Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

STUDY ON MECHANICAL BEHAVIOR OF CABLE - STAYED BRIDGE SUPPORT SYSTEM IN MULTI - FULCRUM UNBALANCED ROTATION

With the maturity and wide application of the bridge rotation construction technology, the single-fulcrum spherical hinge balance rotation can not meet the need of crossing over the high-speed railway catenary and other obstacles, so the unbalanced rotation construction is often needed. In order to ensure the stability and safety of the unbalanced rotation process, a multi-pivot rotation method is proposed. In this paper, the railway cable-stayed bridge over Harbin West Avenue is taken as the research object, and the multi-fulcrum rotating construction method over the metal contact network is adopted. The Abaqus finite element model is established, the influence of different rotation angular velocity, friction coefficient of slideway and position of support foot on the force of support system in the course of rotation is studied. The results show that, compared with the traditional single-pivot rotation, the force on the multi-pivot rotation support foot becomes the main force component, and the force on the spherical hinge decreases. The rotation angular velocity is positively correlated with Mises stress of the support foot and the spherical hinge. The friction coefficient of the slideway has a great influence on the force of the support foot. When the friction coefficient of the slideway changes in order of 0.02,0.04,0.06,0.08 and 0.1, the friction stress of the outer edge of the support foot increases linearly. Considering the force of spherical hinge and support foot, the best position of supporting foot is 7.3 m from the center of spherical hinge. The research in this paper can be used for reference in the future multi-pivot unbalanced rotation construction

Cold Atmospheric-Pressure Plasma Caused Protein Damage in Methicillin-Resistant \u3ci\u3eStaphylococcus aureus\u3c/i\u3e Cells in Biofilms

Biofilms formed by multidrug-resistant bacteria are a major cause of hospital-acquired infections. Cold atmospheric-pressure plasma (CAP) is attractive for sterilization, especially to disrupt biofilms formed by multidrug-resistant bacteria. However, the underlying molecular mechanism is not clear. In this study, CAP effectively reduced the living cells in the biofilms formed by methicillin-resistant Staphylococcus aureus, and 6 min treatment with CAP reduced the S. aureus cells in biofilms by 3.5 log10. The treatment with CAP caused the polymerization of SaFtsZ and SaClpP proteins in the S. aureus cells of the biofilms. In vitro analysis demonstrated that recombinant SaFtsZ lost its self-assembly capability, and recombinant SaClpP lost its peptidase activity after 2 min of treatment with CAP. Mass spectrometry showed oxidative modifications of a cluster of peaks differing by 16 Da, 31 Da, 32 Da, 47 Da, 48 Da, 62 Da, and 78 Da, induced by reactive species of CAP. It is speculated that the oxidative damage to proteins in S. aureus cells was induced by CAP, which contributed to the reduction of biofilms. This study elucidates the biological effect of CAP on the proteins in bacterial cells of biofilms and provides a basis for the application of CAP in the disinfection of biofilms

Unleashing floret fertility in wheat through the mutation of a homeobox gene

Floret fertility is a key determinant of the number of grains per inflorescence in cereals. During the evolution of wheat (Triticum sp.), floret fertility has increased, such that current bread wheat (Triticum aestivum) cultivars set three to five grains per spikelet. However, little is known regarding the genetic basis of floret fertility. The locus Grain Number Increase 1 (GNI1) is shown here to be an important contributor to floret fertility. GNI1 evolved in the Triticeae through gene duplication. The gene, which encodes a homeodomain leucine zipper class I (HD-Zip I) transcription factor, was expressed most abundantly in the most apical floret primordia and in parts of the rachilla, suggesting that it acts to inhibit rachilla growth and development. The level of GNI1 expression has decreased over the course of wheat evolution under domestication, leading to the production of spikes bearing more fertile florets and setting more grains per spikelet. Genetic analysis has revealed that the reduced-function allele GNI-A1 contributes to the increased number of fertile florets per spikelet. The RNAi-based knockdown of GNI1 led to an increase in the number of both fertile florets and grains in hexaploid wheat. Mutants carrying an impaired GNI-A1 allele out-yielded WT allele carriers under field conditions. The data show that gene duplication generated evolutionary novelty affecting floret fertility while mutations favoring increased grain production have been under selection during wheat evolution under domestication

SARS-CoV-2 bivalent mRNA vaccine with broad protection against variants of concern

IntroductionThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has rapidly spread around the globe. With a substantial number of mutations in its Spike protein, the SARS-CoV-2 Omicron variant is prone to immune evasion and led to the reduced efficacy of approved vaccines. Thus, emerging variants have brought new challenges to the prevention of COVID-19 and updated vaccines are urgently needed to provide better protection against the Omicron variant or other highly mutated variants.Materials and methodsHere, we developed a novel bivalent mRNA vaccine, RBMRNA-405, comprising a 1:1 mix of mRNAs encoding both Delta-derived and Omicron-derived Spike proteins. We evaluated the immunogenicity of RBMRNA-405 in BALB/c mice and compared the antibody response and prophylactic efficacy induced by monovalent Delta or Omicron-specific vaccine with the bivalent RBMRNA-405 vaccine in the SARSCoV-2 variant challenge.ResultsResults showed that the RBMRNA-405 vaccine could generate broader neutralizing antibody responses against both Wuhan-Hu-1 and other SARS-CoV-2 variants, including Delta, Omicron, Alpha, Beta, and Gamma. RBMRNA-405 efficiently blocked infectious viral replication and lung injury in both Omicron- and Delta-challenged K18-ACE2 mice.ConclusionOur data suggest that RBMRNA-405 is a promising bivalent SARS-CoV-2 vaccine with broad-spectrum efficacy for further clinical development

