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• First adoption of Paterno-Buchi reaction in studying milk lipid fine structure. • Unsaturated fatty acids show a non-specific regio-distribution in phospholipids. • Regioisomers are widespread in milk phospholipid species. • Double-bond isomers of C18:1 and C18:2 are widely detected in milk phospholipids. Hundreds of phospholipid (PL) species with defined fatty acid (FA) composition have been identified previously in bovine milk using liquid chromatography tandem mass spectrometry (LC-MS/MS). Paterno-Buchi photochemical reaction coupled with LC-MS/MS was applied in this study to further unravel the regio-distribution and double bond (DB) locations of FAs. Using SPE-purified PLs and 2-acetylpyridine as the photochemical derivatization reagent, we were able to reveal the non-specific regio-distribution of unsaturated FAs and the widespread occurrence of regioisomers in milk PLs. Although Δ9 and Δ9,12 were found to be the predominant DB location(s) for C18:1 and C18:2 respectively, other DB positional isomers such as C18:1Δ11, C18:1Δ12 and C18:1Δ13 and C18:2Δ9,11 were widely detected in PL structures, implying that the minor isomers of C18:1 and C18:2 equally participate in the synthesis of PLs. Our study provides novel information on the fine structure of milk PLs and further underlines the complexity of milk lipid composition. [ABSTRACT FROM AUTHOR]

The practice of crime risk mapping, enabled by the utilization of geospatial big data such as street view images, has received significant research attention. However, in situations where available data is scarce, mapping models may suffer from underfitting and generate inaccurate spatial pattern estimations of crime risk. The covert nature of pickpocketing crimes results in limited observed areas relevant to such criminal events, leading to insufficient coverage of geospatial data. Moreover, the location of crime is also influenced by socio-economic characteristics that may introduce biases into crime risk estimates. These factors render it challenging for the model to capture a valid crime risk pattern, potentially yielding misleading conclusions. Therefore, effectively extracting crime risk with limited data remains a challenge, especially when relying on easily accessible, widespread, and unbiased geospatial data. To address this challenge, we propose a novel crime risk assessment framework based on deep anomaly detection techniques, assuming that urban landscape anomalies carry deep crime risk information. We take Shenzhen as the study area and map the distribution of pickpocketing risk using street view images, accurately revealing the spatial aggregation of pickpocketing crime risk. Our findings indicate that pickpocketing crime in China is caused by regional economic conditions, built environment factors, and human routine activities. This study provides valuable insights for policing and prevention strategies aimed at addressing pickpocketing crimes in large Chinese cities. By leveraging our proposed crime risk assessment framework, decision-makers can allocate resources more efficiently and develop targeted interventions to mitigate crime risks. • A deep learning-based model for mining pickpocketing crimes from street view images is proposed. • With the idea of anomaly detection, crime risk information can be extracted from a small number of street view images with crime labels. • The reliable parcel-level pickpocketing risk distribution was mapped in China's megacity. • Pickpocketing crime in China is multifactorial and spatially heterogeneous. • Pickpocketing crime risk is aggregated and affected by neighbourhood socioeconomic conditions and the built environment. [ABSTRACT FROM AUTHOR]

• A new seismic barrier for underground structures is proposed to control the Rayleigh waves. • The soil acceleration response is reduced within the frequency range of 0.5–5 Hz. • The maximum layer drift of the subway station is reduced by over 40 %. • The internal forces of structural components are reduced by 70 % on average. • A novel index is proposed to evaluate the performance of seismic barriers. This research introduces a novel seismic mitigation approach for civil engineering constructions, utilizing seismic metamaterials, and particularly studies its application to underground structures. This approach involves the implementation of a regional seismic barrier, designed in the form of piles periodically clamped to a concrete foundation. The efficacy of this seismic barrier is explored through comprehensive numerical simulations. A wave propagation study is conducted to investigate the attenuation efficiency for Rayleigh waves in the frequency domain. Besides, dynamic analysis is performed on a two-layer and three-span subway station subjected to actual seismic recordings. The results from the wave propagation study reveal a notable reduction in acceleration response within the frequency range of 0.5 Hz–5 Hz when employing the seismic barrier, in contrast to the free-field scenario. Dynamic analysis demonstrates that the implementation of the proposed seismic barrier leads to a significant decrease in both the maximum layer drift and the structural internal forces of the subway station. Moreover, the relative displacement of soil proves to be a pertinent metric for evaluating the seismic control efficacy of the proposed barrier for underground structures. [ABSTRACT FROM AUTHOR]

