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Within the scope of this study, the evolution of microstructures and mechanical properties in as-extruded Mg–Sc alloys were systematically explored with Mn content from 0.3 to 0.9 wt.%. The results showed that a completely recrystallized crystal structure was obtained in Mg-xMn-Sc alloys, and the average grain size decreased from 12.75 to 3.35 μm with the increase of Mn content. In addition, the increment of Mn content reduced the texture intensity from 14.95 to 7.65. Thus, Mg–Sc alloys’ tensile yield and ultimate strength significantly improved. Meanwhile, Mn addition led to the activation of non-basal slip, especially prismatic slip, which significantly enhanced the ductility of alloys. Moreover, the activation of non-basal slip by alloying Mn can be attributed to the reduction of the critical shear stress (CRSS) gap between basal slip and non-basal slip.

This work systematically investigated the influence of the micro-Mn addition on the microstructures and mechanical properties of Mg-Er alloys. To investigate their deformation mechanisms during tensile testing, electron back-scattered diffraction, transmission electron microscopy, slip trace analysis, and visco-plastic self-consistent polycrystal constitutive (VPSC) modeling were used. The study showed that the as-solid solution samples only consist of the α-Mg phase. All samples exhibit a complete dynamic recrystallized (DRXed) microstructure with an average grain size of 2.79 µm after hot extrusion. The ductility first increases from 26.02 % to 35.34 % and then remains unchanged with the increment of Mn content. Meanwhile, the yield strength significantly increases from 95 MPa to 200 MPa. According to VPSC results, the initial slip resistance (τ0) difference between prismatic and basal slips decreases from 109 MPa to 92 MPa and τ0 between pyramidal and basal slip systems from 129 MPa to 112 MPa. Both the VPSC and two-beam diffraction results confirmed that pyramidalslip andslip were activated during tensile deformation. The quantitative analysis of the slip trace line verified that the volume of non-basal slip reached 65 % when the content of Mn was increased to 0.9 wt%. Mn in solid solution increased the activity of pyramidaland prismaticdislocations during deformation, which is beneficial for accommodating c-axis strain. Consequently, the ambient ductility of Mg-2Er alloy with Mn addition is improved.

Light buoy is a navigation aid sign to guide ship navigation, which plays an important role in ensuring the safety of ship navigation. To predict the offset distance of light buoy and provide accurate position information of light buoy for ship navigation safety, and to solve the problem of low prediction accuracy caused by the excessive amplitude of initial training data in the traditional multiplicative seasonal model, a grey optimization multiplicative seasonal model is proposed. By analyzing the characteristics of the time series of the light buoy offset data, as well as the regularity and seasonal characteristics of the light buoy offset distance, an optimization model is established. Based on the real offset distance data of the 1 # light buoy in Meizhou Bay, the model is simulated, and the prediction accuracy of the model is evaluated by MAPE, RMSE, and the Monte Carlo sampling method. The results show that, compared to the traditional multiplicative seasonal model, the average absolute percentage error of the grey optimization multiplicative seasonal model is reduced by 6.66%, and the root mean square error is reduced by 2.96. It shows that the model can effectively deal with the problem that the error of the prediction results caused by the instability of the initial time series of the traditional multiplicative seasonal model is too large, which provides a new idea for the prediction of the offset distance of the light buoy and the navigation safety of the ship.

The additions of alloying elements can significantly improve the mechanical properties of magnesium (Mg) alloys, mainly due to the fact that their additions change the critical shear stresses (CRSS) for dislocation slips. In this work, experimental and computational methods were used to explore the mechanisms responsible for the roles of Sm element addition in affecting the mechanical properties of Mg. The results showed that the addition of Sm obviously improves the microstructure and mechanical properties. It promotes the formation of twins and beneficially activated the non-basal slip at the initial stage of plastic deformation, resulting in a high ductility. The Visco-Plastic Self-Consistent (VPSC) and the two-beam diffraction results confirmed that the pyramidal slip and prismatic slip were activated during tensile testing. The quantitative analysis of slip traces verified that the volume of non-basal slips reached 35 % after Sm addition. The additions of Sm with solid solution increased the activities of pyramidal dislocation during deformation, which was beneficial to accommodate the c-axis strain, and finally improved the room temperature ductility of Mg. First-principle calculations demonstrate that the solute Sm atoms would reduce the stacking fault energy for basal and prismatic slips.

