Search

• Adaptively allocate computing resource to input regions for better network inference. • Patch sampling algorithm reduces redundant calculation costs in overlapping regions. • Comparable performance is achieved on two datasets by comparing with SOTA methods. [Display omitted] In this paper, we propose an entropy-dynamic resolution detection (EDRdet) method for object detection in aerial images. Most conventional object detection methods usually detect each region in aerial images directly with a fixed resolution, so that the resolution of the hard-to-detect region in the whole aerial image is not enough and that of the easy-to-detect region is not necessary. We argue that different resolutions of regions are required for efficient and accurate object detection in aerial images. Our EDRdet dynamically adjusts the resolution of each region via measuring the challenge of the detection process, therefore, our model can achieve a good trade-off between the computational cost and the accuracy effectively. Specifically, our EDRdet searches hard-to-detect regions through low-resolution global context information, and inputs higher-resolution patches to supplement lost feature at low resolution for fine-grained detection. We apply the entropy to determine the regions in aerial images required to be detected at higher resolutions, and where the entropy represents the uncertainty of the detection result. Moreover, we design a patch sampling algorithm to make the selected regions sparse to further improve the efficiency of patch generation. Extensive experiments on the DOTA and Visdrone2019 datasets verify that our EDRdet can reduce the computational cost and improve the model accuracy effectively. [ABSTRACT FROM AUTHOR]

In this study, steel fiber-reinforced concrete (SFRC) beam structures with regular oriented steel fibers are designed and prepared by using an assembled solenoid device. Fiber orientation is not arranged in a single direction but is similar to the direction of tensile stress of the beam in bending load conditions. With application of inductive test method and image processing technology, fiber orientation coefficient of SFRC was evaluated and increased from 0.5 to 0.7–0.8 after fiber alignment. To describe and analyze the flexural behavior of SFRC beams with regular oriented fibers and glass fiber-reinforced polymer (GFRP) rebars, 14 concrete beams with different parameters were fabricated and subjected to a four-point bending test. Specifically, the parameters included fiber content, fiber orientation, GFRP reinforcement ratio and different layer sections. The results showed that optimizing fiber orientations to the tension zone of the section led to a higher flexural strength and can well restrain the development of crack widths with comparison to randomly distributed fibers. The peak load and energy absorption increased by 7 % and 12.4 % after fiber alignment with a fiber volume fraction of 1.5 %. The combination of randomly and regularly distributed steel fibers in the form of two-layer composite members can be an efficient solution to improve the flexural performance for purpose of the optimum distribution and direction of steel fibers, and reduce the engineering cost. • A magnetic orientation device is designed for SFRC beams with regular oriented fibers. • Fibers had well arrangements with high fiber orientation coefficient. • A flexural test of the ASFRC beams was conducted and investigated. • ASFRC improved the first cracking load and initial stiffness of beams. • The experimental results indicated great flexural performance after aligning fibers to the tension zone. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis suffers from unsatisfied accuracy under variant working conditions, due to class-imbalance and distribution discrepancy of faulty data. Class-imbalance will cause classification bias that makes the diagnostic model tend to misclassify faulty classes, while the distribution discrepancy caused by variant working conditions will further reduce the model's generalization. Thus, this paper is mainly focusing on solving the two challenges simultaneously, and an Iterative Resampling Deep Decoupling Domain Adaptation (IRDDDA) model is proposed. The model consists of a feature extractor, domain classifier, label classifier and feature resampler modules, and it adopts a decoupling two-stage training framework. In the first stage, the model learns domain invariant features from the source domain and target domain based on original class-imbalance datasets. In the second stage, the feature extractor is partially fixed, and the feature-based resampling method is used to adjust the biased classifier caused by the class-imbalance problem. An iterative training strategy is adopted to simultaneously update the upper-layers of the feature extractor and adjust the classifier till convergence. The method is verified on the CWRU open dataset and the dataset collected from our bearing test rig. The verification results show that our method can achieve a better fault diagnostic performance overall, especially in minority faulty classes. • Class-imbalance and distribution discrepancy problem are simultaneously considered. • Deep decoupling domain adaptation framework is proposed. • An iterative feature-based resampling training strategy is adopted. • The method learns transferable invariant features and the calibrated classifier. [ABSTRACT FROM AUTHOR]

The stable reliability analysis of dams is a challenging topic in geotechnical engineering safety. Because there are more complex system failure modes and high-dimensional parameters in large solid structures, it is difficult to calculate failure probability efficiently and accurately using general reliability analysis methods. Most methods suffer from complex iterative processes and the curse of dimensionality. In this study, an improved importance sampling method based on multi-objective optimization is proposed and applied to reliability analysis for dam sliding. The optimization function is constructed from the limit state surface and the original probability density. The optimal sampling center is selected from the optimization results by considering the dominant performance function as well as the coefficient of variation of the weight function. Four numerical examples and a case study of the sliding stability of a gravity dam demonstrate the accuracy and efficiency of the proposed method. The results show that the proposed method is not sensitive to the specific multi-objective optimization algorithm used. Simulations using multiple sampling centers perform better overall than simulations using a single sampling center. In particular, the proposed method remains applicable to parallel systems with 20 high-dimensional parameters that are virtually immune to the curse of dimensionality. [ABSTRACT FROM AUTHOR]

• Seepage failure probability of high core rockfill dams during RDD is investigated. • The randomness of the parameters is estimated by Bayesian inference. • Seepage failure is most likely to occur in the mid-term of RDD. Rapid drawdown (RDD) is an abnormal reservoir flood control operation that may cause seepage failure of high core rockfill dams. In this paper, a novel framework combining Bayesian inference and transient unsaturated seepage simulation is proposed to evaluate the seepage failure probability of the core during RDD. The dynamic hydraulic boundaries utilized in the simulation are first determined by the performance of water release structures. The extreme learning machine (ELM) is then employed as the surrogate model to establish the mapping relationship between material parameters with simulated seepage pressure and hydraulic gradient. The Bayesian inference method is applied to characterize the uncertainty of material parameters based on seepage monitoring data. Finally, the posterior distribution samples of random parameters are utilized to calculate the seepage failure probability of the core. The seepage safety of the core under different drawdown rates is studied with a real 186 m high core rockfill dam. The results reveal that a faster RDD rate can lead to an increased probability of seepage failure. The maximum failure probability is 35.56 % for the four release structures working together (about 0.316 m/h), higher than 23.16 % for the three release structures (about 0.165 m/h). It is also found that the peak of seepage failure probability often occurs in the middle stage of RDD. Therefore, to ensure the safety of the project, it is important to control the RDD rate and carefully monitor the seepage pressure. From the point of view of seepage failure, the proposed method for evaluating the safety of core rockfill dams under RDD is of high guiding significance to the risk assessment of similar projects under extreme conditions. [ABSTRACT FROM AUTHOR]

