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In this study, an inverse identification strategy based on the finite element model updating (FEMU) method is proposed to calibrate the parameters of a temperature and strain rate dependent constitutive model. The mechanical responses of a dedicated cruciform specimen with heterogeneous temperature field under biaxial loading are employed to supply information to the inverse scheme. A combination of Particle Swarm Optimization (PSO) and SIMPLEX optimization algorithm is employed to find the optimal values of the material parameters. In order to validate the proposed identification strategy, a virtual experiment is designed and performed with a reference material constitutive model. The proposed strategy is proved to be feasible as all seven parameters of the constitutive model are accurately identified. In addition, the influences of measurement noise of force, temperature, and strain data are analyzed by means of a sensitivity study. The experimental data after the localized necking should be avoided for parameter identification. The proposed inverse identification strategy shows good robustness to strain noise.

In real-world systems, scaling has been critical for improving the translation quality in autoregressive translation (AT), which however has not been well studied for non-autoregressive translation (NAT). In this work, we bridge the gap by systematically studying the impact of scaling on NAT behaviors. Extensive experiments on six WMT benchmarks over two advanced NAT models show that scaling can alleviate the commonly-cited weaknesses of NAT models, resulting in better translation performance. To reduce the side-effect of scaling on decoding speed, we empirically investigate the impact of NAT encoder and decoder on the translation performance. Experimental results on the large-scale WMT20 En-De show that the asymmetric architecture (e.g. bigger encoder and smaller decoder) can achieve comparable performance with the scaling model, while maintaining the superiority of decoding speed with standard NAT models. To this end, we establish a new benchmark by validating scaled NAT models on the scaled dataset, which can be regarded as a strong baseline for future works. We release code and system outputs at https://github.com/DeepLearnXMU/Scaling4NAT., Comment: 13 pages, Findings of ACL 2023

This paper presents the application of triangle configuration B-splines (TCB-splines) for representing and analyzing the Kirchhoff-Love shell in the context of isogeometric analysis (IGA). The Kirchhoff-Love shell formulation requires global $C^1$-continuous basis functions. The nonuniform rational B-spline (NURBS)-based IGA has been extensively used for developing Kirchhoff-Love shell elements. However, shells with complex geometries inevitably need multiple patches and trimming techniques, where stitching patches with high continuity is a challenge. On the other hand, due to their unstructured nature, TCB-splines can accommodate general polygonal domains, have local refinement, and are flexible to model complex geometries with $C^1$ continuity, which naturally fit into the Kirchhoff-Love shell formulation with complex geometries. Therefore, we propose to use TCB-splines as basis functions for geometric representation and solution approximation. We apply our method to both linear and nonlinear benchmark shell problems, where the accuracy and robustness are validated. The applicability of the proposed approach to shell analysis is further exemplified by performing geometrically nonlinear Kirchhoff-Love shell simulations of a pipe junction and a front bumper represented by a single patch of TCB-splines.

Based on the industry and commerce annual report data, the present study uses the continuous-time nonparametric, parametric, and semi-parametric estimation to investigate the determinants influencing the withdrawal behaviour of foreign-invested enterprises. Through the survival analysis of 3,858 foreign-invested enterprises located in China from 2013 to 2020, the study found that operation profit, enterprise size and enterprise age have significantly negative impacts on the probability of enterprise withdrawal. At the industry-level and region-level, the improvement of industry entry rate and regional business environment ranking can significantly increase the probability of enterprise survival. The rise of the regional GDP growth rate and wage rate can significantly increase the probability of enterprise withdrawal. The study also found that the influence of some variables on enterprise withdrawal varies with different withdrawal patterns. After applying multiple models for estimation, similar results were replicated, which reinforced the validity of the conclusions offered in the present study.

Additional file 11: Table S2. Clinical information of AD and control CSF in Fig. 1d.

Fluorine-doped graphene supported PbO (F-rGO/PbO) composite was prepared through a facile water bath method to study an improvement strategy for its catalytic performance in thermal decomposition of ammonium perchlorate (AP). The characterization results showed that the PbO with the size of 20 ∼ 30 nm are highly dispersed on the surface of F-doped graphene (F-rGO). Compared with other samples, the F-rGO/PbO exhibited superior catalytic performance, as 3 wt% of catalyst added in AP, the peak temperature for HTD reduced by 124°C, and the peak temperature for LTD increased by 8°C. The released apparent specific heat of the thermal decomposition of AP catalyzed by F-rGO/PbO (3603.7 J/g) is five times of that of pure AP (603.5 J/g). The doping of F atoms in graphene increased BET surface areas of the F-rGO carrier and enhanced the content of hydroxyl groups on the surface of graphene, which improved the thermal decomposition rate of AP. While the highly dispersed PbO NPs further increased BET surface areas of F-rGO/PbO to provide more active sites during the thermal decomposition of AP.

Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient frames without awareness of the class-specific saliency scores, which neglect the implicit association between the saliency of frames and its belonging category. To alleviate this issue, we devise a novel Temporal Saliency Query (TSQ) mechanism, which introduces class-specific information to provide fine-grained cues for saliency measurement. Specifically, we model the class-specific saliency measuring process as a query-response task. For each category, the common pattern of it is employed as a query and the most salient frames are responded to it. Then, the calculated similarities are adopted as the frame saliency scores. To achieve it, we propose a Temporal Saliency Query Network (TSQNet) that includes two instantiations of the TSQ mechanism based on visual appearance similarities and textual event-object relations. Afterward, cross-modality interactions are imposed to promote the information exchange between them. Finally, we use the class-specific saliencies of the most confident categories generated by two modalities to perform the selection of salient frames. Extensive experiments demonstrate the effectiveness of our method by achieving state-of-the-art results on ActivityNet, FCVID and Mini-Kinetics datasets. Our project page is at https://lawrencexia2008.github.io/projects/tsqnet ., Comment: Accepted by ECCV 2022

Additional file 1.

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.

Additional file 10: Table S1. Clinical information of human post-mortem tissues in Fig. 1a, c, e-g, and Fig. S1a-b.

Bonahon-Wong-Yang introduced a new version of the volume conjecture by using the intertwiners between two isomorphic irreducible representations of the skein algebra.The intertwiners are built from surface diffeomorphisms; they made the volume conjecture when diffeomorphisms are pseudo-Anosov. In this paper we calculated all the intertwiners for the closed torus, producing some interesting results. Moreover we considered the periodic diffeomorphisms, and introduced the corresponding conjecture. For some special cases we made precise calculations for intertwiners and proved our conjecture.

A framework of compositionally graded Ti-Al alloys was proposed to elucidate the Al alloying dependent deformation mechanism at room temperature. Slip trace analysis demonstrated low-alloyed Ti-Al model materials were plastically deformed by prismatic slip, pyramidal slip, stacking faults, and deformation twins. Increasing Al concentration promoted chemical ordering, thus suppressing the dislocation motion and deformation twinning. The mechanism behind such a kind of composition-mediated deformation physics was discussed. Our findings are expected to be applicable in the design of next-generation high-performance structural materials through the tailoring of the chemical ordering of solute atoms in substitutional solid solution.

Although current proposed compression schemes achieve better performance than traditional data compression schemes, they have not fully exploited the spatial and temporal correlations among the data, and the design of the projection (measurement) matrix cannot satisfy the requirement of real scenarios adaptively. Hence, well-designed clustering algorithm is needed to further explore strong spatial correlation, and an adaptive measurement matrix is also needed to ensure exact data recovery. In this paper, we propose a fog-based optimized Kronecker-supported compression scheme to address the above shortcomings and achieve better compression results in the industrial Internet of Things (IIoT). Our scheme first leverages a k-means-based clustering algorithm that explores the spatial correlation among sensory data, which can obtain better compression effects with less communication overhead. It then develops a novel Kronecker-supported two-dimensional data compression mechanism at the fog node, which can ensure the recovery of the original data from the compressed data with high precision; this mechanism can also reduce the communication overhead between fog and cloud nodes significantly. Next, a Kronecker concatenated measurement matrix optimization problem is formulated for meeting the requirement of real scenarios adaptively, and an efficient solution algorithm is developed to obtain the optimal value and ensure that the stringent precision requirements of industrial applications are satisfied. Finally, simulation results show that our proposed scheme is energy efficient and can achieve better clustering results and recovery performance for sensory data, for example, the energy consumption is reduced by 6.8 percent after clustering operation, and the relative reconstruction error of temperature data is improved by an average of 15.8 percent with the same energy saving effect.

Non-intrusive load monitoring technology is one of the key technologies for the construction of ubiquitous power Internet of Things, and load identification is the core content of non-intrusive load monitoring technology. When non-invasive load monitoring technology uses low dimensional feature data, the identification accuracy of similar loads is low. This paper designs a local mean decomposition algorithm(LMD) to decompose the low dimensional load features, and uses the load identification model established by k-nearest neighbor algorithm (KNN) to realize the identification of similar feature loads under low dimensional features. After non-intrusive load monitoring public data set simulation verification, the load identification model combined with LMD and KNN can realize accurate identification of similar loads.

