Speeding Research Articles

Novel Hybrid Optimization Technique for Solar Photovoltaic Output Prediction Using Improved Hippopotamus Algorithm

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems.

Modular Assembled Localized Hybridization Chain Reaction for In Situ mRNA Amplified Imaging.

As a nonenzymatic DNA signal amplification technique, localized hybridization chain reaction (LHCR) was designed to improve the limitations in response speed and low sensitivity of conventional free diffusional HCR (hybridization chain reaction). However, it is still confronted with the challenges of complicated DNA scaffolds with low loading capacity and a time-consuming process of diffusion. Herein, we introduced modular assembly of a DNA minimal scaffold for coassembly of DNA hairpins for amplified fluorescence imaging of mRNA in situ. DNA hairpins were spatially bound to two Y-shaped modules to form H-shaped DNA modules, and then multiple H-shaped DNA modules can further assemble into an H-module-based hairpin scaffold (HHS). Benefiting from highly spatial localization and high loading capacity, the HHS system showed higher sensitivity and faster speed. It has also been proven to work perfectly in vitro and in vivo, which could provide a promising bioanalysis system for low abundance biomolecule detection.

Observations and Simulations of the Winds at a Bridge in Hong Kong during Two Tropical Cyclone Events in 2023

The impact of tropical cyclones on the operation of bridges and railways are mostly dependent on the forecast wind speed trend (upward, downward or staying steady) at present in Hong Kong. There are requests to forecast the exceedance of wind speed thresholds for such operations with a lead time of many hours ahead. This study considers the technical feasibility of forecasting the wind speed along a recently built bridge in Hong Kong with a coupled mesoscale numerical weather prediction (NWP)–computational fluid dynamics (CFD) model. This bridge features numerous anemometers where the coupled model can be verified. It is found that these two tropical cyclone cases are very challenging, especially in representing the wind structure of the cyclone because of its relatively small circulation. As such, the timing of the maximum wind could be 2 to 4 h earlier than the actual observation. The maximum wind speed from CFD modeling could be higher than that from the NWP model alone by 5 to 10 m/s, which shows that CFD modeling could add value in the forecasting of wind speed exceedance, although the maximum simulated value is still lower than the actual observation by as much as 10 m/s. As a result, while the general wind speed trend may be forecast, the exceedance of definite values of wind speed limits is still practically rather challenging, given the present two cases of tropical cyclones.

The tales of two cities: use of evidence for introducing 20 miles per hour speed limits in Edinburgh and Belfast (United Kingdom)

BackgroundIn 2016, large-scale 20 miles per hour speed limits were introduced in the United Kingdom cities of Edinburgh and Belfast. This paper investigates the role that scientific evidence played in the policy decisions to implement lower speed limits in the two cities.MethodsUsing a qualitative case study design, we undertook content analysis of a range of documents to explore and describe the evolution of the two schemes and the ways in which evidence informed decision-making. In total, we identified 16 documents for Edinburgh, published between 2006 and 2016, and 19 documents for Belfast, published between 2002 and 2016.FindingsIn both cities, evidence on speed, collisions and casualties was important for initiating discussions on large-scale 20 mph policies. However, the narrative shifted over time to the idea that 20 mph would contribute to a wider range of aspirations, none of which were firmly grounded in evidence, but may have helped to neutralize opposing discourses.Discussion and conclusionsThe relationship between evidence and decision-making in Edinburgh and Belfast was neither simple nor linear. Widening of the narrative appears to have helped to frame the idea in such a way that it had broad acceptability, without which there would have been no implementation, and probably a lot more push back from vested interests and communities than there was.

Dynamic response mechanisms of thin UHMWPE under high-speed impact

This study systematically explores the dynamic response mechanisms and energy dissipation mechanisms of ultra-high-molecular-weight polyethylene (UHMWPE) fiber laminated plates with thicknesses of 1.1 mm, 2.8 mm, and 5.4 mm under high-speed steel ball impacts through experimental, theoretical analysis, and dimensional analysis. By analyzing the damage mechanisms from experimental results, the main modes of energy absorption were identified, and a relatively accurate predictive model for ballistic limit speed was established. The results show that under high-speed steel ball impacts, the primary failure modes of UHMWPE fiber laminated plates are localized permanent bulging plastic deformation and perforation failure caused by compressive shear. The ballistic limit speeds for UHMWPE with thicknesses of 1.1 mm, 2.8 mm, and 5.4 mm are respectively 233.5 m/s, 415.7 m/s, and 602.9 m/s. The theoretical calculations of the energy model align well with the experimental results, indicating that as the speed increases, the proportion of tensile energy absorption gradually decreases, consistent with the observed phenomena. Near the ballistic limit, there are significant turning points in the level of energy absorption and the height of the rear bulge. The ballistic limit increases linearly with target thickness, and the unit area density energy absorption also increases continuously. Finally, this study established a dimensionless ballistic limit model considering the mechanical properties of fibers and the matrix, which through regression analysis, can accurately describe the ballistic limits of various strengths and types of fiber laminated plates, suitable for predicting the ballistic performance of various composite materials.

