Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model.

Epistemic uncertainty in data-driven landslide susceptibility assessment often tends to be increased by the limited accuracy of an individual model, as well as uncertainties associated with the selection of non-landslide samples. To address these issues, this paper centers on the landslide disaster in Ji'an City, China, and proposes a heterogeneous ensemble learning method incorporating frequency ratio (FR) and semi-supervised sample expansion. Based on the superimposed results of 12 environmental factor frequency ratios (FFR), non-landslide samples were selected and input into light gradient boosting machine (LightGBM), random forest (RF), and convolutional neural network (CNN) models for prediction along with historical landslide samples. The predicted probability values are integrated by four heterogeneous ensemble strategies to expand samples from high-confidence results. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), partition frequency ratio (PFR), and other verification methods. The results demonstrate that the negative sample based on FFR sampling is more accurate than the random sampling method, and the FR-SSELR model based on frequency ratio sampling and semi-supervised ensemble strategy exhibits the highest performance (AUC = 0.971, ACC = 0.941). A more reasonable landslide susceptibility map was drawn based on this model, with the lowest percentage of landslides in the low and very low susceptibility zones (sum of PFR = 0.194), as well as the highest percentage of landslides in the high and very high susceptibility zones (sum of PFR = 6.800). Furthermore, the FR-SSELR model improved economic benefits by 3.82-14.2%, offering valuable guidance for decision-making regarding landslide management and the sustainability of Ji'an City.

Insulin-like growth factor-1 in myocardial ischemia-reperfusion injury: A review.

Myocardial ischemia-reperfusion injury (MIRI) is a severe damage inflicted on the ischemic myocardium when blood flow is restored, and it commonly occurs in a wide range of cardiovascular diseases. Presently, no effective clinical treatment exists for MIRI. Accumulating evidence indicates that insulin-like growth factor-1 (IGF-1) plays a role in the intricate chain of cardiovascular events, in addition to its well-recognized growth-promoting and metabolic effects. IGF-1, a member of the insulin family, exhibits a broad spectrum of protective effects against ischemia/reperfusion injury in various tissues, especially the myocardium. In particular, earlier research has demonstrated that IGF-1 reduces cellular oxidative stress, improves mitochondrial function, interacts with noncoding RNAs, and activates cardiac downstream protective genes and protective signaling channels. This review aimed to summarize the role of IGF-1 in MIRI and elucidate its related mechanisms of action. In addition, IGF-1-related interventions for MIRI, such as ischemic preconditioning and post-conditioning, were discussed. The purpose of this review was to provide evidence supporting the activation of IGF-1 in MIRI and advocate its use as a therapeutic target.

Fault diagnosis of rotor based on Semi-supervised Multi-Graph Joint Embedding.

Traditional graph embedding methods only consider the pairwise relationship between fault data. But in practical applications, the relationship of high-dimensional fault data usually is multiple classes corresponding to multiple samples. Therefore, the hypergraph structure is introduced to fully portray the complex structural relationship of high-dimensional fault data. However, during the construction of the hypergraph, the hyperedge weight is usually set as the sum of the similarities between every two vertices contained within the hyperedge, and this "averaging effect" causes the relationship between data sample points with high similarity to be weakened, while the relationship between data sample points with low similarity to be strengthened. This phenomenon also leads to the hypergraph cannot accurately portray the relationship of high-dimensional data, which reduces the fault classification accuracy. To address this issue, a novel dimensionality reduction method named Semi-supervised Multi-Graph Joint Embedding (SMGJE) is proposed and applied to rotor fault diagnosis. SMGJE constructs simple graphs and hypergraphs with the same sample points and characterizes the structure of high-dimensional data in a multi-graph joint embedding. The edges of the simple graph are the direct description of the similarity between sample points so that SMGJE can overcome this "averaging effect" of the hypergraph. The effectiveness of the proposed method is verified by two different fault datasets.