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The current dialog model suffers from the lack of grammatical accuracy and content relevance. To provide interactive content with high quality, the study constructs an intelligent human-computer interaction dialog model using end to end neural network and emotion information. The study utilizes the transformer-based bidirectional coding model and the end to end neural network model to realize the interactive dialogue, and embeds emotional information in the model to realize the intelligent human-computer interaction dialog. The outcomes demonstrated that the classification accuracy of the research-designed sentiment classification model on the three datasets was 89.97%, 90.15%, and 91.44%, respectively. The research-designed model consistently outperformed the other models in terms of emotion accuracy as well as Distinct-1 and Distinct-2 score values, which were 0.068, 0.204, and 80.425%, respectively. Meanwhile, the average accuracy of the research-designed model was higher than 90% on all three datasets. The emotional quality and content quality of the model during the dialog process were higher than several more popular emotional interaction dialog models. The comprehensive analysis outcomes reveal that the research-designed model is able to generate high-quality emotion-based responses in the conversation, providing users with a more intelligent human-computer interaction experience.

With the rapid progress of computer vision and deep learning techniques, accurately predicting continuous human motions from very few image inputs and generating high-quality 3D human models has become a cutting-edge research direction in this field. Despite the achievements in 2D to 3D conversion techniques, it is still a great challenge to capture coherent movements from limited image frames and generate texture-rich 3D models. In this paper, we propose a 3D clothed human body generation method based on Inter-Frame Motion Prediction (IFMP for short) of 2D images, which is capable of not only predicting a series of coherent human body motions, but also reconstructing a detailed textured 3D human body model from only two image frames. The method automatically focuses on key parts of the image through action coding and uses a conditional generative adversarial network to generate a series of consecutive intermediate frame images. A depth-aware implicit function representation is combined to map a 3D model from the 2D image, and high quality textures of the human body in a clothed state are obtained by texture mapping and model detail enhancement methods. Finally, the experimental results validate the advantages of the IFMP method in image action coherence prediction, as well as verifying the effectiveness of the human 3D model generated by the method in terms of geometric accuracy and texture quality.

Pleurotus eryngii (PE) has been sought after for its various health benefits and high content of phenolic compounds. This study explored the feasibility of steam explosion (SE)-assisted extraction of polysaccharides with high antioxidant capacities from PE. An orthogonal experimental design (OED) was used to optimize the SE-assisted extraction of PE. The influence of the optimized SE-assisted extraction on the physicochemical properties of PE polysaccharides was determined by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), monosaccharide compositional analysis and antioxidant capacity assays. Under optimal SE conditions, SE-assisted extraction increased the polysaccharide yield by 138% compared to extraction without SE-assistance. In addition, SEM demonstrated that SE-assisted extraction markedly altered the spatial structure of Pleurotus eryngii polysaccharides (PEP), and monosaccharide compositional analysis revealed that this pretreatment significantly increased the proportions of some monosaccharides, such as glucose, rhamnose and arabinose, in the isolated PEP. FTIR spectra indicated no change in the major chemical functional groups of PEP. PEP extracted by SE-assisted extraction had significantly increased free radical scavenging and antioxidant capacities. In conclusion, SE-assisted extraction appears to be a novel polysaccharide extraction technology, which markedly increases extraction yields and efficiency and can increase the biological activity of polysaccharide extracts.

In this study, the effects of Z-0°, Z-67°, Z-90°, I-67°, and S-67° scanning strategies on the surface morphology, microstructure, and corrosion resistance of the specimens in SLM316L were systematically studied. The results show that the partition scanning path can effectively improve the manufacturing quality of the specimen, reduce the cumulative roughness layer by layer, and increase the density of the specimen. The scan path of the island partition of the fine partition is better than that of the strip partition; moreover, the 67° rotation between each layer reduces the accumulation of the height difference of the melt pool, fills the scanning gap of the previous layer, and improves the molding quality of the sample. Electrochemical tests were performed in an aqueous solution of NaCl (3.5 wt%), including open-circuit potential (OCP), dynamic potential polarization, and electrochemical impedance spectroscopy (EIS). The results show that the specimen with a 67° rotation between each layer achieves stability of the surface potential in a short time, and the I-67° specimen exhibits good corrosion performance, while the Z-0° specimen has the worst corrosion resistance.

