Search Results (114)

Pulse and glide (PnG) has limited application in urban traffic flows, particularly in emerging mixed traffic flows comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), as well as at signalized intersections. In light of this, green wave coordination is applied to the urban network of multiple signalized intersections. Under perception asymmetries, HDVs lack environmental perception capabilities, while CAVs are equipped with perception sensors of varying performance. CAVs could activate the PnG mode and set its average speed based on signal phase and safety status, enabling assessment of fuel savings and safety. The findings reveal that (i) excluding idling fuel consumption, when the traffic volume is low and market penetration rate (MPR) of CAVs exceeds 70%, CAVs could significantly reduce regional average fuel consumption by up to 8.8%. (ii) Compared to HDVs, CAVs could achieve a fuel saving rate (FSR) ranging from 7.1% to 50%. In low-traffic-volume conditions, CAVs with greater detection ranges could swiftly activate the PnG mode to achieve fuel savings, while in higher-traffic-volume conditions, more precise sensing aids effectiveness. (iii) the PnG mode could ensure safety for CAVs and HDVs, with CAVs equipped with highly precise sensing exhibiting particularly robust safety performance. Full article

Recharging and battery swapping are of great significance for extending the driving range of autonomous vehicles (AVs). However, if an AV cannot recharge or swap batteries in a timely manner, the consequences are more serious than for a traditional human-driven vehicle, as there is a lack of human assistance in an AV. To address this challenge, this study proposes the joint routing optimization of AVs under recharging and battery-swapping modes. Firstly, a multi-objective model is defined for the joint routing optimization problem of AVs, which minimizes the total distance, idling time, and charging waiting time of AVs while meeting all user demands. The user demand is described as a directed arc consisting of a departure node and a destination at random locations and times, and the AVs need to plan their routes to sequentially access all user demand arcs and recharge or swap batteries in a timely manner. Secondly, an improved artificial plant community (APC) algorithm is proposed to solve the NP-hard problem, including a recharging scheme and a hybrid scheme comprising recharging and swapping. In the seeding operation, random seeds are generated to enhance global search capabilities, and optimal solution learning is added in the fruiting operation to improve local search capabilities. In the growing operation, population optimization is strengthened to improve convergence performance. Thirdly, a benchmark test set was developed based on a real scenario in Wuhan, China. Compared to some baseline algorithms, the results show that the proposed APC algorithm exhibits better performance in solving the NP-hard problem. Full article

Nitrated aromatic compounds (NACs) are critical toxic components of PM_2.5, and accurately identifying their sources is vital for effective urban air quality improvement. However, the lack of real-world emission data for construction machinery has introduced significant uncertainties into NACs source apportionment and emission inventories, particularly in urban areas where such machinery is widely used. Here, we characterized NACs, including nitrated polycyclic aromatic hydrocarbons (NPAHs) and nitrophenols (NPs), emissions from forklifts and excavators at construction sites in China. It is found that construction machinery emitted significantly higher NACs levels compared to on-road vehicles, with average NPAHs and NPs emission factors of 340.1 and 562.0 μg kg⁻¹ fuel for forklifts and 459.0 and 1381.1 μg kg⁻¹ fuel for excavators. Emissions during working modes were 1.1–1.6 times higher than during idling for forklifts and excavators. A key finding was the dominance of 5-nitroacenaphthene and 1-nitropyrene, which contrasts sharply with the observed emissions in other sources. We believed that combining the 5-nitroacenaphthene and 1-nitropyrene during the source apportionment using the receptor model would make it possible to separate the contributions of construction machinery. Notably, the light absorption of 45 NACs from both forklifts and excavators collectively accounted for approximately 30% of the total methanol-soluble brown carbon—a significantly higher contribution ratio compared to other emission sources. Furthermore, while construction machinery accounted for less than 5% of urban vehicle numbers, its toxic equivalent quotients can reach 4 to 6 times that of on-road vehicles with the nonnegligible potential toxicity. These results highlight the urgent need for stricter emission controls on construction machinery to reduce NACs-related adverse environmental effects in urban environments. Our findings provide valuable insights for constructing NACs emission inventories and refining NACs source apportionment methods in urban atmospheric studies. Full article

