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Number connection test A (NCT-A) and digit symbol test (DST), the preferential neuropsychological tests to detect minimal hepatic encephalopathy (MHE) in China, haven't been standardized in Chinese population. We aimed to establish the norms based on a multi-center cross-sectional study and to detect MHE in cirrhotic patients. NCT-A and DST were administered to 648 healthy controls and 1665 cirrhotic patients. The regression-based procedure was applied to develop demographically adjusted norms for NCT-A and DST based on healthy controls. Age, gender, education, and age by education interaction were all predictors of DST, while age, gender, and education by gender interaction were predictors of log10 NCT-A. The predictive equations for expected scores of NCT-A and DST were established, and Z-scores were calculated. The norm for NCT-A was set as Z ≤ 1.64, while the norm for DST was set as Z ≥ − 1.64. Cirrhotic patients with concurrent abnormal NCT-A and DST results were diagnosed with MHE. The prevalence of MHE was 8.89% in cirrhotic patients, and only worse Child–Pugh classification (P = 0.002, OR = 2.389) was demonstrated to be the risk factor for MHE. The regression-based normative data of NCT-A and DST have been developed to detect MHE in China. A significant proportion of Chinese cirrhotic patients suffered from MHE, especially those with worse Child–Pugh classification. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions. [ABSTRACT FROM AUTHOR]

The multiple edge sites on 2D nanosheets or nanoplates usually supply more active sites which result in the enhancement of gas sensing performance. Manufacturing porous structure, with high density of edge sites coupled under the irradiation of UV light, have great potential in NO2 gas selectivity at room temperature. In this work, annealing engineered active edge sites of 2D SnO2 porous nanoplates were synthesized by a facile hydrothermal route. SnO2-600 2D semicircle nanoplates with small holes on its edge are attribute to their high sensitivity and selectivity to NO2 gas under the excitation of 365 nm UV light at room temperature. Density functional theory calculations show that the adsorption energy of −0.74 eV and the obvious variation in band gap before and after the interaction of NO2 on SnO2 (110). These results indicate high performance of gas sensitivity of SnO2 to NO2. The present studies provide useful strategies for the development of high selective room temperature SnO2-based NO2 sensors. Highlights: The SnO2 porous nanoplates with multiple active edge sites were successfully synthesized by a feasible hydrothermal method via adjusting annealing temperature. The fabricated SnO2-600 sensor displayed excellent NO2-sensing performance with fast response and recovery properties with the aid of UV excitation at room temperature. The gas sensing mechanism and gas selectivity mechanism are investigated by density functional theory calculations. [ABSTRACT FROM AUTHOR]

In waterway regulation engineering, submerged reefs are quite susceptible to the repetitive oblique strike from drop hammer and impact drilling, leading to a repetitive compression–shear impacting. Understanding the dynamic responses and failure mechanism of saturated rocks subjected to repetitive compression–shear impacting is thus of great significance for the efficient removal of submerged reefs. In this study, using the split Hopkinson pressure bar (SHPB) apparatus, the repetitive compression–shear impacting tests were conducted on the saturated and dry sandstone specimen from the harbor excavation project in Guoyuan Port (Chongqing, China). Our experimental results indicate that the increasing loading rate or shear component in the dynamic loading has a negative influence on the bearing capacity of saturated sandstone subjected to repetitive impacting. The deformability of saturated rocks increases as the impacting number or the shear component in the dynamic loading. The energy absorption ability as well as the energy utilization of the saturated rocks is highly improved by implementing repetitive compression–shear impacting, and such improvement yields an ascension limit with the increasing shear component in the dynamic loading. As the shear component introduces into the repetitive impacting loading, the failure patterns of rocks change from the relative tensile mode with a large deformation band along the loading direction to the combined compression–shear mode with two parallel large deformation bands inclined to the loading direction, the water content between the mineral grains promotes the shear failure in rock dynamic testing. These results can provide a better understanding of the dynamic responses and failure properties of saturated rocks subjected to repetitive compression–shear impacting. Highlights: Illustrated influences of repetitive compression–shear impacting on the bearing capacity and deformability of saturated and dry rocks. Investigated energy dissipation of saturated and dry rocks under repetitive combined compression–shear impacting. Revealed failure patterns of saturated and dry rocks under repetitive combined compression–shear impacting. [ABSTRACT FROM AUTHOR]

In this study, the inclined sandstone specimen is introduced into cyclic loading tests to fulfill the coupled compression-shear loading state. 21 cyclic coupled compression-shear loading tests are conducted on inclined specimens under different loading conditions, including maximum stress levels ranging in 0.80, 0.85, 0.90 and 0.95, and amplitude levels varying in 0.40, 0.50, 0.60 and 0.70. Our testing results systematically revealed the influence of cyclic loading parameters on fatigue mechanical response of rocks under coupled compression-shear loading, regarding the deformation characteristics, energy evolution, damage variable and failure mechanism. Under higher maximum stress or cyclic amplitude, inclined rocks are characterized by larger elastic modulus and higher dissipated energy, resulting in less irreversible strain, faster damage accumulation and shorter fatigue life. Furthermore, a fatigue life prediction method is proposed based on the energy dissipation, and its reliability is verified by comparing with experimental results. In addition, the progressive cracking behavior of rocks is analyzed during the fatigue tests by virtue of digital image correlation technique. Under cyclic coupled compression-shear loading, rock specimens are featured by a prominent shear-dominated failure along its short diagonal direction combined with local tensile damage along the loading orientation. [ABSTRACT FROM AUTHOR]

The Houston-Dallas (I-45) corridor is the busiest route among 18 traffic corridors in Texas, USA. The expected population growth and the surge in passenger mobility may result in a significant impact on the regional environment. This study uses a life cycle framework to predict and evaluate the net changes of environmental impact associated with the potential development of a high-speed rail (HSR) System along the I-45 corridor through its life cycle. The environmental impact is estimated in terms of CO2 and greenhouse gas (GHG) emissions per vehicle/passenger-kilometers traveled (V/PKT) using life cycle assessment. The analyses are performed referring to the Ecoinvent 3.4 inventory database through the phases: material extraction and processing, infrastructure construction, vehicle manufacturing, system operation, and end of life. The environmental benefit is evaluated by comparing the potential development of the HSR system with those of the existing transportation systems. The vehicle component, especially operation and maintenance of vehicles, is the primary contributor to the total global warming potential with about 93% of the life cycle GHG emissions. For the infrastructure component, 56.76% of GHG emissions result from the material extraction and processing phase (23.75 kgCO2eq/VKT). Various life cycle emissions of HSR except PM are significantly lower than for passenger cars. [ABSTRACT FROM AUTHOR]