Speeding Research Articles

Mapping Noise from Motorised Transport in the Context of Infrastructure Management

Noise pollution presents significant challenges for urban infrastructure management, highlighting the need for practical assessment tools such as noise maps. These maps enable the visualization and geo-referencing of noise levels, identifying areas requiring immediate intervention and long-term strategic responses. Road sections with traffic exceeding 3 million vehicles per year were selected for measurement. The findings are presented in detail, revealing that the Long-term Day-Night Average Noise Level (Lden) exceeds acceptable limits, affecting approximately 1.899 km2 and impacting around 1200 residents within the exceedance zone. Similarly, the equivalent noise level (LAeq) surpasses acceptable thresholds over an area of 1.220 km2, affecting an additional 700 residents. Notably, there were no exceedances of the key noise impact indicators, including high annoyance (HA), high sleep disturbance (HSD), and ischemic heart disease (IHD). Changes in traffic organisation were implemented to address areas that exceed the applicable noise standards, including a ban on trucks and the introduction of local speed limits. The measures have successfully mitigated the noise problem in Grodzisk County (Poland). Further anti-noise initiatives are planned, including planting vegetation along the roadways.

Substitution load revisited: a high proportion of deaths can be selective.

Haldane's Dilemma refers to the concern that the need for many "selective deaths" to complete a substitution (i.e. selective sweep) creates a speed limit to adaptation. However, discussion of this concern has been marked by confusion, especially with respect to the term "substitution load". Here we distinguish different historical lines of reasoning, and identify one, focused on finite reproductive excess and the proportion of deaths that are "selective" (i.e. causally contribute to adaptive allele frequency changes), that has not yet been fully addressed. We develop this into a more general theoretical model that can apply to populations with any life history, even those for which a generation or even an individual are not well defined. The actual speed of adaptive evolution is coupled to the proportion of deaths that are selective. The degree to which reproductive excess enables a high proportion of selective deaths depends on the details of when selection takes place relative to density regulation, and there is therefore no general expression for a speed limit. To make these concepts concrete, we estimate both reproductive excess, and the proportion of deaths that are selective, from a dataset measuring survival of 517 different genotypes of Arabidopsis thaliana grown in eight different environmental conditions. In this dataset, a much higher proportion of deaths contribute to adaptation, in all environmental conditions, than the 10% cap that was anticipated as substantially restricting adaptation during historical discussions of speed limits.

Speed limits and thermodynamic uncertainty relations for quantum systems with the non-Hermitian Hamiltonian

Speed limits and thermodynamic uncertainty relations for quantum systems with the non-Hermitian Hamiltonian

Modeling of Tank Vehicle Rollover Risk Assessment on Curved–Slope Combination Sections for Sustainable Transportation Safety

Tank vehicles are highly prone to rollover accidents, especially on curved–slope combination sections, which can cause hazardous chemical spills, endangering the environment, public safety, and human health. Therefore, it is crucial to conduct research aimed at reducing the risk of such incidents. Method: The rollover risk of tank vehicles under various loading conditions while traveling on curved–slope combination sections was investigated using driver–vehicle–road dynamics simulation. A multiple linear regression model was then developed to further quantify the impact of key factors on the rollover risk. Results: The results revealed that the road curve radius, vehicle operating speed, and liquid cargo fill level have the greatest impact on a tank vehicle’s rollover risk, and higher fill levels, higher speeds, and steeper downhill slopes all amplify the impact of curve radius on the rollover risk. In some cases, adhering to the road’s speed limit alone was insufficient to ensure the safe passage of the tank vehicle through curves. Conclusions: This study introduced, for the first time, a rollover risk assessment model for tank vehicles operating on curved–slope combination sections. The findings reveal effective methods to improve the transportation safety of tank vehicles. Practical Applications: The findings of this study can assist transportation agencies in selecting routes with lower rollover risks for tank vehicles with different configurations, as well as guide the development of loading standards and curve speed limits. This will effectively reduce rollover accidents of tank vehicles and support sustainable, safer transportation practices.

