Search

The lattice reduction aided(LRA) algorithm has attracted widely attention of researchers in MIMO systems because of its low complexity to achieve full diversity. However, most lattice reduction auxiliary algorithms do not directly improve system performance. In this paper, a modified lattice reduction successive interference cancellation (SIC) algorithm is proposed for MIMO OFDM system mild changing the complexity of LRA algorithm. At present, the MMSE SQRD algorithm has the problem of regarding too much useful signals as the residual interference which cannot be cancelled by successive interference cancellation. Since the diagonal elements of useful signals are non-zero, the components of the symbols to be detected still present in the interference term, treating all the signals as interference will be resulted in performance loss. To solve this problem, the modified matrix is proposed.by moving this symbol component into the signal term, the proposed algorithm can provide the better performance than MMSE SQRD algorithm. The analysis shows that the LRA-MMSE-SIC algorithm of proposed in this paper significantly improves the system performance with mild increasing the complexity. The simulation results show that the performance of the proposed algorithm is improved by 1dB and 8dB compared with the comparison algorithm when the bit error rate is 10−3.

In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are still insufficiently addressed, such as the receptive field problem, small sample problem, and feature fusion problem. To tackle the above problems, we proposed a two-branch convolutional neural network with a polarized full attention mechanism for HSI classification. In the proposed network, two-branch CNNs are implemented to efficiently extract the spectral and spatial features, respectively. The kernel sizes of the convolutional layers are simplified to reduce the complexity of the network. This approach can make the network easier to be trained and fit the network to small sample size conditions. The one-shot connection technique is applied to improve the efficiency of feature extraction. An improved full attention block, named polarized full attention, is exploited to fuse the feature maps and provide global contextual information. Experimental results on several public HSI datasets confirm the effectiveness of the proposed network.

Weighted gene co-expression network analysis (WGCNA) is a classic systematic biological method, which can be used to identify co-expressed modules, investigate relationships between modules and specific traits, and screen hub genes in the networks. Verticillium wilt, caused by the fungal pathogen Verticillium dahliae , can cause severe fibre quality reduction and yield loss of cotton. Studying on cotton genes and molecular mechanisms related to defense responses against V. dahliae can shed light on cotton breeding. In this study, 21 transcriptome data of Gossypium barbadense seedling roots infected by V. dahliae were used to investigate differentially expressed genes (DEGs). By filtering out the genes with low variation, 35,647 genes were selected for WGCNA. In total, 22,850 DEGs were identified under V. dahliae infection, with 4685 in common at all inoculated time points. Co-expression network analysis identified 18 modules, in which five modules significantly associated with V. dahliae infection (black, mediumpurple3, darkolivegreen, plum3 positively correlated with the 2 h, 6 h, 48 h, and 72 h inoculated time points, respectively; mediumpurple2 negatively correlated with the 2 h inoculated time point). GO and KEGG enrichment analysis were performed on these five specific modules, which could be enriched in GO terms and metabolic pathways, such as cellular response to stimulus, calcium ion binding and flavonoid biosynthesis. The hub genes were screened by calculating gene connectivity in the corresponding networks, and they may play important roles in the resistance against biotic/abiotic stresses. In summary, by WGCNA, a few defense-related co-expressed modules and hub genes were identified. The results of this study would be beneficial for further understanding of the molecular mechanisms of pathogen resistance in cotton, and provide new gene resources for cotton breeding in the future.

With the development of enterprises and their gradual growth, their device terminals continue to expand in terms of types, numbers, and application ranges. The form of terminal security protection is becoming increasingly severe, and terminal vulnerabilities and viruses emerge endlessly. A high-quality, efficient, and secure corporate network and terminal environment is an important guarantee for the sound development of enterprises. However, the commonly used monitoring methods of existing equipment terminals, especially the detection methods for encrypted traffic, have been unable to meet the needs of some enterprises for real-time monitoring, rapid identification and timely blocking of high-risk behaviors of terminals. In this paper, an encryption traffic monitoring method for end users is proposed to realize abnormal user traffic detection. Deep neural network model is used to extract communication data features and abnormal traffic features for similarity comparison, so as to judge whether it is abnormal traffic.

