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During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data.

First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method.

The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers.

First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.

The flammability of poly-acrylate (PA) resin is a major disadvantage in applications that require flame resistance. Many studies, including the authors’ previous study, have proved that covalent-incorporated phosphorous-containing (P-containing) monomer onto the PA resin can exhibit better flame resistance than that by an additive approach. However, other properties such as thermal stability, coating properties are still deteriorated. To further improve the flame-retardancy and other comprehensive properties of the P-containing PA resin, in this study, melamine formaldehyde(MF) resin was used not only as a curing agent to enhance the coating properties of the PA resin, but also as a nitrogen-containing (N-containing) resin to form a P-N synergistic effect and therefore further improve its flame retardancy.

Epoxy resin phosphorous acid-modified (EPPA-modified) PA (EPPA-PA) resin was first prepared and then using MF resin as curing agent. The flame retardancy of the cured resin was tested by the limiting oxygen index (LOI) and UL 94 methods. The thermal stability of the cured resin was studied by TGA. The coating technology such as adhesion property, pencil hardness and anti-solvent properties were characterized according to methods of International Standards ISO2409-1992, ISO 15184-1998 and ISO-15184-2012, respectively. The micro-char morphology of the char residue was observed by SEM.

The results showed that by using MF resin as curing agent has provided the PA resin with excellent coating properties and thermal stability, but also gave a P-N synergistic effect which has greatly enhanced the flame retardancy of the cured resin. The cured resin system containing only 1.7 Wt.% P content and 5.3 Wt.% N content can reach a LOI of 26.9 per cent and pass the V-0 rating in the UL-94 test.

This resin system releases formaldehyde due to the MF resin.

It is expected that the large-scale production of this EPPA-PA resin cured by MF resin system will enable practical industrial applications.

This method for the synthesis of a P- and N-containing PA resin system is newfangled.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Taking attribution theory as an overarching framework, the study aims to examine how employees attribute and respond to a colleague's approach crafting.

Two complementary studies, including a scenario experiment (Study 1; N = 114) and an online survey (Study 2; N = 220), were conducted to test the hypothesized model.

Study 1 found support for the attribution of a prosocial motive to approach crafting, which in turn led to more social support and less social undermining among observers. This mediation was stronger when the job crafter was perceived as less other-oriented. Study 2 replicated the findings of Study 1 and further showed that when observers attributed both high impression management and prosocial motives to approach crafting, the positive relationship between their prosocial motive attribution and social support for the job crafter got weakened, while the negative relationship between their prosocial motive attribution and social undermining of the job crafter was strengthened.

The findings demonstrate that approach crafting gives rise to specific attributions and reactions toward the job crafter, which enrich the understanding of the social consequences of job crafting in the workplace.

The purpose of this paper is to develop an equation for the synergistic corrosion of SRB and CO₂ based on the D-W model.

The bacterial types in the a and ß pipelines were studied by the most probable number method, and the corrosion morphology of L360 in pipeline water samples was studied by surface analysis. The corrosion rate of L360 was studied using the weight loss method. The gray correlation method was used to calculate the degree of correlation between the influencing factors of corrosion under the synergistic effect of CO₂ and SRB. The curve obtained from PIPESIM software and experiments data was then fitted using multiple non-linear regression method by MATLAB software.

The equation was used to predict the corrosion of the ß pipeline for verification, and it was found that seven out of ten excavation sites were within a 20% error range.

Using the gray correlation method, an equation that considers synergistic corrosion of SRB and CO₂ has been developed based on the D-W model. The equation could be used to predict the corrosion rate of shale gas gathering pipelines through SRB and CO₂ synergistic corrosion.

Cascaded DC-DC converters system is the main structure of distributed power system, and it has complex nonlinear phenomena during operation, which affect the power quality. Therefore, the dynamic behavior of the cascaded buck converter and boost converter system, as one of the typical cascaded DC-DC converters systems is analyzed.

