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Multicomponent crystalline materials construct more complex and diversified structures with different combinations and modes, which expands the material structure space and application fields. In this work, we have successfully synthesized two two-component ionic hydrogen-bonded organic frameworks (iHOF-32and iHOF-33) and a three-component iHOF (iHOF-34). Guanidine hydrochloride which is rich in N and H and has a short chain length strengthens the hydrogen bonding network of iHOF, thereby aiding in the efficient migration of protons. The rigid architecture of 1,1′-diamino-4,4′-bipyridine diiodide furnishes additional scaffolding for the iHOFs and enhances its robustness under harsh conditions. iHOF-34accomplishes the combination of the ultra-high proton conductivity of iHOF-32and the robustness of iHOF-33by the synergistic interaction among the three components of ligands. In addition, iHOF-34was fabricated as a composite membrane with a peak proton conductivity of 1.55 × 10−1S cm−1, which is 3.8 times higher than that of recast Nafion, and has extensive potential in fuel cells. The self-assembly strategy of multicomponent HOFs opens up new avenues for constructing diverse structures while promising new opportunities for applications in the fields of proton conduction and energy storage.

Current studies for brain-muscle modulation often analyze selected properties in electrophysiological signals, leading to a partial understanding. This article proposes a cross-modal generative model that converts brain activities measured by electroencephalography (EEG) to corresponding muscular responses recorded by electromyography (EMG). Examining the generation process in the model highlights how the motor cue, representing implicit motor information hidden within brain activities, modulates the interaction between brain and muscle systems. The proposed model employs a two-stage generation process to bridge the semantic gap in cross-modal signals. Initially, the shared movement-related information between EEG and EMG signals is extracted using a contrastive learning framework. These shared representations act as conditional vectors in the subsequent EMG generation stage based on generative adversarial networks (GANs). Experiments on a self-collected multimodal electrophysiological signal data set show the algorithm’s superiority over existing time series generative methods in cross-modal EMG generation. Further insights derived from the model’s inference process underscore the brain’s strategy for muscle control during movements. This research provides a data-driven approach for the neuroscience community, offering a comprehensive perspective of brain-muscular modulation.

Recent camouflaged object detection (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from the high intrinsic similarity between camouflaged objects and their background, objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To this end, we propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and videos, i.e., zooming in and out. Specifically, our approach employs the zooming strategy to learn discriminative mixed-scale semantics by the multi-head scale integration and rich granularity perception units, which are designed to fully explore imperceptible clues between candidate objects and background surroundings. The former's intrinsic multi-head aggregation provides more diverse visual patterns. The latter's routing mechanism can effectively propagate inter-frame differences in spatiotemporal scenarios and be adaptively deactivated and output all-zero results for static representations. They provide a solid foundation for realizing a unified architecture for static and dynamic COD. Moreover, considering the uncertainty and ambiguity derived from indistinguishable textures, we construct a simple yet effective regularization, uncertainty awareness loss, to encourage predictions with higher confidence in candidate regions. Our highly task-friendly framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.

Photochromic hydrogen-bonded organic frameworks (HOFs) can introduce different luminescent functional groups to achieve synergistic controlled multiple color change properties, which are in great demand for diverse information encryption applications. We report in this paper switchable photochromic and photoluminescent dual luminescent functional group HOFs constructed with synergistic effects by N,N′-bis(2-phenylalanine)-1,4,5,8-naphthalenediimine (H2PheNDI) and benzenecarboximidamide 4,4′-azobis(hydrochloride) (AZBH). The crystal powder of iHOF-41is orange–red in color, which can be changed to black under the irradiation of a 365 nm ultraviolet (UV) light source for 15 min. The photoisomerization rate of the crystal solution under continuous UV irradiation for 5 h was close to 99%. The composite membranes can achieve the properties of photochromism and photoluminescence when they are discolored under 365 nm UV irradiation and, at the same time, excite red bright fluorescence. This work achieves the construction of HOFs based on switching biluminescent functional groups and explores the synergistic mechanism of the photoisomerization process and photochromism as well as its practical application in information encryption.

Multidrug crystals, comprising multiple drugs in the same crystal lattice, can be used to develop drug combinations with improved physicochemical properties. The investigation on polymorphism of a given multidrug crystal aids in finding the optimal solid form for the development of combined formulations. In this work, three polymorphs (GTB−RH I, GTB−RH II, and GTB−RH III) and two hydrates (GTB−RH·4.25H2Oand GTB−RH·1.25H2O) of a multidrug crystal involving gefitinib (GTB) and rhein (RH) were successfully obtained and fully characterized by X-ray diffraction (XRD), 1H nuclear magnetic resonance (1H NMR), thermogravimetric analysis (TG), differential scanning calorimetry (DSC), Fourier transform infrared (FT-IR), and dynamic vapor sorption (DVS) measurements. Thermal analysis reveals that GTB−RH Iand GTB−RH IIare monotropically related, while GTB−RH IIand GTB−RH IIIare enantiotropically related. GTB−RH Iis a metastable form, GTB−RH IIis the stable form at lower temperature, and GTB−RH IIIis the stable form at higher temperature. The transition of GTB−RH IIto GTB−RH IIIoccurs upon heating to 190 °C. Slurry conversion experiments indicate that GTB−RH·4.25H2Ois the stable form in aqueous solutions, while GTB−RH IIis the stable form in nonaqueous solutions. Accelerated stability tests show that GTB−RH·1.25H2Oand GTB−RH Iconverted to GTB−RH·4.25H2O, while GTB−RH·4.25H2O, GTB−RH II, and GTB−RH IIIremained intact up to three months. In addition, dissolution experiments demonstrate the apparent solubility of GTB and RH in the following order: GTB−RH III> GTB−RH II> GTB−RH I> GTB−RH·1.25H2O> GTB−RH·4.25H2O. This work shows that GTB−RH IIIhas great potential to be developed into a combined formulation as it exhibits simultaneously and significantly improved solubility for both GTB and RH.