Design and implementation of a driving safety assistant system based on driver behavior

<p>These days, road accidents are one of Morocco's biggest problems. Fatigue, drowsiness, and driver behavior are among the primary causes.This research aims to develop an embedded system by image processing and computer vision to ensure driving safety by monitoring driver behavior and assist drivers to awaken from micro-sleep or fatigue due to long driving hours and various other reasons. Indeed, the driver inattention, drowsiness or driver fatigue can be detected. The suggested method is designed to support drivers if needed, based on the vehicle velocity. Once the driver crosses a certain speed limit, the program starts face detection and analyzing this data to determine whether the driver is tired, sleepy, or inattentive. This activates different alarm depending on the criticality level. It can sound a voice alert to help him wake up and drive more cautiously. The system is based on AI algorithms in image processing based on OpenCV libraries and the Python language to capture the movements of the driver's eyes and head when starting the automobile. Every algorithm is run on a Raspberry-Pi 4 card, and numerous experimentation series have demonstrated overall credible performance with success accuracy of over 93% in EAR and MAR calculations.</p>

Adaptive Cruise Control System: A Literature Survey

Adaptive cruise control (ACC) assists automobiles in preserving a safe following distance and adhering to speed limits. This advanced driver-assistance system (ADAS) modifies the car's speed to keep a safe gap from oncoming traffic. All vehicle types include combustion engines, pure electric vehicles, hybrid electric vehicles, and methods of operation; controllers are designed to react to cruise control signals and provide an efficient route profile according to the surrounding environment and instantaneous vehicle performance characteristics. ACC uses a perception system to measure the forward vehicle's current distance, speed, and acceleration relative to the host vehicle. Some of these systems use lasers, radar, cameras, or a combination of these sensors to determine the distance and speed of the leading vehicle. Other systems even use wireless communication to collect data from surrounding vehicles. ACC can help reduce stress on long drives, increase road safety, prevent accidents, and enhance traffic flow energy efficiency. This paper aims to introduce a comprehensive study of the research on ACC and mention different controlling techniques used to deal with the problem. Furthermore, a discussion of each method with its cons and pros is mentioned too. First, an introduction to the ACC system and control approaches with a brief discussion of their main principle is presented. Next, various application cases of ACC are presented. These applications include lateral dynamics, wireless technology, energy vehicles, navigation data, and practical experimental tests. At last, future guidance and challenges are discussed.

Traffic collisions and micromobility: A comparison between personal mobility devices and bicycles based on police reports

Introduction: The recent increase in the use of bicycles and personal mobility devices (PMDs), including mostly E-scooters, is associated with a rapid rise in injuries. Understanding the main crash scenarios leading to these injuries is essential to evaluate and improve preventive and protective measures, especially for PMDs, which are often equated with bicycles. The objective of this study is to identify and compare the most common two-party collision scenarios for bicycles and PMDs, and to identify factors affecting injury severity. Method: Crashes involving at least one PMD or one bicycle and another road user were analyzed from the 2019–2022 French police-reported road crashes database. We investigated the rider, the other vehicle, the road, and the crash scenarios characteristics (pre-crash maneuvers, impact zone on vehicles) and their joint effect on injury severity (hospitalization or fatality: yes/no). Results: We included 16,302 bicycle crashes and 4,118 PMD crashes in the analysis. Most of these collisions (75%) were against a car. The most frequent and the most severe collision scenario was the side-on-head for both bicycles (51%) and PMDs (58%); 67% of both bicycles and PMDs were going straight before the collision. Main factors associated with increased injury severity included colliding with a greater size vehicle, age above 50, and riding on roads with a higher speed limit. Bicycles remained at a higher risk of severe injury than PMDs after accounting for adjustment factors. Conclusions: Although collision scenarios appear similar for bicycles and PMDs, differences in other crash characteristics and injury severity suggest that these two modes of transportation should not be equated in crash investigations. Practical implications: These findings emphasize the need to primarily investigate side-on-head collisions with a moving car for both PMDs and bicycles in order to develop, evaluate, and improve protective devices to reduce the risk of injuries.

