Hasil untuk "Transportation engineering"

Liping Ren, Wei Zhou, Bojing Sun et al.

Abstract Defect-engineered magnetic γ-Fe2O3 ultrathin nanosheets/mesoporous black TiO2 hollow sphere heterojunctions (γ-Fe2O3/b-TiO2) are fabricated by a metal-ion intervened hydrothermal technique and high-temperature hydrogenation, which exhibit wide-spectrum response and magnetic separation. The specific surface area, pore size and pore volume of the resultant γ-Fe2O3/b-TiO2 with hollow structure are ∼63 m2 g−1, 10.5 nm and 0.14 cm3 g−1, respectively. After surface hydrogenation, α-Fe2O3 nanosheets are converted to γ-Fe2O3 ultrathin nanosheets (∼6 nm) combined with the formation of surface defects. The ultrathin nanosheet structure facilitates the surface engineering and also favors the diffusion and transportation of photogenerated charge carriers. The apparent rate constant (k) of defect-engineered γ-Fe2O3/b-TiO2 photocatalytic degradation biotoxic tetracycline is ∼3 times higher than that of α-Fe2O3/b-TiO2 under AM 1.5 irradiation. The enhancement is attributed to the introduction of narrow bandgap unit-cell-thick γ-Fe2O3 nanosheets, the hollow structure and the defect engineering, which are beneficial to solar-light-harvesting and rapid electron transport, and spatial separation of photogenerated charge carriers. The photocatalytic degradation mechanism is also proposed. The novel magnetic γ-Fe2O3/b-TiO2 heterojunction is a promising photocatalyst for recovering the domestic sewage in environment.

Xianwu Huang, Jiahui Huang, Dong Yang et al.

MXenes as an emerging two‐dimensional (2D) material have attracted tremendous interest in electrochemical energy‐storage systems such as supercapacitors. Nevertheless, 2D MXene flakes intrinsically tend to lie flat on the substrate when self‐assembling as electrodes, leading to the highly tortuous ion pathways orthogonal to the current collector and hindering ion accessibility. Herein, a facile strategy toward multi‐scale structural engineering is proposed to fabricate high‐performance MXene hydrogel supercapacitor electrodes. By unidirectional freezing of the MXene slurry followed by a designed thawing process in the sulfuric acid electrolyte, the hydrogel electrode is endowed with a three‐dimensional (3D) open macrostructure impregnated with sufficient electrolyte and H+‐intercalated microstructure, which provide abundant active sites for ion storage. Meanwhile, the ordered channels bring through‐electrode ion and electron transportation pathways that facilitate electrolyte infiltration and mass exchange between electrolyte and electrode. Furthermore, this strategy can also be extended to the fabrication of a 3D‐printed all‐MXene micro‐supercapacitor (MSC), delivering an ultrahigh areal capacitance of 2.0 F cm–2 at 1.2 mA cm–2 and retaining 1.2 F cm–2 at 60 mA cm–2 together with record‐high energy density (0.1 mWh cm–2 at 0.38 mW cm–2).

José de Jesús Montaño López, Lisset A. Duran, J. Avalos

M. Adegboye, O. Ojuederie, P. Talia et al.

The issues of global warming, coupled with fossil fuel depletion, have undoubtedly led to renewed interest in other sources of commercial fuels. The search for renewable fuels has motivated research into the biological degradation of lignocellulosic biomass feedstock to produce biofuels such as bioethanol, biodiesel, and biohydrogen. The model strain for biofuel production needs the capability to utilize a high amount of substrate, transportation of sugar through fast and deregulated pathways, ability to tolerate inhibitory compounds and end products, and increased metabolic fluxes to produce an improved fermentation product. Engineering microbes might be a great approach to produce biofuel from lignocellulosic biomass by exploiting metabolic pathways economically. Metabolic engineering is an advanced technology for the construction of highly effective microbial cell factories and a key component for the next-generation bioeconomy. It has been extensively used to redirect the biosynthetic pathway to produce desired products in several native or engineered hosts. A wide range of novel compounds has been manufactured through engineering metabolic pathways or endogenous metabolism optimizations by metabolic engineers. This review is focused on the potential utilization of engineered strains to produce biofuel and gives prospects for improvement in metabolic engineering for new strain development using advanced technologies.