The data distribution of rolling bearings varies under different operating conditions, and it is difficult to obtain a large amount of labeled data. Most existing work focuses solely on domain alignment and neglects the assessment of cross-domain transferability. In this article, a fault diagnosis method for rolling bearing based on deep adversarial transfer learning with transferability measurement (DATLTM) is proposed. First, the source domain and target domain data under different working conditions of rolling bearings are divided by empirical wavelet transform (EWT) and input the source domain and its subband data together with the target domain data into the deep neural network to generate a pretrained model, where the network is composed of a feature extraction module constructed by 1-D convolutional neural network (CNN) and a domain adaptive module that uses entropy regularized Wasserstein distance (ERWD) to measure the distribution difference. Subsequently, transferability measurement is conducted based on the logarithm of maximum evidence (LogME) evaluation index and the existing pretrained models. Target domain sample reconstruction is performed, and the source domain data are reintroduced into the network for training. The effectiveness and advantages of the proposed method were demonstrated through variable operating conditions tasks on Case Western Reserve University (CWRU) and self-conducted bearing fault datasets. Among them, on the six transfer tasks of self-conducted bearing fault datasets, compared with other transfer learning diagnosis methods, the proposed method has the highest cross-domain diagnosis accuracy in five tasks.

Blind deconvolution has been demonstrated to be an effective tool for rolling element bearing localized defect detection. However, the successful utilization of deconvolution methods largely depends on the selection of several tunable configuration parameters, otherwise it may lead to the incomplete extraction of malfunction information. Additionally, the effectiveness of some multiple-transient-oriented deconvolution approaches strongly relies on the accurate set of the specific prior impulse period, whose application may be severely confined in practice. Coping with the aforementioned problems, an efficient as well as automatic bearing fault detection and diagnosis method based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and Bayesian optimization (BO) is proposed in this article, where the configuration parameters and the potential impulse period can be optimized jointly in a sequential decision-making framework. To alleviate the influence of trend terms and intricate interferences in the raw signal to some extent and promote the deconvolution result, a preprocessing method coined selective coarse enveloping (SCE) is developed based on the kurtosis indicator. In the methodological aspect, the signal after preprocessing is analyzed by the presented BO-based MOMEDA (BO-MOMEDA) method, and the period of the purified signal can be obtained, with the fault type of bearing being identified. Furthermore, both simulation analysis and experimental verifications profoundly corroborate the efficiency and superiority of the proposed method, compared with other contradistinctive methods.

A novel alkali metal-based metal–organic framework (Na-MOF) has been successfully prepared under solvothermal conditions using tetrakis(4-phosphonatophenyl)ethylene (H8TPE) as a ligand and NaOH as a metal source. The single crystal structure of Na-MOFshowed that the phosphonate groups and solvent water molecules formed a two-dimensional hydrogen bonding network. The phosphonic acid group has the ability to induce proton adsorption and release, forming an H-bonding network of proton conduction channels inside the metal phosphonate for efficient proton transport. The results showed that the proton conductivity of Na-MOFincreased with the increase in temperature and relative humidity (RH). Na-MOFexhibits superprotonic conductivity of 1.03 × 10–1S·cm–1at 60 °C and 98% RH. As a solid-state proton conductor, its high proton conductivity shows good application prospects.

As induction motor is one of the most preponderant driving equipment in modern industrial systems, timely detection and maintenance of motor failures can efficaciously circumvent economic losses and catastrophic accidents. Though vast majority of machinery fault diagnosis approaches are based on vibration analysis, there may be circumstances that it is impossible to install vibratory sensors in some sophisticated equipment or harsh environments. In view of this, a fault diagnosis method for induction motor based on multiscale acoustic entropy feature selection and hierarchical adaptive neuro-fuzzy inference system (HANFIS) with localized recurrent input is presented in this article. First, multiscale entropy features, which can reflect the health state of the running motor, are extracted from the collected acoustic signals. Subsequently, to avoid the dimensionality disaster problem and remove irrelevant features, the Relief-F algorithm is used for feature selection. Next, based on the fuzzy formalisms, a newly designed HANFIS is utilized for motor fault identification, where the localized recurrent input mechanism allows for injecting important features again into the model to prevent it from forgetting significant discriminative information, to further promote the overall diagnostic accuracy. Finally, experiments on two acoustic datasets of induction motor and comparisons with existing approaches sufficiently verify the effectiveness and superiority of the proposed method.

ABSTRACT Compared with the traditional concrete, the concrete fully made with the lightweight aggregate exhibits remarkable reduction in mechanical properties and is not suitable for the structural elements. To improve [...]

Generative (generalized) zero-shot learning [(G)ZSL] models aim to synthesize unseen class features by using only seen class feature and attribute pairs as training data. However, the generated fake unseen features tend to be dominated by the seen class features and thus classified as seen classes, which can lead to inferior performances under zero-shot learning (ZSL), and unbalanced results under generalized ZSL (GZSL). To address this challenge, we tailor a novel balanced semantic embedding generative network (BSeGN), which incorporates balanced semantic embedding learning into generative learning scenarios in the pursuit of unbiased GZSL. Specifically, we first design a feature-to-semantic embedding module (FEM) to distinguish real seen and fake unseen features collaboratively with the generator in an online manner. We introduce the bidirectional contrastive and balance losses for the FEM learning, which can guarantee a balanced prediction for the interdomain features. In turn, the updated FEM can boost the learning of the generator. Next, we propose a multilevel feature integration module (mFIM) from the cycle-consistency branch of BSeGN, which can mitigate the domain bias through feature enhancement. To the best of our knowledge, this is the first work to explore embedding and generative learning jointly within the field of ZSL. Extensive evaluations on four benchmarks demonstrate the superiority of BSeGN over its state-of-the-art counterparts.

Cysteine cathepsin B (CTS-B) is a crucial enzyme that is overexpressed in numerous malignancies and contributes to the invasion and metastasis of cancer. Therefore, this study sets out to develop and evaluate an activity-based multimodality theranostic agent targeting CTS-B for cancer imaging and therapy. A CTS-B activity-based probe, BMX2, was synthesized and labeled efficiently with 68Ga and 90Y to produce 68Ga-BMX2 for multimodality imaging and 90Y-BMX2 for radiation therapy. The affinity and specificity of BMX2 binding with the CTS-B enzyme were determined by fluorescent western blots using recombined active human CTS-B enzyme (rh-CTS-B) and four cancer cell lines including HeLa, HepG2, MCF7, and U87MG, with CA074 as the CTS-B inhibitor for control. Confocal laser scanning microscope imaging and cell uptake measurement were also performed. Then, in vivo PET imaging and fluorescence imaging were acquired on HeLa xenografts. Finally, the therapeutic effect of 90Y-BMX2 was tested. BMX2 could be specifically activated by rh-CTS-B and stably bound to the enzyme. The binding of BMX2 with CTS-B is time-dependent and enzyme concentration-dependent. Although CTS-B expression varied between cell lines, all showed significant uptake of BMX2 and 68Ga-BMX2. In vivo optical and PET imaging showed a high tumor uptake of BMX2 and 68Ga-BMX2 and accumulation for more than 24 h. 90Y-BMX2 could significantly inhibit HeLa tumor growth. The development of 68Ga/90Y-BMX2, a radioactive and fluorescent dual modality theranostic agent, demonstrated an effective theranostic approach for PET diagnostic imaging, fluorescence imaging, and radionuclide therapy of cancers, which may have a potential for clinical translation for cancer theranostics in the future.