Patial-frequency aware zero-centric residual unfolding network for MRI reconstruction.

Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality. Compressed Sensing (CS) theory has enabled the acquisition of partial k-space data, which can then be effectively reconstructed to recover the original image using advanced reconstruction algorithms. Recently, deep learning has been widely applied to MRI reconstruction, aiming to reduce the artifacts in the image domain caused by undersampling in k-space and enhance image quality. As deep learning continues to evolve, the undersampling factors in k-space have gradually increased in recent years. However, these layers are limited in compensating for reconstruction errors in the unsampled areas, impeding further performance improvements. To address this, we propose a learnable spatial-frequency difference-aware module that complements the learnable data consistency layer, mapping k-space domain differences to the spatial image domain for perceptual compensation. Additionally, inspired by wavelet decomposition, we introduce explicit priors by decomposing images into mean and residual components, enforcing a refined zero-mean constraint on the residuals while maintaining computational efficiency. Comparative experiments on the FastMRI and Calgary-Campinas datasets demonstrate that our method achieves superior reconstruction performance against seven state-of-the-art techniques. Ablation studies further confirm the efficacy of our model's architecture, establishing a new pathway for enhanced MRI reconstruction.

Enhancing High-Speed Railway Timetable Resilience: A Two-Level Spatiotemporal Network Model Focused on Disturbance Absorption

Abstract Enhancing the resilience of train timetables can effectively improve their resistance and recovery capabilities against operational disturbances, thereby ensuring the stable operation of the railway system and the quality of passenger transportation services. This paper defined timetable resilience as three parts: disturbance absorptive capacity, resistance capacity, and post-disturbance recovery capacity. These capacities were quantitatively evaluated using the buffer time of trains, the number of train delays at departures and arrivals, and the duration of delay state. The evaluation metrics of the three resilience capacities and the total travel time of trains were used as the objective to establish a spatiotemporal network model. Utilizing actual operational data from the Beijing–Shanghai high-speed railway in China, the model was validated through a case study. Sensitivity analysis of the model's key parameters was conducted under two experimental scenarios: routine operational disturbances and speed restrictions on specific sections. Results showed that our model can effectively reduce the delay time and the number of delays in both the routine operational disturbance scenario and the 300 km/h speed restriction scenario with a frequent disturbance value of 2 min. Moreover, the model's performance with 3-min frequent disturbance outperformed that with 2-min frequent disturbance in speed restriction at 250 km/h and 200 km/h, yielding a 30% improvement.

Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging

Background: The global burden of respiratory diseases such as influenza, tuberculosis, and viral pneumonia necessitates rapid, accurate diagnostic tools to improve healthcare responses. Current methods, including RT-PCR and chest radiography, face limitations in accuracy, speed, accessibility, and cost-effectiveness, especially in resource-constrained settings, often delaying treatment and increasing transmission. Methods: This study introduces an Enhanced Multi-Model Deep Learning (EMDL) approach to address these challenges. EMDL integrates an ensemble of five pre-trained deep learning models (VGG-16, VGG-19, ResNet, AlexNet, and GoogleNet) with advanced image preprocessing (histogram equalization and contrast enhancement) and a novel multi-stage feature selection and optimization pipeline (PCA, SelectKBest, Binary Particle Swarm Optimization (BPSO), and Binary Grey Wolf Optimization (BGWO)). Results: Evaluated on two independent chest X-ray datasets, EMDL achieved high accuracy in the multiclass classification of influenza, pneumonia, and tuberculosis. The combined image enhancement and feature optimization strategies significantly improved diagnostic precision and model robustness. Conclusions: The EMDL framework provides a scalable and efficient solution for accurate and accessible pulmonary disease diagnosis, potentially improving treatment efficacy and patient outcomes, particularly in resource-limited settings.