Most membrane-less organelles (MLOs) formed by LLPS contain both nucleic acids and IDR-rich proteins. Currently while IDRs are well-recognized to drive LLPS, nucleic acids are thought to exert non-specific electrostatic/salt effects. TDP-43 functions by binding RNA/ssDNA and its LLPS was characterized without nucleic acids to be driven mainly by PLD-oligomerization, which may further transit into aggregation characteristic of various neurodegenerative diseases. Here by NMR, we discovered unexpectedly for TDP-43 PLD: 1) ssDNAs drive and then dissolve LLPS by multivalently and specifically binding Arg/Lys. 2) LLPS is driven by nucleic-acid-binding coupled with PLD-oligomerization. 3) ATP and nucleic acids universally interplay in modulating LLPS by competing for binding Arg/Lys. However, the unique hydrophobic region within PLD renders LLPS to exaggerate into aggregation. The study not only unveils the first residue-resolution mechanism of the nucleic-acid-driven LLPS of TDP-43 PLD, but also decodes a general principle that not just TDP-43 PLD, all Arg/Lys-containing IDRs are cryptic nucleic-acid-binding domains that may phase separate upon binding nucleic acids. Strikingly, ATP shares a common mechanism with nucleic acids in binding IDRs, thus emerging as a universal mediator for interactions between IDRs and nucleic acids, which may underlie previously-unrecognized roles of ATP at mM in physiology and pathology.

Ternary magnesium-based alloys with an aluminum content of 0.5 wt.% and a varying manganese content of x = 1, 2, and 3 wt.% (Mg-xMn-0.5Al) were prepared and characterized. The study focused on the effect of Mn addition on the formation of ultrafine grains (UFGs, average grain size

Most membrane-less organelles (MLOs) formed by LLPS contain both nucleic acids and IDR-rich proteins. Currently while IDRs are well-recognized to drive LLPS, nucleic acids are thought to exert non-specific electrostatic/salt effects. TDP-43 functions by binding RNA/ssDNA and its LLPS was characterized without nucleic acids to be driven mainly by PLD-oligomerization, which may further transit into aggregation characteristic of various neurodegenerative diseases. Here by NMR, we discovered unexpectedly for TDP-43 PLD: 1) ssDNAs drive and then dissolve LLPS by multivalently and specifically binding Arg/Lys. 2) LLPS is driven by nucleic-acid-binding coupled with PLD-oligomerization. 3) ATP and nucleic acids universally interplay in modulating LLPS by competing for binding Arg/Lys. However, the unique hydrophobic region within PLD renders LLPS to exaggerate into aggregation. The study not only unveils the first residue-resolution mechanism of the nucleic-acid-driven LLPS of TDP-43 PLD, but also decodes a general principle that not just TDP-43 PLD, all Arg/Lys-containing IDRs are cryptic nucleic-acid-binding domains that may phase separate upon binding nucleic acids. Strikingly, ATP shares a common mechanism with nucleic acids in binding IDRs, thus emerging as a universal mediator for interactions between IDRs and nucleic acids, which may underlie previously-unrecognized roles of ATP at mM in physiology and pathology.

Diabetic nephropathy (DN) is a common complication of diabetes. Tamsulosin is a selective α1-AR antagonist. α1-AR is expressed widely in kidney tissues and has displayed its various physiological functions. However, whether Tamsulosin has affects DN is unknown. To our knowledge, this is the first time it has been examined whether Tamsulosin possesses a beneficial effect in high glucose-challenged glomerular endothelial cells (GECs). Firstly, we found that Tamsulosin reduced high glucose-induced expressions of TNF-α, IL-6, and IL-8. Secondly, Tamsulosin alleviated high glucose-induced expressions of MMP-2 and MMP-9. Thirdly, Tamsulosin inhibited the expressions of VCAM-1 and ICAM-1. Importantly, our results indicate that Tamsulosin inhibited high glucose-induced expressions of fibrosis factors such as Col-1 and TGF-β1. Additionally, we found that Tamsulosin ameliorated oxidative stress via reducing the generation of ROS and preventing the activation of p38. Mechanistically, we found that Tamsulosin attenuated high glucose-induced activation of NF-κB. Based on these findings, we conclude that Tamsulosin could attenuate high glucose-induced injury in GECs through alleviating oxidative stress and inflammatory response., Graphical abstract

