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Emergency bridges are crucial to providing emergency relief supplies to those isolated residents affected by natural disasters. To increase the bridge’s load-bearing capacity and adaptability for different obstacles in military or natural disaster relief, a novel Bi-Plane V (BPV) shaped prestressed unbonded retrofit cable system was applied to reinforce an emergency bridge assembled using glass fiber-reinforced polymer (GFRP) rectangular tubes and steel components. The reinforcing system can prestress steel wire cables by adjusting the length of the vertical brace without using a hydraulic jack or any surface preparation. A pre-tightened teeth connection (PTTC) was efficaciously devised for fastening pultruded GFRP rectangular tubes and steel sleeves. All the parts including the prestressed system and bridge modules can be assembled and disassembled rapidly through single lug and yoke plate joints. Bending tests were conducted to investigate the structural responses under three different conditions. Results demonstrated that the novel prestressed cable system enhanced the overall stiffness of the bridge, improving the mechanical performance and stress state of the GFRP tubes in bridge modules. The dynamic parameter identification test results indicated that by applying about 9.85 kN cable force in each trackway, the first-order natural vibration frequency increased about 37.5 %. Vehicle-through load test results showed that the bridge had the bearing capacity of medium-sized cars, and the corresponding impact factors at about 5 km/h are within the limit of the code.

Abstract Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Abstract In data-driven materials design where the target materials have limited data, the transfer machine learning from large known source materials, becomes a demanding strategy especially across different crystal structures. In this work, we proposed a deep transfer learning approach to predict thermodynamically stable perovskite oxides based on a large computational dataset of spinel oxides. The deep neural network (DNN) source domain model with “Center-Environment” (CE) features was first developed using the formation energy of 5329 spinel oxide structures and then was fine-tuned by learning a small dataset of 855 perovskite oxide structures, leading to a transfer learning model with good transferability in the target domain of perovskite oxides. Based on the transferred model, we further predicted the formation energy of potential 5329 perovskite structures with combination of 73 elements. Combining the criteria of formation energy and structure factors including tolerance factor (0.7 < t ≤ 1.1) and octahedron factor (0.45 < μ

Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models for scientific research. In this Perspective, we discuss a few central challenges faced by ML practitioners in developing meaningful representations, including handling the complexity of real-world industry-relevant materials, combining theory and experimental data sources, and describing scientific phenomena across timescales and length scales. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices.

Soft composites are widely employed in industrial and biomedical fields, which often serve as load-bearing structural materials by virtue of a special combination of high strength, high toughness, and low flexural stiffness. Understanding the toughening mechanism of such composites is crucial for designing the next-generation soft materials. In this review, we give an overview of recent progress in soft composites, focusing on the design strategy, mechanical properties, toughening mechanisms, and relevant applications. Fundamental design strategies for soft composites that dissipate energy at different length scales are firstly described. By subsequently elucidating the synergistic effects of combining soft and hard phases, we show how a resulting composite can achieve unprecedented mechanical performance by optimizing the energy dissipation. Relevant toughening models are discussed to interpret the superior strength and fracture toughness of such soft composites. We also highlight relevant applications of these soft composites by taking advantage of their special mechanical responses.

