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Revenue management (RM) is a significant technique to improve revenue with limited resources. With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China, it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.

This paper proposes the theory and framework of generalized RM of railway passenger transport (RMRPT), and the thoughts and methods of the main techniques in RMRPT, involving demand forecasting, line planning, inventory control, pricing strategies and information systems, are all studied and elaborated. The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT. The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.

The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.

The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.

This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO₂ and PM_2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.

Results suggest that the carbon trading mechanism induces 2.7% of CO₂ emission reduction and 6.7% of PM_2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.

This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure.

Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.

The purpose of the study is to assess the differential impact of gender and cancer sites on mental burden across different types of cancer and control patients.

The paper is based on a primary survey undertaken in 2015–2016 of 600 cancer and 200 control patients across five hospitals of Nepal. The data was analyzed using propensity score matching methods and treatment effect weighting estimators.

The authors find that of all the types of patients covered under this study, cervical cancer patients suffered from a greater intensity of anxiety and lack of functional wellbeing. On an average, all other female, male cancer patients, and control patients experience significantly lower intensity of mental burden in the range of 1.83, 2.63 and 3.31, respectively when compared to patients of cervical cancer. The results are robust across all the four treatment effect estimators and through all the measures of mental burden. The implications of suffering from cervical cancer, as a unique gynecological cancer was studied in-depth. An effect size analysis pointed out to the dysfunctional familial relationship as additional causes of concern for cervical cancer patients.

An important finding that emerged is that female cancer patients especially those who have cervical cancer should be given special attention because they appear to be the most vulnerable group. Further work is needed to delineate the reasons behind a cervical cancer patient facing higher amount of stress.

This paper aims to explore the mechanism of influence on the subordinate's sense of gain at work (SGW) in terms of the coaching leadership behavior (CL), supervisor-subordinate guanxi (SSG) and commitment-based practice of human resource management (CHRM).

Based on the survey of 584 employees from 50 firms operating in China, this study explores the effect of CL on employees’ SGW, which concerns two dimensions: sense of material gain and sense of spiritual gain.

Results show that the CL has a significant positive influence on both the subordinate’s sense of material gain and his/her sense of spiritual gain, in which there exists a mediating effect of SSG and moderating effects of CHRM for the influence.

This study not only develops the theory of SGW, but also provides a scientific basis and policy suggestions for employers to implement in order to enhance their employees’ SGW.

Few integrative studies have examined the impact of CL on employees’ SGW. Based on the Need-to-Belong Theory, this study adds new empirical evidence and constructs a theoretical model for the mechanism of influence on the SGW, examines the influence of CL on the subordinate’s SGW and finds a mechanism of transmission (SSG) and a boundary condition (CHRM) for the influence.

The purpose of this paper is to provide insights into the current practice, challenges and future development trends of intelligent compaction (IC) technology from a bibliometric perspective.

A bibliometric analysis on IC-relevant studies is presented. Through this quantitative manner, insights into the current IC research practice and development trends have been derived from the perspectives of publications and citations, spatial distribution, knowledge construction, structural variations, existing problems, and conclusions and recommendations.

Currently, IC applications are confronted with the issues of intelligent compaction measurement values (ICMVs) applicability, autonomous control, specifications and applications. To address the issues, three potential research directions are identified: a comprehensive ICMV measurement system that is designated for single layer analysis; autonomous control mechanisms with integrated management capabilities that can efficiently collaborate all stakeholders; and a standardized application workflow and the cost-benefit evaluation of IC in the context of the full life cycle.

The literature used in this paper is collected from the Web of Science. Although the database covers almost all the important publications in IC field, studies not indexed by the database are not considered.

This research quantitatively analyzes the current IC practice and development trends from the perspectives of bibliometric analysis. It provides an overview of the knowledge construction and development of IC technology. The discussions about the problems and the suggested solutions can be useful for those interested in this field.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.