Optical analog computing for salient object detection in complex scenes via dielectric metasurface

Optical analog computing overcomes the throughput and computational speed limitations of traditional digital computing, facilitating salient object detection on platforms that rapidly process massive image data, such as optical remote sensing. Further device miniaturization and integration can be achieved by replacing filters in optical analog computing with metasurface. Based on the combination of optical analog computing and dielectric metasurface, we proposed an optical analog cross-correlation operation operating in the mid-infrared wavelength band to achieve the real-time and compact salient object detection. Even in extremely complex scenes, this cross-correlation operation can be used to identify and segment target patterns based on several target features accurately. The proposed optical analog cross-correlation operation may find applications in object tracking, medical image segmentation, and person re-identification.

A train trajectory optimization method based on the safety reinforcement learning with a relaxed dynamic reward

Train trajectory optimization (TTO) is an effective way to address energy consumption in rail transit. Reinforcement learning (RL), an excellent optimization method, has been used to solve TTO problems. Although traditional RL algorithms use penalty functions to restrict the random exploration behavior of agents, they cannot fully guarantee the safety of the process and results. This paper proposes a proximal policy optimization based safety reinforcement learning framework (S-PPO) for the train trajectory optimization, including a safe action rechoosing mechanism (SARM) and a relaxed dynamic reward mechanism (RDRM) combining a relaxed sparse reward and a dynamic dense reward. SARM guarantees that the new states generated by the agent consistently adhere to the environmental security constraints, thereby enhancing sampling efficiency and facilitating algorithm convergence. RDRM makes it easier for agents to obtain successful samples by relaxing time constraints, which also offers a better balance between exploration and exploitation. The experimental results show that S-PPO can significantly improve performance and obtain better train operation trajectories than soft constraint methods, and the convergence process is smoother. Finally, it was demonstrated that S-PPO exhibits good adaptability across various speed limit tracks.

Enhancing Traffic Efficiency and Sustainability through Strategic Placement of Roadside Units and Variable Speed Limits in a Connected Vehicle Environment

Enhancing Traffic Efficiency and Sustainability through Strategic Placement of Roadside Units and Variable Speed Limits in a Connected Vehicle Environment

Energetic cost as a consequence of parallel transporting speed limit

Time-optimal unitary operating quantum systems and energy-efficient quantum gates are vital in quantum engineering. Here, we establish the connection between the energetic cost and the quantum speed limit for parallel evolving of a quantum system. Our results show that the energetic cost is the consequence of parallel transporting speed limit, which opens up the critical role of parallel transport of the quantum state for the quantum technologies such as the quantum simulation, the quantum information processing. Our investigations present a route to construct the energetic-efficient quantum gate. In addition, our theoretical framework provides an alternative method for calculating quantum speed limit. Two typical relevant systems are employed to illustrate our results: the spin-1/2 particle in the rotating magnetic field and the entanglement swap gate.

Drivers’ Knowledge, Attitude and Practices Towards Traffic Rules and Regulations

Research frequently reports that the majority of crashes (up to 80%), are the fault of drivers. Further, the WHO reports that most fatal crashes occur in low- and middle-income countries (LMIC). In LMIC, including Pakistan, most drivers have low levels of knowledge about traffic rules and regulations, have negative attitudes towards rules and regulations and often violate road safety rules. This study used a structured survey of drivers in Karachi, Pakistan (n=260). Most respondents were aware that helmet use for motorbike riders is compulsory (74.6%), agreed it was important to follow road signs (74.2%) and wear a seat belt (64.6%). However, over half did not know the speed limit in residential areas (54.6%) and almost half considered drug use when driving was not dangerous or were against the penalty or fine for violation of traffic laws (45.4%). In addition, 45.8% of all drivers thought driving licenses were unnecessary. This study suggests there may be low levels of driver knowledge of certain road rules (e.g. speed limit), negative attitudes about certain safe driving practices (drug driving), and low levels of value placed on driving license in Karachi, Pakistan. Analysis suggest that knowledge and attitude affect driver practices in Karachi, Pakistan. Strengthening enforcement and related public communication measures about traffic rules and greater penalties that are sufficiently deterring for the violators of the traffic rules is needed. There must also be a formal mechanism to issue the driving licence after a proper test of the knowledge of road rules and driving following those rules.