The geothermal resources in mountainous areas are usually controlled by neotectonic faults. To minimize the risk in site selection for geothermal drilling, the controlling mechanism must be identified. Based on the neotectonic control theory for geothermal resources, the occurrence of the geothermal resources in the mountainous area in southeastern Zhangjiakou city was investigated. The investigation was divided into three stages. Firstly, field investigation for neotectonics was carried out to characterize the kinematics and the stress state of the faults, including fault occurrences, tectonic stages, and paleostress analysis, and in total 19 neotectonic faults were confirmed. Based on Riedel shear model and present stress field, their conductivity for geothermal water was inferred; secondly, geophysical and geochemical surveys were applied to identify the stress state and characterize the occurrence of the potential water conductive faults. The results showed that the combined use of magnetotelluric sounding (MT), controllable source audio magnetotelluric sounding (CSAMT), and 2D seismologic survey is effective in delineating fault occurrence, strata distribution, and water conductivity within 1,000 m depth, while measurement of 222Rn activity in soil gas across the neotectonic faults is effective to investigate the groundwater conductivity of the faults, and all the neotectonic faults in the study area striking clockwise from N5°E to N25°W are considered to be water conductive; thus, the areas at the intersection zones of neotectonic faults in the basin in groundwater runoff and discharge regions are promising targets for geothermal exploration. This conceptual model was verified by the geothermal fields already discovered in the study area and proved to be reasonable, and then a potential drilling site was predicted and proved to be successful. It is suggested that this workflow for geothermal exploration is suitable for Zhangjiakou city and may also be applied to other mountainous areas.

The gas diffusion layer (GDL) is an important component of proton exchange membrane fuel cells (PEMFCs), and its porosity distribution has considerable effects on the transport properties and durability of PEMFCs. A 3-D two-phase flow computation fluid dynamics model was developed in this study, to numerically investigate the effects of three different porosity distributions in a cathode GDL: gradient-increasing (Case 1), gradient-decreasing (Case 3), and uniform constant (Case 2), on the gas–liquid transport and performance of PEMFCs; the novelty lies in the porosity gradient being along the channel direction, and the physical properties of the GDL related to porosity were modified accordingly. The results showed that at a high current density (2400 mA·cm−2), the GDL of Case 1 had a gas velocity of up to 0.5 cm·s−1 along the channel direction. The liquid water in the membrane electrode assembly could be easily removed because of the larger gas velocity and capillary pressure, resulting in a higher oxygen concentration in the GDL and the catalyst layer. Therefore, the cell performance increased. The voltage in Case 1 increased by 8% and 71% compared to Cases 2 and 3, respectively. In addition, this could ameliorate the distribution uniformity of the dissolved water and the current density in the membrane along the channel direction, which was beneficial for the durability of the PEMFC. The distribution of the GDL porosity at lower current densities had a less significant effect on the cell performance. The findings of this study may provide significant guidance for the design and optimization of the GDL in PEMFCs.

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN , PhysNet , SchNet , MPNN , MPNN-transformer , and TorchMD-NET . We employ these AI models along with the benchmark QM9 , hMOF , and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.

Fog computing emerges as a great candidate to mitigate the unsolved problems of cloud computing. Data migration between fog nodes and datacenters is placing huge bandwidth requirements on substrate optical networks. To increase network capacity and to improve network flexibility, broad attention has been given to flexible-grid technology. This paper addresses the issue of how to migrate substrate optical networks from the fixed-grid network era to the flexible-grid network era gradually, due to the unbearable capital expenditure and service stability. First, we model the network migration and elucidate the upgrade probability by calculating the node demands for upgrading. Then, based on the upgrade probability, three schemes are proposed to construct Potential Upgrade Nodes Group (PUNG). Besides, to maintain the traffic stability, a Migration-aware Service Provisioning (MSP) scheme is proposed based on PUNG. Numerical results illustrate that the proposed MSP scheme can effectively enhance the traffic stability compared to Non-migration-aware Service Provisioning (NSP) scheme, and the connection interrupted ratio is excessively reduced.

Entrepreneurship education complements vocational education in helping students develop their career prospects. This empirical study comprehensively analyzed sample data of 13,885 students from 40 “Double High-level Plan” higher vocational colleges in China using robust standard error regression analysis and other methods. The results showed that Entrepreneurship Practice (EP), Entrepreneurship Curriculum (EC), and Integration of Entrepreneurship Education and Professional Education (IEEPE) have a significant positive effect on Entrepreneurship Education Performance (EEP), with EP being the most important factor. Furthermore, ascribed factors (gender, household registration, only child or not, whether parents have entrepreneurial experience) and self-achieved factors (double high-level type, school area, subject major, whether to accept social entrepreneurship education) were found to affect students' perception of investment in entrepreneurship education. The study summarizes the existing problems of entrepreneurship education in “Double High-level Plan” higher vocational colleges and proposes four suggestions: pursue the integrated development of entrepreneurship education and “Double High-level” construction, advance both theoretical education and practical education, promote digital reform of the “three teaches” (teachers, teaching materials, and teaching methods), and develop entrepreneurship education in a comprehensive and balanced manner. This has certain theoretical and practical significance for the improvement of entrepreneurship education in other developing countries.

