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Bridges play a significant role in urban areas, and their performance and safety are highly related to the carbon emissions of infrastructure systems. Previous studies have mainly offered maintenance policies that balance structural safety with overall costs. Considering the goal of achieving near-zero global carbon emissions by 2050, this study proposes a policymaking agent based on a convolutional autoencoder–structured deep-Q network (ConvAE-DQN) for managing deteriorating bridges at the network level while considering sustainability performance. This agent considers environmental, economic, and safety metrics, including spatially correlated structural failure probability, traffic volume, bridge size, and others, which are transformed into a multiattribute utility model to form the reward function. Reinforcement learning is employed to optimize the life-cycle maintenance planning to minimize the total carbon emissions and economic costs while maximizing regional safety performance. The proposed method is substantiated by developing sustainable life-cycle maintenance policies for an existing bridge network in Northern China. It is found that the proposed ConvAE-DQN policymaking agent could output efficient and sustainable life-cycle maintenance policies, which are annually stable and easy to schedule. The utility-based reward function enhances the stability and convergence efficiency of the algorithm. This study also assesses the impact of budget levels on network-level bridge safety and carbon footprint. [ABSTRACT FROM AUTHOR]

Purpose We aimed to evaluate whether the addition of pemetrexed is effective in improving progression-free survival (PFS) in epidermal growth factor receptor (EGFR)-mutated patients with or without concomitant alterations. Materials and Methods This multicenter clinical trial was conducted in China from June 15, 2018, to May 31, 2019. A total of 92 non-small cell lung cancer (NSCLC) patients harboring EGFR-sensitive mutations were included and divided into concomitant and non-concomitant groups. Patients in each group were randomly treated with EGFR-tyrosine kinase inhibitor (TKI) monotherapy or EGFR-TKI combined with pemetrexed in a ratio of 1:1. PFS was recorded as the primary endpoint. Results The overall median PFS of this cohort was 10.1 months. There were no significant differences in PFS between patients with and without concomitant and between patients received TKI monotherapy and TKI combined with pemetrexed (p=0.210 and p=0.085, respectively). Stratification analysis indicated that patients received TKI monotherapy had a significantly longer PFS in non-concomitant group than that in concomitant group (p=0.002). In concomitant group, patients received TKI combined with pemetrexed had a significantly longer PFS than patients received TKI monotherapy (p=0.013). Molecular dynamic analysis showed rapidly emerging EGFR T790M in patients received TKI monotherapy. EGFR mutation abundance decreased in patients received TKI combined chemotherapy, which supports better efficacy for a TKI combined chemotherapy as compared to TKI monotherapy. A good correlation between therapeutic efficacy and a change in circulating tumor DNA (ctDNA) status was found in 66% of patients, supporting the guiding role of ctDNA minimal residual disease (MRD) in NSCLC treatment. Conclusion EGFR-TKI monotherapy is applicable to EGFR-sensitive patients without concomitant alterations, while a TKI combined chemotherapy is applicable to EGFR-sensitive patients with concomitant alterations. CtDNA MRD may be a potential biomarker for predicting therapeutic efficacy. [ABSTRACT FROM AUTHOR]

As one of the most important urban infrastructure systems, the road network plays a vital role in post-earthquake emergency response. Quantitative evaluation of the emergency response functionality of road network under earthquakes can provide decision support for emergency planning and future construction. In this paper, a novel network performance metric is proposed to consider both the special traffic demand in emergency response and the supply capability of road network. On this basis, an integrated probabilistic assessment framework for post-earthquake network emergency response functionality is presented, considering various uncertainties. In the framework, a penalty-based approach is proposed to consider the impact of partially degraded links on network functionality. At last, the proposed methodology is illustrated on the road network of Tangshan city, China, under different seismic scenarios. [ABSTRACT FROM AUTHOR]

This paper aims to assess the seismic fragility of vehicle‐bridge‐interaction (VBI) systems considering the effects of vehicle types, traffic conditions, and road surface qualities. A stochastic nonlinear mechanical model for the earthquake‐VBI system is developed, and the fragility functions for the proposed VBI model are derived by considering the relevant probabilistic seismic demand parameters. On the basis of a typical four‐span continuous prestressed concrete highway bridge in China, a complete numerical model for the VBI system is built considering multiple uncertainties from bridge and vehicle parameters, as well as the road surface qualities. A total of 120 real ground motion records with different combinations of magnitude‐source‐to‐site distance (M‐R) and earthquake intensity characteristics are selected. Meanwhile, 80 scenarios in terms of different combinations of vehicle types, vehicle speeds, and road surface irregularities are defined. In this context, 96,000 nonlinear time‐history analyses are performed, and the developed fragility models are applied to the VBI system at both component and system levels. Results indicate that the fragilities of pier drift, bearing shear strain, and the overall VBI system increase with the increase of the vehicle weight or the decrease of the vehicle speed, while the vertical deck displacement is dominated by the vehicle weight. It is also found that the road surface quality has a negligible effect on both component and system fragilities. [ABSTRACT FROM AUTHOR]