Junchen Ye, Yuhao Mao, Ke Cheng et al.

Given the swift proliferation of structural health monitoring (SHM) technology within tunnel engineering, there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction. In contrast to other SHM datasets, the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations. Nevertheless, most methodologies fail to adequately combine these types of correlations. Hence, the objective of this study is to develop spatiotemporal recurrent neural network (ST-RNN) model, which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems. ST-RNN consists of two moduli: a temporal module employing recurrent neural network (RNN) to capture temporal dependencies, and a spatial module employing multilayer perceptron (MLP) to capture spatial correlations. To confirm the efficacy of the model, several commonly utilized methods are chosen as baselines for conducting comparative analyses. Furthermore, parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process. The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced. The experimental findings indicate that the ST-RNN model, incorporating both spatiotemporal modules, exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data. This affirms the reliability of the proposed model.

R. Jhaveri, A. Revathi, K. Ramana et al.

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing automated and intelligent applications that can handle data in fields such as health, cyber-security, and intelligent transportation systems. There are a range of strategies in the field of machine learning, including reinforcement learning, semi-supervised, unsupervised, and supervised algorithms. This study provides a complete study of managing real-time engineering applications using machine learning, which will improve an application's capabilities and intelligence. This work adds to the understanding of the applicability of various machine learning approaches in real-world applications such as cyber security, healthcare, and intelligent transportation systems. This study highlights the research objectives and obstacles that Machine Learning approaches encounter while managing real-world applications. This study will act as a reference point for both industry professionals and academics, and from a technical standpoint, it will serve as a benchmark for decision-makers on a range of application domains and real-world scenarios.

Justin Panich, B. Fong, S. Singer

Decelerating global warming is one of the predominant challenges of our time and will require conversion of CO2 to usable products and commodity chemicals. Of particular interest is the production of fuels, because the transportation sector is a major source of CO2 emissions. Here, we review recent technological advances in metabolic engineering of the hydrogen-oxidizing bacterium Cupriavidus necator H16, a chemolithotroph that naturally consumes CO2 to generate biomass. We discuss recent successes in biofuel production using this organism, and the implementation of electrolysis/artificial photosynthesis approaches that enable growth of C. necator using renewable electricity and CO2. Last, we discuss prospects of improving the nonoptimal growth of C. necator in ambient concentrations of CO2.

Yan Zhang, Xiupeng Shi, Shenmin Zhang et al.

Road accidents wreck lives. Could technology stop them from happening? Driving better road safety with technology and artificial intelligence are the key elements considered by several carmakers. The key aspect of transportation in the future is to build an ecosystem comprising autonomous, connected, electric and shared mobility. The evolution of autonomous vehicles (AVs) can potentially aid transportation to people and be deployed to resolve mobility-related pain for drivers and safety on roads while changing lanes. Thus, the intelligent assistance system should be smart enough to track such vehicles while deviating into another lane. In this paper, we propose a lane change prediction framework for feature learning, with the aim to have a deep and comprehensive understanding of lane change behaviors, meanwhile, reach a high performance based on the selected features. A time-step dataset with more than 1000 features is constructed from vehicle trajectory data. To identify the key features involved in the original feature set, an XGBoost-based three-step feature learning algorithm is proposed, which integrates the feature importance ranking, metric selection and recursive feature elimination. After analyzing the accuracy of test data from different time segment positions, the sliding window method is applied on a time-step dataset with filtered features to properly select time segments, which are flattened into corresponding time-series dataset for model prediction. In our case studies, a publicly available dataset, Next Generation SIMulation (NGSIM), is adopted to conduct experiments of feature learning and lane change prediction, where we achieved a new state-of-art accuracy with 97.6% under the time-series data of 75 selected features and 1-second window size with predictor XGBoost after adopting proposed three-step method, which is superior to the other state-of-the-art feature selection methods.

Yuan Ma, Xinhui Wang, Ting Su et al.