The development and utilization of wind energy can help promote the carbon neutrality. Condition monitoring and fault diagnosis based on supervisory control and data acquisition (SCADA) can effectively improve wind turbine reliability and reduce operation and maintenance (O&M) costs. However, the complex data characteristics (e.g., time-varying and distribution-free) make it difficult to use a unified model to comprehensively evaluate the turbine state. To finely detect and analyze the turbine faulty mode, a hierarchical condition monitoring and fault diagnosis (CMFD) method is proposed, which consists of variable-level and turbine-level. First, the SCADA data is divided into two blocks: the nonstationary part with time-varying distribution, and the stationary part with time-invariant but non-Gaussian distribution. At the variable-level, this paper proposed a local monitoring unit based on sparse cointegration analysis (SCA) and independent component analysis (ICA) to detect faulty samples of different variable blocks. And the fault-related variables are isolated based on the proposed distributed reconstruction. At the turbine-level, the evaluation results from variable-level are fused: on the one hand, Bayesian inference is used to integrate monitoring statistics of stationary and nonstationary parts to comprehensively evaluate the operational state of the turbine; on the other hand, the root fault subsystem is deduced from the fault-related variables isolated from the two parts based on Gaussian process regression. The proposed method is applied in the operation and maintenance of a real wind farm, and the performance advantages are verified by the comparison with popular benchmarks. • An innovative hierarchical CMFD strategy for wind turbines is proposed which consists of variable-level and turbine-level methods. • At the variable-level, the monitoring and isolated methods are proposed based on SCA and ICA. • At the turbine-level, Bayesian inference is used to integrate variable-level monitoring results, and the root faulty subsystem is deduced based on Gaussian process regression. [ABSTRACT FROM AUTHOR]

The failure mode is a dominant factor of mechanical performance of braided composite tube subjected to axial compression. The failure mode was modified by tailoring stack-up sequence of composite tubes hybridized by bi/tri-axial braided laminate. The thermal photographing and micro-CT scanning methods were employed to monitor the in-situ damage progression and post-mortem specimen, respectively. The results show that the delamination was easy to be induced at the interface nearby tri-axial braided laminate. It attributes to the combination between the crack caused by local buckling of the axial yarns and the crack caused by shear deformation of braiding yarns. By the illumination of this behavior, delamination is controlled to occur at the different position along thickness direction of braided tube by adjusting the stacking sequence of braided laminates. When delamination position transfer from the inner/outer sides to the middle of tube wall, the failure mode transfer from the progressive folding to splaying, which increase the specific energy absorption by 45.77%. [ABSTRACT FROM AUTHOR]

The powder immersion reaction assisted coating (PIRAC) method was used to fabricate a coating on the graphite surface. The graphite was immersed into Cr powder and annealed at 1173–1373 K. The iodine powder was admixed with the Cr powder to investigate the contribution of iodine to the growth and mechanical properties of the coating. After 4 h of PIRAC treatment with or without iodine, Cr7C3- and Cr23C6-based coatings were formed, respectively. The iodinated samples had a higher hardness and thickness, and the friction coefficient was slightly higher than that of the iodine-free sample. The mechanisms for the formation of PIRAC-chromium carbide coatings are explained, and the variations in the produced coatings are discussed. [ABSTRACT FROM AUTHOR]

Fully convolutional neural networks (FCNs) have shown great effects in the saliency detection task. In this paper, we present an FCN based model to aggregate multi-level convolutional features for salient object detection. The main contribution of this work is to make full use of the information extracted by the network while preserving the characteristics of each level. We first propose a novel Intra-stage Feature Aggregating (IFA) module to enhance the detailed information by integrating feature maps from different layers within the same stage of the ResNet-50. We then design an Inter-stage Feature Fusion (IFF) module which exploits contextual information by combining the features from neighboring stages to make each feature map more discriminative. Finally, we propose a Deeply Supervised Group Recursive Prediction (DGR) module to refine the details of the predicted saliency maps and generate the final saliency map. Our approach performs favorably against state-of-the-art methods on five popular datasets and achieves a real-time speed of 30 fps. [ABSTRACT FROM AUTHOR]

A multi-turbine condition monitoring method using supervisory control and data acquisition (SCADA) data for large-scale wind farm is proposed. The method takes the difference between the SCADA data of each turbine with the median of other remaining turbines, and establishes condition vector consisting of the differences. Considering the autocorrelation of turbine SCADA data, vector autoregression (VAR) model is used to remove the autocorrelation in the condition vector of wind farm. Hotelling and multivariate exponentially weighted moving average (MEWMA) control chart are applied to monitor the residual vector. An industrial wind farm example is given to illustrate the proposed method. Compared with the existing turbine condition monitoring charts, the false alarm of proposed method is reduced for considering the autocorrelation of operation data, and monitoring strategy using MEWMA improves detected rate and expedites alarm time compared with Hotelling. The proposed method realizes monitoring multiple turbines simultaneously in farm by a fault indicator, which has important theoretical and engineering significance to the practical operations and maintenance activities in large-scale wind farm. • Through the cross-reference between the turbines in wind farm, the operation state would be detected more conveniently. • VAR-Hotelling and VAR-MEWMA were established to monitor the wind farm and locate the faulty turbine. • An industry example verified that proposed method could detect the faulty turbines and expedite fault warning. [ABSTRACT FROM AUTHOR]

The instability of wind power has a serious impact on the power grid system, and an accurate wind power forecasting is greatly demanded in practice. Due to the time-varying nature, the distribution of weather variables such as wind speed, wind direction and temperature and/or the functional relationship of the power output relating to these weather variables are likely to change over time, which are often known as "concept drifts". However, most of the prediction models usually fail to adapt to such concept drifts. This would deteriorate the prediction accuracy, especially for the short-term prediction with high accuracy requirements. Motivated by this, this paper proposes an adaptive short-term wind power prediction method, aimed at automatically detecting the occurrence of concept drifts and updating the forecast model accordingly. The proposed method consists of three main steps, extract sample-related and sequence-related features of the weather data, cluster the data based on the similarity of the features extracted, and develop a new power prediction model with automatic drift detection and adaptation for each cluster. The comparison results show that the prediction performance of the proposed method is superior to that of the competitive methods. • Develop a new method, DDA-LightGBM, for very short-term. • The new method can take weather drifts into account. • Compare DDA-LightGBM with a wide variety of prediction models. • Show that DDA-LightGBM is able to provide a better forecasting accuracy. [ABSTRACT FROM AUTHOR]

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughly investigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequence data, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models. A comprehensive understanding of heterogeneous tumor subpopulations will benefit drug sensitivity prediction and combination drug treatment design. Deep learning models are powerful and extensively used in drug sensitivity prediction and in inferring drug–target interactions. Single-cell sequencing techniques offer precise and accurate profiling of tumor subpopulations and reveal subtle differences in their response to drug treatments. Applying deep transfer learning to predict drug sensitivity allows us to not only take advantage of prior knowledge obtained from massive bulk sequencing data but also utilize the heterogeneous landscapes generated by single-cell sequencing techniques. The integration of single-cell multi-omic data for drug sensitivity prediction using transfer learning methods poses a special challenge. [ABSTRACT FROM AUTHOR]

