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At present, the cage guide inspection robot used for data acquisition of deep vertical shaft can only carry a small number of sensors. There is no safety protection device, so there are potential safety hazards. In addition, most of the deep vertical shaft data visualization programs use 3D GIS for rendering and display. It is difficult to transplant and has long development cycle. In order to solve the above problems, a data acquisition and analysis system for deep vertical shaft is designed. The cage guide inspection robot is improved by adding a wheel lock device to ensure the safety of the robot during operation. The cage guide inspection robot with wheel lock is used as mobile platform. The data acquisition of deep vertical shaft is realized by infrared camera, photoelectric encoder, ultrasonic ranging module and various sensors on the robot. The cloud server plus front-end visualization panel is used for data processing and display. The cloud server receives various data sent by the cage guide inspection robot and classifies and processes the data. The data of temperature and humidity sensor and the gas sensor is directly stored in the corresponding folder. The convolutional neural network (CNN) is applied to process the video data and analyze whether there are cracks or deformation in the cage guide and the shaft wall. The analysis results are stored in the corresponding folder. The front-end visual panel is lightweighted. Asynchronous JavaScript and XML (Ajax) are used to read data from the cloud server regularly. And JavaScript is used to write the host computer interface display program to improve the portability of the system. The test results show that the cage guide inspection robot with wheel lock used as the data acquisition device can improve the reliability and safety of the system. The data processing and display method of the visualization panel and cloud server reduces the memory occupied by the host computer software to less than 5 MB. The page refreshes quickly and the portability is strong.

As a green and recyclable thermoplastic material, polypropylene (PP) has a good application prospect in high voltage cable line. However, its thermodynamic performance is difficult to directly meet the requirements of the cable line, so the PP material needs to be modified to meet the performance requirements. Therefore, based on molecular simulation technology, a nano-CaCO3/PP composite model with doping mass fraction of 3% and 7% nano-CaCO3 and doped grafted silane coupling agent was built, the data of thermal and mechanical properties were calculated, and the changes of its properties were analyzed from a microscopic perspective. The results show that the thermal and mechanical properties of the composites model with grafting number 2 improved the best. In terms of thermal properties, the glass transition temperature (Tg) value increased by 49 K, the free volume fraction decreased by 2.52%, and the thermal conductivity was the highest. In terms of mechanical properties, the rigidity decreased the most, and the toughness improved the most. [ABSTRACT FROM AUTHOR]

Maintaining high fundamental mode purity in low‐loss single‐ring hollow‐core photonic crystal fiber remains a key challenge. Here a double‐clad anti‐resonant‐reflecting design is reported that, through modal filtering, provides high suppression of higher‐order modes while maintaining a low‐loss fundamental mode. The structure consists of a ring of six thin‐walled capillaries fused to the inner wall of a thicker‐walled capillary that is in turn fused at a single point to the silica outer cladding. Greater than 40 dB m−1 suppression of higher order modes over a 6 nm wide window whose central wavelength can be tuned by adjusting the gas pressure is observed. The novel design paves the way to practical applications of such fibers in high‐quality laser beam delivery and low‐noise optical‐signal transmission. [ABSTRACT FROM AUTHOR]