Salient object detection is a critical task in the field of computer vision. However, existing detection methods still face certain challenges, such as the inability to effectively integrate multimodal features and the blurring of detection result boundaries. To address these issues, this paper proposes a novel RGB-D salient object detection method that combines multimodal feature fusion and contour-guiding techniques. Initially, we employ ResNet50 as the backbone network, and by removing its final pooling layer and fully connected layer, we construct a fully convolutional network specifically for feature extraction from RGB images and depth images. Subsequently, we leverage channel attention mechanism and spatial attention mechanism separately to optimize the RGB image features and depth image features. Following this, we design an interactive feature fusion module to blend the optimized features, thereby obtaining the multimodal fusion features. Furthermore, based on the localization ability of high-level fusion features, we constrain the low-level fusion features, eliminate non-salient objects, and generate salient object contour features. Eventually, we use this contour feature to guide the recognition process of salient objects, resulting in salient objects with clear boundaries. Our approach has been validated across seven RGB-D salient object detection datasets. The experimental results indicate an improvement of 0.21% ~ 1.84% and 0.32% ~ 1.25% respectively in maxF and S metrics, compared to the best competing methods (CMINet, CIR-Net, and CPFP).

Abstract Background Colorectal cancer (CRC), a commonly diagnosed cancer often develops slowly from benign polyps called adenoma to carcinoma. Altered gut microbiota is implicated in colorectal carcinogenesis. It is warranted to find non-invasive progressive microbiota biomarkers that can reflect the dynamic changes of the disease. This study aimed to identify and evaluate potential progressive fecal microbiota gene markers for diagnosing advanced adenoma (AA) and CRC. Results Metagenome-wide association was performed on fecal samples from different cohorts of 871 subjects (247 CRC, 234 AA, and 390 controls). We characterized the gut microbiome, identified microbiota markers, and further constructed a colorectal neoplasms classifier in 99 CRC, 94 AA, and 62 controls, and validated the results in 185 CRC, 140 AA, and 291 controls from 3 independent cohorts. 21 species and 277 gene markers were identified whose abundance was significantly increased or decreased from normal to AA and CRC. The progressive gene markers were distributed in metabolic pathways including amino acid and sulfur metabolism. A diagnosis model consisting of four effect indexes was constructed based on the markers, the sensitivities of the Adenoma Effect Index 1 for AA, Adenoma Effect Index 2 for high-grade dysplasia (HGD) adenoma were 71.3% and 76.5%, the specificities were 90.5% and 90.3%, respectively. CRC Effect Index 1 for all stages of CRC and CRC Effect Index 2 for stage III–IV CRC to predict CRC yielded an area under the curve (AUC) of 0.839 (95% CI 0.804–0.873) and 0.857 (95% CI 0.793–0.921), respectively. Combining with fecal immunochemical test (FIT) significantly improved the sensitivity of CRC Effect Index 1 and CRC Effect Index 2 to 96.7% and 100%. Conclusions This study reports the successful diagnosis model establishment and cross-region validation for colorectal advanced adenoma and carcinoma based on the progressive gut microbiota gene markers. The results suggested that the novel diagnosis model can significantly improve the diagnostic performance for advanced adenoma.

We propose a time-variant reliability analysis framework to quantitatively predict the lifetime of the lattice structures fabricated by selective laser melting (SLM), including confirming hybrid uncertainties, establishing a hybrid model, and proposing an efficient time-variant reliability method. We first design and manufacture a representative and complex L-shaped body-centred cubic (BCC) lattice structure utilising the SLM method, followed by morphology and microstructure observations to indicate the necessity of accounting for material uncertainty. Further considering loading fluctuation, we develop an effective time-variant reliability analysis method utilising the mixed probability and convex set model. One benchmark numerical example has been employed to shed a light on the high computational efficiency and acceptable computational accuracy of the developed time-variant reliability method. Finally, the proposed framework is performed to a real L-shaped BCC lattice structure to predict its lifetime, finding that the failure probability after ten years can reach more than 40 times the initial design.

Aiming at the problems of large water secretion, poor suspensibility and low strength of cemented aeolian sand (AS)-fly ash (FA) backfill (CAFB) mixtures, CAFB was doped with fine coal gangue (CG) particles crushed to less than 4 mm and configured as cemented aeolian sand-coal gangue-fly ash backfill (CACFB) mixtures, in which coal gangue accounted for 8% of the mass ratio of the slurry. Through UCS and rheological experiments, using the response surface methodology and an orthogonal design, the following conclusions were drawn: (1) With the increase in ordinary Portland cement (PO) and slurry concentration, the UCS of the CACFB increased. (2) With the increase in the FA dosage, the UCS of the CACFB decreased first and then increased due to the gradual increase in FA dosage, destroying the reasonable ratio of the material and leading to the reduction in the material’s UCS, and with the growth in time, the volcanic ash effect of the FA caused the UCS of the material to increase. (3) With the increases in slurry concentration, the yield stress and viscosity coefficient of the slurry increased. (4) Reasonable proportions for CACFB should ensure the strength characteristics and rheological properties of the material. Through theoretical and experimental research, the final reasonable proportions were as follows: the concentrations of slurry, AS, CG, FA and PO were 77.5%, 42%, 8%, 17.5% and 10%, respectively. This ensured that the UCSs of the CACFB at 3 d, 7 d and 28 d were 1.2 MPa, 2.5 MPa and 4.3 MPa, respectively; the yield stress of the CACFB was 495 Pa, and the viscosity coefficient was 3.97 Pa·s. These reasonable proportions of the CACFB can meet the strength index and flow property of material industrial experiments.