Supplemental material, sj-docx-1-cix-10.1177_11769351211027592 for Re-Evaluate Fusion Genes in Prostate Cancer by Ting Wei, Ji Lu, Tao Ma, Haojie Huang, Jean-Pierre Kocher and Liguo Wang in Cancer Informatics

During the machining of powder metallurgy (PM) superalloy parts, the machined surface bears severe plastic deformation which leads to the transformation of microstructure and mechanical properties. White layer which reflects the transformation of microstructure and mechanical properties, often appears on the top of machined surface during hard machining of PM superalloy. White layer has significant effect on the machined surface integrity. The effects of cutting speed on the white layer formation have been investigated in order to reveal the transformation of the microstructure and mechanical properties in the machined surface of PM superalloy FGH95. Results show that white layer thickness increases with the increasing of cutting speed. The machined surface exhibits densification with no obvious structural characteristics. FGH95 superalloy bulk material exists in the form of Ni-based solid solution, while the microstructure of white layer is significantly different from that of bulk materials. It's because of the microstructure of Ni-based solid solution which has transformed during the cutting of FGH95. The higher the cutting speed is, the more obvious the grain refinement is. A higher cutting speed could also lead to higher values of hardness in white layer. Residual stresses in the machined surface of FGH95 are tensile in all cutting conditions, which show an increasing trend with the increasing of cutting speed. This research can provide the theoretical basis for the investigation and controlling of machined surface quality.

NAC genes, specific to plants, play important roles in plant development as well as in response to biotic and abiotic stresses. Here, a novel gene encoding a NAC domain, named as GhSNAC3, was isolated from upland cotton (Gossypium hirsutum L.). Sequence analyses showed that GhSNAC3 encodes a protein of 346 amino acids with an estimated molecular mass of 38.4 kDa and pI of 8.87. Transient localization assays in onion epidermal cells confirmed GhSNAC3 is a nuclear protein. Transactivation studies using a yeast system revealed that GhSNAC3 functions as a transcription activator. Quantitative real-time polymerase chain reaction analysis indicated that GhSNAC3 was induced by high salinity, drought and abscisic acid treatments. We overexpressed GhSNAC3 in tobacco by using Agrobacterium-mediated transformation. Transgenic lines produced longer primary roots and more fresh weight under salt and drought stresses as compared to wild-type plants. Collectively, our results indicated that overexpression of GhSNAC3 in tobacco can enhance drought and salt tolerances.

Lots of heat generated in the cutting deformation zone can cause the temperature rising of cutting tool and workpiece during the cutting process. Tool temperature is mainly depended on the heat energy entering into tool in the secondary deformation zone. Higher working temperature can weaken the cutting tool strength and then deteriorate the tool wear. Advanced coating layer deposited on the tool substrate can change the tool-chip friction and affect the heat partition in the tool-chip interface. In order to reveal the mechanism of heat conduction in the secondary deformation zone, the model of heat partition into cutting tool was built. With the aid of finite element method (FEM), the relative parameters which were necessary for the solving of heat partition model were quantified. Actual cutting experiments were carried out to the verification of the FEM models by means of the cutting force and temperature measurement. Influences of coatings (i.e., coating materials, coating thickness) and cutting speeds on the heat partition were analyzed. And then, rake face temperatures obtained at four kinds of coated tools were compared. The investigation results showed that TiC coating appears as the maximum value and Al2O3 coating appears as the minimum value of heat partition coefficient into cutting tool among these four kinds of coatings (i.e., TiC, TiN, TiAlN, Al2O3). With the increasing of coating thickness and cutting speeds, heat partition into cutting tool all showed a decrease trend. Al2O3-coated tool had the maximum rake face temperature among these four kinds of coated tools; however, the location of peak temperature on the rake face of Al2O3-coated tool was the shortest distance from the tool tip.

In this work, the authors propose a split-gate resurf stepped oxide with P-pillar (SGRSOP) UMOS structure. The P-pillar trench under the source electrode in SGRSOP U-shape metal-oxide-semiconductor (UMOS) serves the purpose of simultaneously achieving the following: (1) it has formed a local super junction (SJ) structure with the N-type drift. The P-pillar modulates the electric field distribution in the local SJ and increases the N− drift region doping concentration. (2) It has suppressed the parasitic bipolar junction transistor (BJT) effect by adding the P-pillar in the N − drift region, enlarges the boundary of the snapback. As compared with the SGRSO UMOS, the SGRSOP UMOS only add the P-pillar structure, while it has the better performance. The simulation results show that the SGRSOP UMOS reduces the on-state specific resistance (R SP) and increases the transconductance (g m), improves the figure of merit and its UIS ruggedness is as good as that of the split-gate resurf stepped oxide UMOS.