The purpose of this experiment was to study the effects of fermented feed on the growth performance, meat quality, and economic benefits of broilers. A total of 1 600 AA broilers were divided into four groups with four replicates in each group and 100 broilers in each replicate. The control group was fed with basal diet, and the experimental group was fed with 2.5%, 5.0%, and 10.0% fermented feed, respectively. The trial period was 42 days. The results showed that from 22 to 42 days old, body weight, average daily gain, and average daily feed intake of 5.0% group and 10.0% group at 42 days of age were significantly increased (P<0.05), and the ratio of feed to gain was significantly decreased (P<0.05). From 1 to 42 days old, the average daily gain and average daily feed intake of 5.0% group and 10.0% group increased significantly (P<0.05), and the feed to gain ratio decreased significantly (P<0.05). The pH24 h value, L* value, b* value, and cooking loss of muscle in 5.0% group and 10.0% group decreased significantly (P<0.05). Shear stress in of muscle in 2.5% group, 5.0% group and 10.0% group decreased significantly (P<0.05). The contentrations of glycine and inosinic acid in muscle of 5.0% group and 10.0% group increased significantly (P<0.05). Compared with the control group, the weight gain of 2.5% group, 5.0% group, and 10.0% group increased by 2.55%, 8.74%, and 12.26%, respectively, and the breeding profit increased by 2.69%, 11.73%, and 13.15%, respectively. The study indicates that broilers fed proper amount of fermented feed can improve growth performance, meat quality, and breeding profit, and it is more suitable to feed 5%~10% fermented feed. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic (DA) neurons and aggregation of α‐synuclein (α‐syn). Ferroptosis, a form of cell death induced by iron accumulation and lipid peroxidation, is involved in the pathogenesis of PD. It is unknown whether melatonin receptor 1 (MT1) modulates α‐syn and ferroptosis in PD. Here, we used α‐syn preformed fibrils (PFFs) to induce PD models in vivo and in vitro. In PD mice, α‐syn aggregation led to increased iron deposition and ferroptosis. MT1 knockout exacerbated these changes and resulted in more DA neuronal loss and severe motor impairment. MT1 knockout also suppressed the Sirt1/Nrf2/Ho1/Gpx4 pathway, reducing resistance to ferroptosis, and inhibited expression of ferritin Fth1, leading to more release of ferrous ions. In vitro experiments confirmed these findings. Knockdown of MT1 enhanced α‐syn PFF‐induced intracellular α‐syn aggregation and suppressed expression of the Sirt1/Nrf2/Ho1/Gpx4 pathway and Fth1 protein, thereby aggravating ferroptosis. Conversely, overexpression of MT1 reversed these effects. Our findings reveal a novel mechanism by which MT1 activation prevents α‐syn‐induced ferroptosis in PD, highlighting the neuroprotective role of MT1 in PD. [ABSTRACT FROM AUTHOR]

Objective To explore the clinical manifestation, pathological features, treatment regimen and prognosis of primary thyroid lymphoma (PTL) patients. Methods We analyzed retrospectively the clinicopathologic features, diagnosis and treatment of 13 PTL patients confirmed by pathology. Results All patients were B cell non-Hodgkin lymphoma, including three cases of extranodal marginal zone lymphoma and 10 cases of diffuse large B cell lymphoma; 10 patients were in the early stage (stage Ⅰ and Ⅱ) and three patients with advanced stage (stage Ⅲ-Ⅳ); only one patient accepted surgical resection and the rest accepted multi-drug chemotherapy or combined with local radiotherapy; the median survival time was 99.5 months and the 5-year survival rate was 100%. Conclusion The incidence of PTL is low. Thyroid nodules are the main symptoms. More than half of them are accompanied with Hashimoto's thyroiditis. Main pathological type is B cell non-Hodgkin lymphoma. CHOP-based chemotherapy is effective for most of the patients with good prognosis.

The Bayesian network classifiers (BNCs) learned from labeled training data are expected to generalize to fit unlabeled testing data based on the independent and identically distributed (i.i.d.) assumption, whereas the asymmetric independence assertion demonstrates the uncertainty of significance of dependency or independency relationships mined from data. A highly scalable BNC should form a distinct decision boundary that can be especially tailored to specific testing instance for knowledge representation. To address the issue of asymmetric independence assertion, in this paper we propose to learn k-dependence Bayesian multinet classifiers in the framework of multistage classification. By partitioning training set and pseudo training set according to high-confidence class labels, the dependency or independency relationships can be fully mined and represented in the topologies of the committee members. Extensive experimental results indicate that the proposed algorithm achieves competitive classification performance compared to single-topology BNCs (e.g., CFWNB, AIWNB and SKDB) and ensemble BNCs (e.g., WATAN, SA2DE, ATODE and SLB) in terms of zero-one loss, root mean square error (RMSE), Friedman test and Nemenyi test. [ABSTRACT FROM AUTHOR]

