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Specialty coffee is increasingly produced and consumed as part of routine life in many cities in modern China, but the social and cultural shifts it has engendered yet to be systematically examined. By examining the intersections between social media and the operations of independent Chinese coffeehouses in the field of taste, this paper puts forward the idea of new taste among Chinese millennials, which comprises individual subjectivity, heterogeneous social relationships, and forms of class distinction. Using taste-oriented keyword searches on WeChat official accounts, 20 articles were returned and analyzed in terms of their textural and visual orientations to examine the processes underlying how taste is influenced in the consumption of specialty coffee in China. Findings suggest the importance of taste makers in this process, from routine creation of aesthetic ambience in the coffeehouses to the construction of affective taste spaces online, and the establishment of taste cycles from online to offline, which all underpin class privilege. Moreover, the emergence of an 'urban café community' appears to be characterized by specific forms of belonging resulting from a productive effect of the interplay between independent coffeehouses and consumers in everyday urban life in which a set of aesthetic boundaries reside. Second, these digital consumers distinguish themselves socially by positioning themselves as having a cosmopolitan taste grounded in coffee appreciation instead of merely consuming coffee for physiological benefits. These findings extend taste propositions through engagement of Chinese digital millennial consumers to uncover the underlying cultural classifications. [ABSTRACT FROM AUTHOR]

Firms' collaborative activities have created increasing opportunities for eco-innovation in modern society. Based on unbalanced panel data from the Chinese National Innovation Survey between 2011 and 2015, this paper explored the influences of different modes of cooperation, i.e., vertical cooperation, horizontal cooperation, and mixed cooperation, on the eco-innovation of Chinese manufacturing firms. The results indicated that three types of cooperation all had positive and statistically significant effects on the firms' eco-innovation, and mixed cooperation had promoted eco-innovation more dramatically. The extent of such impacts may vary depending on the heterogeneity of the characteristic of enterprises. We also verified that knowledge spillovers from cooperative partners have played a mediating role between cooperation and eco-innovation. Our results suggest the potential benefits of diversified collaborative activities and appropriate intellectual property protection for firms' eco-innovation in China. [ABSTRACT FROM AUTHOR]

Accurate train arrival delay prediction is critical for real-time train dispatching and for the improvement of the transportation service. This study proposes a data-driven method that combines eXtreme Gradient Boosting (XGBoost) and a Bayesian optimization (BO) algorithm to predict train arrival delays. First, eleven characteristics that may affect the train arrival time at the next scheduled station are identified as independent variables. Second, an XGBoost prediction model that captures the relation between the train arrival delays and various railway system characteristics is established. Third, the BO algorithm is applied to the hyperparameter optimization of the XGBoost model to improve the prediction accuracy. Subsequently, case studies using data from two high-speed railway (HSR) lines in China are performed to analyze the prediction efficiency and accuracy of the proposed model for different delay bins and at different stations. The results on two HSR lines demonstrate that the proposed method outperforms other benchmark models regarding the performance metrics of the determination coefficient (0.9889/0.9905), root-mean-squared error (2.686/1.887), and mean absolute error (0.896/ 0.802). In addition, the statistical test is carried out using Friedman Test (FT) and Wilcoxon Signed Rank Test (WSRT) to validate the efficacy of the proposed method. Furthermore, the train arrival delays at different abnormal events can also be accurately forecasted using the proposed method; the results indicate that the proposed method outperforms other benchmark methods, especially in the prediction of long delays caused by specific abnormal events. • Train arrival delays under different scenes is predicted with big data. • A novel method combining XGBoost and BO is proposed to predict train delays. • Large-scale multi-attribute data are adopted to improve the prediction stability. • The proposed method produced significantly improved forecasting results compared to the benchmarks. [ABSTRACT FROM AUTHOR]

The terrestrial ecosystem productivity and foundation of regional ecosystem services have been significantly influenced by recent urbanization processes. This study assesses the changes in terrestrial ecosystem productivity in Jiangsu from the years of 2000 to 2015 in response to the urbanization. A linear model that incorporates the traditional equalization method is proposed to improve the estimation accuracy of net primary productivity (NPP) loss. Results revealed that the land area of urban construction expanded rapidly during the research period to encompass an area of 8672.8 km2. The rate of expansion was highest during 2005–2010. Additionally, the expansion rate of urban construction land was considerably higher in southern Jiangsu compared to the northern areas. The NPP exhibited a rising tendency from the year of 2000 to 2015, and varied from 33.30 to 40.23 Tg C/y. It was higher in the central parts, which include the cities of Yancheng and Nantong. The increase in urban construction land has resulted in a significant reduction in the terrestrial ecosystem productivity, i.e. a cumulative NPP loss of 2.55–2.88 Tg C during the research period. The NPP losses due to the conversion from cropland to constrction land were the highest, followed by the wetland. The work in this paper indicates that the rate of future productivity losses in terrestrial ecosystem in northern Jiangsu would be faster than the southern areas. [ABSTRACT FROM AUTHOR]

