The Qitianling pluton in southern Hunan, China, has spatially and genetically influenced the formation and distribution of a series of polymetallic deposits, including Xintianling, Baoshan, Huangshaping, and Furong. These deposits host a variety of tungsten- and tin-related deposits, often regarded as strategic and critical rare metals, and comprise one of the most prominent reserves globally. A thorough understanding of the structure of the Qitianling pluton is essential for insights into the development and evolution of the metallogenic system in southern Hunan. Working towards the goal of investigating regional structural features and magma emplacements model, we have generated three-dimensional (3-D) electrical resistivity models of the Qitianling pluton and its surrounding areas to upper-crustal depth using magnetotelluric (MT) data that range from 1000 Hz to 0.001 Hz. The results reveal that the upper-crust of southern Hunan is mainly characterized by high resistivity with multiple unique conductive zones. The high-resistivity anomalies (>1000 Ω·m) are interpreted to represent the Qitianling pluton. In addition, they correspond very well to a negative residual Bouguer gravity anomaly. Moreover, the morphology of the feature aligns with low-velocity obtained by modelling reflected seismic waves. Conductive anomalies (<30 Ω·m) near the sides of the pluton that extend through the upper crust likely indicate the presence of the Chenzhou-Linwu deep-seated fault system, which may have served as a pathway for the upward migration and emplacement of magma/hydrothermal fluids. Conductive features (<30 Ω·m) beneath the Qitianling pluton are inferred to represent ancient magma reservoirs where assimilation and mixing processes occurred before magma emplacement. Based on the geophysical models and the available geological data, a multi-stage magma emplacement model of the Qitianling pluton is proposed, which provides new insights into the W-Sn polymetallic mineralization system and the regional magmatic evolution within southern Hunan.

Rain erosion may cause substantial damage to aircrafts during supersonic flight. Such event is investigated here via high-speed waterjet impact on composite laminates. An experimental setup is developed to produce waterjets with the speed up to 700m/s and a finite element model of the waterjet-composite impact event is established. The consistency of experiment and simulation results validates the adopted numerical methods. The distribution of the water-hammer pressure is non-uniform and the maximum pressure occurs near the contact periphery when the water is about to eject laterally. After a high-speed (300∼560m/s) waterjet impacts a composite laminate, the impacted surface depression is observed, and the typical surface damage presents a central region with no visible surface damage surrounded by a faded “failure ring” with resin removal, matrix cracking and minor fiber fracture. Delamination occurs at the interfaces of adjacent layers with unequal dimensions and longitudinal matrix cracking appears on the back surface. Both the velocity and the diameter of waterjets are crucial factors on CFRP damage extents. Water-hammer pressure, the stagnation pressure and propagation of stress waves are failure mechanisms for most matrix damage in CFRP impacted by waterjets.

In China, storm surge disasters cause severe damages in coastal regions. One of the most critical tasks is to predict affected regions and their relative damage levels to support decision-making. This study develops a two-stage retrieval model to search the most similar past disaster case to complete prediction. Based on spatial attributes of cases, the top-ranking past cases with a similar location to the target case are selected. Among these past cases, the most similar past case is selected by disaster attribute similarities. Three typical storm surge case studies have been used and implemented into this proposed model, and the results show that all the most affected regions can be predicted. The proposed model simplifies the prediction process and updates results quickly. This study provides valuable information for the government to make real-time response plans.

The urban expressway system serves as a key role in the roadway transportation system. It provides an efficient and comfortable approach for long-distance travel within the city. However, the safety status of the urban expressways is becoming a critical issue as the high-frequent traffic crashes have severely influenced the traffic operations. Among the safety influencing factors, including traffic operational parameters (such as traffic speed and volume), geometric features and traffic participants’ characteristics (such as vehicle roadway usage patterns), the traffic operational parameters and geometric features have been widely investigated. However, the impacts of traffic participants’ characteristics on traffic safety have never been examined. This unprecedented study aims to link vehicles’ roadway usage patterns with traffic safety through crash frequency analyses. First, the roadway usage patterns were identified using Latent Class Cluster Analysis (LCCA) based on their traveling rates. Then, the hourly-based crash frequency analysis data were formulated with traffic operational parameters, geometric features and crash data. Finally, crash frequency analysis models were developed to unveil the relationships between the crash occurrence and their influencing factors. The modeling results showed that the Random Effects Hurdle Negative Binomial Model (REHNBM) provided better goodness-of-fit. And it concluded that higher proportions of vehicles with low-level roadway usage pattern would substantially enhance the possibility of crash occurrence; while the proportions of vehicles with the medium-high-level roadway usage pattern had negative impacts on crash occurrence probability. Finally, safety improvement recommendations and strategies based on the modeling results were put forward.

Predicting the spatial distribution of direct economic losses from typhoon storm surge disasters is crucial for supporting emergency response efforts. Using case-based reasoning, we developed a preliminary method for completing predictions about the impact of typhoon storm surge disasters for multiple coastal regions. We proposed retrieval, reuse, and revision algorithms to predict the following effects of a typhoon storm surge disaster: total direct economic losses and their grades, the affected regions, and individual direct economic losses and their grades in each affected region. We tested 33 such disaster cases using the developed method, and the predicted results were as follows. In about 70% of the cases, all recorded affected regions were predicted; the grades of the total direct economic losses were accurate in over 70% of the cases; in 63% of the cases, the grades of direct economic losses in each affected region were partly accurate, and they were all accurate in 30% of the cases. These promising results suggest that the proposed method can support disaster managers to respond adequately to typhoon storm surge disasters for multiple coastal regions.

Crash frequency prediction models have been an important subject of safety research that unveils a relationship between crash occurrences and their influencing factors. Recently, the hourly-based refined-scale crash frequency analysis becomes attractive since it holds the benefits of introducing time-varying explanatory information (e.g. traffic volume and operating speed). However, crash frequency data with short time intervals possess the analytical issues of excessive zeros and unobserved heterogeneity. In this study, a marginalized random effects hurdle negative binomial (MREHNB) model was, for the first time, developed in which the hurdle modelling structure handles the excessive zeros issue and site-specific random effect terms capture the factors associated with unobserved heterogeneity. Moreover, the marginalized inference approach was introduced here to obtain the marginal mean inference for the overall population rather than subject-specific estimations. Empirical analyses were conducted based on data from the Shanghai urban expressway system, and the MREHNB model was compared with the HNB (hurdle negative binomial) and the REHNB (random effects hurdle negative binomial) model. In terms of model goodness-of-fits, REHNB and MREHNB model showed substantial improvement compared to the HNB model while there was no distinct difference between the REHNB and MREHNB models. However, as for the estimated parameters, the MREHNB model provided better inference precisions. Furthermore, the MREHNB model provided interesting findings for the crash contributing factors, for example, higher ratios of local vehicles within the traffic volume would enhance the probability of crash occurrence; and a non-linear relationship was concluded between traffic volume and crash frequency with the moderate level of volume held the highest crash occurrence probability. Finally, in-depth analyses about the modeling results and the model technique were discussed. The results will assist in designing more efficient control strategies for near real-time traffic management.