We aimed to determine whether treatment effect size differed between randomised controlled phase II trials and subsequent phase III trials and examine potential predictor of positive phase III trials. We searched MEDLINE for randomised controlled phase II studies published from January 2006 to December 2015. Matched phase III trials that investigated same intervention in the same setting of the same cancer were identified through Web of Science, ClinicalTrials.gov and conference proceedings. For each pair of phase II and phase III trials, we extracted hazard ratios (HRs) with 95% confidence intervals (CIs) for both overall survival (OS) and progression-free survival (PFS) and evaluated the differences by ratio of HRs (rHRs): the HR for phase II trial to that for phase III trial. A summary rHR was obtained through a random-effect meta-analysis. Univariable analyses were conducted to identify predictors of positive phase III trials. We identified 57 pairs of phase II and phase III trials. Compared with phase III trials, treatment effect sizes of PFS were, on average, 26% larger in phase II trials (rHR = 0.74, P < 0.001, 95% CI: 0.68–0.80). Treatment effect sizes of OS were 27% greater in phase II trials than in phase III trials (rHR = 0.73, P < 0.001, 95% CI: 0.66–0.79). Fifteen (26.3%) phase III trials were positive, and the only predictor of positive phase III trials was positive phase II trials Treatment effects in randomised controlled phase II trials were greater than those in matched phase III trials. Caution must be taken when interpreting promising results from randomised controlled phase II trials. • Progression-free survival benefit was 26% larger in phase II than phase III trial. • Overall survival benefits was 27% larger in phase II than phase III trial. • The only predictor of positive phase III trials was positive phase II trials. [ABSTRACT FROM AUTHOR]

• The Si-O-Cs groups are identified as active sites. • The promotion of Brønsted acid sites in Cs dispersion was revealed. • The maximum MA production reaches up to 7.23 mmol·g Cat −1·h−1. • The deactivation behavior of catalyst was demonstrated. With the gradual depletion of petroleum resources and overcapacity of coal industry, the one-step synthesis of methyl acrylate (MA) from the coal-based methyl acetate (Ma) and formaldehyde via aldol condensation, regarded as a supplementary route for the petroleum-based propene selective oxidation, receives significant attention in decades. Herein, a series of Cs/Beta zeolite catalysts were developed for this transformative process using incipient wetness impregnation with varied Cs loading amount and SiO 2 /Al 2 O 3 ratio. The physicochemical properties of both prepared Cs/Beta zeolite catalyst series and Beta zeolite supports were characterized by using FT-IR, ICP, XRF, physical N 2 adsorption–desorption isotherms, NH 3 -TPD, Py-IR, 31P-MAS NMR, XRD and XPS, in combination with catalytic evaluation experiments. The effects of Brønsted acid sites, SiO 2 /Al 2 O 3 ratio and Cs loading on the catalytic performance and catalyst deactivation were systematically investigated. The Brønsted acid sites on Beta zeolite support showed promotional effects on the dispersion of loaded Cs species and formation of (Al-O)-Si-O-Cs groups which were crucial to the improvement of catalytic activity and selectivity. However, the excessive Brønsted acid sites on Cs/Beta or Beta zeolite would lead to the heavy side reactions, including the hydrolysis of Ma and dehydration and further aromatization of methanol (MeOH). As a result, the highest formation rate of 7.23 mmol·g Cat −1·h−1 for MA could be achieved with a relatively high selectivity of 85% on the optimal 17.5Cs/Beta40 catalyst. While the undesired catalytic deactivation behavior resulting from the accumulation of alkyl-substituted single-ring aromatic species in the micropores of catalyst was found during the reaction by employing FT-IR, GC–MS, TGA and UV–vis analysis, which could be easily regenerated by thermal treatment at 400 °C and air atmosphere. [ABSTRACT FROM AUTHOR]

The endoplasmic reticulum (ER) is the main site for protein synthesis, folding, and secretion, and accumulation of the unfolded/misfolded proteins in the ER may induce ER stress. ER stress is an important participant in various intracellular signaling pathways. Prolonged- or high-intensity ER stress may induce cell apoptosis. Osteoporosis, characterized by imbalanced bone remodeling, is a global disease caused by many factors, such as ER stress. ER stress stimulates osteoblast apoptosis, increases bone loss, and promotes osteoporosis development. Many factors, such as the drug's adverse effects, metabolic disorders, calcium ion imbalance, bad habits, and aging, have been reported to activate ER stress, resulting in the pathological development of osteoporosis. Increasing evidence shows that ER stress regulates osteogenic differentiation, osteoblast activity, and osteoclast formation and function. Various therapeutic agents have been developed to counteract ER stress and thereby suppress osteoporosis development. Thus, inhibition of ER stress has become a potential target for the therapeutic management of osteoporosis. However, the in-depth understanding of ER stress in the pathogenesis of osteoporosis still needs more effort. [Display omitted] • ER stress is implicated in bone remodeling. • ER stress mediates osteogenic differentiation and osteoblast activity. • ER stress regulates osteoclast formation and function. • ER stress has become a target for osteoporosis treatment. [ABSTRACT FROM AUTHOR]

Highlights • A method for simulation of spatial variability of gravity dams was established. • Mean value of the tensile strength has a great influence on the plastic damage. • Research results can be used as a reference for gravity dams with similar profiles. Abstract A method to simulate the random field of the strength parameters of gravity dams considering their spatial variability based on the theory of random fields and the autocorrelation function is proposed. Taking a gravity dam project as an example, the elastic modulus and tensile strength are selected as random variables and the corresponding random fields are generated. Based on the concrete damage plasticity (CDP) model, the effects of the spatial variability of parameters on the dynamic response characteristics of gravity dams are expressed from the standpoints of damage development, residual displacement, and energy dissipation. The impacts of the mean value, coefficients of variation, autocorrelation distance, realizations of the tensile strength random field, and seismic inputs on the structural damage are also investigated. It is found that the spatial variability of parameters, especially the tensile strength, has a great influence on the dynamic damage of gravity dams, and that increasing the mean value of the tensile strength can improve their seismic performance. [ABSTRACT FROM AUTHOR]

In industry, mass transfer limitation is a major and widespread problem in most of vegetable oil epoxidation reactors. To improve the quality of products and the efficiency of production, it is essential to understand the law of mass transfer in the epoxidation reaction system. In present work, epoxidation of fatty acid methyl esters with performic acid generated in situ was studied in a stirred vessel at 50–80 °C. At first, the Sauter mean diameter (D 32 ) was determined and correlated, the correlation can predict the D 32 evolution of the reaction. Then, individual experiments were performed to separately determine the intrinsic kinetic parameters and the mass transfer parameters. Finally, a macroscopic kinetic model was developed, which can well describe the effect of droplet size on the reaction. Noteworthy, the mass transfer limitation was significantly increased when reaction temperature rises to 80 °C. [ABSTRACT FROM AUTHOR]

