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A direct numerical simulation database (Zuo et al., J. Fluid Mech. vol. 877, 2019, 167–195) is analyzed to investigate the size of the separation bubble and the relation with the pressure rise in the conical shock wave/turbulent boundary layer interaction. The characteristics of the separation bubbles in the supersonic turbulent boundary layer induced by conical shock waves are discussed. The inviscid reflected conical shock theory is analyzed, to conclude that regular reflection transition to Mach reflection will occur somewhere along the spanwise direction in the interaction zone. Then, the typical shape of a turbulent separation bubble in the conical shock wave/turbulent boundary layer interaction is described. The scaling between the size of the turbulent separation bubble and moderate conical shock intensities is established, giving a potential alternative method to rapidly predict the size of turbulent separation bubbles, especially in the supersonic viscous corrections under condition of boundary layer separation.

Recent years have witnessed the emergence of worldwide megalopolises and the accompanying public safety events, making urban safety a top priority in modern urban management. Among various threats, dangerous goods such as gas and hazardous chemicals transported through cities have bred repeated tragedies and become the deadly “bomb” we sleep with every day. While tremendous research efforts have been devoted to dealing with dangerous goods transportation (DGT) issues, further study is still in great need to quantify this problem and explore its intrinsic dynamics from a big data perspective. In this article, we present a novel system called DGeye , to feature a fusion between DGT trajectory data and residential population data for dangers perception and prediction. Specifically, DGeye first develops a probabilistic graphical model-based approach to mine spatio-temporally adjacent risk patterns from population-aware risk trajectories. Then, DGeye builds the novel causality network among risk patterns for risk pain-point identification, risk source attribution, and online risky state prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye in real-life DGT risk management. As a case in point, our report powered by DGeye successfully drove the government to lay down gas pipelines for the famous Guijie food street in Beijing.

Hypersonic non-equilibrium air flows over a laminar double-cone configuration are numerically investigated. In this paper, the effects of thermochemical non-equilibrium gas, chemical non-equilibrium gas, and thermally perfect gas models on the prediction of the wall-surface heat transfer and pressure are investigated. Comparing to the high-enthalpy experimental data, the results of simulations indicate that all models have a similar tendency, and underpredict the length of the separation region, while the heat transfer peaks are overpredicted. The experimental data for surface pressure and heat transfer is insensitive to the choice of the thermally perfect gas model and non-equilibrium thermochemistry models. Quantitatively, the thermochemical non-equilibrium gas and thermally perfect gas models predict the same separation and reattachment locations within the uncertainty in the simulation, but the chemical non-equilibrium gas model predicts a smaller separation region.

Fraud detection from massive user behaviors is often regarded as trying to find a needle in a haystack. In this paper, we suggest abnormal behavioral patterns can be better revealed if both sequential and interaction behaviors of users can be modeled simultaneously, which however has rarely been addressed in prior work. Along this line, we propose a COllective Sequence and INteraction (COSIN) model, in which the behavioral sequences and interactions between source and target users in a dynamic interaction network are modeled uniformly in a probabilistic graphical model. More specifically, the sequential schema is modeled with a hierarchical Hidden Markov Model, and meanwhile it is shifted to the interaction schema to generate the interaction counts through Poisson factorization. A hybrid Gibbs-Variational algorithm is then proposed for efficient parameter estimation of the COSIN model. We conduct extensive experiments on both synthetic and real-world telecom datasets in different scales, and the results show that the proposed model outperforms some competitive baseline methods and is scalable. A case is further presented to show the precious explainability of the model.

In recent years, short text topic modeling has drawn considerable attentions from interdisciplinary researchers. Various customized topic models have been proposed to tackle the semantic sparseness nature of short texts. Most (if not all) of them follow the bag-of-words assumption, which, however, is not adequate since word order and phrases are often critical to capturing the meaning of texts. On the other hand, while some existing topic models are sensitive to word order, they do not perform well on short texts due to the severe data sparseness. To address these issues, we propose the Pseudo-document-based Topical N-Grams model (PTNG), which alleviates the data sparsity problem of short texts while is sensitive to word order. Extensive experiments on three real-world data sets with state-of-the-art baselines demonstrate the high quality of topics learned by PTNG according to UCI coherence scores and more discriminative semantic representation of short texts according to classification results.

Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers’ information from their employees’ online ratings and comments, which enables EBE from an employee’s perspective. To this end, in this article, we propose a method named Company Profiling–based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes--based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-the-art baselines and enable a comprehensive understanding of EBE.

Fraud detection is usually compared to finding a needle in a haystack and remains a challenging task because fraudulent acts are buried in massive amounts of normal behavior and true intentions may be disguised in a single snapshot. Indeed, fraudulent incidents usually take place in consecutive time steps to gain illegal benefits, which provides unique clues for probing fraudulent behavior by considering a complete behavioral sequence rather than detecting fraud from a snapshot of behavior. Additionally, fraudulent behavior may involve different parties, such that the interaction patterns between sources and targets can help distinguish fraudulent acts from normal behavior. Therefore, in this paper, we model the attributed behavioral sequences generated from consecutive behaviors in order to capture the sequential patterns, while those that deviate from the pattern can be detected as fraudulence. Considering the characteristics of the behavioral sequence, we propose a novel model, NHA-LSTM, by augmenting the traditional LSTM with a modified forget gate, where the interval time between consecutive time steps is considered. Furthermore, we design a self-historical attention mechanism to allow for long time dependencies, which can help identify repeated or cyclical appearances. In addition, we propose an enhanced network embedding method, FraudWalk, to construct embeddings for the nodes in the interaction network with regard to higher-order interactions and particular time constraints for revealing potential group fraudulence. The node embeddings, along with the feature vectors, are fed into the model to capture the interactions between sources and targets. To validate the effectiveness of sequential behavior embeddings, we experiment on a real-world telecommunication dataset with prediction and classification tasks based on the learned embeddings. The experimental results show that the learned embeddings can better identify fraudulent behavior. Finally, we visualize the weights of the attention mechanism to provide a rational interpretation of human behavioral patterns.

We carry out a parametric study of conical shock-wave/turbulent boundary-layer interactions by means of numerical simulation of the Reynolds-averaged Navier–Stokes (RANS) equations, with the eventu...

Next location prediction has aroused great interests in the era of internet of things (IoT). With the ubiquitous deployment of sensor devices, e.g., GPS and Wi-Fi, IoT environment offers new opportunities for proactively analyzing human mobility patterns and predicting user’s future visit in low cost, no matter outdoor and indoor. In this paper, we consider the problem of next location prediction in IoT environment via a session-based manner. We suggest that user’s future intention in each session can be better inferred for more accurate prediction if patterns hidden inside both trajectory and signal strength sequences collected from IoT devices can be jointly modeled, which however existing state-of-the-art methods have rarely addressed. To this end, we propose a trajectory and sIgnal sequence (TSIS) model, where the trajectory transition regularities and signal temporal dynamics are jointly embedded in a neural network based model. Specifically, we employ gated recurrent unit (GRU) for capturing the temporal dynamics in the multivariate signal strength sequence. Moreover, we adapt gated graph neural networks (gated GNNs) on location transition graphs to explicitly model the transition patterns of trajectories. Finally, both the low-dimensional representations learned from trajectory and signal sequence are jointly optimized to construct a session embedding, which is further employed to predict the next location. Extensive experiments on two real-world Wi-Fi based mobility datasets demonstrate that TSIS is effective and robust for next location prediction compared with other competitive baselines.

Web service composition has been increasingly challenging in recent years due to the escalating number of services and the diversity of task objectives. Despite many researches have already addressed the optimization of multiple Quality of Service (QoS) attributes, most of the currently available methods have to build a large web service dependency graph, which may incur excessive memory consumption and extreme inefficiency. To address these issues, we present a novel web service composition method by optimizing composition-segment candidates. Firstly, we formalize the web service composition problem as a Mixed-Integer Linear Programming (MILP) model and introduce some effective techniques for complex cases, and then a standard solver can be applied to this model. Afterwards, a candidate optimization method is proposed to solve the MILP model efficiently, which runs sharply fast without building a web service dependency graph. Experimental results on both Web Service Challenge 2009’s datasets and substantial datasets randomly generated show that the proposed method outperforms the state-of-art while achieving a much ideal tradeoff among all the objectives with better performance.

Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics’ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint (HSOC), which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.

Aspect-based opinion mining is finding elaborate opinions towards a subject such as a product or an event. With explosive growth of opinionated texts on the Web, mining aspect-level opinions has become a promising means for online public opinion analysis. In particular, the boom of various types of online media provides diverse yet complementary information, bringing unprecedented opportunities for cross media aspect-opinion mining. Along this line, we propose CAMEL, a novel topic model for complementary aspect-based opinion mining across asymmetric collections. CAMEL gains information complementarity by modeling both common and specific aspects across collections, while keeping all the corresponding opinions for contrastive study. An auto-labeling scheme called AME is also proposed to help discriminate between aspect and opinion words without elaborative human labeling, which is further enhanced by adding word embedding-based similarity as a new feature. Moreover, CAMEL-DP, a nonparametric alternative to CAMEL is also proposed based on coupled Dirichlet Processes. Extensive experiments on real-world multi-collection reviews data demonstrate the superiority of our methods to competitive baselines. This is particularly true when the information shared by different collections becomes seriously fragmented. Finally, a case study on the public event “2014 Shanghai Stampede” demonstrates the practical value of CAMEL for real-world applications.

High Strength Structural (HSS) steels, with nominal yield strength greater than 450 MPa exhibit phenomena including Lode angle dependence of yield, and accelerated cyclic softening at large strains that cannot be conveniently simulated by existing constitutive models that are developed primarily for conventional strength structural steels. This paper presents an experimental study consisting of 25 coupon scale tests (monotonic and cyclic) as well as 2 large scale cantilever column tests. According to experimental results, a classical combined isotropic and kinematic hardening constitutive model, the Armstrong and Frederick (A-F) model is identified by using the particle swarm optimization (PSO) algorithm. However, the yield plateau region and full-range softening behavior of HSSs under cyclic loading are difficult to be captured by the A-F model. A new constitutive model, termed the HSS-TD model with consideration of the yield plateau and three-stage strength softening, is formulated to simulate the response of HSS steels. The HSS-TD model shows significantly lower errors relative to the conventional A-F model, when assessed relative to test data. Moreover, the limitations of the HSS-TD model including influence of strain history and nonproportional loading are discussed.

Chaboche model has gained prominent utilization in numerical simulation on cyclic behaviors of metal structure. However, the calibration of its material parameter may produce considerable cost. A simplified calibration way is of great importance to ease its application. To this end, a re-evaluation on chaboche model material parameters and simulated elastic-plastic laws was conducted regarding various steel grades. Futhermore, the characterisitcs of isotropic softening was coupled with the present model hardening rule. The kinematic rule was correlated with cumulative plastic strain. The modified model was then calibrated and validated base upon the coupon test result on 41 batches of steels, whose yield stress ranges from 100 MPa to 890 MPa. The calibrated material parameter were then correlated with corresponding monotonic mechanical properties. Accordingly, a simplified calibration procedure, based upon which the material parameters of modifed Chaboche model can be fastly estimated by yield strength and yield ratio, was constructed. As for low yield point steels, mild steels and high strength steels, this calibration method can ease the calibration of modified Chaboche model considerably.

The relationship between von Mises equivalent stress σ ¯ and equivalent plastic strain e ¯ (PEEQ), termed as von Mises stress-strain, plays an important role in numerical simulations for ductile fracture of structural metallic materials with large plastic strain. The standard uniaxial tensile test on cylindrical specimens is the most straightforward method to derive the ( σ ¯ − e ¯ ) data. However, the distribution of the stress and strain along the necking section of the tensile specimen is no longer uniform in the post-necking process, which induces difficulty in acquiring the ( σ ¯ − e ¯ ) data. The existing correction methods to identify von Mises stress-strain in the post-necking regime need instantaneous geometry of the notched region and ignore influence of the initial geometry of the specimen, which induces these methods are inconvenient for application and inapplicable for circumferentially notched tensile specimens to determine ( σ ¯ − e ¯ ) of each individual material zone in a hybrid coupon (e.g. weldment). In the present study, influences of initial geometry of the specimen and plastic hardening property of material on necking process are investigated firstly. It is found that the shrinking behavior of the minimum cross-section of the specimen is determined by the geometric parameter of specimen and plastic hardening exponent of materials. Based on an extensive parametric study a modified power law function to characterize the von Mises stress-strain curve from tensile tests on circumferentially notched specimens without measuring instantaneous geometry of cross-section is proposed.