In the existing research on the intelligent navigation of ships, navigation route planning often regards light buoys as fixed obstructions. However, due to factors such as water ripples, the position of the buoys keeps periodically changing. If the buoys are set to a fixed range of avoidance areas in the process of ship navigation, it is easy to allow a collision between the ship and the light buoys. Therefore, based on historical motion trajectory data of the buoys, a SARIMA-based time-series prediction model is proposed to estimate the offset position of a given buoy in a specified time. Furthermore, the collision-free path planning approach is presented to dynamically recommend an accurate sailing path. The results of the simulation experiment show that this method can effectively deal with collisions of ships caused by the offset position of the light buoys during the navigation of the large and low-speed autonomous ships.

FMS-like tyrosine kinase 3 (FLT3) serves as an important drug target for acute myeloid leukemia (AML), and gene mutations of FLT3 have been closely associated with AML patients with an incidence rate of ~ 30%. However, the mechanism of the clinically relevant F691L gatekeeper mutation conferred resistance to the drug gilteritinib remained poorly understood. In this study, multiple microsecond molecular dynamics (MD) simulations, end-point free energy calculations, and dynamic correlated and network analyses were performed to investigate the molecular basis of gilteritinib resistance to the FLT3-F691L mutation. The simulations revealed that the resistant mutation largely induced the conformational changes of the activation loop (A-loop), the phosphate-binding loop, and the helix αC of the FLT3 protein. The binding abilities of the gilteritinib to the wild-type and the F691L mutant were different through the binding free energy prediction. The simulation results further indicated that the driving force to determine the binding affinity of gilteritinib was derived from the differences in the energy terms of electrostatic and van der Waals interactions. Moreover, the per-residue free energy decomposition suggested that the four residues (Phe803, Gly831, Leu832, and Ala833) located at the A-loop of FLT3 had a significant impact on the binding affinity of gilteritinib to the F691L mutant. This study may provide useful information for the design of novel FLT3 inhibitors specially targeting the F691L gatekeeper mutant.

Pancreatic diseases, a serious threat to human health, have garnered considerable research interest, as they are associated with a high mortality rate. However, owing to the uncertain etiology and complex pathophysiology, the treatment of pancreatic diseases is a challenge for clinicians and researchers. Exosomes, carriers of intercellular communication signals, play an important role in the diagnosis and treatment of pancreatic diseases. Exosomes are involved in multiple stages of pancreatic disease development, including apoptosis, immune regulation, angiogenesis, cell migration, and cell proliferation. Thus, extensive alterations in the quantity and variety of exosomes may be indicative of abnormal biological behaviors of pancreatic cells. This phenomenon could be exploited for the development of exosomes as a new biomarker or target of new treatment strategies. Several studies have demonstrated the diagnostic and therapeutic effects of exosomes in cancer and inflammatory pancreatic diseases. Herein, we introduce the roles of exosomes in the diagnosis and treatment of pancreatic diseases and discuss directions for future research and perspectives of their applications.

Aimed at the problem of the large error caused by uncertain factors in the fitting process of the traditional multiplicative seasonal model, the advantages of the Markov chain in this study are applied to the multiplicative seasonal model to optimize the prediction results. Based on the residual value between the theoretical and actual values, the values of different intervals are divided into states. The transition probability matrix is established through different probabilities; then, the weighted sum of different prediction probabilities is carried out to select the optimal prediction state. The real number of Meizhou Bay portlight buoys is used to verify the prediction effect of the model, and MAE, MAPE, RMSE, RRMSE, SSE, R2 are used to calculate the error between the predicted value and the actual value. The results show that compared to the traditional multiplicative seasonal model and other prediction models, the prediction MAE of the MC-SARIMA model is decreased by 2.19003794, the MAPE is decreased by 0.66%, the RMSE is decreased by 2.092671823, the RRMSE is decreased by 0.006221352, the SSE is decreased by 404.0231931, and the R2 is increased by 0.224686247. It shows that the multiplicative seasonal model optimized by the Markov chain can predict the azimuth data of the light buoy more effectively than the traditional multiplicative seasonal model and other prediction models.