Recently, network traffic has been growing exponentially and almost reached the physical capacity limit of single mode fibers. Space division multiplexing (SDM) is a promising technology to overcome the looming fiber capacity crunch. Especially, few-mode multi-core fibers (FM-MCFs) can aggregate multiple cores into one fiber and two or more modes can be transmitted in one core, which can greatly increase the capacity yet introduce crosstalk constraints including inter- and intra-core crosstalk. To our best knowledge, there is no accurate crosstalk calculation model study in SDM optical networks with FM-MCFs. To address this issue, we first introduce the machine learning into the crosstalk prediction phase and propose a novel crosstalk estimation model (CEM) exploiting the beam propagation method called CEM-beam propagation method (BPM)-machine learning (ML), which can be used to evaluate the crosstalk during the design for the resource allocation scheme. Then, a crosstalk aware core, mode, and spectrum assignment (CA-CMSA) strategy is presented. The simulation results for crosstalk estimation at the wavelength level indicate that the crosstalk at lower frequencies is less than that at higher frequencies. Thus, the lower frequencies are always the first choice in the spectrum resource assignment phase. In addition, for our specific training set, the Levenberg-Marquardt (LM) algorithm based on machine learning performs better on the training, including regression values measurement and time consumption. The simulation results of the proposed CA-CMSA scheme also show that the resource allocation algorithm based on LM can improve resource utilization without increasing total connection set-up time. Thus, it will be the best choice for the resource assignment in SDM networks with FM-MCFs.

Achievable capacity of optical fiber is approaching its physical limitation in frequency domain. Space division multiplexing (SDM) technology can scale the network capacity using multi-core fiber and multi-mode fiber. In order to provide high-speed transmission services with fine granularities, SDM enabled elastic optical networks (SDM-EONs) become a promising candidate of future optical transport networks. However, since the spectrum status in SDM-EONs becomes more complex with the introduction of spatial dimension, the issue of spectrum fragmentation will be more serious in SDM-EONs compared in simple EONs. To remedy the issue of spectrum fragmentation in SDM-EONs, we propose a crosstalk-aware spectrum defragmentation (CASD) algorithm based on a metric, i.e., spectrum compactness (SC), which we define to measure the spectrum status in the SDM-EONs. Simulation results show that the proposed CASD algorithm can achieve better performance than a benchmark algorithm in terms of blocking probability and spectrum utilization. We also compare CASD algorithm with different SC thresholds in bandwidth blocking probability and spectrum utilization. Among them, CASD algorithm with SC threshold of 50 performs the best. It can achieve low spectrum moving times as well as low defragmentation latency.

Inter-datacenter services such as data replication and virtual machine migration contribute significantly to the underlying network traffic. These bulk-data transfer services can tolerate certain delay if the completion time is guaranteed with a pre-defined deadline. In this paper, we propose joint frequency and time-domain optimization algorithms of static routing and spectrum assignment (RSA) for these deadline-driven bulk-data transfer requests in elastic optical networks. We formulate the problem as an integer linear program (ILP) and then propose six scheduling schemes, which combine three request ordering strategies with two RSA algorithms. Simulation results show that compared with ILP, our schemes can achieve the same performance in terms of spectrum utilization with efficient time complexity. Furthermore, based on the numerical results and computational efficiency, we provide a guideline on choosing scheduling schemes.