Naturalistic driving analysis of situational, behavioral, and psychosocial determinants of speeding

The present analysis used full-trip naturalistic driving data along with driver behavioral and psychosocial surveys to understand the individual and contextual predictors of speeding. The data were collected over a three-week period from 44 drivers and contain 3,798 full trips, with drivers speeding 7.8 % of the time. Speeding events were identified as periods when participants traveled at a velocity greater than five mph over the speed limit for at least five seconds. Data were analyzed using the Comprehensive Driver Profile (CDP) framework which uses principal component analysis (dimensionality reduction), random forest (predictive modeling), k-means clustering (grouping and profiling), and bootstrapping (profile stability) to decompose the predictive variables and driver characteristics. The final dataset included 188 candidate independent variables from the CDP framework and one dependent variable (speeding). Nine variables emerged as significant predictors of speeding onset with an AUC of 0.88, including the percent of trip time spent idling and speeding, highway driving in low traffic conditions, and positive attitudes toward phone use. Percent of trip speeding was associated with a higher likelihood of speeding by up to 42 percent, and percent trip idling was associated with it by up to 30 percent. Driver profile clusters revealed four types: Traffic & Idling Speeders, Infrequent Speeders, Frequent Speeders, and Situational Speeders. The present analysis demonstrates the importance of situational factors and individual differences in motivating speeding behavior. Countermeasures targeting speeding may be more effective if they address the root causes of the behavior in addition to the behavior itself.

Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences

Understanding individuals’ distinct movement patterns is crucial for health, rehabilitation, and sports. Recently, we developed a machine learning-based framework to show that “gait signatures” describing the neuromechanical dynamics governing able-bodied and post-stroke gait kinematics remain individual-specific across speeds. However, we only evaluated gait signatures within a limited speed range and number of participants, using only sagittal plane (i.e., 2D) joint angles. Here we characterized changes in gait signatures across a wide range of speeds, from very slow (0.3 m/s) to exceptionally fast (above the walk-to-run transition speed) in 17 able-bodied young adults. We further assessed whether 3D kinematic and/or kinetic (ground reaction forces, joint moments, and powers) data would improve the discrimination of gait signatures. Our study showed that gait signatures remained individual-specific across walking speeds: Notably, 3D kinematic signatures achieved exceptional accuracy (99.8%, confidence interval (CI) 99.1–100%) in classifying individuals, surpassing both 2D kinematics and 3D kinetics. Moreover, participants exhibited consistent, predictable linear changes in their gait signatures across the entire speed range. These changes were associated with participants’ preferred walking speeds, balance ability, cadence, and step length. These findings support gait signatures as a tool to characterize individual differences in gait and predict speed-induced changes in gait dynamics.

Impact of traffic signs on driving speed at mountain highway tunnel entrances − The role of low-volume intermittent information

Driver’s perception of speed is the basis of driving safety, and installing speed limit signs at tunnel entrances is an intuitive means of controlling a driver’s driving speed. Therefore, tunnel entrance signs should be set up effectively to ensure each signage can fulfil its intended effect. Quantifying the information volume conveyed by traffic signs, discovering the impact of different information volumes on driving speeds, and understanding the effects of typical signs are the prerequisites for the effective use of signage. This study collected the speed variations of 40 drivers driving at nine mountainous highway tunnels with different entrance traffic signs through a naturalistic driving real vehicle test. The effect of low-volume intermittent information at tunnel entrances is identified in terms of the black hole effect on drivers (BHD), white hole effect on drivers (WHD), velocity fluctuation trend (VFT), and effect of speed limit signs (ELS). The study’s results confirm that the effect of speed limit signage with traffic sign information volume (TSIV) in the range of 8.904 to 33.318 bit is significant. In comparison, the sign will lose its effectiveness in guiding drivers to control their speed when TSIV is greater than 58.641 bits. Using low-volume information speed limit signs or tunnel warning signs repeated twice before entering a tunnel can alert drivers and regulate their driving speeds. The speed limit sign (sign ①) for each type of vehicle is set individually, and the combined speed limit and no lane change sign (sign ②) used in two stacks have a strong speed control effect. Moreover, the sign ②, when used alone, has the best timeliness; the effective distance of speed control can reach 1522 m; using it before short tunnels or long tunnels less than 1500 m can achieve better effectiveness.

