The southern Tibet in China is characterized by the widespread distribution of high-temperature geothermal systems that possess significant potential for development and utilization. However, current studies in this area have been predominantly focused on geothermal systems with felsic rock reservoirs, while studies on geothermal systems with carbonate reservoirs are scarce. Thus, the large-scale exploitation of such geothermal resources remains limited. In this study, we selected the typical carbonate geothermal system of Quzhuomu in southern Tibet as the research object. Based on the hydrochemical and isotopic (delta D, delta 18O, delta 13C, delta 34S, and delta 11B) characteristics of the geothermal water, we investigated the geochemical origin of Quzhuomu geothermal water, evaluated the reservoir temperature of the geothermal system, identified its heat source, and ultimately proposed a genetic mechanism for the geothermal system. The chemical components of the geothermal water showed that it primarily originated from the dissolution of marine carbonate rocks and tourmaline granite. Further, an improved mineral assemblage geothermometer showed that the reservoir temperature varied in the range 118-150 degrees C (mean, 128 degrees C). Comparative analysis between a typical magmatic geothermal system, the Gudui system, which is also located in the Sangri-Cuona rift zone, and the Quzhuomu system revealed that the strong surface geothermal manifestations and high reservoir temperature in Quzhuomu are closely related to a deepseated magma chamber. However, the hydrochemical composition of the Quzhuomu geothermal water is not influenced by the magmatic fluids differentiated from the magma. These findings are of great significance with respect to the scientific and rational utilization of geothermal resources in the Quzhuomu geothermal field as they provide valuable insights for estimating the reservoir temperature of carbonate geothermal systems like the Quzhuomu system and investigating their genetic mechanisms.

Whether plate subduction or mantle plume activity dominated the formation of the Archean crust is hotly debated. Neoarchean crust-mantle interaction and crustal evolution are instructive for researching continental crustal growth and reworking, and cratonic evolution processes. The Jiaoliao microblock contains abundant Archean rocks and is the largest and oldest microblock in the eastern part of the North China Craton. We present new data of the petrology, geochemistry, and geochronology of amphibolite enclaves in Neoarchean granitoids and amphibolite lenticles in Neoarchean supracrustal rocks from the western Jiaoliao microblock in eastern Hebei. Bulk rock composition indicates low to moderate SiO2 (45.2-56.6 wt%) and high MgO (Mg# = 0.44-0.75) contents, depletion in high field strength elements (Nb, Ta, Zr, Hf) and enrichment in light rare earth elements (Rb, Ba). The protoliths were calc-alkaline gabbros and gabbroic diorites with LA-ICP-MS zircon U-Pb ages of 2554-2482 Ma, positive zircon epsilon Hf(t) values of +1.64 to +5.45, and TDM1 model ages peaking at ca. 2.7 Ga. They were derived from partial melting of lithospheric mantles that were subsequently enriched by different degrees of slab-derived fluids and sedimentary melts. Subsequent amphibolite-facies metamorphism affected these intrusive rocks, suggested by mineral assemblages of magnesio-hornblende, plagioclase (An = 1-30), magnesian biotite with minor edenite, and phlogopite. We conduct a comparative analysis of the four main crystalline basements in eastern Hebei, northern Liaoning, eastern Shandong, and western Shandong within and around the Jiaoliao microblock. The main body of the Jiaoliao microblock is suggested to be located in eastern Hebei - northern Liaoning - eastern Shandong and the formation of the microblock from the Mesoarchean to early Paleoproterozoic can be divided into three stages, including the crustal growth and reworking at 3.17-2.85 Ga, the mantle plume activity restricted in Shandong at 2.77-2.60 Ga, and the subduction and accretion at 2.78-2.45 Ga. The late Neoarchean arc-related magmatism in northern Liaoning, eastern Hebei, eastern Shandong, and western Shandong indicates the subduction of oceanic crust beneath the western margin of the Jiaoliao microblock, which is like the modern plate tectonics regime.