• A lightweight nonlinear response prediction model is established based on 1D-CNN. • The performance of 1D-CNN is excellent. • The randomness of ground motions and soil parameters are considered simultaneously. • The calculation cost of stochastic seismic response has been significantly reduced. • A comprehensive stochastic dynamic analysis is conducted using PDEM. The randomness of ground motions and soil properties has a significant impact on the seismic response of underground structures. The probability density evolution method (PDEM) is a powerful stochastic dynamic analysis method. By decoupling the physical space and probability space, any number of random excitation variables can be incorporated into the dynamic system, thereby accurately capturing the time-varying evolution of structural dynamic response. However, due to the expensive computational cost of nonlinear time history analysis (NLTHA) of underground structures, the application of the PDEM that requires many calculation samples is severely limited. In view of this, this paper proposes a lightweight stochastic seismic response analysis method for the subway station structure based on deep learning and PDEM. Firstly, mathematical models of stochastic ground motions and random soil shear wave velocity profiles are constructed. Two types of random excitations are coupled using the high-dimensional space optimization point selection method. The one-dimensional convolutional neural network (1D-CNN) is used as a proxy model to map the free field's nonlinear time-history response to the subway station structure's nonlinear time-history response. The computational cost of stochastic dynamics analysis has been significantly reduced. Subsequently, the predicted results are combined with PDEM to conduct a comprehensive analysis and evaluation of the subway station structure's seismic response and seismic performance. The research results indicate that the proposed 1D-CNN model has superior predictive performance, with a computational cost of approximately 92.5% less than the traditional process. The evolution of the subway station structure's inter-storey drift ratio (IDR) is mainly influenced by stochastic ground motions. However, under the combined random excitation, the peak value of IDR is larger and its occurrence time is earlier, so the establishment of earthquake evacuation plans should refer to this working condition. Both the randomness of soil properties and the randomness of ground motions need to be considered, otherwise, underground structures' seismic performance will be overestimated. This study provides new technologies and directions for seismic analysis and design of underground structures. [ABSTRACT FROM AUTHOR]

• A novel seismic intensity measure for underground structures is proposed. • The seismic response characteristics of underground structures can be considered. • The rigidity degradation of the dynamic system can be considered. • The period range significantly affecting the seismic response for underground structures is obtained. The purpose of this study is to present a novel approach to constructing intensity measures (IMs) for underground structures using seismic response spectrums. The proposed IM takes the form of an integral of the product of the response spectrum and an arbitrary function that reflects the relative importance of each range of period to the seismic response of underground structures. To obtain the arbitrary function, an artificial neural network (ANN) technique is employed. To demonstrate the effectiveness of this method, we develop two different soil-subway station dynamic systems, referred to as Case I and Case II. Based on both cases, the probabilistic seismic demand model (PSDM) for the proposed IM and eight existing IMs are developed. The performance of the proposed IM is compared with that of the existing IMs in terms of their proficiency, practicality, and efficiency, as well as their ability to describe the probabilistic distribution of damage measure (DM). The results indicate that the proposed IM outperforms the existing IMs since it has the highest proficiency for both cases. Moreover, the fragility curves calculated using the proposed IM are more effective than those calculated using traditional IMs, with narrower uncertainty ranges, which can help reduce the uncertainty in evaluating seismic risk. Comparing the function solved by the ANN with the seismic response of subway stations under different periods of harmonic excitation, it can be found that the solved function and the seismic response reach their peak points at the same period. This finding suggests that the proposed IM can account for the interaction between the surrounding soil and underground structures, as well as the softening of soil layers during earthquakes, making it a suitable IM for underground structures. [ABSTRACT FROM AUTHOR]