Nowadays, most of the time we need to obtain the location information of the signature in the documents for signature verification or other tasks. Manual processing can be completed for a small amount of documents, if the amount of documents is large, it will consume huge manpower and material resources to do it .When using deep learning methods to intelligently locate signatures, but there are several difficulties: First, for documents such as Chinese contracts, files, etc. the signature is covered by seal coverage or other interference in most cases, which brings a lot of interference to the location of the signature.Second , it is hard to get real-scene contracts, bills which can be used as training sets.Third, the signature itself has fewer features, this brings certain difficulties to the intelligent location. This paper proposes an improved feature extraction network based on EfficientNet to adapt to detection targets with fewer features and more interference. Training datasets mainly comes from algorithm synthesis.Finally we tested on 1000 scanned documents containing 2048 handwritten signatures,the test results on real-scenes data set containing Chinese handwritten signatures can achieve more than 90% of AP, Precision, and Recall.

The Global Stratotype Section and Point of the mid-Carboniferous boundary was selected in the Arrow Canyon section within the lower Bird Spring Formation at the first evolutionary appearance of the conodont Declinognathodus noduliferus (Ellison and Graves) sensu lato. The studied boundary beds, spanning a 38.8-m interval from the Upper Mississippian into the Lower Pennsylvanian, occur in bryozoan-brachiopod-crinoidal grainstones/packstones with conodonts and foraminifers. The conodonts of the studied interval belong to the shallow-water Adetognathus-Rhachistognathus biofacies. The Adetognathus and Rhachistognathus species/subspecies A. lautus (=A. gigantus), A. spathus, R. minutus minutus, R. muricatus, R. primus, R. prolixus, R. websteri and transitional forms dominate the succession. Gnathodus girtyi simplex, G. lanei n. sp., G. defectus and transitional forms between them are abundant. The elements of the boundary-marker D. noduliferus s. l. and transitional forms between G. girtyi simplex and D. inaequalis are less common. The occurrences of Idiognathoides spp. are very rare. Neognathodus spp. only occur in the upper part of the studied interval. The mid- Carboniferous boundary stratotype has been studied by many workers but its conodonts have been rarely described systematically. The detailed updated descriptions of themost stratigraphically important taxa are given in this paper. The evolution of Gnathodus, Adetognathus and Rhachistognathus species within the mid–Carboniferous interval is discussed. Four lineages are summarized: 1) C. unicornis, A. unicornis, A. lautus and A. spathus; 2) G. girtyi girtyi, G. g. simplex and D. inaequalis; 3) G. lanei and G. defectus; and 4) R. prolixus, R. muricatus and R. websteri/R. primus/R. minutus.

Short Retraction Notice This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused. Please see the article page for more details. The full retraction notice in PDF is preceding the original paper which is marked "RETRACTED".

Biaxial tensile test methods using cruciform specimens have been drawing attention as an effective approach to characterize sheet metal formability. The cruciform specimens usually adopt a thickness reduction at the central area to achieve a relatively large strain gradient for localized necking and fracture investigation. For conventional tests, many criteria have been proposed to determine the forming limits at necking, however, none has been widely accepted and applied to the cruciform specimens with non-uniformed thickness reduction. Therefore, in this work, different existing necking criteria are applied and discussed for numerical procedure in order to define an appropriate necking criterion for the thickness reduced cruciform specimens. AA6061-T4 sheet with a thickness of 2mm is used as a target material. The predicted forming limit curves are compared for different criteria, and the feasibility of each criterion is discussed in detail. Thanks to two dedicated specimens proposed by the authors, forming limits are determined for a wide range of strain states.

Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost, thereby providing a feasible way to acquire high-resolution remote sensing images, which are difficult to obtain due to the high cost of acquisition equipment and complex weather. Clearly, image super-resolution is a severe ill-posed problem. Fortunately, with the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. In this paper, we propose a network based on the generative adversarial network (GAN) to generate high resolution remote sensing images, named the multi-attention generative adversarial network (MA-GAN). We first designed a GAN-based framework for the image SR task. The core to accomplishing the SR task is the image generator with post-upsampling that we designed. The main body of the generator contains two blocks; one is the pyramidal convolution in the residual-dense block (PCRDB), and the other is the attention-based upsample (AUP) block. The attentioned pyramidal convolution (AttPConv) in the PCRDB block is a module that combines multi-scale convolution and channel attention to automatically learn and adjust the scaling of the residuals for better results. The AUP block is a module that combines pixel attention (PA) to perform arbitrary multiples of upsampling. These two blocks work together to help generate better quality images. For the loss function, we design a loss function based on pixel loss and introduce both adversarial loss and feature loss to guide the generator learning. We have compared our method with several state-of-the-art methods on a remote sensing scene image dataset, and the experimental results consistently demonstrate the effectiveness of the proposed MA-GAN.

