Interest in natural fiber-reinforced polymer (NFRP) composites is growing rapidly in the transportation sector, especially as a replacement material for metals and synthetic fiber composites. The heightened interest is directly related to a need to produce lightweight and fuel efficient vehicles. Further, stringent legislation and greater environmental awareness is forcing transportation industries to select materials with a smaller carbon footprint. In such a context, NFRP composite materials are a good choice due to their low cost, low environmental impact, and relatively equivalent properties to metals and other composites. Most prior studies have examined commodity plastics such as polypropylene, polyethylene, and epoxy as the primary polymer matrix in NFRP composites and little work has addressed engineering plastics. Engineering plastics, which includes polycarbonate, polyamides, and polystyrene, are high performance thermoplastics with superior properties but relatively higher cost than commodity plastics. It has been claimed that even after recycling, engineering plastics properties are superior to those of commodity plastics, and thus, utilization of recycled engineering plastic in NFRP composites can help reduce waste and lower composite material costs. The aim of this review article is to explore the current status of engineering plastics reinforced with natural fibers such as flax, hemp, jute, and sisal and to examine their use in automotive, aerospace, and maritime applications. Properties and processing techniques of engineering plastics reinforced with natural fibers are also studied.
[Objective] To achieve cost efficiency and low-carbon transformation in rail transit, the planning, design, and implementation of green and low-carbon urban rail transit systems is investigated. [Method] Aiming at life-cycle sustainable development, through comprehensive route selection and laying methods optimization (mainly elevated), Shenzhen Metro Line 6 innovatively applies systematic technologies such as comprehensive vibration and noise reduction technology for elevated lines, prefabricated and assembled green construction (such as U-shaped beams and floating plate tracks), distributed photovoltaic power generation, regenerative braking energy feedback, ecological sponge vehicle depot design, intelligent cloud platform and TOD (transit oriented development) model intensive land utilization, etc., to explore green and low-carbon planning, design and practice paths. [Result & Conclusion] The green and low-carbon technology system of Shen-zhen Metro Line 6 has demonstrated remarkable outcomes: construction costs are reduced by approximately 45% (10.9 billion yuan) compared to a fully underground scheme, with a 2.4 million-ton carbon emission reduction in construction-phase. The operational energy consumption is decreased by 40% (annual electricity savings of 33.6 million kW·h), with accumulated carbon emission reduction of 580 000 tons in 25 years. The annual power generation and energy saving by using photovoltaic and regenerative braking technologies are 6.78 million kW·h；the land development income is expected to reach 30 billion yuan（RMB）. This case provides a replicable technology integration solution for urban rail transit to achieve the carbon peak and carbon neutrality（shorten as "dual carbon"）goals, proving that the green, low-carbon and high-quality sustainable development can be collaboratively achieved through systematic innovation.
Ali Raza, Khaled Mohamed Elhadi, Muhammad Abid
et al.
Waste tyre rubber has become an environmental and health concern that needs to be sustainably managed to avoid fire hazards and save natural resources. This research work aims to study the structural behavior of glass fiber reinforced polymer (glass-FRP) reinforced rubberized concrete (GRC) compressive elements under monotonic axial compression loads. Nine GRC circular compressive elements with different axial and crosswise reinforcement ratios were fabricated. All the elements were 300 mm in diameter and 1200 mm in height. A 3D nonlinear finite element equation (FEM) was suggested for the GRC compressive elements using a commercial package ABAQUS. A parametric study has been done to examine the effect of various parameters of GRC elements. The test outcomes revealed that the ductility of GRC elements ameliorated with the lessening in the spaces of glass-FRP ties. The addition of rubberized concrete improved the ductility of GRC elements. The damage to GRC elements occurred due to the vertical cracking along the height of the elements. The estimates of FEM were in close agreement with the test outcomes. The suggested empirical equation depending on the 600 test elements, which considered the lateral confinement effect of FRP ties, presented higher accuracy than previous equations.
Mauro Femminella, Martina Palmucci, Gianluca Reali
et al.
In modern cloud computing, the need for flexible and scalable orchestration of services, combined with robust security measures, is paramount. In this paper, we propose an innovative approach for managing secure cloud bursting in Kubernetes, combining Attribute-Based Encryption (ABE) with Kubernetes labeling. Our model addresses the challenges of complexity, cost, and data protection compliance by leveraging both Kubernetes and ABE. We introduce an attribute-based bursting component that uses Kubernetes labels for orchestration, and an encryption component that employs ABE for data protection. This unified management model ensures data confidentiality while enabling efficient cloud bursting. Our approach combines the strengths of label-based orchestration with fine-grained encryption, providing a technologically advanced yet user-friendly solution for secure cloud bursting. We present a proof-of-concept implementation that demonstrates the feasibility and effectiveness of our model. Our approach offers a unified solution that complies with security and privacy laws while meeting the needs of contemporary cloud-based systems.
Telecommunication, Transportation and communications
Pavlos Tafidis, Mehdi Gholamnia, Payam Sajadi
et al.
Abstract Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.
Transportation engineering, Transportation and communications
Dongbin Xiong, Shaozhuan Huang, Daliang Fang
et al.