Purpose The purpose of this paper is to overcome the shortcomings of existing multivariate grey incidence models that cannot analyze the similarity of behavior matrixes. Design/methodology/approach First, the feasibility of using gradient to measure the similarity of continuous functions is analyzed theoretically and intuitively. Then, a grey incidence degree is constructed for multivariable continuous functions. The model employs the gradient to measure the local similarity, as incidence coefficient function, of two functions, and combines local similarity into global similarity, as grey incidence degree by double integral. Third, the gradient incidence degree model for behavior matrix is proposed by discretizing the continuous models. Furthermore, the properties and satisfaction of grey incidence atom of the proposed model are research, respectively. Finally, a financial case is studied to examine the validity of the model. Findings The proposed model satisfies properties of invariance under mean value transformation, multiple transformation and linear transformation, which proves it is a model constructed from similarity perspective. Meanwhile, the case study shows that proposed model performs effectively. Practical implications The method proposed in the paper could be used in financial multivariable time series clustering, personalized recommendation in e-commerce, etc., when the behavior matrixes need to be analyzed from trend similarity perspective. Originality/value It will promote the accuracy of multivariate grey incidence model.

Loading of Au nanoparticles (AuNPs) onto environmentally sensitive polymer microgels is increasingly used to regulate their optical properties and catalytic activities. Herein, we synthesized composite polymer microgels composed of poly(N-isopropylacrylamide-co-3-methacryloxypropyltrimethoxysilane)/poly(acrylic acid) with a core–shell structure, and AuNPs were controllably loaded onto/into the network chains of the polymer microgels using seed-mediated growth. The prepared AuNPs composite materials possessed good pH sensitivity; the electromagnetic coupling between the AuNPs could thus be tuned via the swelling/deswelling of the polymer microgels under various acidic/alkaline conditions. More importantly, the coordination interaction between the carboxyl groups in the PAA chains and Cu2+ ions changed by varying the copper ion content under various pH conditions, thus enabling regulation of the localized surface plasmon resonance of the AuNPs. These structural features of the prepared composite microgels enabled to tailor the optical performance of the AuNPs by varying the pH of the surrounding medium.

Functional nanonetwork-structured polymers with inbuilt poly(acrylic acid) linings were successfully synthesized by combining surface-initiated atom transfer radical polymerization (SI-ATRP) and hypercrosslinking chemistry, and demonstrated superior adsorption performances toward basic dyes and heavy metal ions.

A new uniform shallow trenches termination (ST 2 ) with field plate for high-voltage VDMOS transistor is proposed in this Letter. In ST 2 structure, the electric field near the source is decreased and the depletion region is extended by the field plate, besides, many electric field peaks are introduced in shallow trenches under the field plate and the average electric field strength is increased, both improve the characteristic of the breakdown voltage (BV). Simulation results show the BV of ST 2 VDMOS is 706 V with a termination length L d = 132 μm, which reaches 97% of that of the parallel-plane junction, and the influence of the edge curvature effect is almost entirely eliminated. The ST 2 structure is compatible with conventional CMOS process with only one additional mask and its fabrication is low-cost.