GPR (Ground penetrating radar) is one kind of the detection technologies with rapid, non-destruction, uninterrupted detection, and high-resolution. GPR can be applied to detect underground holes and loose soil layer and the other baneful anomalies, and therefore is widely applied to hole survey of the subway project in recent years. Based on briefly describing engineering geological background between F station and L station of a subway project in Beijing, this paper gives the arrangement method of GPR survey line in the construction area of shield method, analyzes detection results, verifies an anomaly by spading, determines the distribution range and its characteristic of the hole anomalies. The results showed that three obvious anomalies were detected in the area between F station and L station, and seriously loose soil layer was the cause of No. 1 and No. 3 anomalies, and hole was the cause of No. 2 anomaly. Thereby the anomalies of loose soil layer and hole could be detected accurately by the GPR technology, and then basic data of underground holes could be obtained. On the base of analyzing these data, effective measures could be adopted to avoid the hidden danger, then the safety of subway construction and surrounding environment of the subway project could be ensured.

The superplastic deformation mechanism of Mg-7.0Al-0.4Zn alloy was studied by EBSD technology. Results indicate that with increasing of strain amount, the texture of {0002}//ED is strengthened obviously, and intragranular sliding plays a main role during the accommodated deformation. Twins have started at the medium and the final deformation stages; when approaching to failure, intragranular sliding vanishes, and twins replace it to play a main role. But the contribution of twins to deformation is relatively small.

The geological conditions of coal-bed methane (CBM) are complex in China, so it is difficult to predict the production of CBM wells. Methodology of artificial intelligence was introduced in the mining of CBM. According to the characteristics of target block reservoir, the gray correlation analysis technology is used for analyzing the degree of correlation between each parameter and CBM production. And then the BP artificial neural network model is used in prediction and evaluation of CBM wells fracturing production. Application results show that the method improves the prediction and evaluation accuracy of CBM production.

In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC) dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods.

Currently, the maintenance mode of the power transformers is regular maintenance, which has been widely used. It is key to correctly evaluate the reliability level of the transformer in operation condition because it is directly related to the success of maintenance; on this basis, the lifetime evaluation and optimized maintenance model based on hot spot temperature (HST) in terms of the power transformer is developed in this chapter. In this model, the reaction speed theory of Arrhenius and Weibull distribution is considered as the fundamental theories, which are used to describe the aging process of the transformer and get the transformer failure rate \(\lambda \) ; and then the equations with exponential form are adopted to compute HST, namely, to calculate the top oil temperature relative to the environmental temperature \(\Delta {{\Theta }_{TO}}()\) the increment of top oil temperature \(\Delta {{\Theta }_{H}}\)and the hysteretic temperature \({{\Theta }_{Ae}}()\)so as to get HST (equals to the sum of the aforementioned three variables). Meanwhile, based on the model, the software analysis system based on Java language and Mysql database has also been developed, in which the transformer maintenance process was optimized and the statistical analysis was made for the hot spot area. Finally, the diagnosis results were obtained correspondingly, indicating that the designed model and system can effectively reduce the maintenance frequency, improve the utilization coefficient of the transformer, and thus improve the equipment’s reliability.

Based on the mechanized mathematics and WU Wen-tsun elimination method, using oil film forces of short-bearing model and Muszynska’s dynamic model, the dynamical behavior of rotor-bearing system and its stability of motion are investigated. As example, the concept of Wu characteristic set and Maple software, whirl parameters of short-bearing model, which is usually solved by the numerical method, are analyzed. At the same time, stability of zero solution of Jeffcott rotor whirl equation and stability of self-excited vibration are studied. The conditions of stable motion are obtained by using theory of nonlinear vibration.

A 0.1-1.2 GHz power amplifier using 0.18-μm CMOS technology is presented with a small chip area. With 3.3V supply, the measurement results in this band indicated that the gain is better than 20 dB, the S11 and S22 is less than - 18 dB and -10 dB, respectively. The saturated output power is 20.5 dBm and 19.5 dBm at 433 MHz and 900 MHz with the corresponding PAE of 27% and 19.5%, respectively. The chip area is only 0.414 mm 2 .

Based on controlling the power quality of the 6.3kV power system of a coal mine in North China, a topology consist of Passive Power Filter (PPF), Thyristor Switched Capacitor (TSC) and Fixed Compensation Capacitor (FC) has been studied. Combining the thermodynamics modeling theory with network topology analysis method, the parameters of PPF, TSC and FC was proposed and optimized. Though there is a 5.th single tuning filter only, the measured results show that the average harmonic current injected into the power system completely accord with Chinese national standard GB/T-14549-93 and the power factor is increased to 0.99 without any possibility of over-compensation or under-compensation. Due to the action of the PPF the negative effect that caused by zero-voltage-switch of TSC was eliminated by proposed topology. The study of this paper gives the corner stone of theory and method for realizing smart grid system with Chinese characteristics.