The development of high-performance proton exchange membrane fuel cells (PEMFCs) is crucial yet challenging. Enrichment of proton transport pathways by doping ionic hydrogen-bonded organic frameworks (iHOFs) in Nafion matrix is important for further development of high-performance PEMFCs. In this work, an iHOF material (iHOF-9) based on arylphosphonate anions and guanidinium cations with the three-dimensional (3D) hydrogen-bonded network was synthesized, which exhibits an ultrahigh proton conductivity of 4.38 × 10–2S·cm–1at 90 °C and 98% RH. In addition, by mixing iHOF into Nafion matrix, we have fabricated the high-performance hybrid membranes, and the maximum proton conductivity value can achieve 6.61 × 10–2S·cm–1for 9%-iHOF-9/Nafionmembrane at 90 °C and 98% RH. iHOF-9/Nafionmembranes were used to fabricate the proton exchange membranes for application in H2/O2fuel cells. The maximum power density of 9%-iHOF-9/Nafionreached 1092.07 mW·cm–2after stabilization for 10 h at 80 °C and 100% RH, which is a 33.99% improvement compared to the recast Nafion membrane. This work doped ultrahigh conductivity iHOF into the Nafion matrix as an emerging proton exchange membrane material, injecting new possibilities for the development of new energy sources.

Lithium‑oxygen batteries (LOBs) have very high theoretical energy density, but the cycle performance is not satisfactory due to numerous obstacles such as the poor interfacial stability of liquid electrolyte (LE) on both cathode and Li metal anode, the leakage of liquid electrolyte, the cross-over of oxygen and moisture, etc. Quasi-solid-state polymer electrolytes are feasible choices to deal with the problems associated with LE and Li anode. Here, a novel strategy is used to prepare quasi-solid-state polymer electrolytes by confining LE in polymer crystals. The trioxymethylene is polymerized in LE, and the as formed polyoxymethylene (POM) crystallizes with the formation of lamellar crystals of hierarchical micro/nano architectures, which confine the liquid exponents in LE and become quasi-solid-state POM electrolytes (POMEs). The POME-30 has high ionic conductivity, wide electrochemical stability window and high Li+transference number. Molecular dynamics simulation shows that there exist channels for Li+to migrate faster on the interface of POM lamellar crystals. POME-30 has excellent interfacial compatibility with Li anode, and can promote more uniform Li deposition and stripping. The in situ formed quasi-solid-state Li|POME-30|O2batteries can be cycled for 143 cycles in O2and 67 cycles in ambient air. XPS analysis results suggest that the cross-over of H2O and CO2can be greatly suppressed. Moreover, the quasi-solid-state Li|POME-30|air pouch battery can be stably cycled for over 30 cycles, showing great safety performance even folded or cut. Our strategy of in situ integrating rigid lamellar crystals in LE has paved a new way for exploring advanced polymer electrolyte, which is sure to promote the development of high energy-density lithium metal batteries.

The purpose of co-salient object detection (CoSOD) is to detect the salient objects that co-occur in a group of relevant images. CoSOD has been significantly prospered by recent advances in convolutional neural networks (CNNs). However, it shows general limitations in modeling long-range feature dependencies, which is crucial for CoSOD. In the vision transformer, the self-attention mechanism is utilized to capture global dependencies but unfortunately destroy local spatial details, which are also essential for CoSOD. To address the above issues, we propose a dual network structure, called TCNet, which can efficiently excavate both local information and global representations for co-saliency learning via the parallel interaction of Transformers and CNNs. Specifically, it contains three critical components, i.e., the mutual consensus module (MCM), the consensus complementary module (CCM), and the group consistent progressive decoder (GCPD). MCM aims to capture the global consensus from high-level features of these two branches as a guide for the following integration of consensus cues of both branches at each level. Next, CCM is designed to effectively fuse the consensus of local information and global contexts from different levels of the two branches. Finally, GCPD is developed to maintain group feature consistency and predict accurate co-saliency maps. The proposed TCNet is evaluated on five challenging CoSOD benchmark datasets using six widely used metrics, showing that our proposed method is superior to other existing cutting-edge methods for co-salient object detection.

Objective: Epidemiology studies indicate that green tea polyphenols (GTP) perform a protective effect on cardiovascular diseases, but the underlying mechanisms are complex. The present study aimed to investigate the effect of GTP on high-fat diets (HFD) induced-early vascular aging. Methods: Six-week young adult Wistar rats were fed with standard chow or HFD in the presence and absence of GTP (200 mg/kg body weight) for 18 weeks. In vitroexperiment, human umbilical vascular endothelial cells (HUVECs) were treated with palmitic acid (PA) and GTP. Results: The results showed that GTP alleviated the disorganized arterial wall and the increased intima-media thickness induced by HFD. In addition, the vascular oxidative injury was suppressed following GTP treatment. Furthermore, GTP elevated the ratio of LC3-II/LC3-I and suppressed expression of p62/SQSTM1, and restored SIRT3 expression in the aorta of HFD rats. Consistently, in cultured HUVECs, GTP inhibited cell senescence indicated by SA-β-gal and promoted endothelial autophagy compared with the PA treatment group. The activity of SIRT3 was specifically inhibited by 3-TYP, and the protective effect of GTP was consequently abolished. Conclusion: The findings indicated that GTP protected against early vascular senescence in young HFD rats via ameliorating oxidative injury and promoting autophagy which was partially regulated by the SIRT3 pathway.

