Dynamic analysis of a train-bridge system to vessel collision and running safety of high-speed trains

A dynamic analysis model is established for a train-bridge system subjected to a vessel collision-load. A (32+48+32) m continuous bridge with PC box girders and a CRH2 high-speed train are considered as a case study. The whole histories of the train running on the bridge are simulated while the vessel collision load acting on the pier, based on which the dynamic responses of the bridge and the running safety indices of the train on the bridge are evaluated. The results show that the dynamic responses of the bridge are greatly increased by the vessel collision load, resulting in a big influence on the running safety of high-speed train

Bridge damage identification from moving load induced deflection based on wavelet transform and Lipschitz exponent

The wavelet transform and Lipschitz exponent perform well in detecting signal singularity.With the bridge crack damage modeled as rotational springs based on fracture mechanics, the deflection time history of the beam under the moving load is determined with a numerical method. The continuous wavelet transformation (CWT) is applied to the deflection of the beam to identify the location of the damage, and the Lipschitz exponent is used to evaluate the damage degree. The influence of different damage degrees,multiple damage, different sensor locations, load velocity and load magnitude are studied.Besides, the feasibility of this method is verified by a model experiment

A novel time computation model based on Algorithm complexity for high level data intensive scientific workflow design and scheduling

Scientific workflow offers a framework for cooperation between remote and shared resources on a grid computing environment (GCE) for scientific discovery. One major function of scientific workflow is to schedule a collection of computational subtasks in well-defined orders for efficient outputs by estimating task duration at runtime. In this paper, we propose a novel time computation model based on algorithm complexity (termed as TCMAC model) for high-level data intensive scientific workflow design. The proposed model schedules the subtasks based on their durations and the complexities of participant algorithms. Characterized by utilization of task duration computation function for time efficiency, the TCMAC model has three features for a full-aspect scientific workflow including both dataflow and control-flow: (1) provides flexible and reusable task duration functions in GCE; (2) facilitates better parallelism in iteration structures for providing more precise task durations; and (3) accommodates dynamic task durations for rescheduling in selective structures of control flow. We will also present theories and examples in scientific workflows to show the efficiency of the TCMAC model, especially for control-flow

Genomic Analysis and Antimicrobial Components of M7, an Aspergillus terreus Strain Derived from the South China Sea

As a typical filamentous fungus, Aspergillus species are highly adaptive to diverse ecological habitats, represented by their occurrence in both terrestrial and marine environments; this could plausibly be ascribed to their preeminent biological diversity and metabolic variability. In this context, marine-derived Aspergillus fungi have recently attracted great interest as a promising potential source of biologically active compounds. The present study depicts the genomic and chemical profiles of M7, a strain of Aspergillus terreus isolated from mussels in the South China Sea; the crude extracts of its soybean fermentation exhibit potent growth-inhibitory properties against A. baumannii and P. aeruginosa. Subsequently, functional genomics analysis based on sequences implied a considerable biosynthetic potential of the strain, which is substantiated by the 75 biosynthetic gene clusters (BGCs) identified via genome mining; the majority (49 BGCs) were functionally unknown. Representatively, the putative biosynthetic pathways of terramide A and terramide B, the bacteriostatic products obtained through chemical separation and characterized from the fermentation, could not be allocated to any known BGC, highlighting the metabolic potency and diversity of this strain. Meanwhile, based on a comprehensive analysis of fermentation conditions, we confirmed that the presence of environmental iron was inversely correlated with antimicrobial characteristics of the strain M7, presumably due to the interference in the biosynthetic pathway or bioactive mechanisms of the antimicrobial components, e.g., terramide A and B. Our observations provide genomic and biochemical insight into the metabolic and ecological novelties of this strain, underpinning the diversity of biosynthetic flexibility and adaptive strategies of marine Aspergillus fungi

Efficiency Comparison of Public Hospitals under Different Administrative Affiliations in China: A Pilot City Case

This study seeks to measure the efficiency disparity and productivity change of tertiary general public hospitals in Wuhan city, central China from the perspective of administrative affiliations by using panel data from 2013 to 2017. Sample hospitals were divided into three categories, namely provincial hospitals, municipal hospitals, and other levels of hospitals. Data envelopment analysis with bootstrapping technique was used to estimate efficiency scores, and a sensitive analysis was performed by varying the specification of model by considering undesirable outputs to test robustness of estimation, and efficiency evolution analysis was carried out by using the Malmquist index. The results indicated that the average values of provincial hospitals and municipal hospitals have experienced efficiency improvement over the period, especially after the initiation of Pilot Public Hospital Reform, but hospitals under other affiliations showed an opposite trend. Meanwhile, differences of administrative subordination in technical efficiency of public hospitals emerged, and the disparity was likely to grow over time. The higher efficiency of hospitals affiliated with municipality, as compared with those governed by province and under other administrative affiliations, may be attributed to better governance and organization structure

Prediction model for the water jet falling point in fire extinguishing based on a GA-BP neural network.

Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored external factors, such as environmental conditions and the status of the fire engine, even though the water jet falling point location prediction was often associated with these parameters and showed a nonlinear relationship. This paper constructed a BP (Back Propagation) neural network model. The fire gun nozzle characteristics were included as model inputs, and the water discharge point coordinates were the model outputs; thus, the model could precisely predict the water discharge point with small error and high precision to determine an accurate firing position and allow for the timely adjustment of the spray gun. To improve the slow convergence and local optimality problems of the BP neural network (BPNN), this paper further used a genetic algorithm to optimize the BPNN (GA-BPNN). The BPNN can be used to optimize the weights in the network to train them for global optimization. A genetic algorithm was introduced into the neural network approach, and the water jet landing prediction model was further improved. The simulation results showed that the prediction accuracy of the GA-BP model was better than that of the BPNN alone. The established model can accurately predict the location of the water jet, making the prediction results more useful for firefighters

Editable Neural Radiance Fields Convert 2D to 3D Furniture Texture

Our work presents a neural network designed to convert textual descriptions into 3D models. By leveraging the encoder-decoder architecture, we effectively combine text information with attributes such as shape, color, and position. This combined information is then input into a generator to predict new furniture objects, which are enriched with detailed information like color and shape.[1] The predicted furniture objects are subsequently processed by an encoder to extract feature information, which is then utilized in the loss function to propagate errors and update model weights. After training the network, we can generate new 3D objects solely based on textual input, showcasing the potential of our approach in generating customizable 3D models from descriptive text.[2