Orthopedic Mg–Zn–Ca alloy is prepared by solution pretreatment and then processed by high pressure torsion (HPT). The results show that the second phases in solution treated (ST) alloy processed by HPT disperse uniformly. While the second phases in HPT-treated cast alloy without solution pretreatment are not completely fragmented and distributed non-uniformly. After five revolutions, the grain refinement is completed. The microhardness of the alloys is significantly improved after HPT, and the microhardness distribution is homogeneous in HPT-treated ST alloy. The corrosion resistance of HPT-treated ST alloy is more excellent than that of HPT-treated cast alloy in physiological environment.

This paper takes the high voltage power system dynamic reactive power compensation at the conditions of the mine load smooth changes, studies the negative effects inhibition of the HVC contactor switched capacitors reactive power compensation device in ambient temperature perturbation. The basic principles and structure of intelligent reactive power compensation device based on contactor zero-crossing switching control and the response time compensation method for different ambient temperature and operating voltage are proposed, and the expert decision-making switched control strategy is given. The research can effectively eliminate the adverse effects of capacitor inrush current and voltage flicker in the moment of switching, and ensure safe and stable operation of the grid under conditions that satisfy the system reactive power compensation.

In order to effectively improve the identification precision of the submersible motor temperature sensorless, The long cable parameter perturbation analysis theory is applied to the submersible motor temperature identification, Firstly this paper uses the distributed parameter method for the analysis of cable parameters distribution, then building and simplifying the model of long cable, rotor and stator in the submersible motor with SolidWorks. Because SolidWorks and ANSYS has a good interface, which can be sheathed cables, submersible motor model into ANSYS, therefore we can get the submersible motor and cable's temperature distribution maps by using the ANSYS thermal field analysis function, finally comparing the simulation results with the measured data to verify the theory that is presented in this paper.

Rotor bar broken fault is one of the common faults of the submersible motor. In order not to affect the production efficiency of submersible motor, the fault should be detected in time. For the purpose of solving the problem of the rotor crack detection, this dissertation has designed a method based on the analysis of the stator current to detect the rotor bar breaking fault. Through the experiments on submersible motor, we analyze on the stator current spectrum by means of HHT algorithm of the three different states of submersible motor—normal, minor fault and complete fault, and discriminate the fault in accordance with the frequency and amplitude of the current spectrum. Furthermore, the BP neural networks are also applied into the research to identify the rotor bar breaking fault in order to increase the accuracy of the discrimination results. The experiment and the research end up with a better result, and it verifies the feasibility and accuracy of the method.

Considering the influence of surge current and voltage flickering on the distribution power system induced by medium frequency arc furnaces, the Hybrid Active Power Filter (HAPF) is applied to suppress the harmonics and compensate reactive power of 10kV bus of a steel mill. In order to study superiority effect and stability of HAPF, the Graph Theory is used to analyze its complex topology. By Graph Theory (GT) the HAPF can be described by several nodes that are connected by the power flows. Considering the important effect of LCL-filter of APF for suppressing harmonics such as high-frequency switch harmonic, its parameters are calculated by derived dynamic differential equations. Simulation and actual measure results show that the idea of combining the Graph Theory with dynamics modeling method is an effective path to design HAPF.

In order to reduce possible resonance induced by harmonics, this paper focus on analyzing sensitivity and stability of passive power filter (PPF) that plays an important role in hybrid active power filter (HAPF). To minimize the capacity of HAPF and forecast its stability, a power system analysis framework (PSAF) that is professional software for analyzing the power distribution system is used to analyze PPF based on Graph Theory. The filtering effect and ability to compensate reactive power is demonstrated by proposed method that is useful and suitable to any HAPF. Comparing to other methods, proposed method shows the advantages of approximately physical simulation and higher calculation precision due to designing detail system parameters. It is implemented in an HAPF with a 3000kVA that installed in a coal mill in Northern China. The simulation and measured results show that the proposed method is not only easy to be calculated and implemented, but also very effective in reducing harmonics and compensating reactive power.