Novel application of sinh cosh optimizer for robust controller design in hybrid photovoltaic-thermal power systems

Load frequency control (LFC) is critical for maintaining stability in interconnected power systems, addressing frequency deviations and tie-line power fluctuations due to system disturbances. Existing methods often face challenges, including limited robustness, poor adaptability to dynamic conditions, and early convergence in optimization. This paper introduces a novel application of the sinh cosh optimizer (SCHO) to design proportional–integral (PI) controllers for a hybrid photovoltaic (PV) and thermal generator-based two-area power system. The SCHO algorithm’s balanced exploration and exploitation mechanisms enable effective tuning of PI controllers, overcoming challenges such as local minima entrapment and limited convergence speeds observed in conventional metaheuristics. Comprehensive simulations validate the proposed approach, demonstrating superior performance across various metrics. The SCHO-based PI controller achieves faster settling times (e.g., 1.6231 s and 2.4615 s for frequency deviations in Area 1 and Area 2, respectively) and enhanced robustness under parameter variations and solar radiation fluctuations. Additionally, comparisons with the controllers based on the salp swarm algorithm, whale optimization algorithm, and firefly algorithm confirm its significant advantages, including a 25–50% improvement in integral error indices (IAE, ITAE, ISE, ITSE). These results highlight the SCHO-based PI controller’s effectiveness and reliability in modern power systems with hybrid and renewable energy sources.

SUV-pedestrian crash severity modelling considering unobserved heterogeneity in means and variances

SUV-type vehicles (light trucks, minivans, vans, SUVs) pose significant risks to pedestrians due to their size and weight. This study analyzed pedestrian crashes involving SUVs using Louisiana traffic crash data (2017–2021) with a random parameter ordered probit model to account for unobserved heterogeneity. Factors linked to higher pedestrian injury severity include winter conditions, divided roads, midblock crossings, business/residential areas, and drivers aged 25–65. Lower injury severity is associated with state highways, city streets, low-speed roads (<25 mph), undivided two-way roads, and intersections. Stability of parameters was observed over time, particularly for crashes involving single SUVs and pedestrians in normal conditions. Variations in variables like "white dashed line" and "proceeding straight ahead" highlight the influence of daylight and speed limits. At the policy level, integrating these findings into traffic management and urban development practices can support a more effective approach to reducing pedestrian injuries in SUV-related crashes.

A coordinated control framework of freeway continuous merging areas considering traffic risks and energy consumption.

Freeway continuous merging areas in a short distance exist continuous multiple ramps. In these areas, traffic flow and vehicle interactions are more complex, and traffic crashes and congestion are more frequent, which has been a major concern influencing traffic operation of freeways. Active traffic management (ATM) measures can improve traffic efficiency and reduce traffic risks in merging areas. Previous studies have focused on variable speed limit (VSL) control or ramp metering (RM) to address traffic problems in merging areas, whereas the problem of comprehensively ameliorating for traffic risks on mainlines and ramps by coordinating VSL and RM control strategies has rarely been explored. This study introduces a Bi-level Programming Model capable of coordinating controls of traffic risks (e.g., Crash Risk and Congestion Risk) in freeway continuous merging areas. The upper-level model aims to minimize the crash risk, the congestion risk, and vehicle energy consumption by VSL control. While the lower-level model focuses on the ramp control by minimizing the congestion risk and energy consumption of the ramp. Then an extended Cell Transmission Model (CTM) (it is based on VSL and RM control) is utilized to simulate the traffic flow of merging areas, based on which a traffic risk evaluation model and a Bi-level coordinated control model for the continuous merging areas are developed. The results demonstrate the constructed method outperforms other control strategies for improving the safety and efficiency of freeways. Specifically, the proposed control framework in the continuous merging areas of freeways reduces the average crash risk (ACR), average mainline congestion risk (AMCI), and average energy consumption (AEC) by 14.10%, 19.52%, and 8.86%, respectively. The research results could be potentially applied to active and coordinated traffic management of freeways.