Detrimental lithium polysulfide (LiPS) shuttle effects and sluggish electrochemical conversion kinetics in lithium-sulfur (Li-S) batteries severely hinder their practical application. Separator modification has been extensively investigated as an effective strategy to address above issues. Nevertheless, in the case of functional separators, how to effectively block the LiPSs from diffusion while enabling the rapid Li ion transport remains a challenge. Herein, by using an "oxidation-etching" method, MXene membranes are presented with controllable in-plane pores as interlayer to regulate Li ion transportation and LiPS immobilization. Porous MXene membranes with optimized pore density and size can simultaneously anchor LiPS and ensure fast Li ion diffusion. Consequently, even with pure sulfur cathode, the improved Li-S batteries deliver excellent rate performance up to 2 C with a reversible capacity of 677.6 mAh g-1 and long-term cyclability over 500 cycles at 1 C with a low capacity decay of 0.07% per cycle. This work sheds new insights into the design of high-performance interlayers with manipulated nanochannels and tailored surface chemistry to regulate LiPSs trapping and Li ion diffusion in Li-S batteries.
The Panjian structure is an important longitudinal connecting member of Song Dynasty hall-type buildings in China. To study the lateral force resistance of such structures, a refined finite element model of Song-style hall-type single-room four-column space timber frame containing Panjian structure was established based on the official building code Yingzao Fashi of Song Dynasty. The Panjian and inner E’fang in the lower-ping part, the Panjian in the upper-ping part, and Guazi-gong Panjian and Man-gong Panjian in the roof ridge part were investigated. The model hysteresis curves of all three parts of the longitudinal timber frame were found to be S-shaped, with obvious pinching effect, fuller at both ends and centrosymmetric shape. The Panjian structures in the lower-ping and upper-ping parts, and the Guazi-gong Panjian in the roof ridge part increased the energy dissipation and lateral stiffness of the timber frame. The Man-gong Panjian in the roof ridge part, however, was detrimental to the lateral resistance of the structure. Throughout the test, the Panjian structures were relatively intact, with large plastic damage occurring at both ends of the E’fang. For the repair and testing of similar ancient buildings, some reference suggestions are provided.
With the development of the hydrogen fuel cell automobile industry, higher requirements are put forward for the construction of hydrogen energy infrastructure, the matching of parameters and the control strategy of hydrogen filling rate in the hydrogenation process of hydrogenation station. Fuel for hydrogen fuel cell vehicles comes from hydrogen refueling stations. At present, the technological difficulty of hydrogenation is mainly reflected in the balanced treatment of reducing the temperature rise of hydrogen and shortening the filling time during the fast filling process. The Joule-Thomson (JT) effect occurs when high-pressure hydrogen gas passes through the valve assembly, which may lead to an increase in hydrogen temperature. The JT effect is generally reflected by the JT coefficient. According to the high pressure hydrogen in the pressure reducing valve, the corresponding JT coefficients were calculated by using the VDW equation, RK equation, SRK equation and PR equation, and the expression of JT effect temperature rise was deduced, which revealed the hydrogen temperature variation law in the process of reducing pressure. Make clear the relationship between charging parameters and temperature rise in the process of decompression; the flow and thermal characteristics of hydrogen in the process of decompression are revealed. This study provides basic support for experts to achieve throttling optimization of related pressure control system in hydrogen industry.
Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful application of Transformer architecture in natural language processing (NLP) tasks, a novel Transformer method called LeViT was introduced for automatic asphalt pavement image classification. LeViT consists of convolutional layers, transformer stages where Multi-layer Perception (MLP) and multi-head self-attention blocks alternate using the residual connection, and two classifier heads. To conduct the proposed methods, three different sources of pavement image datasets and pre-trained weights based on ImageNet were attained. The performance of the proposed model was compared with six state-of-the-art (SOTA) deep learning models. All of them were trained based on transfer learning strategy. Compared to the tested SOTA methods, LeViT has less than 1/8 of the parameters of the original Vision Transformer (ViT) and 1/2 of ResNet and InceptionNet. Experimental results show that after training for 100 epochs with a 16 batch-size, the proposed method acquired 91.56% accuracy, 91.72% precision, 91.56% recall, and 91.45% F1-score in the Chinese asphalt pavement dataset and 99.17% accuracy, 99.19% precision, 99.17% recall, and 99.17% F1-score in the German asphalt pavement dataset, which is the best performance among all the tested SOTA models. Moreover, it shows superiority in inference speed (86 ms/step), which is approximately 25% of the original ViT method and 80% of some prevailing CNN-based models, including DenseNet, VGG, and ResNet. Overall, the proposed method can achieve competitive performance with fewer computation costs. In addition, a visualization method combining Grad-CAM and Attention Rollout was proposed to analyze the classification results and explore what has been learned in every MLP and attention block of LeViT, which improved the interpretability of the proposed pavement image classification model.
Satyam Panchal, Krishna Gudlanarva, Manh-Kien Tran
et al.
In this paper, an analogous study of the velocity and temperature profiles inside microchannel cooling plates (with hydraulic diameter of 6 mm), placed on a large pouch-type LiFePO<sub>4</sub> battery, is presented using both the laboratory and simulation techniques. For this, we used reverse engineering (RE), computed tomography (CT) scanning, Detroit Engineering Products (DEP) MeshWorks 8.0 for surface meshing of the cold plate, and STAR CCM+ for steady-state simulation. The numerical study was conducted for 20 A (1C) and 40 A (2C) and different operating temperatures. For experimental work, three heat flux sensors were used and were intentionally pasted at distributed locations, out of which one was situated near the negative tab (anode) and the other was near the positive tab (cathode), because the heat production is high near electrodes and the one near the mid body. Moreover, the realizable <i>k</i>-ε turbulence model in STAR CCM+ is used for simulation of the stream in a microchannel cooling plate, and the computational fluid dynamics (CFD) simulations under constant current (CC) discharge load cases are studied. Later, the validation is conducted with the lab data to ensure sufficient cooling occurs for the required range of temperature. The outcome of this research work shows that as C-rates and ambient temperature increase, the temperature contours of the cooling plates also increase.