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It is crucial to diagnose transmission line faults quickly and accurately. Belief rule base (BRB) has strong nonlinear modeling ability, allowing for the effective utilization of both qualitative knowledge and quantitative data related to these faults. To ensure accurate diagnosis results, the model must account for uncertainty and ignorance. Additionally, interpretability of the results is essential for improving diagnostic credibility. Therefore, a transmission line fault diagnosis model based on interpretable BRB with power set (PBRB-I) is proposed in this paper. Firstly, transmission line faults and data are analyzed, with the use of the Spearman correlation coefficient for data preprocessing. Secondly, according to the characteristics of transmission lines, interpretable modeling criteria are defined. Then, a power set identification framework is utilized to represent ignorance. Finally, the evidence reasoning (ER) algorithm is applied as a reasoning tool, and a parameter optimization method with interpretable constraints based on the projection covariance matrix adaptive evolutionary strategy (P-CMA-ES) is proposed. In the case study, the PBRB-I model demonstrates an accuracy of 91.11%, and it exhibits high performance stability across different data allocation ratios. It not only shows outstanding accuracy but also effectively expresses ignorance and produces interpretable results.

Abstract Background We have reported a positive correlation between S100 calcium-binding protein (S100) A8/S100A9 and sepsis-induced lung damage before. However, limited knowledge exists concerning the biological role of S100A8/A9 in pulmonary vascular endothelial barrier dysfunction, as well as the diagnostic value of S100A8/A9 in sepsis. Methods Sepsis was induced in C57BL/6J mice and S100A9-knockout (KO) mice through the cecal ligation and puncture (CLP). Pulmonary vascular leakage was determined by measuring extravasated Evans blue (EB). Reverse transcription polymerase chain reaction and the histological score were used to evaluate inflammation and lung injury, respectively. Recombinant S100A8/A9 (rhS100A8/A9) was used to identify the effects of S100A8/A9 on endothelial barrier dysfunction in human umbilical vein endothelial cells (HUVECs). Additionally, the diagnostic value of S100A8/A9 in sepsis was assessed using receiver operating characteristic. Results S100A8/A9 expression was up-regulated in the lungs of CLP-operated mice. S100A9 KO significantly reversed CLP-induced hypothermia and hypotension, resulting in an improved survival rate. S100A9 KO also decreased the inflammatory response, EB leakage, and histological scores in the lungs of CLP-operated mice. Occludin and VE-cadherin expressions were decreased in the lungs of CLP-operated mice; However, S100A9 KO attenuated this decrease. Moreover, CLP-induced signal transducer and activator of transcription 3 (STAT3) and p38/extracellular signal-regulated kinase (ERK) signalling activation and apoptosis were mitigated by S100A9 KO in lungs. In addition, rhS100A8/A9 administration significantly decreased occludin and VE-cadherin expressions, increased the phosphorylated (p)-ERK/ERK, p-p38/p38, and B-cell leukaemia/lymphoma 2 protein (Bcl-2)-associated X protein/Bcl-2 ratios in HUVECs. Conclusion The present study demonstrated S100A8/A9 aggravated sepsis-induced pulmonary inflammation, vascular permeability, and lung injury. This was achieved, at least partially, by activating the P38/STAT3/ERK signalling pathways. Moreover, S100A8/A9 showed the potential as a biomarker for sepsis diagnosis.

Abstract Accurately predicting the capacity of lithium battery is conducive to improving its safety. Affected by complex internal electrochemical reaction and external use conditions, the prediction accuracy is difficult to guarantee; at the same time, the existing prediction methods are unexplainable, resulting in the inability to trace the prediction process. Therefore, a capacity prediction model based on belief rule base with interpretability and interval optimization strategy is proposed in this paper. First, the reasoning process is designed according to interpretability modeling criteria. Second, to achieve a balance of accuracy and interpretability, based on the whale optimization algorithm, a model parameter optimization method using interval optimization strategy is proposed. Finally, through a case study, the model's effectiveness is verified. Comparison with other models shows that the proposed model has certain advantages in accuracy and interpretability.

