This study proposes three mathematical programming models with distinct optimization objectives for transfer optimization in a bi-modal public transport network. To improve the applicability of the models and expedite the solution process, some acceleration techniques, including eliminating redundant constraints and incorporating valid inequalities, are suggested. The models and solution methods are applied to a small toy network and a real-life bi-modal public transport network. The results indicate that compared to the third model, the second model can reduce the total transfer waiting time by 12.29% to 30.31%, while the longest transfer waiting time may increase by 4.35% to 22.22%. Furthermore, the third model, which prioritizes minimizing the longest transfer waiting time, may increase the total or average transfer waiting time. The results suggest that decision-makers need to make a trade-off between reducing total passenger transfer waiting time (for efficiency) and reducing the longest passenger transfer waiting time (for fairness). .

To facilitate the shift from conventional to electric buses, the required charging infrastructure must be deployed. This study models the charging station location selection problem for fixed-line public transport services consisting of electric buses. The model considers the deadheading time of electric buses between the final stop of their trip and the locations of the potential charging stations with the objective of minimizing vehicle running costs. The problem is solved at a strategic level; therefore, several parameters of day-to-day operations, such as deadheading distances, are included as aggregate data considering their average values. In addition, it considers different charger types (slow and fast), which are subject to a day-ahead scheduling of the charging sessions of the buses. The developed model is a mixed-integer nonlinear program, which is reformulated as a mixed-integer linear program and can be solved efficiently for large networks with more than 1940 bus trips and 336 charging installation options. The model is applied in the Athens metropolitan area, demonstrating its potential as a decision support tool for selecting charging station locations and charger types in large public transport networks.

Constructed wetlands (CWs) have been proven to effectively immobilize plastic particles. However, little is known about the differences in the impact of varying sized plastic particles on nitrous oxide (N₂O) release, as well as the intervention mechanisms in CWs. Here, we built a lab-scale wetland model and introduced plastic particles of macro-, micro-, and nano-size at 100 μg/L for 370 days. The results showed that plastic particles of all sizes reduced N₂O release in CWs, with the degrees being the strongest for the Nano group, followed by Micro and Macro groups. Meanwhile, ¹⁵N- and ¹⁸O-tracing experiment revealed that the ammoxidation process contributed the most N₂O production, followed by denitrification. While for every N₂O-releasing process, the contributing proportion of N₂O in nitrification-coupled denitrification were most significantly cut down under exposing to macro-sized plastics and had an obvious increase in nitrifier denitrification in all groups, respectively. Finally, we revealed the three mechanism pathways of N₂O release reduction with macro-, micro-, and nano-sized plastics by impacting carbon assimilation (RubisCO activity), ammonia oxidation (gene amo abundance and HAO activity), and N-ion transmembrane and reductase activities, respectively. Our findings thus provided novel insights into the potential effects of plastic particles in CWs as an eco-technology.

Transfer optimization in public transport (PT) networks can be achieved through coordinated timetabling and vehicle scheduling. Traditionally, the coordinated timetabling problem is solved first before proceeding to the vehicle scheduling problem. The integration of these two problems can help further reduce the total operation cost and improve the level of service, especially when timetables of different PT lines are well-coordinated at transfer stations. This work addresses the integrated PT timetable coordination and vehicle scheduling problem while ensuring that each PT line is dispatched with an even headway. We first separately formulate two integer linear programming models for the timetable coordination and vehicle scheduling problems. Next, the two models are integrated into a bi-objective integer linear programming model for the integrated timetable coordination and vehicle scheduling problem. For small size PT networks, the model can be solved by using an ɛ-constraint method, together with off-the-shelf optimization solvers. For large-size problems, two constraint-reduction procedures are developed to reduce the number of redundant constraints so as to reduce the computation complexity and improve the solution process. Finally, the models and solution method are applied to a numerical example and a real-world bus rapid transit (BRT) network in Chengdu, China. Computation results show that the solution generated by the sequential optimization approach is usually dominated by the Pareto-optimal solutions generated by the integrated optimization approach. Our findings suggest that it is not a wise decision to use the solution generated by the sequential optimization approach or the solution with the minimum fleet size generated by the integrated optimization approach. For practical implementation, it is recommended to choose the solution that has a fleet size of one more vehicle than the minimum fleet size.