With the increasing maritime activities, a great demand of wide-area maritime digital data services is needed. Therefore, Narrowband Internet of Things (NB-IoT) that can provide wide coverage has been expected as an application for maritime communication networks (MCNS). In this paper, we aim to enhance the spectral efficiency in NB-IoT by reducing the cyclic prefix (CP) overhead in random access signal without causing interference. The key point of the proposed scheme is the symbols transmitted for multiple times repeatedly in NB-IoT. Specifically, all CP are removed and multi-path fading effect is eliminated by using a repeated symbol to cover the disturbed symbol to construct a circular convolution structure of the channel with the same effect as adding CP. In addition, a single-tap equalization is still appropriate. To validate the effectiveness of the proposed scheme, simulation results are carried out with respect to the bit error ratio (BER).

In this paper, we utilize the original noise subspace and its conjugate to reconstruct a new estimator for the fast joint estimation of DOA and inter‐subarray errors in the partly calibrated arrays (PCA), namely fast‐rank‐reduce (F‐RARE). Based on the new estimator, the collection of true DOA estimation and its virtual mirror can be obtained by searching over only half of the total angular field‐of‐view. Then, the true DOA estimation can be selected by using the conventional beamforming (CBF) technique. Additionally, the inter‐subarray errors can also be computed by a finite number of subspace decompositions. Numerical simulation illustrates that compared with the conventional RARE algorithm, the new method can not only reduce the computational burden by a half but also provide a similar estimation accuracy. In this paper, we investigate the fast joint estimation of direction‐of‐arrival (DOA) and inter‐subarray errors in the partly calibrated arrays (PCA). Since the conventional algorithm is accompanied with heavy computational burden, we propose a new algorithm called fast‐rank‐reduce (F‐RARE), where half of the searching range is reduced. This means that the computational complexity of F‐RARE is significantly lower than that of the conventional algorithm. To verify this, the theoretical complexity is analyzed, and the improvement of computational efficiency is visually seen in the form of the lower theoretical complexity and the shorter simulation time. In the meantime, the new method is independent of inter‐subarray gain/phase errors and displacement errors. Finally, the simulation results show that the new method exhibits a similar estimation performance compared to the conventional algorithm. We propose a new low‐complexity algorithm by using original noise‐subspace and its conjugateThe direction‐of‐arrival and inter‐subarray error can be estimated by using the new algorithmThe efficiency of the new algorithm is higher than the conventional algorithm by simulationPartly calibrated array We propose a new low‐complexity algorithm by using original noise‐subspace and its conjugate The direction‐of‐arrival and inter‐subarray error can be estimated by using the new algorithm The efficiency of the new algorithm is higher than the conventional algorithm by simulation Partly calibrated array

Important efforts are currently under way in order to develop further the nascent field of plasmonic photocatalysis, striving for improved efficiencies and selectivities. A significant fraction of such efforts has been focused on distinguishing, understanding, and enhancing specific energy-transfer mechanisms from plasmonic nanostructures to their environment. Herein, we report a synthetic strategy that combines two of the main physical mechanisms driving plasmonic photocatalysis into an engineered system by rationally combining the photochemical features of energetic charge carriers and the electromagnetic field enhancement inherent to the plasmonic excitation. We do so by creating hybrid photocatalysts that integrate multiple plasmonic resonators in a single entity, controlling their joint contribution through spectral separation and differential surface functionalization. This strategy allows us to create complex hybrids with improved photosensitization capabilities, thanks to the synergistic combination of two photosensitization mechanisms. Our results show that the hot electron injection can be combined with an energy-transfer process mediated by the near-field interaction, leading to a significant increase in the final photocatalytic response of the material and moving the field of plasmonic photocatalysis closer to energy-efficient applications. Furthermore, our multimodal hybrids offer a test system to probe the properties of the two targeted mechanisms in energy-related applications such as the photocatalytic generation of hydrogen and open the door to wavelength-selective photocatalysis and novel tandem reactions.

During efforts to solve common technical challenges to the comprehensive recovery of rare precious metals associated with copper smelting, Xiangguang Copper (XG) independently developed a new process to treat anode slime using technology that captures precious metal using bismuth. This paper focuses on the introduction and application of this technology for capturing precious metals with bismuth. It also discusses silver electrolysis at high current density and bismuth recovery from precious metals slag. The new process shows good adaptability of raw materials, elimination of lead pollution, low energy consumption, environmental protection and high comprehensive recovery rate of metals, which promotes the technological progress of comprehensive recovery and utilization of associated resources in copper smelting process.

As the high-power density and environmentally friendly energy resources, proton exchange membrane fuel cells (PEMFCs) have a promising future in portable power generation. Herein, the hybrid Nafion membranes of ionic hydrogen-bonded organic frameworks (iHOFs) for PEMFC applications are demonstrated. By adjusting the position of sulfonic groups on naphthalene disulfonic acid compounds, four iHOFs with different types of hydrogen bonds were synthesized successfully based on 1,1′-diamino-4,4′-bipyridylium and naphthalene disulfonic acid. The formation of hydrogen bond interactions between amino and sulfonate groups provides a rich hydrogen bond network, which makes such iHOFs have high conductivity, and the maximum value is 2.76 × 10–3S·cm–1at 100 °C and 98% RH. Besides, composite membrane materials were obtained by mixing Nafion and iHOFs, and the maximum proton conductivity values can achieve 1.13 × 10–2S·cm–1for 6%-iHOF-3/Nafionand 2.87 × 10–3S·cm–1for 6%-iHOF-4/Nafionmembranes at 100 °C under 98% RH. Through the H2/O2fuel cell performance test by using iHOF/Nafion as the solid electrolyte, the maximum power and current density values of hybrid membranes are 0.36 W·cm–2and 1.10 A·cm–2for 6%-iHOF-3/Nafionand 0.42 W·cm–2and 1.20 A·cm–2for 6%-iHOF-4/Nafionat 80 °C and 100% RH. This work provides a practicable approach for establishing high-performance proton exchange hybrid membranes by doping high proton-conducting iHOFs into the Nafion matrix.