At present, the existing gait recognition systems are focusing on developing methods to extract robust gait feature from silhouette images and they indeed achieved great success. However, gait can be sensitive to appearance features such as clothing and carried items. Compared with appearance-based method, model-based gait recognition is promising due to the robustness against these variations. In recent years, with the development of human pose estimation, the difficulty of model-based gait recognition methods has been mitigated. In this paper, to resist the increase of subjects and views variation, local features are built and a siamese network is proposed to maximize the distance of samples from the same subject. We leverage recent advances in action recognition to embed human pose sequence to a vector and introduce Spatial-Temporal Graph Convolution Blocks (STGCB) which has been commonly used in action recognition for gait recognition. Experiments on the very large population dataset named OUMVLP-Pose and the popular dataset, CASIA-B, show that our method archives some state-of-the-art (SOTA) performances in model-based gait recognition. The code and models of our method are available at https://github.com/timelessnaive/Gait-for-Large-Dataset after being accepted.

Offline Chinese handwriting text recognition is a long-standing research topic in the field of pattern recognition. In previous studies, text detection and recognition are separated, which leads to the fact that text recognition is highly dependent on the detection results. In this paper, we propose a robust end-to-end Chinese text page spotter framework. It unifies text detection and text recognition with text kernel that integrates global text feature information to optimize the recognition from multiple scales, which reduces the dependence of detection and improves the robustness of the system. Our method achieves state-of-the-art results on the CASIA-HWDB2.0-2.2 dataset and ICDAR-2013 competition dataset. Without any language model, the correct rates are 99.12% and 94.27% for line-level recognition, and 99.03% and 94.20% for page-level recognition, respectively.

Additional file 2: Table S1. List of all primer sequences.

With the development of computer science and artificial intelligence, the industrial field is gradually becoming unmanned. In this paper, a 3D point cloud image is created with an RGBD camera, the scene is restored through dense 3D reconstruction, and then the color information in the 3D point cloud image is recognized to obtain the 3D coordinates of the area to be painted. Finally, the coordinates are sent to the painting robot to achieve the purpose of automatic painting. This article describes the visual 3D reconstruction part. Unlike the current painting system, this system can automatically obtain the position information without the need to fix the position of the object. In the end, the system can realize the dense reconstruction of 3D point clouds of objects, which provides a certain reference for industrial unmanned management.

The spreading COVID-19 pandemic has brought the world to a halt in 2020. One of the major challenges is the lack of effective antiviral drugs. Drug and vaccine development typically takes years; a practical approach to formulate knowledge-based prescriptions is to conduct in silico screening for drugs and compounds that has the potential to disrupt viral protein functions. By evaluating the dataset from the “Shennong project”, an in silico screening of the DrugBank library against SARS-CoV-2 proteins, we identified chlorogenic acid and rutin displayed a strong affinity with diverse viral proteins. Chlorogenic acid is naturally present in coffee in large quantity, and rutin is available as nutraceutical products, both compounds are considered safe to consume, hence could potentially aid the recovery or treatment for COVID-19 patients at low health risk. We emphasise that the results require further clinical clarification, the impact of this work shall be examined by professionals carefully.

Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct few-shot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying "dog" from "laptop" is often trivial) to the meta-learner. In this paper, we propose an adaptive task sampling method to improve the generalization performance. Unlike instance based sampling, task based sampling is much more challenging due to the implicit definition of the task in each episode. Therefore, we accordingly propose a greedy class-pair based sampling method, which selects difficult tasks according to class-pair potentials. We evaluate our adaptive task sampling method on two few-shot classification benchmarks, and it achieves consistent improvements across different feature backbones, meta-learning algorithms and datasets., Comment: ECCV2020

In this work, we introduce {\em topological representations of a quiver} as a system consisting of topological spaces and its relationships determined by the quiver. Such a setting gives a natural connection between topological representations of a quiver and diagrams of topological spaces. First, we investigate the relation between the category of topological representations and that of linear representations of a quiver via $P(\Gamma)$-$\mathcal{TOP}^o$ and $k\Gamma$-Mod, concerning (positively) graded or vertex (positively) graded modules. Second, we discuss the homological theory of topological representations of quivers via $\Gamma$-limit $Lim^{\Gamma}$ and using it, define the homology groups of topological representations of quivers via $H_n$. It is found that some properties of a quiver can be read from homology groups. Third, we investigate the homotopy theory of topological representations of quivers. We define the homotopy equivalence between two morphisms in $\textbf{Top}\mathrm{-}\textbf{Rep}\Gamma$ and show that the parallel Homotopy Axiom also holds for top-representations based on the homotopy equivalence. Last, we mainly obtain the functor $At^{\Gamma}$ from $\textbf{Top}\mathrm{-}\textbf{Rep}\Gamma$ to $\textbf{Top}$ and show that $At^{\Gamma}$ preserves homotopy equivalence between morphisms. The relationship is established between the homotopy groups of a top-representation $(T,f)$ and the homotopy groups of $At^{\Gamma}(T,f)$., Comment: 32 pages