State Compensation Model in Adaptive Event-Triggered Predictive Control: A Novel Approach to Mitigating Moving Bottlenecks

Moving bottlenecks, characterized by their high frequency and unpredictability, pose significant challenges to timely response and management, often resulting in road congestion and increased risk of traffic accidents. To address these issues, this paper proposes an adaptive event-triggered variable speed limit (AET-VSL) method based on a state compensation model, which emphasizes the concept of symmetry in the optimization of multi-segment speed limits. This symmetry approach facilitates a balanced and efficient control strategy that adjusts speed limits in a way that harmonizes traffic flow across multiple road segments, reducing congestion and improving overall traffic stability. The state compensation model builds on the classical METANET traffic flow model, incorporating coordination between road segments to reduce congestion while minimizing disruptions to traffic flow stability. By dynamically adjusting speed limits using real-time traffic data, the AET-VSL method addresses fluctuations in traffic conditions and ensures adaptive control to manage bottlenecks efficiently. A simulation framework was employed to evaluate the proposed strategy across varying traffic scenarios. Results demonstrate that AET-VSL outperforms traditional methods, providing consistent improvements in traffic performance. For instance, under low-traffic-flow conditions, AET-VSL reduced waiting time (WT) by 41.36%, potential collisions (PCs) by 51.92%, and fuel consumptionfuel consumption (FC) by 34.07%. This study highlights the novelty and effectiveness of AET-VSL, offering a scalable and reliable solution for dynamic traffic management and showcasing its potential to enhance traffic safety and efficiency.

Log End Face Feature Extraction and Matching Method Based on Swin Transformer V2

This study proposes a novel log end face feature extraction and matching method based on Swin Transformer V2, aiming to address limitations in accuracy and speed faced by traditional deep learning models, like InceptionResNetV2 and Vision Transformer. Accurate log identification is crucial for forestry and wood supply chain management, especially given the growing reliance on timber imports to meet industrial demands in construction, furniture manufacturing, and paper production. Our dataset comprises images of coniferous timber, specifically Scots pine (Pinus sylvestris L.), reflecting its significance as an essential imported resource in China’s timber industry. By leveraging Swin Transformer V2 as the backbone, our method enhances feature extraction and achieves a significant accuracy improvement from 84.0% to 97.7% under random rotation angles while reducing the average matching time per log to 0.249 s. The model was evaluated under fixed and random rotation augmentations, and the results demonstrated Swin Transformer V2’s superior clustering ability, as confirmed by t-SNE visualization. Unlike InceptionResNetV2, the proposed model maintains high accuracy and efficiency even as the feature database size increases, making it suitable for large-scale applications. This approach provides a more accurate and efficient solution for log end-face recognition, supporting the development of high-throughput wood identification systems critical for forestry automation and the global timber trade.

Hidden domain boundary dynamics toward crystalline perfection

A central paradigm of nonequilibrium physics concerns the dynamics of heterogeneity and disorder, impacting processes ranging from the behavior of glasses to the emergent functionality of active matter. Understanding these complex mesoscopic systems requires probing the microscopic trajectories associated with irreversible processes, the role of fluctuations and entropy growth, and the timescales on which nonequilibrium responses are ultimately maintained. Approaches that illuminate these processes in model systems may enable a more general understanding of other heterogeneous nonequilibrium phenomena, and potentially define ultimate speed and energy cost limits for information processing technologies. Here, we apply ultrafast single-shot X-ray photon correlation spectroscopy to resolve the nonequilibrium, heterogeneous, and irreversible mesoscale dynamics during a light-induced phase transition in a (PbTiO3)16/(SrTiO3)16 superlattice. Such ferroelectric superlattice systems are a useful platform to study phase transitions and topological dynamics due to their high degree of tunability. This provides an approach for capturing the nucleation of the light-induced phase, the formation of transient mesoscale defects at the boundaries of the nuclei, and the eventual annihilation of these defects, even in systems with complex polarization topologies. We identify a nonequilibrium correlation response spanning >10 orders of magnitude in timescales, with multistep behavior similar to the plateaus observed in supercooled liquids and glasses. We further show how the observed time-dependent long-time correlations can be understood in terms of stochastic and non-Markovian dynamics of domain walls, encoded in waiting-time distributions with power-law tails. This work defines possibilities for probing the nonequilibrium and correlated dynamics of disordered and heterogeneous media.