For traditional integrated navigation system based on SINS/GNSS, the integrated navigation system will work in standalone inertial navigation when GNSS signals are lost. In this situation, the divergence of SINS error is fast without GNNS information correction. Besides, the divergence of SINS error is related to the motion mode of the vehicle. This paper proposes a BP neural network (BPNN) aided integrated navigation method based on vehicle motion learning. The inputs of the neural network are 10-dimension, including the outputs of the IMU, the three attitude angles estimated by SINS, and the GNSS interruption lasting time. The outputs are three predictions of SINS position error. When GNSS is effective, the neural network is used to learn the SINS error divergence characteristics under several basic motion modes of the vehicle. When the GNSS signal is disturbed, the neural network is used to correct the position error of the SINS to improve the navigation accuracy. Finally, the effectiveness of the algorithm is verified by using an 8-shape motion of the vehicle.

Fraud detection is usually regarded as finding a needle in haystack, which is a challenging task because fraudulences are buried in massive normal behaviors. Indeed, a fraudulent incident usually takes place in consecutive time steps to gain illegal benefits, which provides unique clues to probing frauds by considering a complete behavioral sequence, rather than detecting frauds from a snapshot of behaviors. Also, fraudulent behaviors may entail different parties, such that the interaction pattern between sources and targets can help distinguish frauds from normal behaviors. Therefore, in this paper, we model the attributed behavioral sequences generated from consecutive behaviors, in order to capture the sequential patterns, while those deviate from the pattern can be regarded as fraudulence. Considering the characteristics of behavioral sequence, we propose a novel model, HAInt-LSTM, by augmenting traditional LSTM with a modified forget gate where interval time between consecutive time steps are considered. Meanwhile, we employ a self-historical attention mechanism to allow for long-time dependencies, which can help identify repeated or cyclical appearances. In addition, we encode the source information as an interaction module to enhance the learning of behavioral sequences. To validate the effectiveness of the learned sequential behavior representations, we experiment on real-world telecommunication dataset under both supervised and unsupervised scenarios. Experimental results show that the learned representations can better identify fraudulent behaviors, and also show a clear cut with normal sequences in the lower dimensional embedding space through visualization. Last but not least, we visualize the weights of attention mechanism to provide rational interpretation of human behavioral periodicity.

Ultra-high strength concrete (UHSC) and Ultra-high performance concrete (UHPC) with high compressive strength and good durability have been increasingly used in engineering structures. In the present study, uniaxial and triaxial compression tests on UHSC and UHPC are carried out by using servo-hydraulic actuators to investigate the mechanical properties of UHSC and UHPC under various stress states. In triaxial compression tests, the confining pressure ranging from 0 to 50 MPa is applied on UHSC and UHPC specimens. The mechanical responses of UHSC and UHPC specimens under various confining pressures, including peak strength, axial and circumferential strain are recorded in the loading process. According to the elastoplastic behavior of UHSC and UHPC specimens in tests, the influence of confining pressure on the compressive strength and plastic deformation performances of UHSC and UHPC are studied. It is found that the compressive strength and plastic deformation capacity of UHSC and UHPC tend to be higher with the increasing of confining pressure. The enhancement of compressive strength of UHPC is slightly lower than that of UHSC However, due to the lateral restraint effect of the confining pressure and the tensile action of the steel fibers inside the UHPC specimen, failure of the cylindrical specimen are delayed more significantly with increasing of confining pressure. Failure surfaces based on Ottosen’s criterion for UHSC and UHPC are identified accroding to test data. By comparing and analyzing the tests results on UHSC, UHPC and previously tested C200, it is concluded the effect of confining pressure on the enhancement of compressive strength of concretes has a negative relationship with the uniaxial compressive strength of concretes.

A RANS (Reynolds-averaged Navier-Stokes simulation) study is performed on an internal duct at a free-stream Mach number 4.0, to investigate the internal conical shock wave/boundary layer interaction (CSBLI) developing from an axisymmetric wall surface. An effort is devoted on the analysis of the flow features of the internal CSBLI phenomena through a simple physical model. The work aims to extend the investigation of Zuo et al. [1] on external CSBLI. The numerical strategy is first described and the capability of turbulence models to capture SBLI physics is then assessed by comparison with experiments. In the interacted region a small recirculation bubble is identified, and the foot of the conical shock forms a λ system. Unlike the external CSBLI case, a horseshoe pattern of separation whose strength and size are reduced away from the symmetry plane is observed. The topology of flow separation is similar to the one of two-dimensional closed separation. The pressure in the pipe increases rapidly by the effect of multi-reflected conical shock waves. The wall-surface pressure in the separation bubble continues to increase due to three-dimensional area compression effect.