Electric vehicles (EVs) are experiencing rapid global adoption driven by environmental concerns and fuel security. This article presents a new dual-source converter based on a hybrid modular multilevel configuration (DCHMMC) designed for optimal cell utilisation in EV battery systems. Contrary to conventional converters that can either charge or discharge the cells using a single source, thereby leaving several cells/modules (Ms) idle during each time step, the proposed converter enables the integration of two sources that can utilise the cells simultaneously. This dual source feature minimises idle cells/Ms, enhances energy efficiency, and supports flexible bidirectional power flow. The proposed converter operates in three distinct modes. The first involves dual-source charging for fast charging and improved vehicle availability. The second involves one source charging while the other discharges for dynamic operation. Finally, the last involves dual-source discharging for maximum power delivery and support vehicle-to-grid (V2G) operation. The simulation results demonstrated smooth multilevel sinusoidal output voltages (Vout_a and Vout_b), each with a peak of 350 V, generated simultaneously using 132 cells (six cells per M, 22 Ms). The total harmonic distortion (THD) values for Vout_a and Vout_b were 0.42% and 2.25%, respectively, confirming the high-quality performance. Furthermore, only 0–36 cells and 0–6 Ms were idle during operation, showing improved cell utilisation. Full article

Due to the diverse needs of users, such as the requirement for rapid charging in time-sensitive situations and the need to minimize battery power consumption to extend battery life when the device is idle, a wireless charging system that combines fast and slow charging capabilities is crucial for adapting to various usage scenarios. This paper proposes a multi-mode wireless charging system based on a reconfigurable transmitter, which can simultaneously charge different types of batteries with both fast and slow charging capabilities. By applying different control logic to the power devices in the reconfigurable inverter, the system can achieve four operating modes: two different constant current (CC) modes and two different constant voltage (CV) modes. Furthermore, the system can switch between these modes by configuring the MOSFETs operating states: two three-coil configurations are used for the two CC modes, while two two-coil configurations are used for the two CV modes. Therefore, the system exhibits high versatility. To verify the theoretical analysis of the proposed system, an experimental prototype with an output specification of 3 A/2.2 A/78 V/65 V is built. Full article

Equivalent energy consumption minimization methods of energy management systems have been implemented as a rule-based strategy to enhance electric propulsion system efficiency. This study compares the efficiencies of different systems by applying variable- and constant-speed generators with battery hybrid systems, measuring fuel consumption. In the same scenario, the variable-speed operation showed a notable improvement of 10.36% compared to the conventional system. However, in the verification of hybrid system efficiency, onshore charged energy cannot be considered a reduction in fuel consumption. Instead, when converting onshore energy usage into equivalent fuel consumption for comparative analysis, both hybrid constant- and variable-speed operation modes achieved efficiency enhancements ranging from 5.5% to 9.79% compared to the conventional, nonequivalent constant-speed operation mode. Conversely, the nonequivalent variable-speed operation mode demonstrated an efficiency that was 5.41% higher than that of the hybrid constant-speed operation mode. In contrast, the battery-integrated variable-speed operation mode indicated a system efficiency approximately equal to that of the nonequivalent variable-speed operation mode. For vessels with load profiles characterized by prolonged periods of idling or low-load operations, a battery-integrated hybrid system could be a practical solution. This study demonstrates the necessity of analyzing load profiles, even when aiming for the optimal operational set points of the generator engine. Full article

Emissions from civil aviation not only degrade the environmental quality around airports but also have the significant effects on climate change. According to the flight schedules, aircraft/engine combination information and revised emission factors from the International Civil Aviation Organization (ICAO) Aircraft Engine Emission Databank (EEDB) based on meteorological data, the emissions of climate forcers (CFs: BC, CH₄, CO₂, H₂O, and N₂O), conventional air pollutants (CAPs: CO, HC, NO_X, OC, PM_2.5, and SO₂), and hazardous heavy metals (HMs: As, Cu, Ni, Se, Cr, Cd, Hg, Pb, and Zn) from flights of civil aviation of eight airports in Shandong in 2018 and 2020 are estimated in this study. Moreover, the study quantifies the impact of COVID-19 on civil aviation emissions (CFs, CAPs, and HMs) in Shandong, revealing reductions of 47.45%, 48.03%, and 47.45% in 2020 compared to 2018 due to flight cuts. By 2020, total emissions reach 9075.44 kt (CFs), 35.57 kt (CAPs), and 0.51 t (HMs), with top contributors being Qingdao Liuting International Airport (ZSQD) (39.60–40.37%), Shandong Airlines (26.56–28.92%), and B738 aircraft (42.98–46.70%). As byproducts of incomplete fuel combustion, the shares of CO (52.40%) and HC (47.76%) emissions during taxi/ground idle mode are significant. In contrast, emissions during cruise phase are the dominant contributor of other species with a share of 74.67–95.61% of the associated total emissions. The findings highlight the disproportionate role of specific airlines, aircraft, and operational phases in regional aviation pollution. By bridging gaps in localized emission inventories and flight-phase analyses, this research supports targeted mitigation strategies, such as fleet modernization and ground operation optimization, to improve air quality in Shandong. The study highlights how sudden shifts in demand, such as those caused by pandemics, can significantly alter emission profiles, providing insights for sustainable aviation planning. Full article