The bacterial RNA polymerase (RNAP) requires an additional type of subunit called the σ factor to recognise the promoter element of the gene and to form the holoenzyme that is capable of transcription initiation. The sigma factor within the core enzyme separates the double-stranded DNA that helps to initiate the transcription in the active channel of the holoenzyme. As each of the sigma factors binds at the specific promoter region upstream of the gene, it can guide the assembly of the holoenzyme and consequently regulates the transcription of the gene. The different types of σ factor that bind to the RNAP core enzyme can regulate the transcription of different sets of genes, should the bacteria require to survive through the change in the environment. The σ factor can be regulated, and the proteins that regulate the sigma factor are therefore essential in the survival of the bacteria. The thesis includes two parts. The first part of the thesis describes the NMR assignments and the solution structure of the receiver domain of RssB, a σS regulator that plays an essential role in the regulated proteolysis of the σS. Additionally, the NMR titration of one of the anti-adapter protein IraD with the receiver domain of RssB helps to understand the binding interface of the complex. This solution structure provided the basis for future studies on the regulation of the σS. The second part of the thesis describes the characterisation of the N-terminal CARF domain of RtcR, a transcriptional regulator of the RNA repairing system Rtc that interacts with σN . In this study, the protein was found to form a protein-RNA-protein complex with the 16S rRNA and the RNA cyclase protein RtcA. This finding provided the basis for further studies on this protein-RNA complex and its biological significance in the regulation of σN-dependent genes. Open Access

In this work, a series of low solid phase, economic and excellent performance water-based drilling fluids (WBDFs) were obtained by introducing nanoparticles into conventional WBDFs. Fe3O4 nanoparticles which were surface modified by poly (sodium p-styrene sulfonate) (PSSS) through 3-(trimethoxysilyl) proryl methacrylate (TMSPMA) were adopted in low solid WBDFs containing 4 wt % bentonite (BT). The effect of temperature on rheological property and fluid filtration was investigated. When the concentration of Fe3O4/PSSS nanoparticles reached 0.1 wt %, shear thinning of WBDFs will be perfect at different temperatures. When the concentration of Fe3O4/PSSS nanoparticles reached 0.25 wt %, the WBDFs had superior rheological properties, as compared to other concentrations of the particles in salt environment. Rheological curves provide a closely fit for the Bingham model at low temperature, however, the curves fit Herschel-Bulkley model at higher temperature. Rheological tests indicated that higher concentration of Fe3O4/PSSS nanoparticles can effectively improve salt (KCl) tolerance. Under high temperature and high press (HTHP) filtration test, WBDFs with Fe3O4/PSSS nanoparticles exhibited minimum filtration loss volume with the concentration of Fe3O4/PSSS nanoparticles as low as 0.1 wt % at different temperatures. For salt tolerance test, the higher concentration of the Fe3O4/PSSS nanoparticles leads to the lower the amount of fluid loss at the same concentration of KCl. With the concentration of nanoparticles grows to 0.1 wt %, the amount of fluid loss volume was minimum, indicating that Fe3O4/PSSS nanoparticles in WBDFs improve the filtration. The WBDFs with performance improved by Fe3O4/PSSS nanoparticles can be used as effective drilling fluids even under high temperature and high salinity, demonstrating that the modified fluids are potential candidates for drilling in deep and salty formation.

We develop a thermal-elastic stress model using the finite element method to predict three-dimensional anisotropic stress in AlN single crystals homoepitaxially grown by the physical vapor transport process; we also perform numerical experiments for a 1-in. AlN crystal surrounded by different cone-tube designs and grown along various orientations. The influences of the cone-tube shape and the growth orientation on the stresses inside the AlN crystal are investigated in detail. The simulation results show that the von Mises stress exceeds 1.11 GPa under all specified growth conditions, while the anisotropy is negligible. The resolved shear stresses are strongly dependent on the thermal gradient inside the growing crystal and the growth orientation. Strong anisotropy of the resolved shear stress is observed upon tilting of the growth orientation. The resolved shear stress along {0001}⟨1120⟩ primary slip system reveals that the c-axis growing crystal is under tensile stress along all three primary slip dire...

"Elastic-plastic mechanics" is a required course for engineering postgraduates in petroleum colleges and universities, such as students who major in oil and gas storage and transportation engineering. It is the basis for personnel engaged in structural safety assessment. In the past teaching process, the teaching effects of this course were unsatisfactory. There are many reasons for this phenomenon, such as the strong theoretical nature of this course, the need for a large number of formula derivation, the high requirements for students' mechanical foundation and mathematical foundation, the lack of appropriate teaching materials and the slackness of students' minds. Based on years of teaching experience, the teaching team has reformed the existing teaching materials of “Elastic-plastic mechanics” to meet the needs of petroleum colleges and universities. In this reform, we have referred to a large number of published textbooks of “Elasticplastic mechanics” and the experience of textbook construction at home and abroad. The newly compiled textbook of “Elastic-plastic mechanics” plays a certain role in improving the teaching quality of “Elastic-plastic mechanics” in petroleum colleges and universities.