An effective way to improve the flight capability and environmental adaptability of aerospace vehicles is to design a morphing aircraft structure, which can actively change their aerodynamic shape. During different physiological activities, the abdomens of bees are regularly deformed to improve the efficiency of movement. This was used as an inspiration for the design of a nose cone for aerospace vehicles. Based on the deformation mechanism of the bee abdomen, a bionic design of a variable configuration morphing nose cone (MNC) mechanism with high sensitivity is proposed, which can realize real-time reverse self-locking of the mechanism via auxiliary branch chains. Theoretical models for the kinematics and dynamics of the MNC mechanism under two configurations are derived, verifying that the proposed mechanism has good kinematic and dynamic characteristics. The theoretical models are validated through simulation studies. In addition, static finite element simulations are performed to verify that the auxiliary locking branch chain can significantly improve the bearing capacity of the proposed mechanism. Moreover, the force and thermal parameters of different configurations of the MNC under corresponding typical working conditions are analyzed, enabling the improvement of the aerospace vehicle aerodynamic characteristics by the morphing of its nose cone. Finally, based on the analytic hierarchy process, different drag and heat flux reduction schemes are comprehensively evaluated. [ABSTRACT FROM AUTHOR]

Composite tubes have an asymmetry deformation and loading distribution under transverse bending. This work investigated the effect of the stacking sequence on the critical damage to a hybrid tube with 45° and 60° braided layers. In-situ infrared thermal imaging was adopted to observe the initiation and development of internal damage. As the outermost reinforcing material in the stacking structure, the 60° braided layer with high contact strength inhibited damage to the contact area at the top and improved the bending strength of the specimen at the initial stage of loading. As the inner reinforcing material in the stacking structure, the 45° braided layer with high bending toughness maintained the high stability of the load-carrying capacity during bending and restrained the tensile failure at the bottom of the tube. However, the interlaminar interface of braided layers with different braiding angles reduced the interlaminar fracture toughness. The interlaminar delamination failure developed rapidly as the bending at the bottom of the tube increased. This delamination damage determines the peak load-carrying capacity of the laminated structure. • The mechanical behavior of the stacking structure braided tube with the outer 60° braid and the inner 45° braid be studied. • Infrared thermography is applied to detect and analyze damage distribution degree in the loading process. • The load and damage distribution of stacking structure braided tubes are analyzed. [ABSTRACT FROM AUTHOR]

We propose a method to improve the measure of real-valued time series irreversibility which contains two tools: the directed horizontal visibility graph and the Kullback–Leibler divergence. The degree of time irreversibility is estimated by the Kullback–Leibler divergence between the i n and o u t degree distributions presented in the associated visibility graph. In our work, we reframe the i n and o u t degree distributions by encoding them with different embedded dimensions used in calculating permutation entropy(PE). With this improved method, we can not only estimate time series irreversibility efficiently, but also detect time series irreversibility from multiple dimensions. We verify the validity of our method and then estimate the amount of time irreversibility of series generated by chaotic maps as well as global stock markets over the period 2005–2015. The result shows that the amount of time irreversibility reaches the peak with embedded dimension d = 3 under circumstances of experiment and financial markets. [ABSTRACT FROM AUTHOR]

Livestock wastewater contains a large amount of nutrients that are available for recovery. In this study, a proof of concept process based on forward osmosis (FO) was proposed and investigated for in situ formation of struvite from digested swine wastewater. This FO system took advantage of a drawback – reverse solute flux (RSF) and used the reverse-fluxed Mg 2+ for struvite precipitation, thereby accomplishing recovery of both water and nutrient. With 0.5 M MgCl 2 as a draw solution, high-purity struvite formed spontaneously in the feed solution and the water flux through the FO membrane reached 3.12 LMH. The precipitated struvite was characterized and exhibited a similar composition to that of commercial struvite. The FO system achieve >50% water recovery and >93% ammonium nitrogen removal from the digested swine wastewater; in particular, it obtained >99% phosphate recovery, much higher than 60.7% removal via direct MgCl 2 addition (without FO treatment). The recovered products (both struvite and water) could potentially generate a value of 1.35 $ m −3 . The results of this study have demonstrated the feasibility of nutrient recovery from livestock wastewater promoted by FO treatment. [ABSTRACT FROM AUTHOR]

Most of the plasticizers used during poly (vinyl chloride) processing are typically toxic petroleum-based phthalates. Furthermore, large amount of edible oils become waste all over the world, fortunately, waste cooking oils have the potential to be an economic alternative for synthesizing different biomaterials. This study aimed to synthesis an improved properties and cost competitiveness cleaner bio-plasticizer using waste cooking oil methyl esters as staring raw materials. In this paper, waste cooking oil methyl esters were structurally modified i.e., transesterification and epoxidation reaction, to prepare epoxide waste cooking oil 2-ethylhexyl esters. The in-situ epoxidation was mainly studied by response surface methodology, with optimum conditions: reaction time was 7 h, molar ratio of C C bonds/FA/H 2 O 2 was 1.00/0.35/2.41, and reaction temperature was 65.84 °C with a maximum relative percentage conversion to oxirane of 92%. Also, the plasticizing effectiveness of epoxide waste cooking oil 2-ethylhexyl esters to substitute toxic plasticizer dioctyl phthalate were studied. Results showed that the mechanical and thermal performance of PVC samples was significantly improved with partial substitutions, while the migration resistance was slightly decreased. Comparison with conventional epoxy compound highlighted that epoxide waste cooking oil 2-ethylhexyl esters had superior low temperature property, oxidation stability, and remarkably reduced the glass transition temperature of PVC. In conclusion, the prepared epoxide via structural modification from waste cooking oil methyl esters proved to be an effective alternative to dioctyl phthalate, replacing about 40% of the total plasticizer. This may lead to the eco-development of waste cooking oil materials, and makes the plasticizing process more environmentally friendly. [ABSTRACT FROM AUTHOR]

A novel and facile post-grafting strategy, via a Schiff base chemical reaction between aldehyde and −NH 2 groups within NH 2 -MIL-125(Ti) has been developed to construct aromatic heterocycle-grafted metal organic frameworks (MOFs) photocatalysts. The improved conjugated system in MOFs showed lower band gaps and their excellent catalytic activities were manifested by selective oxidation of alcohols to corresponding aldehydes under visible light irradiation with high conversion and selectivity. Density functional theory (DFT) was applied for simulating the band gaps and electron orbits of heterocycle-grafted MOFs. The post-synthetic aromatic heterocycles in the MOFs network did not influence the original framework, but effectively expanded its π-delocalized system and promoted the separation and transfer of photo-excited charge carriers. As a result, the aromatic heterocycle-grafted MOFs exhibit significantly enhanced visible-light photo-catalytic activity for selective oxidation of alcohols over pristine NH 2 -MIL-125(Ti). Especially, the NH 2 -MIL-125(Ti) grafted by 2-quinolinecarboxaldehyde and 3-thiophenecarboxaldehyde showed better performance (more than 85% conversion of benzyl alcohol) due to the excellent photon adsorption and charge carriers transfer of aromatic heterocycles grafted on MOFs. This general grafting strategy can be utilized to graft large numbers of aromatic rings with different molecular structures onto NH 2 -MIL-125(Ti). [ABSTRACT FROM AUTHOR]

Transfer entropy (TE) now is widely used in the data mining and economic field. However, TE itself demands that time series intend to be stationary and meet Markov condition. Naturally, we are interested in investigating the effect of the nonlinear transformation of the two series on the TE. Therefore, the paper is designed to study the TE of five nonlinear “volatile” transformations based on the data which are generated by the linear modeling and the logistic maps modeling, as well as the dataset that come from financial markets. With only one of the TE of nonlinear transformations fluctuating around the TE of original series, the TE of others all have increased with different degrees. [ABSTRACT FROM AUTHOR]