The use of cemented Aeolian sand-fly ash backfill (CAFB) material to fill the mining area to improve the surface subsidence damage caused by underground coal mining is in the development stage. Their performance with large overflow water and strength loss is not well understood. Few research has been conducted to understand the effects of aeolian sand and coal gangue on the rheological properties of CAFB with plasticizers. Therefore, this study aims to investigate the effects of a plasticizer on the rheological properties, specifically yield stress and viscosity, of CAFB prepared with aeolian sand and coal gangue. CAFB mixes containing 0%, 0.05%, and 0.1% plasticizers were prepared, and yield stress and viscosity were determined at different intervals. Additional tests, such as thermal analysis and zeta potential analysis, were also conducted. It was found that the rheological properties of CAFB are the comprehensive manifestation of the composite characteristics of various models. Reasonable particle size distribution and less plasticizer can ensure the stability of the slurry structure and reduce the slurry settlement and the risk of pipe blocking. The findings of this study will be beneficial in the design and production of CAFB material.

Slow transit constipation (STC) is one of the most frequent gastrointestinal diagnoses. In this study, we conducted a quantitative metagenomics study in 118 Chinese individuals. These participants were divided into the discovery cohort of 50 patients with STC and 40 healthy controls as well as a validation cohort of 16 patients and 12 healthy controls. We found that the intestinal microbiome of patients with STC was significantly different from that of healthy individuals at the phylum, genus, and species level. Patients with STC had markedly higher levels of Alistipes and Eubacterium and lower abundance of multiple species belonging to the Roseburia genus. Patients with STC gene expression levels and the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology pathway (such as fatty acid biosynthesis, butanoate metabolism, and methane metabolism pathways) enrichment were also substantially different from those of healthy controls. These microbiome and metabolite differences may be valuable biomarkers for STC. Our findings suggest that alteration of the microbiome may lead to constipation by changing the levels of microbial-derived metabolites in the gut. Above findings may help us in the development of microbial drugs.

Abstract The application of prestresses is a valuable strategy for enhancing the overall mechanical performances of structural materials. Residual stresses, acting as prestresses, exist naturally in biological structural materials, such as the nacre with the 3D “brick‐and‐mortar” arrangement. Although regulation of the tablets sliding has recently been demonstrated to be vital to improve toughness in synthetic nacre‐like structures, the effects of prestresses on the tablets‐sliding mechanism in these nacre‐like structures remain unclear. Here, by a combination of simulation, additive manufacturing, and drop tower testing the authors reveal that, at a critical prestress, synergistic effects between the prestress‐enhanced tablets sliding and prestress‐weakened structural integrality result in optimized impact resistance of nacre‐like structures. Furthermore, the prestressing strategy is easily implemented to a designed nacre‐inspired separator to enhance the impact resistance of lithium batteries. The findings demonstrate that the prestressing strategy combined with bioinspired architectures can be exploited for enhancing the impact resistance of engineering structural materials and energy storage devices.

Abstract Background The assessment and characterization of the gut microbiome has become a focus of research in the area of human autoimmune diseases. Ankylosing spondylitis is an inflammatory autoimmune disease and evidence showed that ankylosing spondylitis may be a microbiome-driven disease. Results To investigate the relationship between the gut microbiome and ankylosing spondylitis, a quantitative metagenomics study based on deep shotgun sequencing was performed, using gut microbial DNA from 211 Chinese individuals. A total of 23,709 genes and 12 metagenomic species were shown to be differentially abundant between ankylosing spondylitis patients and healthy controls. Patients were characterized by a form of gut microbial dysbiosis that is more prominent than previously reported cases with inflammatory bowel disease. Specifically, the ankylosing spondylitis patients demonstrated increases in the abundance of Prevotella melaninogenica, Prevotella copri, and Prevotella sp. C561 and decreases in Bacteroides spp. It is noteworthy that the Bifidobacterium genus, which is commonly used in probiotics, accumulated in the ankylosing spondylitis patients. Diagnostic algorithms were established using a subset of these gut microbial biomarkers. Conclusions Alterations of the gut microbiome are associated with development of ankylosing spondylitis. Our data suggest biomarkers identified in this study might participate in the pathogenesis or development process of ankylosing spondylitis, providing new leads for the development of new diagnostic tools and potential treatments.