Background: Previous studies on unilateral internal carotid artery occlusive disease have focused on the mechanisms of ipsilateral hemispheric stroke, and contralateral stroke is considered to be an accidental phenomenon. Little is known about the relationship between severe stenosis (including occlusion) of the unilateral extracranial segment of the internal carotid artery and contralateral cerebral stroke, and the infarct patterns and pathogenesis require further study. The purpose of this study was to investigate the clinical characteristics and pathogenesis of contralateral acute stroke with unilateral extracranial internal carotid artery stenosis (including occlusion). Methods: Thirty‐four patients were enrolled in this study, and all patients underwent routine clinical evaluation, including medical history, physical examination, laboratory tests, and various imaging evaluations. The morphological characteristics of diffusion‐weighted magnetic resonance imaging were applied to determine infarct patterns. The etiological classification was confirmed according to the TOAST classification. Results: There were six distinctive lesion patterns: small subcortical infarcts (six patients), large subcortical infarcts (one patient), diffuse infarcts (eight patients), multiple anterior circulation infarcts (eight patients), multiple posterior circulation infarcts (two patients), and multiple anterior and posterior circulation infarcts (nine patients). Conclusion: Diffuse and multiple infarcts were the most common topographic patterns in ischemic stroke contralateral to internal carotid artery stenosis or occlusion. Hemodynamic impairment of the contralateral hemisphere due to hypoperfusion and blood theft is regarded as the basis of stroke occurrence. Low ischemic tolerance and embolism are the main causes of acute ischemic stroke. [ABSTRACT FROM AUTHOR]