Mg–Al binary alloys in the concentration range from 0 to 4.0 wt.% Al have been prepared under conventional casting conditions. The as-cast Mg and Mg–Al alloys after solution treatment were processed via hot extrusion at 350 °C. The results show that Al has a positive influence on grain refinement and solution strengthening. The as-extruded Mg–Al alloys are fully recrystallized, and the tensile yield strength of the binary alloys is two times higher than that of pure Mg. Furthermore, the elongations of Mg–Al alloys are much higher than that of pure Mg. In addition, Mg and Mg–Al alloys were further studied by the viscoplastic self-consistent (VPSC) model to explore the activation and evolution of deformation modes. The simulation results match well with the experimental results.

LncRNAs have been proven to play crucial roles in various processes of breast cancer. LncRNA FGF13-AS1 has been identified as one of the 25 downregulated lncRNAs in breast cancer through analyzing data from two cohorts and TCGA by another group of our lab. In this study, we report that FGF13-AS1 expression is decreased in breast cancer tissue compared with corresponding normal tissue, and the downregulation of FGF13-AS1 is associated with poor prognosis. Functional studies show that FGF13-AS1 inhibits breast cancer cells proliferation, migration, and invasion by impairing glycolysis and stemness properties. Mechanistically, FGF13-AS1 reduces the half-life of c-Myc (Myc) mRNA by binding RNA-binding proteins, insulin-like growth factor 2 mRNA binding proteins (IGF2BPs) and disrupting the interaction between IGF2BPs and Myc mRNA. Furthermore, Myc transcriptionally inhibits FGF13-AS1, forming a feedback loop in this signaling pathway. These results reveal for the first time that FGF13-AS1 functions as a tumor suppressor by inhibiting glycolysis and stemness properties of breast cancer cells, and the FGF13-AS1/IGF2BPs/Myc feedback loop could be a novel therapeutic target for breast cancer patients.

In this paper, according to the water area of light buoy, the migration rule of light buoy in main channel is counted, and the frequency of light buoy passing through a certain position point in the process of migration is calculated, and the model is verified by buoy position data. An anomaly detection algorithm based on improved adaptive DBSCAN clustering is designed. The size of the ε neighborhood is adaptive according to the wind speed, wave height, and drift distance span of the water area where the light buoy is located. The experimental results show that the improved adaptive DBSCAN clustering algorithm can solve the problem that the common DBSCAN clustering algorithm takes the “hot” water area of the light buoy position or the most likely area in the light buoy migration process as the noise point.

The control of the ship's course determines the correctness of navigation, ensures the correct destination, and plays a significant role in the navigation of the ship. Based on RBF neural network and ship mathematical model, a predictive controller using Euclidean distance to calculate center vector for network learning is designed to predict and adjust the three parameters of PID in real time and online, so as to obtain the optimal PID parameters faster. By supervised learning ensuring the course to meet the requirements and the stable navigation of the ship. Then the simulation experiment is carried out to verify the accuracy of the designed controller in Simulink environment of MATLAB software. Compared with the traditional PID controller, this method of this paper reduces the overshoot of the system and the setting time, and improves the accuracy of the ship's course automatic control simultaneously. In addition, the simulation results of ship course control simulation demonstrate the better adaptability, robustness and anti-interference ability of the intelligent PID control strategy.

This paper investigates the distributed finite-time event-triggered bipartite consensus control for multiagent systems over antagonistic networks. Under the constraint of energy conservation, a distributed nonlinear finite-time control protocol only depending upon local information is proposed coupled with event-triggered strategies, where controllers of agents at triggered instants are only updated to reduce the computation. It is proved that when the antagonistic network is structurally balanced with a spanning tree, a necessary and sufficient condition is established to guarantee all agents to reach consensus values with identical magnitude but opposite signs. More interestingly, the settling time depending on the initial state is obtained over the whole process. Comparing to asymptotic control algorithms, the proposed control method has better disturbance rejection properties and convergence rate. Simulations are given to demonstrate the effectiveness of the theoretical results.