Porcine circovirus 4 (PCV4) was identified in 2019 as a novel circovirus species and then proved to be pathogenic to piglets. However, there is a lack of its prevalence in the Southwest of China. To investigate whether PCV4 DNA existed in the Southwest of China, 374 samples were collected from diseased pigs during 2021–2022 and detected by a real-time PCR assay. The results showed that the positive rate of PCV4 was 1.34% (5/374) at sample level, and PCV4 was detected in two of 12 cities, demonstrating that PCV4 could be detected in pig farms in the Southwest of China, but its prevalence was low. Furthermore, one PCV4 strain (SC-GA2022ABTC) was sequenced in this study and shared a high identity (98.1–99.7%) with reference strains at the genome level. Combining genetic evolution analysis with amino acid sequence analysis, three genotypes PCV4a, PCV4b, and PCV4c were temporarily identified, and the SC-GA2022ABTC strain belonged to PCV4c with a specific amino acid pattern (239V for Rep protein, 27N, 28R, and 212M for Cap protein). Phylogenetic tree and amino acid alignment showed that PCV4 had an ancient ancestor with mink circovirus. In conclusion, the present study was the first to report the discovery and the evolutionary analysis of the PCV4 genome in pig herds of the Southwest of China and provide insight into the molecular epidemiology of PCV4. [ABSTRACT FROM AUTHOR]

As possible candidate screening instruments for benign paroxysmal positional vertigo (BPPV), studies to validate the Dizziness Handicap Inventory (DHI) sub-scale (5-item and 2-item) and total scores are rare in China. From May 2014 to December 2014, 108(55 with and 53 without BPPV) patients complaining of episodic vertigo in the past week from a vertigo outpatient clinic were enrolled for DHI evaluation, as well as demographic and other clinical data. Objective BPPV was subsequently determined by positional evoking maneuvers under the record of optical Frenzel glasses. Cronbach's coefficient α was used to evaluate the reliability of psychometric scales. The validity of DHI total, 5-item and 2-item questionnaires to screen for BPPV was assessed by receiver operating characteristic (ROC) curves. It revealed that the DHI 5-item questionnaire had good internal consistency (Cronbach's coefficient α = 0.72). Area under the curve of total DHI, 5-item and 2-item scores for discriminating BPPV from those without was 0.678 (95 % CI 0.578-0.778), 0.873(95 % CI 0.807-0.940) and 0.895(95 % CI 0.836-0.953), respectively. It revealed 74.5 % sensitivity and 88.7 % specificity in separating BPPV and those without, with a cutoff value of 12 in the 5-item questionnaire. The corresponding rate of sensitivity and specificity was 78.2 and 88.7 %, respectively, with a cutoff value of 6 in 2-item questionnaire. The present study indicated that both 5-item and 2-item questionnaires in the Chinese version of DHI may be more valid than DHI total score for screening objective BPPV and merit further application in clinical practice in China. [ABSTRACT FROM AUTHOR]

More than a dozen molybdenum prospects, distributed within a zone about 200 km long and 20 km wide, have been discovered in the Eastern Qinling orogenic belt, central China. These deposits are mainly hosted within small, dominantly granoporphyritic and quartz monzonitic intrusions of Cretaceous age. They can be classified into five groups based on spatial relationships between the ores and intrusions: porphyry-type, transitional-type (between porphyry and skarn), skarn-type, vent breccia-type, and hydrothermal vein-type Mo deposits. Regional geologic relations and deep-seated structures define the presence of the Luanchuan syncline, an EW-striking west-dipping depression located beneath the Mo-bearing metallogenic zone. Because the axis of the syncline is west-dipping in the study area and the crust overlying it is thickened, the stress is inhomogeneous, resulting in a S-N compressive stress in the deep crust and development of a set of NE-oriented extension-shear faults. These faults are superimposed on the W-E-striking deep-seated faults parallel to the ancient continental margin. The two groups form a chequerboard-like assemblage of deep-seated faults, providing channels for ascending ore fluids derived from crust-mantle interaction and space for subsequent ore precipitation. These faults controlled the emplacement of a series of intermediate-acid granite magmas and also contributed significantly to the distribution of the various Eastern Qinling ore deposits. [ABSTRACT FROM AUTHOR]