Patient unpunctuality substantially complicates appointment scheduling in integrated telemedicine–traditional outpatient systems. The current research frequently ignores behavioral distinctions between telemedicine patients and outpatients, while neglecting to measure the intangible burden on physicians from service mode switches. To address these gaps, this study incorporates patient heterogeneity and introduces two novel cost metrics. Specifically, we implement penalties for service-mode switching and penalties for consecutive telemedicine sessions. We develop a Stochastic Mixed-Integer Programming (SMIP) model. This stochastic model is transformed into a deterministic Mixed-Integer Linear Programming (MILP) formulation via Sample Average Approximation (SAA). Linearization techniques enhance computational efficiency. In numerical experiments, the dual-penalty model yields balanced schedules with moderate patient mix, reducing physician overtime by 62.5% and service mode switches by 55% compared to baseline approaches. Sensitivity analysis confirms that narrowing outpatient unpunctuality ranges significantly reduces patient waiting and overtime, while raising telemedicine patient proportions bolsters system stability at the cost of increased physician idle time. These insights offer actionable guidance for healthcare institutions managing integrated online–offline services. Full article

With the development of a quantum computer in the near future, classical public-key cryptography will face the challenge of being vulnerable to quantum algorithms, such as Shor’s algorithm. As communication technology advances rapidly, a great deal of personal information is being transmitted over the Internet. Based on our observation that the Kyber algorithm exhibits a significant number of idle cycles during execution when implemented following the conventional software procedure, this paper proposes a high-throughput scheduling for Kyber by parallelizing the SHA-3 function, the sampling algorithm, and the NTT computations to improve hardware utilization and reduce latency. We also introduce the 8-stage pipelined SHA-3 architecture and multi-mode polynomial arithmetic module to increase area efficiency. By also optimizing the hardware architecture of the various computational modules used by Kyber, according to the implementation result, an aggregate throughput of 877.192 kOPS in Kyber KEM can be achieved on TSMC 40 nm. In addition, our design not only achieves the highest throughput among existing studies but also improves the area and power efficiencies. Full article

Pulse and Glide (PnG), as a fuel-saving technique, has primarily been applied to manual transmission vehicles. So, its effectiveness when integrated with a novel vehicle type like connected and automated vehicles (CAVs) remains largely unexplored. On the other hand, CAVs have evidently received less attention regarding energy conservation, and their prominent perception capabilities clearly exhibit individual variations. In light of this, this study investigates the impacts of PnG combined with CAVs on energy conservation and safety within the emerging mixed traffic flow composed of CAVs with varying sensing accuracies. The results indicate the following: (i) compared to the traditional driving modes, the PnG can achieve a maximum fuel-saving rate of 39.53% at Fuel Consumption with Idle (FCI), reducing conflicts by approximately 30% on average; (ii) CAVs, equipped with sensors boasting a greater detection range, markedly enhance safety during vehicle operation and contribute to a more uniform distribution of individual fuel consumption; (iii) PnG modes with moderate acceleration, such as 1–2 m/s², can achieve excellent fuel consumption while ensuring safety and may even slightly enhance the operational efficiency of the intersection. The findings could provide a theoretical reference for the transition of transportation systems toward sustainability. Full article

