High strength and high toughness are usually mutually exclusive in engineering materials. In ceramics, improving toughness usually relies on the introduction of a metallic or polymeric ductile phase, but this decreases the material's strength and stiffness as well as its high-temperature stability. Although natural materials that are both strong and tough rely on a combination of mechanisms operating at different length scales, the relevant structures have been extremely difficult to replicate. Here, we report a bioinspired approach based on widespread ceramic processing techniques for the fabrication of bulk ceramics without a ductile phase and with a unique combination of high strength (470 MPa), high toughness (22 MPa m(1/2)), and high stiffness (290 GPa). Because only mineral constituents are needed, these ceramics retain their mechanical properties at high temperatures (600 °C). Our bioinspired, material-independent approach should find uses in the design and processing of materials for structural, transportation and energy-related applications.
Qingsheng Gao, Wenbiao Zhang, Zhangping Shi
et al.
As the key of hydrogen economy, electrocatalytic hydrogen evolution reactions (HERs) depend on the availability of cost‐efficient electrocatalysts. Over the past years, there is a rapid rise in noble‐metal‐free electrocatalysts. Among them, transition metal carbides (TMCs) are highlighted due to their structural and electronic merits, e.g., high conductivity, metallic band states, tunable surface/bulk architectures, etc. Herein, representative efforts and progress made on TMCs are comprehensively reviewed, focusing on the noble‐metal‐like electronic configuration and the relevant structural/electronic modulation. Briefly, specific nanostructures and carbon‐based hybrids are introduced to increase active‐site abundance and to promote mass transportation, and heteroatom doping and heterointerface engineering are encouraged to optimize the chemical configurations of active sites toward intrinsically boosted HER kinetics. Finally, a perspective on the future development of TMC electrocatalysts is offered. The overall aim is to shed some light on the exploration of emerging materials in energy chemistry.
It has long been known that social factors influence health. However, a recent upsurge of interest in addressing social needs within the context of health care delivery has emerged,1-3 driven in part by a recognition that achieving high-quality, high-value health care may require attention to nonmedical factors such as housing, food, and transportation. Addressing social determinants of health may be important for any person during periods of increased need (eg, after discharge from the hospital) and particularly important for addressing health disparities in communities with greater social need. A new report4 from a consensus committee of the National Academies of Sciences, Engineering, and Medicine provides recommendations to guide practice and policy discussions in this area.
Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.
Unmanned aerial vehicles (UAVs) are gaining considerable interest in transportation engineering in order to monitor and analyze traffic. This systematic review surveys the scientific contributions in the application of UAVs for civil engineering, especially those related to traffic monitoring. Following the PRISMA framework, 34 papers were identified in five scientific databases. First, this paper introduces previous works in this field. In addition, the selected papers were analyzed, and some conclusions were drawn to complement the findings. It can be stated that this is still a field in its infancy and that progress in advanced image processing techniques and technologies used in the construction of UAVs will lead to an explosion in the number of applications, which will result in increased benefits for society, reducing unpleasant situations, such as congestion and collisions in major urban centers of the world.
This study addresses two major limitations in the current evaluation system for urban rail train drivers’ emergency handling capability: the lack of clearly defined criteria, and an overemphasis on technical skills to the neglect of psychological factors. We innovatively construct a multidimensional evaluation framework based on the Physio-Psycho-Machine-Environment-Management (PPMEM) model. Through a systematic analysis of the core components of emergency response capability and its influencing factors, a mechanism model rooted in “Human-Machine-Environment-Management” theory is established. Empirically, 30 key influencing factors were identified and categorized into seven dimensions: cognitive, physiological, skill-based, psychological, equipment, environmental, and managerial. A mixed-methods approach was adopted. During the qualitative phase, a system of influencing factors was determined through field studies and in-depth expert interviews. In the quantitative phase, a questionnaire survey was administered to employees of Kunming Rail Transit Operations Co., Ltd. (N = 538 valid responses), and a multidimensional evaluation model was developed using structural equation modeling (SEM) with Amos 26 Graphics. The results indicate that the total effects of latent variables on emergency handling capability, in descending order, are: psychological factors (β = 0.214) > physiological factors (β = 0.212) > environmental factors (β = 0.205) > equipment status (β = 0.126) > cognitive factors (β = 0.105) = skill-based factors (β = 0.105) > managerial factors (β = 0.102). Notably, psychological, physiological, and environmental factors all exhibited effect sizes exceeding the significant threshold of 0.2, constituting a core group of determinants for emergency response performance. Therefore, metro operators should prioritize improvements in drivers’ workload management, mental health support, and environmental adaptability, supplemented by targeted skill and cognitive training, as well as policy refinement. These measures will contribute to a systematic enhancement of emergency response capabilities. The findings provide both a theoretical foundation and practical guidance for strengthening emergency management systems in urban rail transit.
Feras Alasali, Mohammed I. Abuashour, Waleed Hammad
et al.