The objective of the experiment was to find the minimum light intensity which could improve reproduction by examining its effect on ovarian gene expression, reproductive performance and body weight of rabbit does with three different light intensities: 60 (L), 80 (M), and 100 (H) lx. A total of 144 Rex-rabbits submitted to a 49-day reproductive regimen were used in this study. Ovaries were collected and relative abundance of mRNA for ovarian proteins of interest was examined with real-time PCR. Amount of protein for proteins of interest was examined by immunohistochemistry. Reproductive performance and doe bodyweight of the first three consecutive reproductive periods after initiation of the light intensity treatments were evaluated. The results provided evidence that light intensity had no effect on relative abundance of estradiol receptor-α (ER-α), follicle stimulating hormone receptor (FSHR), luteinizing hormone receptor (LHR), gonadotropin releasing hormone receptor 1 (GnRHR1) and progesterone receptor (PGR) mRNA. The relative abundance of growth hormone receptor (GHR) mRNA was, however, greater in Group L than M and H ( P < 0.05). No difference was observed for all reproductive indices as a result of submission to the three light intensities ( P > 0.05). The bodyweight of the does in Group L was greater than the other two groups at first insemination, second insemination and the second postpartum period ( P < 0.05). There was no difference in bodyweight after the second postpartum period ( P > 0.05). These observations suggest that light intensity between 60 and 100 lx has no effect on the reproductive performance of rabbit does, however, the amounts of GHR mRNA and growth hormone (GH) protein were affected and the greater light intensity had a negative effect on bodyweight between the time of the first insemination and the second partum period. [ABSTRACT FROM AUTHOR]

• The usage of correlation between maximum temperature and peak load. • The reconstruction of CT images identifies the debonding distribution and degree. • A novel inspection way of combing infrared imaging and micro CT reconstruction methods. • The role of axial yarns in axial crushing. In this work, the effect of axial yarns on damage behavior of two-dimensional triaxially braided composite(2DTBC) tubes subjected to axial compression was investigated. A novel damage detection method combining infrared thermography and CT scanning was presented. In the area without axial yarns, yarn crossovers were separated from each other due to interlacing patterns. Axial compression caused the braiding yarns to shear deformation like scissors. Inter-yarn de-bonding appeared in crossovers and could not connect. Consequently, it caused a progressively folding failure mode. In contrast, local buckling occurred on axial yarns because of their primary loading behavior. Cracks along the axial yarns formed, which would combine with debonding crossovers caused by the sheared braiding yarn. It resulted in significant inter-layer delamination and consequently splaying failure mode. The energy absorption of composites tube with 18 axial yarns increased by 79.9% at the cost of 27.8% fiber usage more than 3 axial yarns. [ABSTRACT FROM AUTHOR]

• H atoms detaching from S i 2 C (G e) 2 H 2 induces net spin density without destroying stability. • Two-dimensional S i 2 C (G e) 2 H structures demonstrate obvious bipolar magnetic characteristics. • Two structures of S i 2 C 2 H exhibit ferromagnetic ground phases and S i 2 G e 2 H exhibit antiferromagnetic ground phases. • Bipolar magnetic characteristics can be effectively modulated and transformed under external strain field. Two-dimensional bipolar magnetic semiconductors demonstrate great application potential in developing novelty spintronic devices. Here, we explore the electronic and magnetic characteristics of diamane-like structures S i 2 C (G e) 2 H 2 and S i 2 C (G e) 2 H based on the first-principles calculations. The results uncover that S i 2 C (G e) 2 H 2 are wide bandgap semiconductors and S i 2 C (G e) 2 H are bipolar magnetic semiconductors. Further calculations demonstrate the flexible magnetic structures for S i 2 C 2 H -I, S i 2 C 2 H -II, S i 2 G e 2 H -I and S i 2 G e 2 H -II based on the external strain field. Significantly, the magnetic type transforming from bipolar magnetic semiconductor to half semiconductor happens when appropriate strain is applied on the structure with a narrow spin-conserved energy gap. Our work enrich the diversity of 2D magnetic semiconductors and put forward a series of materials for the candidates of spintronic applications. [ABSTRACT FROM AUTHOR]

The estimation of acid dew point temperature (T adp ) of flue gas, which is mainly determined by SO 3 concentration and steam concentration (C H2O ) in flue gas, is crucial for coal-fired boiler system design. In this work, the heterogeneous and homogeneous effects for the conversion of SO 2 to SO 3 in the oxy-fuel flue gas have been examined. An updated formula for T adp estimation of flue gas was proposed, which is: T adp = 186 + 20 lgC H 2 O + 26 lg ( C SO 2 * ( 127.65 C SO 2 * − 0.585 + lg Aar × α fly C SO 2 * 1 2 × ( 15.191 + 0.071 Fe + 0.044 Al − 0.058 Ca − 0.117 Mg ) × 10 − 2 ) ) The updated formula shows that the main effects of the T adp were the steam concentration, initial SO 2 concentration (C SO2 , ppm), ash content of raw coal (A ar , %), fly ash proportion of the inherent coal ash (α fly , %), main mineral contents of coal ash (Fe, Al, Ca, Mg, g/kg). The formula was then fully validated by a large amount of data obtained from laboratory-scale experiments, pilot-scale experiments and industrial scale coal-fired power plants. The results showed that, the heterogeneous effect for the conversion of SO 2 to SO 3 contributed to 1.33 to 20.86% of the total SO 3 formed, smaller than the homogenous effect. Furthermore, the influence of the heterogeneous effect for oxy-fuel process was 1.2–1.6 times larger than that for the conventional air-fired process. The accuracy of the updated formula was proven much higher than the conventional empirical formula for air-firing combustion. The relative deviation of the calculation for oxy-fuel combustion by the updated formula was < 7.8%. [ABSTRACT FROM AUTHOR]

Photochemical devices show great potential in applications ranging from electrical energy storage to future smart grids. Here, we report a metal-free composite film cell based on carbon dots (CDots) and polyaniline (PANI), which could achieve 24/7 discharge and photo-charging in seawater. CDots/PANI film cell has an open-circuit-voltage (OCV) of 0.95 V, a maximum power density of 380 mW m2, and the ability to discharge continuously for 40 h under solar irradiation of AM 1.5 G. It couples the reduction reaction of PANI, and the CDot-catalyzed oxygen reduction reactions (ORR) during discharge. While the light-induced CDot-catalyzed oxygen evolution reaction (OER) and the reduction reaction of PANI during charging. In particular, in-situ TPV tests were used to verify the effect of the light-induced OER and the kinetic properties of the catalyst on the charge transfer at the interface. We believe this research may provide new ideas for sustainable energy conversion and storage. Carbon dots-based film cell can achieve 24/7 discharge and photo-charging in seawater, is capable of storing energy and utilizes the redox reaction of polyaniline, the ORR/OER catalyzed by carbon dots for discharge and charging, and TPV tests were used to explore the OER process and catalytic mechanism during charging. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel observer-based synchronization problem for discrete-time complex networks with Markov switching topologies is concerned. • An non-homogenous observer with an asynchronous topological structure and gain is developed to estimate the state of nodes. • Improve the existing threshold of dynamic event-triggered scheme into a diagonal threshold matrix. • To unify the works of the synchronization and state observation, an augmented error system is established. In this paper, the issue of observer-based synchronization for discrete complex networks with Markov switching topologies is investigated. First, the asynchronous phenomenon between the observer and the node is considered. By resorting to a hidden Markov model (HMM), an non-homogenous observer with an asynchronous topological structure and gain is developed to estimate the state of nodes. Second, for saving precious communication resources, the remote controller updates the control signal based on a dynamic event-triggered scheme (DETS). In this scheme, the threshold of DETS is improved into a diagonal threshold matrix, and the desired system performance can be obtained by flexibly arranging each entry of threshold matrix. To unify the works of the synchronization and state observation, an augmented error system (AES) is established. Subsequently, sufficient conditions for H ∞ synchronization of complex networks with asynchronous constraints are proposed by choosing a suitable Lyapunov function. Finally, two simulation examples are implemented to demonstrate the effectiveness of the above results. [ABSTRACT FROM AUTHOR]