This paper presents an experimental and numerical investigation on the bearing behavior of multi-bolt high strength steel connections in single bolt line. Three grades of high strength steel with nominal yield stress of 550 MPa, 690 MPa and 890 MPa were used to fabricate thirty lap connections with two or three bolts. It is found that the deformation capability of high strength steel plate near bolt hole is sufficient to meet the deformation demand of compatibility in multi-bolt connection even with an intended bolt hole misalignment of 2 mm for M24 high strength bolt. The comparison with Eurocode3 and AISC 360-16 is conducted. When no partial or safety factors are considered, the original formulae of Eurocode3 already has sufficient safety while AISC 360-16 gives unsafe predictions. Numerical models were established and verified by test results. With the obtained results, the full-range distribution of internal load among different bolts can be divided into four stages. Features for each stage are summarized and the effect of bolt hole misalignment on the distribution pattern is evaluated. Numerical analysis results indicate that, behavior of an individual bolt in multi-bolt connection may be influenced by surrounding bolts. The formation of critical yielding band between different bolts may explain this influence. Based on the experimental and numerical analyses, this paper proposed a new ultimate bearing resistance model for multi-bolt connections. The proposed model is capable of describing the nonlinearity in the ultimate bearing resistance of multi-bolt connection and achieves improved predicting accuracy compared to classical models.

The high strength steel (HSS) with a nominal yield stress not less than 460 N/mm2 has been increasingly used in engineering structures. Compared with mild steels, HSSs have higher strength but lower ductility. This paper is concerned with the ductile fracture prediction of the HSS in a wide range of stress triaxiality and Lode angle. Experimental investigation is conducted to study the influence of stress triaxiality and Lode angle on fracture ductility of three different HSSs (Q550, Q690 and Q890). The experimental results are used to calibrate three typical fracture criteria: Void Growth Model (VGM), Combined Fracture Model (CFM) and Bai-Wierzbicki Model (BWM). It is found that without of consideration of Lode angle, the VGM fails to give an accurate prediction of fracture responses of all types of experiments. Compared with VGM, the CFM gives better fitting results. However, because the stress triaxiality dependence and Lode angle dependence are separated in CFM, there still exists discrepancy between prediction and real responses. The BWM gives the most accurate fitting results and prediction for all tests on three different HSSs. Moreover, it is found that elongation at fracture fails to describe the fracture ductility of HSS in a wide range of stress states. Based on parametric study results, it is found that with increasing Lode angle dependence of plasticity, the Lode angle dependence of fracture ductility of HSSs tend to decrease.

In order to evaluate the slip performance of hybrid connections between high strength steel and mild steel, 30 hybrid connection specimens between high strength steel (Q550, Q690 and Q890) and mild steel (Q235 and Q345) with shot-blasted surface are designed to measure slip factors. The test results are compared with the slip factors measured from the connections with the same grade of high strength steel. According to the test results, the slip factors of hybrid connections are lower than the slip factor of 0.5 for mild steel specified in existing design code. Furthermore, the influence of steel strength grade on slip factor is investigated based on friction mechanism. It can be concluded that with the increase of steel grade, the slip factor of hybrid connection tends to decrease due to the reduction of surface roughness and the reduced potential increase in contact surface by plastic deformation. The slip factor of 0.45 is recommended for hybrid connections between Q550 or Q690 and Q235 mild steel. The slip factor of 0.40 is recommended for hybrid connections between Q550 or Q690 and Q345 mild steel. For hybrid connections between Q890 and mild steel Q235 or Q345, slip factor of 0.40 is recommended.