A method is presented for the estimation of DOA, and polarization parameters of far-field sources based on four order cumulant. The method adopts ULA array, and every sensor consists of orthogonal dipole-pairs with the same coordinate axis direction, which measure the x-axis and y-axis electric-field components of the far-field signals. The method constructs three four-order cumulant matrixes by receiving polarized array data, and estimates the parameters of far-field according to the MP algorithm. The method does not require any spectral peak search and easy parameter pairing only, can be applied to additive Gaussian white noise environment. The performance of the proposed method is verified by computer simulations.

The time-frequency distribution (TFD) method has received a considerable amount of attention in Direction of Arrival (DOA) estimation of multi-senor and time space processing, which lead to improved spatial resolution and source separation performances. Based on the characteristic of the different time-frequency distribution in time-frequency domain, a novel approach of Time-frequency DOA estimation is proposed. Through computation of the high order power of spatial time-frequency matrix, the signal subspace or noise subspace is estimated without the eigenvalue decomposition (EVD) of spatial time-frequency matrix. Compared with the conventional approaches of DOA estimation based on EVD, the presented method has lower computational complexity without performance degradation. The effectiveness and high resolution is verified via computer simulations.

A new concept ‘propagator’ for array signal processing is recently introduced, the properties of which have been proven useful for DO A (Direction of Arrival) estimation. This attractive concept is the propagator which can easily be extracted from the cross spectral matrix of the received signals for DOA estimation without Eigen-decomposition. But supplying propagator for DOA estimation has to know the number of sources received by array, which limits the application of propagator in Radar, Sonar and passive detection system. Based on multivariate analysis method, a joint estimation of the number of sources and DOA by propagator method (PM) is presented. It shows that the performance of proposed method with less complexity, effectiveness and high resolution.

Data association mainly decided whether the data from different sensors stood for the same target in multi-target information fusion. Tradition data association always took the joint probabilistic data association (JPDA) algorithm as the priority means which depend on the state measurement and was complex with long computation time. A PDA algorithm used in the paper based on gray correlation analysis not only calculated with multiple features of the target, but also was easy and accurate and had short computation time. Compared to the weight factor given by subjective judgments of the experts, a new PDA algorithm was proposed based on entropy weight and gray correlation analysis. The algorithm made the data more theoretical by given the weight factor of entropy weight of the target features adaptively. Experiments showed that the proposed algorithm was effective in engineering.

Classification is an important technique of hyperspetral imagery processing. In traditional classification methods of hyperspectral imagery, all classes are treated equally. Some of them, however, should be given more regard, and so, it is significant to emphasize particularly on the analysis effect of classes of interest. In this case, two kinds of processing methods are proposed to protect classes of interest in process of least square SVM based classification: deleting training samples and changing diagonal elements. In former method, by deleting samples of uninterested classes in process of SVM training, interested classes are left and their classification accuracies are improved greatly. In latter method, by attaching different weights to diagonal elements of punishment matrix, samples of interested classes are given more regard and so the corresponding classification accuracies are improved. Elaborate experiments show that the proposed methods can improve the classification effect of classes of interest.

In traditional spectral unmixing methods of hyperspectral imagery, all endmembers (EMs) take part in the process of each pixel unmixing, and each EM is invariable in the whole processing. This kind of ways discounts unmixing accuracy by introducing some EMs unrelated with mixed pixel to be analyzed, and by the inaccuracy representation of each class by a fixed EM. So the traditional way of spectral unmixing can not get satisfied result. In this case, the paper proposes spectral unmixng ways based on flexibly selected EMs to improve unmixing accuracy. In the first way, partly EMs corresponding to classes of interest are selected to be used in the whole process of spectral unmixing. In the second way, related EMs of a mixed pixel are concluded by utilizing spatial information, then spectral unmixing is done with only related classes considered. In the third way, each class EM is adjusted regionally in each locally divided area. And so, more accuracy EMs are used in spectral unmixing. Experimental results show that spectral unmixing based on flexibly selected EMs can get better performance.

Based on the self-tuning theory, a new self-tuning recursive predictor is proposed for nonstationary time series. Very good results were obtained by using the predictor for the prediction of electric power systems.