Spiking neural network (SNN) is interesting due to its strong bio-plausibility and high energy efficiency. However, its performance is falling far behind conventional deep neural networks (DNNs). In this paper, considering a general class of single-spike temporal-coded integrate-and-fire neurons, we analyze the input-output expressions of both leaky and nonleaky neurons. We show that SNNs built with leaky neurons suffer from the overly-nonlinear and overly-complex input-output response, which is the major reason for their difficult training and low performance. This reason is more fundamental than the commonly believed problem of nondifferentiable spikes. To support this claim, we show that SNNs built with nonleaky neurons can have a less-complex and less-nonlinear input-output response. They can be easily trained and can have superior performance, which is demonstrated by experimenting with the SNNs over two popular network intrusion detection datasets, i.e., the NSL-KDD and the AWID datasets. Our experiment results show that the proposed SNNs outperform a comprehensive list of DNN models and classic machine learning models. This paper demonstrates that SNNs can be promising and competitive in contrast to common beliefs., Comment: Published in the 25th International Conference on Pattern Recognition (ICPR'2020), January 2021

To ensure successful future planetary landing mission, the lander must be capable of detecting and assessing terrain hazard in the nominal landing zone. This paper presents an innovative method of planetary terrain hazard detection. The concept of feature density is introduced into the process of terrain hazard detection. Moreover, the statistical information of the terrain feature density is utilized to represent the terrain hazard level. Opposed to other methods of hazard detection, the research in this paper focuses on how to build a unified terrain representation by the terrain features rather than detecting the specific one or several types of hazard obstacles in traditional sense. Computer simulations demonstrates that the method presented in this paper is able to conduct multitype of complex terrain hazard detecting by the terrain feature detected in the visual image obtained from onboard camera during the landing phase.

Real-time accurate detection of three-dimensional (3D) objects is a fundamental necessity for self-driving vehicles. Most existing computer vision approaches are based on convolutional neural networks (CNNs). Although the CNN-based approaches can achieve high detection accuracy, their high energy consumption is a severe drawback. To resolve this problem, novel energy efficient approaches should be explored. Spiking neural network (SNN) is a promising candidate because it has orders-of-magnitude lower energy consumption than CNN. Unfortunately, the studying of SNN has been limited in small networks only. The application of SNN for large 3D object detection networks has remain largely open. In this paper, we integrate spiking convolutional neural network (SCNN) with temporal coding into the YOLOv2 architecture for real-time object detection. To take the advantage of spiking signals, we develop a novel data preprocessing layer that translates 3D point-cloud data into spike time data. We propose an analog circuit to implement the non-leaky integrate and fire neuron used in our SCNN, from which the energy consumption of each spike is estimated. Moreover, we present a method to calculate the network sparsity and the energy consumption of the overall network. Extensive experiments have been conducted based on the KITTI dataset, which show that the proposed network can reach competitive detection accuracy as existing approaches, yet with much lower average energy consumption. If implemented in dedicated hardware, our network could have a mean sparsity of 56.24% and extremely low total energy consumption of 0.247mJ only. Implemented in NVIDIA GTX 1080i GPU, we can achieve 35.7 fps frame rate, high enough for real-time object detection.

The size of the ship-induced wave directly affects the bank, slope protection and ship-to-ship interference under the condition of head-on situation. This paper used the conventional computational fluid dynamics analysis software STAR-CCM+, the models such as VOF multiphase flow model, 6DOF kinematics model and RNG k-e turbulence model combined with RANS numerical method were used to simulate the coupling of the ship-induced wave in head-on situation in the inland river shallow channel based on CFD technology. The coupling and reflection of ship-induced wave and the effect on the two ship hull and the bank are further analyzed. Using the 6-DOF and overlapping meshing model, the motion of the two ships in different situations from the approaching to leaving of the two ships was carried out, and the variation of the ship-induced wave height between the two ships was observed and discussed. Through numerical calculation and simulation, it is found that the ship-induced wave has obvious interference to the ship-to-ship interaction, and the ship-induced wave height changes significantly, and the waveform coupling response is realistic. The result can be used as a reference for research and design departments.