Hydrogels have been extensively used as scaffolds in tissue engineering for cell adhesion, proliferation, migration, and differentiation because of their high-water content and biocompatibility similarity to the extracellular matrix. However, submicron or nanosized pore networks within hydrogels severely limit cell survival and tissue regeneration. In recent years, the application of macroporous hydrogels in tissue engineering has received considerable attention. The macroporous structure not only facilitates nutrient transportation and metabolite discharge but also provides more space for cell behavior and tissue formation. Several strategies for creating and functionalizing macroporous hydrogels have been reported. This review began with an overview of the advantages and challenges of macroporous hydrogels in the regulation of cellular behavior. In addition, advanced methods for the preparation of macroporous hydrogels to modulate cellular behavior were discussed. Finally, future research in related fields was discussed.

Wei Li, Lin Zhao, Xiaoli Jiang et al.

The electrocatalytic performance of nanomaterials can be enhanced by fine‐tuning the coordination environment and number of low‐coordination atoms. Confinement engineering is the most effective strategy for the precise chemical synthesis of electrocatalysts through the modulation of electron transfer properties, atomic arrangement, and molecular structure in a confined region. It not only alters the coordination environments to adjust the formation mechanism of active centers, but also regulates the physicochemical properties of electrocatalysts. Consequently, the nucleation, transportation, and stabilization of intermediate species in electrocatalysis are optimized, and then improve the performance covering activity, stability, and selectivity. In this review, confinement engineering is introduced in terms of confined definition, classification, construction, and basic principles. Then, the latest advances in the confinement engineering of electrocatalysts for the oxygen reduction reaction, hydrogen evolution reaction, oxygen evolution reaction, nitrogen reduction reaction, and carbon dioxide reduction reaction are systematically evaluated. Furthermore, using representative experimental results and theoretical calculations, the structure‐activity relationships between confinement engineering and electrocatalytic performance are illustrated. Finally, potential challenges and future development prospects of confined electrocatalysts are highlighted, with a focus on controlling the construction of confined environments, investigating uncommon catalytic properties in confined regions, and practical applications.

Pengfei He, Haitao Cao, Jianhua Dong et al.

Composite geotextiles are frequently utilized in water delivery canals and other projects in cold regions. However, the peak strength of the soil-geotextile interface can be degrade due to continuous wet-dry-freeze-thaw (WDFT) cycles, potentially compromising the stability of the infrastructure. This paper presents the use of a temperature-controlled direct shear system to conduct direct shear tests on the interface between the soil and the geotextile composite under a variety of normal pressures, temperatures, and WDFT cycles. The study shows that WDFT has minimal effect on the shear stress-shear displacement curve at positive temperatures. At negative temperatures, the curve transitions from strain hardening to strain softening. At negative temperatures, the average increase in peak strength was 39 %, 56.9 %, and 65.3 % after a single WDFT cycle at −2 °C, −6 °C, and −10 °C, respectively. A gradual decline in peak strength was observed with each subsequent cycle, although the peak strength remained slightly higher than the initial strength. The average growth rates were 6.6 %, 19.3 %, and 29.2 % after ten cycles, respectively. The interfacial cohesion and friction angle exhibited similar trends to those of peak strength. The sensitivity analysis revealed that temperature and WDFT cycles exert a considerable influence on the interface cohesion.

FAN Liqian

Objective In existing train control systems based on moving blocking, there is a significant safety margin between adjacent trains. This leads to a mismatch between the transportation efficiency of a segment and its actual capacity needs, especially during peak passenger flow periods. Virtual formation trains can increase line capacity and better adapt to imbalanced commuter flows. Therefore, it is essential to develop a high-precision tracking control strategy suitable for virtual formation trains. Method To address the operational control challenges of virtual formation trains, a tracking control strategy based on sliding mode control is proposed. A train dynamics model and a virtual formation train operation control model are developed. A non-singular terminal sliding mode controller is designed, its performance is tested through simulations. Result & Conclusion Under the proposed virtual formation train control strategy, both the position and speed tracking errors of the virtual formation trains can asymptotically converge to zero along the sliding mode surface within a finite time. In the simulation verification tests involving four trains, when the operating state of the lead train changes, the other trains in the virtual formation quickly follow the expected speed trajectory, demonstrating the effectiveness of the proposed control strategy for virtual formation train tracking control.

Dipak Mahat, K. Niranjan, Chikkala S K V R Naidu et al.