Optimizing Breast Cancer Diagnosis with Machine Learning Algorithms

Breast cancer is the most common disease among women worldwide, and it continues to pose a serious threat to global health [2]. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods, while effective, have limitations in consistency and speed. Medical diagnostics have undergone a revolution with the introduction of machine learning (ML), which provides tools to analyze complex data and increase diagnosis accuracy. Using the Breast Cancer Wisconsin (Diagnostic) Dataset[1], this investigation dives into the application of several machine learning (ML) algorithms to improve the effectiveness of the diagnosis of breast cancer in terms of accuracy. Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Support Vector Classifier (SVC), XGBoost Classifier (XGBC), and Convolutional Neural Network (CNN) are used. Improved performance metrics were obtained across models by applying optimization approaches, particularly genetic algorithm for the selection of features, following preprocessing and initial training. RandomForestClassifier, XGBClassifier, and CNN notably showed significant enhancements in accuracy, ROC AUC score, recall, precision, and F1 score post-optimization. Ensemble methods and deep learning architectures proved effective in handling complex data and reducing overfitting, with Random Forest Classifier standing out as consistently superior. Conversely, Decision Tree Classifier and Support Vector Classifier exhibited mixed results, showcasing the importance of optimization strategies. This research shows the capability of ML in augmenting breast cancer diagnostics, advocating for further exploration of ensemble methods and advanced neural networks to refine diagnostic tools and improve patient outcomes.

Optimizing passengers’ experience: A goal-oriented reinforcement learning speed control approach for urban railway trains

Prolonged vibration can be uncomfortable for passengers utilizing urban rail transit systems. This study proposes an automatic speed control framework for urban railway trains to reduce vertical vibrations experienced by passengers. We suggest the concept of the “segmented comfort speed limit” to represent the vertical passing comfort of oncoming sections. This speed limit is calculated from 1/3 octave band acceleration and smoothed through lag-type speed control mode. The deep deterministic policy gradient algorithm with hindsight experience replay mechanism (HER-DDPG) is designed, to balance safety, comfort, and energy efficiency driving. Verify the speed control framework based on HER-DDPG through the rail data collected from Dalian Metro Line 12. Compared with the DDPG-based model, the vertical comfort is improved by 2.34%, and the longitudinal acceleration and total energy consumption are reduced by 45% and 8.1%. Compared with the real-world train control trajectory, HER-DDPG improves vertical comfort by 9.76% and reduces energy consumption by 12.4%. The results show that the proposed framework can effectively improve the ride experience of passengers.

Enhanced pilot protection scheme for half-wavelength transmission line based on the conservation of traveling wave energy

The half-wavelength transmission system is a viable technology for long-distance and large-capacity power transmission. It can safely and efficiently transmit a large amount of clean energy to remote load centers. However, the traditional traveling wave protection methods have reliability and speed limitations for HWTL lines. To overcome these limitations, this paper proposes a novel and robust pilot protection scheme based on the conservation of traveling wave energy at the protection installation. The proposed scheme uses the constant equation relationship between fault-traveling waves, reflected waves and refracted waves at the measurement end to significantly improve the reliability and effectiveness of the protection mechanism. The blocking protection operation logic constructed by the inequality directional element is used to improve the rapidity of the pilot protection scheme. The theoretical analysis and real-time digital simulator (RTDS) digital-analog hybrid test demonstrate that the action equation of the proposed protection scheme is explicit to the operating value. The novel scheme exhibits enhanced resilience against high transition resistance and the impact of impulse corona while ensuring both reliability and rapidity.© 2017 Elsevier Inc. All rights reserved.

Local high temperature phenomena near the complex interfaces of thermal barrier coatings based on non-Fourier model

Thermal barrier coatings (TBCs) consist of a metal substrate (Sub), a bond coat (BC), and a ceramic top coat (TC). The ceramic coating is a porous dielectric layer with a significant relaxation time that cannot be ignored. In high-temperature environments, an irregularly shaped thermally grown oxide layer (TGO) forms between the BC and the TC. Therefore, using non-Fourier model to describe the thermal contact process of TBCs with complex interface can get more accurate results. However, it is difficult to obtain the analytical solution of this model. This paper introduces a finite volume method based on unstructured meshes to address the non-Fourier heat transfer process in the complex interfaces of multilayer TBCs. The gradient reconstruction technique is employed to calculate the cell derivatives on the surface. The method's reliability is confirmed through a comparison with a two-dimensional analytical solution. The results indicate that the thermal wave travels at a limited speed within the TC layer. After passing through the interface, the thermal wave travels very fast within the BC and Sub layers, due to the influence of thermal wave reflection and superposition, obvious local high temperature will be formed near the interface. In addition, the effects of thermal conductivity, relaxation time and interface shapes on the non-Fourier heat conduction process of TBCs are also discussed. The results of this paper are useful for the structural design and failure mechanism research in TBCs.