Cerium oxalate hydrate particles were synthesized by precipitation technique under external magnetic field with respective intensities of 0, 0.1, 0.3, and 0.6 T. The particle morphologies were investigated by scanning electron microscopy (SEM) and transmission electron microscope (TEM). Results show that external magnetic field has a great influence on the growth behavior of cerium oxalate hydrate particles. Rod-like and plate-like cerium oxalate hydrate particles with smaller size are formed without the applying of external magnetic field. However, the extremely big rod-like particles with the average size of 12.47 μm were prepared under external magnetic field of 0.6 T. The increasing magnetic field intensity makes the grain grow quickly. Thermogravimetric analysis supports the presence of 8.5 H2O molecules associated with cerium oxalate. Further, the growth mechanism of cerium oxalate hydrate under external magnetic field was briefly discussed.

A series of 17β-amide-2-methoxyestradiol compounds were synthesized with an aim to enhance the antiproliferative effect of 2-methoxyestradiol. The antiproliferative activity of 2-methoxyestradiol analogs against human cancer cells was investigated. 2-methoxy-3-benzyloxy-17β-chloroacetamide-1,3,5(10)-triene (5e) and 2-methoxy-3-hydroxy-17β-butyramide-1,3,5(10)-triene (6c) had comparable or better antitumor activity than 2-methoxyestradiol. The elimination half-life of 6c (t1/2β=240.93min) is ten times longer than 2-ME and the area under the curve was seven times (AUC0-tmin=2068.20±315.74μgmL-1min) higher than 2-ME, respectively. Whereas 5e had similar pharmacokinetic behavior with 2-ME (t1/2β=22.28min) with a t1/2β of 29.5 min. 6c had higher blood concentration, longer actuation duration and better suppression rate against S180 mouse ascites tumor than 2-methoxyestradiol.

In the process of photovoltaic power generation, it is particularly important to choose a suitable inverter control strategy. Aiming at problems such as the poor dynamic response of the traditional controller in the three-phase LCL grid-connected inverter, the current harmonic disturbance caused by the grid voltage distortion and the inability to achieve the optimal tracking of the AC quantity, this paper presented a method to control voltage by fuzzy PI, control current by discrete QPR and improved DBC. In the voltage outer loop, the fuzzy control principle was used to adjust the P and I parameters in real time, and its output was used as the reference value for the current inner loop. In the current inner loop, the repeated control improved deadbeat and discretized form of QPR were used for compound control. The simulation results show that the control strategy greatly improves the distortion of the grid-connected current, which can optimally track the AC volume, and makes the entire system have good stability and dynamic response capabilities.

A series of 1,2-bis(imidazolidin-2-imine)benzene nickel complexes with different substituents on the imidazolidin-2-imine or benzene ring was synthesized and characterized. The complexes exhibited a tetrahedral configuration, and Ni2 bearing a phenyl substituent on the imine ligand provided highest steric hinderance around nickel center with a buried volume percentage of 53.1%. The nickel complexes in the presence methylaluminoxane (MAO) were highly active for the addition polymerization of norbornene and exhibited good thermal stability, where Ni3 bearing fluorine substituent on the benzene ligand backbone showed highest activity of 1.68 × 106 g·mol−1·h−1 at 100 °C to produce high molecular weight polynorbornene (1.2 × 105 g·mol−1). Most importantly, the catalysts promoted the copolymerization of norbornene and styrene with high activity (～105 g·mol−1·h−1), high molecular weight (~104 g·mol−1), and tunable styrene content (up to 17 mol%) and glass transition temperature. The linear relationship between the glass transition temperature and styrene content indicated the formation of the uniform copolymers. A preference for the norbornene insertion in each catalytic system was investigated by the evaluation of the monomer reactivity ratios.

Container technology has a series of advantages such as low physical resource consumption, fast startup speed, high concurrency, and can run in a variety of environments. It is widely used in scenarios such as big data and cloud computing. Container technology has certain advantages in performance, but there are some shortcomings in security. The container technology shares the kernel with the host, and its security mainly depends on the host. Once the attacker breaks through the host’s defense, he can easily access the files deployed in the container, steal or tamper with the file data, and cause losses to users and users. In response to the above problems, this paper proposes a container-oriented isolation control technology, which realizes further isolation of files inside the container by adding domain names to programs and files. If the program domain name matches the file part, the files in the current container cannot be accessed, and the security of the files in the container can be effectively ensured after the host is compromised.

