Search

Leak detection has significant implications for the long-term stable operation of water distribution networks (WDNs). This study presented a novel leak detection method by calculating the angular variance between a pressure vector and other vectors in the database, to evaluate the presence of an anomaly in a network. The top priority for this method was to establish a reliable dataset collected from the pressure sensors, which is generated by EPANET 2.2. Numerous node water demand data in normal conditions were generated by the Monte Carlo method, and leak conditions with various leak flows were simulated by creating leak holes in the pipes. Through learning the composite normal and abnormal data in a certain proportion, the angle-based outlier detection model was employed to identify abnormal events. This angle-based method was applied in an actual WDN and the identification performance for anomalies was compared with that of previous detection methods. The results indicated that the novel method proposed in this study could significantly improve the accuracy and efficiency of leak detection compared to the threshold-based and distance-based detection methods. HIGHLIGHTS The proposed method for leak detection in the WDN combines the hydraulic model with outlier detection.; Numerous normal and leak scenarios are simulated by the hydraulic model to calculate residuals.; A clustering algorithm is used to determine the optimal locations of pressure sensors.; The performance of the proposed method is compared with the distance-based method in the literature.;

BackgroundLung cancer is the deadliest and most diagnosed type of cancer worldwide. The 5-year survival rate of lung adenocarcinoma (LUAD) dropped significantly when tumor stages advanced. Patients who received surgically resecting at the pre-invasive stage had a 5-year survival rate of nearly 100%. However, the study on the differences in gene expression profiles and immune microenvironment among pre-invasive LUAD patients is still lacking.MethodsIn this study, the gene expression profiles of three pre-invasive LUAD stages were compared using the RNA-sequencing data of 10 adenocarcinoma in situ (AIS) samples, 12 minimally invasive adenocarcinoma (MIA) samples, and 10 invasive adenocarcinoma (IAC) samples.ResultsThe high expression levels of PTGFRN (Hazard Ratio [HR] = 1.45; 95% Confidence Interval [CI]: 1.08-1.94; log-rank P = 0.013) and SPP1 (HR = 1.44; 95% CI: 1.07-1.93; log-rank P = 0.015) were identified to be associated with LUAD prognosis. Moreover, the early LUAD invasion was accompanied by the enhancement of antigen presentation ability, reflected by the increase of myeloid dendritic cells infiltration rate (Cuzick test P < 0.01) and the upregulation of seven important genes participating in the antigen presentation, including HLA-A (Cuzick test P = 0.03), MICA (Cuzick test P = 0.01), MICB (Cuzick test P = 0.01), HLA-DPA1 (Cuzick test P = 0.04), HLA-DQA2 (Cuzick test P < 0.01), HLA-DQB1 (Cuzick test P = 0.03), and HLA-DQB2 (Cuzick test P < 0.01). However, the tumor-killing ability of the immune system was inhibited during this process, as there were no rising cytotoxic T cell activity (Cuzick test P = 0.20) and no increasing expression in genes encoding cytotoxic proteins. ConclusionIn all, our research elucidated the changes in the immune microenvironment during early-stage LUAD evolution and may provide a theoretical basis for developing novel early-stage lung cancer therapeutic targets.