Engineering reports : open access最新文献

This paper introduces a novel clustering approach that enhances the traditional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm by integrating a grid search method and Nash Equilibrium principles and addresses the limitations of DBSCAN parameterization, particularly its inefficiency with big data. The use of Nash equilibrium allows the identification of clusters with different densities and the determination of DBSCAN parameters and the selection of cells from the network, and significantly improves the efficiency and accuracy of the clustering process. The proposed method divides data into grid cells, applies DBSCAN to each cell, and then merges smaller clusters, capitalizing on dynamic parameter calculation and reduced computational complexity. The performance of the proposed method was assessed over 3 big-size and 11 middle-size datasets. The achieved results implied the superiority of the proposed method to DBSCAN, ST-DBSCAN, P-DBSCAN, GCBD, and CAGS methods in terms of clustering accuracy (purity) and processing time.

As global urbanization continues to expand, the challenges associated with traffic congestion and road safety have become more pronounced. Traffic accidents remain a major global concern, with road crashes resulting in approximately 1.19 million deaths annually, as reported by the WHO. In response to this critical issue, this research presents a novel deep learning-based approach to vehicle classification aimed at enhancing traffic management systems and road safety. The study introduces a real-time vehicle classification model that categorizes vehicles into seven distinct classes: Bus, Car, Truck, Van or Mini-Truck, Two-Wheeler, Three-Wheeler, and Special Vehicles. A custom dataset was created with images taken in varying traffic conditions, including different times of day and locations, ensuring accurate representation of real-world traffic scenarios. To optimize performance, the model leverages the YOLOv8 deep learning framework, known for its speed and precision in object detection. By using transfer learning with pre-trained YOLOv8 weights, the model improves accuracy and efficiency, particularly in low-resource environments. The model's performance was rigorously evaluated using key metrics such as precision, recall, and mean average precision (mAP). The model achieved a precision of 84.6%, recall of 82.2%, mAP50 of 89.7%, and mAP50–95 of 61.3%, highlighting its effectiveness in detecting and classifying multiple vehicle types in real-time. Furthermore, the research discusses the deployment of this model in low-and middle-income countries where access to high-end traffic management infrastructure is limited, making this approach highly valuable in improving traffic flow and safety. The potential integration of this system into intelligent traffic management solutions could significantly reduce accidents, improve road usage, and provide real-time traffic control. Future work includes enhancing the model's robustness in challenging weather conditions such as rain, fog, and snow, integrating additional sensor data (e.g., LiDAR and radar), and applying the system in autonomous vehicles to improve decision-making in complex traffic environments.

CFRD have been widely used worldwide because of their superior structural stability and economy. However, with the increasing of dam height, the issue of deformation control is becoming increasingly prominent, especially the panel deformation control which closely related to the internal rockfill deformation. Comparing with the monitoring data of a single monitoring point, the slope plane deformation can reflect the deformation characteristics of the dam rockfill area more effective. To ensure the long-term stability and safe operation of the CFRD, construction and analysis the slope plane deformation of the CFRD is particularly important. In order to improve the accuracy of prediction, a method of modeling and analysis of dam slope plane deformation based on multi-points monitoring data is proposed in this paper. Based on the actual monitoring data of the Hongjiadu CFRD, this paper comprehensively analyzes the influence of the filling process, water level change, and time effect on the settlement of the CFRD during the construction and impoundment periods, and establishes the settlement statistical model of all monitoring points. By studying the relationship between the parameters of the settlement statistical model of each monitoring point and its own position coordinates, a spatial–temporal distribution model of the dam slope plane is established by using the thin plate spline interpolation method, and it is used to predict the settlement of the dam slope plane. This method has great advantages in the prediction accuracy. By considering the temporal and spatial distribution characteristics, this method can effectively capture the whole and local deformation of the dam body and improve the prediction accuracy of settlement. Compared with the traditional single point monitoring method, the proposed method provides more accurate prediction results on the basis of multi-point data collaborative analysis, provides a new idea for dam displacement monitoring and settlement prediction, and has high engineering application and popularization value.

