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This study aims to improve the passenger accessibility of passenger demands in the end-of-operation period.

A mixed integer nonlinear programming model for last train timetable optimization of the metro was proposed considering the constraints such as the maximum headway, the minimum headway and the latest end-of-operation time. The objective of the model is to maximize the number of reachable passengers in the end-of-operation period. A solution method based on a preset train service is proposed, which significantly reduces the variables of deciding train services in the original model and reformulates it into a mixed integer linear programming model.

The results of the case study of Wuhan Metro show that the solution method can obtain high-quality solutions in a shorter time; and the shorter the time interval of passenger flow data, the more obvious the advantage of solution speed; after optimization, the number of passengers reaching the destination among the passengers who need to take the last train during the end-of-operation period can be increased by 10%.

Existing research results only consider the passengers who take the last train. Compared with previous research, considering the overall passenger demand during the end-of-operation period can make more passengers arrive at their destination. Appropriately delaying the end-of-operation time can increase the proportion of passengers who can reach the destination in the metro network, but due to the decrease in passenger demand, postponing the end-of-operation time has a bottleneck in increasing the proportion of passengers who can reach the destination.

This paper aims to identify which types of creative R&D employees prefer which daily and weekly working schedules.

This paper builds on an original repeated survey of creative R&D employees from Estonia and presents multinomial logit regression estimates based on a sample of 153 individuals from 11 entities.

The probability of women preferring their weekly work to be concentrated in three to four days is 20 percentage points higher than in men, and the case is similar for less-educated creative R&D employees. The more educated prefer the standard five-day working week. Men have a stronger preference for their week of work to be dispersed over six to seven days. Sleep patterns appear to relate to working time preferences as morning-type individuals have a stronger preference for a working day with fixed start and end times. Those who sleep 7 h or more per day prefer the standard five-day working week more, while employees who sleep less than 7 h favour a working week of six to seven days. Employees who desire more creativity intensity at work have a stronger preference for irregular daily working hours, as do those with poorer general health.

The results indicate that individual characteristics have a significant impact on the preferences for working time arrangements. Similar working time regulations for all employees appear outdated, therefore, and may make work inefficient and harm individual well-being, at least for creative R&D employees.

This paper describes a small research project undertaken in a technical college in Qatar on the use of iPads in the classroom. iPads were trialed for a semester each in mathematics and physics classes; students completed pre- and post-surveys. Classroom observations were carried out and interviews were conducted with both faculty (N=3) and students (N=19). Over 80% of students reported positively on the iPad as being “helpful” to “very helpful” for learning new things and course materials, for increasing their interaction with online course materials and getting course information and for exploring additional material related to course topics. Faculty perceptions of iPad use in class were also positive.

The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.

This study aims at developing an understanding of action pathways when adverse events force micro-enterprises to change their operations.

This qualitative study draws upon empirical data collected from entrepreneurs facing the same adverse event—the COVID-19 pandemic—to build theory on different types of actions that micro-enterprises take and what leads up to these actions.

The findings suggest three types of action pathways. The first pathway is set off by losses stretched out over time and generates open-ended actions. The second pathway is set off by immediate losses and generates survival-oriented actions. The third pathway is set off by potential long-term losses and generates developmental-oriented actions.

This study offers novel insights into action pathways in response to adverse events, heterogeneity of such actions and processes that precede the choice of actions. It also expands the existing literature by showcasing actual actions instead of desired actions, which have already been extensively studied.

Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.

A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.

The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.

Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

While studies about business-to-business (B2B) relationships have mainly addressed buyer–supplier long-term exchanges, focusing on social outcomes such as trust, commitment and cooperation, there is little research that explores the social outcomes which stem from short-term B2B transactions. The purpose of this paper is to explain buyers’ intention to renew a contract after discrete and time-delimited transactions by suggesting a model that complements social exchange theory with theories of fairness. In detail, this study aims to determine how evaluations of economic and social outcomes are complemented by both procedural fairness and distributive fairness.

The hypotheses are tested in the social couponing industry with a survey of a sample of 199 firms purchasing advertising services from daily deal websites. Data are analyzed using covariance-based structural equation modeling (CB-SEM).

The findings reveal direct effects of procedural fairness on social outcomes (satisfaction) and distributive fairness on the intention to renew a contract, negative moderating effect of procedural fairness on the relationship between economic outcomes (campaign effectiveness) and social outcomes (satisfaction).

In discrete, time-delimited transactions, high levels of procedural fairness may partially compensate for low levels of economic outcomes and prevent a reduction in social outcomes. Hence, when economic outcomes are influenced largely by external, uncontrollable conditions, the buyer seems to appreciate the supplier’s efforts to behave fairly.

Social outcomes matter even in discrete transactions and considerations of fairness should be integrated in the management of discrete transactions. Sharing economic outcomes fairly is not sufficient to secure the buyer’s intention to renew the contract.

This study proposes and tests a model that complements social exchange theory with theories of fairness and explains contract renewal in discrete, time-delimited transactions, encompassing both economic outcomes and social outcomes.

This study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry.

The authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program.

The sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study.

Digital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses.

Multi-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.

This study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.

The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality.

To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator.

In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator.

A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.

Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.

This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.

Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.

The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.