It is very important to estimate the mechanical parameters based on monitoring data for investigating the operation behavior of concrete dams. In this paper, an advanced Bayesian parameter estimation method for concrete dams is proposed. The Empirical Mode Decomposition (EMD) is adopted to extract the aging component of the observed deformation sequence, and the hydrostatic component is separated from the remaining periodic term through the statistical regression model (SRM). A hybrid response surface method (HRSM) is proposed as a surrogate model for finite element method (FEM) simulation. The likelihood function, which reflects the error between the observed and simulated deformation, is constructed, then the Bayesian formula for parameter estimation is established. The transitional Markov chain Monte Carlo (TMCMC) algorithm is used to obtain the parameter posterior distribution. The proposed method is applied to estimate the elastic modulus of the GD concrete dam, the parameter posterior distribution is obtained through the fusion of monitoring information and parameter prior information. This method can also avoid the issues of multiple solutions and unreasonable parameter combinations, which may exist in traditional deterministic inversion methods. The case study shows that the proposed method has good applicability in the parameter estimation of concrete dams. [ABSTRACT FROM AUTHOR]

The health monitoring and operation behavior assessment of earth dams are research focuses in geotechnical engineering. Due to the limited monitoring data and the uncertainty of material properties, existing monitoring data-based interpolation methods and mechanism-based finite element method (FEM) can not accurately estimate the dam deformation field. Therefore, studying an accurate estimation method of the deformation field is important to guarantee the serviceability of earth dams. In this paper, a FEM-Bayesian kriging (FBK) method is proposed, in which the FEM is introduced into the Bayesian kriging framework as the source of soft data, taking the FEM results as a trend surface of the deformation field, the spatial distribution of the random residual is determined by the exact monitoring data, thereby the FEM results are corrected. In this method, data-rich but low-precision FEM results and limited but high-precision monitoring data are coupled to approach the real deformation field. A numerical experiment is designed to verify the accuracy of the proposed method, and the estimation error of the FBK is reduced by about 80% compared with traditional methods. The case study of an actual earth dam also shows that the proposed method can effectively characterize the deformation behavior of earth dams. [ABSTRACT FROM AUTHOR]

• Data-based models with EMD are proposed for deformation monitoring of concrete dams. • The proposed models can well simulate the irreversible component of concrete dam deformation and significantly decrease the over-fitting levels. • A new model selection criterion, namely Over-fitting coefficient, is proposed for evaluating and selecting dam health monitoring models. • The Over-fitting coefficient can better reflect over-fitting levels of individual monitoring models compared to traditional model selection criteria. Data-based models are extensively applied in concrete dam health monitoring. In this paper, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the irreversible component of deformation monitoring sequence. Consequently proper mathematical formulation of the irreversible component of deformation monitoring model for concrete dams can be established, and the problem of improperly assuming the mathematical form of irreversible component when constructing data-based models is well addressed. The periodic component of the deformation time series is modelled by regression and three machine learning algorithms (i.e., Extreme learning machine, Support Vector Machine based on Grid search and Cross-Verification, and Back Propagation Neural Networks optimized by Genetic Algorithm and Simulated Annealing) respectively. The data-based models with the EMD are able to effectively improve the performance of formulating irreversible component of concrete dam deformation and decrease the over-fitting levels. A new model selection criterion, namely Over-fitting coefficient, is proposed for evaluating and selecting the optimum model among candidate monitoring models. The Over-fitting coefficient can better reflect over-fitting levels of individual monitoring models compared to traditional model selection criteria. As a case study, the displacement and strain monitoring data from the YL gravity dam are modeled by data-based models with the EMD and machine learning algorithms. It is shown that data-based models with the EMD are of higher prediction accuracy and lower rate of false alarm in deformation monitoring than traditional models. The Over-fitting coefficient can better reflect over-fitting levels of monitoring models compared to the error measurement criteria and information criteria. The case study highlights the merits of the proposed data-based models and the model selection criterion for health monitoring of concrete dams. [ABSTRACT FROM AUTHOR]

• Epoxidation was performed continuously in a packed bed reactor with orifice plates. • The periodical dynamic variation of droplet size was characterized. • Two phases were well mixed at a low flow velocity in the reactor. • Heat transfer was intensified significantly and no thermal runaway occurred. • The average reaction rate in the reactor was greatly increased. In this work, a packed bed reactor with orifice plates (PBR@OP) was designed and applied to Prileschajew epoxidation. The reactor performance such as mass/heat transfer and reaction characteristics were studied and compared with packed bed reactor (PBR) and stirred-tank reactor (STR). In PBR@OP, the periodical dynamic variation of droplet size was characterized. Liquid-liquid two phases were well mixed at a low flow velocity, resulting in a significant reduction of total reactor length and pressure drop as compared with PBR. The heat transfer coefficient and thermal stability of PBR@OP were analyzed and a method to determine the critical diameter of the reactor for the epoxidation was developed. The continuous epoxidation of fatty acid methyl ester (FAME) in PBR@OP was carried out with a simpler and safer process, and the rate of transfer process and reaction were greatly enhanced as compared with that in STR. This reactor presents an economic choice for the process intensification of continuous epoxidation. [Display omitted] [ABSTRACT FROM AUTHOR]