Aiming at the uncertainties such as human's subjectivity of evaluation index weight and inconsistent distribution in ship maneuverability evaluation, Cluster Analysis, which is a comprehensive evaluation method of ship maneuverability, is proposed. Based on IMO maneuverability index, this method regards one ship with multiple maneuverability evaluation factors as a point in multidimensional space, uses Euclidean distance to measure the similarity between sample points, and classifies and evaluates ship maneuverability. By analyzing and calculating the maneuverability experimental data of 8 ships, the maneuverability of each ship is ranked. The results show that the method is not only scientific and objective in theory, but also simple and reliable in practical application. It is helpful to improve the objectivity of comprehensive evaluation of ship maneuverability.

Large amounts of silicon cutting waste (SCW) are generated during Si wafers producing process. In this paper, SCW was mixed with Al powder to prepare Al–Si alloys by a one-step smelting process in corundum crucibles. The influences of smelting temperature (1000 °C, 1200 °C and 1500 °C) on the products of each zone (surface layer zone, loose granular zone and blocky products zone) were investigated. Al–Si alloys in the form of granular and blocky were prepared and the blocky Al–Si alloys mainly concentrated in the blocky products zone. The increase of smelting temperature can promote the aggregation of Al–Si alloy particles. The yields of Al–Si alloy blocks obtained at 1000 °C, 1200 °C and 1500 °C were 0%, 58% and 69%, respectively. The Si contents of Al–Si alloy blocks at 1200 °C and 1500 °C were 15.8 wt% and 17.1 wt% respectively. After compacting the raw materials, the yields of the blocky Al–Si alloys obtained at 1000 °C, 1200 °C and 1500 °C were increased to 65%, 72% and 79% and the corresponding Si contents of the blocky Al–Si alloys were increased to 16.0 wt%, 16.5 wt% and 17.3 wt% respectively. The reaction mechanism of the alloying process was also investigated.

A novel sandwich grain structure of an Mg–Mn–Ce alloy was achieved by controlling the content of Mn in the traditional extrusion process. The solid-state Mn was dynamically precipitated during the extrusion. These finely precipitated particles hindered the grain boundary migration and refined the dynamically recrystallized grains. A sandwich-like grain structure was formed in as-extruded Mg–Mn–Ce alloys with Mn content increasing from 0.3 wt% to 0.9 wt%. This sandwich-like grain structure induced a tensile yield stress (TYS) that was improved from 86.5 MPa to 166.7 MPa; the Mg-0.9Mn-0.5Ce alloy exhibited improved yield isotropy. Here we discuss the formation mechanism for the sandwich-like grain structure and the strengthening mechanism in detail.

Fabricating bimodal grain structure is an effective way to improve the mechanical properties of Mg alloys. In present work, the various Mn contents (0, 0.5, 1.0, 1.5 wt%) are added in Mg-1.0Gd alloy to fabricate bimodal grain structure and improve the mechanical properties. After extrusion at 300 °C, bimodal grain structure is obtained in the Mg-1.0Gd-1.0Mn and Mg-1.0Gd-1.5Mn alloys. With the increment of Mn content, the average grain sizes of dynamic recrystallization (DRX) grains are refined from 8.6 to 1.2 μm. The mechanical properties are significantly improved. The Mg-1.0Gd-1.5Mn alloy exhibits the best tensile yield stress (TYS) and ultimate tensile stress (UTS), which is 280 MPa and 293 MPa. Comparing with the Mg-1.0Gd alloy, the improvement of TYS and UTS are 165 MPa (143%) and 98 MPa (51%). The enhancement of the strength is mainly attributed to the bimodal grain structure. The formation of the bimodal grain structure and the strengthening mechanisms are also discussed.