In order to gain a true signal from the signal polluted by time-variant correlative noises, an adaptive noise canceler (ANC) is proposed. We develop an observation model using the ARMA process. The time-variant parameter vector of the ARMA model is described by using a generalized random walk model. Under the conditions that input and output noises are jointly normal white noises, we deduce the adaptive Kalman filter (AKF) of the parameter vector and the maximum a posterior (MAP) estimator of the noise statistics. The ANC consists of the AKF and the MAP estimator. Simulation results are obtained by using a conventional smooth signal with sinusoidal components as well as the "non-smooth" signal of recorded oil well colour spectrum data with time-variant correlative noise.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Superplasticity and deformation mechanism of Mg-7.0Al-0.2Zn (AZ70) alloys without typical fine-grained structure was investigated. It is indicated that the AZ70 alloy exhibits superior superplasticity and the maximum elongation of 191.5% was obtained at 380 °C and 1×10 −3 s −1 . It is worth mentioned that high-strain-rate ( =1×10 −2 s −1 ) superplasticity with elongation of 161.5% was achieved at 380 °C. The mean grain sizes increase with temperature increasing and strain rate decreasing. The grain boundary sliding (GBS) is the dominant deformation mode of the superplasticity, which is verified by grains remaining equiaxial during deformation. Cavities form and grow up along grain boundary. And then the growth and connection of cavities results in the final failure. SEM morphology of the failure surface shows typical tenacity failure characteristics with quantities of uniform dimples.

Yu Wang,1,* Hanning Zha,1,* Xiang Fang,2 Tianjiao Shen,1 Kunyun Pan,1 Jianping Zhang,1 Keqiang He,1 Sheng Wang,1 Liguo Hu1 1Department of Anesthesiology, The First Affiliated Hospital of USTC (Anhui Provincial Hospital), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People’s Republic of China; 2Department of Anesthesiology, Hefei BOE Hospital, Hefei, Anhui, People’s Republic of China*These authors contributed equally to this workCorrespondence: Sheng Wang; Liguo Hu, Email iamsheng2020@ustc.edu.cn; hulg@163.comObjective: The dose selection of ropivacaine for spinal anesthesia in clinical work mainly depends on the experience of the anesthesiologist. In this study, a prospective and modified up-down sequential allocation design was used to provide the optimal dose selection of ropivacaine for spinal anesthesia.Patients and methods: This study was divided into two stages, and a total of 164 elderly patients with elective hip fractures were included. In stage I, the dose of ropivacaine was selected using the up-down sequential method of height correction, and the 50% effective dose (ED50) and 95% effective dose (ED95) were obtained. A nomogram for predicting satisfactory anesthesia and a formula for predicting the optimal dose was also given in this stage. In stage II, the dose of ropivacaine was calculated by using the optimal dose prediction formula, so as to evaluate the efficacy and safety of the model.Results: The ED50 and ED95 of the stage I were 7.036 mg (95%CI 6.549– 7.585 mg) and 8.709 mg (95%CI 7.902– 14.275 mg), respectively. And provided a nomogram predicting satisfactory anesthesia with a C-index of 0.847 (95%CI 0.774– 0.92). The optimal dose prediction formula of ropivacaine was calculated, including variables for age, gender, height, and weight. This formula was found to be 90% efficient. It is worth mentioning that the incidence of direct transfer to the ward in the two stages was as high as 86.84% and 93.33%, respectively, and no patients were transferred to the ICU in stage II.Conclusion: The ED50 and ED95 of ropivacaine were 7.036 mg and 8.709 mg, respectively, and the nomograms are sufficiently accurate to predict satisfactory anesthesia. Beyond that, the dose prediction equation provided in this study has high efficacy and safety, and can guide the dose selection of spinal anesthesia in elderly patients with hip fracture in clinical practice.Clinical trials registration: ChiCTR2100046982Keywords: ropivacaine, dose selection, elder, spinal anesthesia, Hip fracture, sequential design

Mineralisation of fibrillar collagen with biomimetic process-directing agents has enabled scientists to gain insight into the potential mechanisms involved in intrafibrillar mineralisation. Here, by using polycation- and polyanion-directed intrafibrillar mineralisation, we challenge the popular paradigm that electrostatic attraction is solely responsible for polyelectrolyte-directed intrafibrillar mineralisation. Because there is no difference when a polycationic or a polyanionic electrolyte is used to direct collagen mineralisation, we argue that additional types of long-range non-electrostatic interactions are responsible for intrafibrillar mineralisation. Molecular dynamics simulations of collagen structures in the presence of extrafibrillar polyelectrolytes show that the outward movement of ions and intrafibrillar water through the collagen surface occurs irrespective of the charges of polyelectrolytes, resulting in the experimentally verifiable contraction of the collagen structures. The need to balance electroneutrality and osmotic equilibrium simultaneously to establish Gibbs-Donnan equilibrium in a polyelectrolyte-directed mineralisation system establishes a new model for collagen intrafibrillar mineralisation that supplements existing collagen mineralisation mechanisms.