The destination choice model considers each individual as a fundamental analytical unit, thereby providing increased flexibility in accommodating a wide range of factors that influence destination choice behaviors compared to aggregate trip distribution models. However, in practical applications, practitioners have found that there are still influencing factors that cannot be represented in the model, resulting in a huge deviation between the predicted trip distribution and the actual trip distribution. To enhance the predictive accuracy of the destination choice model, this paper proposes a new computational method based on a combination of the traditional destination choice model and the empirical Bayes (EB) method. It can integrate additional information from trip frequencies in the survey data with the information from various influencing factors in the traditional destination choice model. The proposed EB model is developed for commuting trips based on 2019 Shanghai Household Travel Survey data, and the model is validated using the survey data and mobile signaling data. For comparisons, both the traditional destination choice model and the proposed EB method are applied to distribute trips. The analytical results indicate that the EB method outperforms the traditional model in all the goodness-of-fit indicators, and the prediction errors are reduced in all the validation procedures. It is therefore expected that the proposed EB method based on Bayesian statistics may improve the predictive capability of the destination choice model for trip distribution.

In China, a developing country, the car ownership level is much lower than that in developed countries, but transportation policies have been implemented to discourage car ownership and mitigate traffic congestion. However, car ownership (considered as car availability in this paper, meaning that an individual has access to a household private car) may influence travelers’ well-being. To highlight the interrelation between car ownership and travelers’ well-being, this paper develops a probit-based discrete-continuous model to analyze the relationship between car ownership and the duration of commuters’ three major non-work outdoor activities (Act1: shopping and dining; Act2: leisure and entertainment; and Act3: visiting relatives or friends) in Xiaoshan District, Hangzhou, China. Empirical results indicate strong effects of individual and household socio-demographics, built environment attributes, and work-related characteristics on the car ownership decision and the duration of three non-work activities. The analysis shows positive correlations in unobserved factors between the car ownership decision and the duration of Acts1–3, indicating a mutually promotive relationship. Similarly, negative correlations among the duration of Acts1–3 show that non-work activities’ duration is mutually substitutive. These findings will help to better understand commuters’ car ownership decisions and non-work outdoor activity behavior restricted by fixed work schedules in developing countries, which can, in turn, better evaluate the impact of transportation policies (such as car ownership restriction) on travel demand as well as well-being, and provide decision support for the formulation of transportation policies.

Giving priority to the development of public transit is an important way to achieve efficient, convenient, safe, comfortable, economic, reliable, green and low-carbon sustainable development. In view of the highly dispersed and regular passenger flow, demand responsive transit is an important complementary means for traditional public transport to improve passenger satisfaction. However, high operating costs and low load factor will have a bad impact on the operation of public transport and reduce passenger satisfaction. In this work, firstly, by analyzing the demand frequency of historical travel stations, the stations with high demand are extracted by time periods as high probability travel points; On this basis, a dynamic vehicle dispatching optimization model is established, and the static vehicle dispatching is carried out with the goal of minimizing the running mileage of the bus system; Finally, based on the initial static route and the later real-time travel demand, the accurate dynamic planning algorithm is used to optimize the dynamic route with the goal of minimizing the change of the system mileage, so as to achieve timely response to the demand. The results show that the two-phase scheduling optimization model based on the station extraction strategy can provide a reasonable real-time vehicle scheduling and route optimization scheme, improve the utilization rate of vehicles and the passenger load factor, and provide a theoretical basis and application guidance for actual vehicle scheduling.