Examining driving stability and traffic capacity: A simulation study on appropriate speed limits in expressway work zones.

In order to obtain proper speed limits in expressway work zones, CarSim and TruckSim software were used to determine the critical car and truck safe speeds and then VISSIM software was used to determine the traffic capacities and their corresponding speed limits under different upstream transition area lengths and road adhesion coefficients. The results show that critical car and truck safe speeds increase exponentially while traffic capacity and its corresponding speed limit increase logarithmically with rising road adhesion coefficient under a constant upstream transition area length, and critical car and truck safe speeds increase as a power function while traffic capacity and its corresponding speed limit increase exponentially with rising upstream transition area length under a constant road adhesion coefficient. Because Road Traffic Signs and Markings- Part 4: Work Zone (RTSM, GB 5768.4-2017) only gave the relationship between speed limit and upstream transition area length without considering road adhesion coefficient, the obtained determination method of optimal speed limit in expressway work zone in this paper has better reference value.

Enhancement of Electrical Parameter Extraction from Solar Cells Using a Hybrid Genetic Algorithm with the Levenberg-Marquardt Method

Accurate modeling and simulation of solar photovoltaic (PV) systems are critical for optimizing their performance and efficiency. This requires precise determination of electrical parameters of solar cells, such as photocurrent (Iph), saturation current (I0), series resistance (Rs), shunt resistance (Rsh), and ideality factor (n). Traditional numerical methods for parameter extraction often face limitations in complexity, speed, and assumption dependencies. To address these issues, this study proposes a hybrid method that combines a genetic algorithm with the Levenberg-Marquardt algorithm (GALM) for solar cell parameter extraction. The genetic algorithm provides a robust initial estimate of the parameters, which is then refined by the Levenberg-Marquardt algorithm to achieve high accuracy. The performance of the proposed GALM method is validated using experimental data from a 57-mm silicon solar cell from R.T.C. France. Results indicate that the GALM method achieves one of the lowest RMSE values compared to other optimization techniques, demonstrating its effectiveness in accurately extracting solar cell parameters and closely matching the experimental I-V data. This contributes significantly to optimizing the performance and efficiency of PV systems.

Ultrafast laser frequency tuning based on equivalent optical phase sampling and accumulation

Ultrafast laser frequency tuning is a crucial function in numerous applications, including light detection and ranging (LiDAR), optical coherence tomography (OCT), and spectroscopy. In general, laser frequency tuning is realized via the motion of mechanical structures, frequency-swept optical filters, or the Pockels effect in the laser cavity. Nevertheless, the maximum frequency tuning rate and sweep rate are smaller than 107 THz/s and 1 GHz, respectively, due to the inherent speed limitation of the existing tuning mechanisms. Here, we propose and demonstrate a brand-new concept for ultrafast laser frequency tuning, which is realized based on equivalent optical phase sampling and accumulation in the laser cavity. By inserting an electro-optic phase modulator (PM) into the laser cavity and properly setting the period of the driving signal applied to the PM to make its integer multiple slightly deviate from the round trip time of the laser cavity, a linear mapping from the voltage of the driving signal to the output laser frequency is realized via equivalent optical phase sampling and accumulation. This remarkable feature provides a rapid way to manipulate the laser frequency, breaking the frequency tuning speed limitation of the existing approaches. In the experiment, a record-breaking frequency tuning rate of 1.522 × 109 THz/s and a sweep rate of 2223.97 MHz are simultaneously realized. Moreover, the center wavelength and the frequency tuning range can be easily reconfigured. This work paves the way for ultrafast laser frequency tuning in a customized wavelength range.