Background: S100 calcium-binding protein A9 (S100A9) is a danger-associated molecular pattern molecule that mediates the inflammatory response. Inflammation is essential in aging-related cardiovascular diseases. However, less is known regarding the role of S100A9 in vascular aging. Methods: S100A9 null mice were used to investigate the role of S100A9 in aging-related pathologies. Artery rings were used to measure the functional characteristics of vascular with a pressurized myograph. Telomere length, Sirtuin activity, oxidative stress, and endothelial nitric oxide synthetase (eNOS) activity were used to elevate vascular senescence. Intraperitoneal glucose tolerance (IPGTT) and insulin sensitivity test (IST) were employed to investigate the effects of S100A9 on insulin resistance. Inflammation response was reflected by the concentration of inflammatory cytokines. The Toll-like receptor 4 (TLR4) and receptor for advanced glycation end products (RAGE) inhibitors were used to identify the downstream molecular mechanisms of S100A9 in aging-induced senescence in endothelial cells. Results: S100A9 expression in vascular increased with aging in mice and humans. Deficiency of S100A9 alleviated vascular senescence in aged mice, as evidenced by increased telomere length, Sirtuin activity, and eNOS activity. Meanwhile, S100A9 knockout improved endothelium-dependent vasodilatation and endothelial continuity in aged mice. Moreover, the increased insulin resistance, oxidative stress, and inflammation were mitigated by S100A9 deletion in aged mice. In vitro, S100A9 induced senescence in endothelial cells, and that effect was blunted by TLR4 but not RAGE inhibitors. Conclusion: The present study suggested that S100A9 may contribute to aging-related pathologies and endothelial dysfunction via the TLR4 pathway. Therefore, targeting S100A9/TLR4 signaling pathway may represent a crucial therapeutic strategy to prevent age-related cardiovascular diseases.

Abstract Background S100 calcium binding protein A9 (S100A9) is a pro-inflammatory alarmin associated with several inflammation-related diseases. However, the role of S100A9 in lung injury in sepsis has not been fully investigated. Therefore, the present study aimed to determine the role of S100A9 in a lipopolysaccharide (LPS)-induced lung injury murine model and its underlying molecular mechanisms. Methods LPS was utilized to induce sepsis and lung injury in C57BL/6 or NOD-like receptor family pyrin domain containing 3 (NLRP3)−/− mice. To investigate the effects of S100A9 blockade, mice were treated with a specific inhibitor of S100A9. Subsequently, lung injury and inflammation were evaluated by histology and enzyme‑linked immunosorbent assay (ELISA), respectively. Furthermore, western blot analysis and RT-qPCR were carried out to investigate the molecular mechanisms underlying the effects of S100A9. Results S100A9 was upregulated in the lung tissues of LPS-treated mice. However, inhibition of S100A9 alleviated LPS-induced lung injury. Additionally, S100A9 blockade also attenuated the inflammatory responses and apoptosis in the lungs of LPS-challenged mice. Furthermore, the increased expression of NLRP3 was also suppressed by S100A9 blockade, while S100A9 blockade had no effect on NLRP3−/− mice. In vitro, S100A9 downregulation mitigated LPS-induced inflammation. Interestingly, these effects were blunted by NLRP3 overexpression. Conclusion The results of the current study suggested that inhibition of S100A9 could protect against LPS-induced lung injury via inhibiting the NLRP3 pathway. Therefore, S100A9 blockade could be considered as a novel therapeutic strategy for lung injury in sepsis.

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decisionmakers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis. [ABSTRACT FROM AUTHOR]

Prediction systems are an important aspect of intelligent decisions. In engineering practice, the complex system structure and the external environment cause many uncertain factors in the model, which influence the modeling accuracy of the model. The belief rule base (BRB) can implement nonlinear modeling and express a variety of uncertain information, including fuzziness, ignorance, randomness, etc. However, the BRB system also has two main problems: Firstly, modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy. Secondly, interpretability is not considered in the optimization process of current research, resulting in the destruction of the interpretability of BRB. To balance the accuracy and interpretability of the model, a self-growth belief rule base with interpretability constraints (SBRB-I) is proposed. The reasoning process of the SBRB-I model is based on the evidence reasoning (ER) approach. Moreover, the self-growth learning strategy ensures effective cooperation between the data-driven model and the expert system. A case study showed that the accuracy and interpretability of the model could be guaranteed. The SBRB-I model has good application prospects in prediction systems. [ABSTRACT FROM AUTHOR]