Urban highway bridges are crucial for regional economic development and population mobility, but they are vulnerable to seismic hazards. Accurately predicting the seismic demands of bridges is essential. Several commonly-used methods for seismic demand estimates, such as cloud analysis, incremental dynamic analysis, and multiple stripe analysis, are computationally expensive and subject to theoretical assumptions and convergence issues, especially for portfolios of bridges. Machine learning techniques can be effective, but their lack of interpretability hinders understanding of the surrogate model's predictions. To address these challenges, this study develops a Bayesian-optimized interpretable ensemble learning surrogate model for predicting and interpreting the critical seismic demands of urban highway bridges.Thousands of continuous prestressed concrete girder bridges with typical geometric configurations in China are generated, and 120 ground motion records with different frequency, waveform, and duration characteristics are chosen. The seismic demand dataset is established based on nonlinear time-history analyses for urban highway bridges, accounting for different types of uncertainties. The surrogate models are developed based on the XGBoost ensemble learning method, with hyperparameters automatically and efficiently determined by Bayesian optimization technique. Compared to the random search and grid search methods, Bayesian optimization is approximately 8 times and 29 times faster, respectively, and it also achieves the highest prediction accuracy. Meanwhile, the relationship of each hyperparameter in the surrogate model is also revealed. The prediction results of the proposed model are compared with other machine learning models. The SHAP algorithm is subsequently used to explain the model predictions regarding the feature importance, global interpretations, and feature dependency analysis. The study quantifies the contribution of each structural feature and seismic intensity measure to the selected critical seismic demand and identifies the interaction effects between different input variables in the prediction. • An interpretable surrogate model for seismic demand predictions of bridges is developed. • Bayesian-optimized model tuning is developed to determine the optimal hyperparameters. • Key structure and quake characteristics for demand predictions are quantified and interpretated. • Interaction effects between different input characteristics on demand predictions are revealed. [ABSTRACT FROM AUTHOR]

International Marine Organization is promoting stricter carbon emission reduction targets, and the influx of environmental regulations presents compliance risks for maritime sector. Stakeholders need to take decarbonization performance indicators into account in ship routing to maintain regulatory compliance. To meet decarbonization goals, a decision support method related to environmental regulatory compliance is necessary for voyage optimization. To this end, this paper presents a digital twin-enabled framework for real-time decarbonization regulatory compliance prognosis in ship routing. The proposed framework tracks real-time carbon intensity and identifies opportunities for reducing operational risks associated with decarbonization goals. The digital twin approach leverage real-time environmental and operational data for improved estimation of vessel-specific regulatory compliance probability along the voyage. An improved dynamic programming algorithm is used for ship routing. The presented framework is versatile in dealing with various decarbonization regulations to provide routing decision support. An illustrated case of a container ship sailing across the South China sea is presented to demonstrate the applicability of the framework. [Display omitted] • A digital twin framework for prognosis of ship decarbonization regulatory compliance is developed. • Regulatory compliance is estimated by probabilistic analysis from a data-driven emission model. • The proposed framework is versatile to deal with different decarbonization regulations including EEXI and CII. • A case study of real-time ship routing considering compliance probability is presented. [ABSTRACT FROM AUTHOR]

In the context of global warming, the increases of temperature may affect tree growth and thus disturb ecosystem balance. In this study, we explored the main limiting factors for radial growth of Pinus sylvestris var. mongolica and Larix gmelinii in the Mohe area of Greater Khingan Mountains by using growth-climate response function analysis and moving correlation analysis, as well as the interspecific difference of the responses of radial growth to rapid warming. The results showed that the radial growth of P. sylvestris var. mongolica and L. gmelinii was affected by both temperature and precipitation. P. sylvestris var. mongolica was more sensitive to climate change than L. gmelinii , and its sensitivity to climate factors was more stable than L. gmelinii. The radial growth of P. sylvestris var. mongolica was significantly positively correlated with the monthly mean temperature and the monthly mean minimum temperature of the growing season, while that of L. gmelinii was significantly positively correlated with the monthly mean temperature and the monthly mean maximum temperature of winter. Precipitation in winter promoted the growth of P. sylvestris var. mongolica , whereas precipitation in the late growing season of the previous year inhibited the radial growth of L. gmelinii . After the rapid warming in 1990, the limiting effect of precipitation on P. sylvestris var. mongolica changed from negative to significantly positive, with the inhibition effect of high temperature being greater than the promotion effect. The inhibitory effect of high temperature on L. gmelinii was enhanced, and the limiting effect of precipitation on L. gmelinii was also enhanced after heating up. The growth rate decreased significantly, with obvious difference being observed in the correlations between the growth rate of two species with temperature and precipitation. Our results could provide scientific basis for forest ecosystem management and protection in Greater Khingan Mountains.