alexandria engineering journal最新文献

The interaction between a vehicle's tire and the road surface is pivotal for a driver's control over the vehicle's movements. It serves as the fundamental link between the vehicle and the road. The modeling of tires holds significant importance in contemporary vehicle design, playing a critical role in assessing aspects such as vehicle handling, ride comfort, and road load analysis. This study focuses on investigating the impact of the enveloping behavior characteristics of a pneumatic tire on the performance of a suspension system. The analysis of the vehicle's ride comfort utilizes a half-car model. Unlike a previous model with a single point of contact with the road, the presented suspension system, coupled with a four-degree-of-freedom rigid ring tire model, offers a more precise estimation of both ride comfort and road holding. The primary emphasis of this research lies in the modeling and evaluation of the proposed suspension system's performance. A comprehensive computer model of the entire system is developed using MATLAB software. This work enhances the existing framework by incorporating both a Multi-Objective Genetic Algorithm (MOGA) and a Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the damping and stiffness coefficients of the passive suspension. This approach allows for a detailed comparison of the optimization capabilities and effectiveness of both algorithms in refining vehicle ride comfort. The results from MATLAB simulations highlight performance improvements, and the comparative analysis of MOGA and MOPSO provides insights into the selection of optimization techniques for suspension system design.

With the rapid development of Telematics, Vehicle Self-Organizing Networks (VANETs) play an increasingly critical role in Intelligent Transportation Systems (ITS). Especially in the environment without roadside assistance units (RSUs), how to effectively manage inter-vehicle communication and improve the stability and communication efficiency of the network has become a hot topic of current research. In this paper, a Deep Reinforcement Learning-based Intelligent QoS-optimized efficient routing algorithm for vehicular networks (DRLIQ) is proposed for VANETs with/without RSU environments, and routing methods are proposed respectively. Among them, in RSU-free environment, the DRLIQ algorithm utilizes the powerful processing capability of deep reinforcement learning to intelligently select the optimal data transmission path by dynamically learning and adapting to the changes in the vehicular network, thus effectively reducing communication interruptions and delays, and improving the accuracy of data transmission. The performance of the DRLIQ algorithm under different vehicle densities is evaluated in simulation experiments and compared with current popular algorithms. The experimental results show that the DRLIQ algorithm outperforms the comparison algorithms in reducing the number of communication interruptions, BER and network delay, especially in vehicle-dense environments. In addition, the DRLIQ algorithm shows higher adaptability and stability in coping with network topology changes and vehicle dynamics.

The present study investigates the significance of the utilization of biodiesel derived from waste cooking oil in diesel engines and its impact on engine output and environmental pollution after blending with diesel and 1-pentanol. The higher values of the ignition index and comprehensive performance index of 4.34 $\times$ ${10}^{-4}$mass/min°C² and 13.71$\times$ ${10}^{-6}$ mass²/min² °C³, respectively, for D70B20P10 (70 % diesel, 20 % biodiesel & 10 % 1-pentanol by volume) indicate superior combustion performance. At full load, carbon monoxide and unburned hydrocarbon emissions from 1-pentanol blended fuels decrease significantly, ranging from 40 % to 52 % and 30.76–46.15 % compared to diesel. The D70B20P10 also showed an improved thermal efficiency of 28.68 % among all tested fuels at full load but slightly lower than diesel (29.56 %). Diesel demonstrated superior in-cylinder pressure (ICP) of 77 bar and heat release rate (HRR) of 41.1 J/ºCA owing to its excellent combustion characteristics. Introducing 5–10 % 1-pentanol in blend improved combustion, elevating ICP and HRR, attributed to enhanced fuel atomization and oxygen content. The sustainable process index value obtained for a global index per person is $0.002\times {10}^{-4}$cap Litre-1, which lies between 0 and 1, indicates sustainability and compatibility of biodiesel production demonstration by universal biofuels (Andhra Pradesh).

We introduce a generalized version of a unit distribution called power unit exponential probability distribution (PUEPrD) using the power transformation of the unit exponential probability distribution. Some statistical properties of the proposed distribution are derived. For some selected parameter cases, we have demonstrated that the hazard function of the proposed distribution can be shaped by increasing and bathtub curves. Twelve estimation methods such as maximum likelihood, Anderson–Darling, Cramer–von-Mises, maximum product spacings, least squares, weighted least squares, right tail Anderson Darling, left-tail Anderson Darling, minimum spacing absolute distance, minimum spacing absolute-log distance, Anderson Darling left-tail second order, Kolmogorov are used to estimate the parameters of the suggested distribution. A numerical simulation study is conducted to check the efficiency of the parameter estimates of the proposed model. With the help of some real-life data sets, the flexibility and usefulness of the PUEPrD are illustrated. As a result of two real data analyses, we observe that the fit of the proposed distribution to the data is superior to its competitors according to the examined criteria.

So far in the literature, numerous probabilistic models and families of probabilistic models have been suggested and put into practice. A large portion of these probabilistic models are developed and updated by introducing new parameters ranging up to five. There are only a few methods that are introduced without adding additional parameters. This paper also contributed to the development of a probabilistic model without adding additional parameters. The proposed model is introduced by incorporating the weighted distributional strategy and the weighted Ramos–Louzada distribution. Therefore, the generalized weighted Ramos–Louzada distribution is a suitable name for the newly proposed model. The derivation of the estimators based on the maximum likelihood for this novel model is presented. Certain distributional properties of the generalized weighted Ramos–Louzada distribution are derived. In order to validate the effectiveness and superiority of the generalized weighted Ramos–Louzada distribution, three applications are chosen as examples. The first two applications are considered from the physical sciences, while, the third application is taken from the musical area. We consider four statistical tests to show the validatity of the proposed model over some well-known established models. The findings from the statistical tests consistently indicate that the proposed model performs better than its rivals.