The high strength steel (HSS) with a nominal yield stress not less than 460 N/mm2 has been increasingly used in engineering structures. An accurate yield criterion which describes the elastoplastic behavior of high strength steels under complex stress states, is significant for the analysis and design of high strength steel structures. For ductile metal materials, the most generally used the von Mises yield criterion neglects the effects of stress triaxiality and Lode angle on the metal plasticity. However, due to the reduce ductility with the increase in strength, such yield criterion may not applicable to HSSs. In the present paper, the tests of six types of specimens are carried out to evaluate effects of stress triaxiality and Lode angle on the plastic behavior of HSSs (Q550, Q690 and Q890). The applicability of von Mises yield criterion is examined firstly using experimental results and finite element analyses. It demonstrates that an appropriate yield function of HSSs should consider effects of the Lode angle while the influence of stress triaxiality is negligible. Based on the experimental and numerical results, a yield function for HSSs is proposed, to essentially simulate the elastoplastic behavior of high strength steel under complex stress states.

Given the rich real-life applications of network mining as well as the surge of representation learning in recent years, network embedding has become the focal point of increasing research interests in both academic and industrial domains. Nevertheless, the complete temporal formation process of networks characterized by sequential interactive events between nodes has yet seldom been modeled in the existing studies, which calls for further research on the so-called temporal network embedding problem. In light of this, in this paper, we introduce the concept of neighborhood formation sequence to describe the evolution of a node, where temporal excitation effects exist between neighbors in the sequence, and thus we propose a Hawkes process based Temporal Network Embedding (HTNE) method. HTNE well integrates the Hawkes process into network embedding so as to capture the influence of historical neighbors on the current neighbors. In particular, the interactions of low-dimensional vectors are fed into the Hawkes process as base rate and temporal influence, respectively. In addition, attention mechanism is also integrated into HTNE to better determine the influence of historical neighbors on current neighbors of a node. Experiments on three large-scale real-life networks demonstrate that the embeddings learned from the proposed HTNE model achieve better performance than state-of-the-art methods in various tasks including node classification, link prediction, and embedding visualization. In particular, temporal recommendation based on arrival rate inferred from node embeddings shows excellent predictive power of the proposed model.

In recent years, anomaly detection has become a focal point of data mining, and numerous efforts have been made to conduct extensive researches on the theories and techniques for detecting abnormal data points. Although the amount of anomaly data is relatively small, they can potentially bring huge losses to social economy, public resources and individual properties. Thus, we propose an unsupervised anomaly detection framework named AEKNN, which aims to incorporate the advantages of automatically learnt representation by deep neural network to boost anomaly detection performance. The framework combines the training of an autoencoder and a k-th nearest neighbor based outlier detection method. We further validate the performance of our proposed model with an extensive experimental study on three UCI datasets. The parameter sensitivity results demonstrate that the proposed algorithm can scale well with respect to both dataset size, data feature dimensionality and anomaly class proportion.

The penetration paths of chloride ions in recycled aggregate concrete (RAC) are of significant interest and have not been well studied previously. This study used X-ray computed tomography (X-CT) as a novel approach to investigate chloride ion penetration paths in RAC. The results indicate that X-CT can be used for the constant monitoring of chloride ion penetration paths in RAC, and the influence of mix proportions on the chloride ion penetration can be understood through the X-CT visualization.

With the rapid development of online social media, the short text has become the prevalent format for information of Internet. Due to the severe data sparsity issue, accurately discovering knowledge behind these short texts remains a critical challenge. Since regular topic models, such as the Latent Dirichlet Allocation (LDA), can not perform well on short texts, many efforts have been put on building different types of probabilistic topic models for short texts. Inducing topics from dense word-word space instead of sparse document-word space becomes an emerging solution for avoiding data sparsity issue, and the representative one is the Word Network Topic Model (WNTM). However, the word-word space building procedure of WNTM often imports much irrelevant information. In light of this, we propose the Robust WNTM (RWNTM), which can filter out unrelated information during the sampling. The experimental results demonstrate that our method can learn more coherent topics and is more accurate in text classification, as compared with WNTM and other state-of-the-arts.