Photocatalytic water-splitting to generate hydrogen (H 2) and hydrogen peroxide (H 2 O 2) is a green and renewable method to convert solar energy into chemical energy. The water impurity in organic solvents is usually hard to be removed. Here, we firstly used an organic semiconductor (DAnTMS) synthesized from 9,10-dibromoanthracene and trimethylsilylacetylene to convert the water impurity in organic solvent into H 2 and H 2 O 2 by photocatalysis. Under visible light, for 300.0 μL water in 20.0 mL methyl cyanide (MeCN) system, the DAnTMS catalyst can produce H 2 and H 2 O 2 with rate of 245.9 μmol g−1 h−1 and 3923.4 μmol g−1 h−1, respectively. Furthermore, a practical photocatalytic experiment was performed in 20.0 mL commercially available MeCN (99.8%, H 2 O ≤ 0.01%) with 10.0 mg DAnTMS as catalyst, in which, the total 15.4 μmol H 2 and 176.7 μmol H 2 O 2 were generated after 12 h reaction, suggesting the water impurity in MeCN was completely removed. In present catalytic system, on DAnTMS, O 2 and H 2 O are reduced into H 2 O 2 and H 2 , respectively, while, H 2 O is oxidized into H 2 O 2 through a 2e- pathway. An organic semiconductor was used to convert the water impurity in MeCN (98%, H 2 O ≤ 0.01%) into H 2 and H 2 O 2 by photocatalysis. [Display omitted] • DAnTMS was used to convert the water impurity in organic solvent into H 2 and H 2 O 2 by photocatalysis. • TPV tests were used to explore the half-reaction process and catalytic mechanism. • The extrapolation was applied on the TPV curves. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis suffers from class imbalances and distributional discrepancies of fault data under different working conditions. The class imbalance of the fault class increases the difficulty of learning the classification boundary of the diagnostic model for the minority class. Furthermore, the diversities of feature distributions decrease the diagnostic model's generalizability under various working conditions. Thus, in this paper, a deep adversarial transfer learning model for imbalanced bearing fault diagnosis (deep Imba-DA) is proposed to overcome these problems. In the proposed method, a cost-sensitive deep classifier is used to solve the class imbalance problem, and the domain adversarial subnet with the intraclass maximum mean discrepancy (MMD) is used to minimize the marginal and conditional distributional discrepancy between the source (data under one working condition) and target domain (data under another working condition) simultaneously. The performance of deep Imba-DA is evaluated and analyzed on the Case Western Reserve University (CWRU) and Paderborn datasets. The results show that deep Imba-DA outperforms other baseline methods on bearing diagnostic tasks. • An imbalanced bearing fault diagnostic model under variant conditions is proposed. • A joint transfer network with marginal and conditional distribution is developed. • Pseudo label strategy guarantees a good accuracy and fast convergence. [ABSTRACT FROM AUTHOR]

Statistical analysis of SRAM has emerged as a challenging issue because the SRAM cell failure probability is extremely small. In this paper, we develop a novel efficient sampling, searching and estimating method to capture the probability of SRAM failure. Particularly, we propose an innovative Adaptive Multi-Level Sliding-Window (AMLSW) method to find the failure boundary in the parameter space with less computational cost. The proposed AMLSW method applies an integrated optimization engine to adaptively explore the failure boundary by sampling a sequence of parameter points and calculating the simulation result in sliding windows. Its key features include ellipsoid transformation, multi-level grid partition and sliding window algorithm to make our method efficient and accurate for finding the SRAM failure probability and failure boundary in parameter space. Furthermore, we provide theoretical analysis on our new AMLSW method. The experimental results of a commercial 65 nm SRAM cell demonstrate that the AMLSW method achieves 1.7–40× runtime speed-up over the existing methods without surrendering accuracy, and dramatically high speed-up over normal M-C sampling method. Moreover, this new method provides the failure boundary in the parameter space as shown in experiments. [ABSTRACT FROM AUTHOR]

Modeling social tagging plays a critical role in identifying statistical regularities and structural principles common to social tagging systems. Existing modeling approaches only consider imitations or background knowledge of users. However, common interests among users are ignored. In this paper, latent interactions are applied to present the common interests, and dynamic patterns in empirical data are investigated. Furthermore, the latent interaction driven model (LIDM) is proposed to model social tagging. Experimental results show that the tag frequency distribution generated by LIDM is consistent with that in real-world data. Moreover, the latent interaction graph generated by LIDM has a higher average clustering coefficient and lower average shortest path compared with that generated by preferential attachment methods. This demonstrates that LIDM outperforms traditional methods. [ABSTRACT FROM AUTHOR]

An increasing number of users interact, collaborate, and share information through social networks. Unprecedented growth in social networks is generating a significant amount of unstructured social data. From such data, distilling communities where users have common interests and tracking variations of users' interests over time are important research tracks in fields such as opinion mining, trend prediction, and personalized services. However, these tasks are extremely difficult considering the highly dynamic characteristics of the data. Existing community detection methods are time consuming, making it difficult to process data in real time. In this paper, dynamic unstructured data is modeled as a stream. Tag assignments stream clustering (TASC), an incremental scalable community detection method, is proposed based on locality-sensitive hashing. Both tags and latent interactions among users are incorporated in the method. In our experiments, the social dynamic behaviors of users are first analyzed. The proposed TASC method is then compared with state-of-the-art clustering methods such as StreamKmeans and incremental k -clique; results indicate that TASC can detect communities more efficiently and effectively. [ABSTRACT FROM AUTHOR]

Single-event transient (SET) distributions and cross-sections are characterized for 16 nm bulk FinFET inverters with different threshold voltages and driving strengths using the heavy ion 181Ta with the energy of 1.07GeV and the Linear Energy Transfer (LET) of 85 MeV·cm2/mg. Lower threshold voltage makes shorter SET pulses and smaller cross-section, and reduces the upper limit of the SET pulse width, which is advantageous to harden the FinFET gates against the high LET heavy ion irradiation. Enhancing the driving strength changes the layout topology, leading to complicated impact on the radiation sensitivity for FinFET inverters. The source region is laid at one side of the drain region in P or N type transistor of the inverter with driving strength X1 (INV1), while there are two source regions at both sides of the drain region in P or N type transistor in the inverter with driving strength X2 (INV2), which makes INV2 is more SET tolerant than INV1. There are interdigitated two drain regions and three source regions in P or N type transistor of the inverter with driving strength X4 (INV4), which expands the sensitive area and cross-section. The test results indicate that the max SET pulse width is approximately proportional to the ratio of sensitive area to driving strength for the FinFET inverters, that is the max SET pulse width of INV4 is between INV1 and INV2. [ABSTRACT FROM AUTHOR]

