Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism

How to achieve high-precision detection and real-time deployment of the lithology intelligent identification system has significant engineering implications in the geotechnical, geological, water conservation, and mining disciplines. In this study, a lightweight lithology intelligent identification model is proposed to overcome this problem. The MobileNetV2 model is utilized as the basic backbone network to decrease network operation parameters. Furthermore, channel attention and spatial attention methods are incorporated into the model to improve the network’s extraction of complicated and abstract petrographic elements. In addition, based on the findings of network training, computing power performance, test results, and Grad-CAM interpretability analysis and comparison tests with Resnet101, InceptionV3, and MobileNetV2 models. The training accuracy of the proposed model is 98.59 percent, the training duration is 76 min, and the trained model is just 6.38 megabytes in size. The precision (P), recall (R), and harmonic mean (FI-score) were, respectively, 89.62%, 91.38%, and 90.42%. Compared to the three competing models, the model presented in this work strikes a better balance between lithology recognition accuracy and speed, and it gives greater consideration to the rock feature area. Wider and more uniform, strong anti-interference capability, improved robustness and generalization performance of the model, which can be deployed in real-time on the client or edge devices and has some promotion value

Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level

Energy storage such as battery and thermal energy storage is an effective approach to shift building peak load and alleviate grid stress at a building cluster level. However, due to the heterogeneous performance of different types of storage (e.g., response speed, charge/discharge efficiency and rate, storage capacity) and highly diversified energy use patterns of individual buildings, the multi-energy storage should be properly selected and optimally designed for individual buildings to achieve effective load shifting. The optimal deployment of multi-energy storage at a cluster level is a challenging optimization problem due to the nonlinear dynamic performance of the multi-energy storage and the high dimensionality as a result of a large number of buildings. To tackle the challenges, this study proposes a data-driven surrogate optimization method that optimally deploys multi-energy storage at a cluster level to minimize the building cluster energy bill under demand response programs. The method utilizes data-driven surrogate models to accurately predict demand response performance of individual buildings with multi-energy storage. An iterative optimization with automated energy-storage-option screening is developed to optimize the multi-energy storage configurations and design parameters. For a case study including 21 buildings, by optimally deploying multi-energy storage including battery, cooling TES tank, and building-integrated TES, the method reduced the building cluster energy bill by 8%–181% as compared to baseline cases. The optimal deployment method effectively identifies the buildings with better potential to adopt demand-side management and balances the pros and cons of the energy storage options, increasing demand response incentives by 12%–31%. The proposed method can be used in practice to facilitate the deployment of energy storage and improve engagement of buildings in demand response

The Association between Polymorphism of CARD8 rs2043211 and Susceptibility to Arteriosclerosis Obliterans in Chinese Han Male Population

Background and aims: Cholesterol crystals have been shown to cause inflammation. As a response to cholesterol crystal accumulation, the NLRP3 inflammasome is activated to produce IL-1β which eventually leads to atherosclerotic lesions. As a part of innate immunity, CARD8 is involved in the modulation of above mentioned inflammatory activities. The primary objective of this study was to investigate the association between polymorphism of CARD8 rs2043211 and susceptibility to arteriosclerosis obliterans (ASO) in Chinese Han male population. Methods: 758 male arteriosclerosis obliterans patients and 793 male controls were genotyped for rs2043211 with the TaqMan allele assays. Fasting blood-glucose (FBG), total cholesterol (TC), triglycerides (TG), urea nitrogen, creatinine, Serum uric acid, high density lipoprotein, low density lipoprotein, ALT, AST, and IL-1β in the blood were detected for all subjects. Clinical data were recorded to analyze the genotype-phenotype. Independent samples t-test was used to perform the comparisons between two groups. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to measure the strength of relationship in the genotype distribution and allele frequencies between patients and controls. The analysis of variance was used for a genotype-phenotype analysis of the ASO patients. Results: The genotypic and allelic frequencies in the ASO group were significantly different from that in the control group (P = 0.014 by genotype, P = 0.003 by allele). Those carrying the genotype TT had a higher risk for ASO than those carrying the genotype AA (OR = 1.494, 95%CI1.131-1.974, P = 0.005).The difference was also significant after the adjustment for the history of smoking, TC, LDL, fasting blood glucose, systolic blood pressure and BMI(OR = 1.525, 95%CI1.158-2.009, P = 0.003). Conclusion: Our finding suggests that the polymorphism of CARD8 rs2043211 is probably associated with the development of ASO in Chinese Han male population