Mechanical engineering businesses rely heavily on effective supply chain and logistics management to increase their productivity, efficiency, and competitiveness. Recent years have seen the rise of artificial intelligence (AI) approaches as potent instruments for improving logistics and supply chain operations. This abstract gives a thorough introduction to Ant Colony Optimization (ACO), an AI method inspired by nature, and how it may be used to improve mechanical engineering's supply chain and logistics. The foraging strategies of ants served as inspiration for the development of Ant Colony Optimization (ACO), a metaheuristic algorithm. It has garnered a lot of interest as a useful tool for supply chain and logistics optimization because of its capacity to tackle difficult optimization challenges. Inventory management, transportation routing, production scheduling, and demand forecasting are just some of the mechanical engineering problems that may be tackled with the help of ACO. Inventory optimization is a key use case for ACO in the context of mechanical engineering supply chain management. By modeling how ants locate food sources, ACO is able to ascertain optimum stock levels. To cut down on carrying costs and stockouts, it helps find the sweet spot between overstocking and understocking of raw materials and finished goods. Likewise, transportation route optimization is greatly aided by ACO. Transporting both inputs and outputs quickly and cheaply is crucial for factories. Taking into account variables like traffic, fuel prices, and delivery windows, ACO can determine the most efficient routes for trucks. This not only improves customer satisfaction through on-time deliveries but also decreases transportation expenses. Mechanical engineers may also use ACO to enhance production scheduling. Algorithms for Achieving Maximum Efficiency (ACOs) may plan the flow of production such that downtime, wasted materials, and lost revenue are kept to a minimum. Mechanical engineering firms may boost output and shorten manufacturing times by optimizing their production plans. Despite the inherent uncertainty in demand forecasting, ACO can improve prediction accuracy. Algorithms for adaptive costing and optimization (ACO) can aid mechanical engineering companies in making better judgments on production volumes and inventory levels by assessing past demand data and continuously revising forecasts based on real-time information. Overproduction and underproduction are avoided, resulting in cost savings and better service to customers. ACO may also be utilized to improve the process of finding and working with vendors. It may take into account several criteria, including supplier dependability, cost, and turnaround time, to select the most suitable vendors for mechanical engineering businesses. In addition to lowering material acquisition costs, this also guarantees a steady supply of high-quality raw materials. In conclusion, ACO-driven AI optimization of mechanical engineering's supply chain and logistics has several advantages, including lower costs, more efficiency, and happier clients. Companies in the mechanical engineering sector can gain an edge by implementing ACO algorithms into their inventory management, transportation routing, production scheduling, demand forecasting, and supplier selection processes. To remain competitive and resilient in the ever-changing area of mechanical engineering, the use of AI techniques like ACO will become increasingly vital as technology progresses.

Ahmad Nahar Quttoum, Ayoub Alsarhan, Mohammad Aljaidi et al.

Worldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid’s transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a <i>real-time pricing</i> model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the <i>shortest</i> least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a <i>preference function</i> with multiple weight variables that vary according to a route’s distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model.

Sicong Zhu, Yongdi Qiao, Wenjie Peng et al.

To estimate traffic facility-oriented particulate matter (PM) emissions, emission factors are both necessary and critical for traffic planners and the community of traffic professionals. This study used locally calibrated laser-scattering sensors to collect PM emission concentrations in a tunnel. Emission factors of both light-duty and heavy-duty vehicles were found to be higher in autumn compared to summer. Based on this study’s data analysis, PM emissions, in terms of mass, have a strong seasonal effect. The study also conducted a PM composition test on normal days and during haze events. Preliminary results suggested that the transformation of gaseous tailpipe emissions to PM is significant within the tunnel during a haze event. This study, therefore, recommends locally calibrated portable devices to monitor mobile-source traffic emissions. The study suggests that emission factor estimation of traffic modeling packages should consider the dynamic PM formation mechanism. The study also presents traffic policy implications regarding PM emission control.

Shambhavi Sharma, Ahtesham Bakht, Muhammad Jahanzaib et al.