Rapeseed (Brassica napusL.) is an important source of edible vegetable oil and feed protein; however, seed pigments affect the quality of rapeseed oil and the feed value of the residue from oil pressing. Here, we used a population of rapeseed recombinant inbred lines (RILs) derived from the black-seeded male parent cultivar Zhongyou 821 and the yellow-seeded female parent line GH06 to map candidate genes controlling seed pigments in embryos and the seed coat. We detected 94 quantitative trait loci (QTLs) for seed pigments (44 for embryos and 50 for seed coat), distributed over 15 of the 19 rapeseed chromosomes. These included 28 QTLs for anthocyanidin content, explaining 2.41–44.66% of phenotypic variation; 24 QTLs for flavonoid content, explaining 2.41–20.26% of phenotypic variation; 16 QTLs for total phenol content, accounting for 2.74–23.68% of phenotypic variation; and 26 QTLs for melanin content, accounting for 2.37–24.82% of phenotypic variation, indicating that these traits are under multigenic control. Consensus regions on chromosomes A06, A09 and C08 were associated with multiple seed pigment traits, including 15, 19 and 10 QTLs, respectively, most of which were major QTLs explaining >10% of the phenotypic variation. Based on the annotation of the B. napus“Darmor-bzh” reference genome, 67 candidate genes were predicted from these consensus QTLs regions, and 12 candidate genes were identified as potentially involved in pigment accumulation by RNA-seq and qRT-PCR analysis. These preliminary results provide insight into the genetic architecture of pigment biosynthesis and lay a foundation for exploring the molecular mechanisms underlying seed coat color in B. napus.

Plasmonic metasurfaces with the photothermal effect have been increasingly investigated for optofluidics. Meanwhile, along with the expanding application of circularly polarized light, a growing number of investigations on chiral plasmonic metasurfaces have been conducted. However, few studies have explored the chirality and the thermal-induced convection of such systems simultaneously. This paper aims to theoretically investigate the dynamics of the thermally induced fluid convection of a chiral plasmonic metasurface. The proposed metasurface exhibits giant circular dichroism in absorption and thus leads to a strong photothermal effect. On the basis of the multiphysical analysis, including optics, thermodynamics, and hydrodynamics, we propose a concept of chiral spectroscopy termed optofluidic circular dichroism. Our results show that different fluid velocities of thermally induced convection appear around a chiral plasmonic metasurface under different circularly polarized excitation. The chiral fluid convection is induced by an asymmetric heat distribution generated by absorbed photons in the plasmonic heater. This concept can be potentially used to induce chiral fluid convection utilizing the chiral photothermal effect. Our proposed structure can potentially be used in various optofluidics applications related to biochemistry, clinical biology, and so on.

Supplemental Digital Content is available in the text.Identifying outcome predictors for ischemic stroke is beneficial for choosing correct intervention protocols. Thus, it is necessary to systemically evaluate histological outcome-associated changes such as hemodynamics, behavior, and body weight during the early phase of ischemia. Here, 50 mice were subjected to 45-min middle cerebral artery occlusion (MCAO) using Longa’s method. Hemodynamic changes were monitored by Doppler laser probe, and behaviors were evaluated by scales while the tissues were visualized by staining. The results by correlation analysis demonstrated that with a probe located near the posterior boundary zone of MCA territory, the latency of anoxic depolarization, as well as the cerebral blood flow reduction during MCAO were confirmed to be predictors for the infarct volume on day 3 post-ischemia; histology showed that the risk of a space-occupying secondary hemorrhage was significantly correlated with the increase of infarct volume versus the traditional Bederson’s neurological deficit scale, a renewed combined behavioral scoring method performed nicely to reflect the severity of tissue lesions. Weight loss was a valuable metric for the enlargement of both infarct volume and secondary hemorrhage. Monitoring changes during early-phase ischemia may benefit the optimization of ischemia models and the discovery of potential intervention targets.See Video Abstract, http:/links.lww.com/WNR/A601).

•We have indicated moxibustion treatment is able to resist inflammation in CIA rats effectively.•Using GC–MS metabolomics technique, we detect novel metabolites in the moxibustion antiarthritic process, which may aid in advanced understanding of arthritis and therapeutic mechanism of moxibustion.

As one of the most promising alternative refrigerants, 1,1,1,3,3,3-hexafluoro-2-methoxypropane (HFE-356 mmz) is considered to have the potential to be applied in a high temperature heat pump and the organic Rankine cycle. The gaseous pvTproperties of HFE-356 mmz were measured using the isochoric method in the temperature range from 333.068 K to 451.226 K, the pressure range from 104.9 to 1997.3 kPa, and the density range from 75.0 mol·m–3to 882.0 mol·m–3. The three-term truncated virial equation of state for gaseous HFE-356 mmz was also fitted based on the experimental data.

In the rapidly evolving field of organic solar cells (OSCs), the charge transfer (CT) process at the photoactive layer interface is pivotal for device performance. This article focuses on recent progress of Z‐type CT pathway, an unconventional process in OSCs that differs significantly from traditional donor–acceptor systems. The Z‐type process shows the potential for high open‐circuit voltage devices. Herein, some applications in solar cells that are inspired by the Z‐scheme in natural photosynthesis are first introduced. Subsequently, studies on mechanisms of charge generation and transfer in bilayer OSCs with Z‐type characteristics are presented. In analogy to the devices in OSCs, other types of optoelectronic devices with similar electron transfer pathways are discussed. Lastly, challenges and potential opportunities are addressed, and the need for further research to harness the potential of Z‐type devices in OSCs is emphasized. The charge transfer process is of great importance in organic solar cells (OSCs). In this article, recent progress of an unconventional charge transport pathway in organic solar cell, called Z‐type devices, is discussed. The progress of its application and mechanism is introduced, and it is compared with similar processes in other organic photovoltaics.

Proposed a technology enterprise value assessment model based on BP neural network, considering that the technology enterprise has the characteristics of asset-light and high growth, whose value is difficult to evaluate. The model not only includes the non-financial indicators with intellectual property and the financial indicators with performance, but also has the advantages of artificial intelligence. Through analysing the model, it can be seen that increasing the intellectual property indicators can reduce the assessment deviation and the traingda is optimal in the negative gradient learning functions.

The ability to control and manipulate temperature at nanoscale dimensions has the potential to impact applications including heat-assisted magnetic recording, photothermal therapies, and temperature-driven reactivity. One challenge with controlling temperature at nanometer dimensions is the need to mitigate heat diffusion, such that the temperature only changes in well-defined nanoscopic regions of the sample. Here we demonstrate the ability to use far-field laser excitation to actively shape the thermal near-field in individual gold nanorod heterodimers by resonantly pumping either the in-phase or out-of-phase hybridized dipole plasmon modes. Using single-particle photothermal heterodyne imaging, we demonstrate localization bias in the photothermal intensity due to preferential heating of one of the nanorods within the pair. Theoretical modeling and numerical simulation make explicit how the resulting photothermal images encode wavelength-dependent temperature biases between each nanorod within a heterodimer, demonstrating the ability to actively manage the thermal near-field by simply tuning the color of incident light.