The utilization of geothermal energy has gradually increased in northern China because of its unique advantages as a heating supply. However, the sustainable exploitation of geothermal energy usually requires a comprehensive investigation of the geothermal water circulation pattern prevailing at a proposed site. During the exploitation of geothermal energy resources at Nanpu Sag in northern China, thermal anomalies were found to exist in two adjacent regions: the Caofeidian and the Matouying. To reconcile the anomalies and to examine both the source of recharge water and the geothermal systems’ circulation dynamics, a comprehensive investigation was performed using multiple chemical and isotopic tracers (δ 2 H, δ 18 O, 87 Sr/ 86 Sr, δ 13 C, and 14 C). The total dissolved solids (TDS) of the geothermal water are approximately 750 mg/L and 1,250 mg/L, respectively. The geothermal water isotopes at the two sites are also different, with average values of -9.3‰ and -8.2‰ for δ 18 O and -73.4‰ and -71‰ for δ 2 H, respectively. Moreover, the 87 Sr/ 86 Sr ratio of geothermal water at Matouying is 0.7185, which is much greater than that of Caofeidian, with an average value of 0.7088. All the results confirm the difference between the two geothermal systems and may explain the two circulation patterns of deep groundwater at Caofeidian and Matouying. The reservoir temperature obtained from theoretical chemical geothermometers is estimated to be 83–92°C at the Caofeidian and 107–137°C at the Matouying, respectively. The corrected 14 C age implies a low circulation rate that would allow sufficient time to heat the water at Caofeidian. In addition, we propose a geothermal conceptual model in our study area. This model could provide key information regarding the geothermal sustainable exploitation and the effective management of geothermal resources.

同济大学建筑与城市规划学院的艺术教学除了面向本学院低年级的基础教学之外,还针对本学院各专业硕、博学生开设系列艺术课程,并面向同济大学所有学院各年级的本科生开设艺术通识课程.同济的艺术教学侧重于培养学生对于空间、材料和艺术形态等进行抽象性思维和创作的能力;除基础课程中的造型基础训练之外,基于空间、材料、造型、文化、传统工艺、当代前沿技术等,平行开设艺术拓展系列课程.美术、建筑等专业教师及校外专家共同参与艺术教学,跨越不同年龄段的师资结构使教学团队具有可持续的发展活力,这也成为同济艺术教育的鲜明特色并呈现着兼容并蓄的同济精神传承.除日常教学之外,在近40年时间中,刘克敏、阴佳等老师也积极投入到城市公共艺术创作实践和研究之中,参与了许多国家级重要城市文化塑造和建设项目.这些作品从多角度为国家文化建设添砖加瓦,也使艺术教学团队的研究和创作不局限于教学,而是为历届学生和未来中国城市建设者植入了具有同济特色的艺术基因,使公共艺术和城市规划、建筑设计、景观设计等专业一样,成为城市更新和文化塑造与精神文明建设的重要组成部分,是城市建设与发展不可或缺的环节.

针对目前建筑信息模型(BIM)消防疏散路径人工绘制的耗时问题,从提高设计效率出发,提出了一种基于深度Q学习(DQN)与A*结合的混合算法,并以此开发了一种基于该算法的BIM疏散自动设计工具.首先,房间疏散路径使用A*算法进行绘制;然后使用改进的DQN算法确定楼层疏散中疏散门至安全出口的路径再以A*算法绘制.在DQN算法的基础上重新设计了奖励矩阵赋值及增加了奖励矩阵验证机制提高了绘制正确性;最后,使用以该算法为基础的疏散设计自动化工具对实际项目进行了实验.结果表明,该算法不仅能正确绘制路线并且比手工绘制效率提高2～3倍.通过服务器部署及硬件的升级为该算法效率进一步提升提供了可能.目前基于该算法的自动设计工具已在同济设计院上海建筑数字中心的多项实际项目中使用.

For autonomous vehicles and intelligent connected vehicles, the real-time recognition of risky drivers can play an important role in traffic accident prevention. However, the external environment substantially impacts driving behavior and driving risk and is usually costly to acquire. Existing risky driver recognition models often ignore external environment information or assume this information is given. We propose two hierarchical two-layer context-aware machine learning structures. The first layer can speculate external context, for example, traffic states. The second layer recognizes risky drivers based on the contextual information speculated from the first layer. The German Highway Drone Dataset is used to establish risky driver recognition and traffic state recognition models. Rear-end collision risk and side collision risk are evaluated for each vehicle. Drivers with high collision risk are labeled as risky drivers. By analyzing vehicle trajectory data from three traffic states: free-flow, saturated, and congested, we find that traffic states have a significant influence on vehicle's longitudinal speed, lateral speed, longitudinal acceleration/deceleration, and collision risk. Six classifiers, including SVM, KNN, RF, Adaboost, Extra trees, and XGBoost, are applied to train recognition models. Results show that the proposed structures can significantly improve model's ability to recognize risky drivers.

