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Considering the growing green awareness and increasingly stringent emission regulations, heavy-emitting supply chains are required to re-schedule their operations for environmental responsibility. Although coordination helps the supply chains overcome the decentralised disadvantages to achieve desirable profits, literature considering option contracts under emission constraints, especially combining warehousing contracts, remains scarce. This paper fills this research gap with the novelty in the dual-contract-coordinated decision analysis for achieving profit maximum and emission reduction targets considering customers' green awareness through option and warehousing contracts, based on the originality of using the Lagrange-Stackelberg optimisation method, which overcomes the difficulty in expressing the first-mover's decisions and simplifies the problem-solving process. Analytical and numerical results show that Pareto-efficient coordination can be fully achieved by the option contract if the warehousing contract ensures the same inventory costs before and after coordination. Otherwise, partial coordination also raises insiders' profitability only through the option contract. Purchasing extra emissions with green investment is the best in most cases. The contract-maker should deliberate its contract settings including the option and wholesale prices, as well as warehousing, to develop Pareto-efficient coordination. Sustainability comes at a cost, but coordination raises profitability and emission mitigation in a well-built ETS market. [ABSTRACT FROM AUTHOR]

Traffic volume is increasing dramatically due to the quick development of technologies like online gaming, on‐demand video streaming, and the Internet of Things (IoT). The telecommunications industry's large‐scale expansion is increasing its energy usage and carbon footprint. Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. In this regard, a convolutional neural network‐based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. This method can more effectively lower energy consumption and carbon emissions when compared to other hierarchical algorithms based on conventional deep neural networks and non‐hierarchical algorithms. The suggested method is tested in three typical complicated networks with different network parameters to show its suitability in different network scenarios. [ABSTRACT FROM AUTHOR]

In this study, we utilized Selective Laser Melting (SLM) technology to fabricate a magnesium alloy, and subsequently subject it to micro-arc oxidation treatment. We analyzed and compared the microstructure, elemental distribution, wetting angle, and corrosion resistance of the SLM magnesium alloy both before and after the micro-arc oxidation process. The findings indicate that the SLM magnesium alloy exhibits surface porosity defects ranging from 2% to 3.2%, which significantly influence the morphology and functionality of the resulting film layer formed during the micro-arc oxidation process. These defects manifest as pores on the surface, leading to an uneven distribution of micropores with varying sizes across the layer. The surface roughness of the 3D-printed magnesium alloy exhibits a high roughness value of 180 nanometers. The phosphorus (P) content is lower within the film layer compared to the surface, suggesting that the Mg3(PO4)2 phase predominantly resides on the surface, whereas the interior is primarily composed of MgO. The micro-arc oxidation process enhances the hydrophilicity and corrosion resistance of the SLM magnesium alloy, thereby potentially endowing it with bioactivity. Additionally, the increased surface roughness post-treatment promotes cell proliferation. However, certain inherent defects present in the SLM magnesium alloy samples negatively impact the improvement of their corrosion resistance. [ABSTRACT FROM AUTHOR]

This study focuses on solving the problem of metro first/last mile, studying the method for designing microcirculation bus routes and coordinating schedules considering the impact of shared bicycles. First, we propose a bilevel mixed-integer programming model for designing microcirculation bus routes and coordinating schedules considering the impact of shared bicycles. The upper-level model minimizes the weighted sum of the travel time cost of passengers and the operating cost of public transport enterprises, and the lower-level model maximizes the number of passengers served by microcirculation bus routes. Then, an improved genetic algorithm is developed to solve the model, called the Monte Carlo adaptive genetic algorithm (M-GAI). Finally, the proposed model and algorithm are evaluated using the case study in the area near the Fubao metro station of Shenzhen Metro Line 3. Results show that if the impact of shared bicycles is not considered, the passenger demand will be greater than the actual value, and the operating cost of public transport enterprises will be increased by 36%. Compared with GAI, the average number of iterations of M-GAI is reduced by 31%, and the objective function value is decreased by 4%. In addition, when the number of routes increases, the average waiting time of passengers is shortened, the average attendance rate of microcirculation buses increases, and the average empty distance of each vehicle is shortened. However, the operating cost of public transport enterprises will increase with the number of routes. Finally, when weight factors α and β are 0.6 and 0.4, respectively, and the sum of the travel time cost of passengers and the operating cost of public transport enterprises reach optimal. [ABSTRACT FROM AUTHOR]

Automated vehicles equipped with adaptive cruise control systems (ACC-AVs) are prevalent and the ensuing issue is the feasibility of ACC-AVs' operation on the roads. This study investigates the feasibility of road horizontal curve designs for ACC-AVs from a vehicle dynamics perspective. Following the scenario generation framework, we created and tested several scenarios featuring horizontal geometric elements and design speeds, conducting a safety evaluation based on the critical adhesion coefficient, lateral acceleration, lateral-load transfer rate, together with driving comfort indicators. Results indicate that ACC-AV can navigate on road curves designed with a common minimum radius (Rmin_com) effectively at speeds over 60 km/h , comparable to conventional vehicles. However, both Rmin_com and limited minimum radius (Rmin_lim) designs show limitations. Additionally, the feasible radius ranges for ACC-AV reveal the capability to safely handle sharper curves and maintain higher speeds, suggesting potential for adaptable road design in complex environments. Finally, minimum radius ranges were summarized for ACC-AV safe and comfortable operation on road curves, unveiling the potential risks and reminding designers in curve design controls for ACC-AVs. [ABSTRACT FROM AUTHOR]