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Fully automatic operation (FAO) metro system is the trend of urban rail in modern cities. This study aims to develop a service quality scale for measuring passenger service perception in the context of FAO metro system.

Based on the interviews and the dimensions proposed in the previous service quality scales of the public transport sector, this study develops a multidimension service quality scale. Face-to-face interviews and surveys are conducted to obtain the riding experience and satisfaction of passengers in the metro stations of Beijing Subway Yanfang Line and Beijing Daxing International Airport Express that are FAO metro lines. Reliability, validity and nomological validity of our scale are demonstrated.

Six dimensions (i.e. tangibles, information, safety, efficiency, empathy and innovation) and 22 items are included in the scale through quantitative analysis. It is found that all of the six dimensions have a significant relationship with passenger satisfaction. Especially information and innovation are the more important dimensions of FAO metro system service quality.

This study contributes to the service quality literature in the public transport sector taking the FAO metro system as the object. The findings may provide a guide for the FAO metro operators to evaluate and improve their service quality.

The purpose of this paper is to provide a knowledge-enabled digital twin for smart design (KDT-SD) of aircraft assembly line (AAL) to enhance the AAL efficiency, performance and visibility. Modern AALs usually need to have capabilities such as digital-physical interaction and self-evaluation that brings significant challenges to traditional design method for AAL. The digital twin (DT) combining with reusable knowledge, as the key technologies in this framework, is introduced to promote the design process by configuring, understanding and evaluating design scheme.

The proposed KDT-SD framework is designed with the introduction of DT and knowledge. First, dynamic design knowledge library (DDK-Lib) is established which could support the various activities of DT in the entire design process. Then, the knowledge-driven digital AAL modeling method is proposed. At last, knowledge-based smart evaluation is used to understand and identify the design flaws, which could further improvement of the design scheme.

By means of the KDT-SD framework proposed, it is possible to apply DT to reduce the complexity and discover design flaws in AAL design. Moreover, the knowledge equips DT with the capacities of rapid modeling and smart evaluation that improve design efficiency and quality.

The proposed KDT-SD framework can provide efficient design of AAL and evaluate the design performance in advance so that the feasibility of design scheme can be improved as much as possible.

The paper aims to study how global pharmaceutical companies such as Pfizer have managed the challenges of pharmaceutical patent expiry.

A case study method was applied. The best-selling brand drug over the past 10 years – Lipitor – was chosen as the case target.

For dealing with this, this paper describes all the details of the corresponding strategies of Pfizer before and after patent expiration of Lipitor. Before patent expiry, Pfizer undertook the activities of direct-to-consumer marketing, pricing strategy for competition, legal delay and me-too drug R&D. After patent expiry, Pfizer chose to carry out continuous marketing for brand, rebate strategy, authorized generics and change to over-the-counter. In addition, diversity and globalization strategy was applied before and after patent expiry.

This research provides strong implication for managing pharmaceutical products before and after patent expiry.

It is strongly recommended for both brand and generic drug companies to design strategies to meet the challenges of pharmaceutical patent expiry.

For the global pharmaceutical market, a conclusion can be drawn that, nowadays, the “patent cliff” is the most significant factor influencing decision-makers to consider futuristic policies. Further, it is also a considerably effective solution for reducing health-care costs for policymakers.

This paper contributes to the field of patent expiry management in high-tech industries such as pharmaceuticals.

The purpose of this study is to investigate the impacts of surface ozone pollution on rice profit, output and variable inputs in China.

This study estimates the rice profit function using county-level rice production data and ozone monitoring data in 2014 and 2015 to capture the impact of ozone pollution on rice profit. Then, it uses dual approach to identify the impacts of ozone on the supply of rice and the demand for variable inputs. The ozone concentration data are obtained from 1,412 monitoring stations established by the National Environmental Monitoring Centre of China.

The results show that surface ozone would significantly reduce rice profits; a 1% increase in (the daily average ozone concentration from 9 am to 4 pm) leads to a 0.1% decrease in profits. In addition, ozone has a negative impact on the levels of inputs and the supply of rice, and the elasticities of rice output, fertilizer input and labour input with respect to are −0.87, −0.86 and −0.78%, respectively. These results suggest that ozone pollution affects rice production via two channels: the direct damage on rice growth and the indirect negative impact of reducing variable inputs.

