Search Results (106)

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

As the world transitions to decarbonized fuels, understanding the impact of engine oil on emissions remains crucial. Lubricant-derived particulate emissions can influence air quality and regulatory compliance in future transport. Researchers have predominantly focused on transient driving cycles to replicate real-world conditions and capture the full range of particle size. This emphasis has led to a lack of comprehensive data on oil-related particulate emissions during steady-state operations, particularly for particles smaller than 23 nm. This paper addresses this gap as upcoming regulations, such as Euro 7, are expected to impose stricter limits by extending measurement thresholds down to 10 nm. The investigation was conducted on a 1.0 L gasoline direct injection engine, assessing total particulate number (TPN) emissions using three oil formulations: a baseline oil with mid-ash content and mid-volatility, a low-ash and low-volatility oil (LoLo), and a high-ash and high-volatility oil (HiHi). A DMS500, with and without a catalytic stripper, measured particle size distribution and TPN. Two digital filters were applied to obtain particle number (PN) metrics comparable to condensation particle counters: “F1-PN > 23” with d50 = 23 nm and “F3-PN > 10” with d50 = 10 nm. Sub-23 nm particles dominated emissions, with baseline oil generally producing higher PN emissions except at low loads. Using F1-PN > 23, HiHi exhibited higher PN counts across moderate to high speeds, while F3-PN > 10 revealed lower PN emissions for HiHi at specific conditions, excluding 2250 rpm-fast idle. By a weighted arithmetic mean, HiHi’s emissions were 9.7% higher than LoLo with F1-PN > 23 and 3.6% higher with F3-PN > 10. Oil formulation did not influence nucleation mode diameter. A three-way ANOVA demonstrated that load and speed were the predominant factors affecting emissions over the entire testing map; albeit at specific operating conditions the effect of the oil is evident. This suggests that under steady-state conditions, carbon-based fuel still plays a key role in particle formation. Future work will investigate decarbonised fuels to further isolate the effect of oil on emissions. Full article

Mobile devices, such as smartphones and laptops, face energy consumption challenges due to battery limitations, with Wi-Fi being one of the major sources of energy consumption in these devices. The IEEE 802.11 standard addresses this issue with Power Saving Mode (PSM), which reduces power consumption but increases latency. To mitigate this latency, Adaptive-PSM (A-PSM) dynamically switches between PSM and Constantly Awake Mode (CAM); however, the associated Idle Listening (IL) process still results in high energy consumption. Various strategies have been proposed to optimize IL time; however, Medium Access Control (MAC)-level contention and network delays limit their effectiveness. To overcome these limitations, we propose MILD (Minimizing Idle Listening energy consumption via Down-clocking), a novel scheme that reduces energy consumption without compromising throughput. MILD introduces specialized preambles for Packet Arrival Detection (PAD) and Device Address Recognition (DAR), allowing the client to operate in a down-clocked state during IL and switch to full clocking only when necessary. Experimental results demonstrate that MILD reduces energy consumption by up to 23.6% while maintaining a minimal throughput loss of 12.5%, outperforming existing schemes. Full article

Diesel vehicles are recognized as significant mobile sources of particulate matter emissions. As a renewable and environmentally friendly alternative to conventional fossil diesel, biodiesel offers the benefit of reducing greenhouse gas emissions. However, existing research on biodiesel emissions primarily focuses on primary emissions, with a limited understanding of their impact on secondary organic aerosol (SOA) formation. In this study, a diesel engine test bench was employed under idle conditions using three commonly used biodiesel blends. Exhaust emissions were directly introduced into the HAP-SWFU chamber, a quartz glass smog chamber designed to characterize both primary emissions and SOA formation during the photochemical oxidation process. The black carbon and primary organic aerosol (POA) emission factors for the three biodiesel blends under idle conditions ranged from 0.31 to 0.58 g kg⁻¹ fuel and 0.99 to 1.06 g kg⁻¹ fuel, respectively. The particle size of exhaust particulates peaked between 20 and 30 nm, and nucleation-idle conditions were found to be the dominating mode. The SOA production factor was between 0.92 and 1.15 g kg⁻¹ fuel, and the SOA/POA ratio ranged from 1.35 to 2.37, with an average of 1.86. This study concludes that the POA emission factor for biodiesel under idle conditions is comparable to values reported in previous studies on pure diesel exhaust, with the maximum SOA production factor reduced by 38%. Full article

The northern Taiwan Strait, characterized by deep waters and high wind energy density, presents significant potential for developing floating offshore wind turbines (FOWTs). However, the region is prone to typhoons, with substantial variations in wind speed and direction during typhoon eye passages, posing challenges to FOWT safety and performance. This study investigates the serviceability of a 10 MW FOWT installed offshore of Hsinchu under typical wind and wave conditions during the eye of Typhoon SOULIK. Wind and wave data were sourced from the ERA5 reanalysis database. Simulations were conducted using OrcaFlex 11.4c, which enables fully coupled dynamic analysis of the entire FOWT system, including the mooring system, platform, tower, turbine, and nacelle, facilitating accurate predictions of system behavior in complex offshore environments. This study evaluated scenarios of maximum wind speed, significant wave height, wind–wave misalignment, and minimum wind speed during typhoon eye passage, considering both idle and power production modes in accordance with IEC TS 61400-3-2 requirements. The results indicate that platform yaw motion exceeds IEC limits during typhoon events, particularly in power production mode. This highlights the need for reducing platform motion. It is recommended to further develop control strategies or implement an active control system for the platform to ensure operational reliability. This research provides critical insights into FOWT design and operational challenges in typhoon-prone regions. Full article