Among the world’s most deadly toxins are a class of organophosphates that are used as chemical warfare agents (CWAs). It is imperative to continue to develop novel means for mitigation and protection against these chemical threats. Sensitizing the surface of metal oxide semiconductors with plasmonic nanoparticles for photocatalytic degradation of chemical threats has been a prominent area of research in recent years. Anisotropic silver nanoplateles were purposefully grown on the surface of TiO2fibers, in order to determine the impact of silver nanoparticle shape on (1) the generation of hot electrons by the silver, (2) the subsequent transfer of those electrons from the silver into the TiO2, and (3) the photocatalytic behavior of the Ag–TiO2composite. To elucidate the charge injection properties of the composites, transient absorption experiments (pump–probe experiments) were undertaken. These involved pumping the composite samples with a range of discrete visible wavelengths and probing the composite within the intraband transitions of the TiO2. As a complement to these experiments, the photocatalytic properties of the Ag–TiO2composite fibers were studied via the photocatalytic hydrolysis of methyl paraoxon, a chemical warfare agent simulant. This involved exposure of the methyl paraoxon to either red, green, blue, or white LED illumination. For both the transient absorption and photocatalytic experiments, maximum efficiency was observed for those scenarios in which the resonance of the silver platelets most closely matched the wavelength of incident radiation. Furthermore, the composite with silver nanoplatelets clearly outperformed its counterpart with silver nanospheres, in terms of both charge injection and photocatalytic behavior. We believe these results shall serve as a basis for future catalytic research in which the resonance of anisotropic plasmonic nanoparticles (in a given composite) shall be designed to match the wavelength of incident radiation.

At present, there are fewer studies on the polarization-sensitive mirrored array (PS-MA). Meantime, the estimation accuracy will decrease when the array element spacing is lower than half wavelength. This paper focuses on the aperture expansion of the PS-MA to improve the estimation accuracy. To this end, we establish the received signal model of the 2-D PS-MA with two perpendicular reflectors. Unlike the existing virtual array without polarization information, a novel model of the polarization-sensitive virtual array (PS-VA) is suggested based on the hybrid coarray technique. Additionally, the spatial smoothing (SS) technique is used to recover the rank of the covariance matrix, and the modulus value boundedness (MVB) of the polarization vector is used to reduce the dimension of the polarization-spatial spectrum. Then, the MVB-MA-SS algorithm is proposed for polarization-DOA estimation, which is much more efficient than the standard group-lasso algorithm used in the 1-D PS-MA. Numerical experiments show that the proposed algorithm can provide satisfactory estimation accuracy with significantly reduced computational complexity. [ABSTRACT FROM AUTHOR]

Grape white rot caused by Coniella vitisis a global concern in the grape industry. pH regulation is essential for cell growth, reproductive processes and pathogenicity in phytopathogenic fungi. In this study, we observed that the growth rate, spore production and virulence of C. vitissignificantly declined in alkaline pH, as well as the suppressive effect on secretion of hydrolytic enzymes. Transcriptomic and metabolomic analyses were used to investigate the responses of C. vitisto acidic (pH=5), neutral (pH=7) and alkaline environments (pH=9). We identified 728, 1780 and 3386 differentially expressed genes (DEGs) at pH 5, pH 7 and pH 9, when compared with the host pH (pH=3), and 2122 differently expressed metabolites (DEMs) in negative and positive ion mode. Most DEGs were involved in carbohydrate metabolic process, transmembrane transport, tricarboxylic acid cycle, peptide metabolic process, amide biosynthetic process, and organic acid metabolic process. In addition, metabolomic analysis revealed ABC transporters, indole alkaloid biosynthesis, diterpenoid biosynthesis, and carotenoid biosynthesis pathways in response to the pH change. Furthermore, we found that the aspartate synthesis metabolic route associated with the TCA cycle is a key limiting factor for the growth and development of C. vitisin alkaline environments,and aspartate supplementation enables C. vitisto grow in alkaline environments. Plant cell wall-degrading enzymes (PCWDEs) could contribute to the pathogenicity, when C. vitisinfected at pH 3. Importantly, aflatrem biosynthesis in acidic environment might contribute to the virulence of C. vitisand has a risk of causing human health problems due to its acute neurotoxic effects.

Although silicon (Si) has a high theoretical capacity, the large volume expansion during lithiation has greatly hindered its application in high-energy-density lithium-ion batteries (LIBs). Among the strategies for improving the performance of Si anode, the role of binders should not be underestimated. Here, a novel strategy for designing a cross-linkable binder for Si anode has been proposed. The binder with hydroxyl and nitrile groups can be in situ covalently cross-linked through the amide group in the batteries. The cross-linked binder (c-POAH) shows high elasticity and strong adhesion to Si particles and the current collector. Si||Li half coin cells using the c-POAH binder have excellent cycle performance and the capacity retention ratio is 67.1% after 100 cycles at 0.2 C. Scanning electronic microscopy images show that the c-POAH binder can contribute to suppressing the pulverization of the Si anode. Moreover, the investigation with X-ray photoelectronic spectrum demonstrates that the decomposition of the liquid electrolyte on Si anode has been mitigated and the c-POAH binder can promote the formation of a more stable SEI film. Our strategy of endowing the binder with good elasticity through in situ cross-linking has opened up a new route for developing binders, which will definitely promote the application of Si anodes in high-energy-density LIBs.

The bearing is one of the most critical components of a wind turbine, and its condition determines whether the wind turbine can work normally or not. And in recent years, the increasing number of wind turbines leads to more and more data collected for bearing condition monitoring, which brings a great challenge to data transmission and preservation. In addition, the vibration signal is one-dimensional, and directly using one-dimensional signals can avoid the complex calculations caused by increasing the dimensionality of the data. In this article, a new fault diagnosis method for wind turbine bearings is proposed using one-dimensional data as input. The method is based on sparsely reconstructed gradient projection and fractional Fourier transform (GPSR-FRFT) for feature extraction of the signal, and a modified convolutional long short-term memory (CNN-LSTM) is used for fault classification of the data. Firstly, the signal is sparsely expressed by the GPSR algorithm, and the original signal is compressed and observed using compressed sensing so that the observed signal can be transmitted instead of the original signal, this process can reduce data samples to save storage space. The original signal is recovered by the inverse solution of compressed sensing at the terminal, and this step can reduce noise interference. Secondly, the time-frequency characteristics of the signal are obtained using the FRFT transform. In addition, in order to solve the problem of insufficient learning ability and low accuracy of convolutional long short-term memory (CNN-LSTM), an improvement was made by adding a jump connection layer to the convolutional layer to improve the training speed and learning ability. The resulting signal is input to an improved CNN-LSTM network, and experimental analysis proves the superiority of the proposed method.