Hospital-based surveillance for Road Traffic Injuries (RTIs) in a tertiary care hospital

Road traffic accidents (RTAs) lead to significant mortality, morbidity and economic loss in India. The present study aims to characterize epidemiology of Road traffic injuries (RTIs) with information from multiple data sources and to describe factors associated with RTIs. Patients admitted with RTIs in a tertiary care hospital were included in this cross-sectional observational study. Hospital records, case sheets of RTA victims and interview of the participants with a questionnaire were used for collecting data. Majority (29%) of road traffic accidents occurred between 4pm and 8 pm, with 60% self-fall or skid. Around 23.8% consumed alcohol, 63.3% exceeded speed limit, 85% rescued within 30 minutes, 57% transported to hospital by government ambulance, 30% reached tertiary care directly, 6.5% had reduced GCS score. Young adult male were the major victims of RTAs with speed violation as a common risk behavior. The findings suggest a need for implementation of speed violation detection systems to bring down the occurrence of RTAs.

AI-Enhanced Automatic Traffic Violation Detection and Challan Issuance using GNSS and OBU Sensors in Self-Driving Cars.

The swift progress in self-driving vehicle technology requires strong systems to guarantee adherence to traffic regulations. This study introduces an AI-powered system for automatic detection of traffic violations and issuance of fines in self-driving vehicles utilizing Global Navigation Satellite System (GNSS) and On-Board Unit (OBU) sensors. The system combines AI algorithms with GNSS for accurate geolocation tracking and OBU sensors for monitoring vehicle status. Infractions like running signals, exceeding speed limits, and improper use of lanes are identified instantly. A main server analyzes violation data and automatically generates electronic fines for offenders. The suggested system improves adherence to traffic regulations, minimizes human involvement, and fosters safer roadways by utilizing AI's predictive and analytical features. This novel strategy tackles shortcomings in current traffic enforcement techniques, rendering it a scalable answer for contemporary smart cities. Keywords - Autonomous Vehicles, Traffic Violation Detection, GNSS, OBU Sensors, AI, Smart Cities, Electronic Challan, Traffic Compliance

A hybrid optimization scheme for tuning fractional order PID controller parameters for a DC motor

This paper on a hybrid optimization scheme for tuning fractional order PID (FOPID) controller parameters for a DC motor explored the effectiveness of a hybrid GA-PSO approach in optimizing FOPID controller parameters. The DC motor is faced with the following challenges: brush wear and maintenance, limited speed control range, limited lifespan, size and weight, efficiency complexity of control systems, and spark generation. This problem arises from the poor tuning of the PID controller used in the systems. First, a DC motor was modelled along with an FOPID controller in a MATLAB environment. Secondly, a hybrid scheme (GA-PSO) was designed in MATLAB environment. Furthermore, the performance of the hybrid scheme (GA-PSO) was compared with GA and PSO used distinctly. The GA-PSO hybrid algorithm outperforms other algorithms in terms of rise time of 0.30, settling time of 2.90, overshoot of 5.29, peak to peak of 1.05, and peak values of 0.59. The results show improvements in key performance indicators, offering insights for improving DC motor control.

Investigating Factor Associations in Barrier Crashes through Cluster Correspondence Analysis

Roadside and median barriers have proven effective in preventing crashes; however, a significant number of crashes still occur that involve road barriers across United States. This study focuses on analyzing the factors related to barrier crashes across Texas. The dataset includes 63,475 crashes involving road barriers and covering six years of crash data (2017–2022). Using cluster correspondence analysis (CCA) data mining approach, the study identifies six different clusters of crashes. Each cluster identifies factors contributing to barrier-related crashes. Before implying the CCA, variable importance analysis is also performed to identify the significance of the variables. The analysis highlighted that dry road surface conditions and clear weather are highly associated with high-speed crashes. Driver distraction and absence of traffic control devices can attribute to the crashes on roads with lower speed limits. Moreover, along with other factors, adverse weather conditions are also found to be a contributing factor that can influence the crash frequency and type of crashes. The analysis also implies that non-complex crash type related to a single vehicle are highly correlated with barrier crashes. The study concludes by making several policy-making implications to assist the transportation planners in reducing the frequency and severity of barrier-related crashes.