The rapidly growing global need for environmentally friendly energy solutions has inspired extensive research and development efforts aimed at harnessing the potential of hydrogen energy. Hydrogen, with its diverse applications and relatively straightforward acquisition, is viewed as a promising energy carrier capable of tackling pressing issues, such as carbon emissions reduction and energy storage. This study conducts a preliminary investigation into effective hydrogen generation and storage systems, encompassing methods like water electrolysis, biomass reforming, and solar‐driven processes. Specifically, the study focuses on assessing the potential of nanostructured catalysts and innovative materials to enhance the productivity and versatility of hydrogen energy systems. Additionally, the utilization of novel materials not only improves hydrogen storage capacity and safety but also opens up possibilities for inventive applications, including on‐demand release and efficient transportation. Furthermore, critical factors such as catalyst design, material engineering, system integration, and technoeconomic viability are examined to identify challenges and chart paths for future advancements. The research emphasizes the importance of fostering interdisciplinary collaborations to advance hydrogen energy technologies and contribute to a sustainable energy future.
The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.
Agglomerate fog event poses more serious threat than normal foggy weather to expressway traffic safety, due to its localized nature and suddenly uneven formation. However, vision-based fog detection methods typically estimate visibility for individual images and ignore the difference in the characteristics of even and uneven fog, lacking use of temporal information to differentiate between normal foggy weather and agglomerate fog events. Meanwhile, detection of fog at night faces strong interference from car lights that is always overlooked. This study proposes a nighttime agglomerate fog event detection (AFED) method for videos, taking into account car light interference. Depth disparity feature is constructed based on the information entropy of depth estimation result. In order to build a metric for uneven characteristics in the field of view, we creatively introduce the Moran’s index to establish uneven feature, generating two-dimensional feature time series for each video. By extracting interpretable features from the two-dimensional feature time series after removing car light interference frames, a classification model based on extreme gradient boosting (XGBoost) is built to differentiate agglomerate fog, normal fog, and no fog videos. Experiments are carried out utilizing real monitoring data from roadside surveillance cameras to validate the effectiveness of features and model. Furthermore, a fog event detection dataset containing over 1 500 videos is established, making up data scarcity for vision-based agglomerate fog event detection and providing support for future research.
Erik Giesen Loo, Robert Corbally, Lewis Feely
et al.
Abstract The ability to understand the underlying fundamentals of traffic flow behaviour facilitates improved planning and decision‐making for road operators. This paper presents an overview of the various models which can be used to describe the interaction between the different parameters governing traffic flows. 5‐years of measured data from Ireland's M50 motorway are used to demonstrate the application of traffic flow theory using real data, and a detailed investigation of factors affecting the fundamental traffic behaviour is presented. The road capacity is shown to be impacted by different traffic behaviour during morning and evening‐peak periods, during dry vs. wet weather conditions and between lanes on the approach to junctions. It is demonstrated that the mean vehicle length is an important factor to consider when using traffic flow models. A novel 3‐dimensional fundamental diagram model linking mean vehicle speed, mean vehicle length, and density is introduced which enhances capacity estimation and illustrates the importance of considering vehicle length when using the fundamental diagram to interpret traffic flows and estimate the capacity of the motorway.
The changes in dip angles of different working faces in the area affected by folding structures cause the variability of mining pressure features. In order to solve the above problem, with the seventh mining area of Baojishan Coal Mine as the engineering background, a combination of on-site research, theoretical analysis, numerical simulation, and on-site industrial experiments is used. The dynamic and static loads during mining of different working faces in coal seams with varying dip angles are studied. The results indicate the following points. ① The accumulated acoustic emission(AE) energy of the coal rock composite system with a stiffness value greater than 0 is smaller than that of the coal rock composite system with a stiffness value less than 0. This indicates that when the stiffness value of the coal rock composite system is less than 0, AE energy is more likely to accumulate. When the stiffness value of the coal rock composite system is less than 0, the larger its absolute value, the higher the AE energy can be accumulated. ② As the dip angle of the coal seam increases, the concentrated static load inside the solid coal side of the goaf roadway decreases, and the concentrated static load inside the coal pillar side increases. The hanging top section required for the cracking of the high and thick hard key layer is longer. ③ When the dip angle of the coal seam is small, the combined system of coal and rock in the two sides of the goaf roadway is prone to inducing dynamic failure type II rock burst under the combined action of dynamic and static loads. When the dip angle of the coal seam is large, the coal rock combination system inside the coal pillar side of the goaf roadway is prone to inducing static or dynamic failure type I rock burst under high concentrated static load. ④ During the mining period of the 705 fully mechanized top coal caving face, the coal pillar side of the goaf roadway is prone to inducing static or dynamic failure type I rock burst. After implementing anti erosion measures, the electromagnetic radiation value decreases by up to 67.3%. The coal rock combination system is not easy to induce rock burst.