Currently, the destabilization mechanisms of slopes due to rainfall infiltration are not fully understood. We conducted physical model tests to measure displacement and pore water pressure from rainfall, using the data to validate numerical models. This study explores how rainfall intensity and duration affect these measures across loess slopes with varying steepness. The goal is to understand slope responses to different rainfall conditions. Our findings indicate that steeper gradients see modest increases in displacement and pore water pressure at the top and mid-slope, but these increases are more pronounced at the toe. The changes at the toe and mid-slope are driven by infiltrated rainwater volume and soil compressive behavior, while top-slope displacement is primarily due to infiltration. Continuous deformation was observed during and after the rainfall events. Post-rain, pressure from saturated soil at the slope's apex amplifies pore water pressure at the toe, influenced by gravitational forces and retained water pressure. This underscores the complex interactions affecting slope stability in wet conditions. Understanding loess slopes' responses can improve predictive models and mitigation strategies, reducing infrastructure and safety risks in these vulnerable areas.

Rainwater film depth on runways is one of the important data for the application of Global Reporting Format (GRF) implemented since 2021 by International Civil Aviation Organization (ICAO) for runways' safety. However, it is still a challenge for all airport operators to provide a real-time Runway Conditions Report (RCR) to pilots without interfering with the aircraft take-off and landing. In this paper, an Integrated Sensing and Communication (ISAC) system has been designed to perform an automatic application of the GRF. The system involves antennas from which the signal strength attenuation due to rain (detected by a sensor) is retrieved to measure the depth of the rainwater on runways automatically and in real-time. While measuring, data are immediately computed to present the rain and the runway conditions via visual interface (screen) for the understanding and the use of the airport runway inspectors. The developed system is fully automatic and implemented specially to use during rainy time. The system uses a raspberry pi 4 model B as a computer, Arduino nano, antennas signals, and a raindrop sensor let alone the Python codes developed by the authors. Results obtained show that using the ISAC system to monitor runways' wetness conditions is very easy in real-time, and human presence on the runway is no longer needed. The results also show that the method used herein is the proper solution to the GRF issues in rainy areas, where the accuracy of the contaminant depth measurement is a challenge.

Circuit board analysis plays a critical role in ensuring the reliability of electronic devices by identifying temperature distribution, assessing component health, and detecting potential defects. This study presents a novel approach to infrared image segmentation for circuit boards, integrating Markov Random Field (MRF) and Level Set (LS) techniques to enhance segmentation accuracy and reliability. The proposed method leverages the probabilistic modeling capabilities of MRF and the contour evolution strengths of LS to achieve robust segmentation of infrared images, revealing critical thermal and structural features. Experimental results demonstrate that the proposed MRF-LS method achieves an accuracy of 86%, a precision of 92%, and a recall of 94% on a benchmark dataset of PCB infrared images. These results indicate significant improvements over conventional segmentation methods, including k-means clustering and active contour models, which yielded accuracies of 79% and 81%, respectively. Furthermore, the method shows adaptability for identifying fine-grained temperature anomalies and structural defects, with enhanced resolution for small components. The study also discusses the potential adaptability of the proposed method to other imaging modalities, highlighting its scalability and versatility. These findings underline the utility of the MRF-LS framework as a valuable tool in advancing circuit board analysis, with promising applications in quality control and predictive maintenance for the electronics industry.

This research is surveying a numerical technique to study effects of CuO nanoparticles in Jeffery–Hamel flow with the high levelized of magnetic field in converging and diverging channels. The innovation of this work is that governing equations in this model is solved using coupling the quasi-linearization method (QLM) and meshless method, which is based on radial basis functions (RBFs). Also, the RBF method, no need for pre-defined meshing, reduces the solution of the problem to the solution of a system of algebraic equations by using a set of points within the domain and its boundaries. In addition to, this geometry, the QLM is utilized as a tool for confronting the nonlinearity of the problem and also to reduce the nonlinear boundary problems to a sequence of linear boundary problems which are much simpler to solve. In order to evaluate the convergence analysis of the method, error estimations are made by a residual function denoted. Also, the ability of the present method is shown by comparing it with the numerical method to solve this problem, which is in good agreement. Effects of multivariable parameters are analyzed on magnetic field, nanoparticles volume fraction, and angle of converging and diverging channels. The obtained results show that at angles or Reynolds number of greater in divergent channels, backflow occurs so the high levelized of magnetic field eliminates this phenomenon. Also, the numerical results show that at the angle of channel $��\alpha �=�5�°�$ and $���\mathrm{Re}�=�75�$ which no backflow occurs, the effect of increasing the Hartmann number on the nanofluid velocity from values of $��\mathrm{Ha}�=�500�$ to $��\mathrm{Ha}�=�1000�$ has respectively increased by 35.55 and 66.36%, compare to its initial value.