In the information age, with the development of internet technology, there are more and more various business services, at the same time, network security issues are also increasing. However, traditional protection methods are gradually hard to cope with the complex network environment. Aiming at the credible connection establishment and data communication problems that need to be solved urgently in the credible network, an interentity communication mechanism based on end-to-end credibility assessment is proposed. This mechanism combines trusted computing with network access control mechanisms, integrates network access authority control technology, server identity authentication technology, communication subject measurement technology and network link transparent encryption technology, adopts real-time monitoring of terminal status, dynamic adjustment of access authorities, optimized network access control mechanism and data transmission method, to against security issues of the network communication. After security analysis, this mechanism improves the security of the connection and communication between the terminal and the network. It can effectively avoid security threats to information systems, and make the service of the information system safe and reliable.

We report the fabrication of poly(2-acrylamide-2-methylpropanesulfonic acid) modified SiO2 (SiO2/PAMPS) nanoparticles as plugging agents in water-based muds (WBMs). SiO2/PAMPS nanoparticles were prepared by the copolymerization of 3-methacryloxypropyltrimethoxysilane-functionalized SiO2 nanoparticles and 2-acrylamide-2-methylpropanesulfonic acid. These SiO2/PAMPS nanoparticles were found to exhibit excellent salt tolerance, thermal stability, and good compatibilities with polymeric additives commonly used in WBMs. The plugging capabilities of the nanoparticles were tested with filter cakes on a high-temperature, high-pressure filtration press. The results indicated that SiO2/PAMPS nanoparticles can effectively block the nanopores in filter cake. In addition, WBMs containing SiO2/PAMPS nanoparticles were found to exhibit excellent plugging properties both at ambient temperature and at 80 °C, demonstrating that these nanoparticles are promising plugging agents in WBMs for drilling in shale formations.

This paper proposed a formal verification method for JTAG security based on information flow tracking. The security property script is used to describe the security requirements. Compared with the traditional writing method of assertions, our method does not need to consider much about the design features, which not only greatly reduces the assertion writing time but also can effectively detect the security violations in the design. Based on the generated information-flow tracking model, the proposed method can generate formal constraints and System Verilog Assertions supported by formal verification tools. Experiment of JTAG security verification proves that the proposed method can effectively verify the security functions related to information flow such as access control.

With the development of smart power grid, the operation mode of power grid is time-varying, which leads to the complex security problems of power grid. Buffer overflow has become the most serious security threat of smart grid. The embedded network security equipment needs more ability to resist buffer overflow attack. Based on the basic principle of buffer overflow, a multi-level anti-buffer mechanism is put forward and applied to embedded security devices in this paper, which can fundamentally curb the occurrence of buffer overflow and maintain a good network transmission environment for smart grid.

Drilling fluid is a vital element and is often regarded as the “blood” in the oil industry. Although traditional oil-based drilling fluids have advantages in some harsh cases, the high cost and environmental pollution faced with them limit its application. Water-based drilling fluids (WBDFs) with environmental friendly, low cost, and high performance are important for drilling engineering to solve the problems of low efficiency and wellbore instability caused by poor rheological properties and large filtration loss in drilling operations. In this paper, Fe3O4 nanoparticles modified by poly (acrylic acid) (PAA) through 3-(trimethoxysilyl) proryl methacrylate (TMSPMA) were introduced into WBDFs for enhancing their rheological and plugging performance. Rheological tests indicated that the consistency coefficient (K) of the Fe3O4/PAA nanoparticles/WBDFs decreased at a higher concentration. Incorporated nanoparticles with a concentration of 0.05 wt %, the WBDFs will exhibit good shear-thinning behavior. The results showed that the best performance for Fe3O4/PAA nanoparticles being as a filtration additive in WBDFs was achieved at concentration as low as 0.1 wt %. These results demonstrated that Fe3O4/PAA nanoparticles are effective additives for WBDFs.

The bacterial RNA polymerase (RNAP) requires an additional type of subunit called the σ factor to recognise the promoter element of the gene and to form the holoenzyme that is capable of transcription initiation. The sigma factor within the core enzyme separates the double-stranded DNA that helps to initiate the transcription in the active channel of the holoenzyme. As each of the sigma factors binds at the specific promoter region upstream of the gene, it can guide the assembly of the holoenzyme and consequently regulates the transcription of the gene. The different types of σ factor that bind to the RNAP core enzyme can regulate the transcription of different sets of genes, should the bacteria require to survive through the change in the environment. The σ factor can be regulated, and the proteins that regulate the sigma factor are therefore essential in the survival of the bacteria. The thesis includes two parts. The first part of the thesis describes the NMR assignments and the solution structure of the receiver domain of RssB, a σS regulator that plays an essential role in the regulated proteolysis of the σS. Additionally, the NMR titration of one of the anti-adapter protein IraD with the receiver domain of RssB helps to understand the binding interface of the complex. This solution structure provided the basis for future studies on the regulation of the σS. The second part of the thesis describes the characterisation of the N-terminal CARF domain of RtcR, a transcriptional regulator of the RNA repairing system Rtc that interacts with σN . In this study, the protein was found to form a protein-RNA-protein complex with the 16S rRNA and the RNA cyclase protein RtcA. This finding provided the basis for further studies on this protein-RNA complex and its biological significance in the regulation of σN-dependent genes.