Vehicle-to-everything (V2X) communication is essential in 5G and upcoming networks as it enables seamless interaction between vehicles and infrastructure, ensuring the reliable transmission of critical and time-sensitive data. Challenges like unstable communication in highly mobile vehicular networks, limited channel state information, high transmission overhead, and significant communication costs hinder vehicle-to-vehicle (V2V) communication. To tackle these issues, a unified approach utilizing distributed deep reinforcement learning is proposed to enhance the overall network performance while meeting the quality of service (QoS), latency, and rate requirements. Recognizing the complexity of this NP-hard, non-convex problem, a machine learning framework based on the Markov decision process (MDP) is adopted for a robust strategy. This framework facilitates the formulation of a reward function and the selection of optimal actions with certainty. Furthermore, a spectrum-based allocation framework employing multi-agent deep reinforcement learning (MADRL) is confidently introduced. The deep deterministic policy gradient (DDPG) within this framework enables the exchange of historical data globally during the primary learning phase, effectively removing the need for signal interaction and manual intervention in optimizing system efficiency. The data transmission policy follows an augmented online policy scheme, known as the proximal online policy scheme (POPS), which confidently reduces the computational complexity during the learning process. The complexity is marginally adjusted using the clipping substitute technique with assurance in the learning phase. Simulation results validate that the proposed method outperforms existing decentralized systems in achieving a higher average data transmission rate and ensuring quality of service (QoS) satisfaction confidently.

In addressing sediment accumulation challenges in the water diversion canal and enhancing both water utilization and ecological conditions, this study introduces a fish mouth engineering design, akin to the sediment reduction strategies used at the Dujiangyan water conservancy hub. A specialized two-dimensional hydrodynamic and sediment transport model was developed, specifically tailored to the Qixing Canal section in Ningxia. Utilizing SMS software, an unstructured grid division of the study area was executed. The research employed simulations under two distinct scenarios: direct water diversion and fish mouth engineering at the canal head. These simulations were pivotal in analyzing the variations in sedimentation thickness and sediment concentration across different flow rates and irrigation cycles. It was observed that the use of the fish mouth engineering for water diversion significantly improved the sedimentation thickness, underscores the efficacy of the fish mouth model in altering the structural characteristics of water flow and achieving a redistribution of water flow and sediment within the canal. This approach to fish mouth diversion and sediment reduction substantially curtails sediment accumulation in the irrigation canal, presenting a viable strategy for canal head design in the self-flow irrigation areas of the Yellow River. This research result provides certain reference value for water conservancy engineering organizations.

The mandibular condyle is a rounded bony projection with an upper biconvex, oval surface in the axial plane. Its form differs significantly among different individuals and age groups. This study aims to address the variability in mandibular condyle morphology, which can be indicative of Temporomandibular Joint disorders (TMD). Given the clinical importance of accurate condyle characterization, we developed a novel detection method leveraging deep learning and feature selection technologies. This method explicitly employs the YOLOv8 network to initially identify the region of interest (ROI) in digital panoramic images. Subsequently, the MobileViT system extracts detailed features from these regions. We introduced a modified Mountaineering Team-Based Optimization Algorithm to refine the feature selection process, which efficiently isolates the most relevant features from the extracted set. Our experimental design involved a robust dataset of 3000 digital panoramic images, classified into four distinct morphological types: round, pointed, angled, and flat. We assessed the performance of our developed method through various metrics, focusing on its ability to detect and describe the morphology of the condyle. The results demonstrate a high capability of the model, achieving an accuracy of 81.5% in binary classification and 83.5% in multi-classification scenarios.

This study examines the modified Sardar sub-equation method (MSSEM) for deriving the novel solutions of the (3+1)-dimensional p-type model. This framework is commonly employed to explain the behavior of optical solitons in nonlinear media. The applications of MSSEM allows us to acquire the precise analytical solutions, which incorporate a diverse array of optical soliton solutions. We discuss the dynamical structure of the solitons, bifurcation and chaos theory to develop the multiple soliton solutions, including rational, hyperbolic, exponential, and trigonometric functions and depending on the principle of balancing equation. Moreover, by using bifurcation and chaos theory, we examine the governing model with and without the perturbation term and provide the three-dimensional, two-dimensional, and density profiles to improve the clarity of obtained results. The different aspects of the solutions are evident in our visual representations. These solutions are applicable to a wide range of domains, including fluid physics, oceanography, physics, engineering, and nonlinear optics.

Topical medications are common initial treatments for Descemet’s membrane detachment (DMD). It is crucial to understand the aqueous humour (AH) flow and drug transport to facilitate the DMD treatment. This paper presents a mathematical model to analyse AH flow and drug transport in the anterior of the human eye considering gravity orientation effects. Utilizing the finite element method, the model couples AH flow dynamics, temperature-induced buoyancy, and drug transport by convection-diffusion. COMSOL Multiphysics is used to simulate the spatial-temporal distribution of AH and drugs delivery. The findings indicate that the eye positioning influences the directional flow of AH, due to temperature differences, and impacts the delivery of drug concentrations. It is also discovered that eye drops diffuse faster due to tear turnover in the eye yet more eye ointment is delivered as it has a longer contact time in the cornea. By understanding the AH flow and transport process, this proposed model may offer appropriate guidelines for optimizing DMD treatment under various forms of topical drugs and eye positions.