From Simulation to Synthesis: Architectural Modeling with Context-Based Encoding Using Data-Driven Computational Machines

Digital architecture has been a fast-evolving domain since the first computer program was written to imitate the technique of drawings. During the last few decades, various computational modeling tools have been developed to equip architects with the ability to computationally solve complex design problems and produce novel building geometries. However, the rapid propagation of programmable design tools has raised a critical situation where researchers are celebrating the productivity of computing power while losing a proper understanding of what computation is about. This brings digital architecture to a deadlock where most of our state-of-the-art computational models in architecture nowadays are not fundamentally advanced compared with their early beginnings decades ago. This situation motivates this work to build a comprehensive theoretical understanding regarding the capacity and limitations of our modeling techniques, and to explore a way out of the current dilemma. Towards this goal, this work emphasizes that computational modeling in architecture is not about geometric modeling which evaluates the capacity of models by the geometrical complexity of the designs, rather, it has always been a branch of artificial intelligence which helps architects to develop new ideas and concepts by the means of intelligent customized computer programs. Following this aspect, this work first reviews the state-of-the-art computational models as well as their counterparts in artificial intelligence. The review shows that, on a technical level, computational modeling in architecture faces two fundamental obstacles – the combinatorial explosion and the frame problem – resulting a phenomenon that most problem-solving programs in architecture succeeded in small-scale research had failed in larger-scale applications. These obstacles are credited for the conceptual assumption that having a faithful representation of the target is a necessity for dealing with the target. The modeling paradigm that follows this assumption is named as simulation. This work then reveals that the contemporary artificial intelligence challenges the paradigm of simulation by giving up the explicit representation of an object and shifting towards the object’s contextual relations to other objects. For contemporary artificial intelligence, any object can be the context of any object. Hence, the computational process no longer represents specific problem content nor the problem-solving process. This allows contemporary artificial intelligence to bypass the mentioned obstacles and use the same technique to solve problems in different fields. This new data-driven context-based modeling paradigm is named as synthesis. Finally, this work applies the idea of synthesis to architectural modeling, using a series of experiments as demonstrations. These experiments cover a wide range of topics, including urban-scale physics, urban dynamics, and ontologies for representations. The experiments show that by giving up reasoning we outperform the conventional modeling techniques. This derives an important implication that, as now all problems can be solved by the same principle, the new challenge ahead of us is no longer how to solve problems, but what questions to ask. This corresponds to the changing role of computers from the tools for computation, to the machines for automation, and finally to the infrastructures for communication

Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system

This paper presents a method for the automatic generation of a spatial architectural layout from a user-specified architectural program. The proposed approach binds a multi-agent topology finding system and an evolutionary optimization process. The former generates topology satisfied layouts for further optimization, while the latter focuses on refining the layouts to achieve predefined architectural criteria. The topology finding process narrows the search space and increases the performance in subsequent optimization. Results imply that the spatial layout modeling and the multi-floor topology are handled

Plant and Floret Growth at Distinct Developmental Stages During the Stem Elongation Phase in Wheat

Floret development is critical for grain setting in wheat (Triticum aestivum), but more than 50% of grain yield potential (based on the maximum number of floret primordia) is lost during the stem elongation phase (SEP, from the terminal spikelet stage to anthesis). Dynamic plant (e.g., leaf area, plant height) and floret (e.g., anther and ovary size) growth and its connection with grain yield traits (e.g., grain number and width) are not clearly understood. In this study, for the first time, we dissected the SEP into seven stages to investigate plant (first experiment) and floret (second experiment) growth in greenhouse- and field-grown wheat. In the first experiment, the values of various plant growth trait indices at different stages were generally consistent between field and greenhouse and were independent of the environment. However, at specific stages, some traits significantly differed between the two environments. In the second experiment, phenotypic and genotypic similarity analysis revealed that grain number and size corresponded closely to ovary size at anthesis, suggesting that ovary size is strongly associated with grain number and size. Moreover, principal component analysis (PCA) showed that the top six principal components PCs explained 99.13, 98.61, 98.41, 98.35, and 97.93% of the total phenotypic variation at the green anther, yellow anther, tipping, heading, and anthesis stages, respectively. The cumulative variance explained by the first PC decreased with floret growth, with the highest value detected at the green anther stage (88.8%) and the lowest at the anthesis (50.09%). Finally, ovary size at anthesis was greater in wheat accessions with early release years than in accessions with late release years, and anther/ovary size shared closer connections with grain number/size traits at the late vs. early stages of floral development. Our findings shed light on the dynamic changes in plant and floret growth-related traits in wheat and the effects of the environment on these traits