Intelligent Guided Vehicles (IGVs) in U-shaped automated container terminals (ACTs) have longer travel paths than those in conventional vertical layout ACTs, and their interactions with double trolley quay cranes (DTQCs) and double cantilever rail cranes (DCRCs) are more frequent and complex, so the scheduling strategy of a traditional ACT cannot easily be applied to a U-shaped ACT. With the aim of minimizing the maximum task completion times within a U-shaped ACT, this study investigates the integrated scheduling problem of DTQCs, IGVs and DCRCs under the hybrid “loading and unloading” mode, expresses the problem as a Markovian decision-making process, and establishes a disjunctive graph model. A deep reinforcement learning algorithm based on a graph neural network combined with a proximal policy optimization algorithm is proposed. To verify the superiority of the proposed models and algorithms, instances of different scales were stochastically generated to compare the proposed method with several heuristic algorithms. This study also analyses the idle time of the equipment under two loading and unloading modes, and the results show that the hybrid mode can enhance the operational effectiveness. of the U-shaped ACT. Full article

The healthcare sector is undergoing a transformation with the integration of cutting-edge technologies such as machine learning (ML), the Internet-of-Things (IoT), and Cyber–Physical Systems (CPS). However, traditional ML systems often face challenges in real-time processing and resource efficiency, limiting their application in life-critical scenarios. This research explores the potential of edge ML, particularly TinyML with TensorFlow Lite, implemented on microcontroller-based AI sensors for real-time health monitoring. By leveraging model quantization, the system analyzes heart rate variability (HRV) data to deliver continuous and personalized insights into stress levels and sleep quality. Trained on SWELL and ISRUC datasets, the system is highly energy-efficient, consuming 33 mW in idle mode, 66 mW during data collection, and 99 mW during real-time inference, making it suitable for resource-constrained environments. Performance analysis reveals significant demographic variations: younger individuals (18–25) achieved 90% accuracy due to higher HRV and lower baseline stress, while middle-aged (26–50) and older adults (50+) demonstrated declining HRV, reducing accuracy to 82% for the latter. Gender differences were also observed, with males exhibiting greater stress response sensitivity and better accuracy (89%) compared to females. This study underscores the transformative potential of TinyML for real-time, energy-efficient health monitoring and emphasizes the need for demographic-specific optimizations to enhance system reliability and accessibility. Full article

Operational modal analysis (OMA) is essential for long-term health monitoring of offshore wind turbines (OWTs), helping identifying changes in structural dynamic characteristics. OMA has been applied under parked or idle states for OWTs, assuming a linear and time-invariant dynamic system subjected to white noise excitations. The impact of complex operating environmental conditions on structural modal identification therefore requires systematic investigation. This paper studies the applicability of OMA based on covariance-driven stochastic subspace identification (SSI-COV) under various non-white noise excitations, using a DTU 10 MW jacket OWT model as a basis for a case study. Then, a scaled (1:75) 10 MW jacket OWT model test is used for the verification. For pure wave conditions, it is found that accurate identification for the first and second FA/SS modes can be achieved with significant wave energy. Under pure wind excitations, the unsteady servo control behavior leads to significant identification errors. The combined wind and wave actions further complicate the picture, leading to more scattered identification errors. The SSI-COV based modal identification method is suggested to be reliably applied for wind speeds larger than the rated speed and with sufficient wave energy. In addition, this method is found to perform better with larger misalignment of wind and wave directions. This study provides valuable insights in relation to the engineering applications of in situ modal identification techniques under operating conditions in real OWT projects. Full article

Over 50% of an engine’s energy dissipates via the exhaust and cooling systems, leading to considerable energy loss. Effectively harnessing the waste heat generated by the engine is a critical avenue for enhancing energy efficiency. Traditional exhaust heat recovery systems are limited to real-time recovery of exhaust heat primarily for engine warm-up and fail to fully optimize exhaust heat utilization. This paper introduces a novel exhaust heat recovery system leveraging thermal battery technology, which utilizes phase change materials for both heat storage and reutilization. This innovation significantly minimizes the engine’s cold start duration and provides necessary heating for the cabin during start-up. Dynamic models and thermal management system models were constructed. Parameter optimization and calculations for essential components were conducted, and the fidelity of the simulation model was confirmed through experiments conducted under idle warm-up conditions. Four distinct operational modes for engine warm-up are proposed, and strategies for transitioning between these heating modes are established. A simulation analysis was performed across four varying operational scenarios: WLTC, NEDC, 40 km/h, and 80 km/h. The results indicated that the thermal battery-based exhaust heat recovery system notably reduces warm-up time and fuel consumption. In comparison to the cold start mode, the constant speed condition at 40 km/h showcased the most significant reduction in warm-up time, achieving an impressive 22.52% saving; the highest cumulative fuel consumption reduction was observed at a constant speed of 80 km/h, totaling 24.7%. This study offers theoretical foundations for further exploration of thermal management systems in new energy vehicles that incorporate heat storage and reutilization strategies utilizing thermal batteries. Full article