNA [MPO-DNA], and citrullinated histone H3) in peripheral blood were quantified. Random survival forest, LASSO regression, and multivariate Cox regression analyses were used to identify prognostic factors associated with 90-day mortality in ACLF patients and develop a nomogram for visualization, which was followed by evaluation in a validation cohort (n = 40). NET-related marker levels were significantly higher in the non-survival group than in the survival group (p < 0.05). The NET score was constructed by combining MPO-DNA, neutrophil-to-lymphocyte ratio, and age data. The score's diagnostic effectiveness, assessed by the area under the curve, yielded values of 0.83 and 0.77 in the training and validation sets, respectively, markedly surpassing those of other established models (p < 0.05). In both groups, the 90-day mortality rates were 88.8% and 75.0%, respectively, for patients categorized as high risk and 18.0% and 12.5%, respectively, for those classified as low risk. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

Innovation is the driving force for achieving sustainable economic development, and healthy business–government relationships are the foundation and guarantee for promoting the sustainability of digital innovation and entrepreneurship. However, current academic research on the impact of business–government relations on digital innovation and entrepreneurship often neglects the configurational effects of various factors. Therefore, this study constructed an analytical framework from the new dimension of "close" and "clean" business–government relationships, selected 292 Chinese cities as research subjects, and employed the Necessary Condition Analysis (NCA) and Time-Differencing Qualitative Comparative Analysis (TDQCA) methods. From a configurational perspective, it explored the relationship between business–government relations and digital innovation and entrepreneurship. The results showed the following: Firstly, the various business–government relationship factors did not have a single linear impact on digital innovation and entrepreneurship, and configuration was more crucial than a single factor. Secondly, based on the integration of research findings and the theoretical framework, five successful configurations were proposed. However, these configurations possess certain adaptability and need to be tailored to local conditions. Thirdly, analyzing the three "non" condition variables in these five configurations, including "clean" business–government relationships, government efficiency, and new infrastructure, also contributed to enhancing the sustainability of digital innovation and entrepreneurship outcomes. Additionally, the study analyzed the implications of these critical configurations for five key stakeholders: government, enterprises, research institutions and academia, policymakers, and the public. Specifically, the government can implement policies tailored to local conditions to promote the sustainable development of digital innovation and entrepreneurship. These policies include increasing investment in digital infrastructure, simplifying approval processes, and enhancing the efficiency of government services. [ABSTRACT FROM AUTHOR]

Overt hepatic encephalopathy (OHE), a serious complication of liver cirrhosis, is associated with alterations in lipid and lipoprotein metabolism. We evaluated the correlation between high-density lipoprotein cholesterol (HDL-C) levels and transplant-free (TF) mortality in patients with OHE. Patients with OHE admitted to Beijing Ditan Hospital between January 2010 and August 2016 (n = 821) and between September 2016 and December 2020 (n = 480) were included in the training and validation sets, respectively. Independent predictors were explored by a multivariate Cox regression analysis, and the area under the receiver operating characteristic curve (AUC) was used to assess the prognostic value of these factors. The prognostic value of HDL-C was good (AUC at 1 year: 0.745) and was equivalent to that of the Model for End-Stage Liver Disease (MELD) score (AUC at 1 year: 0.788). The optimal threshold values for HDL-C and MELD were 0.5 mmol/L and 17, respectively. The 1-year TF mortality rates in the low-risk (HDL-C ≥ 0.5 mmol/L and MELD < 17) and high-risk (HDL-C < 0.5 mmol/L and MELD ≥ 17) groups were 7.5% and 51.5% in the training set and 10.1% and 48.2% in the validation set, respectively. HDL-C level < 0.5 mmol/L and MELD score > 17 can facilitate the identification of high-risk patients and provide a basis for timely treatment. [ABSTRACT FROM AUTHOR]

Over 50% of patients with hepatitis B virus‐associated hepatocellular carcinoma (HBV‐HCC) are diagnosed at an advanced stage, which is characterized by immune imbalance between CD8+ T cells and regulatory T (Treg) cells that accelerates disease progression. However, there is no imbalance indicator to predict clinical outcomes. Here, we show that the proportion of CD8+ T cells decreases and Treg cells increases in advanced HBV‐HCC patients. During this stage, CD8+ T cells and Treg cells expressed the coinhibitory molecule PD‐1 and the costimulatory molecule ICOS, respectively. Additionally, the ratio between PD‐1+CD8 and ICOS+Tregs showed significant changes. Patients were further divided into high‐ and low‐ratio groups: PD‐1+CD8 and ICOS+Tregs high‐ (PD‐1/ICOShi) and low‐ratio (PD‐1/ICOSlo) groups according to ratio median. Compared with PD‐1/ICOSlo patients, the PD‐1/ICOShi group had better clinical prognosis and weaker CD8+ T cells exhaustion, and the T cell‐killing and proliferation functions were more conservative. Surprisingly, the small sample analysis found that PD‐1/ICOShi patients exhibited a higher proportion of tissue‐resident memory T (TRM) cells and had more stable killing capacity and lower apoptosis capacity than PD‐1/ICOSlo advanced HBV‐HCC patients treated with immune checkpoint inhibitors (ICIs). In conclusion, the ratio between PD‐1+CD8 and ICOS+Tregs was associated with extreme immune imbalance and poor prognosis in advanced HBV‐HCC. These findings provide significant clinical implications for the prognosis of advanced HBV‐HCC and may serve as a theoretical basis for identifying new targets in immunotherapy. [ABSTRACT FROM AUTHOR]