It is critical to improve the effectiveness of demand management in metro systems with passenger departure time choice exactly learned during peak hours. In this study, a practical framework is developed to model departure time choice of metro passengers during peak hours. First, various attributes that influence departure time choice of metro passengers are investigated and the technique for order preference by similarity to ideal solutions (TOPSIS) is used to identify these main attributes. Then, a mixed logit (ML) model of departure time choice that accounts for price endogeneity is developed. To calibrate the model, a stated preference (SP) survey based on D-efficient design is conducted in the Beijing metro system. It is proved that the corrected ML model outperforms the uncorrected ML model according to the collected 1152 sample data. An elasticity analysis of these main attributes is further conducted, which indicates that metro fare and departure time change influence passenger departure time choice more than crowdedness in Beijing metro. Knowledge of these preferences assists traffic managers in balancing passenger departure time to mitigate overcrowding during peak hours. Heterogeneity of passenger socioeconomic and trip characteristics is also concerned taking advantage of ML model. Finally, a ML-based fare discount strategy to ease the crowdedness in Batong Line of Beijing metro is presented and evaluated via an existing simulation tool. [ABSTRACT FROM AUTHOR]

• Three factors are ascertained to measure the effects of disruptions. • Real-time prediction requirements are particularly considered in the model. • The model shows high accuracy in predicting the effects of disruptions. • The model shows strong generalizability on two different high-speed railway lines. Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L , N , and T , respectively. [ABSTRACT FROM AUTHOR]

• The contribution of Pu from PPG close-in fallout in some samples is 41.5–50%. • Pu from the ocean to the coast is mainly controlled by ocean currents and tides. • The migration of Pu can be explained by the idea of "source-sink". • The distribution of Pu in coastal areas reflects the "land-sea interaction". 239Pu, 240Pu in the surface sediments from tidal flat wetlands in northern Jiangsu Province and radial sand ridge in southern Yellow Sea were analyzed by SF-ICP-MS. Tidal flat wetlands and radial sand ridge are the special sediment deposits in coastal area, which are the ideal places to study "land-sea interaction". The 240Pu/239Pu atom ratios in our research samples show that the Pu in most samples is from global fallout, only the Pu in a few samples near Yangtze River Estuary and channel of radial sand ridge is affected by the close-in Pu fallout from Pacific Proving Grounds (PPG), and the contribution of Pu from PPG is 41.5–50%. In composition, since the study samples are mainly sand, the 239+240Pu activities in our samples are lower than those in river sediments of China. The transport of Pu is a very important scientific issue, and researches have shown that Pu is "transported" into the coastal areas and seas from the land- based "source". In addition, the Pu distribution in coastal areas and seas is affected by the input of rivers and ocean currents, which is a manifestation of the "land-sea interaction". [ABSTRACT FROM AUTHOR]

• Deep learning models were employed to predict train delays. • Train operations were modeled as sequences. • Interactions were captured from train groups in the prediction model. • The proposed model shows satisfactory performance on different railway lines. This paper presents a carefully designed train delay prediction model, called FCLL-Net, which combines a fully-connected neural network (FCNN) and two long short-term memory (LSTM) components, to capture operational interactions. The performance of FCLL-Net is tested using data from two high speed railway lines in China. The results show that FCLL-Net has significantly improved prediction performance, over 9.4% on both lines, in terms of the selected absolute and relative metrics compared to the commonly used state-of-the-art models. Additionally, the sensitivity analysis demonstrates that interactions of train operations and weather-related features are of great significance to consider in delay prediction models. [ABSTRACT FROM AUTHOR]

Reducing delays in the metro transit system improves passenger satisfaction and the operational efficiency of the system. However, current delay-recovery strategies tend to reduce delays rather than the operational costs of the metro company. We propose optimized delay-recovery strategies to reduce the delay and increase energy efficiency simultaneously in this study. We classified delays in historical data according to the relationship between the delay time and the following headway, and we present optimized delay-recovery strategies for different initial delay conditions. A multi-objective optimization model was used to minimize the cumulative delay time (CDT) and energy consumption. We added weight factors to the objective functions to reflect the preferences of decision-makers regarding delay recovery and energy savings. We applied the non-dominated sorting genetic algorithm-II (NSGA-II) to solve the proposed model and verified its performance using a case study of the Chengdu Metro. The results showed that the proposed model exhibited excellent performance regarding delay recovery and energy savings. • Optimized strategies were used to reduce the delay and increase the energy efficiency of metro lines. • A multi-objective programming model was proposed to deal with different initial delay scenarios. • The NSGA-II algorithm was used to solve the optimization problem. • Proposed model were verified using a case study of the Chengdu Metro in China. [ABSTRACT FROM AUTHOR]