With the construction of smart grid and smart energy city, a large number of smart terminals will be deployed in non-traditional areas by power grid enterprises. On the one hand, these intelligent terminals will realize flexible interaction with the power grid. On the other hand, they are also vulnerable to external attacks. Therefore, the industrial control security risk of intelligent terminals has become the focus of attention of society and enterprises. After explaining the industrial control risks faced by smart terminals, the protection scheme of smart grid control terminals based on dynamic trust is introduced in detail in this paper, and multiple protection of control terminals in complex network environment is realized.

Land cover change detection based on remote sensing has become increasingly important for protecting the ecological environment. Spatial features of images can be extracted by object-level methods. However, the computational complexity is high when using many features to detect land cover change. Meanwhile, single-constrained change detection (SCCD) methods produce non-objective and inaccurate results. Therefore, we proposed a land cover change detection method: the object-level double constrained change detection (ODCD) method. First, spectral and spatial features were calculated based on multi-scale segmentation results. Second, using the significant difference test (SDT), feature differences among all categories were calculated, and the features with more significant differences were considered as the optimal features. Third, the maximum Kappa coefficient was used as the criterion for determining the optimal change intensity and correlation coefficient. Finally, the ODCD was validated using GF-1 satellite images on March 2016 and February 2017 in north Beiqijia Town, Beijing. Using optimal feature selection, the dimension of features was reduced from 26 to 12. Compared with SCCD methods, the result of the ODCD was more reliable and accurate. Its overall accuracy was 10% higher, overall error was 27% lower, and the Kappa coefficient was 0.22 higher. In conclusion, the ODCD is effective for land cover change detection and can improve computational efficiency.

This study proposes a lever-type vibration isolator with eddy current damping (LVI-ECD), which can broaden the isolation band by adjusting the lever ratio and improve the vibration suppression performance with eddy current damping (ECD). The LVI-ECD composes of a lever with a permanent magnet (PM) at the tip of the lever, a load plate, a base plate and springs, etc. The relative motion between the PM and the conductive base and load plates induces ECD, which can be amplified by the lever structure and thus to generate nonlinear damping effect. The theoretical models of the lever-type vibration isolator (LVI) and LVI-ECD are established and the amplitude frequency relationship considering nonlinear damping is deduced. The effects of the lever ratio, tip mass, material property and cross-section of the lever, geometric parameters of the PM and excitation amplitude on the vibration isolation performance of the LVI-ECD are analyzed. The analytical predictions are consistent with the experimental results, which demonstrates that the peak transmissibility and natural frequency decrease with the increase of the lever ratio. ECD can further improve the vibration suppression performance in the resonance region. The transmissibility tends to be flat with the increase of the lever ratio in the isolation region. The vibration isolation performance can be also improved by increasing the tip mass of the lever. The material property and cross-section of the lever have little effect on vibration isolation. This study provides a guideline to design, model and optimize a passive damped lever-type vibration isolator.

Xincheng gold deposit is located in Xincheng Village of Jiaodong, Laizhou Jincheng, near Bohai Bay, and is one of the largest mines in China. The orebodies of Xincheng gold deposit located in Jiaojia fault zone are mainly located in the pyrite sericite-quartzized cataclasite belt and the pyrite-sericite-quartzized granodioritic cataclasite belt under the main fault plane (Fault Gouge). Based on the exploration data and test data, the occurrence characteristics of I and V major orebodies in the deposit are analyzed, and the mining evaluation is made on the basis of the hydrologic and environmental characteristics of the deposit. The results show that the rock of the Orebody, roof and floor is mainly massive igneous rock and Metamorphic Rock, which has high mechanical strength and is hard and semi-hard rock with simple engineering geological conditions The Ore and waste rock are not easy to decompose harmful substances, have no heat harm, the possibility of local surface deformation caused by mining is not big, the circulation of groundwater is bad, the evaporation is big, the water quality is bad, the mined-out area formed by mining has little influence on the stability of rock mass in the mining area. The main ore body in the study area is of good geological environment.

Power transformer fault diagnosis exerts a vital part in the safe operation of power system. The PSO-SVM based on transformer fault diagnosis still has some shortcomings, such as slow convergence speed and easy to fall into local optimization. This dissertation proposes a transformer diagnosis method based on Improve Particle Swarm Optimization to support Vector Machine (MPSO-SVM). Adding disturbance to Particle swarm optimization (PSO) to disturb the position of such precocious particles, so as to get rid of local optimum. The case analysis represents that the diagnostic accuracy of MPSO-SVM is higher than that of PSO-SVM and Generalized Regression Neural Network (GRNN), and MPSO-SVM can effectively promote the fault diagnosis performance of transformer.