The Active Traffic Management (ATM) system has been widely used in the United States and the European countries to improve the traffic safety of urban expressways. The accurate real-time crash risk prediction is fundamental to the system running well. Crash data are characterized by small probability, which poses a typical Imbalanced Data Classification problem. Most previous studies mainly improved the prediction methods only in data level or algorithm level, which may be inadequate to predict the crash risk accurately especially in a continuous real-time traffic data environment. The comprehensive imbalanced classification algorithm was examined in this research to build more accurate real-time traffic crash risk prediction model. At the output level, the Youden index method has been proved to be of the best ability to divide the prediction results and Probability Calibration Method was proposed to optimize the prediction results in further. At the data level, Under-sampling and Synthetic Minority Oversampling Technique(SMOTE) methods were compared to solve the imbalanced data classification problem by changing the data distribution. At the algorithm level, the cost-sensitive MLP algorithm and Adaboost algorithm were examined and finally the random sampling cost-sensitive MLP model(RCSMLP) and Rusboost model were constructed by synthesizing the optimization methods from three levels. The sensitivity of the RCSMLP model reached 78.10 % and the specificity of the model reached 81.44 %. The AUC and sensitivity of the Rusboost model reached 0.892 and 0.842 while the specificity of the model reached 0.816, which shows the better performance in dealing with the imbalanced traffic crash risk prediction problem compared to existed prediction models. The proposed method of improving prediction accuracy in this study is universal and can be applied to many other prediction models to predict real-time traffic crash risk.

Rear-end collision crash is one of the most common accidents on the road. Accurate driving style recognition considering rear-end collision risk is crucial to design useful driver assistance systems and vehicle control systems. The purpose of this study is to develop a driving style recognition method based on vehicle trajectory data extracted from the surveillance video. First, three rear-end collision surrogates, Inversed Time to Collision (ITTC), Time-Headway (THW), and Modified Margin to Collision (MMTC), are selected to evaluate the collision risk level of vehicle trajectory for each driver. The driving style of each driver in training data is labelled based on their collision risk level using K-mean algorithm. Then, the driving style recognition model's inputs are extracted from vehicle trajectory features, including acceleration, relative speed, and relative distance, using Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and statistical method to facilitate the driving style recognition. Finally, Supporting Vector Machine (SVM) is applied to recognize driving style based on the labelled data. The performance of Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) is also compared with SVM. The results show that SVM overperforms others with 91.7% accuracy with DWT feature extraction method.

Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers' active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

Atoll-shaped and normal garnets in a low-temperature (LT) and ultra-high-pressure (UHP) metamorphic eclogite of the Dabie orogen were studied by detailed petrographic observation and pseudosection modelling. The normal and atoll garnets exhibit pronounced chemical variations, characterized by the overall increase of pyrope and decrease of grossular content from core to rim. A compositional discontinuity between the inclusion-rich core and the inclusion-poor mantle was defined by a sharp decrease in grossular content and an increase in pyrope. Pseudosection modelling using different effective bulk compositions (EBCs) obtained using XRF and the pointcounting method shows that the eclogite experienced two stages of eclogite-facies metamorphism from a prograde amphibole-eclogite-facies (similar to 500-700 degrees C, < 2.2 GPa) to the peak UHP lawsonite-eclogite-facies (similar to 650 degrees C, similar to 3.3 GPa), followed by an isothermal decompression (1TD) exhumation. Garnet essentially did not grow owing to the parallels between the prograde P-T path and the garnet mode isopleths after the formation of the garnet core. The stable field and the compositions of the peak mineral assemblage at LT-UHP conditions insignificantly depend on the scale of equilibrium. Partial lawsonite and omphacite should be subtracted from the EBCs to obtain reasonable P-T estimates/pseudosections that are consistent with the genesis of atoll-garnet-bearing eclogite. Pseudosection modelling indicates that the infiltration of external fluid into the "dry" eclogite weakened the rigid garnet and produced micro-cracks. These cracks served as permeable pathways, leading ultimately to the consumption of previously formed garnet core and the formation of atoll texture. Previous P-T estimates using conventional garnet-omphacite-phengite thermobarometry are less reliable because omphacite was highly altered owing to net transfer reactions involving omphacite. The atoll texture was developed from prograde to peak UHP metamorphism, and the atoll-garnet forming event in the Dabie eclogite predates the final stage of the UHP metamorphism. The previous Lu-Hf date of c. 221 Ma is thus a maximum estimate of the termination of UHP metamorphism.

