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We have developed a dissolved oxygen (DO) method with differential equation (DE) correction. We measured the oxygen content in La-based and Y-based superconductors, and succeeded in measuring the oxygen content simply in one-third of the time required by the iodometric titration method. However, there was a problem with Bi-based superconductors where the measured oxygen content was smaller compared to the iodometric titration method. We hypothesized that not only O2 but also Cl2 gas is generated when dissolving Bi-based superconductors and developed a dissolved oxygen/chlorine (DO/Cl) method with DE correction. This method uses only a dissolved oxygen sensor and a dissolved chlorine sensor to measure the dissolved oxygen and dissolved chlorine content in Bi2Sr2−xLaxCuOy, allowing for the calculation of copper valence and oxygen content. The results from the DO/Cl method with DE correction show that the measured copper valence and oxygen content differ very little from those obtained using the iodometric titration method, with discrepancies within 0.016 and 0.008, respectively. Additionally, this method reduces the measurement time by one-third compared to the iodometric titration method. The results demonstrate that the DO/Cl method with DE correction can effectively measure the copper valence and oxygen content in cuprate superconductors, and using hydrochloric acid as the experimental solution is superior to sulfuric acid and nitric acid. [ABSTRACT FROM AUTHOR]

In the information age, the amount of data is growing exponentially. However, different data sources are heterogeneous, which makes it inconvenient to share and multiplex data. With the rapid development of semantic network, ontology mapping is an effective method to solve this problem. The core of ontology mapping is ontology similarity calculation. Therefore, a calculation method based on graph convolution network was proposed. Firstly, ontologiesare modeled as a heterogeneous graph network, then the graph convolution network was used to learn the text embedding rules, which made ontologies were definedin global unified representation. Lastly, multisource data fusion was completed. The experimental results show that the accuracy of the proposed method is higher than other methods, and the accuracy of multi-source data fusion was effectively improved.

Micro-perforated panel (MPP) absorbers exhibit multiple resonance bands with increased bandwidth narrowing and shifting in higher frequencies, limiting their effectiveness. This study investigates the effects of narrowing and shifting in higher-order resonance bands of MPP absorbers. First, an acoustic impedance model for MPP absorbers is introduced, and the narrowing and shifting coefficients are defined and modeled to quantify these effects. It is observed that a larger ratio of acoustic resistance to acoustic mass is favorable for reducing the narrowing and shifting effects. Subsequently, the theoretical model is validated using a numerical model, and a parametric study is conducted to explore the influence of geometric parameters on the narrowing and shifting effects. The study reveals that decreasing aperture and panel thickness, while increasing perforation ratio and cavity depth, reduces the narrowing and shifting coefficients. Remarkably, ultra-micro-perforated panels (UMPPs) with an aperture below 0.1 mm and perforation constant below 0.0046, having relatively larger acoustic resistance and smaller acoustic mass, demonstrate near-zero band narrowing and shifting. Finally, UMPPs are fabricated using micro-electro-mechanical systems (MEMS) technology, and their normal absorption coefficients are measured. Results align with theoretical predictions, confirming UMPPs' ability to achieve zero narrowing and shifting compared to ordinary MPPs and verifying the study's findings. [ABSTRACT FROM AUTHOR]

Recent adversaries targeting the Industrial Control Systems (ICSs) have started exploiting their sophisticated inherent contextual semantics such as the data associativity among heterogeneous field devices. In light of the subtlety rendered in these semantics, anomalies triggered by such interactions tend to be extremely covert, hence giving rise to extensive challenges in their detection. Driven by the critical demands of securing ICS processes, a Graph-Neural-Network (GNN) based method is presented to tackle these subtle hostilities by leveraging an ICS's advanced contextual features refined from a universal perspective, rather than exclusively following GNN's conventional local aggregation paradigm. Specifically, we design and implement the Graph Sample-and-Integrate Network (GSIN), a general chained framework performing node-level anomaly detection via advanced feature integration, which combines a node's local awareness with the graph's prominent global properties extracted via process-oriented pooling. The proposed GSIN is evaluated on multiple well-known datasets with different kinds of integration configurations, and results demonstrate its superiority consistently on not only anomaly detection performance (e.g., F1 score and AUPRC) but also runtime efficiency over recent representative baselines. [ABSTRACT FROM AUTHOR]