Nacre with a combination of superior strength and toughness is one of the best natural body armors due to its unique brick-and-mortar structure, which is recently regarded as the model system for the lightweight and high impact performance composite design. However, most of the studies of nacreous structure focused on static loadings or simple one-dimensional dynamic load. Our study presents 3D finite element simulation, 3D printing and drop-tower impact testing to reveal the role of interfacial strength and impact velocity in impact resistance of the nacreous structure. An optimal interfacial strength is observed at which the impact resistance reaches a maximum. Sorting of various evolving damage patterns demonstrates that the simultaneous intralayer radial cracks propagation and interlayer delamination lead to a maximal energy dissipation in the presence of the optimal interfacial strength. The phase diagram of damage patterns depending on interfacial strength and impact velocity reveals that the optimal interfacial strength decreases with increasing impact velocity. This study demonstrates that the impact-resistance of nacreous structure under different impact velocities can be enhanced by tuning the interfacial strength, and gives some guidance for advanced protective materials designs. Keywords: Nacreous structure, Impact resistance, Interfacial strength, Finite element analysis, 3D-printing, Drop-tower impact experiment

Graded cellular material is a superb sandwich candidate for blast alleviation, but it has a disadvantage for the anti-blast design of sacrificial cladding, i.e., the supporting stress for the graded cellular material cannot maintain a constant level. Thus, a density graded-uniform cellular sacrificial cladding was developed, and its anti-blast response was investigated theoretically and numerically. One-dimensional nonlinear plastic shock models were proposed to analyze wave propagation in density graded-uniform cellular claddings under blast loading. There are two shock fronts in a positively graded-uniform cladding; while there are three shock fronts in a negatively graded-uniform cladding. Response features of density graded-uniform claddings were analyzed, and then a comparison with the cladding based on the uniform cellular material was carried out. Results showed that the cladding with uniform cellular materials is a good choice for the optimal mass design, while the density graded-uniform cladding is more advantageous from the perspective of the critical length design indicator. A partition diagram for the optimal length of sacrificial claddings under a defined blast loading was proposed for engineering design. Finally, cell-based finite element models were applied to verify the anti-blast response results of density graded-uniform claddings.

A virtual Taylor impact of cellular materials is analyzed with a wave propagation technique, i.e. the Lagrangian analysis method, of which the main advantage is that no pre-assumed constitutive relationship is required. Time histories of particle velocity, local strain, and stress profiles are calculated to present the local stress–strain history curves, from which the dynamic stress–strain states are obtained. The present results reveal that the dynamic-rigid-plastic hardening (D-R-PH) material model introduced in a previous study of our group is in good agreement with the dynamic stress–strain states under high loading rates obtained by the Lagrangian analysis method. It directly reflects the effectiveness and feasibility of the D-R-PH material model for the cellular materials under high loading rates.

Hyperspectral imagery classification is a challenging problem. Wherein, the high number of spectral channels and the high cost of true sample labeling greatly reduce the classification precision. In this paper, we proposed a semi-supervised method, which combine linear discriminant analysis and manifold learning, to improve the precision of hyperspectral imagery classification. Experimental results showed that new method had provided considerable insight on the band extraction problem and the new features were good for land-cover classification.

Abstract Upon publication of the original article [1], it was noted that references 11 and 22 were miscited in the reference list.

Abstract Dynamic compression behaviors of density-homogeneous and density-graded irregular honeycombs are investigated using cell-based finite element models under a constant-velocity impact scenario. A method based on the cross-sectional engineering stress is developed to obtain the one-dimensional stress distribution along the loading direction in a cellular specimen. The cross-sectional engineering stress is contributed by two parts: the node-transitive stress and the contact-induced stress, which are caused by the nodal force and the contact of cell walls, respectively. It is found that the contact-induced stress is dominant for the significantly enhanced stress behind the shock front. The stress enhancement and the compaction wave propagation can be observed through the stress distributions in honeycombs under high-velocity compression. The single and double compaction wave modes are observed directly from the stress distributions. Theoretical analysis of the compaction wave propagation in the density-graded honeycombs based on the R-PH (rigid-plastic hardening) idealization is carried out and verified by the numerical simulations. It is found that stress distribution in cellular materials and the compaction wave propagation characteristics under dynamic compression can be approximately predicted by the R-PH shock model.