More than 40% of the crystalline silicon has been wasted as silicon cutting waste (SCW) during the wafer production process. This waste not only leads to resource wastage but also causes environmental burden. In this paper, SCW produced by the diamond-wire sawing process was recycled by Al-Si alloying process. Cryolite was introduced to the reaction system to dissolve the SiO2 layer existed on the surface of the Si particles in SCW. Alloys with 12.02 wt% of Si were prepared and the mechanism of the alloying process was investigated in detail. The Si-Al-cryolite system and SiO2-Al-cryolite system were studied individually to analyze the reaction process and transferring behavior of Si and SiO2 in SCW. The SiO2 shell was firstly transformed into Si-O-F ions. Then the Si-O-F ions diffused to the reaction interface by the effect of the concentration gradient and were reduced to Si by the aluminothermic reduction reaction: 4Al (l) + 3SiO2 (dissolved in the melt) = 3Si (Al)+ 2Al2O3 (dissolved in the melt). Then the internal Si particles were released into cryolite after the dissolution of SiO2 and transferred to the reaction interface by the effect of gravity. The influences of the mass ratio of Al/SCW and agitation modes on the Si content of the alloys and the Si recovery ratio in SCW were investigated. With the increase of the mass ratio of Al/SCW from 2.2 to 6.5, the Si recovery ratio in SCW increased from 44.08% to 69.05%, but the silicon content of the alloys decreased from 16.06 wt% to 8.83 wt%. Agitation can effectively improve the smelting effect during smelting by which the silicon content of the alloys and the Si recovery ratio in SCW increased from 12.02 wt% and 64.25% to 13.17 wt% and 69.46%, respectively.

The effect of Mn on the microstructure and mechanical properties of as-extruded Mg-0.5Sr alloy were discussed in this work. The results showed that high Mn alloying (2 wt.%) could significantly improve the mechanical properties of the alloys, namely, the tensile and compressive yield strength. The grain size of as-extruded Mg-0.5Sr alloys significantly was refined from 2.78 &mu, m to 1.15 &mu, m due to the pinning effect by fine &alpha, Mn precipitates during the extrusion. Moreover, it also showed that the tensile yield strength and the compressive yield strength of Mg-0.5Sr-2Mn alloy were 32 and 40 percent age higher than those of Mg-0.5Sr alloy, respectively. Moreover, the strain hardening behaviors of the Mg-0.5Sr-2Mn alloy were discussed, which proved that a large number of small grains and texture have an important role in improving mechanical properties.

The growing amount of online reviews is an important external information source for both consumers and producers. While the reviews’ impact on consumers purchasing behaviors has drawn much attenti...

Large amounts of silicon cutting waste (SCW) will be generated as the photovoltaic (PV) industry grows, which not only wastes valuable resources, but also causes environmental issues if SCW is not addressed. In this study, a novel and effective method to recycle Si from SCW by Al–Si alloying with a cryolite additive is proposed. The influence of the smelting parameters including smelting temperature, holding time and cryolite/SCW ratio, on the Si content of alloys and the utilization ratio of SCW was investigated and optimized. The optimal conditions were a smelting temperature of 1150 °C, holding time of 30 min and cryolite/SCW ratio of 6.3. Under these optimized conditions, the Si content of alloys and the utilization ratio of SCW were 12.02 wt% and 64.25%, respectively. The characterization analysis results of the products showed that Al–Si alloys with a uniform distribution of Si were successfully prepared. Furthermore, the mechanism of the alloying process was studied. Cryolite additives can efficiently dissolve the SiO2 film on the surface of Si particles and hence contribute to the subsequent transfer of the exposed Si to molten Al. The study of the slag-metal interface demonstrates that Si is recycled with a highly efficiency from SCW. Despite the relatively large amount of cryolite, the generated slag can be recycled and used as an additive for the next batch of experiments. This method not only recycles silicon scrap for use as valuable raw materials, but also reduces the production cost of Al–Si alloys, and thus, this method is a feasible way to commercially recycle SCW in an economically and environmentally friendly manner.

Offshore platform is an important infrastructure for exploitation of marine resources, and the operation safety is a key issue in the offshore platform industry. Dropped risers and other dropped objects would threat the platform structure and personal safety. In order to analyze the risk of damage caused by the dropped risers, a practical engineering analysis is presented using the engineering simulation platform ANSYS/LS-DYNA, a useful and powerful tool with an explicit dynamic solver. The impact angle of the dropped object may have different impact consequences on the offshore platform deck in respects of dimension and strength. By using ANSYS/LS-DYNA, the dropped riser's impacts on the platform deck under different angles circumstances are simulated and analyzed. Based on the simulation results, the nonlinear fitting approaches of polynomial fitting and BP neural networks were used to analyze the nonlinear mapping between impact angles and collision results. The results of the fitting were analyzed and verified by LS-DYNA and the fitting accuracy of the two methods was further discussed. This method can be used in conventional risk analysis studies for evaluating the risk for offshore platform and subsea pipelines from dropped objects.