Firstly, the studied cascaded system of the buck converter with peak current control and the boost converter with PI current control is introduced and its discrete modeling is built. Then, the Jacobian matrix of the cascaded system is calculated to research the stability when the parameter change. Finally, simulation by PSIM and experiments are carried out to verify the theoretical analysis.

The coexistence of fast and slow time scale bifurcations with the changes of reference current and input voltage are studied in the cascaded system, and using simulation analysis to further study the sensitivity of the inductor current of the front-stage converter and back-stage converter to different parameters.

A discrete model of the cascaded buck converter and boost converter system is established, and its dynamic behavior is analyzed in detail for the first time.

In the presence of coronavirus disease 2019 (COVID-19), due to the social distance restriction, consumers' regular consumption behaviors and patterns have been changing fundamentally. Thereafter, an innovative group buying model has emerged and developed explosively with a specific focus on consumer's location, known as community-based group buying (CGB). The purpose of this paper is to investigate the transfer mechanism of user's trust in dyadic contexts of social and commercial role-playing in the CGB program.

This study adopts an empirical research method, with an online and offline questionnaire survey, a total of 382 responses have been obtained. Then, both descriptive analysis and hierarchical regression analysis are conducted to explore the dual roles of group leader and its corresponding effects on consumers' trust (i.e. emotional trust and behavioral trust) and engagement actions (i.e. purchase and share) in the CGB program.

Results indicate that resident's trust and their perception of group leader's friend role can positively enhance their engagement actions in the CGB programs. Meanwhile, for the purpose of profit maximization, the group leader is more willing to play a friend role in transactions no matter whether the role conflict exists.

Research findings provide some managerial insights for CGB platform on the selection and training of group leaders and the incentive mechanism design.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

Green innovation (GI) in megaprojects has become a significant research topic that attracts both megaproject management scholars' and practitioners' attention. Green transformational leadership (GTL) is acknowledged as an important antecedent to GI in the permanent context. However, limited research investigates the mechanism and condition of how GTL effectively affects GI in the temporary (i.e. megaproject) context. This study seeks to examine the mechanism and condition of GTL in improving GI by assessing the mediating role of green knowledge sharing (GKS) and the moderating effect of innovation climate (IC).

Regression analysis was performed on data obtained from 303 experts who have been involved in megaprojects.

GTL has a significant positive impact on two aspects of GI, including green product innovation (GPDI) and green process innovation (GPCI). Besides, GKS mediates the relationship between GTL and the two aspects of GI. Moreover, IC plays a significantly positive moderating role in the relationship between GTL and GKS and the relationship between GKS and the two aspects of GI.

This study adds knowledge to the theory and practice by unveiling the “black box” between GTL and GI in the temporary (i.e. megaproject) context. First, this study extends the continuing discussion on the direct effect of GTL on GI to the temporary (i.e. megaproject) context. Second, this study facilitates the understanding of the mechanism to generate better GI performance considering the mediating role of GKS and the moderating effect of IC in the temporary (i.e. megaproject) context. The results can illuminate megaproject practitioners on generating better GI performance.

Despite its flourishing development since first proposed, job crafting literature has provided limited insights into why people craft their jobs. This study theoretically develops a two-dimensional integrative framework for the motives of job crafting, including orientation (self-oriented vs work-oriented vs other-oriented) and self-determination (autonomous vs introjected vs external) dimensions. We further investigate the specific motives of job crafting from actor and observer perspectives.

We conducted two critical-incident recall surveys among 120 and 100 employees from varied sectors and organizations, who responded from the actor and observer perspective respectively. 395 and 299 valid open-ended responses were then collected and coded following the steps for content analysis.

Drawing from the proposed two-dimensional theoretical framework, we identified 16 specific job crafting motives from actor and observer perspectives.

Our findings remind managers to pay attention to employees' motives of job crafting and take appropriate managerial actions according to their varied motives.

By incorporating job crafting from the motivation literature and identifying diversified motives that drive employees to engage in job crafting, this qualitative study contributes to both the job crafting literature and the broader application of self-determination theory in the field of organizational behavior.