In the developing country of China, driving used to be considered a travel mode that symbolized identity. However, with the explosive growth of auto ownership, traffic congestion and parking challenges have become increasingly serious in large cities. Some auto owners have switched to urban rail transit for commuting. The reasons behind this shift are worthy of in-depth studies. In this paper, a binary logit model is developed for auto owners in Shanghai, China, to identify influential factors and quantify their impacts on probabilities of choices between auto and rail for commute. The model involves a variety of explanatory variables including rail travel time, station access/egress distance, rail fare, rail waiting time, in-car time as well as some commuters’ socioeconomic and demographic characteristics. The research findings can aid in proposing programs to improve other existing transit facilities and providing a basis for quantitative assessment of alternative improvement programs.

A reconstruction of the pressure-temperature-time (P-T-t) path of high-pressure eclogite-facies rocks in subduction zones may reveal important information about the tectono-metamorphic processes that occur at great depths along the plate interface. The majority of studies have focused on prograde to peak metamorphism of these rocks, whereas after-peak metamorphism has received less attention. Herein, we present a detailed petrological, pseudosection modeling and radiometric dating study of a retrograded eclogite sample from the Sumdo ultrahigh pressure belt of the Lhasa terrane, Tibet. Mineral chemical variations, textural discontinuities and thermodynamic modeling suggest that the eclogite underwent an exhumation-heating period. Petrographic observations and phase equilibria modeling suggest that the garnet cores formed at the pressure peak (similar to 2.5 GPa and similar to 520 degrees C) within the lawsonite eclogite-facies and garnet rims (similar to 1.5 GPa and < 650 degrees C) grew during post peak amphibole eclogite-facies metamorphism. The metamorphic evolution of the Sumdo eclogite is characterized by a clockwise P-T path with a heating stage during early exhumation, a finding that conflicts with previously reported heating-compression P-T paths for the Sumdo eclogite. A garnet-whole rock Lu-Hf age of 266.6 +/- 0.7 Ma, which is consistent with the loosely constrained zircon U-Pb age of 261 +/- 15 Ma within uncertainty, was obtained for the sample. The peak metamorphic temperature of the sample is lower than the Lu-Hf closure temperature of garnet, which combined with the general core-to-rim decrease in the Mn and Lu concentrations and the occurrence of a second maximum Lu peak in the inner rim, is consistent with the Lu-Hf system skewing to the age of the garnet inner rim. Thus the Lu-Hf age likely reflects late eclogite-facies metamorphism. The new U-Pb and Lu-Hf ages, together with previously published radiometric dating results, suggest that the overall growth of garnet spans an interval of similar to 7 million years, which is a minimum estimate of the duration of the eclogite-facies metamorphism of the Sumdo eclogite.

In this paper we mainly discuss the thinking and practice on the teaching reform of Art Modeling Course in Architecture, Urban Planning and Landscape. Based on the analysis of the characteristics of Architecture and traditional teaching pattern of Art Modeling Training, we point out that the deficiency of traditional teaching pattern is lacking of association between teaching process, context and cognition. Combining with the exploration of Art Modeling Course in Tongji University, we attempt to carry on a systematic reform which included setting multiple contexts, focusing on observation and thinking process, integrating and expanding the teaching resources. Reconstructing two associations helps to improve and optimize the teaching of Modeling Course.