The component characteristics of an aeroengine below idle speed are fundamental for start-up process simulations. However, due to experimental limitations, these characteristics must be extrapolated from data above idle speed. Existing extrapolation methods often suffer from insufficient utilization of available data, reliance on specific prior conditions, and an inability to capture unique operating modes (e.g., the stirring mode and turbine mode of compressor). To address these limitations, this study proposes a novel curve-and-surface fitting-based extrapolation method. The key innovations include: (1) extrapolating sub-idle characteristics through constrained curve/surface fitting of limited above-idle data, preserving their continuous and smooth nature; (2) transforming discontinuous isentropic efficiency into a continuous specific enthalpy change coefficient (SECC), ensuring physically meaningful extrapolation across all operating modes; (3) applying constraints during fitting to guarantee reasonable and smooth extrapolation results. Validation on a micro-turbojet engine demonstrates that the proposed method requires only conventional performance parameters (corrected flow, pressure/expansion ratio, and isentropic efficiency) above idle speed, yet successfully supports ground-starting simulations under varying inlet conditions. The results confirm that the proposed method not only overcomes the limitations of existing approaches but also demonstrates broader applicability in practical aeroengine simulations. Full article

Additive manufacturing (AM) is revolutionizing industrial production by enabling the fabrication of complex, customized components with reduced material waste. However, the scheduling of AM machines presents significant challenges in terms of optimizing both time-related performance and energy consumption. This paper introduces a novel multi-objective mixed-integer linear programming (MILP) model for scheduling AM machines with the dual objectives of minimizing makespan and energy consumption. We address the single-machine environment with detailed mathematical formulation that accounts for machine-specific parameters such as power consumption rates during different operational states, including printing, setup, and idle modes. Additionally, we consider part-specific characteristics including height, area requirements, and volume, ensuring practical feasibility constraints are met. The proposed model is validated using a comprehensive set of test problems, with optimal solutions reported for small to medium-sized instances. For larger problem instances, where computational complexity prevents finding optimal solutions within reasonable time limits, we report the best solutions obtained under specified time constraints. Computational experiments demonstrate that our approach effectively balances the trade-off between makespan and energy consumption, providing valuable insights for production planning in AM facilities. The results indicate potential energy savings of up to 18% compared to makespan-only optimization approaches, with minimal impact on overall completion times. Full article

Transportation planning is a complex process that aims to achieve the maximum level of effectiveness in terms of costs, usage of transport resources, reliability of deliveries, and minimizing the negative impact on the environment. Most traditional models focus on cost minimization at the expense of risk, road dynamics, and emissions constraints. In contrast, the current paper presents a mixed-integer linear programming (MILP) model for scheduling fleets, selecting transportation modes in multiple modes of transportation, and meeting emissions regulation requirements according to dynamic transportation requirements. Risk-aware routing and taking the factor of congestion and CO₂ emission limits proposed by the government into consideration, this model can offer a more efficient and flexible optimization strategy. From the case study, we observe the significant result that the proposed model achieves, a 23% reduction in transport costs, a 25% improvement in fleet use, a 33.3% decrease in the delivery delay, and a 24.6% decrease in CO₂ emissions. The model dynamically delivers shipments utilizing both road and rail transportation and improves mode choice by minimizing idle vehicle time. This is confirmed through sensitivity analysis which addresses factors such as traffic congestion, changing fuel prices, and changing environmental standards. Full article

Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals. Full article

Taxi time plays a critical role in airport capacity, aircraft fuel consumption, and emissions. It is defined as the time from touchdown to the gate and from the gate to liftoff. The International Civil Aviation Organization (ICAO) established a standard taxi/ground idle time-in-mode (TIM) of 26 min in the landing and take-off (LTO) cycle for modeling turbine engine aircraft emissions. However, actual taxi times vary significantly across airports. While a simplified standard streamlines emissions modeling, the 26 min assumption may not accurately reflect real-world conditions. While using airport-specific taxi times may not always be practical, hub classifications of U.S. commercial airports may affect taxi time and serve as a compromise between airport-specific taxi times and a simplified standard. Therefore, this study statistically analyzed Federal Aviation Administration (FAA) data from 71 U.S. commercial hub airports to compare reported taxi times with the ICAO’s standard and assess the influence of airport hub classifications. The exploratory findings indicate that the 26 min ICAO taxi/idle TIM does not represent reported taxi times at 70 of the 71 sampled airports. Moreover, total taxi time varied by hub classification: small-hub airports had a mean taxi time of 19.82 min (median: 18 min), medium-hub airports had a mean taxi time of 19.72 min (median: 18.25 min), and large hubs had a mean taxi time of 26.98 min (median: 25.08 min). When hub classifications were ignored, the overall mean taxi time was 23.78 min (median: 22 min), indicating a statistically significant difference between the ICAO’s standard 26 min assumption and the observed taxi times at most airports. Full article