For semiconductors photosensitized with organic dyes or quantum dots, transferred electrons are usually considered thermalized at the conduction band edge. This study suggests that the electrons injected from a plasmonic metal into a thin semiconductor shell can be nonthermal with energy up to the plasmon frequency. In other words, the electrons injected into the semiconductor are still hot carriers. Photomodulated X-ray absorption measurements of the Ti L2,3edge are compared before and after excitation of the plasmon in Au@TiO2core–shell nanoparticles. Comparison with theoretical predictions of the X-ray absorption, which include the heating and state-filling effects from injected hot carriers, suggests that the electrons transferred from the plasmon remain nonthermal in the ∼10 nm TiO2shell, due in part to a slow trapping in defect states. By repeating the measurements for spherical, rod-like, and star-like metal nanoparticles, the magnitude of the nonthermal distribution, peak energy, and number of injected hot electrons are confirmed to be tuned by the plasmon frequency and the sharp corners of the plasmonic nanostructure. The results suggest that plasmonic photosensitizers can not only extend the sunlight absorption spectral range of semiconductor-based devices but could also result in increased open circuit voltages and elevated thermodynamic driving forces for solar fuel generation in photoelectrochemical cells.

Elucidating the underlying principles behind band gap engineering is paramount for the successful implementation of semiconductors in photonic and optoelectronic devices. Recently it has been shown that the band gap of a wide and direct band gap semiconductor, such as ZnO, can be modified upon cocrystallization with amino acids, with the role of the biomolecules remaining unclear. Here, by probing and modeling the light-emitting properties of ZnO–amino acid cocrystals, we identify the amino acids’ role on this band gap modulation and demonstrate their effective chirality transfer to the interband excitations in ZnO. Our 3D quantum model suggests that the strong band edge emission blue-shift in the cocrystals can be explained by a quasi-periodic distribution of amino acid potential barriers within the ZnO crystal lattice. Overall, our findings indicate that biomolecule cocrystallization can be used as a truly bio-inspired means to induce chiral quantum confinement effects in quasi-bulk semiconductors.

The transient optical properties of graphene on a dielectric substrate rely on both the direction of light incidence and anisotropic distribution of excited carriers in graphene. Here, we experimentally and theoretically studied the polarization dependence of transient optical reflection and transmission caused by the interplay between the above two factors. The interplay between the two factors resulted in diverse polarization dependence characteristics and could shift the converting incident angle for inverse polarization dependence in transient optical response. For large angles of incidence, transient optical transmission exhibited a stronger polarization dependence and no sign reversal, compared with transient optical reflection. Our findings can be useful for designing high-speed photonic devices with controllable transient optical response of graphene.

The use of plasmonic metal nanoparticles as photosensitizers has undergone a strong development in the past few years given their ability to increase the activity of semiconductors into the visible and near-infrared regions. The present work reports an experimental and theoretical study on the critical influence that shape anisotropy of gold nanoparticles exerts on the photocatalytic performance of Au–TiO2nanoarchitectures. The obtained results show that for a given amount of metallic material, Au nanostars endow titania with a strongly enhanced catalytic efficiency compared to that found in the presence of Au nanospheres or nanorods. This is ascribed to the ability of nanostars to locally create extremely large electromagnetic field enhancements around their spikes, which ensures an increased population of hot electrons close to the interface between the metal and the semiconductor. Therefore, these nanostructures exhibit a novel regime of photocatalytic activity that could be described as plasmonic hot-spot photocatalysis. Numerical simulations confirm that the hot electron injection is a feasible mechanism behind the photosensitization process and that the nanostars should have the strongest photochemical response. These results pave the way for a more rational design of the plasmonic component in the search for high-performance photocatalytic nanoreactors operating under visible and NIR light.

Excellent and inexpensive materials with proton conductivity have far-reaching significance for the development of clean energy. In this paper, three ionic hydrogen-bonded organic frameworks (iHOFs) were synthesized using naphthalenedisulfonate derivatives with substituents at different positions and 1,1′-diamino-4,4′-bipyridylium. The hydrogen bond networks were formed by sulfonic acid groups and amino groups, while the addition of functional groups such as hydroxyl or amino groups can also enrich the hydrogen bonds, which also makes such iHOFs have high conductivity, in which iHOF-7afforded the highest proton conductivity of 4.5 × 10–3S·cm–1at 100 °C, 85% relative humidity (RH). It is difficult for the crystalline materials to form a membrane, so we prepared the hybrid membranes of iHOF-7using sulfonated poly (ether ether ketone) (SPEEK) or chitosan (CS) as the supporting polymeric matrix. At 85% RH and 100 °C, the highest proton conductivity of the 15%-iHOF-7/SPEEKcomposite membrane can reach 5.1 × 10–3S·cm–1, and at 98% RH and 100 °C, the highest proton conductivity of 15%-iHOF-7/CScan reach to 5.7 × 10–3S·cm–1.

Hydrogen bonding interactions in amorphous indomethacin and amorphous solid dispersions of indomethacin with poly(vinylpyrrolidone), or PVP, and poly(vinylpyrrolidone-co-vinyl acetate), or PVP/VA, were investigated quantitatively using solid-state NMR spectroscopy. Indomethacin that was 13C isotopically labeled at the carboxylic acid carbon was used to selectively analyze the carbonyl region of the spectrum. Deconvolution of the carboxylic acid carbon peak revealed that 59% of amorphous indomethacin molecules were hydrogen bonded through carboxylic acid cyclic dimers, 15% were in disordered carboxylic acid chains, 19% were hydrogen bonded through carboxylic acid and amide interactions, and the remaining 7% were free of hydrogen bonds. The standard dimerization enthalpy and entropy of amorphous indomethacin were estimated to be −38 kJ/mol and −91 J/(mol·K), respectively, using polystyrene as the “solvent”. Polymers such as PVP and PVP/VA disrupted indomethacin self-interactions and formed hydrogen bonds with the drug. The carboxylic acid dimers were almost completely disrupted with 50% (wt) of PVP or PVP/VA. The fraction of disordered carboxylic acid chains also decreased as the polymer content increased. The solid-state NMR results were compared with molecular dynamics (MD) simulations from the literature. The present work highlights the potential of 13C solid-state NMR to detect and quantify various hydrogen bonded species in amorphous solid dispersions as well as to serve as an experimental validation of MD simulations.