Unconstrained Palmprint Recognition (UPR) shows engaging potential owing to its high hygiene and privacy. The unconstrained acquisition usually produces wide variations, against which deep methods resort to large samples that are unavailable in practice, however. We focus on Few-Shot UPR (FS-UPR), a more general problem, recognizing query samples given a few support samples per class. Because scarce samples insufficiently represent potential variations, the augmentation methods train independent hallucinators on large samples to generate more ones. Whereas, the hallucinators trained independently of Few-Shot Learning (FSL) are blind of generating promising samples to boost the downstream FSL. Moreover, training hallucinators requires large samples per class, unavailable from unconstrained palmprint databases. We aim to address FS-UPR via contrastive augmentation merely on the support samples. Observing the variations to be transferable across samples, we exploit low-rank representation to disentangle support samples into principles and variations in embedding space and augment features by variation transfer. To this end, we devise an end-to-end Deep Low-Rank Representation Feature Augmentation Network (DLRR-FAN) to simultaneously learn the embedding space and augmentation features with guaranteed reality and diversity. Furthermore, a Contrastive Recognition Regularizer (CRR) is tailored to secure the discriminability of augmentation features. During each training episode, the task motivates DLRR-FAN to augment such features that correct the biased prototypes towards upcoming query samples with variations unseen in the support samples, namely task-driven prototype correction. Extensive experiments on both the typical and extended FS-UPR tasks demonstrate the efficacy of DLRR-FAN versus the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The experiment was conducted to study the effect of Clostridium butyricum on production performance, serum biochemical indexes and economic benefits of laying hens. Four thousand 38-week-old Hyline brown laying hens were randomly divided into four groups with five replicates in each group and 200 laying hens in each replicate. The laying hens in control group were fed with basal diet, and the laying hens in experimental groups were fed with basal diet supplemented with 250, 500 and 1 000 mg/kg Clostrium butyric, respectively. The pre-test period lasted for 7 d and the formal test period lasted for 60 d. The results showed that the qualified egg rate of 1 000 mg/kg group was significantly higher than that of control group ( P<0.05), and the feed to egg ratio was significantly lower than that of control group ( P<0.05). The Haugh unit of eggs in experimental groups was significantly higher than that in control group ( P<0.05). The intestinal Lactobacillus in 500 mg/kg group and 1 000 mg/kg group was significantly higher than that in control group ( P<0.05), and the number of Escherichia coli in 500 mg/kg group was significantly lower than that in control group ( P<0.05). The T-AOC, GSH-Px and SOD activity in experimental groups were significantly higher than those in control group ( P<0.05), and the MDA content was significantly lower than that in control group ( P<0.05). The content of IgG in serum of laying hens in 1 000 mg/kg group was significantly higher than that in control group ( P<0.05), and the content of IgM in serum of laying hens in experimental groups was significantly higher than that in control group ( P<0.05). The profit per layer of the experimental groups was 8.36%, 17.72% and 19.40% higher than that of control group, respectively. The experiment indicates that dietary supplementation of Clostridium butyricum can improve the production performance and egg quality, and effectively improve the body health level. The dietary supplementation level of Clostridium butyricum is recommended to be 1 000 mg/kg. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease, and is characterized by accumulation of α-synuclein (α-syn). Neuroinflammation driven by microglia is an important pathological manifestation of PD. α-Syn is a crucial marker of PD, and its accumulation leads to microglia M1-like phenotype polarization, activation of NLRP3 inflammasomes, and impaired autophagy and phagocytosis in microglia. Autophagy of microglia is related to degradation of α-syn and NLRP3 inflammasome blockage to relieve neuroinflammation. Microglial autophagy and phagocytosis of released α-syn or fragments from apoptotic neurons maintain homeostasis in the brain. A variety of PD-related genes such as LRRK2, GBA and DJ-1 also contribute to this stability process. Objectives: Further studies are needed to determine how α-syn works in microglia. Methods: A keyword-based search was performed using the PubMed database for published articles. Conclusion: In this review, we discuss the interaction between microglia and α-syn in PD pathogenesis and the possible mechanism of microglial autophagy and phagocytosis in α-syn clearance and inhibition of neuroinflammation. This may provide a novel insight into treatment of PD. [ABSTRACT FROM AUTHOR]

In our latest in-vivo study, Hst1 not only promotes angiogenesis, re-epithelialization and collagen production, but also suppresses inflammation, thereby significantly accelerating acute skin wound healing in mice [[2]]. Keywords: Histatin 1; Minimal active domain; Acute skin wound; Inflammatory response; Oxidative stress EN Histatin 1 Minimal active domain Acute skin wound Inflammatory response Oxidative stress 1 4 4 08/09/22 20220716 NES 220716 Xiao-Xuan Lei, Liu Hang-Hang Cheng and Hai-Yan Lin have contributed equally to this work Dear Editor, The skin barrier can be impaired by acute skin wounds, which may lead to a series of complications. [Extracted from the article]

Background: The presence of circulating plasma cells (cPCs) was associated with a worse prognosis in multiple myeloma patients. However, the underlying mechanisms involved in the migration and invasion of bone marrow myeloma cells (BMMCs) to cPCs remains unclear. Here, we investigate the possible factors related to hematogenous myeloma cell dissemination and potential regulatory mechanisms. Methods: BMMCs and cPCs of five extramedullary plasmacytoma (EMP) patients were selected for single cell RNA sequencing, We found that the expression level of sphingosine-1-phosphate receptor 2 (S1RP2) was lower in cPCs compared with that in BMMCs. Then, we investigated the effect of S1PR2 in cell migration and invasion through pharmacologic inhibition with a S1PR2-selective antagonist JTE-013 or knockdown of S1PR2 expression in MM cell line U266. Results: The results showed that S1PR2 inhibition with JTE-013 or S1PR2-shRNA significantly promoted cell migration and invasion in U266 cells. We measured the expression of invasion-related proteins by Western blot and found that knockdown of S1PR2 could reduce MMP-9 expression in U266 cells. Furthermore, we found NF-κB pathway may mediate the inhibition effects of S1PR2 on cell migration and invasion in MM cells. Conclusion: Our findings demonstrated that S1PR2 downregulation may contribute to the initial extramedullary translocation by promoting cell migration and invasion through NF-κB pathway activation. [ABSTRACT FROM AUTHOR]