A stacked lateral double-diffused metal–oxide–semiconductor field-effect transistor (LDMOS) with enhanced depletion effect by surface substrate is proposed (ST-LDMOS), which is compatible with the traditional CMOS processes. The new stacked structure is characterized by double substrates and surface dielectric trenches (SDT). The drift region is separated by the P-buried layer to form two vertically parallel devices. The doping concentration of the drift region is increased benefiting from the enhanced auxiliary depletion effect of the double substrates, leading to a lower specific on-resistance (R on,sp). Multiple electric field peaks appear at the corners of the SDT, which improves the lateral electric field distribution and the breakdown voltage (BV). Compared to a conventional LDMOS (C-LDMOS), the BV in the ST-LDMOS increases from 259 V to 459 V, an improvement of 77.22%. The R on,sp decreases from 39.62 mΩcm2 to 23.24 mΩcm2 and the Baliga's figure of merit (FOM) of is 9.07 MW/cm2.

Recent years have witnessed the unprecedented growth of online social media, which empower short texts as the prevalent format for information of Internet. Given the nature of sparsity, however, short text topic modeling remains a critical yet much-watched challenge in both academy and industry. Rich research efforts have been put on building different types of probabilistic topic models for short texts, among which the self aggregation methods without using auxiliary information become an emerging solution for providing informative cross-text word co-occurrences. However, models along this line are still rarely seen, and the representative one Self-Aggregation Topic Model (SATM) is prone to overfitting and computationally expensive. In light of this, in this paper, we propose a novel probabilistic model called Pseudo-document-based Topic Model (PTM) for short text topic modeling. PTM introduces the concept of pseudo document to implicitly aggregate short texts against data sparsity. By modeling the topic distributions of latent pseudo documents rather than short texts, PTM is expected to gain excellent performance in both accuracy and efficiency. A Sparsity-enhanced PTM (SPTM for short) is also proposed by applying Spike and Slab prior, with the purpose of eliminating undesired correlations between pseudo documents and latent topics. Extensive experiments on various real-world data sets with state-of-the-art baselines demonstrate the high quality of topics learned by PTM and its robustness with reduced training samples. It is also interesting to show that i) SPTM gains a clear edge over PTM when the number of pseudo documents is relatively small, and ii) the constraint that a short text belongs to only one pseudo document is critically important for the success of PTM. We finally take an in-depth semantic analysis to unveil directly the fabulous function of pseudo documents in finding cross-text word co-occurrences for topic modeling.

Conditional pulse nulling (CPN) receiver is proposed by Dolinar to discriminate pulse position modulation (PPM) signals. The receiver, which uses a beam splitter and an on-off photon detector can outperform the standard quantum limit (SQL) for PPM signals. In this paper, this receiver is applied to multi-pulse PPM (MPPM) and binary quantum coding signals, and a dynamic programming algorithm is proposed to optimize the control strategy. The MPPM signals are used to improve the band-utilization efficiency, and the binary quantum coding enables the communication system to correct the error. Numerical simulation results show that the CPN receiver with optimized strategy outperform SQL for both MPPM and binary quantum coding signals.

We present a quantum receiver for 16-QAM signals discrimination with hybrid structure containing a homodyne receiver and a displacement receiver, which can outperform the SQL, and the performance can be improved by an optimized displacement., Comment: 2 pages, 3 figures, CLEO2015

The short text has been the prevalent format for information of Internet in recent decades, especially with the development of online social media, whose millions of users generate a vast number of short messages everyday. Although sophisticated signals delivered by the short text make it a promising source for topic modeling, its extreme sparsity and imbalance brings unprecedented challenges to conventional topic models like LDA and its variants. Aiming at presenting a simple but general solution for topic modeling in short texts, we present a word co-occurrence network based model named WNTM to tackle the sparsity and imbalance simultaneously. Different from previous approaches, WNTM models the distribution over topics for each word instead of learning topics for each document, which successfully enhance the semantic density of data space without importing too much time or space complexity. Meanwhile, the rich contextual information preserved in the word-word space also guarantees its sensitivity in identifying rare topics with convincing quality. Furthermore, employing the same Gibbs sampling with LDA makes WNTM easily to be extended to various application scenarios. Extensive validations on both short and normal texts testify the outperformance of WNTM as compared to baseline methods. And finally we also demonstrate its potential in precisely discovering newly emerging topics or unexpected events in Weibo at pretty early stages.