High-strong-ductile magnesium alloys by interactions of nanoscale quasi-long period stacking order unit with twin.

Magnesium alloys with high strength in combination of good ductility are especially desirable for applications in transportation, aerospace and bio-implants owing to their high stiffness, abundant raw materials, and environmental friendliness. However, the majority of traditional strengthening approaches including grain refining and precipitate strengthening can usually prohibit dislocation movement at the expense of ductility invariably. Herein, we report an effective strategy for simultaneously enhancing yield strength (205 MPa, 2.41 times) and elongation (23%, 1.54 times) in a Mg-0.2Zn-0.6Y (at.%) alloy at room temperature, based on the formation of a nanosized quasi-long period stacking order unit (QLPSO)-twin structure by ultrahigh-pressure treatment followed by annealing. The formation reason and strong-ductile mechanism of the unique QLPSO-twin structure have been clarified by transmission electron microscopy observations and molecule dynamics simulations. The improved strength is mainly associated with the presence of nanosized QLPSO and the modified <86.3o QLPSO-twin boundary (TB) interface, effectively pinning dislocation movement. Comparatively, the enhanced ductility is related to the <3.7o QLPSO-TB interface and micro-kinks of nanoscale QLPSO, providing some paths for plastic deformation. This strategy on the QLPSO-twin structure might provide an alternative perspective for designing innovative hexagonal close-packed structural materials with superior mechanical properties

A real-world study of anlotinib combined with GS regimen as first-line treatment for advanced pancreatic cancer

BackgroundAnlotinib may boost the efficacy of pancreatic cancer (PC) treatment if timely added to the GS regimen (Gemcitabine, Tegafur-gimeracil-oteracil potassium); however, no data has been published. This study evaluated the safety and efficacy of anlotinib in combination with the GS regimen(hereafter referred to as the A+GS regimen) in the first-line treatment of patients with unresectable or metastatic PC.MethodsPatients with unresectable or metastatic PC treated at Yueyang Central Hospital and Yueyang People’s Hospital between October 2018 and June 2022 were enrolled in this retrospective real-world investigation. Treatment efficacy was evaluated based on the overall survival (OS), progression-free survival (PFS), disease control rate (DCR), and objective response rate (ORR), while the treatment safety was assessed by the frequency of major adverse events (AEs).ResultsSeventy-one patients were included in this study, 41 in the GS group and 30 in the A+GS group. The A+GS group had a longer mPFS than the GS group (12.0 months (95% CI, 6.0–18.0) and 6.0 months (95% CI, 3.0–8.1)), respectively (P = 0.005). mOS was longer in the GS+A group) when compared with the GS group (17.0 months (95%CI, 14.0–20.0) and 10.0 months (95% CI, 7.5–12.5)), respectively (P = 0.018). The GS+A group had higher ORR (50.0% vs 26.8%, P = 0.045) and DCR (83.3% vs 58.5%, P = 0.026). Furthermore, there were no grade 4-5 AEs and no treatment-related deaths, and no discernible increase in AEs in the GS+A group when compared with the GS group.ConclusionThe A+GS regimen therapy holds great promise in managing treatment-naive advanced PC, except that future prospective studies with larger sample sizes and multiple centers are required to determine its efficacy and safety

A hyperchaotic system without equilibrium

Abstract: This article introduces a new chaotic system of 4-D autonomous ordinary differential equations, which has no equilibrium. This system shows a hyper-chaotic attractor. There is no sink in this system as there is no equilibrium. The proposed system is investigated through numerical simulations and analyses including time phase portraits, Lyapunov exponents, and Poincaré maps. There is little difference between this chaotic system and other chaotic systems with one or several equilibria shown by phase portraits, Lyapunov exponents and time series methods, but the Poincaré maps show this system is a chaotic system with more complicated dynamics. Moreover, the circuit realization is also presented