Motivation Deep neural network (DNN) algorithms were utilized in predicting various biomedical phenotypes recently, and demonstrated very good prediction performances without selecting features. This study proposed a hypothesis that the DNN models may be further improved by feature selection algorithms. Results A comprehensive comparative study was carried out by evaluating 11 feature selection algorithms on three conventional DNN algorithms, i.e. convolution neural network (CNN), deep belief network (DBN) and recurrent neural network (RNN), and three recent DNNs, i.e. MobilenetV2, ShufflenetV2 and Squeezenet. Five binary classification methylomic datasets were chosen to calculate the prediction performances of CNN/DBN/RNN models using feature selected by the 11 feature selection algorithms. Seventeen binary classification transcriptome and two multi-class transcriptome datasets were also utilized to evaluate how the hypothesis may generalize to different data types. The experimental data supported our hypothesis that feature selection algorithms may improve DNN models, and the DBN models using features selected by SVM-RFE usually achieved the best prediction accuracies on the five methylomic datasets. Availability and implementation All the algorithms were implemented and tested under the programming environment Python version 3.6.6. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Lane changing is one of the basic driving behaviours, which may induce traffic oscillations and incidents. However, it is difficult to well model the lane-changing decision process due to the complex traffic status. To promote the prediction accuracy of lane-changing decisions, this paper presents a probability lane-changing model by taking into account the memory effect. That is, the lane-changing decision model considers a series of trajectory data rather than the data of a specific time utilized in most existing models. Furthermore, the drivers are classified in terms of lane-changing trajectories, which is expected to further promote the prediction accuracy of the lane-changing decision model. Calibrations and validations are carried out based on the NGSIM data, which indicate that the proposed model can significantly promote the prediction accuracy of lane-changing decisions. [ABSTRACT FROM AUTHOR]

Single sample per person face recognition (SSPP FR), i.e., identifying a person (i.e., data subject) with a single face image only for training, has several attractive potential applications, but it is still a challenging problem. Existing generic learning methods usually leverage prototype plus variation (P+V) model for SSPP FR provided that face samples in the biometric enrolment database are variation-free and thus can be treated as the prototypes of data subjects. However, this condition is not satisfied when these samples are contaminated by nuisance facial variations in the wild, such as varied expressions, poor lightings, and disguises (e.g., wearing scarf). We call this new and practical problem SSPP FR with a ${c}$ ontaminated biometric ${e}$ nrolment database (SSPP-ce FR). Subsequently, a challenging issue will be raised on estimating proper prototypes from the contaminated enrolment samples in SSPP-ce FR. Moreover, the generated variation dictionary also needs to be enhanced because it is simply based on the subtraction of average face from the samples of the same data subject in the generic set, thus containing individual characteristics that can hardly be shared by other data subjects. To address these two issues, we propose a novel synergistic generic learning (SGL) method to study the SSPP-ce FR problem. Compared with the existing generic learning methods, SGL develops a new “learned P + learned V” model to identify new query samples. Specifically, it learns better prototypes for the contaminated samples in the biometric enrolment database by preserving their more discriminative subject-specific portions and learns a representative variation dictionary by extracting the less discriminative intra-subject variants from an auxiliary generic set. The experiments on various benchmark face datasets demonstrate the effectiveness of the proposed SGL method. [ABSTRACT FROM AUTHOR]