Ammonia-oxidizing microorganisms (AOMs, archaea (AOA) and bacteria (AOB)) are primarily responsible for the ammoxidation in constructed wetlands (CWs). However, little is known about evaluating the response of AOA and AOB to engineered nanomaterials (ENMs) and quantifying the shift of their contribution to ammoxidation. Herein, we operated a series of CWs exposing to silver nanoparticles (Ag-NPs), single-walled carbon nanotubes (SWCNTs), and polystyrene nano-sized plastics (PS-NPs) with the wastewater-accumulating concentration of ENMs for 180 days. The results showed that the abundance of AOA amoA gene in situ was far lower than that of AOB, while the abundance ratio of AOA to AOB increased by 15 folds after 180-day experiment. Using DNA stable isotope probing (DNA-SIP) experiment, we found that the active AOB microbiota varied substantially but the AOA was more stable across different groups. Furthermore, the co-occurrence analysis proved that ENMs stress increased the negative coexistence pattern of AOA and AOB; predictive functional profiling showed that the ENMs enhanced the functional advantage of AOA by inhibiting AOB (mainly hydroxylamine oxidation process). Finally, the contribution of AOA increased under exposing to SWCNTs (18.35%), PS-NPs (24.92%), and Ag-NPs (32.14%) compared with control group (0.03%) for 180 days. Despite this, AOB was still the primary executant of ammoxidation in CWs. Overall, in our study, the differences in activities and contributions of AOMs were quantified in CWs, and a significantly negative coexistence relationship between AOA and AOB was revealed when exposed to emerging nanomaterials.

Bus timetables play an important role in improving the level of service and reducing operations costs of a bus transit system. Without dedicated bus lanes, bus travel times, which are important input data for bus timetabling, are usually time-dependent due to recurrent traffic congestion. However, few studies on bus timetabling have explicitly considered such travel time time-dependency in creating timetables. This paper addresses the problem of how to optimally modify an existing single-line bus timetable by slightly shifting vehicle departure times at the departure terminal and holding vehicles at other stops taking into account time-dependent travel times. The problem is mathematically formulated as a nonlinear programming model. According to the special structure and properties of the model, a derivative-free constrained compass search algorithm with revised step-size updating rule is applied to solve it. A case study of a bus line in Beijing, China is conducted to demonstrate the effectiveness and efficiency of the proposed model and solution algorithm. The case study results show that by utilizing the proposed methodology the optimized bus timetable can significantly reduce the total passenger travel time and improve ridership comfort, while rarely increasing the average vehicle cycle time. This study offers a promising and practical methodology for optimizing single-line bus service taking into account time-dependent travel times.

Decreasing the preheating temperature is an effective step to control the energy consumption in the hot rolling process. In order to obtain the lowest preheating temperature to prepare enough thickness of oxide scale in the hot rolling process, the oxidation resistance of commercial steel samples with different Al and Si contents were investigated in this paper. The results indicate that both Al and Si based oxides form at the steel-oxides interface as diffusion barrier but Al provide stronger diffusion resistance than Si in the diffusion-controlling oxidation region. Meanwhile, a three-dimensional oxidation kinetic model has been adopted to depict the oxidation behavior of four types of commercial steel. The oxidation process of automotive steel sample containing with low alloy elements is kinetically determined by interface chemical reaction. Its activation energy is 55.2 ± 6.9 kJ/mol. As for silicon steel containing with relative high alloy elements, its controlling process is determined by diffusion step at low temperature and controlled by chemical reaction rate at high temperature. In order to obtain enough thickness of oxide scale, the lowest preheating temperature of different types steel range from high to low should be the steel with high content of Al (1180 °C), the steel with high content of Si and low content of Al (1130 °C) and the steel with high contents of Si and Al (1030 °C).