The scheme of graphene on a silicon substrate is potentially compatible to the microelectronic technology. But the maintained plasmons have considerable ohmic loss because of silicon’s large permittivity. We introduce air grooves in the silicon surface to reduce the optical thickness of substrate and hence decrease the propagation loss. The properties of graphene plasmons on the corrugated substrates are numerically investigated, in terms of the photon frequency and the geometrical parameters of the corrugated layer, considering both ohmic loss and scattering loss. The plasmons propagation lengths for the corrugated substrates can exceed twice of those for flat silicon in a broadband in mid-infrared. This study may be useful for designing of compact mid-infrared waveguides based on graphene for future photonic integrated circuits.

For the airborne weather radar signal processing, the intensity of the meteorological targets is represented by the physical quantity called reflectivity factor. When observing the middle-or long-range meteorological targets at low altitude from a high-altitude radar platform, due to the incomplete beam filling, the retrieval result of the reflectivity factor is much less than the actual value. In this paper, we firstly introduce a beam filling coefficient into the weather radar equation and then present its estimation method based on the real-time radar operating parameters. The validity of the proposed method is then verified by processing the raw-data of the simulated meteorological targets.

By analysis the form of gas percolation in coalfield fire, the follow conclusion is obtained : under the influence of natural wind pressure and fire pressure, natural ventilation system of coalfield outcrop is formed automatically during coal spontaneous combustion, which exists in two forms, one is the fresh airflow percolation in caving zone and its pore, the other is coal combustion gases migration movements in cracks after sliding. The fluent numerical simulations show that natural ventilation system in coalfield outcrop fire district is a vacuum ventilation system; From inlet to combustion area air intake line, air pressure and gas percolation on linear relationship, and gas percolation meets Darcy law in the porous medium percolation theory.

The Bachuan River is the main water source for urban life and industrial and agricultural production of one county in Chongqing, China, but water shortage is serious due to the water pollution and low development rate at present. In this paper, water resources quantity and quality of the Bachuan River basin were analyzed, and the water demand and supply were calculated. The results indicate that for the polluted river, in addition to the living and production water requirements, the guarantee of ecological water is important for the environment improvement. Reasonable joint regulation of upstream reservoirs is needed to maintain the water supply of living, production and ecological maintenance, and the water resources allocation should be adjusted according to the water inflow in different seasons. At the same time, water environment treatment and water conservation are also necessary to help to improve the water quality, and water transfer projects can be considered if the flow is too small in dry season.

Molecular models for HPMCAS polymer have been developed for molecular dynamics (MD) simulation that attempt to mimic the complex substitution patterns in HPMCAS observed experimentally. These molecular models were utilized to create amorphous HPMCAS solids by cooling of the polymeric melts at different water contents to explore the influence of water on molecular mobility, which plays a critical role in stability and drug release from HPMCAS-based solid matrices. The densities found for the simulated amorphous HPMCAS were 1.295, 1.287, and 1.276 g/cm3at 0.7, 5.7, and 13.2% w/w water, indicating swelling of the polymer with increasing water content. These densities compare favorably with the experimental density of 1.285 g/cm3for commercial HPMCAS-(AQOAT AS-MF) supporting the present HPMCAS models as a realistic representation of amorphous HPMCAS solids. Water molecules were observed to be mostly isolated from each other at a low water content (0.7% w/w), while clusters or strands of water were pervasive and broadly distributed in size at 13.2% w/w water. The average number of first-shell water molecules (nw) increased from 0.17 to 3.5, though the latter is still far below that (8.9) expected for the onset of a separate water phase. Increasing water content from 0.7 to 13.2% w/w was found to reduce the Tgby ∼81 K, similar to experimental observations. Plasticization with increasing water content resulted in increasing polymer mobility and water diffusivity. From 0.7 to 13.2% w/w water, the apparent water diffusivity increased from 1.1 × 10–9to 7.0 × 10–8cm2/s, though non-Einsteinian behavior persisted at all water contents explored. This and the water trajectories in the polymers suggest that water diffusion at 0.7% w/w water follows a “hopping” mechanism. At a higher water content (13.2% w/w) water diffusion follows dual diffusive processes: (1) fast water motions within water clusters; and (2) slower diffusion through the more rigid polymer matrix.

The uniaxial compressive tests on frozen loess with different ice content under loading rate of 1.25mm/min at-6°C are carried out to investigate the effects of ice content on the mechanical behavior of ice. The influence of ice content on stress-strain, elastic modulus, strength and failure strain of frozen loess are analyzed based on the experimental data. The results show that strength and failure strain increase with increasing of ice content. The elastic modulus first increase and then decrease with increasing of ice content.

To model the stress-strain relation of frozen soil under different temperatures, an elasto-plastic constitutive model coupling with temperature variable was proposed. Under axisymmetric condition, elastic strain was calculated by the K-G model coupling with temperature. The plastic strain was calculated by using the DP yield criterion, associated flow rule and isotropic hardening law. All of the elastic and plastic parameters are related to the temperature variable. The simulated results show that the proposed model can predict the deformation behavior of frozen soil under different temperatures.

Steel and concrete composite slab is a new type of bridge deck, which is consist of steel plate and concrete slab connected by shear connectors such as perfobond ribs ( usually abbreviated as PBL), studs and so on. Static experiment about four pieces of one-way steel and concrete composite slab using PBL was carried, and the whole-process structural behaviors and failure mode were investigated. Based on the spatial beam element model with the degenerated theory, the nonlinear finite element analysis on experimental specimens was executed. Such structural behaviors as concrete cracking, yield of steel plate, crushing of concrete, and so on are simulated, and are compared with the experimental results. It can be observed that excellent agreements are achieved between experimental results and numerical simulation.