The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., ${L}_{1}$ -graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A) is the most abundant eukaryote mRNA modification, modulated by regulators known as epigenetic writers, erasers and readers, which are known to serve crucial roles in mRNA metabolism. However, the role of m6A during B-cell development and B-cell tumorigenesis remains poorly understood. By analyzing the gene expression profile of 123 mantle cell lymphoma cases from the Gene Expression Omnibus database, the present study demonstrated that one-half of the m6A regulators were able to predict patient survival in mantle cell lymphoma, notably the m6A.index. The expression levels of the m6A regulators were regarded as good classifiers in mantle cell lymphoma. The m6A.index-low mantle cell lymphoma type exhibited a poor patient survival and lower mRNA levels from the total transcriptome. The m6A regulators may be associated with the cell division and the RNA metabolic pathways, which may result in poor survival of patients with mantle cell lymphoma. [ABSTRACT FROM AUTHOR]

3D Reconstruction of Ultrafast Vortex Pulses Ultrafast optical‐vortex beams carrying orbital angular momentum are of particular importance in many nonlinear‐optics experiments. In article number 2200697, Jinyu Pan, Zhiyuan Huang, Meng Pang, Yuxin Leng, and colleagues developed a novel total electrical‐field measurement set‐up based on a tilted optical interferometer, which can be used to reconstruct, in a self‐referenced manner, the spatio‐temporal information of ultrafast vortex pulses. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association of the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and its receptors, osteoprotegerin (OPG) and death receptor 5 (DR5) with large-artery atherosclerosis (LAA) stroke and its prognosis. Methods: We included patients with LAA stroke (n = 132) according to the TOAST classification system and controls (n = 60). To evaluate the extent and severity of cerebral atherosclerosis, the LAA stroke group was subdivided into 3 subgroups by number of cerebral arteries with atherosclerotic stenosis (≥50%): single, double and multiple (≥3). Plasma levels of TRAIL, OPG and DR5 were measured by ELISA. Ordinal logistic regression was used to analyze the association between the plasma levels of TRAIL, OPG, DR5 and the severity of cerebral atherosclerosis. Prognosis was determined by the Modified Rankin Scale at 3 months after stroke. Receiver operating characteristic (ROC) curve was used to evaluated TRAIL as a predictor of prognosis. Results: Plasma TRAIL level was significantly lower for LAA patients than controls (P<0.001), while plasma OPG and DR5 levels were higher (both P<0.001). Logistic regression analysis revealed that risk of severe cerebral atherosclerosis was reduced significantly with increased plasma level of TRAIL (OR 0.438; 95% CI 0.282–0.681; P<0.001), whereas increased with high plasma levels of OPG and DR5 (OR 2.707; 95% CI 1.702–4.302, P <0.001; OR 3.593; 95% CI 1.878–6.869, P <0.001). Plasma TRAIL level was negatively correlated with the prognosis (r = - 0.372, P <0.001). The optimal cut-off value of TRAIL for prognosis was 848.63 pg/mL. The sensitivity and specificity at this cut-off value were 63.1% and 86.2%, respectively. After adding the plasma TRAIL level into the multivariate model of ROC, the area under the ROC curve was increased from 0.639 to 0.785, but the change was not statistical significant (P = 0.146). Conclusions: TRAIL and its receptors OPG and DR5 may be involved in LAA stroke and the plasma level of TRAIL may be a biomarker predicting the severity of cerebral atherosclerosis and the prognosis of LAA stroke. [ABSTRACT FROM AUTHOR]

Kernel Locality Preserving Projection (KLPP) algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP) can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP) is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC). By combining manifold learning and sparse coding techniques together, GRNMF_SC can efficiently extract the basic vectors from the data space, which preserves the intrinsic manifold structure and also the local features of original data. The target function of our method is easy to propose, while the solving procedures are really nontrivial; in the paper we gave the detailed derivation of solving the target function and also a strict proof of its convergence, which is a key contribution of the paper. Compared with sparseness constrained NMF and GNMF algorithms, GRNMF_SC can learn much sparser representation of the data and can also preserve the geometrical structure of the data, which endow it with powerful discriminating ability. Furthermore, the GRNMF_SC is generalized as supervised and unsupervised models to meet different demands. Experimental results demonstrate encouraging results of GRNMF_SC on image recognition and clustering when comparing with the other state-of-the-art NMF methods. [ABSTRACT FROM AUTHOR]