Detecting change in the Indonesian Seas

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sprintall, J., Gordon, A. L., Wijffels, S. E., Feng, M., Hu, S., Koch-Larrouy, A., Phillips, H., Nugroho, D., Napitu, A., Pujiana, K., Susanto, R. D., Sloyan, B., Yuan, D., Riama, N. F., Siswanto, S., Kuswardani, A., Arifin, Z., Wahyudi, A. J., Zhou, H., Nagai, T., Ansong, J. K., Bourdalle-Badie, R., Chanuts, J., Lyard, F., Arbic, B. K., Ramdhani, A., & Setiawan, A. Detecting change in the Indonesian Seas. Frontiers in Marine Science, 6, (2019):257, doi:10.3389/fmars.2019.00257.The Indonesian seas play a fundamental role in the coupled ocean and climate system with the Indonesian Throughflow (ITF) providing the only tropical pathway connecting the global oceans. Pacific warm pool waters passing through the Indonesian seas are cooled and freshened by strong air-sea fluxes and mixing from internal tides to form a unique water mass that can be tracked across the Indian Ocean basin and beyond. The Indonesian seas lie at the climatological center of the atmospheric deep convection associated with the ascending branch of the Walker Circulation. Regional SST variations cause changes in the surface winds that can shift the center of atmospheric deep convection, subsequently altering the precipitation and ocean circulation patterns within the entire Indo-Pacific region. Recent multi-decadal changes in the wind and buoyancy forcing over the tropical Indo-Pacific have directly affected the vertical profile, strength, and the heat and freshwater transports of the ITF. These changes influence the large-scale sea level, SST, precipitation and wind patterns. Observing long-term changes in mass, heat and freshwater within the Indonesian seas is central to understanding the variability and predictability of the global coupled climate system. Although substantial progress has been made over the past decade in measuring and modeling the physical and biogeochemical variability within the Indonesian seas, large uncertainties remain. A comprehensive strategy is needed for measuring the temporal and spatial scales of variability that govern the various water mass transport streams of the ITF, its connection with the circulation and heat and freshwater inventories and associated air-sea fluxes of the regional and global oceans. This white paper puts forward the design of an observational array using multi-platforms combined with high-resolution models aimed at increasing our quantitative understanding of water mass transformation rates and advection within the Indonesian seas and their impacts on the air-sea climate system. IntroductionJS acknowledges funding to support her effort by the National Science Foundation under Grant Number OCE-1736285 and NOAA’s Climate Program Office, Climate Variability and Predictability Program under Award Number NA17OAR4310257. SH was supported by the National Natural Science Foundation of China (Grant 41776018) and the Key Research Program of Frontier Sciences, CAS (QYZDB-SSW-SYS023). HP acknowledges support from the Australian Government’s National Environmental Science Programme. HZ acknowledges support from National Science Foundation under Grant No. 41876009. RS was supported by National Science Foundation Grant No. OCE-07-25935; Office of Naval Research Grant No. N00014-08-01-0618 and National Aeronautics and Space Administration Grant No. 80NSSC18K0777. SW, MF, and BS were supported by Center for Southern Hemisphere Oceans Research (CSHOR), which is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales and University of Tasmania

The Spatial Patterns of the Crime Rate in London and Its Socio-Economic Influence Factors

This paper analyses the spatial trends and patterns of the crime rates in London and explores how socio-economic characteristics affect crime rates with consideration of the geographic context across London. The 2015 London Crime Statistics and Socio-economic Characteristics datasets were used. First, we investigated the spatial patterns of crime rates through exploratory spatial analysis at the ward level. In addition, both the ordinary least square (OLS) model and geographically weighted regression (GWR) model, which allow the effects of factors to vary in spatial scales, were adopted and compared to explore the potential spatially varying effect across London. The results showed that there exists obvious spatial clustering for the crime rate in central London. Both global and local Moran’s I values indicated the spatial dependence of crime at the ward level. The GWR model performed better in explaining crime rates than the OLS model. Only two factors, namely, the percentage of children aged from 0 to 15 and employment rates, had significant spatial variability in London. The influences of the percentage of children aged 0 to 15 on crime rates are constantly negative over a spatial scale; however, employment rates positively affect crime rates in the north-western areas near the centre of London. Therefore, this paper focuses more on the spatial perspective, which fills the gap in traditional crime analysis, especially on the spatially varying influence of socio-economic status

Intelligent Hybrid Modeling of Complex Leaching System Based on LSTM Neural Network

In order to improve the leaching efficiency of gold ore and reduce the environmental treatment cost of residual sodium cyanide, continuous stirred tank reactors are often connected in a cascade manner. A gold leaching system is a multiphase chemical reaction system, and its kinetic reaction mechanism is complex and affected by random factors. Using intelligent modeling technology to establish a hybrid prediction model of the leaching system, the dynamic performance of the process can be easily analyzed. According to the reaction principle and the theory of substance conservation, a mechanism model is established to reflect the main dynamic performance of the leaching system. In order to improve the global convergence of the optimization target, a particle swarm optimization (PSO) algorithm based on simulated annealing is used to optimize the adjustment parameters in the kinetic reaction velocity model. The multilayer long short-term memory (LSTM) neural network approach is used to compensate for the prediction errors caused by the unmodeled dynamics, and a hybrid model is established. The hybrid prediction model can accurately predict the leaching rate, which provides a reliable basis for guiding production, and also provides a model basis for process optimization, controller design, and operation monitoring. Finally, the superiority and practicability of the hybrid model are verified by a practical leaching industrial system test. The prediction model of key variables in the leaching process is established for the first time using the latest time series prediction technology and intelligent optimization technology. The research results of this paper can provide a good reference and guidance for other research on complex system hybrid modeling

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Abstract Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change