This study estimates the impacts of surface ozone pollution on rice profit and output, and quantifies its influence on variable inputs in China, which provides a better understanding of farmers' adaptation behaviour.

This paper aims to solve the facility location problem of mobility industry call centers comprehensively, considering both investment efficiency and long-term development efficiency.

In this paper, a two-phase decision-making approach within a multi-criteria decision-making (MCDM) framework has been proposed to help select optimal locations among various alternate locations. Both quantitative and qualitative information is collected and processed based on fuzzy set theory and fuzzy analytic hierarchy process. Then the fuzzy technique for order preference by similarity to an ideal solution method is incorporated in the framework to assess the overall feasibility of all alternates.

A real case of a mobility giant in China is applied to verify the effectiveness of the proposed framework. Sensitivity analysis also proves the robustness of the framework.

This two-phase MCDM framework allows the mobility industry call center location to be selected considering economic, human resource and sustainability elements comprehensively. The framework proposed in this paper might be applicable to other companies in the mobility industry when deciding optimal locations of call centers.

At the US passenger stations, train operations approaching terminating tracks rely on the engineer’s compliant behavior to safely stop before the end of the tracks. Noncompliance actions from the disengaged or inattentive engineers would result in hazards to train passengers, train crewmembers and bystanders at passenger stations. Over the past decade, a series of end-of-track collisions occurred at passenger stations with substantial property damage and casualties. This study’s developed systemic model and discussions present policymakers, railway practitioners and academic researchers with a flexible approach for qualitatively assessing railroad safety.

To achieve a system-based, micro-level analysis of end-of-track accidents and eventually promote the safety level of passenger stations, the systems-theoretic accident modeling and processes (STAMP), as a practical systematic accident model widely used in the complex systems, is developed in view of environmental factors, human errors, organizational factors and mechanical failures in this complex socio-technical system.

The developed STAMP accident model and analytical results qualitatively provide an explicit understanding of the system hazards, constraints and hierarchical control structure of train operations on terminating tracks in the US passenger stations. Furthermore, the safety recommendations and practical options related to obstructive sleep apnea screening, positive train control-based collision avoidance mechanisms, robust system safety program plans and bumping posts are proposed and evaluated using the STAMP approach.

The findings from STAMP-based analysis can serve as valid references for policymakers, government accident investigators, railway practitioners and academic researchers. Ultimately, they can contribute to establishing effective emergent measures for train operations at passenger stations and promote the level of safety necessary to protect the public. The STAMP approach could be adapted to analyze various other rail safety systems that aim to ultimately improve the safety level of railroad systems.

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework.

The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level.

MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only.

This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

Student engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research.

The video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different modal data have different granularity, this study proposed the Fast–Slow Neural Network (FSNN) to detect engagement through both observable and internal information, with an asynchrony structure to preserve the sequence information of data with different granularity.

Experimental results show that the proposed algorithm can recognize engagement better than the traditional data fusion methods. The results are also analyzed to figure out the reasons for the better performance of the proposed FSNN.

This study combined multimodal data from observable and internal aspects to improve the accuracy of engagement detection in the classroom. The proposed FSNN used the asynchronous process to deal with the problem of remaining sequential information when facing multimodal data with different granularity.

International students make valuable educational and economic contributions in higher education institutions (HEIs) all across the world. They contribute to the diversity and add different perspective in classrooms. Attracting international students is one of the objectives of HEIs. International students being present all over the world access information about the university online. Hence, all the relevant information must be available on the HEI website. Institutions are also present on social media platforms for interaction purposes. The owned media, including website, social media, discussion forums, etc. of institutions must be regularly monitored and analysed. Equally important is to analyse what students are discussing online about institutions, that is, what are their sentiments. This chapter is an attempt to develop a guide for HEI to draw the interest of international students for an institution through web analytics and social listening. Web analytics will help in understanding the behaviour of the visitor to the HEI website. Key performance indicators that will help in achieve the objective of attracting international students will be identified. On the other hand, analysing social listening on different platforms will help in understanding international students’ perspectives towards a specific institution.