The study investigates the physical properties, strength, and flexibility of the jute/kenaf/glass reinforced composite. The composite is infused with equal quantities of silicon dioxide (SiO₂), nanographene, and multi-walled carbon nanotubes (MWCNTs) at a concentration of 5 wt%, as specified by ASTM standards. The composite was created using a manual lay-up approach followed by compression molding, using a unique 10-layer fiber structure. The S4 (5%) composite specimen increased its tensile strength (TS) from 91 to 101 MPa, tensile modulus (TM) from 7.286 GPa to 10.286 GPa, interlaminar shear strength (ILSS) from 9.39 to 19.71 MPa, and glass-transition temperature (Tg) from 70°C to 85°C. The results show that the 5 wt% MWCNTs have significantly higher TS, TM, ILSS, and Tg than the unfilled composite and SiO₂, nanographene, with increases of 5%, 2%, and 10%, respectively. Incorporating MWCNT nanofillers into composites significantly enhances their mechanical and dynamic characteristics when compared to composites without fillers. The study's findings show that adding MWCNTs to the matrix significantly improves performance when compared to the composite without any fillers. Scanning electron microscopy images reveal the underlying reasons of failure in composites, such as layer separation, material fissures, fiber extraction, and fiber breakage. The incorporation of 5 wt% MWCNTs into the jute/kenaf/glass hybrid composite matrix significantly enhances mechanical and dynamic properties compared to composites without fillers.

In this scientometric review, we characterize the evolution of polaritonics research during the last 60 years. We explore how the understanding and utilization of polaritons lead to the development of advanced technologies. By employing networks, bibliometric analysis, and artificial intelligence techniques, we identify research trends, patterns of international collaboration, and key topics within the field. We combine bibliographic coupling techniques with a comprehensive literature review, with the aim of analyzing the evolution of the most important research fronts. Our study reveals an exponential growth of scientific output, with a high level of specialization in areas such as Optics, Applied Physics, Materials Science, Physics of Condensed Matter, Nanotechnology, and Electrical and Electronic Engineering. We use Self-Organizing Maps to identify the variety of scientometric performance profiles of the most productive countries, as well as the evolution of the world's scientometric profile. In spite of being a research activity predominantly centered in Europe and the United States, the emergence of China during the last 10 years is remarkable. Our study highlights progress in understanding optical phenomena, excitations in specific materials, and the characterization of polaritons at interfaces and nanostructures, underscoring their potential for practical applications such as sensors and optical devices.

Based on the big data of literature in the field of Electroforming processing, knowledge mapping and bibliometric methods are used in this article to conduct a visual analysis of the research hotspots, development trends, and research networks in this field. The results indicate that: (1) Research in the field of Electroforming processing has been shown to have a significant upward trend, reflecting the high attention and investment devoted to Electroforming technology by both academia and industry. Outstanding research performance in this field has been demonstrated by China, South Korea, and the United States, while core research forces in this domain are institutions such as the Chinese Academy of Sciences, Nanjing University of Aeronautics and Astronautics, Dalian University of Technology, and Seoul National University. (2) The co-occurrence analysis of keywords has revealed the close connection between Electroforming processing technology and hot technologies like resistance and memory, showcasing the broad application prospects of Electroforming processing technology across numerous fields, including electronics, materials, and biomedicine. (3) International cooperation in the field of Electroforming has been characterized by diversification and deepening, resulting in the formation of multiple cooperation networks encompassing Asia and Western Europe, North America and Asia, European scientific research powers, and emerging market countries. (4) Attention has continued to be paid to Electroforming technology in terms of precision and multidisciplinary integration. Driven by the increasing demand in aerospace, automobile manufacturing, and electronic circuits, innovations in Electroforming processing technology are constantly being made, thereby promoting the advancement of science and technology as well as the development of new materials.