The resonance band in hollow-core photonic crystal fiber (HC-PCF), while leading to high-loss region in the fiber transmission spectrum, has been successfully used for generating phase-matched dispersive wave (DW). Here, we report that the spectral width of the resonance-induced DW can be largely broadened due to plasma-driven blueshifting soliton. In the experiment, we observed that in a short length of Ar-filled single-ring HC-PCF the soliton self-compression and photoionization effects caused a strong spectral blueshift of the pump pulse, changing the phase-matching condition of the DW emission process. Therefore, broadening of DW spectrum to the longer-wavelength side was obtained with several spectral peaks, which correspond to the generation of DW at different positions along the fiber. In particular, we numerically used the super-Gauss windows with different central wavelengths to filter out these DW spectral peaks and studied the time-domain characteristics of these peaks respectively using Fourier transform method. We observed that these multiple-peaks on the DW spectrum have different delays in the time domain, which is in good agreement with our theoretical prediction. More interestingly, we found that the broadband DW with several spectral peaks can be compressed to ~29 fs after proper dispersion compensation. The results reported here, on the one hand, provide some useful insights into the resonance-induced DW generation process in gas-filled HC-PCFs. On the other hand, the DW-emission mechanism could be used to generate the ultrashort light sources with a wide spectral range through using the proper design of the resonance bands of the HC-PCFs, which has many applications in the ultrafast related experiments. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to investigate the seismic performance improvement effect of coupled shear wall with steel link beams under cyclic lateral loading. The quasi‐static tests on two 1/3‐scaled specimens, including one coupled shear wall with steel link beams and one coupled shear wall with conventional concrete link beams, were conducted. These two specimens were designed with similar lateral initial stiffness and tested under the same gravity and lateral cyclic loading procedure. The specimen with concrete link beams exhibited sequence shear failures of link beams from top to bottom storey after completing 22 loading cycles. However, the specimen with steel link beams sustained ductile and plump hysteretic behavior under the same loading cycles. The lateral load‐resisting capacity of the specimen with steel link beams is 2.2 times of the specimen with concrete link beams. The experimental results demonstrated great advantages of steel link beams in improving seismic performance of conventional coupled shear wall systems. [ABSTRACT FROM AUTHOR]

The underlying mechanism of broadband dispersive‐wave emission within a resonance band of gas‐filled anti‐resonant hollow‐core fiber is studied. Both numerical and experimental results unveiled that the pump pulse with a soliton order of ≈3, launched into the hollow‐core fiber, experienced two stages of pulse compression, resulting in a multi‐peak structure of the emitted dispersive‐wave spectrum. Over the first‐stage pulse compression, a sharp increase of the pulse peak power triggers the first time of dispersive‐wave emission, and simultaneously causes the soliton frequency blue‐shift due to soliton‐plasma interactions. As the central frequency of the blue‐shifting soliton approaches to a resonance band of the hollow‐core fiber, it experiences a fast‐decreasing dispersion value in the fiber waveguide, resulting in the second stage of pulse compression. The second‐stage pulse compression triggers the second time of dispersive‐wave emission with a phase‐matched frequency slightly lower than that at the first stage. Multi‐peak spectra of the output dispersive‐waves and their formation dynamics can be understood using a delicate and unique coupling mechanism among three nonlinear effects including multi‐stage soliton compression, soliton‐plasma interaction, and phase‐matched dispersive‐wave emission. The output broadband dispersive‐wave, exhibiting good coherence and stability, can be potentially compressed to sub‐30 fs duration using a precise chirp‐compensation technique. [ABSTRACT FROM AUTHOR]