Public transportation facilities, especially road crossings, which raise the pathogenic potential of urban environments, are the most conducive places for the transfer of germs between people and the environment. It is necessary to study the variety of the microbiome and describe its unique characteristics to comprehend these relationships. In this investigation, we used 16 S rRNA gene sample sequencing to examine the biological constituents and inhalable, thoracic, and alveolar particles in aerosol samples collected from busy areas in the Gangnam-gu district of the Seoul metropolitan area using a mobile vehicle. We also conducted a comparison analysis of these findings with the previously published data and tested for antibiotic resistance to determine the distribution of bacteria related to the human microbiome and the environment. Actinobacteria, Cyanobacteria, Bacteriodetes, Proteobacteria, and Firmicutes were the top five phyla in the bacterial 16 S rRNA libraries, accounting for >90 % of all readings across all examined locations. The most prevalent classes among the 12 found bacterial classes were Bacilli (45.812 %), Gammaproteobacteria (25.238 %), Tissierellia (13.078 %), Clostridia (5.697 %), and Alphaproteobacteria (5.142 %). The data acquired offer useful information on the variety of bacterial communities and their resistance to antibiotic drugs on the streets of Gangnam-gu, one of the most significant social centers in the Seoul metropolitan area. This work emphasizes the relevance of biological particles and particulate matter in the air, and it suggests more research is needed to perform biological characterization of the ambient particulate matter.

Hans Pohl, Magnus Karlström

We developed and used methodology to analyze scientific publications in Scopus relating to vehicle electrification and associated key enabling technologies: batteries, fuel cells and electric machines with power electronics. The global research landscape was mapped, and an analysis of the 16 most active countries was carried out. Vehicle electrification publications are rewarded with a high citation impact, and they include corporate actors to a great extent. China dominates in vehicle electrification research as well as in the enabling technologies, and China’s position is set to become even more dominating. Battery research has grown rapidly with a high citation impact, whereas the volume of research for the other enabling technologies was more constant during 2017–2021. Automakers’ research that has led to scientific publications was specifically studied. Ford Motor Company was the automaker with the highest number of vehicle electrification publications during 2017–2021. A large share of the automakers’ publications was co-authored with academic actors, and such publications were rewarded with a higher citation impact than those without. However, the share of international co-publications among the automakers was meager. It is concluded that the analysis of vehicle electrification publications gives an overview of the rapidly developing field. Moreover, the analysis of automakers’ involvement in such research is one way of obtaining one perspective on their strategies and priorities.

Patrick Miller, Alexandre G de Barros, L. Kattan et al.

S. Muruganandam, R. Srinivasan

B. Hong, G. Lu, Tianshuai Li et al.

With the rapid development of society and industry, novel technologies and materials related to pavement engineering are constantly emerging. However, with the continuous improvement of people’s demands, pavement engineering also faces more and more enormous challenges that the pavement materials must have excellent engineering properties and environmental benefits. Meanwhile, the intelligence is the mainstream development direction of modern society, and the development trend of future transportation infrastructure. Materials Genome Initiative, a program for the development of new materials that materials design is conducted by up-front simulations and predictions, followed by key validation experiments, the rapid development of science and technology and AI toolset (big data and machine learning) provide new opportunities and strong technical supports for pavement materials development that shorten the development-application cycle of new material, reduce cost and promote the application of new carriers such as intelligent sensing components in transportation engineering, to achieve the intelligence of transportation engineering. However, traditional pavement materials possess several unavoidable shortcomings, indicating that it is exceedingly difficult for them to meet the above requirements for future pavement materials. Therefore, the development of future new pavement materials, which can be designed on-demand as well as possessing enough mechanical properties, high durability, practical functionality, and high environmental protection, is urgent. In recent years, as a “designable” polymer material with various excellent engineering performances, polyurethane (PU) has been widely applied in pavement practices by changing the chemical structures of raw materials and their mix proportions, for instance pavement repairing material, permeable pavement material, tunnel paving material and bridge deck paving materials, etc. Although PU material has been widely applied in practices, a systematically summarization is still quite necessary for further understanding the working mechanism of PU materials and optimization it’s engineering applications. To fill the gap, this article puts forward the special requirements for future transportation infrastructure materials, and introduces the basic properties and working mechanism of PU materials in order to make up for the defects of conventional road materials. Based on this, this article also summarizes the engineering performances and environmental benefits of applying PU as the binder for different road infrastructure materials in recent years. Considering the gene-editable nature of polyurethane, further research of the on-demand design principles of PU pavement materials is recommended. The establishment of raw material gene database, material terminal performance database and their structure-activity relationship are highlighted. The current research is essential to the practice guidance and further optimization of the PU materials for road infrastructures, which in line with the future Carbon neutral policy.