Feature selection is a vital step in many machine learning and data mining tasks. Feature selection can reduce the dimensionality, speed up the learning process, and improve the performance of the learning models. Most of the existing feature selection methods try to find the best feature subset according to a pre-defined feature evaluation criterion. However, in many real-world datasets, there may exist many global or local optimal feature subsets, especially in the high-dimensional datasets. Classical feature selection methods can only obtain one optimal feature subset in a run of the algorithm and they cannot locate multiple optimal solutions. Therefore, this paper considers feature selection as a multimodal optimization problem and proposes a novel feature selection method which integrates the barebones particle swarm optimization (BBPSO) and a neighborhood search strategy. BBPSO is a simple but powerful variant of PSO. The neighborhood search strategy can form several steady sub-swarms in the population and each sub-swarm aims at finding one optimal feature subset. The proposed approach is compared with four PSO based feature selection methods on eight UCI datasets. Experimental results show that the proposed approach can produce superior feature subsets over the comparative methods.

Developing high yield strength and ductility in low–cost Mg alloys using conventional plastic forming process is a tremendous challenge. Mg–Mn–based alloys have drawn considerable attention owing to their low–cost, high ductility and good corrosion resistance. However, the strength of Mg–Mn binary alloy is not satisfactory for industrial scale applications. In the present study, low content of Al alloying in Mg–1Mn alloy is the aim to obtain high strength and ductility of Mg–Mn–based alloys with low cost. Experimental results revealed that phase composition of the targeted Mg–1Mn–xAl (x = 0.3 wt%, 0.5 wt%, 1.0 wt%) alloys were composed of α-Mn + Al8Mn5, Al8Mn5 and Al8Mn5+Al11Mn4 phase, respectively. Al–Mn particles significantly refined the dynamically recrystallized (DRXed) grains. Among them, Al8Mn5 particles exhibited the most effective grain refinement. Therefore, Mg–1Mn–0.5Al alloy containing single-phase Al8Mn5 particles possessed the finest microstructure and exhibited the best mechanical properties. The high performance of the alloy was mainly attributed to the fine DRXed grains according to the Hall–Petch effect and to the large amount of fine Al8Mn5 particles through the dispersion strengthening. Meanwhile, the weak texture of fine grains enhanced the ductility. The fine DRXed grains and numerous Al8Mn5 particles effectively suppressed the extension twining, thus substantially enhanced the compression yield strength, and resulted in improved anisotropy.

Highly-uniform ultrafine Mg–3Al–1Zn alloy was successfully fabricated by a simple conventional extrusion method called continuous forging extrusion (CFE). After CFE, the average grain size of Mg–3Al–1Zn alloy was refined to 0.6 μm. The ultra-fine grain (UFG) AZ31 alloy exhibited an excellent combination of yield strength of 307 MPa and ductility of 26.3%. The improved mechanical properties were mainly attributed to grain boundary strengthening and texture weakening. The formation of dynamic recrystallized submicron grain in the CFE was also discussed.

Carbon-coated Co3O4 microparticles (Co3O4@C) with dendrite-like morphology are prepared by a low-temperature hydrothermal method and subsequent C2H2 chemical vapor deposition (CVD) for the first time. The synthesized precursor Co3O4 has a size of 8–10 μm, which is assembled by lots of aligned Co3O4 wire with 4–5 μm in length. After carbon coating, Co3O4 microparticles were proved to be coated in amorphous carbon of ~10 nm. A plausible formation process of the dendrite-like Co3O4 and Co3O4@C is proposed based on the morphology and structure characterizations of the materials. As an anode material for lithium-ion batteries, the Co3O4@C composite electrode exhibits higher reversible capacity and better cycling performance than the unmodified Co3O4 electrode. The reversible capacity of the Co3O4@C composite after 40 cycles is 500 mAh g−1, much higher than that of Co3O4 (148 mAh g−1). Electrochemical impedance spectra and cyclic voltammogram indicate that the carbon layer coated on Co3O4 by CVD can improve the electrochemical activity and enhance the reversibility of Co3O4 during charge/discharge cycles.