• Difference of damage modes caused by the boundary conditions of primary load-bearing yarns. • Straightening effect of braided yarns with undulations under low-velocity impact. • Variation of off-axial angles leading to the transition of failure modes. The sensitivity of off-axial angle on impact response was investigated by experimental and numerical methods in this work. Three types of braided laminates with different on/off-axial angles in terms of OA00, OA225 and OA45 were prepared and low-velocity impact tests with various energy levels were performed. Meso-level finite element model (FEM) was also developed to reveal impact damage initiation and development. We discovered that the boundary condition of primary load-bearing yarns depending on the off-axial angles caused the difference of structural deformation and stress distribution of braiding yarns during impact process. Due to the restriction of the pressurized tips, the straightening effect of primary load-bearing yarns with heavy waviness caused its out-of-plane movement and related main cracks in OA00 specimen. In addition, the matrix resin cracks were found at the edge of the specimen because of in-plane shear movement of braiding yarns. As for OA45 specimen, more evident bending deformation was observed, resulting in elevated tensile stress of braiding yarns on non-impact side. The variation of off-axial angle from 0 to 45 degree led to the failure mode transforming from the extensive matrix damage on specimen edge to the severe fiber breakage on non-impact side. As a medium off-axial angle, OA225 specimen exhibited a mixture failure mode to resist impact and achieved excellent performance with respect to higher peak force and delayed load dropping. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Background and objectives: Bmi1 and EZH2 are involved in tumorigenesis of gliomas. However, clinicopathologic significance of their expression in gliomas is unknown; especially, the prognostic value of combined expression of Bmi1 and EZH2 has not been explored. Methods: Bmi1 and EZH2 expression in human gliomas and nonneoplastic brain tissues was measured by immunohistochemistry. Results: Both Bmi1 and EZH2 expressions in glioma tissues were significantly higher than those in corresponding nonneoplastic brain tissues (both P<0.001). Additionally, the upregulations of Bmi1 and EZH2 proteins were both significantly associated with advanced WHO grades (both P<0.001) and low KPS (P=0.008 and 0.01, respectively). Moreover, the overall survival of patients with high Bmi1 protein expression (P=0.006) or high EZH2 protein expression (P=0.01) was obviously lower than those with low expressions. More interestingly, glioma patients with combined overexpression of Bmi1 and EZH2 proteins had the shortest overall survival (P<0.001). Furthermore, multivariate analysis showed that Bmi1n expression (P=0.02), EZH2 expression (P=0.03), and combined expression of Bmi1 and EZH2 (P=0.008), were all independent prognostic factors for overall survival in glioma patients. Conclusions: Our data suggest for the first time that the combination of Bmi1 and EZH2 overexpression may be a highly sensitive marker for the prognosis in glioma patients. [Copyright &y& Elsevier]

• We utilize parallel sub networks to automatically reveal saliency clues at different spatial levels. • We propose an end-to-end salient object detection model that learns diversity saliency clues in an iterative manner. • We also provide a novel selective fusion strategy to fuse multi-model saliency clues for a high-performance salient object detection. Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper and more complex, which easily homogenize their learned deep features, resulting in a clear performance bottleneck. In sharp contrast to the conventional "deeper" schemes, this paper proposes a "wider" network architecture which consists of parallel sub-networks with totally different network architectures. In this way, those deep features obtained via these two sub-networks will exhibit large diversity, which will have large potential to be able to complement with each other. However, a large diversity may easily lead to the feature conflictions, thus we use the dense short-connections to enable a recursively interaction between the parallel sub-networks, pursuing an optimal complementary status between multi-model deep features. Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections. Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework. [ABSTRACT FROM AUTHOR]

The computational cost of nonlinear numerical simulation of gravity dams brings resistance to the popularization of seismic risk analysis under the framework of Performance-based Earthquake Engineering. Insufficient seismic records and inappropriate parametric fragility models will mislead the assessment of structural performance and total risk. A computationally efficient methodology suitable for a large number of seismic waves is proposed based on screening for intensity measures and a surrogate model to obtain the non-parametric fragility curves. It specifically integrates the rigorous numerical technique of seismic damage to the dam-reservoir-foundation system, the comprehensive comparison of intensity measures, the surrogate model for classification of limit states, and the seismic risk analysis of gravity dams. A typical high gravity dam and a classical artificial neural network model are employed to illustrate the effectiveness of the proposed methodology through a step-by-step scheme. The result shows that the simulated non-parametric fragility curves are more competent to consider the uncertainty of ground motions. The obtained structural fragility and risk is more accurate than the conventional method. • An non-parametric fragility model and matched scheme are proposed for gravity dams. •Risk analysis has been successfully performed under a large number of ground motions. •Conventional methods underrate the variability of records and lead to biased results. [ABSTRACT FROM AUTHOR]

Niobium‑chromium (Nb Cr) carbide coatings were deposited on graphite using the thermo-reactive deposition and diffusion (TRD) technique. The microstructure, hardness, and coefficient of thermal expansion were characterized to search for the optimal treatment parameters for hard and adherent coatings on graphite. The growth rate increased with treatment temperature for fixed treatment duration and remained constant at a constant temperature for about 6 h. The coatings were found to consist of alternating Nb and Cr layers, and this interesting multilayer structure might be explained by the thermodynamics of oxide reduction and carbide formation reactions. Compared to the single-carbides layer, the hardness of the complex coatings increased from about 2500 HV to above 3000 HV, and displayed a smaller gradient of the depth-dependent hardness from the coating to the substrate. The variation of coefficient of thermal expansion of the samples with temperature was similar to that of the graphite substrate at temperatures from 40 to 450 °C. These results showed the effectiveness of the TRD method for synthesizing hard and adherent carbide coatings on graphite, and for other coating/substrate systems with sharply dissimilar mechanical properties. [Display omitted] • Nb Cr complex carbide layers were formed on graphite materials by the TRD technique for the first time. • The maximum coating thicknesses can reach up to 20 μm, positively correlate with the TRD treatment temperature/time. • The elements distributions and unique alternating Nb Cr multilayer structure in the coatings were reported and discussed. • The coatings showed excellent hardness performance (>2500 HV) with good adhesions to the graphite substrates. [ABSTRACT FROM AUTHOR]

• The transverse impact response of hybrid biaxial/uniaxial braided tube was studied. • The effects of architecture hybridization and stacking sequence were identified. • Proposed FE model provided accurate predication of the impact behaviors. Tubular composites in automobile engineering are susceptible to transverse loading events. This paper deals with transverse low-velocity impacts on interply hybrid tubes made of biaxial/uniaxial braided fabric layers. For comparison, drop weight tests were performed on hybrid tubes with biaxial braided surface layers and uniaxial braided inner layer (BUB), with contrary stacking sequence (UBU), pure biaxial and uniaxial braided tubes (BBB and UUU). Numerical models were established to predict the impact behaviors and evaluate the hybrid effects. X-ray micro-computed tomography (Micro-CT) was also employed to identity the cracking location and characterize the damage mechanism. The results showed that the impact response of braided tube was related to its structural deformation resistance, which was determined by properties of reinforced layers varying with different stacking sequence. The hybrid effect became more obvious at higher impact energy. At impact energy of 15.4 J, hybrid tube (BUB) yielded the highest impact resistance with small structural deformation. The biaxial braided layer protected the specimen surface by constraining intralaminar crack propagation through the interlacing patterns. Meanwhile, it promoted crack tip propagating to the subsequent uniaxial braided layer and transferred the impact load. Thus, the material involvement of the inner layer was improved, which was responsible for providing a higher in-plane tensile property due to its low yarn crimp. [ABSTRACT FROM AUTHOR]

Sub-10 MeV proton-induced single-event transients (SETs) were firstly reported in this work. Sub-10 MeV proton experiment demonstrates that sub-10 MeV proton-induced SET pulse can be wider than 200 ps and the average SET pulse-width increases with proton energy. The analysis indicates that the observed SETs are attributed to recoil ions and SETs induced by oxygen recoils can be much wider than those induced by silicon recoils. The average deposited charge of the recoil ions increases with proton energy, which is the reason for the increased average SET pulse-width as proton energy increases. • Sub-10 MeV proton-induced single-event transients (SETs) were firstly reported in this work. • SETs induced by oxygen recoils can be much wider than those induced by silicon recoils. • The average deposited charge of the recoil ions increases with proton energy. [ABSTRACT FROM AUTHOR]